dbSnp155Composite dbSNP 155 Short Genetic Variants from dbSNP release 155 Variation Description This track shows short genetic variants (up to approximately 50 base pairs) from dbSNP build 155: single-nucleotide variants (SNVs), small insertions, deletions, and complex deletion/insertions (indels), relative to the reference genome assembly. Most variants in dbSNP are rare, not true polymorphisms, and some variants are known to be pathogenic. For hg38 (GRCh38), approximately 998 million distinct variants (RefSNP clusters with rs# ids) have been mapped to more than 1.06 billion genomic locations including alternate haplotype and fix patch sequences. dbSNP remapped variants from hg38 to hg19 (GRCh37); approximately 981 million distinct variants were mapped to more than 1.02 billion genomic locations including alternate haplotype and fix patch sequences (not all of which are included in UCSC's hg19). This track includes four subtracks of variants: All dbSNP (155): the entire set (1.02 billion for hg19, 1.06 billion for hg38) Common dbSNP (155): approximately 15 million variants with a minor allele frequency (MAF) of at least 1% (0.01) in the 1000 Genomes Phase 3 dataset. Variants in the Mult. subset (below) are excluded. ClinVar dbSNP (155): approximately 820,000 variants mentioned in ClinVar. Note: that includes both benign and pathogenic (as well as uncertain) variants. Variants in the Mult. subset (below) are excluded. Mult. dbSNP (155): variants that have been mapped to multiple chromosomes, for example chr1 and chr2, raising the question of whether the variant is really a variant or just a difference between duplicated sequences. There are some exceptions in which a variant is mapped to more than one reference sequence, but not culled into this set: A variant may appear in both X and Y pseudo-autosomal regions (PARs) without being included in this set. A variant may also appear in a main chromosome as well as an alternate haplotype or fix patch sequence assigned to that chromosome. A fifth subtrack highlights coordinate ranges to which dbSNP mapped a variant but with genomic coordinates that are not internally consistent, i.e. different coordinate ranges were provided when describing different alleles. This can occur due to a bug with mapping variants from one assembly sequence to another when there is an indel difference between the assembly sequences: Map Err (155): around 134,000 mappings of 88,000 distinct rsIDs for hg19 and 178,000 mappings of 108,000 distinct rsIDs for hg38. Interpreting and Configuring the Graphical Display SNVs and pure deletions are displayed as boxes covering the affected base(s). Pure insertions are drawn as single-pixel tickmarks between the base before and the base after the insertion. Insertions and/or deletions in repetitive regions may be represented by a half-height box showing uncertainty in placement, followed by a full-height box showing the number of deleted bases, or a full-height tickmark to indicate an insertion. When an insertion or deletion falls in a repetitive region, the placement may be ambiguous. For example, if the reference genome contains "TAAAG" but some individuals have "TAAG" at the same location, then the variant is a deletion of a single A relative to the reference genome. However, which A was deleted? There is no way to tell whether the first, second or third A was removed. Different variant mapping tools may place the deletion at different bases in the reference genome. To reduce errors in merging variant calls made with different left vs. right biases, dbSNP made a major change in its representation of deletion/insertion variants in build 152. Now, instead of assigning a single-base genomic location at one of the A's, dbSNP expands the coordinates to encompass the whole repetitive region, so the variant is represented as a deletion of 3 A's combined with an insertion of 2 A's. In the track display, there will be a half-height box covering the first two A's, followed by a full-height box covering the third A, to show a net loss of one base but an uncertain placement within the three A's. Variants are colored according to functional effect on genes annotated by dbSNP: Protein-altering variants and splice site variants are red. Synonymous codon variants are green. Non-coding transcript or Untranslated Region (UTR) variants are blue. On the track controls page, several variant properties can be included or excluded from the item labels: rs# identifier assigned by dbSNP, reference/alternate alleles, major/minor alleles (when available) and minor allele frequency (when available). Allele frequencies are reported independently by the project (some of which may have overlapping sets of samples): 1000Genomes: The 1000 Genomes dataset contains data for 2,504 individuals from 26 populations. dbGaP_PopFreq: The new source of dbGaP aggregated frequency data (>1 Million Subjects) provided by dbSNP. TOPMED: The TOPMED dataset contains freeze 8 panel that includes about 158,000 individuals. The approximate ethnic breakdown is European(41%), African (31%), Hispanic or Latino (15%), East Asian (9%), and unknown (4%) ancestry. KOREAN: The Korean Reference Genome Database contains data for 1,465 Korean individuals. SGDP_PRJ: The Simons Genome Diversity Project dataset contains 263 C-panel fully public samples and 16 B-panel fully public samples for a total of 279 samples. Qatari: The dataset contains initial mappings of the genomes of more than 1,000 Qatari nationals. NorthernSweden: The dataset contains 300 whole-genome sequenced human samples from the county of Vasterbotten in northern Sweden. Siberian: The dataset contains paired-end whole-genome sequencing data of 28 modern-day humans from Siberia and Western Russia. TWINSUK: The UK10K - TwinsUK project contains 1854 samples from the Department of Twin Research and Genetic Epidemiology (DTR). The dataset contains data obtained from the 11,000 identical and non-identical twins between the ages of 16 and 85 years old. TOMMO: The Tohoku Medical Megabank Project contains an allele frequency panel of 3552 Japanese individuals, including the X chromosome. ALSPAC: The UK10K - Avon Longitudinal Study of Parents and Children project contains 1927 sample including individuals obtained from the ALSPAC population. This population contains more than 14,000 mothers enrolled during pregnancy in 1991 and 1992. GENOME_DK: The dataset contains the sequencing of Danish parent-offspring trios to determine genomic variation within the Danish population. GnomAD: The gnomAD genome dataset includes a catalog containing 602M SNVs and 105M indels based on the whole-genome sequencing of 71,702 samples mapped to the GRCh38 build of the human reference genome. GoNL: The Genome of the Netherlands (GoNL) Project characterizes DNA sequence variation, common and rare, for SNVs and short insertions and deletions (indels) and large deletions in 769 individuals of Dutch ancestry selected from five biobanks under the auspices of the Dutch hub of the Biobanking and Biomolecular Research Infrastructure (BBMRI-NL). Estonian: The dataset contains genetic variation in the Estonian population: pharmacogenomics study of adverse drug effects using electronic health records. Vietnamese: The Kinh Vietnamese database contains 24.81 million variants (22.47 million single nucleotide polymorphisms (SNPs) and 2.34 million indels), of which 0.71 million variants are novel. Korea1K: The dataset contains 1,094 Korean personal genomes with clinical information. HapMap: (HapMap is being retired.) The International HapMap Project contains samples from African, Asian, or European populations. PRJEB36033: The dataset contains ancient Sardinia genome-wide 1240k capture data from 70 ancient Sardinians. HGDP_Stanford: The Stanford HGDP SNP genotyping data consists of ~660,918 tag SNPs in autosomes, chromosome X and Y, the pseudoautosomal region, and mitochondrial DNA, typed across 1043 individuals from all panel populations. Daghestan: The dataset contains genotypes of >550 000 autosomal single-nucleotide polymorphisms (SNPs) in a set of 14 population isolates speaking Nakh-Daghestanian (ND) languages. PAGE_STUDY: The PAGE Study: How Genetic Diversity Improves Our Understanding of the Architecture of Complex Traits. Chileans: The dataset consists of genetic variation on the Chileans using genotype data on ~685,944 SNPs from 313 individuals across the whole-continental country. MGP: MGP contains aggregated information on 267 healthy individuals, representative of the Spanish population that were used as controls in the MGP (Medical Genome Project). PRJEB37584: The dataset contains genome-wide genotype analysis that identified copy number variations in cranial meningiomas in Chinese patients, and demonstrated diverse CNV burdens among individuals with diverse clinical features. GoESP: The NHLBI Grand Opportunity Exome Sequencing Project (GO-ESP) dataset contains 6503 samples drawn from multiple ESP cohorts and represents all of the ESP exome variant data. ExAC: The Exome Aggregation Consortium (ExAC) dataset contains 60,706 unrelated individuals sequenced as part of various disease-specific and population genetic studies. Individuals affected by severe pediatric disease have been removed. GnomAD_exomes: The gnomAD v2.1 exome dataset comprises a total of 16 million SNVs and 1.2 million indels from 125,748 exomes in 14 populations. FINRISK: The FINRISK cohorts comprise the respondents of representative, cross-sectional population surveys that are carried out every 5 years since 1972, to assess the risk factors of chronic diseases (e.g. CVD, diabetes, obesity, cancer) and health behavior in the working age population. PharmGKB: The dataset contains aggregated frequency data for all PharmGKB submissions. PRJEB37766: The Mexican Genomic Database for Addiction Research. The project from which to take allele frequency data defaults to 1000 Genomes but can be set to any of those projects. Using the track controls, variants can be filtered by minimum minor allele frequency (MAF) variation class/type (e.g. SNV, insertion, deletion) functional effect on a gene (e.g. synonymous, frameshift, intron, upstream) assorted features and anomalies noted by UCSC during processing of dbSNP's data Interesting and anomalous conditions noted by UCSC While processing the information downloaded from dbSNP, UCSC annotates some properties of interest. These are noted on the item details page, and may be useful to include or exclude affected variants. Some are purely informational: keyword in data file (dbSnp155.bb) # in hg19# in hg38description clinvar 627817 630503 Variant is in ClinVar. clinvarBenign 275541 276409 Variant is in ClinVar with clinical significance of benign and/or likely benign. clinvarConflicting 16925 16834 Variant is in ClinVar with reports of both benign and pathogenic significance. clinvarPathogenic 56373 56475 Variant is in ClinVar with clinical significance of pathogenic and/or likely pathogenic. commonAll 14904503 15862783 Variant is "common", i.e. has a Minor Allele Frequency of at least 1% in all projects reporting frequencies. commonSome 59633864 62095091 Variant is "common", i.e. has a Minor Allele Frequency of at least 1% in some, but not all, projects reporting frequencies. diffMajor 12748733 13073288 Different frequency sources have different major alleles. overlapDiffClass 198945442 207101421 This variant overlaps another variant with a different type/class. overlapSameClass 29281958 30301090 This variant overlaps another with the same type/class but different start/end. rareAll 906113910 938985356 Variant is "rare", i.e. has a Minor Allele Frequency of less than 1% in all projects reporting frequencies, or has no frequency data. rareSome 950843271 985217664 Variant is "rare", i.e. has a Minor Allele Frequency of less than 1% in some, but not all, projects reporting frequencies, or has no frequency data. revStrand 5540864 6770772 Alleles are displayed on the + strand at the current position. dbSNP's alleles are displayed on the + strand of a different assembly sequence, so dbSNP's variant page shows alleles that are reverse-complemented with respect to the alleles displayed above. while others may indicate that the reference genome contains a rare variant or sequencing issue: keyword in data file (dbSnp155.bb) # in hg19# in hg38description refIsAmbiguous 19 41 The reference genome allele contains an IUPAC ambiguous base (e.g. 'R' for 'A or G', or 'N' for 'any base'). refIsMinor 14950212 15386394 The reference genome allele is not the major allele in at least one project. refIsRare 793081 822757 The reference genome allele is rare (i.e. allele frequency refIsSingleton 694310 712794 The reference genome allele has never been observed in a population sequencing project reporting frequencies. refMismatch 1 18 The reference genome allele reported by dbSNP differs from the GenBank assembly sequence. This is very rare and in all cases observed so far, the GenBank assembly has an 'N' while the RefSeq assembly used by dbSNP has a less ambiguous character such as 'R'. and others may indicate an anomaly or problem with the variant data: keyword in data file (dbSnp155.bb) # in hg19# in hg38description altIsAmbiguous 5294 5361 At least one alternate allele contains an IUPAC ambiguous base (e.g. 'R' for 'A or G'). For alleles containing more than one ambiguous base, this may create a combinatoric explosion of possible alleles. classMismatch 13289 18475 Variation class/type is inconsistent with alleles mapped to this genome assembly. clusterError 373258 459130 This variant has the same start, end and class as another variant; they probably should have been merged into one variant. freqIncomplete 0 0 At least one project reported counts for only one allele which implies that at least one allele is missing from the report; that project's frequency data are ignored. freqIsAmbiguous 4332 4399 At least one allele reported by at least one project that reports frequencies contains an IUPAC ambiguous base. freqNotMapped 1149972 1141935 At least one project reported allele frequencies relative to a different assembly; However, dbSNP does not include a mapping of this variant to that assembly, which implies a problem with mapping the variant across assemblies. The mapping on this assembly may have an issue; evaluate carefully vs. original submissions, which you can view by clicking through to dbSNP above. freqNotRefAlt 74139 110646 At least one allele reported by at least one project that reports frequencies does not match any of the reference or alternate alleles listed by dbSNP. multiMap 799777 286666 This variant has been mapped to more than one distinct genomic location. otherMapErr 91260 195051 At least one other mapping of this variant has erroneous coordinates. The mapping(s) with erroneous coordinates are excluded from this track and are included in the Map Err subtrack. Sometimes despite this mapping having legal coordinates, there may still be an issue with this mapping's coordinates and alleles; you may want to click through to dbSNP to compare the initial submission's coordinates and alleles. In hg19, 55454 distinct rsIDs are affected; in hg38, 86636. Data Sources and Methods dbSNP has collected genetic variant reports from researchers worldwide for more than 20 years. Since the advent of next-generation sequencing methods and the population sequencing efforts that they enable, dbSNP has grown exponentially, requiring a new data schema, computational pipeline, web infrastructure, and download files. (Holmes et al.) The same challenges of exponential growth affected UCSC's presentation of dbSNP variants, so we have taken the opportunity to change our internal representation and import pipeline. Most notably, flanking sequences are no longer provided by dbSNP, because most submissions have been genomic variant calls in VCF format as opposed to independent sequences. We downloaded JSON files available from dbSNP at http://ftp.ncbi.nlm.nih.gov/snp/archive/b155/JSON/, extracted a subset of the information about each variant, and collated it into a bigBed file using the bigDbSnp.as schema with the information necessary for filtering and displaying the variants, as well as a separate file containing more detailed information to be displayed on each variant's details page (dbSnpDetails.as schema). Data Access Note: It is not recommeneded to use LiftOver to convert SNPs between assemblies, and more information about how to convert SNPs between assemblies can be found on the following FAQ entry. Since dbSNP has grown to include over 1 billion variants, the size of the All dbSNP (155) subtrack can cause the Table Browser and Data Integrator to time out, leading to a blank page or truncated output, unless queries are restricted to a chromosomal region, to particular defined regions, to a specific set of rs# IDs (which can be pasted/uploaded into the Table Browser), or to one of the subset tracks such as Common (~15 million variants) or ClinVar (~0.8M variants). For automated analysis, the track data files can be downloaded from the downloads server for hg19 and hg38. file format subtrack dbSnp155.bb hg19 hg38 bigDbSnp (bigBed4+13) All dbSNP (155) dbSnp155ClinVar.bb hg19 hg38 bigDbSnp (bigBed4+13) ClinVar dbSNP (155) dbSnp155Common.bb hg19 hg38 bigDbSnp (bigBed4+13) Common dbSNP (155) dbSnp155Mult.bb hg19 hg38 bigDbSnp (bigBed4+13) Mult. dbSNP (155) dbSnp155BadCoords.bb hg19 hg38 bigBed4 Map Err (155) dbSnp155Details.tab.gz gzip-compressed tab-separated text Detailed variant properties, independent of genome assembly version Several utilities for working with bigBed-formatted binary files can be downloaded here. Run a utility with no arguments to see a brief description of the utility and its options. bigBedInfo provides summary statistics about a bigBed file including the number of items in the file. With the -as option, the output includes an autoSql definition of data columns, useful for interpreting the column values. bigBedToBed converts the binary bigBed data to tab-separated text. Output can be restricted to a particular region by using the -chrom, -start and -end options. bigBedNamedItems extracts rows for one or more rs# IDs. Example: retrieve all variants in the region chr1:200001-200400 bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/snp/dbSnp155.bb -chrom=chr1 -start=200000 -end=200400 stdout Example: retrieve variant rs6657048 bigBedNamedItems dbSnp155.bb rs6657048 stdout Example: retrieve all variants with rs# IDs in a file (myIds.txt) and output to another file (dbSnp155.myIds.bed) bigBedNamedItems -nameFile dbSnp155.bb myIds.txt dbSnp155.myIds.bed The columns in the bigDbSnp/bigBed files and dbSnp155Details.tab.gz file are described in bigDbSnp.as and dbSnpDetails.as respectively. For columns that contain lists of allele frequency data, the order of projects providing the data listed is as follows: 1000Genomes dbGaP_PopFreq TOPMED KOREAN SGDP_PRJ Qatari NorthernSweden Siberian TWINSUK TOMMO ALSPAC GENOME_DK GnomAD GoNL Estonian Vietnamese Korea1K HapMap PRJEB36033 HGDP_Stanford Daghestan PAGE_STUDY Chileans MGP PRJEB37584 GoESP ExAC GnomAD_exomes FINRISK PharmGKB PRJEB37766 The functional effect (maxFuncImpact) for each variant contains the Sequence Ontology (SO) ID for the greatest functional impact on the gene. This field contains a 0 when no SO terms are annotated on the variant. UCSC also has an API that can be used to retrieve values from a particular chromosome range. A list of rs# IDs can be pasted/uploaded in the Variant Annotation Integrator tool to find out which genes (if any) the variants are located in, as well as functional effect such as intron, coding-synonymous, missense, frameshift, etc. Please refer to our searchable mailing list archives for more questions and example queries, or our Data Access FAQ for more information. References Holmes JB, Moyer E, Phan L, Maglott D, Kattman B. SPDI: Data Model for Variants and Applications at NCBI. Bioinformatics. 2019 Nov 18;. PMID: 31738401 Sayers EW, Agarwala R, Bolton EE, Brister JR, Canese K, Clark K, Connor R, Fiorini N, Funk K, Hefferon T et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2019 Jan 8;47(D1):D23-D28. PMID: 30395293; PMC: PMC6323993 Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
dbSnp155ViewVariants Variants Short Genetic Variants from dbSNP release 155 Variation 
dbSnp155 All dbSNP(155) All Short Genetic Variants from dbSNP Release 155 Variation 
dbSnp155Mult Mult. dbSNP(155) Short Genetic Variants from dbSNP Release 155 that Map to Multiple Genomic Loci Variation 
dbSnp155ClinVar ClinVar dbSNP(155) Short Genetic Variants from dbSNP Release 155 Included in ClinVar Variation 
dbSnp155Common Common dbSNP(155) Common (1000 Genomes Phase 3 MAF >= 1%) Short Genetic Variants from dbSNP Release 155 Variation 
dbSnp155ViewErrs Mapping Errors Short Genetic Variants from dbSNP release 155 Variation 
dbSnp155BadCoords Map Err dbSnp(155) Mappings with Inconsistent Coordinates from dbSNP 155 Variation 
dbSnp153Composite dbSNP 153 Short Genetic Variants from dbSNP release 153 Variation Description This track shows short genetic variants (up to approximately 50 base pairs) from dbSNP build 153: single-nucleotide variants (SNVs), small insertions, deletions, and complex deletion/insertions (indels), relative to the reference genome assembly. Most variants in dbSNP are rare, not true polymorphisms, and some variants are known to be pathogenic. For hg38 (GRCh38), approximately 667 million distinct variants (RefSNP clusters with rs# ids) have been mapped to more than 702 million genomic locations including alternate haplotype and fix patch sequences. dbSNP remapped variants from hg38 to hg19 (GRCh37); approximately 658 million distinct variants were mapped to more than 683 million genomic locations including alternate haplotype and fix patch sequences (not all of which are included in UCSC's hg19). This track includes four subtracks of variants: All dbSNP (153): the entire set (683 million for hg19, 702 million for hg38) Common dbSNP (153): approximately 15 million variants with a minor allele frequency (MAF) of at least 1% (0.01) in the 1000 Genomes Phase 3 dataset. Variants in the Mult. subset (below) are excluded. ClinVar dbSNP (153): approximately 455,000 variants mentioned in ClinVar. Note: that includes both benign and pathogenic (as well as uncertain) variants. Variants in the Mult. subset (below) are excluded. Mult. dbSNP (153): variants that have been mapped to multiple chromosomes, for example chr1 and chr2, raising the question of whether the variant is really a variant or just a difference between duplicated sequences. There are some exceptions in which a variant is mapped to more than one reference sequence, but not culled into this set: A variant may appear in both X and Y pseudo-autosomal regions (PARs) without being included in this set. A variant may also appear in a main chromosome as well as an alternate haplotype or fix patch sequence assigned to that chromosome. A fifth subtrack highlights coordinate ranges to which dbSNP mapped a variant but with genomic coordinates that are not internally consistent, i.e. different coordinate ranges were provided when describing different alleles. This can occur due to a bug with mapping variants from one assembly sequence to another when there is an indel difference between the assembly sequences: Map Err (153): around 120,000 mappings of 55,000 distinct rsIDs for hg19 and 149,000 mappings of 86,000 distinct rsIDs for hg38. Interpreting and Configuring the Graphical Display SNVs and pure deletions are displayed as boxes covering the affected base(s). Pure insertions are drawn as single-pixel tickmarks between the base before and the base after the insertion. Insertions and/or deletions in repetitive regions may be represented by a half-height box showing uncertainty in placement, followed by a full-height box showing the number of deleted bases, or a full-height tickmark to indicate an insertion. When an insertion or deletion falls in a repetitive region, the placement may be ambiguous. For example, if the reference genome contains "TAAAG" but some individuals have "TAAG" at the same location, then the variant is a deletion of a single A relative to the reference genome. However, which A was deleted? There is no way to tell whether the first, second or third A was removed. Different variant mapping tools may place the deletion at different bases in the reference genome. To reduce errors in merging variant calls made with different left vs. right biases, dbSNP made a major change in its representation of deletion/insertion variants in build 152. Now, instead of assigning a single-base genomic location at one of the A's, dbSNP expands the coordinates to encompass the whole repetitive region, so the variant is represented as a deletion of 3 A's combined with an insertion of 2 A's. In the track display, there will be a half-height box covering the first two A's, followed by a full-height box covering the third A, to show a net loss of one base but an uncertain placement within the three A's. Variants are colored according to functional effect on genes annotated by dbSNP: Protein-altering variants and splice site variants are red. Synonymous codon variants are green. Non-coding transcript or Untranslated Region (UTR) variants are blue. On the track controls page, several variant properties can be included or excluded from the item labels: rs# identifier assigned by dbSNP, reference/alternate alleles, major/minor alleles (when available) and minor allele frequency (when available). Allele frequencies are reported independently by twelve projects (some of which may have overlapping sets of samples): 1000Genomes: The 1000 Genomes Phase 3 dataset contains data for 2,504 individuals from 26 populations. GnomAD exomes: The gnomAD v2.1 exome dataset comprises a total of 16 million SNVs and 1.2 million indels from 125,748 exomes in 14 populations. TOPMED: The TOPMED dataset contains phase 3 data from freeze 5 panel that include more than 60,000 individuals. The approximate ethnic breakdown is European(52%), African (31%), Hispanic or Latino (10%), and East Asian (7%) ancestry. PAGE STUDY: The PAGE Study: How Genetic Diversity Improves Our Understanding of the Architecture of Complex Traits. GnomAD genomes: The gnomAD v2.1 genome dataset includes 229 million SNVs and 33 million indels from 15,708 genomes in 9 populations. GoESP: The NHLBI Grand Opportunity Exome Sequencing Project (GO-ESP) dataset contains 6503 samples drawn from multiple ESP cohorts and represents all of the ESP exome variant data. Estonian: Genetic variation in the Estonian population: pharmacogenomics study of adverse drug effects using electronic health records. ALSPAC: The UK10K - Avon Longitudinal Study of Parents and Children project contains 1927 sample including individuals obtained from the ALSPAC population. This population contains more than 14,000 mothers enrolled during pregnancy in 1991 and 1992. TWINSUK: The UK10K - TwinsUK project contains 1854 samples from the Department of Twin Research and Genetic Epidemiology (DTR). The DTR dataset contains data obtained from the 11,000 identical and non-identical twins between the ages of 16 and 85 years old. NorthernSweden: Whole-genome sequenced control population in northern Sweden reveals subregional genetic differences. This population consists of 300 whole genome sequenced human samples selected from the county of Vasterbotten in northern Sweden. To be selected for inclusion into the population, the individuals had to have reached at least 80 years of age and have no diagnosed cancer. Vietnamese: The Vietnamese Genetic Variation Database includes about 25 million variants (SNVs and indels) from 406 genomes and 305 exomes of unrelated healthy Kinh Vietnamese (KHV) people. The project from which to take allele frequency data defaults to 1000 Genomes but can be set to any of those projects. Using the track controls, variants can be filtered by minimum minor allele frequency (MAF) variation class/type (e.g. SNV, insertion, deletion) functional effect on a gene (e.g. synonymous, frameshift, intron, upstream) assorted features and anomalies noted by UCSC during processing of dbSNP's data Interesting and anomalous conditions noted by UCSC While processing the information downloaded from dbSNP, UCSC annotates some properties of interest. These are noted on the item details page, and may be useful to include or exclude affected variants. Some are purely informational: keyword in data file (dbSnp153.bb) # in hg19# in hg38description clinvar 454678 453996 Variant is in ClinVar. clinvarBenign 143864 143736 Variant is in ClinVar with clinical significance of benign and/or likely benign. clinvarConflicting 7932 7950 Variant is in ClinVar with reports of both benign and pathogenic significance. clinvarPathogenic 96242 95262 Variant is in ClinVar with clinical significance of pathogenic and/or likely pathogenic. commonAll 12184521 12438655 Variant is "common", i.e. has a Minor Allele Frequency of at least 1% in all projects reporting frequencies. commonSome 20541190 20902944 Variant is "common", i.e. has a Minor Allele Frequency of at least 1% in some, but not all, projects reporting frequencies. diffMajor 1377831 1399109 Different frequency sources have different major alleles. overlapDiffClass 107015341 110007682 This variant overlaps another variant with a different type/class. overlapSameClass 16915239 17291289 This variant overlaps another with the same type/class but different start/end. rareAll 662601770 681696398 Variant is "rare", i.e. has a Minor Allele Frequency of less than 1% in all projects reporting frequencies, or has no frequency data. rareSome 670958439 690160687 Variant is "rare", i.e. has a Minor Allele Frequency of less than 1% in some, but not all, projects reporting frequencies, or has no frequency data. revStrand 3813702 4532511 Alleles are displayed on the + strand at the current position. dbSNP's alleles are displayed on the + strand of a different assembly sequence, so dbSNP's variant page shows alleles that are reverse-complemented with respect to the alleles displayed above. while others may indicate that the reference genome contains a rare variant or sequencing issue: keyword in data file (dbSnp153.bb) # in hg19# in hg38description refIsAmbiguous 101 111 The reference genome allele contains an IUPAC ambiguous base (e.g. 'R' for 'A or G', or 'N' for 'any base'). refIsMinor 3272116 3360435 The reference genome allele is not the major allele in at least one project. refIsRare 136547 160827 The reference genome allele is rare (i.e. allele frequency refIsSingleton 37832 50927 The reference genome allele has never been observed in a population sequencing project reporting frequencies. refMismatch 4 33 The reference genome allele reported by dbSNP differs from the GenBank assembly sequence. This is very rare and in all cases observed so far, the GenBank assembly has an 'N' while the RefSeq assembly used by dbSNP has a less ambiguous character such as 'R'. and others may indicate an anomaly or problem with the variant data: keyword in data file (dbSnp153.bb) # in hg19# in hg38description altIsAmbiguous 10755 10888 At least one alternate allele contains an IUPAC ambiguous base (e.g. 'R' for 'A or G'). For alleles containing more than one ambiguous base, this may create a combinatoric explosion of possible alleles. classMismatch 5998 6216 Variation class/type is inconsistent with alleles mapped to this genome assembly. clusterError 114826 128306 This variant has the same start, end and class as another variant; they probably should have been merged into one variant. freqIncomplete 3922 4673 At least one project reported counts for only one allele which implies that at least one allele is missing from the report; that project's frequency data are ignored. freqIsAmbiguous 7656 7756 At least one allele reported by at least one project that reports frequencies contains an IUPAC ambiguous base. freqNotMapped 2685 6590 At least one project reported allele frequencies relative to a different assembly; However, dbSNP does not include a mapping of this variant to that assembly, which implies a problem with mapping the variant across assemblies. The mapping on this assembly may have an issue; evaluate carefully vs. original submissions, which you can view by clicking through to dbSNP above. freqNotRefAlt 17694 32170 At least one allele reported by at least one project that reports frequencies does not match any of the reference or alternate alleles listed by dbSNP. multiMap 562180 132123 This variant has been mapped to more than one distinct genomic location. otherMapErr 114095 204219 At least one other mapping of this variant has erroneous coordinates. The mapping(s) with erroneous coordinates are excluded from this track and are included in the Map Err subtrack. Sometimes despite this mapping having legal coordinates, there may still be an issue with this mapping's coordinates and alleles; you may want to click through to dbSNP to compare the initial submission's coordinates and alleles. In hg19, 55454 distinct rsIDs are affected; in hg38, 86636. Data Sources and Methods dbSNP has collected genetic variant reports from researchers worldwide for more than 20 years. Since the advent of next-generation sequencing methods and the population sequencing efforts that they enable, dbSNP has grown exponentially, requiring a new data schema, computational pipeline, web infrastructure, and download files. (Holmes et al.) The same challenges of exponential growth affected UCSC's presentation of dbSNP variants, so we have taken the opportunity to change our internal representation and import pipeline. Most notably, flanking sequences are no longer provided by dbSNP, because most submissions have been genomic variant calls in VCF format as opposed to independent sequences. We downloaded JSON files available from dbSNP at ftp://ftp.ncbi.nlm.nih.gov/snp/archive/b153/JSON/, extracted a subset of the information about each variant, and collated it into a bigBed file using the bigDbSnp.as schema with the information necessary for filtering and displaying the variants, as well as a separate file containing more detailed information to be displayed on each variant's details page (dbSnpDetails.as schema). Data Access Note: It is not recommeneded to use LiftOver to convert SNPs between assemblies, and more information about how to convert SNPs between assemblies can be found on the following FAQ entry. Since dbSNP has grown to include approximately 700 million variants, the size of the All dbSNP (153) subtrack can cause the Table Browser and Data Integrator to time out, leading to a blank page or truncated output, unless queries are restricted to a chromosomal region, to particular defined regions, to a specific set of rs# IDs (which can be pasted/uploaded into the Table Browser), or to one of the subset tracks such as Common (~15 million variants) or ClinVar (~0.5M variants). For automated analysis, the track data files can be downloaded from the downloads server for hg19 and hg38. file format subtrack dbSnp153.bb hg19 hg38 bigDbSnp (bigBed4+13) All dbSNP (153) dbSnp153ClinVar.bb hg19 hg38 bigDbSnp (bigBed4+13) ClinVar dbSNP (153) dbSnp153Common.bb hg19 hg38 bigDbSnp (bigBed4+13) Common dbSNP (153) dbSnp153Mult.bb hg19 hg38 bigDbSnp (bigBed4+13) Mult. dbSNP (153) dbSnp153BadCoords.bb hg19 hg38 bigBed4 Map Err (153) dbSnp153Details.tab.gz gzip-compressed tab-separated text Detailed variant properties, independent of genome assembly version Several utilities for working with bigBed-formatted binary files can be downloaded here. Run a utility with no arguments to see a brief description of the utility and its options. bigBedInfo provides summary statistics about a bigBed file including the number of items in the file. With the -as option, the output includes an autoSql definition of data columns, useful for interpreting the column values. bigBedToBed converts the binary bigBed data to tab-separated text. Output can be restricted to a particular region by using the -chrom, -start and -end options. bigBedNamedItems extracts rows for one or more rs# IDs. Example: retrieve all variants in the region chr1:200001-200400 bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/snp/dbSnp153.bb -chrom=chr1 -start=200000 -end=200400 stdout Example: retrieve variant rs6657048 bigBedNamedItems dbSnp153.bb rs6657048 stdout Example: retrieve all variants with rs# IDs in file myIds.txt bigBedNamedItems -nameFile dbSnp153.bb myIds.txt dbSnp153.myIds.bed The columns in the bigDbSnp/bigBed files and dbSnp153Details.tab.gz file are described in bigDbSnp.as and dbSnpDetails.as respectively. For columns that contain lists of allele frequency data, the order of projects providing the data listed is as follows: 1000Genomes GnomAD exomes TOPMED PAGE STUDY GnomAD genomes GoESP Estonian ALSPAC TWINSUK NorthernSweden Vietnamese UCSC also has an API that can be used to retrieve values from a particular chromosome range. A list of rs# IDs can be pasted/uploaded in the Variant Annotation Integrator tool to find out which genes (if any) the variants are located in, as well as functional effect such as intron, coding-synonymous, missense, frameshift, etc. Please refer to our searchable mailing list archives for more questions and example queries, or our Data Access FAQ for more information. References Holmes JB, Moyer E, Phan L, Maglott D, Kattman B. SPDI: Data Model for Variants and Applications at NCBI. Bioinformatics. 2019 Nov 18;. PMID: 31738401 Sayers EW, Agarwala R, Bolton EE, Brister JR, Canese K, Clark K, Connor R, Fiorini N, Funk K, Hefferon T et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2019 Jan 8;47(D1):D23-D28. PMID: 30395293; PMC: PMC6323993 Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
dbSnpArchive dbSNP Archive dbSNP Track Archive Variation Description This composite track contains information about single nucleotide polymorphisms (SNPs) and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP, available from ftp.ncbi.nih.gov/snp. You can click into each track for a version/subset-specific description. This collection includes numbered versions of the entire dbSNP datasets (All SNP) as well as three tracks with subsets of the items in that version. Here is information on each of the subsets: dbSNP 153: The dbSNP build 153 is composed of 5 subtracks. Click the track for a description of the subtracks. Common SNPs: SNPs that have a minor allele frequency of at least 1% and are mapped to a single location in the reference genome assembly. Frequency data are not available for all SNPs, so this subset is incomplete. Flagged SNPs: SNPs flagged as clinically associated by dbSNP, mapped to a single location in the reference genome assembly, and not known to have a minor allele frequency of at least 1%. Frequency data are not available for all SNPs, so this subset may include some SNPs whose true minor allele frequency is 1% or greater. Mult. SNPs: SNPs that have been mapped to multiple locations in the reference genome assembly. The default maximum weight for this track is 1, so unless the setting is changed in the track controls, SNPs that map to multiple genomic locations will be omitted from display. When a SNP's flanking sequences map to multiple locations in the reference genome, it calls into question whether there is true variation at those sites, or whether the sequences at those sites are merely highly similar but not identical. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors: Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP Weight can be 0, 1, 2, 3 or 10. Weight = 1 are the highest quality alignments. Weight = 0 and weight = 10 are excluded from the data set. A filter on maximum weight value is supported, which defaults to 1 on all tracks except the Mult. SNPs track, which defaults to 3. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nih.gov/snp/organisms/ organism_tax_id/database/ (for human, organism_tax_id = human_9606; for mouse, organism_tax_id = mouse_10090). The fasta files were downloaded from ftp://ftp.ncbi.nih.gov/snp/organisms/ organism_tax_id/rs_fasta/ Coordinates, orientation, location type and dbSNP reference allele data were obtained from files like b138_SNPContigLoc.bcp.gz and b138_ContigInfo.bcp.gz. b138_SNPMapInfo.bcp.gz provides the alignment weights. Functional classification was obtained from files like b138_SNPContigLocusId.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access Note: It is not recommeneded to use LiftOver to convert SNPs between assemblies, and more information about how to convert SNPs between assemblies can be found on the following FAQ entry. The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation files can be downloaded in their entirety for hg38, hg19, and mm10 as (snp*.txt.gz). You can also make queries using the UCSC Genome Browser JSON API or public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download in the genome's snp*Mask folder. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exlcude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
dbSnp153ViewVariants Variants Short Genetic Variants from dbSNP release 153 Variation 
dbSnp153 All dbSNP(153) All Short Genetic Variants from dbSNP Release 153 Variation 
dbSnp153Mult dbSNP(153) Mult. Short Genetic Variants from dbSNP Release 153 that Map to Multiple Genomic Loci Variation 
dbSnp153ClinVar dbSNP(153) in ClinVar Short Genetic Variants from dbSNP Release 153 Included in ClinVar Variation 
dbSnp153Common Common dbSNP(153) Common (1000 Genomes Phase 3 MAF >= 1%) Short Genetic Variants from dbSNP Release 153 Variation 
dbSnp153ViewErrs Mapping Errors Short Genetic Variants from dbSNP release 153 Variation 
dbSnp153BadCoords Map Err dbSnp(153) Mappings with Inconsistent Coordinates from dbSNP 153 Variation 
snp151Common Common SNPs(151) Simple Nucleotide Polymorphisms (dbSNP 151) Found in >= 1% of Samples Variation Description This track contains information about a subset of the single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 151, available from ftp.ncbi.nlm.nih.gov/snp. Only SNPs that have a minor allele frequency (MAF) of at least 1% and are mapped to a single location in the reference genome assembly are included in this subset. Frequency data are not available for all SNPs, so this subset is incomplete. Allele counts from all submissions that include frequency data are combined when determining MAF, so for example the allele counts from the 1000 Genomes Project and an independent submitter may be combined for the same variant. dbSNP provides download files in the Variant Call Format (VCF) that include a "COMMON" flag in the INFO column. That is determined by a different method, and is generally a superset of the UCSC Common set. dbSNP uses frequency data from the 1000 Genomes Project only, and considers a variant COMMON if it has a MAF of at least 0.01 in any of the five super-populations: African (AFR) Admixed American (AMR) East Asian (EAS) European (EUR) South Asian (SAS) In build 151, dbSNP marks approximately 38M variants as COMMON; 23M of those have a global MAF < 0.01. The remainder should be in agreement with UCSC's Common subset. The selection of SNPs with a minor allele frequency of 1% or greater is an attempt to identify variants that appear to be reasonably common in the general population. Taken as a set, common variants should be less likely to be associated with severe genetic diseases due to the effects of natural selection, following the view that deleterious variants are not likely to become common in the population. However, the significance of any particular variant should be interpreted only by a trained medical geneticist using all available information. The remainder of this page is identical on the following tracks: Common SNPs(151) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(151) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(151) - SNPs mapping in more than one place on reference assembly. All SNPs(151) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors. If a SNP has more than one of these attributes, the stronger color will override the weaker color. The order of colors, from strongest to weakest, is red, green, blue, gray, and black. Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Non-coding (ncRNA): (nc_transcript_variant) are colored blue. Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP. Before dbSNP build 147, weight had values 1, 2 or 3, with 1 being the highest quality (mapped to a single genomic location). As of dbSNP build 147, dbSNP now releases only the variants with weight 1. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period >= 12) is shown in lower case, and matching bases are indicated by a "+". Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b151_GRCh37p13/database/data/organism_data/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b151_GRCh38p7/database/data/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b151_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b151_GRCh38p7/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b151_SNPContigLoc_N.bcp.gz and b151_ContigInfo_N.bcp.gz. (N = 105 for hg19, 108 for hg38) b151_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b151_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp151*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp151 All SNPs(151) Simple Nucleotide Polymorphisms (dbSNP 151) Variation Description This track contains information about single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 151, available from ftp.ncbi.nlm.nih.gov/snp. Three tracks contain subsets of the items in this track: Common SNPs(151): SNPs that have a minor allele frequency of at least 1% and are mapped to a single location in the reference genome assembly. Frequency data are not available for all SNPs, so this subset is incomplete. Flagged SNPs(151): SNPs flagged as clinically associated by dbSNP, mapped to a single location in the reference genome assembly, and not known to have a minor allele frequency of at least 1%. Frequency data are not available for all SNPs, so this subset may include some SNPs whose true minor allele frequency is 1% or greater. Mult. SNPs(151): SNPs that have been mapped to multiple locations in the reference genome assembly. There are very few SNPs in this category because dbSNP has been filtering out almost all multiple-mapping SNPs since build 149. The default maximum weight for this track is 1, so unless the setting is changed in the track controls, SNPs that map to multiple genomic locations will be omitted from display. When a SNP's flanking sequences map to multiple locations in the reference genome, it calls into question whether there is true variation at those sites, or whether the sequences at those sites are merely highly similar but not identical. The remainder of this page is identical on the following tracks: Common SNPs(151) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(151) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(151) - SNPs mapping in more than one place on reference assembly. All SNPs(151) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors. If a SNP has more than one of these attributes, the stronger color will override the weaker color. The order of colors, from strongest to weakest, is red, green, blue, gray, and black. Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Non-coding (ncRNA): (nc_transcript_variant) are colored blue. Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP. Before dbSNP build 147, weight had values 1, 2 or 3, with 1 being the highest quality (mapped to a single genomic location). As of dbSNP build 147, dbSNP now releases only the variants with weight 1. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period >= 12) is shown in lower case, and matching bases are indicated by a "+". Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b151_GRCh37p13/database/data/organism_data/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b151_GRCh38p7/database/data/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b151_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b151_GRCh38p7/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b151_SNPContigLoc_N.bcp.gz and b151_ContigInfo_N.bcp.gz. (N = 105 for hg19, 108 for hg38) b151_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b151_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp151*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp151Flagged Flagged SNPs(151) Simple Nucleotide Polymorphisms (dbSNP 151) Flagged by dbSNP as Clinically Assoc Variation Description This track contains information about a subset of the single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 151, available from ftp.ncbi.nlm.nih.gov/snp. Only SNPs flagged as clinically associated by dbSNP, mapped to a single location in the reference genome assembly, and not known to have a minor allele frequency of at least 1%, are included in this subset. Frequency data are not available for all SNPs, so this subset probably includes some SNPs whose true minor allele frequency is 1% or greater. The significance of any particular variant in this track should be interpreted only by a trained medical geneticist using all available information. For example, some variants are included in this track because of their inclusion in a Locus-Specific Database (LSDB) or mention in OMIM, but are not thought to be disease-causing, so inclusion of a variant in this track is not necessarily an indicator of risk. Again, all available information must be carefully considered by a qualified professional. The remainder of this page is identical on the following tracks: Common SNPs(151) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(151) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(151) - SNPs mapping in more than one place on reference assembly. All SNPs(151) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors. If a SNP has more than one of these attributes, the stronger color will override the weaker color. The order of colors, from strongest to weakest, is red, green, blue, gray, and black. Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Non-coding (ncRNA): (nc_transcript_variant) are colored blue. Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP. Before dbSNP build 147, weight had values 1, 2 or 3, with 1 being the highest quality (mapped to a single genomic location). As of dbSNP build 147, dbSNP now releases only the variants with weight 1. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period >= 12) is shown in lower case, and matching bases are indicated by a "+". Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b151_GRCh37p13/database/data/organism_data/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b151_GRCh38p7/database/data/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b151_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b151_GRCh38p7/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b151_SNPContigLoc_N.bcp.gz and b151_ContigInfo_N.bcp.gz. (N = 105 for hg19, 108 for hg38) b151_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b151_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp151*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp151Mult Mult. SNPs(151) Simple Nucleotide Polymorphisms (dbSNP 151) That Map to Multiple Genomic Loci Variation Description This track contains information about a subset of the single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 150, available from ftp.ncbi.nlm.nih.gov/snp. Only SNPs that have been mapped to multiple locations in the reference genome assembly are included in this subset. When a SNP's flanking sequences map to multiple locations in the reference genome, it calls into question whether there is true variation at those sites, or whether the sequences at those sites are merely highly similar but not identical. Since build 149, dbSNP has been filtering out almost all such "SNPs" so there are very few items in this track. The default maximum weight for this track is 3, unlike the other dbSNP build 150 tracks which have a maximum weight of 1. That enables these multiply-mapped SNPs to appear in the display, while by default they will not appear in the All SNPs(150) track because of its maximum weight filter. The remainder of this page is identical on the following tracks: Common SNPs(150) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(150) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(150) - SNPs mapping in more than one place on reference assembly. All SNPs(150) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors. If a SNP has more than one of these attributes, the stronger color will override the weaker color. The order of colors, from strongest to weakest, is red, green, blue, gray, and black. Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Non-coding (ncRNA): (nc_transcript_variant) are colored blue. Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP. Before dbSNP build 147, weight had values 1, 2 or 3, with 1 being the highest quality (mapped to a single genomic location). As of dbSNP build 147, dbSNP now releases only the variants with weight 1. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period >= 12) is shown in lower case, and matching bases are indicated by a "+". Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b150_GRCh37p13/database/data/organism_data/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b150_GRCh38p7/database/data/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b150_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b150_GRCh38p7/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b150_SNPContigLoc_N.bcp.gz and b150_ContigInfo_N.bcp.gz. (N = 105 for hg19, 107 for hg38) b150_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b150_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp150*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp150Mult Mult. SNPs(150) Simple Nucleotide Polymorphisms (dbSNP 150) That Map to Multiple Genomic Loci Variation Description This track contains information about a subset of the single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 150, available from ftp.ncbi.nlm.nih.gov/snp. Only SNPs that have been mapped to multiple locations in the reference genome assembly are included in this subset. When a SNP's flanking sequences map to multiple locations in the reference genome, it calls into question whether there is true variation at those sites, or whether the sequences at those sites are merely highly similar but not identical. Since build 149, dbSNP has been filtering out almost all such "SNPs" so there are very few items in this track. The default maximum weight for this track is 3, unlike the other dbSNP build 150 tracks which have a maximum weight of 1. That enables these multiply-mapped SNPs to appear in the display, while by default they will not appear in the All SNPs(150) track because of its maximum weight filter. The remainder of this page is identical on the following tracks: Common SNPs(150) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(150) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(150) - SNPs mapping in more than one place on reference assembly. All SNPs(150) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors. If a SNP has more than one of these attributes, the stronger color will override the weaker color. The order of colors, from strongest to weakest, is red, green, blue, gray, and black. Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Non-coding (ncRNA): (nc_transcript_variant) are colored blue. Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP. Before dbSNP build 147, weight had values 1, 2 or 3, with 1 being the highest quality (mapped to a single genomic location). As of dbSNP build 147, dbSNP now releases only the variants with weight 1. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period >= 12) is shown in lower case, and matching bases are indicated by a "+". Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b150_GRCh37p13/database/data/organism_data/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b150_GRCh38p7/database/data/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b150_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b150_GRCh38p7/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b150_SNPContigLoc_N.bcp.gz and b150_ContigInfo_N.bcp.gz. (N = 105 for hg19, 107 for hg38) b150_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b150_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp150*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp150 All SNPs(150) Simple Nucleotide Polymorphisms (dbSNP 150) Variation Description This track contains information about single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 150, available from ftp.ncbi.nlm.nih.gov/snp. Three tracks contain subsets of the items in this track: Common SNPs(150): SNPs that have a minor allele frequency of at least 1% and are mapped to a single location in the reference genome assembly. Frequency data are not available for all SNPs, so this subset is incomplete. Flagged SNPs(150): SNPs flagged as clinically associated by dbSNP, mapped to a single location in the reference genome assembly, and not known to have a minor allele frequency of at least 1%. Frequency data are not available for all SNPs, so this subset may include some SNPs whose true minor allele frequency is 1% or greater. Mult. SNPs(150): SNPs that have been mapped to multiple locations in the reference genome assembly. There are very few SNPs in this category because dbSNP has been filtering out almost all multiple-mapping SNPs since build 149. The default maximum weight for this track is 1, so unless the setting is changed in the track controls, SNPs that map to multiple genomic locations will be omitted from display. When a SNP's flanking sequences map to multiple locations in the reference genome, it calls into question whether there is true variation at those sites, or whether the sequences at those sites are merely highly similar but not identical. The remainder of this page is identical on the following tracks: Common SNPs(150) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(150) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(150) - SNPs mapping in more than one place on reference assembly. All SNPs(150) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors. If a SNP has more than one of these attributes, the stronger color will override the weaker color. The order of colors, from strongest to weakest, is red, green, blue, gray, and black. Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Non-coding (ncRNA): (nc_transcript_variant) are colored blue. Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP. Before dbSNP build 147, weight had values 1, 2 or 3, with 1 being the highest quality (mapped to a single genomic location). As of dbSNP build 147, dbSNP now releases only the variants with weight 1. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period >= 12) is shown in lower case, and matching bases are indicated by a "+". Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b150_GRCh37p13/database/data/organism_data/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b150_GRCh38p7/database/data/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b150_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b150_GRCh38p7/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b150_SNPContigLoc_N.bcp.gz and b150_ContigInfo_N.bcp.gz. (N = 105 for hg19, 107 for hg38) b150_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b150_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp150*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp150Common Common SNPs(150) Simple Nucleotide Polymorphisms (dbSNP 150) Found in >= 1% of Samples Variation Description This track contains information about a subset of the single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 150, available from ftp.ncbi.nlm.nih.gov/snp. Only SNPs that have a minor allele frequency (MAF) of at least 1% and are mapped to a single location in the reference genome assembly are included in this subset. Frequency data are not available for all SNPs, so this subset is incomplete. Allele counts from all submissions that include frequency data are combined when determining MAF, so for example the allele counts from the 1000 Genomes Project and an independent submitter may be combined for the same variant. dbSNP provides download files in the Variant Call Format (VCF) that include a "COMMON" flag in the INFO column. That is determined by a different method, and is generally a superset of the UCSC Common set. dbSNP uses frequency data from the 1000 Genomes Project only, and considers a variant COMMON if it has a MAF of at least 0.01 in any of the five super-populations: African (AFR) Admixed American (AMR) East Asian (EAS) European (EUR) South Asian (SAS) In build 151 (which has replaced build 150 on the dbSNP web and download site), dbSNP marks approximately 38M variants as COMMON; 23M of those have a global MAF < 0.01. The remainder should be in agreement with UCSC's Common subset. The selection of SNPs with a minor allele frequency of 1% or greater is an attempt to identify variants that appear to be reasonably common in the general population. Taken as a set, common variants should be less likely to be associated with severe genetic diseases due to the effects of natural selection, following the view that deleterious variants are not likely to become common in the population. However, the significance of any particular variant should be interpreted only by a trained medical geneticist using all available information. The remainder of this page is identical on the following tracks: Common SNPs(150) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(150) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(150) - SNPs mapping in more than one place on reference assembly. All SNPs(150) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors. If a SNP has more than one of these attributes, the stronger color will override the weaker color. The order of colors, from strongest to weakest, is red, green, blue, gray, and black. Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Non-coding (ncRNA): (nc_transcript_variant) are colored blue. Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP. Before dbSNP build 147, weight had values 1, 2 or 3, with 1 being the highest quality (mapped to a single genomic location). As of dbSNP build 147, dbSNP now releases only the variants with weight 1. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period >= 12) is shown in lower case, and matching bases are indicated by a "+". Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b150_GRCh37p13/database/data/organism_data/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b150_GRCh38p7/database/data/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b150_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b150_GRCh38p7/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b150_SNPContigLoc_N.bcp.gz and b150_ContigInfo_N.bcp.gz. (N = 105 for hg19, 107 for hg38) b150_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b150_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp150*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp150Flagged Flagged SNPs(150) Simple Nucleotide Polymorphisms (dbSNP 150) Flagged by dbSNP as Clinically Assoc Variation Description This track contains information about a subset of the single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 150, available from ftp.ncbi.nlm.nih.gov/snp. Only SNPs flagged as clinically associated by dbSNP, mapped to a single location in the reference genome assembly, and not known to have a minor allele frequency of at least 1%, are included in this subset. Frequency data are not available for all SNPs, so this subset probably includes some SNPs whose true minor allele frequency is 1% or greater. The significance of any particular variant in this track should be interpreted only by a trained medical geneticist using all available information. For example, some variants are included in this track because of their inclusion in a Locus-Specific Database (LSDB) or mention in OMIM, but are not thought to be disease-causing, so inclusion of a variant in this track is not necessarily an indicator of risk. Again, all available information must be carefully considered by a qualified professional. The remainder of this page is identical on the following tracks: Common SNPs(150) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(150) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(150) - SNPs mapping in more than one place on reference assembly. All SNPs(150) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors. If a SNP has more than one of these attributes, the stronger color will override the weaker color. The order of colors, from strongest to weakest, is red, green, blue, gray, and black. Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Non-coding (ncRNA): (nc_transcript_variant) are colored blue. Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP. Before dbSNP build 147, weight had values 1, 2 or 3, with 1 being the highest quality (mapped to a single genomic location). As of dbSNP build 147, dbSNP now releases only the variants with weight 1. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period >= 12) is shown in lower case, and matching bases are indicated by a "+". Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b150_GRCh37p13/database/data/organism_data/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b150_GRCh38p7/database/data/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b150_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b150_GRCh38p7/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b150_SNPContigLoc_N.bcp.gz and b150_ContigInfo_N.bcp.gz. (N = 105 for hg19, 107 for hg38) b150_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b150_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp150*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp147Mult Mult. SNPs(147) Simple Nucleotide Polymorphisms (dbSNP 147) That Map to Multiple Genomic Loci Variation Description This track contains information about a subset of the single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 147, available from ftp.ncbi.nlm.nih.gov/snp. Only SNPs that have been mapped to multiple locations in the reference genome assembly are included in this subset. When a SNP's flanking sequences map to multiple locations in the reference genome, it calls into question whether there is true variation at those sites, or whether the sequences at those sites are merely highly similar but not identical. The default maximum weight for this track is 3, unlike the other dbSNP build 147 tracks which have a maximum weight of 1. That enables these multiply-mapped SNPs to appear in the display, while by default they will not appear in the All SNPs(147) track because of its maximum weight filter. The remainder of this page is identical on the following tracks: Common SNPs(147) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(147) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(147) - SNPs mapping in more than one place on reference assembly. All SNPs(147) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors: Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Non-coding (ncRNA): (nc_transcript_variant) are always colored blue. Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP. Before dbSNP build 147, weight had values 1, 2 or 3, with 1 being the highest quality (mapped to a single genomic location). As of dbSNP build 147, dbSNP now releases only the variants with weight 1. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period >= 12) is shown in lower case, and matching bases are indicated by a "+". Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b147_GRCh37p13/database/organism_data/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b147_GRCh38p2/database/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b147_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b147_GRCh38p2/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b147_SNPContigLoc_N.bcp.gz and b147_ContigInfo_N.bcp.gz. (N = 105 for hg19, 107 for hg38) b147_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b147_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp147*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp147Flagged Flagged SNPs(147) Simple Nucleotide Polymorphisms (dbSNP 147) Flagged by dbSNP as Clinically Assoc Variation Description This track contains information about a subset of the single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 147, available from ftp.ncbi.nlm.nih.gov/snp. Only SNPs flagged as clinically associated by dbSNP, mapped to a single location in the reference genome assembly, and not known to have a minor allele frequency of at least 1%, are included in this subset. Frequency data are not available for all SNPs, so this subset probably includes some SNPs whose true minor allele frequency is 1% or greater. The significance of any particular variant in this track should be interpreted only by a trained medical geneticist using all available information. For example, some variants are included in this track because of their inclusion in a Locus-Specific Database (LSDB) or mention in OMIM, but are not thought to be disease-causing, so inclusion of a variant in this track is not necessarily an indicator of risk. Again, all available information must be carefully considered by a qualified professional. The remainder of this page is identical on the following tracks: Common SNPs(147) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(147) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(147) - SNPs mapping in more than one place on reference assembly. All SNPs(147) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors: Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Non-coding (ncRNA): (nc_transcript_variant) are always colored blue. Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP. Before dbSNP build 147, weight had values 1, 2 or 3, with 1 being the highest quality (mapped to a single genomic location). As of dbSNP build 147, dbSNP now releases only the variants with weight 1. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period >= 12) is shown in lower case, and matching bases are indicated by a "+". Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b147_GRCh37p13/database/organism_data/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b147_GRCh38p2/database/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b147_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b147_GRCh38p2/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b147_SNPContigLoc_N.bcp.gz and b147_ContigInfo_N.bcp.gz. (N = 105 for hg19, 107 for hg38) b147_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b147_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp147*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp147Common Common SNPs(147) Simple Nucleotide Polymorphisms (dbSNP 147) Found in >= 1% of Samples Variation Description This track contains information about a subset of the single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 147, available from ftp.ncbi.nlm.nih.gov/snp. Only SNPs that have a minor allele frequency of at least 1% and are mapped to a single location in the reference genome assembly are included in this subset. Frequency data are not available for all SNPs, so this subset is incomplete. The selection of SNPs with a minor allele frequency of 1% or greater is an attempt to identify variants that appear to be reasonably common in the general population. Taken as a set, common variants should be less likely to be associated with severe genetic diseases due to the effects of natural selection, following the view that deleterious variants are not likely to become common in the population. However, the significance of any particular variant should be interpreted only by a trained medical geneticist using all available information. The remainder of this page is identical on the following tracks: Common SNPs(147) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(147) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(147) - SNPs mapping in more than one place on reference assembly. All SNPs(147) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors: Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Non-coding (ncRNA): (nc_transcript_variant) are always colored blue. Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP. Before dbSNP build 147, weight had values 1, 2 or 3, with 1 being the highest quality (mapped to a single genomic location). As of dbSNP build 147, dbSNP now releases only the variants with weight 1. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period >= 12) is shown in lower case, and matching bases are indicated by a "+". Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b147_GRCh37p13/database/organism_data/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b147_GRCh38p2/database/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b147_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b147_GRCh38p2/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b147_SNPContigLoc_N.bcp.gz and b147_ContigInfo_N.bcp.gz. (N = 105 for hg19, 107 for hg38) b147_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b147_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp147*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp147 All SNPs(147) Simple Nucleotide Polymorphisms (dbSNP 147) Variation Description This track contains information about single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 147, available from ftp.ncbi.nlm.nih.gov/snp. Three tracks contain subsets of the items in this track: Common SNPs(147): SNPs that have a minor allele frequency of at least 1% and are mapped to a single location in the reference genome assembly. Frequency data are not available for all SNPs, so this subset is incomplete. Flagged SNPs(147): SNPs flagged as clinically associated by dbSNP, mapped to a single location in the reference genome assembly, and not known to have a minor allele frequency of at least 1%. Frequency data are not available for all SNPs, so this subset may include some SNPs whose true minor allele frequency is 1% or greater. Mult. SNPs(147): SNPs that have been mapped to multiple locations in the reference genome assembly. The default maximum weight for this track is 1, so unless the setting is changed in the track controls, SNPs that map to multiple genomic locations will be omitted from display. When a SNP's flanking sequences map to multiple locations in the reference genome, it calls into question whether there is true variation at those sites, or whether the sequences at those sites are merely highly similar but not identical. The remainder of this page is identical on the following tracks: Common SNPs(147) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(147) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(147) - SNPs mapping in more than one place on reference assembly. All SNPs(147) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors: Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Non-coding (ncRNA): (nc_transcript_variant) are always colored blue. Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP. Before dbSNP build 147, weight had values 1, 2 or 3, with 1 being the highest quality (mapped to a single genomic location). As of dbSNP build 147, dbSNP now releases only the variants with weight 1. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period >= 12) is shown in lower case, and matching bases are indicated by a "+". Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b147_GRCh37p13/database/organism_data/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b147_GRCh38p2/database/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b147_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b147_GRCh38p2/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b147_SNPContigLoc_N.bcp.gz and b147_ContigInfo_N.bcp.gz. (N = 105 for hg19, 107 for hg38) b147_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b147_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp147*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp146Mult Mult. SNPs(146) Simple Nucleotide Polymorphisms (dbSNP 146) That Map to Multiple Genomic Loci Variation Description This track contains information about a subset of the single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 146, available from ftp.ncbi.nih.gov/snp. Only SNPs that have been mapped to multiple locations in the reference genome assembly are included in this subset. When a SNP's flanking sequences map to multiple locations in the reference genome, it calls into question whether there is true variation at those sites, or whether the sequences at those sites are merely highly similar but not identical. The default maximum weight for this track is 3, unlike the other dbSNP build 146 tracks which have a maximum weight of 1. That enables these multiply-mapped SNPs to appear in the display, while by default they will not appear in the All SNPs(146) track because of its maximum weight filter. The remainder of this page is identical on the following tracks: Common SNPs(146) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(146) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(146) - SNPs mapping in more than one place on reference assembly. All SNPs(146) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors: Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP Weight can be 0, 1, 2, 3 or 10. Weight = 1 are the highest quality alignments. Weight = 0 and weight = 10 are excluded from the data set. A filter on maximum weight value is supported, which defaults to 1 on all tracks except the Mult. SNPs track, which defaults to 3. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b146_GRCh37p13/database/organism_data/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b146_GRCh38p2/database/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b146_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b146_GRCh38p2/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b146_SNPContigLoc_N.bcp.gz and b146_ContigInfo_N.bcp.gz. (N = 105 for hg19, 107 for hg38) b146_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b146_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp146*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp146Flagged Flagged SNPs(146) Simple Nucleotide Polymorphisms (dbSNP 146) Flagged by dbSNP as Clinically Assoc Variation Description This track contains information about a subset of the single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 146, available from ftp.ncbi.nih.gov/snp. Only SNPs flagged as clinically associated by dbSNP, mapped to a single location in the reference genome assembly, and not known to have a minor allele frequency of at least 1%, are included in this subset. Frequency data are not available for all SNPs, so this subset probably includes some SNPs whose true minor allele frequency is 1% or greater. The significance of any particular variant in this track should be interpreted only by a trained medical geneticist using all available information. For example, some variants are included in this track because of their inclusion in a Locus-Specific Database (LSDB) or mention in OMIM, but are not thought to be disease-causing, so inclusion of a variant in this track is not necessarily an indicator of risk. Again, all available information must be carefully considered by a qualified professional. The remainder of this page is identical on the following tracks: Common SNPs(146) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(146) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(146) - SNPs mapping in more than one place on reference assembly. All SNPs(146) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors: Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP Weight can be 0, 1, 2, 3 or 10. Weight = 1 are the highest quality alignments. Weight = 0 and weight = 10 are excluded from the data set. A filter on maximum weight value is supported, which defaults to 1 on all tracks except the Mult. SNPs track, which defaults to 3. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b146_GRCh37p13/database/organism_data/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b146_GRCh38p2/database/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b146_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b146_GRCh38p2/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b146_SNPContigLoc_N.bcp.gz and b146_ContigInfo_N.bcp.gz. (N = 105 for hg19, 107 for hg38) b146_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b146_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp146*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp146Common Common SNPs(146) Simple Nucleotide Polymorphisms (dbSNP 146) Found in >= 1% of Samples Variation Description This track contains information about a subset of the single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 146, available from ftp.ncbi.nih.gov/snp. Only SNPs that have a minor allele frequency of at least 1% and are mapped to a single location in the reference genome assembly are included in this subset. Frequency data are not available for all SNPs, so this subset is incomplete. The selection of SNPs with a minor allele frequency of 1% or greater is an attempt to identify variants that appear to be reasonably common in the general population. Taken as a set, common variants should be less likely to be associated with severe genetic diseases due to the effects of natural selection, following the view that deleterious variants are not likely to become common in the population. However, the significance of any particular variant should be interpreted only by a trained medical geneticist using all available information. The remainder of this page is identical on the following tracks: Common SNPs(146) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(146) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(146) - SNPs mapping in more than one place on reference assembly. All SNPs(146) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors: Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP Weight can be 0, 1, 2, 3 or 10. Weight = 1 are the highest quality alignments. Weight = 0 and weight = 10 are excluded from the data set. A filter on maximum weight value is supported, which defaults to 1 on all tracks except the Mult. SNPs track, which defaults to 3. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b146_GRCh37p13/database/organism_data/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b146_GRCh38p2/database/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b146_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b146_GRCh38p2/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b146_SNPContigLoc_N.bcp.gz and b146_ContigInfo_N.bcp.gz. (N = 105 for hg19, 107 for hg38) b146_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b146_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp146*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp146 All SNPs(146) Simple Nucleotide Polymorphisms (dbSNP 146) Variation Description This track contains information about single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 146, available from ftp.ncbi.nih.gov/snp. Three tracks contain subsets of the items in this track: Common SNPs(146): SNPs that have a minor allele frequency of at least 1% and are mapped to a single location in the reference genome assembly. Frequency data are not available for all SNPs, so this subset is incomplete. Flagged SNPs(146): SNPs flagged as clinically associated by dbSNP, mapped to a single location in the reference genome assembly, and not known to have a minor allele frequency of at least 1%. Frequency data are not available for all SNPs, so this subset may include some SNPs whose true minor allele frequency is 1% or greater. Mult. SNPs(146): SNPs that have been mapped to multiple locations in the reference genome assembly. The default maximum weight for this track is 1, so unless the setting is changed in the track controls, SNPs that map to multiple genomic locations will be omitted from display. When a SNP's flanking sequences map to multiple locations in the reference genome, it calls into question whether there is true variation at those sites, or whether the sequences at those sites are merely highly similar but not identical. The remainder of this page is identical on the following tracks: Common SNPs(146) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(146) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(146) - SNPs mapping in more than one place on reference assembly. All SNPs(146) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors: Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP Weight can be 0, 1, 2, 3 or 10. Weight = 1 are the highest quality alignments. Weight = 0 and weight = 10 are excluded from the data set. A filter on maximum weight value is supported, which defaults to 1 on all tracks except the Mult. SNPs track, which defaults to 3. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b146_GRCh37p13/database/organism_data/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b146_GRCh38p2/database/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b146_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b146_GRCh38p2/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b146_SNPContigLoc_N.bcp.gz and b146_ContigInfo_N.bcp.gz. (N = 105 for hg19, 107 for hg38) b146_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b146_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp146*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp144Mult Mult. SNPs(144) Simple Nucleotide Polymorphisms (dbSNP 144) That Map to Multiple Genomic Loci Variation Description This track contains information about a subset of the single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 144, available from ftp.ncbi.nih.gov/snp. Only SNPs that have been mapped to multiple locations in the reference genome assembly are included in this subset. When a SNP's flanking sequences map to multiple locations in the reference genome, it calls into question whether there is true variation at those sites, or whether the sequences at those sites are merely highly similar but not identical. The default maximum weight for this track is 3, unlike the other dbSNP build 144 tracks which have a maximum weight of 1. That enables these multiply-mapped SNPs to appear in the display, while by default they will not appear in the All SNPs(144) track because of its maximum weight filter. The remainder of this page is identical on the following tracks: Common SNPs(144) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(144) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(144) - SNPs mapping in more than one place on reference assembly. All SNPs(144) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors: Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP Weight can be 0, 1, 2, 3 or 10. Weight = 1 are the highest quality alignments. Weight = 0 and weight = 10 are excluded from the data set. A filter on maximum weight value is supported, which defaults to 1 on all tracks except the Mult. SNPs track, which defaults to 3. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b144_GRCh37p13/database/organism_data/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b144_GRCh38p2/database/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b144_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b144_GRCh38p2/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b144_SNPContigLoc_N.bcp.gz and b144_ContigInfo_N.bcp.gz. (N = 105 for hg19, 107 for hg38) b144_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b144_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp144*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp144Flagged Flagged SNPs(144) Simple Nucleotide Polymorphisms (dbSNP 144) Flagged by dbSNP as Clinically Assoc Variation Description This track contains information about a subset of the single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 144, available from ftp.ncbi.nih.gov/snp. Only SNPs flagged as clinically associated by dbSNP, mapped to a single location in the reference genome assembly, and not known to have a minor allele frequency of at least 1%, are included in this subset. Frequency data are not available for all SNPs, so this subset probably includes some SNPs whose true minor allele frequency is 1% or greater. The significance of any particular variant in this track should be interpreted only by a trained medical geneticist using all available information. For example, some variants are included in this track because of their inclusion in a Locus-Specific Database (LSDB) or mention in OMIM, but are not thought to be disease-causing, so inclusion of a variant in this track is not necessarily an indicator of risk. Again, all available information must be carefully considered by a qualified professional. The remainder of this page is identical on the following tracks: Common SNPs(144) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(144) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(144) - SNPs mapping in more than one place on reference assembly. All SNPs(144) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors: Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP Weight can be 0, 1, 2, 3 or 10. Weight = 1 are the highest quality alignments. Weight = 0 and weight = 10 are excluded from the data set. A filter on maximum weight value is supported, which defaults to 1 on all tracks except the Mult. SNPs track, which defaults to 3. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b144_GRCh37p13/database/organism_data/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b144_GRCh38p2/database/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b144_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b144_GRCh38p2/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b144_SNPContigLoc_N.bcp.gz and b144_ContigInfo_N.bcp.gz. (N = 105 for hg19, 107 for hg38) b144_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b144_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp144*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp144Common Common SNPs(144) Simple Nucleotide Polymorphisms (dbSNP 144) Found in >= 1% of Samples Variation Description This track contains information about a subset of the single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 144, available from ftp.ncbi.nih.gov/snp. Only SNPs that have a minor allele frequency of at least 1% and are mapped to a single location in the reference genome assembly are included in this subset. Frequency data are not available for all SNPs, so this subset is incomplete. The selection of SNPs with a minor allele frequency of 1% or greater is an attempt to identify variants that appear to be reasonably common in the general population. Taken as a set, common variants should be less likely to be associated with severe genetic diseases due to the effects of natural selection, following the view that deleterious variants are not likely to become common in the population. However, the significance of any particular variant should be interpreted only by a trained medical geneticist using all available information. The remainder of this page is identical on the following tracks: Common SNPs(144) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(144) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(144) - SNPs mapping in more than one place on reference assembly. All SNPs(144) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors: Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP Weight can be 0, 1, 2, 3 or 10. Weight = 1 are the highest quality alignments. Weight = 0 and weight = 10 are excluded from the data set. A filter on maximum weight value is supported, which defaults to 1 on all tracks except the Mult. SNPs track, which defaults to 3. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b144_GRCh37p13/database/organism_data/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b144_GRCh38p2/database/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b144_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b144_GRCh38p2/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b144_SNPContigLoc_N.bcp.gz and b144_ContigInfo_N.bcp.gz. (N = 105 for hg19, 107 for hg38) b144_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b144_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp144*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp144 All SNPs(144) Simple Nucleotide Polymorphisms (dbSNP 144) Variation Description This track contains information about single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 144, available from ftp.ncbi.nih.gov/snp. Three tracks contain subsets of the items in this track: Common SNPs(144): SNPs that have a minor allele frequency of at least 1% and are mapped to a single location in the reference genome assembly. Frequency data are not available for all SNPs, so this subset is incomplete. Flagged SNPs(144): SNPs flagged as clinically associated by dbSNP, mapped to a single location in the reference genome assembly, and not known to have a minor allele frequency of at least 1%. Frequency data are not available for all SNPs, so this subset may include some SNPs whose true minor allele frequency is 1% or greater. Mult. SNPs(144): SNPs that have been mapped to multiple locations in the reference genome assembly. The default maximum weight for this track is 1, so unless the setting is changed in the track controls, SNPs that map to multiple genomic locations will be omitted from display. When a SNP's flanking sequences map to multiple locations in the reference genome, it calls into question whether there is true variation at those sites, or whether the sequences at those sites are merely highly similar but not identical. The remainder of this page is identical on the following tracks: Common SNPs(144) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(144) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(144) - SNPs mapping in more than one place on reference assembly. All SNPs(144) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors: Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP Weight can be 0, 1, 2, 3 or 10. Weight = 1 are the highest quality alignments. Weight = 0 and weight = 10 are excluded from the data set. A filter on maximum weight value is supported, which defaults to 1 on all tracks except the Mult. SNPs track, which defaults to 3. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b144_GRCh37p13/database/organism_data/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b144_GRCh38p2/database/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b144_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b144_GRCh38p2/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b144_SNPContigLoc_N.bcp.gz and b144_ContigInfo_N.bcp.gz. (N = 105 for hg19, 107 for hg38) b144_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b144_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp144*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp142Mult Mult. SNPs(142) Simple Nucleotide Polymorphisms (dbSNP 142) That Map to Multiple Genomic Loci Variation Description This track contains information about a subset of the single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 142, available from ftp.ncbi.nih.gov/snp. Only SNPs that have been mapped to multiple locations in the reference genome assembly are included in this subset. When a SNP's flanking sequences map to multiple locations in the reference genome, it calls into question whether there is true variation at those sites, or whether the sequences at those sites are merely highly similar but not identical. The default maximum weight for this track is 3, unlike the other dbSNP build 142 tracks which have a maximum weight of 1. That enables these multiply-mapped SNPs to appear in the display, while by default they will not appear in the All SNPs(142) track because of its maximum weight filter. The remainder of this page is identical on the following tracks: Common SNPs(142) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(142) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(142) - SNPs mapping in more than one place on reference assembly. All SNPs(142) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors: Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP Weight can be 0, 1, 2, 3 or 10. Weight = 1 are the highest quality alignments. Weight = 0 and weight = 10 are excluded from the data set. A filter on maximum weight value is supported, which defaults to 1 on all tracks except the Mult. SNPs track, which defaults to 3. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b142_GRCh37p13/database/organism_data/ for hg19 and from ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b142_GRCh38/database/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b142_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b142_GRCh38/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b142_SNPContigLoc_N.bcp.gz and b142_ContigInfo_N.bcp.gz. (N = 105 for hg19, 106 for hg38) b142_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b142_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp142*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp142Flagged Flagged SNPs(142) Simple Nucleotide Polymorphisms (dbSNP 142) Flagged by dbSNP as Clinically Assoc Variation Description This track contains information about a subset of the single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 142, available from ftp.ncbi.nih.gov/snp. Only SNPs flagged as clinically associated by dbSNP, mapped to a single location in the reference genome assembly, and not known to have a minor allele frequency of at least 1%, are included in this subset. Frequency data are not available for all SNPs, so this subset probably includes some SNPs whose true minor allele frequency is 1% or greater. The significance of any particular variant in this track should be interpreted only by a trained medical geneticist using all available information. For example, some variants are included in this track because of their inclusion in a Locus-Specific Database (LSDB) or mention in OMIM, but are not thought to be disease-causing, so inclusion of a variant in this track is not necessarily an indicator of risk. Again, all available information must be carefully considered by a qualified professional. The remainder of this page is identical on the following tracks: Common SNPs(142) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(142) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(142) - SNPs mapping in more than one place on reference assembly. All SNPs(142) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors: Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP Weight can be 0, 1, 2, 3 or 10. Weight = 1 are the highest quality alignments. Weight = 0 and weight = 10 are excluded from the data set. A filter on maximum weight value is supported, which defaults to 1 on all tracks except the Mult. SNPs track, which defaults to 3. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b142_GRCh37p13/database/organism_data/ for hg19 and from ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b142_GRCh38/database/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b142_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b142_GRCh38/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b142_SNPContigLoc_N.bcp.gz and b142_ContigInfo_N.bcp.gz. (N = 105 for hg19, 106 for hg38) b142_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b142_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp142*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp142Common Common SNPs(142) Simple Nucleotide Polymorphisms (dbSNP 142) Found in >= 1% of Samples Variation Description This track contains information about a subset of the single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 142, available from ftp.ncbi.nih.gov/snp. Only SNPs that have a minor allele frequency of at least 1% and are mapped to a single location in the reference genome assembly are included in this subset. Frequency data are not available for all SNPs, so this subset is incomplete. The selection of SNPs with a minor allele frequency of 1% or greater is an attempt to identify variants that appear to be reasonably common in the general population. Taken as a set, common variants should be less likely to be associated with severe genetic diseases due to the effects of natural selection, following the view that deleterious variants are not likely to become common in the population. However, the significance of any particular variant should be interpreted only by a trained medical geneticist using all available information. The remainder of this page is identical on the following tracks: Common SNPs(142) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(142) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(142) - SNPs mapping in more than one place on reference assembly. All SNPs(142) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors: Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP Weight can be 0, 1, 2, 3 or 10. Weight = 1 are the highest quality alignments. Weight = 0 and weight = 10 are excluded from the data set. A filter on maximum weight value is supported, which defaults to 1 on all tracks except the Mult. SNPs track, which defaults to 3. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b142_GRCh37p13/database/organism_data/ for hg19 and from ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b142_GRCh38/database/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b142_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b142_GRCh38/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b142_SNPContigLoc_N.bcp.gz and b142_ContigInfo_N.bcp.gz. (N = 105 for hg19, 106 for hg38) b142_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b142_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp142*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp142 All SNPs(142) Simple Nucleotide Polymorphisms (dbSNP 142) Variation Description This track contains information about single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 142, available from ftp.ncbi.nih.gov/snp. Three tracks contain subsets of the items in this track: Common SNPs(142): SNPs that have a minor allele frequency of at least 1% and are mapped to a single location in the reference genome assembly. Frequency data are not available for all SNPs, so this subset is incomplete. Flagged SNPs(142): SNPs flagged as clinically associated by dbSNP, mapped to a single location in the reference genome assembly, and not known to have a minor allele frequency of at least 1%. Frequency data are not available for all SNPs, so this subset may include some SNPs whose true minor allele frequency is 1% or greater. Mult. SNPs(142): SNPs that have been mapped to multiple locations in the reference genome assembly. The default maximum weight for this track is 1, so unless the setting is changed in the track controls, SNPs that map to multiple genomic locations will be omitted from display. When a SNP's flanking sequences map to multiple locations in the reference genome, it calls into question whether there is true variation at those sites, or whether the sequences at those sites are merely highly similar but not identical. The remainder of this page is identical on the following tracks: Common SNPs(142) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(142) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(142) - SNPs mapping in more than one place on reference assembly. All SNPs(142) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors: Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP Weight can be 0, 1, 2, 3 or 10. Weight = 1 are the highest quality alignments. Weight = 0 and weight = 10 are excluded from the data set. A filter on maximum weight value is supported, which defaults to 1 on all tracks except the Mult. SNPs track, which defaults to 3. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b142_GRCh37p13/database/organism_data/ for hg19 and from ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b142_GRCh38/database/organism_data/ for hg38. The fasta files were downloaded from ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b142_GRCh37p13/rs_fasta/ for hg19 and from ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b142_GRCh38/rs_fasta/ for hg38. Coordinates, orientation, location type and dbSNP reference allele data were obtained from b142_SNPContigLoc_N.bcp.gz and b142_ContigInfo_N.bcp.gz. (N = 105 for hg19, 106 for hg38) b142_SNPMapInfo_N.bcp.gz provided the alignment weights. Functional classification was obtained from b142_SNPContigLocusId_N.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp142*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp141Mult Mult. SNPs(141) Simple Nucleotide Polymorphisms (dbSNP 141) That Map to Multiple Genomic Loci Variation Description This track contains information about a subset of the single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 141, available from ftp.ncbi.nih.gov/snp. Only SNPs that have been mapped to multiple locations in the reference genome assembly are included in this subset. When a SNP's flanking sequences map to multiple locations in the reference genome, it calls into question whether there is true variation at those sites, or whether the sequences at those sites are merely highly similar but not identical. The default maximum weight for this track is 3, unlike the other dbSNP build 141 tracks which have a maximum weight of 1. That enables these multiply-mapped SNPs to appear in the display, while by default they will not appear in the All SNPs(141) track because of its maximum weight filter. The remainder of this page is identical on the following tracks: Common SNPs(141) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(141) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(141) - SNPs mapping in more than one place on reference assembly. All SNPs(141) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors: Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP Weight can be 0, 1, 2, 3 or 10. Weight = 1 are the highest quality alignments. Weight = 0 and weight = 10 are excluded from the data set. A filter on maximum weight value is supported, which defaults to 1 on all tracks except the Mult. SNPs track, which defaults to 3. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nih.gov/snp/organisms/ organism_tax_id/database/ (for human, organism_tax_id = human_9606; for mouse, organism_tax_id = mouse_10090). The fasta files were downloaded from ftp://ftp.ncbi.nih.gov/snp/organisms/ organism_tax_id/rs_fasta/ Coordinates, orientation, location type and dbSNP reference allele data were obtained from b141_SNPContigLoc.bcp.gz and b141_ContigInfo.bcp.gz. b141_SNPMapInfo.bcp.gz provided the alignment weights. Functional classification was obtained from b141_SNPContigLocusId.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp141*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp141Flagged Flagged SNPs(141) Simple Nucleotide Polymorphisms (dbSNP 141) Flagged by dbSNP as Clinically Assoc Variation Description This track contains information about a subset of the single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 141, available from ftp.ncbi.nih.gov/snp. Only SNPs flagged as clinically associated by dbSNP, mapped to a single location in the reference genome assembly, and not known to have a minor allele frequency of at least 1%, are included in this subset. Frequency data are not available for all SNPs, so this subset probably includes some SNPs whose true minor allele frequency is 1% or greater. The significance of any particular variant in this track should be interpreted only by a trained medical geneticist using all available information. For example, some variants are included in this track because of their inclusion in a Locus-Specific Database (LSDB) or mention in OMIM, but are not thought to be disease-causing, so inclusion of a variant in this track is not necessarily an indicator of risk. Again, all available information must be carefully considered by a qualified professional. The remainder of this page is identical on the following tracks: Common SNPs(141) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(141) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(141) - SNPs mapping in more than one place on reference assembly. All SNPs(141) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors: Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP Weight can be 0, 1, 2, 3 or 10. Weight = 1 are the highest quality alignments. Weight = 0 and weight = 10 are excluded from the data set. A filter on maximum weight value is supported, which defaults to 1 on all tracks except the Mult. SNPs track, which defaults to 3. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nih.gov/snp/organisms/ organism_tax_id/database/ (for human, organism_tax_id = human_9606; for mouse, organism_tax_id = mouse_10090). The fasta files were downloaded from ftp://ftp.ncbi.nih.gov/snp/organisms/ organism_tax_id/rs_fasta/ Coordinates, orientation, location type and dbSNP reference allele data were obtained from b141_SNPContigLoc.bcp.gz and b141_ContigInfo.bcp.gz. b141_SNPMapInfo.bcp.gz provided the alignment weights. Functional classification was obtained from b141_SNPContigLocusId.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp141*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp141Common Common SNPs(141) Simple Nucleotide Polymorphisms (dbSNP 141) Found in >= 1% of Samples Variation Description This track contains information about a subset of the single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 141, available from ftp.ncbi.nih.gov/snp. Only SNPs that have a minor allele frequency of at least 1% and are mapped to a single location in the reference genome assembly are included in this subset. Frequency data are not available for all SNPs, so this subset is incomplete. The selection of SNPs with a minor allele frequency of 1% or greater is an attempt to identify variants that appear to be reasonably common in the general population. Taken as a set, common variants should be less likely to be associated with severe genetic diseases due to the effects of natural selection, following the view that deleterious variants are not likely to become common in the population. However, the significance of any particular variant should be interpreted only by a trained medical geneticist using all available information. The remainder of this page is identical on the following tracks: Common SNPs(141) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(141) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(141) - SNPs mapping in more than one place on reference assembly. All SNPs(141) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors: Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP Weight can be 0, 1, 2, 3 or 10. Weight = 1 are the highest quality alignments. Weight = 0 and weight = 10 are excluded from the data set. A filter on maximum weight value is supported, which defaults to 1 on all tracks except the Mult. SNPs track, which defaults to 3. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nih.gov/snp/organisms/ organism_tax_id/database/ (for human, organism_tax_id = human_9606; for mouse, organism_tax_id = mouse_10090). The fasta files were downloaded from ftp://ftp.ncbi.nih.gov/snp/organisms/ organism_tax_id/rs_fasta/ Coordinates, orientation, location type and dbSNP reference allele data were obtained from b141_SNPContigLoc.bcp.gz and b141_ContigInfo.bcp.gz. b141_SNPMapInfo.bcp.gz provided the alignment weights. Functional classification was obtained from b141_SNPContigLocusId.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp141*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
snp141 All SNPs(141) Simple Nucleotide Polymorphisms (dbSNP 141) Variation Description This track contains information about single nucleotide polymorphisms and small insertions and deletions (indels) — collectively Simple Nucleotide Polymorphisms — from dbSNP build 141, available from ftp.ncbi.nih.gov/snp. Three tracks contain subsets of the items in this track: Common SNPs(141): SNPs that have a minor allele frequency of at least 1% and are mapped to a single location in the reference genome assembly. Frequency data are not available for all SNPs, so this subset is incomplete. Flagged SNPs(141): SNPs flagged as clinically associated by dbSNP, mapped to a single location in the reference genome assembly, and not known to have a minor allele frequency of at least 1%. Frequency data are not available for all SNPs, so this subset may include some SNPs whose true minor allele frequency is 1% or greater. Mult. SNPs(141): SNPs that have been mapped to multiple locations in the reference genome assembly. The default maximum weight for this track is 1, so unless the setting is changed in the track controls, SNPs that map to multiple genomic locations will be omitted from display. When a SNP's flanking sequences map to multiple locations in the reference genome, it calls into question whether there is true variation at those sites, or whether the sequences at those sites are merely highly similar but not identical. The remainder of this page is identical on the following tracks: Common SNPs(141) - SNPs with >= 1% minor allele frequency (MAF), mapping only once to reference assembly. Flagged SNPs(141) - SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated" -- not necessarily a risk allele! Mult. SNPs(141) - SNPs mapping in more than one place on reference assembly. All SNPs(141) - all SNPs from dbSNP mapping to reference assembly. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. On the track controls page, SNPs can be colored and/or filtered from the display according to several attributes: Class: Describes the observed alleles Single - single nucleotide variation: all observed alleles are single nucleotides (can have 2, 3 or 4 alleles) In-del - insertion/deletion Heterozygous - heterozygous (undetermined) variation: allele contains string '(heterozygous)' Microsatellite - the observed allele from dbSNP is a variation in counts of short tandem repeats Named - the observed allele from dbSNP is given as a text name instead of raw sequence, e.g., (Alu)/- No Variation - the submission reports an invariant region in the surveyed sequence Mixed - the cluster contains submissions from multiple classes Multiple Nucleotide Polymorphism (MNP) - the alleles are all of the same length, and length > 1 Insertion - the polymorphism is an insertion relative to the reference assembly Deletion - the polymorphism is a deletion relative to the reference assembly Unknown - no classification provided by data contributor Validation: Method used to validate the variant (each variant may be validated by more than one method) By Frequency - at least one submitted SNP in cluster has frequency data submitted By Cluster - cluster has at least 2 submissions, with at least one submission assayed with a non-computational method By Submitter - at least one submitter SNP in cluster was validated by independent assay By 2 Hit/2 Allele - all alleles have been observed in at least 2 chromosomes By HapMap (human only) - submitted by HapMap project By 1000Genomes (human only) - submitted by 1000Genomes project Unknown - no validation has been reported for this variant Function: dbSNP's predicted functional effect of variant on RefSeq transcripts, both curated (NM_* and NR_*) as in the RefSeq Genes track and predicted (XM_* and XR_*), not shown in UCSC Genome Browser. A variant may have more than one functional role if it overlaps multiple transcripts. These terms and definitions are from the Sequence Ontology (SO); click on a term to view it in the MISO Sequence Ontology Browser. Unknown - no functional classification provided (possibly intergenic) synonymous_variant - A sequence variant where there is no resulting change to the encoded amino acid (dbSNP term: coding-synon) intron_variant - A transcript variant occurring within an intron (dbSNP term: intron) downstream_gene_variant - A sequence variant located 3' of a gene (dbSNP term: near-gene-3) upstream_gene_variant - A sequence variant located 5' of a gene (dbSNP term: near-gene-5) nc_transcript_variant - A transcript variant of a non coding RNA gene (dbSNP term: ncRNA) stop_gained - A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript (dbSNP term: nonsense) missense_variant - A sequence variant, where the change may be longer than 3 bases, and at least one base of a codon is changed resulting in a codon that encodes for a different amino acid (dbSNP term: missense) stop_lost - A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript (dbSNP term: stop-loss) frameshift_variant - A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three (dbSNP term: frameshift) inframe_indel - A coding sequence variant where the change does not alter the frame of the transcript (dbSNP term: cds-indel) 3_prime_UTR_variant - A UTR variant of the 3' UTR (dbSNP term: untranslated-3) 5_prime_UTR_variant - A UTR variant of the 5' UTR (dbSNP term: untranslated-5) splice_acceptor_variant - A splice variant that changes the 2 base region at the 3' end of an intron (dbSNP term: splice-3) splice_donor_variant - A splice variant that changes the 2 base region at the 5' end of an intron (dbSNP term: splice-5) In the Coloring Options section of the track controls page, function terms are grouped into several categories, shown here with default colors: Locus: downstream_gene_variant, upstream_gene_variant Coding - Synonymous: synonymous_variant Coding - Non-Synonymous: stop_gained, missense_variant, stop_lost, frameshift_variant, inframe_indel Untranslated: 5_prime_UTR_variant, 3_prime_UTR_variant Intron: intron_variant Splice Site: splice_acceptor_variant, splice_donor_variant Molecule Type: Sample used to find this variant Genomic - variant discovered using a genomic template cDNA - variant discovered using a cDNA template Unknown - sample type not known Unusual Conditions (UCSC): UCSC checks for several anomalies that may indicate a problem with the mapping, and reports them in the Annotations section of the SNP details page if found: AlleleFreqSumNot1 - Allele frequencies do not sum to 1.0 (+-0.01). This SNP's allele frequency data are probably incomplete. DuplicateObserved, MixedObserved - Multiple distinct insertion SNPs have been mapped to this location, with either the same inserted sequence (Duplicate) or different inserted sequence (Mixed). FlankMismatchGenomeEqual, FlankMismatchGenomeLonger, FlankMismatchGenomeShorter - NCBI's alignment of the flanking sequences had at least one mismatch or gap near the mapped SNP position. (UCSC's re-alignment of flanking sequences to the genome may be informative.) MultipleAlignments - This SNP's flanking sequences align to more than one location in the reference assembly. NamedDeletionZeroSpan - A deletion (from the genome) was observed but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NamedInsertionNonzeroSpan - An insertion (into the genome) was observed but the annotation spans more than 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) NonIntegerChromCount - At least one allele frequency corresponds to a non-integer (+-0.010000) count of chromosomes on which the allele was observed. The reported total sample count for this SNP is probably incorrect. ObservedContainsIupac - At least one observed allele from dbSNP contains an IUPAC ambiguous base (e.g., R, Y, N). ObservedMismatch - UCSC reference allele does not match any observed allele from dbSNP. This is tested only for SNPs whose class is single, in-del, insertion, deletion, mnp or mixed. ObservedTooLong - Observed allele not given (length too long). ObservedWrongFormat - Observed allele(s) from dbSNP have unexpected format for the given class. RefAlleleMismatch - The reference allele from dbSNP does not match the UCSC reference allele, i.e., the bases in the mapped position range. RefAlleleRevComp - The reference allele from dbSNP matches the reverse complement of the UCSC reference allele. SingleClassLongerSpan - All observed alleles are single-base, but the annotation spans more than 1 base. (UCSC's re-alignment of flanking sequences to the genome may be informative.) SingleClassZeroSpan - All observed alleles are single-base, but the annotation spans 0 bases. (UCSC's re-alignment of flanking sequences to the genome may be informative.) Another condition, which does not necessarily imply any problem, is noted: SingleClassTriAllelic, SingleClassQuadAllelic - Class is single and three or four different bases have been observed (usually there are only two). Miscellaneous Attributes (dbSNP): several properties extracted from dbSNP's SNP_bitfield table (see dbSNP_BitField_v5.pdf for details) Clinically Associated (human only) - SNP is in OMIM and/or at least one submitter is a Locus-Specific Database. This does not necessarily imply that the variant causes any disease, only that it has been observed in clinical studies. Appears in OMIM/OMIA - SNP is mentioned in Online Mendelian Inheritance in Man for human SNPs, or Online Mendelian Inheritance in Animals for non-human animal SNPs. Some of these SNPs are quite common, others are known to cause disease; see OMIM/OMIA for more information. Has Microattribution/Third-Party Annotation - At least one of the SNP's submitters studied this SNP in a biomedical setting, but is not a Locus-Specific Database or OMIM/OMIA. Submitted by Locus-Specific Database - At least one of the SNP's submitters is associated with a database of variants associated with a particular gene. These variants may or may not be known to be causative. MAF >= 5% in Some Population - Minor Allele Frequency is at least 5% in at least one population assayed. MAF >= 5% in All Populations - Minor Allele Frequency is at least 5% in all populations assayed. Genotype Conflict - Quality check: different genotypes have been submitted for the same individual. Ref SNP Cluster has Non-overlapping Alleles - Quality check: this reference SNP was clustered from submitted SNPs with non-overlapping sets of observed alleles. Some Assembly's Allele Does Not Match Observed - Quality check: at least one assembly mapped by dbSNP has an allele at the mapped position that is not present in this SNP's observed alleles. Several other properties do not have coloring options, but do have some filtering options: Average heterozygosity: Calculated by dbSNP as described in Computation of Average Heterozygosity and Standard Error for dbSNP RefSNP Clusters. Average heterozygosity should not exceed 0.5 for bi-allelic single-base substitutions. Weight: Alignment quality assigned by dbSNP Weight can be 0, 1, 2, 3 or 10. Weight = 1 are the highest quality alignments. Weight = 0 and weight = 10 are excluded from the data set. A filter on maximum weight value is supported, which defaults to 1 on all tracks except the Mult. SNPs track, which defaults to 3. Submitter handles: These are short, single-word identifiers of labs or consortia that submitted SNPs that were clustered into this reference SNP by dbSNP (e.g., 1000GENOMES, ENSEMBL, KWOK). Some SNPs have been observed by many different submitters, and some by only a single submitter (although that single submitter may have tested a large number of samples). AlleleFrequencies: Some submissions to dbSNP include allele frequencies and the study's sample size (i.e., the number of distinct chromosomes, which is two times the number of individuals assayed, a.k.a. 2N). dbSNP combines all available frequencies and counts from submitted SNPs that are clustered together into a reference SNP. You can configure this track such that the details page displays the function and coding differences relative to particular gene sets. Choose the gene sets from the list on the SNP configuration page displayed beneath this heading: On details page, show function and coding differences relative to. When one or more gene tracks are selected, the SNP details page lists all genes that the SNP hits (or is close to), with the same keywords used in the function category. The function usually agrees with NCBI's function, except when NCBI's functional annotation is relative to an XM_* predicted RefSeq (not included in the UCSC Genome Browser's RefSeq Genes track) and/or UCSC's functional annotation is relative to a transcript that is not in RefSeq. Insertions/Deletions dbSNP uses a class called 'in-del'. We compare the length of the reference allele to the length(s) of observed alleles; if the reference allele is shorter than all other observed alleles, we change 'in-del' to 'insertion'. Likewise, if the reference allele is longer than all other observed alleles, we change 'in-del' to 'deletion'. UCSC Re-alignment of flanking sequences dbSNP determines the genomic locations of SNPs by aligning their flanking sequences to the genome. UCSC displays SNPs in the locations determined by dbSNP, but does not have access to the alignments on which dbSNP based its mappings. Instead, UCSC re-aligns the flanking sequences to the neighboring genomic sequence for display on SNP details pages. While the recomputed alignments may differ from dbSNP's alignments, they often are informative when UCSC has annotated an unusual condition. Non-repetitive genomic sequence is shown in upper case like the flanking sequence, and a "|" indicates each match between genomic and flanking bases. Repetitive genomic sequence (annotated by RepeatMasker and/or the Tandem Repeats Finder with period Data Sources and Methods The data that comprise this track were extracted from database dump files and headers of fasta files downloaded from NCBI. The database dump files were downloaded from ftp://ftp.ncbi.nih.gov/snp/organisms/ organism_tax_id/database/ (for human, organism_tax_id = human_9606; for mouse, organism_tax_id = mouse_10090). The fasta files were downloaded from ftp://ftp.ncbi.nih.gov/snp/organisms/ organism_tax_id/rs_fasta/ Coordinates, orientation, location type and dbSNP reference allele data were obtained from b141_SNPContigLoc.bcp.gz and b141_ContigInfo.bcp.gz. b141_SNPMapInfo.bcp.gz provided the alignment weights. Functional classification was obtained from b141_SNPContigLocusId.bcp.gz. The internal database representation uses dbSNP's function terms, but for display in SNP details pages, these are translated into Sequence Ontology terms. Validation status and heterozygosity were obtained from SNP.bcp.gz. SNPAlleleFreq.bcp.gz and ../shared/Allele.bcp.gz provided allele frequencies. For the human assembly, allele frequencies were also taken from SNPAlleleFreq_TGP.bcp.gz . Submitter handles were extracted from Batch.bcp.gz, SubSNP.bcp.gz and SNPSubSNPLink.bcp.gz. SNP_bitfield.bcp.gz provided miscellaneous properties annotated by dbSNP, such as clinically-associated. See the document dbSNP_BitField_v5.pdf for details. The header lines in the rs_fasta files were used for molecule type, class and observed polymorphism. Data Access The raw data can be explored interactively with the Table Browser, Data Integrator, or Variant Annotation Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server for hg38 and hg19 (snp141*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. Orthologous Alleles (human assemblies only) For the human assembly, we provide a related table that contains orthologous alleles in the chimpanzee, orangutan and rhesus macaque reference genome assemblies. We use our liftOver utility to identify the orthologous alleles. The candidate human SNPs are a filtered list that meet the criteria: class = 'single' mapped position in the human reference genome is one base long aligned to only one location in the human reference genome not aligned to a chrN_random chrom biallelic (not tri- or quad-allelic) In some cases the orthologous allele is unknown; these are set to 'N'. If a lift was not possible, we set the orthologous allele to '?' and the orthologous start and end position to 0 (zero). Masked FASTA Files (human assemblies only) FASTA files that have been modified to use IUPAC ambiguous nucleotide characters at each base covered by a single-base substitution are available for download: GRCh37/hg19, GRCh38/hg38. Note that only single-base substitutions (no insertions or deletions) were used to mask the sequence, and these were filtered to exclude problematic SNPs. References Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11. PMID: 11125122; PMC: PMC29783 
cons100way Conservation Vertebrate Multiz Alignment & Conservation (100 Species) Comparative Genomics Downloads for data in this track are available: Multiz alignments (MAF format), and phylogenetic trees PhyloP conservation (WIG format) PhastCons conservation (WIG format) Description This track shows multiple alignments of 100 vertebrate species and measurements of evolutionary conservation using two methods (phastCons and phyloP) from the PHAST package, for all species. The multiple alignments were generated using multiz and other tools in the UCSC/Penn State Bioinformatics comparative genomics alignment pipeline. Conserved elements identified by phastCons are also displayed in this track. PHAST/Multiz are built from chains ("alignable") and nets ("syntenic"), see the documentation of the Chain/Net tracks for a description of the complete alignment process. PhastCons is a hidden Markov model-based method that estimates the probability that each nucleotide belongs to a conserved element, based on the multiple alignment. It considers not just each individual alignment column, but also its flanking columns. By contrast, phyloP separately measures conservation at individual columns, ignoring the effects of their neighbors. As a consequence, the phyloP plots have a less smooth appearance than the phastCons plots, with more "texture" at individual sites. The two methods have different strengths and weaknesses. PhastCons is sensitive to "runs" of conserved sites, and is therefore effective for picking out conserved elements. PhyloP, on the other hand, is more appropriate for evaluating signatures of selection at particular nucleotides or classes of nucleotides (e.g., third codon positions, or first positions of miRNA target sites). Another important difference is that phyloP can measure acceleration (faster evolution than expected under neutral drift) as well as conservation (slower than expected evolution). In the phyloP plots, sites predicted to be conserved are assigned positive scores (and shown in blue), while sites predicted to be fast-evolving are assigned negative scores (and shown in red). The absolute values of the scores represent -log p-values under a null hypothesis of neutral evolution. The phastCons scores, by contrast, represent probabilities of negative selection and range between 0 and 1. Both phastCons and phyloP treat alignment gaps and unaligned nucleotides as missing data, and both were run with the same parameters. See also: lastz parameters and other details and chain minimum score and gap parameters used in these alignments. UCSC has repeatmasked and aligned all genome assemblies, and provides all the sequences for download. For genome assemblies not available in the genome browser, there are alternative assembly hub genome browsers. Missing sequence in any assembly is highlighted in the track display by regions of yellow when zoomed out and by Ns when displayed at base level (see Gap Annotation, below). Primate subset OrganismSpeciesRelease dateUCSC versionAlignment type BaboonPapio hamadryasMar 2012Baylor Panu_2.0/papAnu2Reciprocal best net BushbabyOtolemur garnettiiMar 2011Broad/otoGar3Syntenic net ChimpPan troglodytesFeb 2011CSAC 2.1.4/panTro4Syntenic net Crab-eating macaqueMacaca fascicularisJun 2013Macaca_fascicularis_5.0/macFas5Syntenic net GibbonNomascus leucogenysOct 2012GGSC Nleu3.0/nomLeu3Syntenic net GorillaGorilla gorilla gorillaMay 2011gorGor3.1/gorGor3Reciprocal best net Green monkeyChlorocebus sabaeusMar 2014Chlorocebus_sabeus 1.1/chlSab2Syntenic net HumanHomo sapiensDec 2013GRCh38/hg38reference species MarmosetCallithrix jacchusMar 2009WUGSC 3.2/calJac3Syntenic net OrangutanPongo pygmaeus abeliiJuly 2007WUGSC 2.0.2/ponAbe2Reciprocal best net RhesusMacaca mulattaOct 2010BGI CR_1.0/rheMac3Syntenic net Squirrel monkeySaimiri boliviensisOct 2011Broad/saiBol1Syntenic net Euarchontoglires subset Brush-tailed ratOctodon degusApr 2012OctDeg1.0/octDeg1Syntenic net ChinchillaChinchilla lanigeraMay 2012 ChiLan1.0/chiLan1Syntenic net Chinese hamsterCricetulus griseusJul 2013C_griseus_v1.0/criGri1Syntenic net Chinese tree shrewTupaia chinensisJan 2013TupChi_1.0/tupChi1Syntenic net Golden hamsterMesocricetus auratusMar 2013MesAur1.0/mesAur1Syntenic net Guinea pigCavia porcellusFeb 2008Broad/cavPor3Syntenic net Lesser Egyptian jerboaJaculus jaculusMay 2012JacJac1.0/jacJac1Syntenic net MouseMus musculusDec 2011GRCm38/mm10Syntenic net Naked mole-ratHeterocephalus glaberJan 2012Broad HetGla_female_1.0/hetGla2Syntenic net PikaOchotona princepsMay 2012OchPri3.0/ochPri3Syntenic net Prairie voleMicrotus ochrogasterOct 2012MicOch1.0/micOch1Syntenic net RabbitOryctolagus cuniculusApr 2009Broad/oryCun2Syntenic net RatRattus norvegicusJul 2014RGSC 6.0/rn6Syntenic net SquirrelSpermophilus tridecemlineatusNov 2011Broad/speTri2Syntenic net Laurasiatheria subset AlpacaVicugna pacosMar 2013Vicugna_pacos-2.0.1/vicPac2Syntenic net Bactrian camelCamelus ferusDec 2011CB1/camFer1Syntenic net Big brown batEptesicus fuscusJul 2012EptFus1.0/eptFus1Syntenic net Black flying-foxPteropus alectoAug 2012ASM32557v1/pteAle1Syntenic net CatFelis catusNov 2014ICGSC Felis_catus 8.0/felCat8Syntenic net CowBos taurusJun 2014Bos_taurus_UMD_3.1.1/bosTau8Syntenic net David's myotis batMyotis davidiiAug 2012ASM32734v1/myoDav1Syntenic net DogCanis lupus familiarisSep 2011Broad CanFam3.1/canFam3Syntenic net DolphinTursiops truncatusOct 2011Baylor Ttru_1.4/turTru2Reciprocal best net Domestic goatCapra hircusMay 2012CHIR_1.0/capHir1Syntenic net Ferret Mustela putorius furoApr 2011MusPutFur1.0/musFur1Syntenic net HedgehogErinaceus europaeusMay 2012EriEur2.0/eriEur2Syntenic net HorseEquus caballusSep 2007Broad/equCab2Syntenic net Killer whaleOrcinus orcaJan 2013Oorc_1.1/orcOrc1Syntenic net MegabatPteropus vampyrusJul 2008Broad/pteVam1Reciprocal best net Little brown batMyotis lucifugusJul 2010Broad Institute Myoluc2.0/myoLuc2Syntenic net Pacific walrusOdobenus rosmarus divergensJan 2013Oros_1.0/odoRosDiv1Syntenic net PandaAiluropoda melanoleucaDec 2009BGI-Shenzhen 1.0/ailMel1Syntenic net PigSus scrofaAug 2011SGSC Sscrofa10.2/susScr3Syntenic net SheepOvis ariesAug 2012ISGC Oar_v3.1/oviAri3Syntenic net ShrewSorex araneusAug 2008Broad/sorAra2Syntenic net Star-nosed moleCondylura cristataMar 2012ConCri1.0/conCri1Syntenic net Tibetan antelopePantholops hodgsoniiMay 2013PHO1.0/panHod1Syntenic net Weddell sealLeptonychotes weddelliiMar 2013LepWed1.0/lepWed1Reciprocal best net White rhinocerosCeratotherium simumMay 2012CerSimSim1.0/cerSim1Syntenic net Afrotheria subset AardvarkOrycteropus afer aferMay 2012OryAfe1.0/oryAfe1Syntenic net Cape elephant shrewElephantulus edwardiiAug 2012EleEdw1.0/eleEdw1Syntenic net Cape golden moleChrysochloris asiaticaAug 2012ChrAsi1.0/chrAsi1Syntenic net ElephantLoxodonta africanaJul 2009Broad/loxAfr3Syntenic net ManateeTrichechus manatus latirostrisOct 2011Broad v1.0/triMan1Syntenic net TenrecEchinops telfairiNov 2012Broad/echTel2Syntenic net Mammal subset ArmadilloDasypus novemcinctusDec 2011Baylor/dasNov3Syntenic net OpossumMonodelphis domesticaOct 2006Broad/monDom5Net PlatypusOrnithorhynchus anatinusMar 2007WUGSC 5.0.1/ornAna1Reciprocal best net Tasmanian devilSarcophilus harrisiiFeb 2011WTSI Devil_ref v7.0/sarHar1Net WallabyMacropus eugeniiSep 2009TWGS Meug_1.1/macEug2Reciprocal best net Aves subset BudgerigarMelopsittacus undulatusSep 2011WUSTL v6.3/melUnd1Net ChickenGallus gallusNov 2011ICGSC Gallus_gallus-4.0/galGal4Net Collared flycatcherFicedula albicollisJun 2013FicAlb1.5/ficAlb2Net Mallard duckAnas platyrhynchosApr 2013BGI_duck_1.0/anaPla1Net Medium ground finchGeospiza fortisApr 2012GeoFor_1.0/geoFor1Net ParrotAmazona vittataJan 2013AV1/amaVit1Net Peregrine falconFalco peregrinusFeb 2013F_peregrinus_v1.0/falPer1Net Rock pigeonColumba liviaFeb 2013Cliv_1.0/colLiv1Net Saker falconFalco cherrugFeb 2013F_cherrug_v1.0/falChe1Net Scarlet macawAra macaoJun 2013SMACv1.1/araMac1Net Tibetan ground jayPseudopodoces humilisJan 2013PseHum1.0/pseHum1Net TurkeyMeleagris gallopavoDec 2009TGC Turkey_2.01/melGal1Net White-throated sparrowZonotrichia albicollisApr 2013ASM38545v1/zonAlb1Net Zebra finchTaeniopygia guttataFeb 2013WashU taeGut324/taeGut2Net Sarcopterygii subset American alligatorAlligator mississippiensisAug 2012allMis0.2/allMis1Net Chinese softshell turtlePelodiscus sinensisOct 2011PelSin_1.0/pelSin1Net CoelacanthLatimeria chalumnaeAug 2011Broad/latCha1Net Green seaturtleChelonia mydasMar 2013CheMyd_1.0/cheMyd1Net LizardAnolis carolinensisMay 2010Broad AnoCar2.0/anoCar2Net Painted turtleChrysemys picta belliiMar 2014v3.0.3/chrPic2Net Spiny softshell turtleApalone spiniferaMay 2013ASM38561v1/apaSpi1Net X. tropicalisXenopus tropicalisSep 2012JGI 7.0/xenTro7Net Fish subset Atlantic codGadus morhuaMay 2010Genofisk GadMor_May2010/gadMor1Net Burton's mouthbreederHaplochromis burtoniOct 2011AstBur1.0/hapBur1Net FuguTakifugu rubripesOct 2011FUGU5/fr3Net LampreyPetromyzon marinusSep 2010WUGSC 7.0/petMar2Net MedakaOryzias latipesOct 2005NIG/UT MEDAKA1/oryLat2Net Mexican tetra (cavefish)Astyanax mexicanusApr 2013Astyanax_mexicanus-1.0.2/astMex1Net Nile tilapiaOreochromis niloticusJan 2011Broad oreNil1.1/oreNil2Net Princess of BurundiNeolamprologus brichardiMay 2011NeoBri1.0/neoBri1Net Pundamilia nyerereiPundamilia nyerereiOct 2011PunNye1.0/punNye1Net Southern platyfishXiphophorus maculatusJan 2012Xiphophorus_maculatus-4.4.2/xipMac1Net Spotted garLepisosteus oculatusDec 2011LepOcu1/lepOcu1Net SticklebackGasterosteus aculeatusFeb 2006Broad/gasAcu1Net TetraodonTetraodon nigroviridisMar 2007Genoscope 8.0/tetNig2Net Yellowbelly pufferfishTakifugu flavidusMay 2013version 1 of Takifugu flavidus genome/takFla1Net Zebra mbunaMaylandia zebraMar 2012MetZeb1.1/mayZeb1Net ZebrafishDanio rerioSep 2014GRCz10/danRer10Net Table 1. Genome assemblies included in the 100-way Conservation track. Display Conventions and Configuration In full and pack display modes, conservation scores are displayed as a wiggle track (histogram) in which the height reflects the size of the score. The conservation wiggles can be configured in a variety of ways to highlight different aspects of the displayed information. Click the Graph configuration help link for an explanation of the configuration options. Pairwise alignments of each species to the human genome are displayed below the conservation histogram as a grayscale density plot (in pack mode) or as a wiggle (in full mode) that indicates alignment quality. In dense display mode, conservation is shown in grayscale using darker values to indicate higher levels of overall conservation as scored by phastCons. Checkboxes on the track configuration page allow selection of the species to include in the pairwise display. Note that excluding species from the pairwise display does not alter the the conservation score display. To view detailed information about the alignments at a specific position, zoom the display in to 30,000 or fewer bases, then click on the alignment. Gap Annotation The Display chains between alignments configuration option enables display of gaps between alignment blocks in the pairwise alignments in a manner similar to the Chain track display. The following conventions are used: Single line: No bases in the aligned species. Possibly due to a lineage-specific insertion between the aligned blocks in the human genome or a lineage-specific deletion between the aligned blocks in the aligning species. Double line: Aligning species has one or more unalignable bases in the gap region. Possibly due to excessive evolutionary distance between species or independent indels in the region between the aligned blocks in both species. Pale yellow coloring: Aligning species has Ns in the gap region. Reflects uncertainty in the relationship between the DNA of both species, due to lack of sequence in relevant portions of the aligning species. Genomic Breaks Discontinuities in the genomic context (chromosome, scaffold or region) of the aligned DNA in the aligning species are shown as follows: Vertical blue bar: Represents a discontinuity that persists indefinitely on either side, e.g. a large region of DNA on either side of the bar comes from a different chromosome in the aligned species due to a large scale rearrangement. Green square brackets: Enclose shorter alignments consisting of DNA from one genomic context in the aligned species nested inside a larger chain of alignments from a different genomic context. The alignment within the brackets may represent a short misalignment, a lineage-specific insertion of a transposon in the human genome that aligns to a paralogous copy somewhere else in the aligned species, or other similar occurrence. Base Level When zoomed-in to the base-level display, the track shows the base composition of each alignment. The numbers and symbols on the Gaps line indicate the lengths of gaps in the human sequence at those alignment positions relative to the longest non-human sequence. If there is sufficient space in the display, the size of the gap is shown. If the space is insufficient and the gap size is a multiple of 3, a "*" is displayed; other gap sizes are indicated by "+". Codon translation is available in base-level display mode if the displayed region is identified as a coding segment. To display this annotation, select the species for translation from the pull-down menu in the Codon Translation configuration section at the top of the page. Then, select one of the following modes: No codon translation: The gene annotation is not used; the bases are displayed without translation. Use default species reading frames for translation: The annotations from the genome displayed in the Default species to establish reading frame pull-down menu are used to translate all the aligned species present in the alignment. Use reading frames for species if available, otherwise no translation: Codon translation is performed only for those species where the region is annotated as protein coding. Use reading frames for species if available, otherwise use default species: Codon translation is done on those species that are annotated as being protein coding over the aligned region using species-specific annotation; the remaining species are translated using the default species annotation. Codon translation uses the following gene tracks as the basis for translation: Gene TrackSpecies UCSC GenesHuman, Mouse RefSeq GenesCow, Frog (X. tropicalis) Ensembl Genes v73Atlantic cod, Bushbaby, Cat, Chicken, Chimp, Coelacanth, Dog, Elephant, Ferret, Fugu, Gorilla, Horse, Lamprey, Little brown bat, Lizard, Mallard duck, Marmoset, Medaka, Megabat, Orangutan, Panda, Pig, Platypus, Rat, Soft-shell Turtle, Southern platyfish, Squirrel, Tasmanian devil, Tetraodon, Zebrafish no annotationAardvark, Alpaca, American alligator, Armadillo, Baboon, Bactrian camel, Big brown bat, Black flying-fox, Brush-tailed rat, Budgerigar, Burton's mouthbreeder, Cape elephant shrew, Cape golden mole, Chinchilla, Chinese hamster, Chinese tree shrew, Collared flycatcher, Crab-eating macaque, David's myotis (bat), Dolphin, Domestic goat, Gibbon, Golden hamster, Green monkey, Green seaturtle, Hedgehog, Killer whale, Lesser Egyptian jerboa, Manatee, Medium ground finch, Mexican tetra (cavefish), Naked mole-rat, Nile tilapia, Pacific walrus, Painted turtle, Parrot, Peregrine falcon, Pika, Prairie vole, Princess of Burundi, Pundamilia nyererei, Rhesus, Rock pigeon, Saker falcon, Scarlet Macaw, Sheep, Shrew, Spiny softshell turtle, Spotted gar, Squirrel monkey, Star-nosed mole, Tawny puffer fish, Tenrec, Tibetan antelope, Tibetan ground jay, Wallaby, Weddell seal, White rhinoceros, White-throated sparrow, Zebra Mbuna, Zebra finch Table 2. Gene tracks used for codon translation. Methods Pairwise alignments with the human genome were generated for each species using lastz from repeat-masked genomic sequence. Pairwise alignments were then linked into chains using a dynamic programming algorithm that finds maximally scoring chains of gapless subsections of the alignments organized in a kd-tree. The scoring matrix and parameters for pairwise alignment and chaining were tuned for each species based on phylogenetic distance from the reference. High-scoring chains were then placed along the genome, with gaps filled by lower-scoring chains, to produce an alignment net. For more information about the chaining and netting process and parameters for each species, see the description pages for the Chain and Net tracks. An additional filtering step was introduced in the generation of the 100-way conservation track to reduce the number of paralogs and pseudogenes from the high-quality assemblies and the suspect alignments from the low-quality assemblies: the pairwise alignments of high-quality mammalian sequences (placental and marsupial) were filtered based on synteny; those for 2X mammalian genomes were filtered to retain only alignments of best quality in both the target and query ("reciprocal best"). The resulting best-in-genome pairwise alignments were progressively aligned using multiz/autoMZ, following the tree topology diagrammed above, to produce multiple alignments. The multiple alignments were post-processed to add annotations indicating alignment gaps, genomic breaks, and base quality of the component sequences. The annotated multiple alignments, in MAF format, are available for bulk download. An alignment summary table containing an entry for each alignment block in each species was generated to improve track display performance at large scales. Framing tables were constructed to enable visualization of codons in the multiple alignment display. Phylogenetic Tree Model Both phastCons and phyloP are phylogenetic methods that rely on a tree model containing the tree topology, branch lengths representing evolutionary distance at neutrally evolving sites, the background distribution of nucleotides, and a substitution rate matrix. The all-species tree model for this track was generated using the phyloFit program from the PHAST package (REV model, EM algorithm, medium precision) using multiple alignments of 4-fold degenerate sites extracted from the 100-way alignment (msa_view). The 4d sites were derived from the RefSeq (Reviewed+Coding) gene set, filtered to select single-coverage long transcripts. This same tree model was used in the phyloP calculations; however, the background frequencies were modified to maintain reversibility. The resulting tree model: all species. PhastCons Conservation The phastCons program computes conservation scores based on a phylo-HMM, a type of probabilistic model that describes both the process of DNA substitution at each site in a genome and the way this process changes from one site to the next (Felsenstein and Churchill 1996, Yang 1995, Siepel and Haussler 2005). PhastCons uses a two-state phylo-HMM, with a state for conserved regions and a state for non-conserved regions. The value plotted at each site is the posterior probability that the corresponding alignment column was "generated" by the conserved state of the phylo-HMM. These scores reflect the phylogeny (including branch lengths) of the species in question, a continuous-time Markov model of the nucleotide substitution process, and a tendency for conservation levels to be autocorrelated along the genome (i.e., to be similar at adjacent sites). The general reversible (REV) substitution model was used. Unlike many conservation-scoring programs, phastCons does not rely on a sliding window of fixed size; therefore, short highly-conserved regions and long moderately conserved regions can both obtain high scores. More information about phastCons can be found in Siepel et al. 2005. The phastCons parameters used were: expected-length=45, target-coverage=0.3, rho=0.3. PhyloP Conservation The phyloP program supports several different methods for computing p-values of conservation or acceleration, for individual nucleotides or larger elements ( http://compgen.cshl.edu/phast/). Here it was used to produce separate scores at each base (--wig-scores option), considering all branches of the phylogeny rather than a particular subtree or lineage (i.e., the --subtree option was not used). The scores were computed by performing a likelihood ratio test at each alignment column (--method LRT), and scores for both conservation and acceleration were produced (--mode CONACC). Conserved Elements The conserved elements were predicted by running phastCons with the --viterbi option. The predicted elements are segments of the alignment that are likely to have been "generated" by the conserved state of the phylo-HMM. Each element is assigned a log-odds score equal to its log probability under the conserved model minus its log probability under the non-conserved model. The "score" field associated with this track contains transformed log-odds scores, taking values between 0 and 1000. (The scores are transformed using a monotonic function of the form a * log(x) + b.) The raw log odds scores are retained in the "name" field and can be seen on the details page or in the browser when the track's display mode is set to "pack" or "full". Credits This track was created using the following programs: Alignment tools: lastz (formerly blastz) and multiz by Minmei Hou, Scott Schwartz and Webb Miller of the Penn State Bioinformatics Group Chaining and Netting: axtChain, chainNet by Jim Kent at UCSC Conservation scoring: phastCons, phyloP, phyloFit, tree_doctor, msa_view and other programs in PHAST by Adam Siepel at Cold Spring Harbor Laboratory (original development done at the Haussler lab at UCSC). MAF Annotation tools: mafAddIRows by Brian Raney, UCSC; mafAddQRows by Richard Burhans, Penn State; genePredToMafFrames by Mark Diekhans, UCSC Tree image generator: phyloPng by Galt Barber, UCSC Conservation track display: Kate Rosenbloom, Hiram Clawson (wiggle display), and Brian Raney (gap annotation and codon framing) at UCSC The phylogenetic tree is based on Murphy et al. (2001) and general consensus in the vertebrate phylogeny community. Thanks to Giacomo Bernardi for help with the fish relationships. References Phylo-HMMs, phastCons, and phyloP: Felsenstein J, Churchill GA. A Hidden Markov Model approach to variation among sites in rate of evolution. Mol Biol Evol. 1996 Jan;13(1):93-104. PMID: 8583911 Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 2010 Jan;20(1):110-21. PMID: 19858363; PMC: PMC2798823 Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, Clawson H, Spieth J, Hillier LW, Richards S, et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 2005 Aug;15(8):1034-50. PMID: 16024819; PMC: PMC1182216 Siepel A, Haussler D. Phylogenetic Hidden Markov Models. In: Nielsen R, editor. Statistical Methods in Molecular Evolution. New York: Springer; 2005. pp. 325-351. DOI: 10.1007/0-387-27733-1_12 Yang Z. A space-time process model for the evolution of DNA sequences. Genetics. 1995 Feb;139(2):993-1005. PMID: 7713447; PMC: PMC1206396 Chain/Net: Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Multiz: Blanchette M, Kent WJ, Riemer C, Elnitski L, Smit AF, Roskin KM, Baertsch R, Rosenbloom K, Clawson H, Green ED, et al. Aligning multiple genomic sequences with the threaded blockset aligner. Genome Res. 2004 Apr;14(4):708-15. PMID: 15060014; PMC: PMC383317 Lastz (formerly Blastz): Chiaromonte F, Yap VB, Miller W. Scoring pairwise genomic sequence alignments. Pac Symp Biocomput. 2002:115-26. PMID: 11928468 Harris RS. Improved pairwise alignment of genomic DNA. Ph.D. Thesis. Pennsylvania State University, USA. 2007. Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 Phylogenetic Tree: Murphy WJ, Eizirik E, O'Brien SJ, Madsen O, Scally M, Douady CJ, Teeling E, Ryder OA, Stanhope MJ, de Jong WW, Springer MS. Resolution of the early placental mammal radiation using Bayesian phylogenetics. Science. 2001 Dec 14;294(5550):2348-51. PMID: 11743200 
cons100wayViewalign Multiz Alignments Vertebrate Multiz Alignment & Conservation (100 Species) Comparative Genomics 
multiz100way Multiz Align Multiz Alignments of 100 Vertebrates Comparative Genomics 
cons100wayViewphastcons Element Conservation (phastCons) Vertebrate Multiz Alignment & Conservation (100 Species) Comparative Genomics 
phastCons100way Cons 100 Verts 100 vertebrates conservation by PhastCons Comparative Genomics 
cons100wayViewelements Conserved Elements Vertebrate Multiz Alignment & Conservation (100 Species) Comparative Genomics 
phastConsElements100way 100 Vert. El 100 vertebrates Conserved Elements Comparative Genomics 
cons100wayViewphyloP Basewise Conservation (phyloP) Vertebrate Multiz Alignment & Conservation (100 Species) Comparative Genomics 
phyloP100way Cons 100 Verts 100 vertebrates Basewise Conservation by PhyloP Comparative Genomics 
cpgIslandExt CpG Islands CpG Islands (Islands < 300 Bases are Light Green) Regulation Description CpG islands are associated with genes, particularly housekeeping genes, in vertebrates. CpG islands are typically common near transcription start sites and may be associated with promoter regions. Normally a C (cytosine) base followed immediately by a G (guanine) base (a CpG) is rare in vertebrate DNA because the Cs in such an arrangement tend to be methylated. This methylation helps distinguish the newly synthesized DNA strand from the parent strand, which aids in the final stages of DNA proofreading after duplication. However, over evolutionary time, methylated Cs tend to turn into Ts because of spontaneous deamination. The result is that CpGs are relatively rare unless there is selective pressure to keep them or a region is not methylated for some other reason, perhaps having to do with the regulation of gene expression. CpG islands are regions where CpGs are present at significantly higher levels than is typical for the genome as a whole. The unmasked version of the track displays potential CpG islands that exist in repeat regions and would otherwise not be visible in the repeat masked version. By default, only the masked version of the track is displayed. To view the unmasked version, change the visibility settings in the track controls at the top of this page. Methods CpG islands were predicted by searching the sequence one base at a time, scoring each dinucleotide (+17 for CG and -1 for others) and identifying maximally scoring segments. Each segment was then evaluated for the following criteria: GC content of 50% or greater length greater than 200 bp ratio greater than 0.6 of observed number of CG dinucleotides to the expected number on the basis of the number of Gs and Cs in the segment The entire genome sequence, masking areas included, was used for the construction of the track Unmasked CpG. The track CpG Islands is constructed on the sequence after all masked sequence is removed. The CpG count is the number of CG dinucleotides in the island. The Percentage CpG is the ratio of CpG nucleotide bases (twice the CpG count) to the length. The ratio of observed to expected CpG is calculated according to the formula (cited in Gardiner-Garden et al. (1987)): Obs/Exp CpG = Number of CpG * N / (Number of C * Number of G) where N = length of sequence. The calculation of the track data is performed by the following command sequence: twoBitToFa assembly.2bit stdout | maskOutFa stdin hard stdout \ | cpg_lh /dev/stdin 2&gt; cpg_lh.err \ | awk '{&dollar;2 = &dollar;2 - 1; width = &dollar;3 - &dollar;2; printf("%s\t%d\t%s\t%s %s\t%s\t%s\t%0.0f\t%0.1f\t%s\t%s\n", &dollar;1, &dollar;2, &dollar;3, &dollar;5, &dollar;6, width, &dollar;6, width*&dollar;7*0.01, 100.0*2*&dollar;6/width, &dollar;7, &dollar;9);}' \ | sort -k1,1 -k2,2n &gt; cpgIsland.bed The unmasked track data is constructed from twoBitToFa -noMask output for the twoBitToFa command. Data access CpG islands and its associated tables can be explored interactively using the REST API, the Table Browser or the Data Integrator. All the tables can also be queried directly from our public MySQL servers, with more information available on our help page as well as on our blog. The source for the cpg_lh program can be obtained from src/utils/cpgIslandExt/. The cpg_lh program binary can be obtained from: http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/cpg_lh (choose "save file") Credits This track was generated using a modification of a program developed by G. Miklem and L. Hillier (unpublished). References Gardiner-Garden M, Frommer M. CpG islands in vertebrate genomes. J Mol Biol. 1987 Jul 20;196(2):261-82. PMID: 3656447 
cpgIslandSuper CpG Islands CpG Islands (Islands < 300 Bases are Light Green) Regulation Description CpG islands are associated with genes, particularly housekeeping genes, in vertebrates. CpG islands are typically common near transcription start sites and may be associated with promoter regions. Normally a C (cytosine) base followed immediately by a G (guanine) base (a CpG) is rare in vertebrate DNA because the Cs in such an arrangement tend to be methylated. This methylation helps distinguish the newly synthesized DNA strand from the parent strand, which aids in the final stages of DNA proofreading after duplication. However, over evolutionary time, methylated Cs tend to turn into Ts because of spontaneous deamination. The result is that CpGs are relatively rare unless there is selective pressure to keep them or a region is not methylated for some other reason, perhaps having to do with the regulation of gene expression. CpG islands are regions where CpGs are present at significantly higher levels than is typical for the genome as a whole. The unmasked version of the track displays potential CpG islands that exist in repeat regions and would otherwise not be visible in the repeat masked version. By default, only the masked version of the track is displayed. To view the unmasked version, change the visibility settings in the track controls at the top of this page. Methods CpG islands were predicted by searching the sequence one base at a time, scoring each dinucleotide (+17 for CG and -1 for others) and identifying maximally scoring segments. Each segment was then evaluated for the following criteria: GC content of 50% or greater length greater than 200 bp ratio greater than 0.6 of observed number of CG dinucleotides to the expected number on the basis of the number of Gs and Cs in the segment The entire genome sequence, masking areas included, was used for the construction of the track Unmasked CpG. The track CpG Islands is constructed on the sequence after all masked sequence is removed. The CpG count is the number of CG dinucleotides in the island. The Percentage CpG is the ratio of CpG nucleotide bases (twice the CpG count) to the length. The ratio of observed to expected CpG is calculated according to the formula (cited in Gardiner-Garden et al. (1987)): Obs/Exp CpG = Number of CpG * N / (Number of C * Number of G) where N = length of sequence. The calculation of the track data is performed by the following command sequence: twoBitToFa assembly.2bit stdout | maskOutFa stdin hard stdout \ | cpg_lh /dev/stdin 2&gt; cpg_lh.err \ | awk '{&dollar;2 = &dollar;2 - 1; width = &dollar;3 - &dollar;2; printf("%s\t%d\t%s\t%s %s\t%s\t%s\t%0.0f\t%0.1f\t%s\t%s\n", &dollar;1, &dollar;2, &dollar;3, &dollar;5, &dollar;6, width, &dollar;6, width*&dollar;7*0.01, 100.0*2*&dollar;6/width, &dollar;7, &dollar;9);}' \ | sort -k1,1 -k2,2n &gt; cpgIsland.bed The unmasked track data is constructed from twoBitToFa -noMask output for the twoBitToFa command. Data access CpG islands and its associated tables can be explored interactively using the REST API, the Table Browser or the Data Integrator. All the tables can also be queried directly from our public MySQL servers, with more information available on our help page as well as on our blog. The source for the cpg_lh program can be obtained from src/utils/cpgIslandExt/. The cpg_lh program binary can be obtained from: http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/cpg_lh (choose "save file") Credits This track was generated using a modification of a program developed by G. Miklem and L. Hillier (unpublished). References Gardiner-Garden M, Frommer M. CpG islands in vertebrate genomes. J Mol Biol. 1987 Jul 20;196(2):261-82. PMID: 3656447 
crossTissueMapsTissueCellType Cross Tissue Nuclei Cross tissue nuclei RNA by tissue and cell type Single Cell RNA-seq Description This track collection shows data from Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. The dataset covers ~200,000 single nuclei from a total of 16 human donors across 25 samples, using 4 different sample preparation protocols followed by droplet based single-cell RNA-seq. The samples were obtained from frozen tissue as part of the Genotype-Tissue Expression (GTEx) project. Samples were taken from the esophagus, skeletal muscle, heart, lung, prostate, breast, and skin. The dataset includes 43 broad cell classes, some specific to certain tissues and some shared across all tissue types. This track collection contains three bar chart tracks of RNA expression. The first track, Cross Tissue Nuclei, allows cells to be grouped together and faceted on up to 4 categories: tissue, cell class, cell subclass, and cell type. The second track, Cross Tissue Details, allows cells to be grouped together and faceted on up to 7 categories: tissue, cell class, cell subclass, cell type, granular cell type, sex, and donor. The third track, GTEx Immune Atlas, allows cells to be grouped together and faceted on up to 5 categories: tissue, cell type, cell class, sex, and donor. Please see the GTEx portal for further interactive displays and additional data. Display Conventions and Configuration Tissue-cell type combinations in the Full and Combined tracks are colored by which cell type they belong to in the below table: Color Cell Type Endothelial Epithelial Glia Immune Neuron Stromal Other Tissue-cell type combinations in the Immune Atlas track are shaded according to the below table: Color Cell Type Inflammatory Macrophage Lung Macrophage Monocyte/Macrophage FCGR3A High Monocyte/Macrophage FCGR3A Low Macrophage HLAII High Macrophage LYVE1 High Proliferating Macrophage Dendritic Cell 1 Dendritic Cell 2 Mature Dendritic Cell Langerhans CD14+ Monocyte CD16+ Monocyte LAM-like Other Methods Using the previously collected tissue samples from the Genotype-Tissue Expression project, nuclei were isolated using four different protocols and sequenced using droplet based single cell RNA-seq. CellBender v2.1 and other standard quality control techniques were applied, resulting in 209,126 nuclei profiles across eight tissues, with a mean of 918 genes and 1519 transcripts per profile. Data from all samples was integrated with a conditional variation autoencoder in order to correct for multiple sources of variation like sex, and protocol while preserving tissue and cell type specific effects. For detailed methods, please refer to Eraslan et al, or the GTEx portal website. UCSC Methods The gene expression files were downloaded from the GTEx portal. The UCSC command line utilities matrixClusterColumns, matrixToBarChartBed, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions or our Data Access FAQ for more information. Credits Thanks to the GTEx Consortium for creating and analyzing these data. References Eraslan G, Drokhlyansky E, Anand S, Fiskin E, Subramanian A, Slyper M, Wang J, Van Wittenberghe N, Rouhana JM, Waldman J et al. Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. Science. 2022 May 13;376(6594):eabl4290. PMID: 35549429; PMC: PMC9383269 
crossTissueMaps Cross Tissue Nuclei Single Nuclei sequenced across many tissues Single Cell RNA-seq Description This track collection shows data from Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. The dataset covers ~200,000 single nuclei from a total of 16 human donors across 25 samples, using 4 different sample preparation protocols followed by droplet based single-cell RNA-seq. The samples were obtained from frozen tissue as part of the Genotype-Tissue Expression (GTEx) project. Samples were taken from the esophagus, skeletal muscle, heart, lung, prostate, breast, and skin. The dataset includes 43 broad cell classes, some specific to certain tissues and some shared across all tissue types. This track collection contains three bar chart tracks of RNA expression. The first track, Cross Tissue Nuclei, allows cells to be grouped together and faceted on up to 4 categories: tissue, cell class, cell subclass, and cell type. The second track, Cross Tissue Details, allows cells to be grouped together and faceted on up to 7 categories: tissue, cell class, cell subclass, cell type, granular cell type, sex, and donor. The third track, GTEx Immune Atlas, allows cells to be grouped together and faceted on up to 5 categories: tissue, cell type, cell class, sex, and donor. Please see the GTEx portal for further interactive displays and additional data. Display Conventions and Configuration Tissue-cell type combinations in the Full and Combined tracks are colored by which cell type they belong to in the below table: Color Cell Type Endothelial Epithelial Glia Immune Neuron Stromal Other Tissue-cell type combinations in the Immune Atlas track are shaded according to the below table: Color Cell Type Inflammatory Macrophage Lung Macrophage Monocyte/Macrophage FCGR3A High Monocyte/Macrophage FCGR3A Low Macrophage HLAII High Macrophage LYVE1 High Proliferating Macrophage Dendritic Cell 1 Dendritic Cell 2 Mature Dendritic Cell Langerhans CD14+ Monocyte CD16+ Monocyte LAM-like Other Methods Using the previously collected tissue samples from the Genotype-Tissue Expression project, nuclei were isolated using four different protocols and sequenced using droplet based single cell RNA-seq. CellBender v2.1 and other standard quality control techniques were applied, resulting in 209,126 nuclei profiles across eight tissues, with a mean of 918 genes and 1519 transcripts per profile. Data from all samples was integrated with a conditional variation autoencoder in order to correct for multiple sources of variation like sex, and protocol while preserving tissue and cell type specific effects. For detailed methods, please refer to Eraslan et al, or the GTEx portal website. UCSC Methods The gene expression files were downloaded from the GTEx portal. The UCSC command line utilities matrixClusterColumns, matrixToBarChartBed, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions or our Data Access FAQ for more information. Credits Thanks to the GTEx Consortium for creating and analyzing these data. References Eraslan G, Drokhlyansky E, Anand S, Fiskin E, Subramanian A, Slyper M, Wang J, Van Wittenberghe N, Rouhana JM, Waldman J et al. Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. Science. 2022 May 13;376(6594):eabl4290. PMID: 35549429; PMC: PMC9383269 
encodeCcreCombined ENCODE cCREs ENCODE Candidate Cis-Regulatory Elements (cCREs) combined from all cell types Regulation Description This track displays the ENCODE Registry of candidate cis-Regulatory Elements (cCREs) in the human genome, a total of 926,535 elements identified and classified by the ENCODE Data Analysis Center according to biochemical signatures. cCREs are the subset of representative DNase hypersensitive sites across ENCODE and Roadmap Epigenomics samples that are supported by either histone modifications (H3K4me3 and H3K27ac) or CTCF-binding data. The Registry of cCREs is one of the core components of the integrative level of the ENCODE Encyclopedia of DNA Elements. Additional exploration of the cCRE's and underlying raw ENCODE data is provided by the SCREEN (Search Candidate cis-Regulatory Elements) web tool, designed specifically for the Registry, accessible by linkouts from the track details page. The cCREs identified in the mouse genome are available in a companion track, here. The related cCREs by Biosample composite track presents ccREs and associated epigenetic signal in all individual biosamples in a large matrix. Additional views of the data are provided by the ENCODE Integrative Megahub. --> Display Conventions and Configuration CCREs are colored and labeled according to classification by regulatory signature: Color UCSC label ENCODE classification ENCODE label red prom promoter-like signature PLS orange enhP proximal enhancer-like signature pELS yellow enhD distal enhancer-like signature dELS pink K4m3 DNase-H3K4me3 DNase-H3K4me3 blue CTCF CTCF-only CTCF-only The DNase-H3K4me3 elements are those with promoter-like biochemical signature that are not within 200bp of an annotated TSS. Methods All individual DNase hypsersensitive sites (DHSs) identified from 706 DNase-seq experiments in humans (a total of 93 million sites from 706 experiments) were iteratively clustered and filtered for the highest signal across all experiments, producing representative DHSs (rDHSs), with a total of 2.2 million such sites in human. The highest signal elements from this set that were also supported by high H3K4me3, H3K27ac and/or CTCF ChIP-seq signals were designated cCRE's (a total of 926,535 in human). Classification of cCRE's was performed based on the following criteria: cCREs with promoter-like signatures (cCRE-PLS) fall within 200 bp of an annotated GENCODE TSS and have high DNase and H3K4me3 signals. cCREs with enhancer-like signatures (cCRE-ELS) have high DNase and H3K27ac with low H3K4me3 max-Z score if they are within 200 bp of an annotated TSS. The subset of cCREs-ELS within 2 kb of a TSS is denoted proximal (cCRE-pELS), while the remaining subset is denoted distal (cCRE-dELS). DNase-H3K4me3 cCREs have high H3K4me3 max-Z scores but low H3K27ac max-Z scores and do not fall within 200 bp of a TSS. CTCF-only cCREs have high DNase and CTCF and low H3K4me3 and H3K27ac. The GENCODE V24 (Ensembl 33) basic gene annotation set was used in this analysis. For further detail about the identification and classification of ENCODE cCREs see the About page of the SCREEN web tool. Data Access The ENCODE accession numbers of the constituent datasets at the ENCODE Portal are available from the cCRE details page. The data in this track can be interactively explored with the Table Browser or the Data Integrator. The data can be accessed from scripts through our API, the track name is "encodeCcreCombined". For automated download and analysis, this annotation is stored in a bigBed file that can be downloaded from our download server. The file for this track is called encodeCcreCombined.bb. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/encode3/ccre/encodeCcreCombined.bb -chrom=chr21 -start=0 -end=100000000 stdout Release Notes This annotation is based on ENCODE data released on or before September 14, 2018. Data from the Common fund supported Roadmap Epigenomics Mapping Consortium (REMC) were included for building the ENCODE cCREs. Please see the 2015 paper on their analysis of reference human genomes for more information. Credits This dataset was produced by the ENCODE Data Analysis Center (ZLab at UMass Medical Center). Please check the ZLab ENCODE Public Hubs for the most updated data. Thanks to Henry Pratt, Jill Moore, Michael Purcaro, and Zhiping Weng, PI for providing this data. Thanks also to the ENCODE Consortium, the ENCODE production laboratories, and the ENCODE Data Coordination Center for generating and processing the datasets used here. References ENCODE Project Consortium. Expanded Encyclopedias of DNA Elements in the Human and Mouse Genomes. Nature. 2020 July 30;583(7818):699-710 ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012 Sep 6;489(7414):57-74. PMID: 22955616; PMC: PMC3439153 ENCODE Project Consortium. A user's guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol. 2011 Apr;9(4):e1001046. PMID: 21526222; PMC: PMC3079585 
fixSeqLiftOverPsl Fix Patches Reference Assembly Fix Patch Sequence Alignments Mapping and Sequencing Description This track shows alignments of fix patch sequences to main chromosome sequences in the reference genome assembly. When errors are corrected in the reference genome assembly, the Genome Reference Consortium (GRC) adds fix patch sequences containing the corrected regions. This strikes a balance between providing the most complete and correct genome sequence, while maintaining stable chromosome coordinates for the original assembly sequences. Fix patches are often associated with incident reports displayed in the GRC Incidents track. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. Mismatching bases are highlighted in red. Several types of alignment gap may also be colored; for more information, see Alignment Insertion/Deletion Display Options. Credits The alignments were provided by NCBI as GFF files and translated into the PSL representation for browser display by UCSC. 
knownGene GENCODE V47 GENCODE V47 Genes and Gene Predictions Description The GENCODE Genes track (version 47, October 2024) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. By default, only the basic gene set is displayed, which is a subset of the comprehensive gene set. The basic set represents transcripts that GENCODE believes will be useful to the majority of users. The track includes protein-coding genes, non-coding RNA genes, and pseudo-genes, though pseudo-genes are not displayed by default. It contains annotations on the reference chromosomes as well as assembly patches and alternative loci (haplotypes). The v47 release was derived from the GTF file that contains annotations only on the main chromosomes. Statistics for this build and information on how they were generated can be found on the GENCODE site. For more information on the different gene tracks, see our Genes FAQ. Display Conventions and Configuration By default, this track displays only the basic GENCODE set, splice variants, and non-coding genes. It includes options to display the entire GENCODE set and pseudogenes. To customize these options, the respective boxes can be checked or unchecked at the top of this description page. This track also includes a variety of labels which identify the transcripts when visibility is set to &quot;full&quot; or &quot;pack&quot;. Gene symbols (e.g. NIPA1) are displayed by default, but additional options include GENCODE Transcript ID (ENST00000561183.5), UCSC Known Gene ID (uc001yve.4), UniProt Display ID (Q7RTP0). Additional information about gene and transcript names can be found in our FAQ. This track, in general, follows the display conventions for gene prediction tracks. The exons for putative non-coding genes and untranslated regions are represented by relatively thin blocks, while those for coding open reading frames are thicker. Coloring for the gene annotations is mostly based on the annotation type: MANE: MANE Select Plus Clinical transcripts. For non-MANE transcripts, the following conventions apply. coding: protein coding transcripts, including polymorphic pseudogenes non-coding: non-protein coding transcripts pseudogene: pseudogene transcript annotations problem: problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) This track contains an optional codon coloring feature that allows users to quickly validate and compare gene predictions. There is also an option to display the data as a density graph, which can be helpful for visualizing the distribution of items over a region. Squishy-pack Display Within a gene using the pack display mode, transcripts below a specified rank will be condensed into a view similar to squish mode. The transcript ranking approach is preliminary and will change in future releases. The transcripts rankings are defined by the following criteria for protein-coding and non-coding genes: Protein_coding genes MANE or Ensembl canonical 1st: MANE Select / Ensembl canonical 2nd: MANE Plus Clinical Coding biotypes 1st: protein_coding and protein_coding_LoF 2nd: NMDs and NSDs 3rd: retained intron and protein_coding_CDS_not_defined Completeness 1st: full length 2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype 1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Methods The GENCODE v47 track was built from the GENCODE downloads file gencode.v47.chr_patch_hapl_scaff.annotation.gff3.gz. Data from other sources were correlated with the GENCODE data to build association tables. Related Data The GENCODE Genes transcripts are annotated in numerous tables, each of which is also available as a downloadable file. One can see a full list of the associated tables in the Table Browser by selecting GENCODE Genes from the track menu; this list is then available on the table menu. Data access GENCODE Genes and its associated tables can be explored interactively using the REST API, the Table Browser or the Data Integrator. The genePred format files for hg38 are available from our downloads directory or in our GTF download directory. All the tables can also be queried directly from our public MySQL servers, with more information available on our help page as well as on our blog. Credits The GENCODE Genes track was produced at UCSC from the GENCODE comprehensive gene set using a computational pipeline developed by Jim Kent and Brian Raney. This version of the track was generated by Jonathan Casper. References Frankish A, Carbonell-Sala S, Diekhans M, Jungreis I, Loveland JE, Mudge JM, Sisu C, Wright JC, Arnan C, Barnes I et al. GENCODE: reference annotation for the human and mouse genomes in 2023. Nucleic Acids Res. 2023 Jan 6;51(D1):D942-D949. PMID: 36420896; PMC: PMC9825462 A full list of GENCODE publications is available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
gnomadVariantsV4.1 gnomAD v4.1 Genome Aggregation Database (gnomAD) Genome and Exome Variants v4.1 Variation Description With the gnomAD v4.1 data release, the v4 Pre-Release track has been replaced with the gnomAD v4.1 track. The v4.1 release includes a fix for the allele number issue. The v4.1 track shows variants from 807,162 individuals, including 730,947 exomes and 76,215 genomes. This includes the 76,156 genomes from the gnomAD v3.1.2 release as well as new exome data from 416,555 UK Biobank individuals. For more detailed information on gnomAD v4.1, see the related blog post. The gnomAD v3.1 track shows variants from 76,156 whole genomes (and no exomes), all mapped to the GRCh38/hg38 reference sequence. 4,454 genomes were added to the number of genomes in the previous v3 release. For more detailed information on gnomAD v3.1, see the related blog post. The gnomAD v3.1.1 track contains the same underlying data as v3.1, but with minor corrections to the VEP annotations and dbSNP rsIDs. On the UCSC side, we have now included the mitochondrial chromosome data that was released as part of gnomAD v3.1 (but after the UCSC version of the track was released). For more information about gnomAD v3.1.1, please see the related changelog. GnomAD Genome Mutational Constraint is based on v3.1.2 and is available only on hg38. It shows the reduced variation caused by purifying natural selection. This is similar to negative selection on loss-of-function (LoF) for genes, but can be calculated for non-coding regions too. Positive values are red and reflect stronger mutation constraint (and less variation), indicating higher natural selection pressure in a region. Negative values are green and reflect lower mutation constraint (and more variation), indicating less selection pressure and less functional effect. Briefly, for any 1kbp window in the genome, a model based on trinucleotide sequence context, base-level methylation, and regional genomic features predicts expected number of mutations, and compares this number to the observed number of mutations using a Z-score (see preprint in the Reference section for details). The chrX scores were added as received from the authors, as there are no de novo mutation data available on chrX (for estimating the effects of regional genomic features on mutation rates), they are more speculative than the ones on the autosomes. The gnomAD Predicted Constraint Metrics track contains metrics of pathogenicity per-gene as predicted for gnomAD v2.1.1 and identifies genes subject to strong selection against various classes of mutation. This includes data on both the gene and transcript level. The gnomAD v2 tracks show variants from 125,748 exomes and 15,708 whole genomes, all mapped to the GRCh37/hg19 reference sequence and lifted to the GRCh38/hg38 assembly. The data originate from 141,456 unrelated individuals sequenced as part of various population-genetic and disease-specific studies collected by the Genome Aggregation Database (gnomAD), release 2.1.1. Raw data from all studies have been reprocessed through a unified pipeline and jointly variant-called to increase consistency across projects. For more information on the processing pipeline and population annotations, see the following blog post and the 2.1.1 README. gnomAD v2 data are based on the GRCh37/hg19 assembly. These tracks display the GRCh38/hg38 lift-over provided by gnomAD on their downloads site. On hg38 only, a subtrack "Gnomad mutational constraint" aka "Genome non-coding constraint of haploinsufficient variation (Gnocchi)" captures the depletion of variation caused by purifying natural selection. This is similar to negative selection on loss-of-function (LoF) for genes, but can be calculated for non-coding regions, too. Briefly, for any 1kbp window in the genome, a model based on trinucleotide sequence context, base-level methylation, and regional genomic features predicts expected number of mutations, and compares this number to the observed number of mutations using a Z-score (see Chen et al 2024 in the Reference section for details). The chrX scores were added as received from the authors, as there are no mutations available for chrX, they are more speculative than the ones on the autosomes. For questions on the gnomAD data, also see the gnomAD FAQ. More details on the Variant type(s) can be found on the Sequence Ontology page. Display Conventions and Configuration gnomAD v4.1 The gnomAD v4.1 track version follows the same conventions and configuration as the v3.1.1 track, except for mouse hovering items. Mouse hover on an item will display the following details about each variant: Position Total Allele Frequency (TotalAF) Genes Annotation FILTER tags from VCF (FILTER) Population with maximum AF (PopMaxAF) Homozygous Individuals Homozygous Individuals in XX samples (chrX and chrY only) Hemizygous Individuals (chrX and chrY only) gnomAD v3.1.1 The gnomAD v3.1.1 track version follows the same conventions and configuration as the v3.1 track, except as noted below. There are additional FILTER field filters: AS_VQSR, indel_stack (chrM only), and npg (chrM only). Where possible, variants overlapping multiple transcripts/genes have been collapsed into one variant, with additional information available on the details page, which has roughly halved the number of items in the bigBed. The bigBed has been split into two files, one with the information necessary for the track display, and one with the information necessary for the details page. For more information on this data format, please see the Data Access section below. The VEP annotation is shown as a table instead of spread across multiple fields. Intergenic variants have not been pre-filtered. gnomAD v3.1 By default, a maximum of 50,000 variants can be displayed at a time (before applying the filters described below), before the track switches to dense display mode. Mouse hover on an item will display many details about each variant, including the affected gene(s), the variant type, and annotation (missense, synonymous, etc). Clicking on an item will display additional details on the variant, including a population frequency table showing allele count in each sub-population. Following the conventions on the gnomAD browser, items are shaded according to their Annotation type: pLoF Missense Synonymous Other Label Options To maintain consistency with the gnomAD website, variants are by default labeled according to their chromosomal start position followed by the reference and alternate alleles, for example &quot;chr1-1234-T-CAG&quot;. dbSNP rsID's are also available as an additional label, if the variant is present in dbSnp. Filtering Options Three filters are available for these tracks: FILTER: Used to exclude/include variants that failed Random Forest (RF), Inbreeding Coefficient (Inbreeding Coeff), or Allele Count (AC0) filters. The PASS option is used to include/exclude variants that pass all of the RF, InbreedingCoeff, and AC0 filters, as denoted in the original VCF. Annotation type: Used to exclude/include variants that are annotated as Probability Loss of Function (pLoF), Missense, Synonymous, or Other, as annotated by VEP version 85 (GENCODE v19). Variant Type: Used to exclude/include variants according to the type of variation, as annotated by VEP v85. There is one additional configurable filter on the minimum minor allele frequency. gnomAD v2.1.1 The gnomAD v2.1.1 track follows the standard display and configuration options available for VCF tracks, briefly explained below. In dense mode, a vertical line is drawn at the position of each variant. In pack mode, &quot;ref&quot; and &quot;alt&quot; alleles are displayed to the left of a vertical line with colored portions corresponding to allele counts. Hovering the mouse pointer over a variant pops up a display of alleles and counts. Filtering Options Four filters are available for these tracks, the same as the underlying VCF: AC0: Allele Count 0 after filtering out low confidence genotypes (GQ &lt; 20; DP &lt; 10; and AB &lt; 0.2 for het calls)) InbreedingCoeff: Inbreeding Coefficient &lt; -0.3 RF: Used to exclude/include variants that failed Random Forest filtering thresholds of 0.055272738028512555, 0.20641025579497013 (probabilities of being a true positive variant) for SNPs, indels) Pass: Variant passes all 3 filters There are two additional filters available, one for the minimum minor allele frequency, and a configurable filter on the QUAL score. UCSC Methods The gnomAD v3.1.1 and v4.1 data is unfiltered. For the v3.1 update only, in order to cut down on the amount of displayed data, the following variant types have been filtered out, but are still viewable in the gnomAD browser: Regulatory Region Variants Downstream/Upstream Gene Variants Transcription Factor Binding Site Variants For the full steps used to create the gnomAD tracks at UCSC, please see the hg38 gnomad makedoc. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API, and the genome annotations are stored in files that can be downloaded from our download server, subject to the conditions set forth by the gnomAD consortium (see below). Variant VCFs can be found in the vcf subdirectory. The v3.1, v3.1.1, and v4.1 variants can be found in a special directory as they have been transformed from the underlying VCF. For the v3.1.1 and v4.1 variants in particular, the underlying bigBed only contains enough information necessary to use the track in the browser. The extra data like VEP annotations and CADD scores are available in the same directory as the bigBed but in the files details.tab.gz and details.tab.gz.gzi. The details.tab.gz contains the gzip compressed extra data in JSON format, and the .gzi file is available to speed searching of this data. Each variant has an associated md5sum in the name field of the bigBed which can be used along with the _dataOffset and _dataLen fields to get the associated external data, as show below: # find item of interest: bigBedToBed genomes.bb stdout | head -4 | tail -1 chr1 12416 12417 854246d79dc5d02dcdbd5f5438542b6e [..omitted for brevity..] chr1-12417-G-A 67293 902 # use the final two fields, _dataOffset and _dataLen (add one to _dataLen to include a newline), to get the extra data: bgzip -b 67293 -s 903 gnomad.v3.1.1.details.tab.gz 854246d79dc5d02dcdbd5f5438542b6e {"DDX11L1": {"cons": ["non_coding_transcript_variant", [..omitted for brevity..] The data can also be found directly from the gnomAD downloads page. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. The mutational constraints score was updated in October 2022 from a previous, now deprecated, pre-publication version. The old version can be found in our archive directory on the download server. It can be loaded by copying the URL into our "Custom tracks" input box. Credits Thanks to the Genome Aggregation Database Consortium for making these data available. The data are released under the Creative Commons Zero Public Domain Dedication as described here. Please note that some annotations within the provided files may have restrictions on usage. See here for more information. References Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alfoldi J, Wang Q, Collins RL, Laricchia KM, Ganna A, Birnbaum DP et al. Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes. doi: https://doi.org/10.1101/531210. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill AJ, Cummings BB et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016 Aug 17;536(7616):285-91. PMID: 27535533; PMC: PMC5018207 Chen S, Francioli LC, Goodrich JK, Collins RL, Kanai M, Wang Q, Alf&#246;ldi J, Watts NA, Vittal C, Gauthier LD et al. A genomic mutational constraint map using variation in 76,156 human genomes. Nature. 2024 Jan;625(7993):92-100. PMID: 38057664 (We added the data in 2021, then later referenced the 2022 Biorxiv preprint, in which the track was not called "Gnocchi" yet) 
gnomadVariants gnomAD Variants Genome Aggregation Database (gnomAD) Genome and Exome Variants Variation Description With the gnomAD v4.1 data release, the v4 Pre-Release track has been replaced with the gnomAD v4.1 track. The v4.1 release includes a fix for the allele number issue. The v4.1 track shows variants from 807,162 individuals, including 730,947 exomes and 76,215 genomes. This includes the 76,156 genomes from the gnomAD v3.1.2 release as well as new exome data from 416,555 UK Biobank individuals. For more detailed information on gnomAD v4.1, see the related blog post. The gnomAD v3.1 track shows variants from 76,156 whole genomes (and no exomes), all mapped to the GRCh38/hg38 reference sequence. 4,454 genomes were added to the number of genomes in the previous v3 release. For more detailed information on gnomAD v3.1, see the related blog post. The gnomAD v3.1.1 track contains the same underlying data as v3.1, but with minor corrections to the VEP annotations and dbSNP rsIDs. On the UCSC side, we have now included the mitochondrial chromosome data that was released as part of gnomAD v3.1 (but after the UCSC version of the track was released). For more information about gnomAD v3.1.1, please see the related changelog. GnomAD Genome Mutational Constraint is based on v3.1.2 and is available only on hg38. It shows the reduced variation caused by purifying natural selection. This is similar to negative selection on loss-of-function (LoF) for genes, but can be calculated for non-coding regions too. Positive values are red and reflect stronger mutation constraint (and less variation), indicating higher natural selection pressure in a region. Negative values are green and reflect lower mutation constraint (and more variation), indicating less selection pressure and less functional effect. Briefly, for any 1kbp window in the genome, a model based on trinucleotide sequence context, base-level methylation, and regional genomic features predicts expected number of mutations, and compares this number to the observed number of mutations using a Z-score (see preprint in the Reference section for details). The chrX scores were added as received from the authors, as there are no de novo mutation data available on chrX (for estimating the effects of regional genomic features on mutation rates), they are more speculative than the ones on the autosomes. The gnomAD Predicted Constraint Metrics track contains metrics of pathogenicity per-gene as predicted for gnomAD v2.1.1 and identifies genes subject to strong selection against various classes of mutation. This includes data on both the gene and transcript level. The gnomAD v2 tracks show variants from 125,748 exomes and 15,708 whole genomes, all mapped to the GRCh37/hg19 reference sequence and lifted to the GRCh38/hg38 assembly. The data originate from 141,456 unrelated individuals sequenced as part of various population-genetic and disease-specific studies collected by the Genome Aggregation Database (gnomAD), release 2.1.1. Raw data from all studies have been reprocessed through a unified pipeline and jointly variant-called to increase consistency across projects. For more information on the processing pipeline and population annotations, see the following blog post and the 2.1.1 README. gnomAD v2 data are based on the GRCh37/hg19 assembly. These tracks display the GRCh38/hg38 lift-over provided by gnomAD on their downloads site. On hg38 only, a subtrack "Gnomad mutational constraint" aka "Genome non-coding constraint of haploinsufficient variation (Gnocchi)" captures the depletion of variation caused by purifying natural selection. This is similar to negative selection on loss-of-function (LoF) for genes, but can be calculated for non-coding regions, too. Briefly, for any 1kbp window in the genome, a model based on trinucleotide sequence context, base-level methylation, and regional genomic features predicts expected number of mutations, and compares this number to the observed number of mutations using a Z-score (see Chen et al 2024 in the Reference section for details). The chrX scores were added as received from the authors, as there are no mutations available for chrX, they are more speculative than the ones on the autosomes. For questions on the gnomAD data, also see the gnomAD FAQ. More details on the Variant type(s) can be found on the Sequence Ontology page. Display Conventions and Configuration gnomAD v4.1 The gnomAD v4.1 track version follows the same conventions and configuration as the v3.1.1 track, except for mouse hovering items. Mouse hover on an item will display the following details about each variant: Position Total Allele Frequency (TotalAF) Genes Annotation FILTER tags from VCF (FILTER) Population with maximum AF (PopMaxAF) Homozygous Individuals Homozygous Individuals in XX samples (chrX and chrY only) Hemizygous Individuals (chrX and chrY only) gnomAD v3.1.1 The gnomAD v3.1.1 track version follows the same conventions and configuration as the v3.1 track, except as noted below. There are additional FILTER field filters: AS_VQSR, indel_stack (chrM only), and npg (chrM only). Where possible, variants overlapping multiple transcripts/genes have been collapsed into one variant, with additional information available on the details page, which has roughly halved the number of items in the bigBed. The bigBed has been split into two files, one with the information necessary for the track display, and one with the information necessary for the details page. For more information on this data format, please see the Data Access section below. The VEP annotation is shown as a table instead of spread across multiple fields. Intergenic variants have not been pre-filtered. gnomAD v3.1 By default, a maximum of 50,000 variants can be displayed at a time (before applying the filters described below), before the track switches to dense display mode. Mouse hover on an item will display many details about each variant, including the affected gene(s), the variant type, and annotation (missense, synonymous, etc). Clicking on an item will display additional details on the variant, including a population frequency table showing allele count in each sub-population. Following the conventions on the gnomAD browser, items are shaded according to their Annotation type: pLoF Missense Synonymous Other Label Options To maintain consistency with the gnomAD website, variants are by default labeled according to their chromosomal start position followed by the reference and alternate alleles, for example &quot;chr1-1234-T-CAG&quot;. dbSNP rsID's are also available as an additional label, if the variant is present in dbSnp. Filtering Options Three filters are available for these tracks: FILTER: Used to exclude/include variants that failed Random Forest (RF), Inbreeding Coefficient (Inbreeding Coeff), or Allele Count (AC0) filters. The PASS option is used to include/exclude variants that pass all of the RF, InbreedingCoeff, and AC0 filters, as denoted in the original VCF. Annotation type: Used to exclude/include variants that are annotated as Probability Loss of Function (pLoF), Missense, Synonymous, or Other, as annotated by VEP version 85 (GENCODE v19). Variant Type: Used to exclude/include variants according to the type of variation, as annotated by VEP v85. There is one additional configurable filter on the minimum minor allele frequency. gnomAD v2.1.1 The gnomAD v2.1.1 track follows the standard display and configuration options available for VCF tracks, briefly explained below. In dense mode, a vertical line is drawn at the position of each variant. In pack mode, &quot;ref&quot; and &quot;alt&quot; alleles are displayed to the left of a vertical line with colored portions corresponding to allele counts. Hovering the mouse pointer over a variant pops up a display of alleles and counts. Filtering Options Four filters are available for these tracks, the same as the underlying VCF: AC0: Allele Count 0 after filtering out low confidence genotypes (GQ &lt; 20; DP &lt; 10; and AB &lt; 0.2 for het calls)) InbreedingCoeff: Inbreeding Coefficient &lt; -0.3 RF: Used to exclude/include variants that failed Random Forest filtering thresholds of 0.055272738028512555, 0.20641025579497013 (probabilities of being a true positive variant) for SNPs, indels) Pass: Variant passes all 3 filters There are two additional filters available, one for the minimum minor allele frequency, and a configurable filter on the QUAL score. UCSC Methods The gnomAD v3.1.1 and v4.1 data is unfiltered. For the v3.1 update only, in order to cut down on the amount of displayed data, the following variant types have been filtered out, but are still viewable in the gnomAD browser: Regulatory Region Variants Downstream/Upstream Gene Variants Transcription Factor Binding Site Variants For the full steps used to create the gnomAD tracks at UCSC, please see the hg38 gnomad makedoc. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API, and the genome annotations are stored in files that can be downloaded from our download server, subject to the conditions set forth by the gnomAD consortium (see below). Variant VCFs can be found in the vcf subdirectory. The v3.1, v3.1.1, and v4.1 variants can be found in a special directory as they have been transformed from the underlying VCF. For the v3.1.1 and v4.1 variants in particular, the underlying bigBed only contains enough information necessary to use the track in the browser. The extra data like VEP annotations and CADD scores are available in the same directory as the bigBed but in the files details.tab.gz and details.tab.gz.gzi. The details.tab.gz contains the gzip compressed extra data in JSON format, and the .gzi file is available to speed searching of this data. Each variant has an associated md5sum in the name field of the bigBed which can be used along with the _dataOffset and _dataLen fields to get the associated external data, as show below: # find item of interest: bigBedToBed genomes.bb stdout | head -4 | tail -1 chr1 12416 12417 854246d79dc5d02dcdbd5f5438542b6e [..omitted for brevity..] chr1-12417-G-A 67293 902 # use the final two fields, _dataOffset and _dataLen (add one to _dataLen to include a newline), to get the extra data: bgzip -b 67293 -s 903 gnomad.v3.1.1.details.tab.gz 854246d79dc5d02dcdbd5f5438542b6e {"DDX11L1": {"cons": ["non_coding_transcript_variant", [..omitted for brevity..] The data can also be found directly from the gnomAD downloads page. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. The mutational constraints score was updated in October 2022 from a previous, now deprecated, pre-publication version. The old version can be found in our archive directory on the download server. It can be loaded by copying the URL into our "Custom tracks" input box. Credits Thanks to the Genome Aggregation Database Consortium for making these data available. The data are released under the Creative Commons Zero Public Domain Dedication as described here. Please note that some annotations within the provided files may have restrictions on usage. See here for more information. References Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alfoldi J, Wang Q, Collins RL, Laricchia KM, Ganna A, Birnbaum DP et al. Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes. doi: https://doi.org/10.1101/531210. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill AJ, Cummings BB et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016 Aug 17;536(7616):285-91. PMID: 27535533; PMC: PMC5018207 Chen S, Francioli LC, Goodrich JK, Collins RL, Kanai M, Wang Q, Alf&#246;ldi J, Watts NA, Vittal C, Gauthier LD et al. A genomic mutational constraint map using variation in 76,156 human genomes. Nature. 2024 Jan;625(7993):92-100. PMID: 38057664 (We added the data in 2021, then later referenced the 2022 Biorxiv preprint, in which the track was not called "Gnocchi" yet) 
gnomadExomesVariantsV4_1 gnomAD v4.1 Exomes Genome Aggregation Database (gnomAD) Exomes Variants v4.1 Variation Description With the gnomAD v4.1 data release, the v4 Pre-Release track has been replaced with the gnomAD v4.1 track. The v4.1 release includes a fix for the allele number issue. The v4.1 track shows variants from 807,162 individuals, including 730,947 exomes and 76,215 genomes. This includes the 76,156 genomes from the gnomAD v3.1.2 release as well as new exome data from 416,555 UK Biobank individuals. For more detailed information on gnomAD v4.1, see the related blog post. The gnomAD v3.1 track shows variants from 76,156 whole genomes (and no exomes), all mapped to the GRCh38/hg38 reference sequence. 4,454 genomes were added to the number of genomes in the previous v3 release. For more detailed information on gnomAD v3.1, see the related blog post. The gnomAD v3.1.1 track contains the same underlying data as v3.1, but with minor corrections to the VEP annotations and dbSNP rsIDs. On the UCSC side, we have now included the mitochondrial chromosome data that was released as part of gnomAD v3.1 (but after the UCSC version of the track was released). For more information about gnomAD v3.1.1, please see the related changelog. GnomAD Genome Mutational Constraint is based on v3.1.2 and is available only on hg38. It shows the reduced variation caused by purifying natural selection. This is similar to negative selection on loss-of-function (LoF) for genes, but can be calculated for non-coding regions too. Positive values are red and reflect stronger mutation constraint (and less variation), indicating higher natural selection pressure in a region. Negative values are green and reflect lower mutation constraint (and more variation), indicating less selection pressure and less functional effect. Briefly, for any 1kbp window in the genome, a model based on trinucleotide sequence context, base-level methylation, and regional genomic features predicts expected number of mutations, and compares this number to the observed number of mutations using a Z-score (see preprint in the Reference section for details). The chrX scores were added as received from the authors, as there are no de novo mutation data available on chrX (for estimating the effects of regional genomic features on mutation rates), they are more speculative than the ones on the autosomes. The gnomAD Predicted Constraint Metrics track contains metrics of pathogenicity per-gene as predicted for gnomAD v2.1.1 and identifies genes subject to strong selection against various classes of mutation. This includes data on both the gene and transcript level. The gnomAD v2 tracks show variants from 125,748 exomes and 15,708 whole genomes, all mapped to the GRCh37/hg19 reference sequence and lifted to the GRCh38/hg38 assembly. The data originate from 141,456 unrelated individuals sequenced as part of various population-genetic and disease-specific studies collected by the Genome Aggregation Database (gnomAD), release 2.1.1. Raw data from all studies have been reprocessed through a unified pipeline and jointly variant-called to increase consistency across projects. For more information on the processing pipeline and population annotations, see the following blog post and the 2.1.1 README. gnomAD v2 data are based on the GRCh37/hg19 assembly. These tracks display the GRCh38/hg38 lift-over provided by gnomAD on their downloads site. On hg38 only, a subtrack "Gnomad mutational constraint" aka "Genome non-coding constraint of haploinsufficient variation (Gnocchi)" captures the depletion of variation caused by purifying natural selection. This is similar to negative selection on loss-of-function (LoF) for genes, but can be calculated for non-coding regions, too. Briefly, for any 1kbp window in the genome, a model based on trinucleotide sequence context, base-level methylation, and regional genomic features predicts expected number of mutations, and compares this number to the observed number of mutations using a Z-score (see Chen et al 2024 in the Reference section for details). The chrX scores were added as received from the authors, as there are no mutations available for chrX, they are more speculative than the ones on the autosomes. For questions on the gnomAD data, also see the gnomAD FAQ. More details on the Variant type(s) can be found on the Sequence Ontology page. Display Conventions and Configuration gnomAD v4.1 The gnomAD v4.1 track version follows the same conventions and configuration as the v3.1.1 track, except for mouse hovering items. Mouse hover on an item will display the following details about each variant: Position Total Allele Frequency (TotalAF) Genes Annotation FILTER tags from VCF (FILTER) Population with maximum AF (PopMaxAF) Homozygous Individuals Homozygous Individuals in XX samples (chrX and chrY only) Hemizygous Individuals (chrX and chrY only) gnomAD v3.1.1 The gnomAD v3.1.1 track version follows the same conventions and configuration as the v3.1 track, except as noted below. There are additional FILTER field filters: AS_VQSR, indel_stack (chrM only), and npg (chrM only). Where possible, variants overlapping multiple transcripts/genes have been collapsed into one variant, with additional information available on the details page, which has roughly halved the number of items in the bigBed. The bigBed has been split into two files, one with the information necessary for the track display, and one with the information necessary for the details page. For more information on this data format, please see the Data Access section below. The VEP annotation is shown as a table instead of spread across multiple fields. Intergenic variants have not been pre-filtered. gnomAD v3.1 By default, a maximum of 50,000 variants can be displayed at a time (before applying the filters described below), before the track switches to dense display mode. Mouse hover on an item will display many details about each variant, including the affected gene(s), the variant type, and annotation (missense, synonymous, etc). Clicking on an item will display additional details on the variant, including a population frequency table showing allele count in each sub-population. Following the conventions on the gnomAD browser, items are shaded according to their Annotation type: pLoF Missense Synonymous Other Label Options To maintain consistency with the gnomAD website, variants are by default labeled according to their chromosomal start position followed by the reference and alternate alleles, for example &quot;chr1-1234-T-CAG&quot;. dbSNP rsID's are also available as an additional label, if the variant is present in dbSnp. Filtering Options Three filters are available for these tracks: FILTER: Used to exclude/include variants that failed Random Forest (RF), Inbreeding Coefficient (Inbreeding Coeff), or Allele Count (AC0) filters. The PASS option is used to include/exclude variants that pass all of the RF, InbreedingCoeff, and AC0 filters, as denoted in the original VCF. Annotation type: Used to exclude/include variants that are annotated as Probability Loss of Function (pLoF), Missense, Synonymous, or Other, as annotated by VEP version 85 (GENCODE v19). Variant Type: Used to exclude/include variants according to the type of variation, as annotated by VEP v85. There is one additional configurable filter on the minimum minor allele frequency. gnomAD v2.1.1 The gnomAD v2.1.1 track follows the standard display and configuration options available for VCF tracks, briefly explained below. In dense mode, a vertical line is drawn at the position of each variant. In pack mode, &quot;ref&quot; and &quot;alt&quot; alleles are displayed to the left of a vertical line with colored portions corresponding to allele counts. Hovering the mouse pointer over a variant pops up a display of alleles and counts. Filtering Options Four filters are available for these tracks, the same as the underlying VCF: AC0: Allele Count 0 after filtering out low confidence genotypes (GQ &lt; 20; DP &lt; 10; and AB &lt; 0.2 for het calls)) InbreedingCoeff: Inbreeding Coefficient &lt; -0.3 RF: Used to exclude/include variants that failed Random Forest filtering thresholds of 0.055272738028512555, 0.20641025579497013 (probabilities of being a true positive variant) for SNPs, indels) Pass: Variant passes all 3 filters There are two additional filters available, one for the minimum minor allele frequency, and a configurable filter on the QUAL score. UCSC Methods The gnomAD v3.1.1 and v4.1 data is unfiltered. For the v3.1 update only, in order to cut down on the amount of displayed data, the following variant types have been filtered out, but are still viewable in the gnomAD browser: Regulatory Region Variants Downstream/Upstream Gene Variants Transcription Factor Binding Site Variants For the full steps used to create the gnomAD tracks at UCSC, please see the hg38 gnomad makedoc. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API, and the genome annotations are stored in files that can be downloaded from our download server, subject to the conditions set forth by the gnomAD consortium (see below). Variant VCFs can be found in the vcf subdirectory. The v3.1, v3.1.1, and v4.1 variants can be found in a special directory as they have been transformed from the underlying VCF. For the v3.1.1 and v4.1 variants in particular, the underlying bigBed only contains enough information necessary to use the track in the browser. The extra data like VEP annotations and CADD scores are available in the same directory as the bigBed but in the files details.tab.gz and details.tab.gz.gzi. The details.tab.gz contains the gzip compressed extra data in JSON format, and the .gzi file is available to speed searching of this data. Each variant has an associated md5sum in the name field of the bigBed which can be used along with the _dataOffset and _dataLen fields to get the associated external data, as show below: # find item of interest: bigBedToBed genomes.bb stdout | head -4 | tail -1 chr1 12416 12417 854246d79dc5d02dcdbd5f5438542b6e [..omitted for brevity..] chr1-12417-G-A 67293 902 # use the final two fields, _dataOffset and _dataLen (add one to _dataLen to include a newline), to get the extra data: bgzip -b 67293 -s 903 gnomad.v3.1.1.details.tab.gz 854246d79dc5d02dcdbd5f5438542b6e {"DDX11L1": {"cons": ["non_coding_transcript_variant", [..omitted for brevity..] The data can also be found directly from the gnomAD downloads page. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. The mutational constraints score was updated in October 2022 from a previous, now deprecated, pre-publication version. The old version can be found in our archive directory on the download server. It can be loaded by copying the URL into our "Custom tracks" input box. Credits Thanks to the Genome Aggregation Database Consortium for making these data available. The data are released under the Creative Commons Zero Public Domain Dedication as described here. Please note that some annotations within the provided files may have restrictions on usage. See here for more information. References Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alfoldi J, Wang Q, Collins RL, Laricchia KM, Ganna A, Birnbaum DP et al. Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes. doi: https://doi.org/10.1101/531210. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill AJ, Cummings BB et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016 Aug 17;536(7616):285-91. PMID: 27535533; PMC: PMC5018207 Chen S, Francioli LC, Goodrich JK, Collins RL, Kanai M, Wang Q, Alf&#246;ldi J, Watts NA, Vittal C, Gauthier LD et al. A genomic mutational constraint map using variation in 76,156 human genomes. Nature. 2024 Jan;625(7993):92-100. PMID: 38057664 (We added the data in 2021, then later referenced the 2022 Biorxiv preprint, in which the track was not called "Gnocchi" yet) 
gnomadGenomesVariantsV4_1 gnomAD v4.1 Genomes Genome Aggregation Database (gnomAD) Genome Variants v4.1 Variation Description With the gnomAD v4.1 data release, the v4 Pre-Release track has been replaced with the gnomAD v4.1 track. The v4.1 release includes a fix for the allele number issue. The v4.1 track shows variants from 807,162 individuals, including 730,947 exomes and 76,215 genomes. This includes the 76,156 genomes from the gnomAD v3.1.2 release as well as new exome data from 416,555 UK Biobank individuals. For more detailed information on gnomAD v4.1, see the related blog post. The gnomAD v3.1 track shows variants from 76,156 whole genomes (and no exomes), all mapped to the GRCh38/hg38 reference sequence. 4,454 genomes were added to the number of genomes in the previous v3 release. For more detailed information on gnomAD v3.1, see the related blog post. The gnomAD v3.1.1 track contains the same underlying data as v3.1, but with minor corrections to the VEP annotations and dbSNP rsIDs. On the UCSC side, we have now included the mitochondrial chromosome data that was released as part of gnomAD v3.1 (but after the UCSC version of the track was released). For more information about gnomAD v3.1.1, please see the related changelog. GnomAD Genome Mutational Constraint is based on v3.1.2 and is available only on hg38. It shows the reduced variation caused by purifying natural selection. This is similar to negative selection on loss-of-function (LoF) for genes, but can be calculated for non-coding regions too. Positive values are red and reflect stronger mutation constraint (and less variation), indicating higher natural selection pressure in a region. Negative values are green and reflect lower mutation constraint (and more variation), indicating less selection pressure and less functional effect. Briefly, for any 1kbp window in the genome, a model based on trinucleotide sequence context, base-level methylation, and regional genomic features predicts expected number of mutations, and compares this number to the observed number of mutations using a Z-score (see preprint in the Reference section for details). The chrX scores were added as received from the authors, as there are no de novo mutation data available on chrX (for estimating the effects of regional genomic features on mutation rates), they are more speculative than the ones on the autosomes. The gnomAD Predicted Constraint Metrics track contains metrics of pathogenicity per-gene as predicted for gnomAD v2.1.1 and identifies genes subject to strong selection against various classes of mutation. This includes data on both the gene and transcript level. The gnomAD v2 tracks show variants from 125,748 exomes and 15,708 whole genomes, all mapped to the GRCh37/hg19 reference sequence and lifted to the GRCh38/hg38 assembly. The data originate from 141,456 unrelated individuals sequenced as part of various population-genetic and disease-specific studies collected by the Genome Aggregation Database (gnomAD), release 2.1.1. Raw data from all studies have been reprocessed through a unified pipeline and jointly variant-called to increase consistency across projects. For more information on the processing pipeline and population annotations, see the following blog post and the 2.1.1 README. gnomAD v2 data are based on the GRCh37/hg19 assembly. These tracks display the GRCh38/hg38 lift-over provided by gnomAD on their downloads site. On hg38 only, a subtrack "Gnomad mutational constraint" aka "Genome non-coding constraint of haploinsufficient variation (Gnocchi)" captures the depletion of variation caused by purifying natural selection. This is similar to negative selection on loss-of-function (LoF) for genes, but can be calculated for non-coding regions, too. Briefly, for any 1kbp window in the genome, a model based on trinucleotide sequence context, base-level methylation, and regional genomic features predicts expected number of mutations, and compares this number to the observed number of mutations using a Z-score (see Chen et al 2024 in the Reference section for details). The chrX scores were added as received from the authors, as there are no mutations available for chrX, they are more speculative than the ones on the autosomes. For questions on the gnomAD data, also see the gnomAD FAQ. More details on the Variant type(s) can be found on the Sequence Ontology page. Display Conventions and Configuration gnomAD v4.1 The gnomAD v4.1 track version follows the same conventions and configuration as the v3.1.1 track, except for mouse hovering items. Mouse hover on an item will display the following details about each variant: Position Total Allele Frequency (TotalAF) Genes Annotation FILTER tags from VCF (FILTER) Population with maximum AF (PopMaxAF) Homozygous Individuals Homozygous Individuals in XX samples (chrX and chrY only) Hemizygous Individuals (chrX and chrY only) gnomAD v3.1.1 The gnomAD v3.1.1 track version follows the same conventions and configuration as the v3.1 track, except as noted below. There are additional FILTER field filters: AS_VQSR, indel_stack (chrM only), and npg (chrM only). Where possible, variants overlapping multiple transcripts/genes have been collapsed into one variant, with additional information available on the details page, which has roughly halved the number of items in the bigBed. The bigBed has been split into two files, one with the information necessary for the track display, and one with the information necessary for the details page. For more information on this data format, please see the Data Access section below. The VEP annotation is shown as a table instead of spread across multiple fields. Intergenic variants have not been pre-filtered. gnomAD v3.1 By default, a maximum of 50,000 variants can be displayed at a time (before applying the filters described below), before the track switches to dense display mode. Mouse hover on an item will display many details about each variant, including the affected gene(s), the variant type, and annotation (missense, synonymous, etc). Clicking on an item will display additional details on the variant, including a population frequency table showing allele count in each sub-population. Following the conventions on the gnomAD browser, items are shaded according to their Annotation type: pLoF Missense Synonymous Other Label Options To maintain consistency with the gnomAD website, variants are by default labeled according to their chromosomal start position followed by the reference and alternate alleles, for example &quot;chr1-1234-T-CAG&quot;. dbSNP rsID's are also available as an additional label, if the variant is present in dbSnp. Filtering Options Three filters are available for these tracks: FILTER: Used to exclude/include variants that failed Random Forest (RF), Inbreeding Coefficient (Inbreeding Coeff), or Allele Count (AC0) filters. The PASS option is used to include/exclude variants that pass all of the RF, InbreedingCoeff, and AC0 filters, as denoted in the original VCF. Annotation type: Used to exclude/include variants that are annotated as Probability Loss of Function (pLoF), Missense, Synonymous, or Other, as annotated by VEP version 85 (GENCODE v19). Variant Type: Used to exclude/include variants according to the type of variation, as annotated by VEP v85. There is one additional configurable filter on the minimum minor allele frequency. gnomAD v2.1.1 The gnomAD v2.1.1 track follows the standard display and configuration options available for VCF tracks, briefly explained below. In dense mode, a vertical line is drawn at the position of each variant. In pack mode, &quot;ref&quot; and &quot;alt&quot; alleles are displayed to the left of a vertical line with colored portions corresponding to allele counts. Hovering the mouse pointer over a variant pops up a display of alleles and counts. Filtering Options Four filters are available for these tracks, the same as the underlying VCF: AC0: Allele Count 0 after filtering out low confidence genotypes (GQ &lt; 20; DP &lt; 10; and AB &lt; 0.2 for het calls)) InbreedingCoeff: Inbreeding Coefficient &lt; -0.3 RF: Used to exclude/include variants that failed Random Forest filtering thresholds of 0.055272738028512555, 0.20641025579497013 (probabilities of being a true positive variant) for SNPs, indels) Pass: Variant passes all 3 filters There are two additional filters available, one for the minimum minor allele frequency, and a configurable filter on the QUAL score. UCSC Methods The gnomAD v3.1.1 and v4.1 data is unfiltered. For the v3.1 update only, in order to cut down on the amount of displayed data, the following variant types have been filtered out, but are still viewable in the gnomAD browser: Regulatory Region Variants Downstream/Upstream Gene Variants Transcription Factor Binding Site Variants For the full steps used to create the gnomAD tracks at UCSC, please see the hg38 gnomad makedoc. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API, and the genome annotations are stored in files that can be downloaded from our download server, subject to the conditions set forth by the gnomAD consortium (see below). Variant VCFs can be found in the vcf subdirectory. The v3.1, v3.1.1, and v4.1 variants can be found in a special directory as they have been transformed from the underlying VCF. For the v3.1.1 and v4.1 variants in particular, the underlying bigBed only contains enough information necessary to use the track in the browser. The extra data like VEP annotations and CADD scores are available in the same directory as the bigBed but in the files details.tab.gz and details.tab.gz.gzi. The details.tab.gz contains the gzip compressed extra data in JSON format, and the .gzi file is available to speed searching of this data. Each variant has an associated md5sum in the name field of the bigBed which can be used along with the _dataOffset and _dataLen fields to get the associated external data, as show below: # find item of interest: bigBedToBed genomes.bb stdout | head -4 | tail -1 chr1 12416 12417 854246d79dc5d02dcdbd5f5438542b6e [..omitted for brevity..] chr1-12417-G-A 67293 902 # use the final two fields, _dataOffset and _dataLen (add one to _dataLen to include a newline), to get the extra data: bgzip -b 67293 -s 903 gnomad.v3.1.1.details.tab.gz 854246d79dc5d02dcdbd5f5438542b6e {"DDX11L1": {"cons": ["non_coding_transcript_variant", [..omitted for brevity..] The data can also be found directly from the gnomAD downloads page. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. The mutational constraints score was updated in October 2022 from a previous, now deprecated, pre-publication version. The old version can be found in our archive directory on the download server. It can be loaded by copying the URL into our "Custom tracks" input box. Credits Thanks to the Genome Aggregation Database Consortium for making these data available. The data are released under the Creative Commons Zero Public Domain Dedication as described here. Please note that some annotations within the provided files may have restrictions on usage. See here for more information. References Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alfoldi J, Wang Q, Collins RL, Laricchia KM, Ganna A, Birnbaum DP et al. Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes. doi: https://doi.org/10.1101/531210. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill AJ, Cummings BB et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016 Aug 17;536(7616):285-91. PMID: 27535533; PMC: PMC5018207 Chen S, Francioli LC, Goodrich JK, Collins RL, Kanai M, Wang Q, Alf&#246;ldi J, Watts NA, Vittal C, Gauthier LD et al. A genomic mutational constraint map using variation in 76,156 human genomes. Nature. 2024 Jan;625(7993):92-100. PMID: 38057664 (We added the data in 2021, then later referenced the 2022 Biorxiv preprint, in which the track was not called "Gnocchi" yet) 
gtexGeneV8 GTEx Gene V8 Gene Expression in 54 tissues from GTEx RNA-seq of 17382 samples, 948 donors (V8, Aug 2019) Expression Description The NIH Genotype-Tissue Expression (GTEx) project was created to establish a sample and data resource for studies on the relationship between genetic variation and gene expression in multiple human tissues. This track shows median gene expression levels in 52 tissues and 2 cell lines, based on RNA-seq data from the GTEx final data release (V8, August 2019). This release is based on data from 17,382 tissue samples obtained from 948 adult post-mortem individuals. Display Conventions In Full and Pack display modes, expression for each gene is represented by a colored bargraph, where the height of each bar represents the median expression level across all samples for a tissue, and the bar color indicates the tissue. Tissue colors were assigned to conform to the GTEx Consortium publication conventions. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The bargraph display has the same width and tissue order for all genes. Mouse hover over a bar will show the tissue and median expression level. The Squish display mode draws a rectangle for each gene, colored to indicate the tissue with highest expression level if it contributes more than 10% to the overall expression (and colored black if no tissue predominates). In Dense mode, the darkness of the grayscale rectangle displayed for the gene reflects the total median expression level across all tissues. The GTEx transcript model used to quantify expression level is displayed below the graph, colored to indicate the transcript class (coding, noncoding, pseudogene, problem), following GENCODE conventions. Click-through on a graph displays a boxplot of expression level quartiles with outliers, per tissue, along with a link to the corresponding gene page on the GTEx Portal. The track configuration page provides controls to limit the genes and tissues displayed, and to select raw or log transformed expression level display. Methods Tissue samples were obtained using the GTEx standard operating procedures for informed consent and tissue collection, in conjunction with the National Cancer Institute Biorepositories and Biospecimen. All tissue specimens were reviewed by pathologists to characterize and verify organ source. Images from stained tissue samples can be viewed via the NCI histopathology viewer. The Qiagen PAXgene non-formalin tissue preservation product was used to stabilize tissue specimens without cross-linking biomolecules. RNA-seq was performed by the GTEx Laboratory, Data Analysis and Coordinating Center (LDACC) at the Broad Institute. The Illumina TruSeq protocol was used to create an unstranded polyA+ library sequenced on the Illumina HiSeq 2000 and HiSeq 2500 platforms to produce 76-bp paired end reads with a coverage goal of 50M (median achieved was ~82M total reads). Sequence reads were aligned to the hg38/GRCh38 human genome using STAR v2.5.3a assisted by the GENCODE 26 transcriptome definition. The alignment pipeline is available here. Gene annotations were produced using a custom isoform collapsing procedure that excluded retained intron and read through transcripts, merged overlapping exon intervals and then excluded exon intervals overlapping between genes. Gene expression levels in TPM were called via the RNA-SeQC tool (v1.1.9), after filtering for unique mapping, proper pairing, and exon overlap. For further method details, see the GTEx Portal Documentation page. UCSC obtained the gene-level expression files, gene annotations and sample metadata from the GTEx Portal Download page. Median expression level in TPM was computed per gene/per tissue. Subject and Sample Characteristics The scientific goal of the GTEx project required that the donors and their biospecimen present with no evidence of disease. The tissue types collected were chosen based on their clinical significance, logistical feasibility and their relevance to the scientific goal of the project and the research community. Summary plots of GTEx sample characteristics are available at the GTEx Portal Tissue Summary page. Data Access The raw data for the GTEx Gene expression track can be accessed interactively through the Table Browser or Data Integrator. Metadata can be found in the connected tables below. gtexGeneModelV8 describes the gene names and coordinates in genePred format. hgFixed.gtexTissueV8 lists each of the 53 tissues in alphabetical order, corresponding to the comma separated expression values in gtexGeneV8. hgFixed.gtexSampleDataV8 has TPM expression scores for each individual gene-sample data point, connected to gtexSampleV8. hgFixed.gtexSampleV8 contains metadata about sample time, collection site, and tissue, connected to the donor field in the gtexDonorV8 table. hgFixed.gtexDonorV8 has anonymized information on the tissue donor. For automated analysis and downloads, the track data files can be downloaded from our downloads server or the JSON API. Individual regions or the whole genome annotation can be accessed as text using our utility bigBedToBed. Instructions for downloading the utility can be found here. That utility can also be used to obtain features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/gtex/gtexGeneV8.bb -chrom=chr21 -start=0 -end=100000000 stdout Data can also be obtained directly from GTEx at the following link: https://gtexportal.org/home/datasets Credits Statistical analysis and data interpretation was performed by The GTEx Consortium Analysis Working Group. Data was provided by the GTEx LDACC at The Broad Institute of MIT and Harvard. References GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science. 2020 Sep 11;369(6509):1318-1330. PMID: 32913098; PMC: PMC7737656 GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013 Jun;45(6):580-5. PMID: 23715323; PMC: PMC4010069 Carithers LJ, Ardlie K, Barcus M, Branton PA, Britton A, Buia SA, Compton CC, DeLuca DS, Peter-Demchok J, Gelfand ET et al. A Novel Approach to High-Quality Postmortem Tissue Procurement: The GTEx Project. Biopreserv Biobank. 2015 Oct;13(5):311-9. PMID: 26484571; PMC: PMC4675181 Mel&#233; M, Ferreira PG, Reverter F, DeLuca DS, Monlong J, Sammeth M, Young TR, Goldmann JM, Pervouchine DD, Sullivan TJ et al. Human genomics. The human transcriptome across tissues and individuals. Science. 2015 May 8;348(6235):660-5. PMID: 25954002; PMC: PMC4547472 DeLuca DS, Levin JZ, Sivachenko A, Fennell T, Nazaire MD, Williams C, Reich M, Winckler W, Getz G. RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics. 2012 Jun 1;28(11):1530-2. PMID: 22539670; PMC: PMC3356847 
jarvis JARVIS JARVIS: score to prioritize non-coding regions for disease relevance Phenotype and Literature Description The "Constraint scores" container track includes several subtracks showing the results of constraint prediction algorithms. These try to find regions of negative selection, where variations likely have functional impact. The algorithms do not use multi-species alignments to derive evolutionary constraint, but use primarily human variation, usually from variants collected by gnomAD (see the gnomAD V2 or V3 tracks on hg19 and hg38) or TOPMED (contained in our dbSNP tracks and available as a filter). One of the subtracks is based on UK Biobank variants, which are not available publicly, so we have no track with the raw data. The number of human genomes that are used as the input for these scores are 76k, 53k and 110k for gnomAD, TOPMED and UK Biobank, respectively. Note that another important constraint score, gnomAD constraint, is not part of this container track but can be found in the hg38 gnomAD track. The algorithms included in this track are: JARVIS - "Junk" Annotation genome-wide Residual Variation Intolerance Score: JARVIS scores were created by first scanning the entire genome with a sliding-window approach (using a 1-nucleotide step), recording the number of all TOPMED variants and common variants, irrespective of their predicted effect, within each window, to eventually calculate a single-nucleotide resolution genome-wide residual variation intolerance score (gwRVIS). That score, gwRVIS was then combined with primary genomic sequence context, and additional genomic annotations with a multi-module deep learning framework to infer pathogenicity of noncoding regions that still remains naive to existing phylogenetic conservation metrics. The higher the score, the more deleterious the prediction. This score covers the entire genome, except the gaps. HMC - Homologous Missense Constraint: Homologous Missense Constraint (HMC) is a amino acid level measure of genetic intolerance of missense variants within human populations. For all assessable amino-acid positions in Pfam domains, the number of missense substitutions directly observed in gnomAD (Observed) was counted and compared to the expected value under a neutral evolution model (Expected). The upper limit of a 95% confidence interval for the Observed/Expected ratio is defined as the HMC score. Missense variants disrupting the amino-acid positions with HMC&lt;0.8 are predicted to be likely deleterious. This score only covers PFAM domains within coding regions. MetaDome - Tolerance Landscape Score (hg19 only): MetaDome Tolerance Landscape scores are computed as a missense over synonymous variant count ratio, which is calculated in a sliding window (with a size of 21 codons/residues) to provide a per-position indication of regional tolerance to missense variation. The variant database was gnomAD and the score corrected for codon composition. Scores &lt;0.7 are considered intolerant. This score covers only coding regions. MTR - Missense Tolerance Ratio (hg19 only): Missense Tolerance Ratio (MTR) scores aim to quantify the amount of purifying selection acting specifically on missense variants in a given window of protein-coding sequence. It is estimated across sliding windows of 31 codons (default) and uses observed standing variation data from the WES component of gnomAD / the Exome Aggregation Consortium Database (ExAC), version 2.0. Scores were computed using Ensembl v95 release. The number of gnomAD 2 exomes used here is higher than the number of gnomAD 3 samples (125 exoms versus 76k full genomes), but this score only covers coding regions. UK Biobank depletion rank score (hg38 only): Halldorsson et al. tabulated the number of UK Biobank variants in each 500bp window of the genome and compared this number to an expected number given the heptamer nucleotide composition of the window and the fraction of heptamers with a sequence variant across the genome and their mutational classes. A variant depletion score was computed for every overlapping set of 500-bp windows in the genome with a 50-bp step size. They then assigned a rank (depletion rank (DR)) from 0 (most depletion) to 100 (least depletion) for each 500-bp window. Since the windows are overlapping, we plot the value only in the central 50bp of the 500bp window, following advice from the author of the score, Hakon Jonsson, deCODE Genetics. He suggested that the value of the central window, rather than the worst possible score of all overlapping windows, is the most informative for a position. This score covers almost the entire genome, only very few regions were excluded, where the genome sequence had too many gap characters. Display Conventions and Configuration JARVIS JARVIS scores are shown as a signal ("wiggle") track, with one score per genome position. Mousing over the bars displays the exact values. The scores were downloaded and converted to a single bigWig file. Move the mouse over the bars to display the exact values. A horizontal line is shown at the 0.733 value which signifies the 90th percentile. See hg19 makeDoc and hg38 makeDoc. Interpretation: The authors offer a suggested guideline of > 0.9998 for identifying higher confidence calls and minimizing false positives. In addition to that strict threshold, the following two more relaxed cutoffs can be used to explore additional hits. Note that these thresholds are offered as guidelines and are not necessarily representative of pathogenicity. PercentileJARVIS score threshold 99th0.9998 95th0.9826 90th0.7338 HMC HMC scores are displayed as a signal ("wiggle") track, with one score per genome position. Mousing over the bars displays the exact values. The highly-constrained cutoff of 0.8 is indicated with a line. Interpretation: A protein residue with HMC score &lt;1 indicates that missense variants affecting the homologous residues are significantly under negative selection (P-value &lt; 0.05) and likely to be deleterious. A more stringent score threshold of HMC&lt;0.8 is recommended to prioritize predicted disease-associated variants. MetaDome MetaDome data can be found on two tracks, MetaDome and MetaDome All Data. The MetaDome track should be used by default for data exploration. In this track the raw data containing the MetaDome tolerance scores were converted into a signal ("wiggle") track. Since this data was computed on the proteome, there was a small amount of coordinate overlap, roughly 0.42%. In these regions the lowest possible score was chosen for display in the track to maintain sensitivity. For this reason, if a protein variant is being evaluated, the MetaDome All Data track can be used to validate the score. More information on this data can be found in the MetaDome FAQ. Interpretation: The authors suggest the following guidelines for evaluating intolerance. By default, the MetaDome track displays a horizontal line at 0.7 which signifies the first intolerant bin. For more information see the MetaDome publication. ClassificationMetaDome Tolerance Score Highly intolerant&le; 0.175 Intolerant&le; 0.525 Slightly intolerant&le; 0.7 MTR MTR data can be found on two tracks, MTR All data and MTR Scores. In the MTR Scores track the data has been converted into 4 separate signal tracks representing each base pair mutation, with the lowest possible score shown when multiple transcripts overlap at a position. Overlaps can happen since this score is derived from transcripts and multiple transcripts can overlap. A horizontal line is drawn on the 0.8 score line to roughly represent the 25th percentile, meaning the items below may be of particular interest. It is recommended that the data be explored using this version of the track, as it condenses the information substantially while retaining the magnitude of the data. Any specific point mutations of interest can then be researched in the MTR All data track. This track contains all of the information from MTRV2 including more than 3 possible scores per base when transcripts overlap. A mouse-over on this track shows the ref and alt allele, as well as the MTR score and the MTR score percentile. Filters are available for MTR score, False Discovery Rate (FDR), MTR percentile, and variant consequence. By default, only items in the bottom 25 percentile are shown. Items in the track are colored according to their MTR percentile: Green items MTR percentiles over 75 Black items MTR percentiles between 25 and 75 Red items MTR percentiles below 25 Blue items No MTR score Interpretation: Regions with low MTR scores were seen to be enriched with pathogenic variants. For example, ClinVar pathogenic variants were seen to have an average score of 0.77 whereas ClinVar benign variants had an average score of 0.92. Further validation using the FATHMM cancer-associated training dataset saw that scores less than 0.5 contained 8.6% of the pathogenic variants while only containing 0.9% of neutral variants. In summary, lower scores are more likely to represent pathogenic variants whereas higher scores could be pathogenic, but have a higher chance to be a false positive. For more information see the MTR-Viewer publication. Methods JARVIS Scores were downloaded and converted to a single bigWig file. See the hg19 makeDoc and the hg38 makeDoc for more info. HMC Scores were downloaded and converted to .bedGraph files with a custom Python script. The bedGraph files were then converted to bigWig files, as documented in our makeDoc hg19 build log. MetaDome The authors provided a bed file containing codon coordinates along with the scores. This file was parsed with a python script to create the two tracks. For the first track the scores were aggregated for each coordinate, then the lowest score chosen for any overlaps and the result written out to bedGraph format. The file was then converted to bigWig with the bedGraphToBigWig utility. For the second track the file was reorganized into a bed 4+3 and conveted to bigBed with the bedToBigBed utility. See the hg19 makeDoc for details including the build script. The raw MetaDome data can also be accessed via their Zenodo handle. MTR V2 file was downloaded and columns were reshuffled as well as itemRgb added for the MTR All data track. For the MTR Scores track the file was parsed with a python script to pull out the highest possible MTR score for each of the 3 possible mutations at each base pair and 4 tracks built out of these values representing each mutation. See the hg19 makeDoc entry on MTR for more info. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated access, this track, like all others, is available via our API. However, for bulk processing, it is recommended to download the dataset. For automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed files that can be downloaded from our download server. Individual regions or the whole genome annotation can be obtained using our tools bigWigToWig or bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tools can also be used to obtain features confined to a given range, e.g., bigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/hmc/hmc.bw stdout Please refer to our Data Access FAQ for more information. Credits Thanks to Jean-Madeleine Desainteagathe (APHP Paris, France) for suggesting the JARVIS, MTR, HMC tracks. Thanks to Xialei Zhang for providing the HMC data file and to Dimitrios Vitsios and Slave Petrovski for helping clean up the hg38 JARVIS files for providing guidance on interpretation. Additional thanks to Laurens van de Wiel for providing the MetaDome data as well as guidance on the track development and interpretation. References Vitsios D, Dhindsa RS, Middleton L, Gussow AB, Petrovski S. Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning. Nat Commun. 2021 Mar 8;12(1):1504. PMID: 33686085; PMC: PMC7940646 Xiaolei Zhang, Pantazis I. Theotokis, Nicholas Li, the SHaRe Investigators, Caroline F. Wright, Kaitlin E. Samocha, Nicola Whiffin, James S. Ware Genetic constraint at single amino acid resolution improves missense variant prioritisation and gene discovery. Medrxiv 2022.02.16.22271023 Wiel L, Baakman C, Gilissen D, Veltman JA, Vriend G, Gilissen C. MetaDome: Pathogenicity analysis of genetic variants through aggregation of homologous human protein domains. Hum Mutat. 2019 Aug;40(8):1030-1038. PMID: 31116477; PMC: PMC6772141 Silk M, Petrovski S, Ascher DB. MTR-Viewer: identifying regions within genes under purifying selection. Nucleic Acids Res. 2019 Jul 2;47(W1):W121-W126. PMID: 31170280; PMC: PMC6602522 Halldorsson BV, Eggertsson HP, Moore KHS, Hauswedell H, Eiriksson O, Ulfarsson MO, Palsson G, Hardarson MT, Oddsson A, Jensson BO et al. The sequences of 150,119 genomes in the UK Biobank. Nature. 2022 Jul;607(7920):732-740. PMID: 35859178; PMC: PMC9329122 
constraintSuper Constraint scores Human constraint scores Phenotype and Literature Description The "Constraint scores" container track includes several subtracks showing the results of constraint prediction algorithms. These try to find regions of negative selection, where variations likely have functional impact. The algorithms do not use multi-species alignments to derive evolutionary constraint, but use primarily human variation, usually from variants collected by gnomAD (see the gnomAD V2 or V3 tracks on hg19 and hg38) or TOPMED (contained in our dbSNP tracks and available as a filter). One of the subtracks is based on UK Biobank variants, which are not available publicly, so we have no track with the raw data. The number of human genomes that are used as the input for these scores are 76k, 53k and 110k for gnomAD, TOPMED and UK Biobank, respectively. Note that another important constraint score, gnomAD constraint, is not part of this container track but can be found in the hg38 gnomAD track. The algorithms included in this track are: JARVIS - "Junk" Annotation genome-wide Residual Variation Intolerance Score: JARVIS scores were created by first scanning the entire genome with a sliding-window approach (using a 1-nucleotide step), recording the number of all TOPMED variants and common variants, irrespective of their predicted effect, within each window, to eventually calculate a single-nucleotide resolution genome-wide residual variation intolerance score (gwRVIS). That score, gwRVIS was then combined with primary genomic sequence context, and additional genomic annotations with a multi-module deep learning framework to infer pathogenicity of noncoding regions that still remains naive to existing phylogenetic conservation metrics. The higher the score, the more deleterious the prediction. This score covers the entire genome, except the gaps. HMC - Homologous Missense Constraint: Homologous Missense Constraint (HMC) is a amino acid level measure of genetic intolerance of missense variants within human populations. For all assessable amino-acid positions in Pfam domains, the number of missense substitutions directly observed in gnomAD (Observed) was counted and compared to the expected value under a neutral evolution model (Expected). The upper limit of a 95% confidence interval for the Observed/Expected ratio is defined as the HMC score. Missense variants disrupting the amino-acid positions with HMC&lt;0.8 are predicted to be likely deleterious. This score only covers PFAM domains within coding regions. MetaDome - Tolerance Landscape Score (hg19 only): MetaDome Tolerance Landscape scores are computed as a missense over synonymous variant count ratio, which is calculated in a sliding window (with a size of 21 codons/residues) to provide a per-position indication of regional tolerance to missense variation. The variant database was gnomAD and the score corrected for codon composition. Scores &lt;0.7 are considered intolerant. This score covers only coding regions. MTR - Missense Tolerance Ratio (hg19 only): Missense Tolerance Ratio (MTR) scores aim to quantify the amount of purifying selection acting specifically on missense variants in a given window of protein-coding sequence. It is estimated across sliding windows of 31 codons (default) and uses observed standing variation data from the WES component of gnomAD / the Exome Aggregation Consortium Database (ExAC), version 2.0. Scores were computed using Ensembl v95 release. The number of gnomAD 2 exomes used here is higher than the number of gnomAD 3 samples (125 exoms versus 76k full genomes), but this score only covers coding regions. UK Biobank depletion rank score (hg38 only): Halldorsson et al. tabulated the number of UK Biobank variants in each 500bp window of the genome and compared this number to an expected number given the heptamer nucleotide composition of the window and the fraction of heptamers with a sequence variant across the genome and their mutational classes. A variant depletion score was computed for every overlapping set of 500-bp windows in the genome with a 50-bp step size. They then assigned a rank (depletion rank (DR)) from 0 (most depletion) to 100 (least depletion) for each 500-bp window. Since the windows are overlapping, we plot the value only in the central 50bp of the 500bp window, following advice from the author of the score, Hakon Jonsson, deCODE Genetics. He suggested that the value of the central window, rather than the worst possible score of all overlapping windows, is the most informative for a position. This score covers almost the entire genome, only very few regions were excluded, where the genome sequence had too many gap characters. Display Conventions and Configuration JARVIS JARVIS scores are shown as a signal ("wiggle") track, with one score per genome position. Mousing over the bars displays the exact values. The scores were downloaded and converted to a single bigWig file. Move the mouse over the bars to display the exact values. A horizontal line is shown at the 0.733 value which signifies the 90th percentile. See hg19 makeDoc and hg38 makeDoc. Interpretation: The authors offer a suggested guideline of > 0.9998 for identifying higher confidence calls and minimizing false positives. In addition to that strict threshold, the following two more relaxed cutoffs can be used to explore additional hits. Note that these thresholds are offered as guidelines and are not necessarily representative of pathogenicity. PercentileJARVIS score threshold 99th0.9998 95th0.9826 90th0.7338 HMC HMC scores are displayed as a signal ("wiggle") track, with one score per genome position. Mousing over the bars displays the exact values. The highly-constrained cutoff of 0.8 is indicated with a line. Interpretation: A protein residue with HMC score &lt;1 indicates that missense variants affecting the homologous residues are significantly under negative selection (P-value &lt; 0.05) and likely to be deleterious. A more stringent score threshold of HMC&lt;0.8 is recommended to prioritize predicted disease-associated variants. MetaDome MetaDome data can be found on two tracks, MetaDome and MetaDome All Data. The MetaDome track should be used by default for data exploration. In this track the raw data containing the MetaDome tolerance scores were converted into a signal ("wiggle") track. Since this data was computed on the proteome, there was a small amount of coordinate overlap, roughly 0.42%. In these regions the lowest possible score was chosen for display in the track to maintain sensitivity. For this reason, if a protein variant is being evaluated, the MetaDome All Data track can be used to validate the score. More information on this data can be found in the MetaDome FAQ. Interpretation: The authors suggest the following guidelines for evaluating intolerance. By default, the MetaDome track displays a horizontal line at 0.7 which signifies the first intolerant bin. For more information see the MetaDome publication. ClassificationMetaDome Tolerance Score Highly intolerant&le; 0.175 Intolerant&le; 0.525 Slightly intolerant&le; 0.7 MTR MTR data can be found on two tracks, MTR All data and MTR Scores. In the MTR Scores track the data has been converted into 4 separate signal tracks representing each base pair mutation, with the lowest possible score shown when multiple transcripts overlap at a position. Overlaps can happen since this score is derived from transcripts and multiple transcripts can overlap. A horizontal line is drawn on the 0.8 score line to roughly represent the 25th percentile, meaning the items below may be of particular interest. It is recommended that the data be explored using this version of the track, as it condenses the information substantially while retaining the magnitude of the data. Any specific point mutations of interest can then be researched in the MTR All data track. This track contains all of the information from MTRV2 including more than 3 possible scores per base when transcripts overlap. A mouse-over on this track shows the ref and alt allele, as well as the MTR score and the MTR score percentile. Filters are available for MTR score, False Discovery Rate (FDR), MTR percentile, and variant consequence. By default, only items in the bottom 25 percentile are shown. Items in the track are colored according to their MTR percentile: Green items MTR percentiles over 75 Black items MTR percentiles between 25 and 75 Red items MTR percentiles below 25 Blue items No MTR score Interpretation: Regions with low MTR scores were seen to be enriched with pathogenic variants. For example, ClinVar pathogenic variants were seen to have an average score of 0.77 whereas ClinVar benign variants had an average score of 0.92. Further validation using the FATHMM cancer-associated training dataset saw that scores less than 0.5 contained 8.6% of the pathogenic variants while only containing 0.9% of neutral variants. In summary, lower scores are more likely to represent pathogenic variants whereas higher scores could be pathogenic, but have a higher chance to be a false positive. For more information see the MTR-Viewer publication. Methods JARVIS Scores were downloaded and converted to a single bigWig file. See the hg19 makeDoc and the hg38 makeDoc for more info. HMC Scores were downloaded and converted to .bedGraph files with a custom Python script. The bedGraph files were then converted to bigWig files, as documented in our makeDoc hg19 build log. MetaDome The authors provided a bed file containing codon coordinates along with the scores. This file was parsed with a python script to create the two tracks. For the first track the scores were aggregated for each coordinate, then the lowest score chosen for any overlaps and the result written out to bedGraph format. The file was then converted to bigWig with the bedGraphToBigWig utility. For the second track the file was reorganized into a bed 4+3 and conveted to bigBed with the bedToBigBed utility. See the hg19 makeDoc for details including the build script. The raw MetaDome data can also be accessed via their Zenodo handle. MTR V2 file was downloaded and columns were reshuffled as well as itemRgb added for the MTR All data track. For the MTR Scores track the file was parsed with a python script to pull out the highest possible MTR score for each of the 3 possible mutations at each base pair and 4 tracks built out of these values representing each mutation. See the hg19 makeDoc entry on MTR for more info. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated access, this track, like all others, is available via our API. However, for bulk processing, it is recommended to download the dataset. For automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed files that can be downloaded from our download server. Individual regions or the whole genome annotation can be obtained using our tools bigWigToWig or bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tools can also be used to obtain features confined to a given range, e.g., bigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/hmc/hmc.bw stdout Please refer to our Data Access FAQ for more information. Credits Thanks to Jean-Madeleine Desainteagathe (APHP Paris, France) for suggesting the JARVIS, MTR, HMC tracks. Thanks to Xialei Zhang for providing the HMC data file and to Dimitrios Vitsios and Slave Petrovski for helping clean up the hg38 JARVIS files for providing guidance on interpretation. Additional thanks to Laurens van de Wiel for providing the MetaDome data as well as guidance on the track development and interpretation. References Vitsios D, Dhindsa RS, Middleton L, Gussow AB, Petrovski S. Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning. Nat Commun. 2021 Mar 8;12(1):1504. PMID: 33686085; PMC: PMC7940646 Xiaolei Zhang, Pantazis I. Theotokis, Nicholas Li, the SHaRe Investigators, Caroline F. Wright, Kaitlin E. Samocha, Nicola Whiffin, James S. Ware Genetic constraint at single amino acid resolution improves missense variant prioritisation and gene discovery. Medrxiv 2022.02.16.22271023 Wiel L, Baakman C, Gilissen D, Veltman JA, Vriend G, Gilissen C. MetaDome: Pathogenicity analysis of genetic variants through aggregation of homologous human protein domains. Hum Mutat. 2019 Aug;40(8):1030-1038. PMID: 31116477; PMC: PMC6772141 Silk M, Petrovski S, Ascher DB. MTR-Viewer: identifying regions within genes under purifying selection. Nucleic Acids Res. 2019 Jul 2;47(W1):W121-W126. PMID: 31170280; PMC: PMC6602522 Halldorsson BV, Eggertsson HP, Moore KHS, Hauswedell H, Eiriksson O, Ulfarsson MO, Palsson G, Hardarson MT, Oddsson A, Jensson BO et al. The sequences of 150,119 genomes in the UK Biobank. Nature. 2022 Jul;607(7920):732-740. PMID: 35859178; PMC: PMC9329122 
omimAvSnp OMIM Alleles OMIM Allelic Variant Phenotypes Phenotype and Literature Description NOTE: OMIM is intended for use primarily by physicians and other professionals concerned with genetic disorders, by genetics researchers, and by advanced students in science and medicine. While the OMIM database is open to the public, users seeking information about a personal medical or genetic condition are urged to consult with a qualified physician for diagnosis and for answers to personal questions. Further, please be sure to click through to omim.org for the very latest, as they are continually updating data. NOTE ABOUT DOWNLOADS: OMIM is the property of Johns Hopkins University and is not available for download or mirroring by any third party without their permission. Please see OMIM for downloads. OMIM is a compendium of human genes and genetic phenotypes. The full-text, referenced overviews in OMIM contain information on all known Mendelian disorders and over 12,000 genes. OMIM is authored and edited at the McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, under the direction of Dr. Ada Hamosh. This database was initiated in the early 1960s by Dr. Victor A. McKusick as a catalog of Mendelian traits and disorders, entitled Mendelian Inheritance in Man (MIM). The OMIM data are separated into three separate tracks: OMIM Alellic Variant Phenotypes (OMIM Alleles) &nbsp;&nbsp;&nbsp;&nbsp;Variants in the OMIM database that have associated dbSNP identifiers. OMIM Gene Phenotypes (OMIM Genes) &nbsp;&nbsp;&nbsp;&nbsp;The genomic positions of gene entries in the OMIM database. The coloring indicates the associated OMIM phenotype map key. OMIM Cytogenetic Loci Phenotypes - Gene Unknown (OMIM Cyto Loci) &nbsp;&nbsp;&nbsp;&nbsp;Regions known to be associated with a phenotype, but for which no specific gene is known to be causative. This track also includes known multi-gene syndromes. This track shows the allelic variants in the Online Mendelian Inheritance in Man (OMIM) database that have associated dbSNP identifiers. Display Conventions and Configuration Genomic positions of OMIM allelic variants are marked by solid blocks, which appear as tick marks when zoomed out. The details page for each variant displays the allelic variant description, the amino acid replacement, and the associated dbSNP and/or ClinVar identifiers with links to the variant's details at those resources. The descriptions of OMIM entries are shown on the main browser display when Full display mode is chosen. In Pack mode, the descriptions are shown when mousing over each entry. Methods This track was constructed as follows: The OMIM allelic variant data file mimAV.txt was obtained from OMIM and loaded into the MySQL table omimAv. The genomic position for each allelic variant in omimAv with an associated dbSnp identifier was obtained from the snp151 table. The OMIM AV identifiers and their corresponding genomic positions from dbSNP were then loaded into the omimAvSnp table. Data Updates This track is automatically updated once a week from OMIM data. The most recent update time is shown at the top of the track documentation page. Data Access Because OMIM has only allowed Data queries within individual chromosomes, no download files are available from the Genome Browser. Full genome datasets can be downloaded directly from the OMIM Downloads page. All genome-wide downloads are freely available from OMIM after registration. If you need the OMIM data in exactly the format of the UCSC Genome Browser, for example if you are running a UCSC Genome Browser local installation (a partial &quot;mirror&quot;), please create a user account on omim.org and contact OMIM via https://omim.org/contact. Send them your OMIM account name and request access to the UCSC Genome Browser 'entitlement'. They will then grant you access to a MySQL/MariaDB data dump that contains all UCSC Genome Browser OMIM tables. UCSC offers queries within chromosomes from Table Browser that include a variety of filtering options and cross-referencing other datasets using our Data Integrator tool. UCSC also has an API that can be used to retrieve data in JSON format from a particular chromosome range. Please refer to our searchable mailing list archives for more questions and example queries, or our Data Access FAQ for more information. Credits Thanks to OMIM and NCBI for the use of their data. This track was constructed by Fan Hsu, Robert Kuhn, and Brooke Rhead of the UCSC Genome Bioinformatics Group. References Amberger J, Bocchini CA, Scott AF, Hamosh A. McKusick's Online Mendelian Inheritance in Man (OMIM). Nucleic Acids Res. 2009 Jan;37(Database issue):D793-6. PMID: 18842627; PMC: PMC2686440 Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005 Jan 1;33(Database issue):D514-7. PMID: 15608251; PMC: PMC539987 
omimContainer OMIM Online Mendelian Inheritance in Man Phenotype and Literature OMIM is a compendium of human genes and genetic phenotypes. The full-text, referenced overviews in OMIM contain information on all known Mendelian disorders and over 12,000 genes. OMIM is authored and edited at the McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, under the direction of Dr. Ada Hamosh. This database was initiated in the early 1960s by Dr. Victor A. McKusick as a catalog of Mendelian traits and disorders, entitled Mendelian Inheritance in Man (MIM). The OMIM data are separated into three separate tracks: OMIM Alellic Variant Phenotypes (OMIM Alleles) - Variants in the OMIM database that have associated dbSNP identifiers. OMIM Gene Phenotypes (OMIM Genes) - The genomic positions of gene entries in the OMIM database. The coloring indicates the associated OMIM phenotype map key. OMIM Cytogenetic Loci Phenotypes: Gene Unknown (OMIM Cyto Loci) - Regions known to be associated with a phenotype, but for which no specific gene is known to be causative. This track also includes known multi-gene syndromes. Clicking into the individual tracks provides additional information including display conventions. 
problematic Problematic Regions Problematic/special genomic regions for sequencing or very variable regions Mapping and Sequencing Description This container track helps call out sections of the genome that often cause problems or confusion when working with the genome. The hg19 genome has a track with the same name, but with many more subtracks, as the GeT-RM and Genome-in-a-Bottle artifact variants do not exist yet for hg38, to our knowledge. If you are missing a track here that you know from hg19 and have an idea how to add it hg38, do not hesitate to contact us. Problematic Regions The Problematic Regions track contains the following subtracks: The UCSC Unusual Regions subtrack contains annotations collected at UCSC, put together from other tracks, our experiences and support email list requests over the years. For example, it contains the most well-known gene clusters (IGH, IGL, PAR1/2, TCRA, TCRB, etc) and annotations for the GRC fixed sequences, alternate haplotypes, unplaced contigs, pseudo-autosomal regions, and mitochondria. These loci can yield alignments with low-quality mapping scores and discordant read pairs, especially for short-read sequencing data. This data set was manually curated, based on the Genome Browser's assembly description, the FAQs about assembly, and the NCBI RefSeq &quot;other&quot; annotations track data. The ENCODE Blacklist subtrack contains a comprehensive set of regions which are troublesome for high-throughput Next-Generation Sequencing (NGS) aligners. These regions tend to have a very high ratio of multi-mapping to unique mapping reads and high variance in mappability due to repetitive elements such as satellite, centromeric and telomeric repeats. The GRC Exclusions subtrack contains a set of regions that have been flagged by the GRC to contain false duplications or contamination sequences. The GRC has now removed these sequences from the files that it uses to generate the reference assembly, however, removing the sequences from the GRCh38/hg38 assembly would trigger the next major release of the human assembly. In order to help users recognize these regions and avoid them in their analyses, the GRC have produced a masking file to be used as a companion to GRCh38, and the BED file is available from the GenBank FTP site. Highly Reproducible Regions The Highly Reproducible Regions track highlights regions and variants from eight samples that can be used to assess variant detection pipelines. The &quot;Highly Reproducible Regions&quot; subtrack comprises the intersection of the reproducible regions across all eight samples, while the &quot;Variants&quot; subtracks contain the reproducible variants from each assayed sample. Both tracks contain data from the following samples: a Chinese Quartet, samples CQ-5, CQ-6, CQ-7, CQ-8 a HapMap Trio, samples NA10385, NA12248, NA12249 a Genome in a Bottle sample, NA12878s Please refer to the Pan et al reference for more information on how these regions were defined. GIAB Problematic Regions The Genome in a Bottle (GIAB) Problematic Regions tracks provide stratifications of the genome to evaluate variant calls in complex regions. It is designed for use with Global Alliance for Genomic Health (GA4GH) benchmarking tools like hap.py and includes regions with low complexity, segmental duplications, functional regions, and difficult-to-sequence areas. Developed in collaboration with GA4GH, the Genome in a Bottle (GIAB) consortium, and the Telomere-to-Telomere Consortium (T2T), the dataset aims to standardize the analysis of genetic variation by offering pre-defined BED files for stratifying true and false positives in genomic studies, facilitating accurate assessments in complex areas of the genome. The creation of the GIAB Problematic Regions tracks involves using a pipeline and configuration to generate stratification BED files that categorize genomic regions based on specific challenges, such as low complexity or difficult mapping, to facilitate accurate benchmarking of variant calls. For more information on the pipeline and configuration used, please visit the following webpage: https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/genome-stratifications/v3.5/README.md. If you have questions or comments, please write to Justin Zook (jzook@nist.gov). Display Conventions and Configuration Each track contains a set of regions of varying length with no special configuration options. The UCSC Unusual Regions track has a mouse-over description, all other tracks have at most a name field, which can be shown in pack mode. The tracks are usually kept in dense mode. The Hide empty subtracks control hides subtracks with no data in the browser window. Changing the browser window by zooming or scrolling may result in the display of a different selection of tracks. Data access The raw data can be explored interactively with the Table Browser or the Data Integrator. For automated download and analysis, the genome annotation is stored in bigBed files that can be downloaded from our download server. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/problematic/comments.bb -chrom=chr21 -start=0 -end=100000000 stdout Methods Files were downloaded from the respective databases and converted to bigBed format. The procedure is documented in our hg38 makeDoc file. Credits Thanks to Anna Benet-Pag&egrave;s, Max Haeussler, Angie Hinrichs, Daniel Schmelter, and Jairo Navarro at the UCSC Genome Browser for planning, building, and testing these tracks. The underlying data comes from the ENCODE Blacklist and some parts were copied manually from the HGNC and NCBI RefSeq tracks. References Amemiya HM, Kundaje A, Boyle AP. The ENCODE Blacklist: Identification of Problematic Regions of the Genome. Sci Rep. 2019 Jun 27;9(1):9354. PMID: 31249361; PMC: PMC6597582 Dwarshuis N, Kalra D, McDaniel J, Sanio P, Alvarez Jerez P, Jadhav B, Huang WE, Mondal R, Busby B, Olson ND et al. The GIAB genomic stratifications resource for human reference genomes. Nat Commun. 2024 Oct 19;15(1):9029. PMID: 39424793; PMC: PMC11489684 Krusche P, Trigg L, Boutros PC, Mason CE, De La Vega FM, Moore BL, Gonzalez-Porta M, Eberle MA, Tezak Z, Lababidi S et al. Best practices for benchmarking germline small-variant calls in human genomes. Nat Biotechnol. 2019 May;37(5):555-560. PMID: 30858580; PMC: PMC6699627 Pan B, Ren L, Onuchic V, Guan M, Kusko R, Bruinsma S, Trigg L, Scherer A, Ning B, Zhang C et al. Assessing reproducibility of inherited variants detected with short-read whole genome sequencing. Genome Biol. 2022 Jan 3;23(1):2. PMID: 34980216; PMC: PMC8722114 
problematicSuper Problematic Regions Problematic/special genomic regions for sequencing or very variable regions Mapping and Sequencing Description This container track helps call out sections of the genome that often cause problems or confusion when working with the genome. The hg19 genome has a track with the same name, but with many more subtracks, as the GeT-RM and Genome-in-a-Bottle artifact variants do not exist yet for hg38, to our knowledge. If you are missing a track here that you know from hg19 and have an idea how to add it hg38, do not hesitate to contact us. Problematic Regions The Problematic Regions track contains the following subtracks: The UCSC Unusual Regions subtrack contains annotations collected at UCSC, put together from other tracks, our experiences and support email list requests over the years. For example, it contains the most well-known gene clusters (IGH, IGL, PAR1/2, TCRA, TCRB, etc) and annotations for the GRC fixed sequences, alternate haplotypes, unplaced contigs, pseudo-autosomal regions, and mitochondria. These loci can yield alignments with low-quality mapping scores and discordant read pairs, especially for short-read sequencing data. This data set was manually curated, based on the Genome Browser's assembly description, the FAQs about assembly, and the NCBI RefSeq &quot;other&quot; annotations track data. The ENCODE Blacklist subtrack contains a comprehensive set of regions which are troublesome for high-throughput Next-Generation Sequencing (NGS) aligners. These regions tend to have a very high ratio of multi-mapping to unique mapping reads and high variance in mappability due to repetitive elements such as satellite, centromeric and telomeric repeats. The GRC Exclusions subtrack contains a set of regions that have been flagged by the GRC to contain false duplications or contamination sequences. The GRC has now removed these sequences from the files that it uses to generate the reference assembly, however, removing the sequences from the GRCh38/hg38 assembly would trigger the next major release of the human assembly. In order to help users recognize these regions and avoid them in their analyses, the GRC have produced a masking file to be used as a companion to GRCh38, and the BED file is available from the GenBank FTP site. Highly Reproducible Regions The Highly Reproducible Regions track highlights regions and variants from eight samples that can be used to assess variant detection pipelines. The &quot;Highly Reproducible Regions&quot; subtrack comprises the intersection of the reproducible regions across all eight samples, while the &quot;Variants&quot; subtracks contain the reproducible variants from each assayed sample. Both tracks contain data from the following samples: a Chinese Quartet, samples CQ-5, CQ-6, CQ-7, CQ-8 a HapMap Trio, samples NA10385, NA12248, NA12249 a Genome in a Bottle sample, NA12878s Please refer to the Pan et al reference for more information on how these regions were defined. GIAB Problematic Regions The Genome in a Bottle (GIAB) Problematic Regions tracks provide stratifications of the genome to evaluate variant calls in complex regions. It is designed for use with Global Alliance for Genomic Health (GA4GH) benchmarking tools like hap.py and includes regions with low complexity, segmental duplications, functional regions, and difficult-to-sequence areas. Developed in collaboration with GA4GH, the Genome in a Bottle (GIAB) consortium, and the Telomere-to-Telomere Consortium (T2T), the dataset aims to standardize the analysis of genetic variation by offering pre-defined BED files for stratifying true and false positives in genomic studies, facilitating accurate assessments in complex areas of the genome. The creation of the GIAB Problematic Regions tracks involves using a pipeline and configuration to generate stratification BED files that categorize genomic regions based on specific challenges, such as low complexity or difficult mapping, to facilitate accurate benchmarking of variant calls. For more information on the pipeline and configuration used, please visit the following webpage: https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/genome-stratifications/v3.5/README.md. If you have questions or comments, please write to Justin Zook (jzook@nist.gov). Display Conventions and Configuration Each track contains a set of regions of varying length with no special configuration options. The UCSC Unusual Regions track has a mouse-over description, all other tracks have at most a name field, which can be shown in pack mode. The tracks are usually kept in dense mode. The Hide empty subtracks control hides subtracks with no data in the browser window. Changing the browser window by zooming or scrolling may result in the display of a different selection of tracks. Data access The raw data can be explored interactively with the Table Browser or the Data Integrator. For automated download and analysis, the genome annotation is stored in bigBed files that can be downloaded from our download server. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/problematic/comments.bb -chrom=chr21 -start=0 -end=100000000 stdout Methods Files were downloaded from the respective databases and converted to bigBed format. The procedure is documented in our hg38 makeDoc file. Credits Thanks to Anna Benet-Pag&egrave;s, Max Haeussler, Angie Hinrichs, Daniel Schmelter, and Jairo Navarro at the UCSC Genome Browser for planning, building, and testing these tracks. The underlying data comes from the ENCODE Blacklist and some parts were copied manually from the HGNC and NCBI RefSeq tracks. References Amemiya HM, Kundaje A, Boyle AP. The ENCODE Blacklist: Identification of Problematic Regions of the Genome. Sci Rep. 2019 Jun 27;9(1):9354. PMID: 31249361; PMC: PMC6597582 Dwarshuis N, Kalra D, McDaniel J, Sanio P, Alvarez Jerez P, Jadhav B, Huang WE, Mondal R, Busby B, Olson ND et al. The GIAB genomic stratifications resource for human reference genomes. Nat Commun. 2024 Oct 19;15(1):9029. PMID: 39424793; PMC: PMC11489684 Krusche P, Trigg L, Boutros PC, Mason CE, De La Vega FM, Moore BL, Gonzalez-Porta M, Eberle MA, Tezak Z, Lababidi S et al. Best practices for benchmarking germline small-variant calls in human genomes. Nat Biotechnol. 2019 May;37(5):555-560. PMID: 30858580; PMC: PMC6699627 Pan B, Ren L, Onuchic V, Guan M, Kusko R, Bruinsma S, Trigg L, Scherer A, Ning B, Zhang C et al. Assessing reproducibility of inherited variants detected with short-read whole genome sequencing. Genome Biol. 2022 Jan 3;23(1):2. PMID: 34980216; PMC: PMC8722114 
grcExclusions GRC Exclusions GRC Exclusion list: contaminations or false duplications Mapping and Sequencing 
encBlacklist ENCODE Blacklist V2 ENCODE Blacklist V2 Mapping and Sequencing 
comments UCSC Unusual Regions UCSC unusual regions on assembly structure (manually annotated) Mapping and Sequencing 
recombAvg Recomb. deCODE Avg Recombination rate: deCODE Genetics, average from paternal and maternal (mat for chrX) Mapping and Sequencing Description The recombination rate track represents calculated rates of recombination based on the genetic maps from deCODE (Halldorsson et al., 2019) and 1000 Genomes (2013 Phase 3 release, lifted from hg19). The deCODE map is more recent, has a higher resolution and was natively created on hg38 and therefore recommended. For the Recomb. deCODE average track, the recombination rates for chrX represent the female rate. This track also includes a subtrack with all the individual deCODE recombination events and another subtrack with several thousand de-novo mutations found in the deCODE sequencing data. These two tracks are hidden by default and have to be switched on explicitly on the configuration page. Display Conventions and Configuration This is a super track that contains different subtracks, three with the deCODE recombination rates (paternal, maternal and average) and one with the 1000 Genomes recombination rate (average). These tracks are in signal graph (wiggle) format. By default, to show most recombination hotspots, their maximum value is set to 100 cM, even though many regions have values higher than 100. The maximum value can be changed on the configuration pages of the tracks. There are two more tracks that show additional details provided by deCODE: one subtrack with the raw data of all cross-overs tagged with their proband ID and another one with around 8000 human de-novo mutation variants that are linked to cross-over changes. Methods The deCODE genetic map was created at deCODE Genetics. It is based on microarrays assaying 626,828 SNP markers that allowed to identify 1,476,140 crossovers in 56,321 paternal meioses and 3,055,395 crossovers in 70,086 maternal meioses. In total, the data is based on 4,531,535 crossovers in 126,427 meioses. By using WGS data with 9,305,070 SNPs, the boundaries for 761,981 crossovers were refined: 247,942 crossovers in 9423 paternal meioses and 514,039 crossovers in 11,750 maternal meioses. The average resolution of the genetic map is 682 base pairs (bp): 655 and 708 bp for the paternal and maternal maps, respectively. The 1000 Genomes genetic map is based on the IMPUTE genetic map based on 1000 Genomes Phase 3, on hg19 coordinates. It was converted to hg38 by Po-Ru Loh at the Broad Institute. After a run of liftOver, he post-processed the data to deal with situations in which consecutive map locations became much closer/farther after lifting. The heuristic used is sufficient for statistical phasing but may not be optimal for other analyses. For this reason, and because of its higher resolution, the DeCODE map is therefore recommended for hg38. As with all other tracks, the data conversion commands and pointers to the original data files are documented in the makeDoc file of this track. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated access, this track, like all others, is available via our API. However, for bulk processing, it is recommended to download the dataset. For automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed files that can be downloaded from our download server. Individual regions or the whole genome annotation can be obtained using our tools bigWigToWig or bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tools can also be used to obtain features confined to a given range, e.g., bigWigToBedGraph -chrom=chr17 -start=45941345 -end=45942345 http://hgdownload.soe.ucsc.edu/gbdb/hg38/recombRate/recombAvg.bw stdout Please refer to our Data Access FAQ for more information. Credits This track was produced at UCSC using data that are freely available for the deCODE and 1000 Genomes genetic maps. Thanks to Po-Ru Loh at the Broad Institute for providing the code to lift the hg19 1000 Genomes map data to hg38. References 1000 Genomes Project Consortium., Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, Hurles ME, McVean GA. A map of human genome variation from population-scale sequencing. Nature. 2010 Oct 28;467(7319):1061-73. PMID: 20981092; PMC: PMC3042601 Halldorsson BV, Palsson G, Stefansson OA, Jonsson H, Hardarson MT, Eggertsson HP, Gunnarsson B, Oddsson A, Halldorsson GH, Zink F et al. Characterizing mutagenic effects of recombination through a sequence-level genetic map. Science. 2019 Jan 25;363(6425). PMID: 30679340 
recombRate2 Recomb Rate Recombination rate: Genetic maps from deCODE and 1000 Genomes Mapping and Sequencing Description The recombination rate track represents calculated rates of recombination based on the genetic maps from deCODE (Halldorsson et al., 2019) and 1000 Genomes (2013 Phase 3 release, lifted from hg19). The deCODE map is more recent, has a higher resolution and was natively created on hg38 and therefore recommended. For the Recomb. deCODE average track, the recombination rates for chrX represent the female rate. This track also includes a subtrack with all the individual deCODE recombination events and another subtrack with several thousand de-novo mutations found in the deCODE sequencing data. These two tracks are hidden by default and have to be switched on explicitly on the configuration page. Display Conventions and Configuration This is a super track that contains different subtracks, three with the deCODE recombination rates (paternal, maternal and average) and one with the 1000 Genomes recombination rate (average). These tracks are in signal graph (wiggle) format. By default, to show most recombination hotspots, their maximum value is set to 100 cM, even though many regions have values higher than 100. The maximum value can be changed on the configuration pages of the tracks. There are two more tracks that show additional details provided by deCODE: one subtrack with the raw data of all cross-overs tagged with their proband ID and another one with around 8000 human de-novo mutation variants that are linked to cross-over changes. Methods The deCODE genetic map was created at deCODE Genetics. It is based on microarrays assaying 626,828 SNP markers that allowed to identify 1,476,140 crossovers in 56,321 paternal meioses and 3,055,395 crossovers in 70,086 maternal meioses. In total, the data is based on 4,531,535 crossovers in 126,427 meioses. By using WGS data with 9,305,070 SNPs, the boundaries for 761,981 crossovers were refined: 247,942 crossovers in 9423 paternal meioses and 514,039 crossovers in 11,750 maternal meioses. The average resolution of the genetic map is 682 base pairs (bp): 655 and 708 bp for the paternal and maternal maps, respectively. The 1000 Genomes genetic map is based on the IMPUTE genetic map based on 1000 Genomes Phase 3, on hg19 coordinates. It was converted to hg38 by Po-Ru Loh at the Broad Institute. After a run of liftOver, he post-processed the data to deal with situations in which consecutive map locations became much closer/farther after lifting. The heuristic used is sufficient for statistical phasing but may not be optimal for other analyses. For this reason, and because of its higher resolution, the DeCODE map is therefore recommended for hg38. As with all other tracks, the data conversion commands and pointers to the original data files are documented in the makeDoc file of this track. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated access, this track, like all others, is available via our API. However, for bulk processing, it is recommended to download the dataset. For automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed files that can be downloaded from our download server. Individual regions or the whole genome annotation can be obtained using our tools bigWigToWig or bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tools can also be used to obtain features confined to a given range, e.g., bigWigToBedGraph -chrom=chr17 -start=45941345 -end=45942345 http://hgdownload.soe.ucsc.edu/gbdb/hg38/recombRate/recombAvg.bw stdout Please refer to our Data Access FAQ for more information. Credits This track was produced at UCSC using data that are freely available for the deCODE and 1000 Genomes genetic maps. Thanks to Po-Ru Loh at the Broad Institute for providing the code to lift the hg19 1000 Genomes map data to hg38. References 1000 Genomes Project Consortium., Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, Hurles ME, McVean GA. A map of human genome variation from population-scale sequencing. Nature. 2010 Oct 28;467(7319):1061-73. PMID: 20981092; PMC: PMC3042601 Halldorsson BV, Palsson G, Stefansson OA, Jonsson H, Hardarson MT, Eggertsson HP, Gunnarsson B, Oddsson A, Halldorsson GH, Zink F et al. Characterizing mutagenic effects of recombination through a sequence-level genetic map. Science. 2019 Jan 25;363(6425). PMID: 30679340 
rmsk RepeatMasker Repeating Elements by RepeatMasker Repeats Description This track was created by using Arian Smit's RepeatMasker program, which screens DNA sequences for interspersed repeats and low complexity DNA sequences. The program outputs a detailed annotation of the repeats that are present in the query sequence (represented by this track), as well as a modified version of the query sequence in which all the annotated repeats have been masked (generally available on the Downloads page). RepeatMasker uses the Repbase Update library of repeats from the Genetic Information Research Institute (GIRI). Repbase Update is described in Jurka (2000) in the References section below. This track and the masking information in our hg38 genome download FASTA files was created in 2010 with the original RepBase library from 2010-03-02 and RepeatMasker 3.0.1. Since April 2019, RepBase is under a commercial license, we cannot distribute it or update the track using the RepBase library without a license. Therefore, and for compatibility with past results, given how central the masking is for many other annotations, we decided to not update the repeatmasking of hg38. However, you can show the small differences between the RepeatMasker 3/RepBase from 2010 and RepeatMasker 4/DFAM from 2020 using the track "RepeatMasker Viz" in the same track group. It contains two subtracks, one with the old and one with the new data. Also, these tracks have many more visusalisation options than the original RepeatMasker track. However, the last track update time of this track at UCSC is not 2010, because we had to add repeatmasking annotations to the rarely used _alt and _fix "patch" sequences of the hg38 genome. The repeatmasking annotations of the main chromosomes were unaffected and have not changed since 2010. For more information on genome patches, see our blog post. Display Conventions and Configuration In full display mode, this track displays up to ten different classes of repeats: Short interspersed nuclear elements (SINE), which include ALUs Long interspersed nuclear elements (LINE) Long terminal repeat elements (LTR), which include retroposons DNA repeat elements (DNA) Simple repeats (micro-satellites) Low complexity repeats Satellite repeats RNA repeats (including RNA, tRNA, rRNA, snRNA, scRNA, srpRNA) Other repeats, which includes class RC (Rolling Circle) Unknown The level of color shading in the graphical display reflects the amount of base mismatch, base deletion, and base insertion associated with a repeat element. The higher the combined number of these, the lighter the shading. A &quot;?&quot; at the end of the &quot;Family&quot; or &quot;Class&quot; (for example, DNA?) signifies that the curator was unsure of the classification. At some point in the future, either the &quot;?&quot; will be removed or the classification will be changed. Methods Data are generated using the RepeatMasker -s flag. Additional flags may be used for certain organisms. Repeats are soft-masked. Alignments may extend through repeats, but are not permitted to initiate in them. See the FAQ for more information. Credits Thanks to Arian Smit, Robert Hubley and GIRI for providing the tools and repeat libraries used to generate this track. References Smit AFA, Hubley R, Green P. RepeatMasker Open-3.0. http://www.repeatmasker.org. 1996-2010. Repbase Update is described in: Jurka J. Repbase Update: a database and an electronic journal of repetitive elements. Trends Genet. 2000 Sep;16(9):418-420. PMID: 10973072 For a discussion of repeats in mammalian genomes, see: Smit AF. Interspersed repeats and other mementos of transposable elements in mammalian genomes. Curr Opin Genet Dev. 1999 Dec;9(6):657-63. PMID: 10607616 Smit AF. The origin of interspersed repeats in the human genome. Curr Opin Genet Dev. 1996 Dec;6(6):743-8. PMID: 8994846 
spliceAIsnvs SpliceAI SNVs SpliceAI SNVs (unmasked) Phenotype and Literature Important: The SpliceAI data on the UCSC Genome Browser is directly from Illumina (See Data Access below). However, since SpliceAI refers to the algorithm, and not the computed dataset, the data on the Broad server or other sources may have some differences between them. Description SpliceAI is an open-source deep learning splicing prediction algorithm that can predict splicing alterations caused by DNA variations. Such variants may activate nearby cryptic splice sites, leading to abnormal transcript isoforms. SpliceAI was developed at Illumina; a lookup tool is provided by the Broad institute. Why are some variants not scored by SpliceAI? SpliceAI only annotates variants within genes defined by the gene annotation file. Additionally, SpliceAI does not annotate variants if they are close to chromosome ends (5kb on either side), deletions of length greater than twice the input parameter -D, or inconsistent with the reference fasta file. What are the differeneces between masked and unmasked tracks? The unmasked tracks include splicing changes corresponding to strengthening annotated splice sites and weakening unannotated splice sites, which are typically much less pathogenic than weakening annotated splice sites and strengthening unannotated splice sites. The delta scores of such splicing changes are set to 0 in the masked files. We recommend using the unmasked tracks for alternative splicing analysis and masked tracks for variant interpretation. Display Conventions and Interpretation Variants are colored according to Walker et al. 2023 splicing imact: Predicted impact on splicing: Score &gt;&#61; 0.2 Not informative: Score &lt; 0.2 and &gt; 0.1 No impact on splicing: Score &lt;&#61; 0.1 Mouseover on items shows the variant, gene name, type of change (donor gain/loss, acceptor gain/loss), location of affected cryptic splice, and spliceAI score. Clicking on any item brings up a table with this information. The scores range from 0 to 1 and can be interpreted as the probability of the variant being splice-altering. In the paper, a detailed characterization is provided for 0.2 (high recall), 0.5 (recommended), and 0.8 (high precision) cutoffs. Methods The data were downloaded from Illumina. The spliceAI scores are represented in the VCF INFO field as SpliceAI=G|OR4F5|0.01|0.00|0.00|0.00|-32|49|-40|-31 Here, the pipe-separated fields contain ALT allele Gene name Acceptor gain score Acceptor loss score Donor gain score Donor loss score Relative location of affected cryptic acceptor Relative location of affected acceptor Relative location of affected cryptic donor Relative location of affected donor Since most of the values are 0 or almost 0, we selected only those variants with a score equal to or greater than 0.02. The complete processing of this track can be found in the makedoc. Data Access These data are not available for download from the Genome Browser. The raw data can be found directly on Illumina. See below for a copy of the license restrictions pertaining to these data. License FOR ACADEMIC AND NOT-FOR-PROFIT RESEARCH USE ONLY. The SpliceAI scores are made available by Illumina only for academic or not-for-profit research only. By accessing the SpliceAI data, you acknowledge and agree that you may only use this data for your own personal academic or not-for-profit research only, and not for any other purposes. You may not use this data for any for-profit, clinical, or other commercial purpose without obtaining a commercial license from Illumina, Inc. References Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, Darbandi SF, Knowles D, Li YI, Kosmicki JA, Arbelaez J, Cui W, Schwartz GB et al. Predicting Splicing from Primary Sequence with Deep Learning. Cell. 2019 Jan 24;176(3):535-548.e24. PMID: 30661751 Walker LC, Hoya M, Wiggins GAR, Lindy A, Vincent LM, Parsons MT, Canson DM, Bis-Brewer D, Cass A, Tchourbanov A et al. Using the ACMG/AMP framework to capture evidence related to predicted and observed impact on splicing: Recommendations from the ClinGen SVI Splicing Subgroup. Am J Hum Genet. 2023 Jul 6;110(7):1046-1067. PMID: 37352859; PMC: PMC10357475 
spliceAI SpliceAI SpliceAI: Splice Variant Prediction Score Phenotype and Literature Important: The SpliceAI data on the UCSC Genome Browser is directly from Illumina (See Data Access below). However, since SpliceAI refers to the algorithm, and not the computed dataset, the data on the Broad server or other sources may have some differences between them. Description SpliceAI is an open-source deep learning splicing prediction algorithm that can predict splicing alterations caused by DNA variations. Such variants may activate nearby cryptic splice sites, leading to abnormal transcript isoforms. SpliceAI was developed at Illumina; a lookup tool is provided by the Broad institute. Why are some variants not scored by SpliceAI? SpliceAI only annotates variants within genes defined by the gene annotation file. Additionally, SpliceAI does not annotate variants if they are close to chromosome ends (5kb on either side), deletions of length greater than twice the input parameter -D, or inconsistent with the reference fasta file. What are the differeneces between masked and unmasked tracks? The unmasked tracks include splicing changes corresponding to strengthening annotated splice sites and weakening unannotated splice sites, which are typically much less pathogenic than weakening annotated splice sites and strengthening unannotated splice sites. The delta scores of such splicing changes are set to 0 in the masked files. We recommend using the unmasked tracks for alternative splicing analysis and masked tracks for variant interpretation. Display Conventions and Interpretation Variants are colored according to Walker et al. 2023 splicing imact: Predicted impact on splicing: Score &gt;&#61; 0.2 Not informative: Score &lt; 0.2 and &gt; 0.1 No impact on splicing: Score &lt;&#61; 0.1 Mouseover on items shows the variant, gene name, type of change (donor gain/loss, acceptor gain/loss), location of affected cryptic splice, and spliceAI score. Clicking on any item brings up a table with this information. The scores range from 0 to 1 and can be interpreted as the probability of the variant being splice-altering. In the paper, a detailed characterization is provided for 0.2 (high recall), 0.5 (recommended), and 0.8 (high precision) cutoffs. Methods The data were downloaded from Illumina. The spliceAI scores are represented in the VCF INFO field as SpliceAI=G|OR4F5|0.01|0.00|0.00|0.00|-32|49|-40|-31 Here, the pipe-separated fields contain ALT allele Gene name Acceptor gain score Acceptor loss score Donor gain score Donor loss score Relative location of affected cryptic acceptor Relative location of affected acceptor Relative location of affected cryptic donor Relative location of affected donor Since most of the values are 0 or almost 0, we selected only those variants with a score equal to or greater than 0.02. The complete processing of this track can be found in the makedoc. Data Access These data are not available for download from the Genome Browser. The raw data can be found directly on Illumina. See below for a copy of the license restrictions pertaining to these data. License FOR ACADEMIC AND NOT-FOR-PROFIT RESEARCH USE ONLY. The SpliceAI scores are made available by Illumina only for academic or not-for-profit research only. By accessing the SpliceAI data, you acknowledge and agree that you may only use this data for your own personal academic or not-for-profit research only, and not for any other purposes. You may not use this data for any for-profit, clinical, or other commercial purpose without obtaining a commercial license from Illumina, Inc. References Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, Darbandi SF, Knowles D, Li YI, Kosmicki JA, Arbelaez J, Cui W, Schwartz GB et al. Predicting Splicing from Primary Sequence with Deep Learning. Cell. 2019 Jan 24;176(3):535-548.e24. PMID: 30661751 Walker LC, Hoya M, Wiggins GAR, Lindy A, Vincent LM, Parsons MT, Canson DM, Bis-Brewer D, Cass A, Tchourbanov A et al. Using the ACMG/AMP framework to capture evidence related to predicted and observed impact on splicing: Recommendations from the ClinGen SVI Splicing Subgroup. Am J Hum Genet. 2023 Jul 6;110(7):1046-1067. PMID: 37352859; PMC: PMC10357475 
lincRNAsAllCellTypeTopView lincRNA RNA-Seq lincRNA RNA-Seq reads expression abundances Genes and Gene Predictions Description This track displays the Human Body Map lincRNAs (large intergenic non coding RNAs) and TUCPs (transcripts of uncertain coding potential), as well as their expression levels across 22 human tissues and cell lines. The Human Body Map catalog was generated by integrating previously existing annotation sources with transcripts that were de-novo assembled from RNA-Seq data. These transcripts were collected from ~4 billion RNA-Seq reads across 24 tissues and cell types. Expression abundance was estimated by Cufflinks (Trapnell et al., 2010) based on RNA-Seq. Expression abundances were estimated on the gene locus level, rather than for each transcript separately and are given as raw FPKM. The prefixes tcons_ and tcons_l2_ are used to describe lincRNAs and TUCP transcripts, respectively. Specific details about the catalog generation and data sets used for this study can be found in Cabili et al (2011). Extended characterization of each transcript in the human body map catalog can be found at the Human lincRNA Catalog website. Expression abundance scores range from 0 to 1000, and are displayed from light blue to dark blue respectively: 01000 Credits The body map RNA-Seq data was kindly provided by the Gene Expression Applications research group at Illumina. References Cabili MN, Trapnell C, Goff L, Koziol M, Tazon-Vega B, Regev A, Rinn JL. Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev. 2011 Sep 15;25(18):1915-27. PMID: 21890647; PMC: PMC3185964 Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010 May;28(5):511-5. PMID: 20436464; PMC: PMC3146043 
nonCodingRNAs Non-coding RNA RNA sequences that do not code for a protein Genes and Gene Predictions Description This is a super track for non-coding RNA data, subtracks represent some form of non-coding RNA data. Credits The body map RNA-Seq data was kindly provided by the Gene Expression Applications research group at Illumina. Genome coordinates for the sno/miRNA track were obtained from the miRBase sequences FTP site and from snoRNABase coordinates download page. References When making use of these data, please cite the folowing articles in addition to the primary sources of the miRNA sequences: Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ. miRBase: tools for microRNA genomics. Nucleic Acids Res. 2008 Jan 1;36(Database issue):D154-8. Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ. miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 2006 Jan 1;34(Database issue):D140-4. Griffiths-Jones S. The microRNA Registry. Nucleic Acids Res. 2004 Jan 1;32(Database issue):D109-11. Weber MJ. New human and mouse microRNA genes found by homology search. You may also want to cite The Wellcome Trust Sanger Institute miRBase and The Laboratoire de Biologie Moleculaire Eucaryote snoRNABase. The following publication provides guidelines on miRNA annotation: Ambros V. et al., A uniform system for microRNA annotation. RNA. 2003;9(3):277-9. Cabili MN, Trapnell C, Goff L, Koziol M, Tazon-Vega B, Regev A, Rinn JL. Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev. 2011 Sep 15;25(18):1915-27. PMID: 21890647; PMC: PMC3185964 Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010 May;28(5):511-5. PMID: 20436464; PMC: PMC3146043 
lincRNAsAllCellType lincRNAsCellType lincRNA RNA-Seq reads expression abundances Genes and Gene Predictions 
lincRNAsCTWhiteBloodCell WhiteBloodCell lincRNAs from whitebloodcell Genes and Gene Predictions 
lincRNAsCTThyroid Thyroid lincRNAs from thyroid Genes and Gene Predictions 
lincRNAsCTTestes_R Testes_R lincRNAs from testes_r Genes and Gene Predictions 
lincRNAsCTTestes Testes lincRNAs from testes Genes and Gene Predictions 
lincRNAsCTSkeletalMuscle SkeletalMuscle lincRNAs from skeletalmuscle Genes and Gene Predictions 
lincRNAsCTProstate Prostate lincRNAs from prostate Genes and Gene Predictions 
lincRNAsCTPlacenta_R Placenta_R lincRNAs from placenta_r Genes and Gene Predictions 
lincRNAsCTOvary Ovary lincRNAs from ovary Genes and Gene Predictions 
lincRNAsCTLymphNode LymphNode lincRNAs from lymphnode Genes and Gene Predictions 
lincRNAsCTLung Lung lincRNAs from lung Genes and Gene Predictions 
lincRNAsCTLiver Liver lincRNAs from liver Genes and Gene Predictions 
lincRNAsCTKidney Kidney lincRNAs from kidney Genes and Gene Predictions 
lincRNAsCThLF_r2 hLF_r2 lincRNAs from hlf_r2 Genes and Gene Predictions 
lincRNAsCThLF_r1 hLF_r1 lincRNAs from hlf_r1 Genes and Gene Predictions 
lincRNAsCTHeart Heart lincRNAs from heart Genes and Gene Predictions 
lincRNAsCTForeskin_R Foreskin_R lincRNAs from foreskin_r Genes and Gene Predictions 
lincRNAsCTColon Colon lincRNAs from colon Genes and Gene Predictions 
lincRNAsCTBreast Breast lincRNAs from breast Genes and Gene Predictions 
lincRNAsCTBrain_R Brain_R lincRNAs from brain_r Genes and Gene Predictions 
lincRNAsCTBrain Brain lincRNAs from brain Genes and Gene Predictions 
lincRNAsCTAdrenal Adrenal lincRNAs from adrenal Genes and Gene Predictions 
lincRNAsCTAdipose Adipose lincRNAs from adipose Genes and Gene Predictions 
spliceAIindels SpliceAI indels SpliceAI Indels (unmasked) Phenotype and Literature Important: The SpliceAI data on the UCSC Genome Browser is directly from Illumina (See Data Access below). However, since SpliceAI refers to the algorithm, and not the computed dataset, the data on the Broad server or other sources may have some differences between them. Description SpliceAI is an open-source deep learning splicing prediction algorithm that can predict splicing alterations caused by DNA variations. Such variants may activate nearby cryptic splice sites, leading to abnormal transcript isoforms. SpliceAI was developed at Illumina; a lookup tool is provided by the Broad institute. Why are some variants not scored by SpliceAI? SpliceAI only annotates variants within genes defined by the gene annotation file. Additionally, SpliceAI does not annotate variants if they are close to chromosome ends (5kb on either side), deletions of length greater than twice the input parameter -D, or inconsistent with the reference fasta file. What are the differeneces between masked and unmasked tracks? The unmasked tracks include splicing changes corresponding to strengthening annotated splice sites and weakening unannotated splice sites, which are typically much less pathogenic than weakening annotated splice sites and strengthening unannotated splice sites. The delta scores of such splicing changes are set to 0 in the masked files. We recommend using the unmasked tracks for alternative splicing analysis and masked tracks for variant interpretation. Display Conventions and Interpretation Variants are colored according to Walker et al. 2023 splicing imact: Predicted impact on splicing: Score &gt;&#61; 0.2 Not informative: Score &lt; 0.2 and &gt; 0.1 No impact on splicing: Score &lt;&#61; 0.1 Mouseover on items shows the variant, gene name, type of change (donor gain/loss, acceptor gain/loss), location of affected cryptic splice, and spliceAI score. Clicking on any item brings up a table with this information. The scores range from 0 to 1 and can be interpreted as the probability of the variant being splice-altering. In the paper, a detailed characterization is provided for 0.2 (high recall), 0.5 (recommended), and 0.8 (high precision) cutoffs. Methods The data were downloaded from Illumina. The spliceAI scores are represented in the VCF INFO field as SpliceAI=G|OR4F5|0.01|0.00|0.00|0.00|-32|49|-40|-31 Here, the pipe-separated fields contain ALT allele Gene name Acceptor gain score Acceptor loss score Donor gain score Donor loss score Relative location of affected cryptic acceptor Relative location of affected acceptor Relative location of affected cryptic donor Relative location of affected donor Since most of the values are 0 or almost 0, we selected only those variants with a score equal to or greater than 0.02. The complete processing of this track can be found in the makedoc. Data Access These data are not available for download from the Genome Browser. The raw data can be found directly on Illumina. See below for a copy of the license restrictions pertaining to these data. License FOR ACADEMIC AND NOT-FOR-PROFIT RESEARCH USE ONLY. The SpliceAI scores are made available by Illumina only for academic or not-for-profit research only. By accessing the SpliceAI data, you acknowledge and agree that you may only use this data for your own personal academic or not-for-profit research only, and not for any other purposes. You may not use this data for any for-profit, clinical, or other commercial purpose without obtaining a commercial license from Illumina, Inc. References Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, Darbandi SF, Knowles D, Li YI, Kosmicki JA, Arbelaez J, Cui W, Schwartz GB et al. Predicting Splicing from Primary Sequence with Deep Learning. Cell. 2019 Jan 24;176(3):535-548.e24. PMID: 30661751 Walker LC, Hoya M, Wiggins GAR, Lindy A, Vincent LM, Parsons MT, Canson DM, Bis-Brewer D, Cass A, Tchourbanov A et al. Using the ACMG/AMP framework to capture evidence related to predicted and observed impact on splicing: Recommendations from the ClinGen SVI Splicing Subgroup. Am J Hum Genet. 2023 Jul 6;110(7):1046-1067. PMID: 37352859; PMC: PMC10357475 
wgEncodeRegTxn Transcription Transcription Levels Assayed by RNA-seq on 9 Cell Lines from ENCODE Regulation Description This track shows transcription levels for several cell types as assayed by high-throughput sequencing of polyadenylated RNA (RNA-seq). Additional views of this dataset and additional documentation on the methods used for this track are available at the ENCODE Caltech RNA-seq page. The data shown here are derived from the Raw Signal view from the paired 75-mer 200 bp insert size reads. The two replicates of the signal were pooled and normalized so that the total genome-wide signal sums to 10 billion. Display Conventions and Configuration By default, this track uses a transparent overlay method of displaying data from a number of cell lines in the same vertical space. Each of the cell lines in this track is associated with a particular color, and these colors are relatively light and saturated so as to work best with the transparent overlay. The color of these tracks match their versions from their lifted source on the hg19 assembly. The colors are consistent with the other hg19 lifted tracks located in the ENCODE Regulation supertrack, with the exception being the DNase tracks, as they were not lifted from hg19 and are colored to reflect similarity of cell types. Credits This track shows data from the Wold Lab at Caltech, as part of the ENCODE Consortium. Release Notes This is release 2 (July 2012) of this track which includes two new subtracks for HeLa-S3 and HepG2. Data Release Policy Primary ENCODE data produced during the 2007-2012 production phase were subject to a restriction period. However, the data here are past those restrictions and are freely available. The full data release policy for ENCODE is available here. 
wgEncodeReg ENCODE Regulation Integrated Regulation from ENCODE Regulation Description These tracks contain information relevant to the regulation of transcription from the ENCODE Project. The Transcription track shows transcription levels assayed by sequencing of polyadenylated RNA from a variety of cell types. The Layered H3K4Me1 and Layered H3K27Ac tracks show where modification of histone proteins is suggestive of enhancer and, to a lesser extent, other regulatory activity. These histone modifications, particularly H3K4Me1, are quite broad. The actual enhancers are typically just a small portion of the area marked by these histone modifications. The Layered H3K4Me3 track shows a histone mark associated with promoters. The DNase I Hypersensitivity tracks indicate where chromatin is hypersensitive to cutting by the DNase enzyme, which has been assayed in a large number of cell types. Regulatory regions, in general, tend to be DNase-sensitive, and promoters are particularly DNase-sensitive. The Txn Factor ChIP tracks show DNA regions where transcription factors, proteins responsible for modulating gene transcription, bind as assayed by chromatin immunoprecipitation with antibodies specific to the transcription factor followed by sequencing of the precipitated DNA (ChIP-seq). These tracks complement each other and together can shed much light on regulatory DNA. The histone marks are informative at a high level, but they have a resolution of just ~200 bases and do not provide much in the way of functional detail. The DNase hypersensitivity assay is higher in resolution at the DNA level and can be done on a large number of cell types since it's just a single assay. At the functional level, DNase hypersensitivity suggests that a region is very likely to be regulatory in nature, but provides little information beyond that. The transcription factor ChIP assay has a high resolution at the DNA level and, due to the very specific nature of the transcription factors, is often informative with respect to functional detail. However, since each transcription factor must be assayed separately, the information is only available for a limited number of transcription factors on a limited number of cell lines. Though each assay has its strengths and weaknesses, the fact that all of these assays are relatively independent of each other gives increased confidence when multiple tracks are suggesting a regulatory function for a region. For additional information, please click on the hyperlinks for the individual tracks above. Also note that additional histone marks and transcription information is available in other ENCODE tracks. This integrative supertrack just shows a selection of the most informative data of most general interest. Display Conventions By default, the transcription and histone mark displays use a transparent overlay method of displaying data from a number of cell lines in a single track. Each of the cell lines in this track is associated with a particular color, and these colors are relatively light and saturated so as to work best with the transparent overlay. The color of the transcription and histone mark tracks match their versions from their lifted source on the hg19 assembly. The DNase tracks, which were not lifted from hg19, are colored differently to reflect similarity of cell types. There are three DNase tracks starting with a transparent overlay DNase Signal Track to allow viewing signals from all 95 cell types in one track. The individual signals and the same coloring scheme can also be found in the DNase HS Track where processed peaks and hotspots are also called out as gray boxes with the darkness of each box reflecting the underlying signal value. Lastly, in the DNase Clusters track all observed hypersensitive regions in the different cell lines at the same location were clustered into a single box where a number to the left of the box indicates how many cell types showed a hypersensitivity region and the darkness of the grey box is proportional to the the maximum value seen from one of the underlying cell lines. Clicking on these item takes you to a details page where additional information displays, such as the list of cell types that combined to form the cluster in the DNase Clusters track. Data Access The raw data for ENCODE 3 Regulation tracks can be accessed from Table Browser or combined with other data-sets through Data Integrator. For automated analysis and downloads, the track data files can be downloaded from our downloads server or queried using the JSON API or the Public SQL Individual regions or the whole genome annotation can be accessed as text using our utility bigBedToBed. Instructions for downloading the utility can be found here. That utility can also be used to obtain features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/wgEncodeRegDnase/wgEncodeRegDnaseUwA549Hotspot.broadPeak.bb -chrom=chr21 -start=0 -end=100000000 stdout For sorting transcription factor binding sites by cell type, we recommend you use the following download file for hg38. Credits Specific labs and contributors for these datasets are listed in the Credits section of the individual tracks in this super-track. The integrative view presented here was developed by Jim Kent at UCSC. Data Use Policy Users may freely download, analyze and publish results based on any ENCODE data without restrictions. Researchers using unpublished ENCODE data are encouraged to contact the data producers to discuss possible coordinated publications; however, this is optional. Users of ENCODE datasets are requested to cite the ENCODE Consortium and ENCODE production laboratory(s) that generated the datasets used, as described in Citing ENCODE. 
wgEncodeRegTxnCaltechRnaSeqNhlfR2x75Il200SigPooled NHLF Transcription of NHLF cells from ENCODE Regulation 
wgEncodeRegTxnCaltechRnaSeqNhekR2x75Il200SigPooled NHEK Transcription of NHEK cells from ENCODE Regulation 
wgEncodeRegTxnCaltechRnaSeqK562R2x75Il200SigPooled K562 Transcription of K562 cells from ENCODE Regulation 
wgEncodeRegTxnCaltechRnaSeqHuvecR2x75Il200SigPooled HUVEC Transcription of HUVEC cells from ENCODE Regulation 
wgEncodeRegTxnCaltechRnaSeqHsmmR2x75Il200SigPooled HSMM Transcription of HSMM cells from ENCODE Regulation 
wgEncodeRegTxnCaltechRnaSeqHepg2R2x75Il200SigPooled HepG2 Transcription of HepG2 cells from ENCODE Regulation 
wgEncodeRegTxnCaltechRnaSeqHelas3R2x75Il200SigPooled HeLa-S3 Transcription of HeLa-S3 cells from ENCODE Regulation 
wgEncodeRegTxnCaltechRnaSeqH1hescR2x75Il200SigPooled H1-hESC Transcription of H1-hESC cells from ENCODE Regulation 
wgEncodeRegTxnCaltechRnaSeqGm12878R2x75Il200SigPooled GM12878 Transcription of GM12878 cells from ENCODE Regulation 
wgEncodeRegMarkH3k4me1 Layered H3K4Me1 H3K4Me1 Mark (Often Found Near Regulatory Elements) on 7 cell lines from ENCODE Regulation Description Chemical modifications (e.g., methylation and acetylation) to the histone proteins present in chromatin influence gene expression by changing how accessible the chromatin is to transcription. A specific modification of a specific histone protein is called a histone mark. This track shows the levels of enrichment of the H3K4Me1 histone mark across the genome as determined by a ChIP-seq assay. The H3K4me1 histone mark is the mono-methylation of lysine 4 of the H3 histone protein, and it is associated with enhancers and with DNA regions downstream of transcription starts. Additional histone marks and other chromatin associated ChIP-seq data is available at the Broad Histone page. Display Conventions and Configuration By default, this track uses a transparent overlay method of displaying data from a number of cell lines in the same vertical space. Each of the cell lines in this track is associated with a particular color, and these colors are relatively light and saturated so as to work best with the transparent overlay. The color of these tracks match their versions from their lifted source on the hg19 assembly. The colors are consistent with the other hg19 lifted tracks located in the ENCODE Regulation supertrack, with the exception being the DNase tracks, as they were not lifted from hg19 and are colored to reflect similarity of cell types. Credits This track shows data from the Bernstein Lab at the Broad Institute, as part of the ENCODE Consortium. Data Release Policy Primary ENCODE data produced during the 2007-2012 production phase were subject to a restriction period. However, the data here are past those restrictions and are freely available. The full data release policy for ENCODE is available here. 
wgEncodeRegMarkH3k4me1Nhlf NHLF H3K4Me1 Mark (Often Found Near Regulatory Elements) on NHLF Cells from ENCODE Regulation 
wgEncodeRegMarkH3k4me1Nhek NHEK H3K4Me1 Mark (Often Found Near Regulatory Elements) on NHEK Cells from ENCODE Regulation 
wgEncodeRegMarkH3k4me1K562 K562 H3K4Me1 Mark (Often Found Near Regulatory Elements) on K562 Cells from ENCODE Regulation 
wgEncodeRegMarkH3k4me1Huvec HUVEC H3K4Me1 Mark (Often Found Near Regulatory Elements) on HUVEC Cells from ENCODE Regulation 
wgEncodeRegMarkH3k4me1Hsmm HSMM H3K4Me1 Mark (Often Found Near Regulatory Elements) on HSMM Cells from ENCODE Regulation 
wgEncodeRegMarkH3k4me1H1hesc H1-hESC H3K4Me1 Mark (Often Found Near Regulatory Elements) on H1-hESC Cells from ENCODE Regulation 
wgEncodeBroadHistoneGm12878H3k4me1StdSig GM12878 H3K4Me1 Mark (Often Found Near Regulatory Elements) on GM12878 Cells from ENCODE Regulation 
spliceAIsnvsMasked SpliceAI SNVs (masked) SpliceAI SNVs (masked) Phenotype and Literature Important: The SpliceAI data on the UCSC Genome Browser is directly from Illumina (See Data Access below). However, since SpliceAI refers to the algorithm, and not the computed dataset, the data on the Broad server or other sources may have some differences between them. Description SpliceAI is an open-source deep learning splicing prediction algorithm that can predict splicing alterations caused by DNA variations. Such variants may activate nearby cryptic splice sites, leading to abnormal transcript isoforms. SpliceAI was developed at Illumina; a lookup tool is provided by the Broad institute. Why are some variants not scored by SpliceAI? SpliceAI only annotates variants within genes defined by the gene annotation file. Additionally, SpliceAI does not annotate variants if they are close to chromosome ends (5kb on either side), deletions of length greater than twice the input parameter -D, or inconsistent with the reference fasta file. What are the differeneces between masked and unmasked tracks? The unmasked tracks include splicing changes corresponding to strengthening annotated splice sites and weakening unannotated splice sites, which are typically much less pathogenic than weakening annotated splice sites and strengthening unannotated splice sites. The delta scores of such splicing changes are set to 0 in the masked files. We recommend using the unmasked tracks for alternative splicing analysis and masked tracks for variant interpretation. Display Conventions and Interpretation Variants are colored according to Walker et al. 2023 splicing imact: Predicted impact on splicing: Score &gt;&#61; 0.2 Not informative: Score &lt; 0.2 and &gt; 0.1 No impact on splicing: Score &lt;&#61; 0.1 Mouseover on items shows the variant, gene name, type of change (donor gain/loss, acceptor gain/loss), location of affected cryptic splice, and spliceAI score. Clicking on any item brings up a table with this information. The scores range from 0 to 1 and can be interpreted as the probability of the variant being splice-altering. In the paper, a detailed characterization is provided for 0.2 (high recall), 0.5 (recommended), and 0.8 (high precision) cutoffs. Methods The data were downloaded from Illumina. The spliceAI scores are represented in the VCF INFO field as SpliceAI=G|OR4F5|0.01|0.00|0.00|0.00|-32|49|-40|-31 Here, the pipe-separated fields contain ALT allele Gene name Acceptor gain score Acceptor loss score Donor gain score Donor loss score Relative location of affected cryptic acceptor Relative location of affected acceptor Relative location of affected cryptic donor Relative location of affected donor Since most of the values are 0 or almost 0, we selected only those variants with a score equal to or greater than 0.02. The complete processing of this track can be found in the makedoc. Data Access These data are not available for download from the Genome Browser. The raw data can be found directly on Illumina. See below for a copy of the license restrictions pertaining to these data. License FOR ACADEMIC AND NOT-FOR-PROFIT RESEARCH USE ONLY. The SpliceAI scores are made available by Illumina only for academic or not-for-profit research only. By accessing the SpliceAI data, you acknowledge and agree that you may only use this data for your own personal academic or not-for-profit research only, and not for any other purposes. You may not use this data for any for-profit, clinical, or other commercial purpose without obtaining a commercial license from Illumina, Inc. References Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, Darbandi SF, Knowles D, Li YI, Kosmicki JA, Arbelaez J, Cui W, Schwartz GB et al. Predicting Splicing from Primary Sequence with Deep Learning. Cell. 2019 Jan 24;176(3):535-548.e24. PMID: 30661751 Walker LC, Hoya M, Wiggins GAR, Lindy A, Vincent LM, Parsons MT, Canson DM, Bis-Brewer D, Cass A, Tchourbanov A et al. Using the ACMG/AMP framework to capture evidence related to predicted and observed impact on splicing: Recommendations from the ClinGen SVI Splicing Subgroup. Am J Hum Genet. 2023 Jul 6;110(7):1046-1067. PMID: 37352859; PMC: PMC10357475 
robustPeaks TSS peaks FANTOM5: DPI peak, robust set Regulation Description The FANTOM5 track shows mapped transcription start sites (TSS) and their usage in primary cells, cell lines, and tissues to produce a comprehensive overview of gene expression across the human body by using single molecule sequencing. Display Conventions and Configuration Items in this track are colored according to their strand orientation. Blue indicates alignment to the negative strand, and red indicates alignment to the positive strand. Methods Protocol Individual biological states are profiled by HeliScopeCAGE, which is a variation of the CAGE (Cap Analysis Gene Expression) protocol based on a single molecule sequencer. The standard protocol requiring 5 &micro;g of total RNA as a starting material is referred to as hCAGE, and an optimized version for a lower quantity (~ 100 ng) is referred to as LQhCAGE (Kanamori-Katyama et al. 2011). hCAGE LQhCAGE Samples Transcription start sites (TSSs) were mapped and their usage in human and mouse primary cells, cell lines, and tissues was to produce a comprehensive overview of mammalian gene expression across the human body. 5&prime;-end of the mapped CAGE reads are counted at a single base pair resolution (CTSS, CAGE tag starting sites) on the genomic coordinates, which represent TSS activities in the sample. Individual samples shown in "TSS activity" tracks are grouped as below. Primary cell Tissue Cell Line Time course Fractionation TSS peaks TSS (CAGE) peaks across the panel of the biological states (samples) are identified by DPI (decomposition based peak identification, Forrest et al. 2014), where each of the peaks consists of neighboring and related TSSs. The peaks are used as anchors to define promoters and units of promoter-level expression analysis. Two subsets of the peaks are defined based on evidence of read counts, depending on scopes of subsequent analyses, and the first subset (referred as a robust set of the peaks, thresholded for expression analysis is shown as TSS peaks. They are named "p#@GENE_SYMBOL" if associated with 5'-end of known genes, or "p@CHROM:START..END,STRAND" otherwise. The summary tracks consist of the TSS (CAGE) peaks and summary profiles of TSS activities (total and maximum values). The summary track consists of the following tracks. TSS (CAGE) peaks the robust peaks TSS summary profiles Total counts and TPM (tags per million) in all the samples Maximum counts and TPM among the samples TSS activity 5&prime;-end of the mapped CAGE reads are counted at a single base pair resolution (CTSS, CAGE tag starting sites) on the genomic coordinates, which represent TSS activities in the sample. The read counts tracks indicate raw counts of CAGE reads, and the TPM tracks indicate normalized counts as TPM (tags per million). Categories of individual samples - Cell Line hCAGE - Cell Line LQhCAGE - fractionation hCAGE - Primary cell hCAGE - Primary cell LQhCAGE - Time course hCAGE - Tissue hCAGE Data Access FANTOM5 data can be explored interactively with the Table Browser and cross-referenced with the Data Integrator. For programmatic access, the track can be accessed using the Genome Browser&apos;s REST API. ReMap annotations can be downloaded from the Genome Browser's download server as a bigBed file. This compressed binary format can be remotely queried through command line utilities. Please note that some of the download files can be quite large. The FANTOM5 reprocessed data can be found and downloaded on the FANTOM website. Credits Thanks to the FANTOM5 consortium, the Large Scale Data Managing Unit and Preventive Medicine and Applied Genomics Unit, the Center for Integrative Medical Sciences (IMS), and RIKEN for providing this data and its analysis. References FANTOM Consortium and the RIKEN PMI and CLST (DGT), Forrest AR, Kawaji H, Rehli M, Baillie JK, de Hoon MJ, Haberle V, Lassmann T, Kulakovskiy IV, Lizio M et al. A promoter-level mammalian expression atlas. Nature. 2014 Mar 27;507(7493):462-70. PMID: 24670764; PMC: PMC4529748 Kanamori-Katayama M, Itoh M, Kawaji H, Lassmann T, Katayama S, Kojima M, Bertin N, Kaiho A, Ninomiya N, Daub CO et al. Unamplified cap analysis of gene expression on a single-molecule sequencer. Genome Res. 2011 Jul;21(7):1150-9. PMID: 21596820; PMC: PMC3129257 Lizio M, Harshbarger J, Shimoji H, Severin J, Kasukawa T, Sahin S, Abugessaisa I, Fukuda S, Hori F, Ishikawa-Kato S et al. Gateways to the FANTOM5 promoter level mammalian expression atlas. Genome Biol. 2015 Jan 5;16(1):22. PMID: 25723102; PMC: PMC4310165 
fantom5 FANTOM5 FANTOM5: Mapped transcription start sites (TSS) and their usage Regulation Description The FANTOM5 track shows mapped transcription start sites (TSS) and their usage in primary cells, cell lines, and tissues to produce a comprehensive overview of gene expression across the human body by using single molecule sequencing. Display Conventions and Configuration Items in this track are colored according to their strand orientation. Blue indicates alignment to the negative strand, and red indicates alignment to the positive strand. Methods Protocol Individual biological states are profiled by HeliScopeCAGE, which is a variation of the CAGE (Cap Analysis Gene Expression) protocol based on a single molecule sequencer. The standard protocol requiring 5 &micro;g of total RNA as a starting material is referred to as hCAGE, and an optimized version for a lower quantity (~ 100 ng) is referred to as LQhCAGE (Kanamori-Katyama et al. 2011). hCAGE LQhCAGE Samples Transcription start sites (TSSs) were mapped and their usage in human and mouse primary cells, cell lines, and tissues was to produce a comprehensive overview of mammalian gene expression across the human body. 5&prime;-end of the mapped CAGE reads are counted at a single base pair resolution (CTSS, CAGE tag starting sites) on the genomic coordinates, which represent TSS activities in the sample. Individual samples shown in "TSS activity" tracks are grouped as below. Primary cell Tissue Cell Line Time course Fractionation TSS peaks TSS (CAGE) peaks across the panel of the biological states (samples) are identified by DPI (decomposition based peak identification, Forrest et al. 2014), where each of the peaks consists of neighboring and related TSSs. The peaks are used as anchors to define promoters and units of promoter-level expression analysis. Two subsets of the peaks are defined based on evidence of read counts, depending on scopes of subsequent analyses, and the first subset (referred as a robust set of the peaks, thresholded for expression analysis is shown as TSS peaks. They are named "p#@GENE_SYMBOL" if associated with 5'-end of known genes, or "p@CHROM:START..END,STRAND" otherwise. The summary tracks consist of the TSS (CAGE) peaks and summary profiles of TSS activities (total and maximum values). The summary track consists of the following tracks. TSS (CAGE) peaks the robust peaks TSS summary profiles Total counts and TPM (tags per million) in all the samples Maximum counts and TPM among the samples TSS activity 5&prime;-end of the mapped CAGE reads are counted at a single base pair resolution (CTSS, CAGE tag starting sites) on the genomic coordinates, which represent TSS activities in the sample. The read counts tracks indicate raw counts of CAGE reads, and the TPM tracks indicate normalized counts as TPM (tags per million). Categories of individual samples - Cell Line hCAGE - Cell Line LQhCAGE - fractionation hCAGE - Primary cell hCAGE - Primary cell LQhCAGE - Time course hCAGE - Tissue hCAGE Data Access FANTOM5 data can be explored interactively with the Table Browser and cross-referenced with the Data Integrator. For programmatic access, the track can be accessed using the Genome Browser&apos;s REST API. ReMap annotations can be downloaded from the Genome Browser's download server as a bigBed file. This compressed binary format can be remotely queried through command line utilities. Please note that some of the download files can be quite large. The FANTOM5 reprocessed data can be found and downloaded on the FANTOM website. Credits Thanks to the FANTOM5 consortium, the Large Scale Data Managing Unit and Preventive Medicine and Applied Genomics Unit, the Center for Integrative Medical Sciences (IMS), and RIKEN for providing this data and its analysis. References FANTOM Consortium and the RIKEN PMI and CLST (DGT), Forrest AR, Kawaji H, Rehli M, Baillie JK, de Hoon MJ, Haberle V, Lassmann T, Kulakovskiy IV, Lizio M et al. A promoter-level mammalian expression atlas. Nature. 2014 Mar 27;507(7493):462-70. PMID: 24670764; PMC: PMC4529748 Kanamori-Katayama M, Itoh M, Kawaji H, Lassmann T, Katayama S, Kojima M, Bertin N, Kaiho A, Ninomiya N, Daub CO et al. Unamplified cap analysis of gene expression on a single-molecule sequencer. Genome Res. 2011 Jul;21(7):1150-9. PMID: 21596820; PMC: PMC3129257 Lizio M, Harshbarger J, Shimoji H, Severin J, Kasukawa T, Sahin S, Abugessaisa I, Fukuda S, Hori F, Ishikawa-Kato S et al. Gateways to the FANTOM5 promoter level mammalian expression atlas. Genome Biol. 2015 Jan 5;16(1):22. PMID: 25723102; PMC: PMC4310165 
wgEncodeRegMarkH3k4me3 Layered H3K4Me3 H3K4Me3 Mark (Often Found Near Promoters) on 7 cell lines from ENCODE Regulation Description Chemical modifications (e.g., methylation and acetylation) to the histone proteins present in chromatin influence gene expression by changing how accessible the chromatin is to transcription. A specific modification of a specific histone protein is called a histone mark. This track shows the levels of enrichment of the H3K4Me3 histone mark across the genome as determined by a ChIP-seq assay. The H3K4Me3 histone mark is the tri-methylation of lysine 4 of the H3 histone protein, and it is associated with promoters that are active or poised to be activated. Additional histone marks and other chromatin associated ChIP-seq data is available at the Broad Histone page. Display Conventions and Configuration By default, this track uses a transparent overlay method of displaying data from a number of cell lines in the same vertical space. Each of the cell lines in this track is associated with a particular color, and these colors are relatively light and saturated so as to work best with the transparent overlay. The color of these tracks match their versions from their lifted source on the hg19 assembly. The colors are consistent with the other hg19 lifted tracks located in the ENCODE Regulation supertrack, with the exception being the DNase tracks, as they were not lifted from hg19 and are colored to reflect similarity of cell types. Credits This track shows data from the Bernstein Lab at the Broad Institute, as part of the ENCODE Consortium. Data Release Policy Primary ENCODE data produced during the 2007-2012 production phase were subject to a restriction period. However, the data here are past those restrictions and are freely available. The full data release policy for ENCODE is available here. 
wgEncodeRegMarkH3k4me3Nhlf NHLF H3K4Me3 Mark (Often Found Near Promoters) on NHLF Cells from ENCODE Regulation 
wgEncodeRegMarkH3k4me3Nhek NHEK H3K4Me3 Mark (Often Found Near Promoters) on NHEK Cells from ENCODE Regulation 
wgEncodeRegMarkH3k4me3K562 K562 H3K4Me3 Mark (Often Found Near Promoters) on K562 Cells from ENCODE Regulation 
wgEncodeRegMarkH3k4me3Huvec HUVEC H3K4Me3 Mark (Often Found Near Promoters) on HUVEC Cells from ENCODE Regulation 
wgEncodeRegMarkH3k4me3Hsmm HSMM H3K4Me3 Mark (Often Found Near Promoters) on HSMM Cells from ENCODE Regulation 
wgEncodeRegMarkH3k4me3H1hesc H1-hESC H3K4Me3 Mark (Often Found Near Promoters) on H1-hESC Cells from ENCODE Regulation 
wgEncodeBroadHistoneGm12878H3k4me3StdSig GM12878 H3K4Me3 Mark (Often Found Near Regulatory Elements) on GM12878 Cells from ENCODE Regulation 
spliceAIindelsMasked SpliceAI indels (masked) SpliceAI Indels (masked) Phenotype and Literature Important: The SpliceAI data on the UCSC Genome Browser is directly from Illumina (See Data Access below). However, since SpliceAI refers to the algorithm, and not the computed dataset, the data on the Broad server or other sources may have some differences between them. Description SpliceAI is an open-source deep learning splicing prediction algorithm that can predict splicing alterations caused by DNA variations. Such variants may activate nearby cryptic splice sites, leading to abnormal transcript isoforms. SpliceAI was developed at Illumina; a lookup tool is provided by the Broad institute. Why are some variants not scored by SpliceAI? SpliceAI only annotates variants within genes defined by the gene annotation file. Additionally, SpliceAI does not annotate variants if they are close to chromosome ends (5kb on either side), deletions of length greater than twice the input parameter -D, or inconsistent with the reference fasta file. What are the differeneces between masked and unmasked tracks? The unmasked tracks include splicing changes corresponding to strengthening annotated splice sites and weakening unannotated splice sites, which are typically much less pathogenic than weakening annotated splice sites and strengthening unannotated splice sites. The delta scores of such splicing changes are set to 0 in the masked files. We recommend using the unmasked tracks for alternative splicing analysis and masked tracks for variant interpretation. Display Conventions and Interpretation Variants are colored according to Walker et al. 2023 splicing imact: Predicted impact on splicing: Score &gt;&#61; 0.2 Not informative: Score &lt; 0.2 and &gt; 0.1 No impact on splicing: Score &lt;&#61; 0.1 Mouseover on items shows the variant, gene name, type of change (donor gain/loss, acceptor gain/loss), location of affected cryptic splice, and spliceAI score. Clicking on any item brings up a table with this information. The scores range from 0 to 1 and can be interpreted as the probability of the variant being splice-altering. In the paper, a detailed characterization is provided for 0.2 (high recall), 0.5 (recommended), and 0.8 (high precision) cutoffs. Methods The data were downloaded from Illumina. The spliceAI scores are represented in the VCF INFO field as SpliceAI=G|OR4F5|0.01|0.00|0.00|0.00|-32|49|-40|-31 Here, the pipe-separated fields contain ALT allele Gene name Acceptor gain score Acceptor loss score Donor gain score Donor loss score Relative location of affected cryptic acceptor Relative location of affected acceptor Relative location of affected cryptic donor Relative location of affected donor Since most of the values are 0 or almost 0, we selected only those variants with a score equal to or greater than 0.02. The complete processing of this track can be found in the makedoc. Data Access These data are not available for download from the Genome Browser. The raw data can be found directly on Illumina. See below for a copy of the license restrictions pertaining to these data. License FOR ACADEMIC AND NOT-FOR-PROFIT RESEARCH USE ONLY. The SpliceAI scores are made available by Illumina only for academic or not-for-profit research only. By accessing the SpliceAI data, you acknowledge and agree that you may only use this data for your own personal academic or not-for-profit research only, and not for any other purposes. You may not use this data for any for-profit, clinical, or other commercial purpose without obtaining a commercial license from Illumina, Inc. References Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, Darbandi SF, Knowles D, Li YI, Kosmicki JA, Arbelaez J, Cui W, Schwartz GB et al. Predicting Splicing from Primary Sequence with Deep Learning. Cell. 2019 Jan 24;176(3):535-548.e24. PMID: 30661751 Walker LC, Hoya M, Wiggins GAR, Lindy A, Vincent LM, Parsons MT, Canson DM, Bis-Brewer D, Cass A, Tchourbanov A et al. Using the ACMG/AMP framework to capture evidence related to predicted and observed impact on splicing: Recommendations from the ClinGen SVI Splicing Subgroup. Am J Hum Genet. 2023 Jul 6;110(7):1046-1067. PMID: 37352859; PMC: PMC10357475 
Total_counts_multiwig Total counts of CAGE reads FANTOM5: Total counts of CAGE reads Regulation Description The FANTOM5 track shows mapped transcription start sites (TSS) and their usage in primary cells, cell lines, and tissues to produce a comprehensive overview of gene expression across the human body by using single molecule sequencing. Display Conventions and Configuration Items in this track are colored according to their strand orientation. Blue indicates alignment to the negative strand, and red indicates alignment to the positive strand. Methods Protocol Individual biological states are profiled by HeliScopeCAGE, which is a variation of the CAGE (Cap Analysis Gene Expression) protocol based on a single molecule sequencer. The standard protocol requiring 5 &micro;g of total RNA as a starting material is referred to as hCAGE, and an optimized version for a lower quantity (~ 100 ng) is referred to as LQhCAGE (Kanamori-Katyama et al. 2011). hCAGE LQhCAGE Samples Transcription start sites (TSSs) were mapped and their usage in human and mouse primary cells, cell lines, and tissues was to produce a comprehensive overview of mammalian gene expression across the human body. 5&prime;-end of the mapped CAGE reads are counted at a single base pair resolution (CTSS, CAGE tag starting sites) on the genomic coordinates, which represent TSS activities in the sample. Individual samples shown in "TSS activity" tracks are grouped as below. Primary cell Tissue Cell Line Time course Fractionation TSS peaks TSS (CAGE) peaks across the panel of the biological states (samples) are identified by DPI (decomposition based peak identification, Forrest et al. 2014), where each of the peaks consists of neighboring and related TSSs. The peaks are used as anchors to define promoters and units of promoter-level expression analysis. Two subsets of the peaks are defined based on evidence of read counts, depending on scopes of subsequent analyses, and the first subset (referred as a robust set of the peaks, thresholded for expression analysis is shown as TSS peaks. They are named "p#@GENE_SYMBOL" if associated with 5'-end of known genes, or "p@CHROM:START..END,STRAND" otherwise. The summary tracks consist of the TSS (CAGE) peaks and summary profiles of TSS activities (total and maximum values). The summary track consists of the following tracks. TSS (CAGE) peaks the robust peaks TSS summary profiles Total counts and TPM (tags per million) in all the samples Maximum counts and TPM among the samples TSS activity 5&prime;-end of the mapped CAGE reads are counted at a single base pair resolution (CTSS, CAGE tag starting sites) on the genomic coordinates, which represent TSS activities in the sample. The read counts tracks indicate raw counts of CAGE reads, and the TPM tracks indicate normalized counts as TPM (tags per million). Categories of individual samples - Cell Line hCAGE - Cell Line LQhCAGE - fractionation hCAGE - Primary cell hCAGE - Primary cell LQhCAGE - Time course hCAGE - Tissue hCAGE Data Access FANTOM5 data can be explored interactively with the Table Browser and cross-referenced with the Data Integrator. For programmatic access, the track can be accessed using the Genome Browser&apos;s REST API. ReMap annotations can be downloaded from the Genome Browser's download server as a bigBed file. This compressed binary format can be remotely queried through command line utilities. Please note that some of the download files can be quite large. The FANTOM5 reprocessed data can be found and downloaded on the FANTOM website. Credits Thanks to the FANTOM5 consortium, the Large Scale Data Managing Unit and Preventive Medicine and Applied Genomics Unit, the Center for Integrative Medical Sciences (IMS), and RIKEN for providing this data and its analysis. References FANTOM Consortium and the RIKEN PMI and CLST (DGT), Forrest AR, Kawaji H, Rehli M, Baillie JK, de Hoon MJ, Haberle V, Lassmann T, Kulakovskiy IV, Lizio M et al. A promoter-level mammalian expression atlas. Nature. 2014 Mar 27;507(7493):462-70. PMID: 24670764; PMC: PMC4529748 Kanamori-Katayama M, Itoh M, Kawaji H, Lassmann T, Katayama S, Kojima M, Bertin N, Kaiho A, Ninomiya N, Daub CO et al. Unamplified cap analysis of gene expression on a single-molecule sequencer. Genome Res. 2011 Jul;21(7):1150-9. PMID: 21596820; PMC: PMC3129257 Lizio M, Harshbarger J, Shimoji H, Severin J, Kasukawa T, Sahin S, Abugessaisa I, Fukuda S, Hori F, Ishikawa-Kato S et al. Gateways to the FANTOM5 promoter level mammalian expression atlas. Genome Biol. 2015 Jan 5;16(1):22. PMID: 25723102; PMC: PMC4310165 
TotalCounts_Rev Total counts of CAGE reads (rev) Total counts of CAGE reads reverse Regulation 
TotalCounts_Fwd Total counts of CAGE reads (fwd) Total counts of CAGE reads forward Regulation 
wgEncodeRegMarkH3k27ac Layered H3K27Ac H3K27Ac Mark (Often Found Near Regulatory Elements) on 7 cell lines from ENCODE Regulation Description Chemical modifications (e.g., methylation and acetylation) to the histone proteins present in chromatin influence gene expression by changing how accessible the chromatin is to transcription. A specific modification of a specific histone protein is called a histone mark. This track shows the levels of enrichment of the H3K27Ac histone mark across the genome as determined by a ChIP-seq assay. The H3K27Ac histone mark is the acetylation of lysine 27 of the H3 histone protein, and it is thought to enhance transcription possibly by blocking the spread of the repressive histone mark H3K27Me3. Additional histone marks and other chromatin associated ChIP-seq data is available at the Broad Histone page. Display Conventions and Configuration By default, this track uses a transparent overlay method of displaying data from a number of cell lines in the same vertical space. Each of the cell lines in this track is associated with a particular color, and these colors are relatively light and saturated so as to work best with the transparent overlay. The color of these tracks match their versions from their lifted source on the hg19 assembly. The colors are consistent with the other hg19 lifted tracks located in the ENCODE Regulation supertrack, with the exception being the DNase tracks, as they were not lifted from hg19 and are colored to reflect similarity of cell types. Credits This track shows data from the Bernstein Lab at the Broad Institute, as part of the ENCODE Consortium. Data Release Policy Primary ENCODE data produced during the 2007-2012 production phase were subject to a restriction period. However, the data here are past those restrictions and are freely available. The full data release policy for ENCODE is available here. 
wgEncodeRegMarkH3k27acNhlf NHLF H3K27Ac Mark (Often Found Near Regulatory Elements) on NHLF Cells from ENCODE Regulation 
wgEncodeRegMarkH3k27acNhek NHEK H3K27Ac Mark (Often Found Near Regulatory Elements) on NHEK Cells from ENCODE Regulation 
wgEncodeRegMarkH3k27acK562 K562 H3K27Ac Mark (Often Found Near Regulatory Elements) on K562 Cells from ENCODE Regulation 
wgEncodeRegMarkH3k27acHuvec HUVEC H3K27Ac Mark (Often Found Near Regulatory Elements) on HUVEC Cells from ENCODE Regulation 
wgEncodeRegMarkH3k27acHsmm HSMM H3K27Ac Mark (Often Found Near Regulatory Elements) on HSMM Cells from ENCODE Regulation 
wgEncodeRegMarkH3k27acH1hesc H1-hESC H3K27Ac Mark (Often Found Near Regulatory Elements) on H1-hESC Cells from ENCODE Regulation 
wgEncodeRegMarkH3k27acGm12878 GM12878 H3K27Ac Mark (Often Found Near Regulatory Elements) on GM12878 Cells from ENCODE Regulation 
Max_counts_multiwig Max counts of CAGE reads FANTOM5: Max counts of CAGE reads Regulation Description The FANTOM5 track shows mapped transcription start sites (TSS) and their usage in primary cells, cell lines, and tissues to produce a comprehensive overview of gene expression across the human body by using single molecule sequencing. Display Conventions and Configuration Items in this track are colored according to their strand orientation. Blue indicates alignment to the negative strand, and red indicates alignment to the positive strand. Methods Protocol Individual biological states are profiled by HeliScopeCAGE, which is a variation of the CAGE (Cap Analysis Gene Expression) protocol based on a single molecule sequencer. The standard protocol requiring 5 &micro;g of total RNA as a starting material is referred to as hCAGE, and an optimized version for a lower quantity (~ 100 ng) is referred to as LQhCAGE (Kanamori-Katyama et al. 2011). hCAGE LQhCAGE Samples Transcription start sites (TSSs) were mapped and their usage in human and mouse primary cells, cell lines, and tissues was to produce a comprehensive overview of mammalian gene expression across the human body. 5&prime;-end of the mapped CAGE reads are counted at a single base pair resolution (CTSS, CAGE tag starting sites) on the genomic coordinates, which represent TSS activities in the sample. Individual samples shown in "TSS activity" tracks are grouped as below. Primary cell Tissue Cell Line Time course Fractionation TSS peaks TSS (CAGE) peaks across the panel of the biological states (samples) are identified by DPI (decomposition based peak identification, Forrest et al. 2014), where each of the peaks consists of neighboring and related TSSs. The peaks are used as anchors to define promoters and units of promoter-level expression analysis. Two subsets of the peaks are defined based on evidence of read counts, depending on scopes of subsequent analyses, and the first subset (referred as a robust set of the peaks, thresholded for expression analysis is shown as TSS peaks. They are named "p#@GENE_SYMBOL" if associated with 5'-end of known genes, or "p@CHROM:START..END,STRAND" otherwise. The summary tracks consist of the TSS (CAGE) peaks and summary profiles of TSS activities (total and maximum values). The summary track consists of the following tracks. TSS (CAGE) peaks the robust peaks TSS summary profiles Total counts and TPM (tags per million) in all the samples Maximum counts and TPM among the samples TSS activity 5&prime;-end of the mapped CAGE reads are counted at a single base pair resolution (CTSS, CAGE tag starting sites) on the genomic coordinates, which represent TSS activities in the sample. The read counts tracks indicate raw counts of CAGE reads, and the TPM tracks indicate normalized counts as TPM (tags per million). Categories of individual samples - Cell Line hCAGE - Cell Line LQhCAGE - fractionation hCAGE - Primary cell hCAGE - Primary cell LQhCAGE - Time course hCAGE - Tissue hCAGE Data Access FANTOM5 data can be explored interactively with the Table Browser and cross-referenced with the Data Integrator. For programmatic access, the track can be accessed using the Genome Browser&apos;s REST API. ReMap annotations can be downloaded from the Genome Browser's download server as a bigBed file. This compressed binary format can be remotely queried through command line utilities. Please note that some of the download files can be quite large. The FANTOM5 reprocessed data can be found and downloaded on the FANTOM website. Credits Thanks to the FANTOM5 consortium, the Large Scale Data Managing Unit and Preventive Medicine and Applied Genomics Unit, the Center for Integrative Medical Sciences (IMS), and RIKEN for providing this data and its analysis. References FANTOM Consortium and the RIKEN PMI and CLST (DGT), Forrest AR, Kawaji H, Rehli M, Baillie JK, de Hoon MJ, Haberle V, Lassmann T, Kulakovskiy IV, Lizio M et al. A promoter-level mammalian expression atlas. Nature. 2014 Mar 27;507(7493):462-70. PMID: 24670764; PMC: PMC4529748 Kanamori-Katayama M, Itoh M, Kawaji H, Lassmann T, Katayama S, Kojima M, Bertin N, Kaiho A, Ninomiya N, Daub CO et al. Unamplified cap analysis of gene expression on a single-molecule sequencer. Genome Res. 2011 Jul;21(7):1150-9. PMID: 21596820; PMC: PMC3129257 Lizio M, Harshbarger J, Shimoji H, Severin J, Kasukawa T, Sahin S, Abugessaisa I, Fukuda S, Hori F, Ishikawa-Kato S et al. Gateways to the FANTOM5 promoter level mammalian expression atlas. Genome Biol. 2015 Jan 5;16(1):22. PMID: 25723102; PMC: PMC4310165 
MaxCounts_Rev Max counts of CAGE reads (rev) Max counts of CAGE reads reverse Regulation 
MaxCounts_Fwd Max counts of CAGE reads (fwd) Max counts of CAGE reads forward Regulation 
wgEncodeRegDnaseClustered DNase Clusters DNase I Hypersensitivity Peak Clusters from ENCODE (95 cell types) Regulation Description This track shows clusters of DNaseI hypersensitivity derived from assays in 95 cell types by the John Stamatoyannapoulos lab at the University of Washington from September 2007 to January 2011, as part of the ENCODE project first production phase. Regulatory regions in general, and promoters in particular, tend to be DNase-sensitive. Additional views of this data sites are displayed from the DNaseI HS track. The peaks in that track are the basis for the clusters shown here, which combine data from peaks from the different cell lines. Please note that track colors for the DNase tracks are based on similiarity of cell types, while there is different coloring for cell types on the ENCODE hg38 Transcription track, Layered H3K4Me1 track, Layered H3K4Me3 track, and Layered H3K27Ac track, which match the coloring used in their previous versions lifted from the hg19 assembly. Display Conventions and Configuration A gray box indicates the extent of the hypersensitive region. The darkness is proportional to the maximum signal strength observed in any cell line. The number to the left of the box shows how many cell lines are hypersensitive in the region. The track can be configured to restrict the display to elements above a specified score in the range 1-1000 (where score is based on signal strength). Methods Raw sequence data files were processed by the UCSC ENCODE DNase analysis pipeline (July 2014 specification), diagrammed here: Credit: Qian Alvin Qin, X. Liu lab Briefly, sequence files were aligned to the hg38 (GRCh38) genome assembly augmented with 'sponge' sequence (ref). Multi-mapped reads were removed, as were reads that aligned to 'sponge' or mitochondiral sequence. Results from all replicates were pooled, and further processed by the Hotspot program to call peaks. Peaks of DNaseI hypersensitivity from the ENCODE DNase Analysis Pipeline at UCSC were assigned normalized scores (by UCSC regClusterMakeTableOfTables) in the range 0-1000 based on the narrowPeak signalValue and then clustered on score (by UCSC regCluster) to generate singly-linked clusters. Additional documentation on the methods used to identify hypersensitive sites are available from the DNaseI HS track. Credits This track is based on sequence data from the University of Washington ENCODE group, with subsequent processing by UCSC. For additional credits and references, see the DNaseI HS track. 
wgEncodeRegDnaseWig DNase Signal DNase I Hypersensitivity Signal Colored by Similarity from ENCODE Regulation Description This track provides an integrated display of DNase hypersensitivity in multiple cell types using overlapping colored graphs of signal density with graph colors assigned to cell types based on similarity of signal. The track is based on results of experiments performed by the John Stamatoyannapoulos lab at the University of Washington from September 2007 to January 2011 as part of the ENCODE project first production phase. The signal graphs displayed here are also included in the comprehensive DNaseI HS track, which also provides peak and region calls and uses the same coloring based on similiarity of cell types (please note there is different coloring on the ENCODE hg38 Transcription track, Layered H3K4Me1 track, Layered H3K4Me3 track, and Layered H3K27Ac track, which match the coloring used in their previous versions lifted from the hg19 assembly). Methods Raw sequence data files were processed by the UCSC ENCODE DNase analysis pipeline described in the DNaseI HS track description. Signal graphs were normalized so the average value genome-wide is 1. Colors for the signal graphs were assigned by the UCSC BigWigCluster tool. The cell types were clustered into a binary tree, a rainbow was cast to the leaf nodes providing coloring based on similarity. Credit: Chris Eisenhart, J. Kent lab Credits The processed data for this track were generated at UCSC. Credits for the primary data underlying this track are included in the DNaseI HS track description. References Miga KH, Eisenhart C, Kent WJ. Utilizing mapping targets of sequences underrepresented in the reference assembly to reduce false positive alignments. Nucleic Acids Res. 2015 Nov 16;43(20):e133. PMID: 26163063 Thurman RE, Rynes E, Humbert R, Vierstra J, Maurano MT, Haugen E, Sheffield NC, Stergachis AB, Wang H, Vernot B et al. The accessible chromatin landscape of the human genome. Nature. 2012 Sep 6;489(7414):75-82. PMID: 22955617; PMC: PMC3721348 See also the references in the DNaseI HS track. 
wgEncodeRegDnaseUwBe2cWig BE2_C Sg BE2_C neuroblastoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwWerirb1Wig WERI-Rb-1 Sg WERI-Rb-1 retinoblastoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwMcf7Estradiol100nm1hrWig MCF-7 estr 1h Sg MCF-7 mammary adenocarcinoma cell line (estradi 1h) DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwMcf7Estradiolctrl0hrWig MCF-7 estr 0h Sg MCF-7 mammary adenocarcinoma cell line (estradi 0h) DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwMcf7Wig MCF-7 Sg MCF-7 mammary adenocarcinoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwSknmcWig SK-N-MC Sg SK-N-MC neuroepithelioma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHelas3Wig HeLa-S3 Sg HeLa-S3 cervical epithelial adenocarcinoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdlyadWig HMVEC-dLy-Ad Sg HMVEC-dLy-Ad dermal MV endothelial cell, lymph DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHrpepicWig HRPEpiC Sg HRPEpiC retinal pigment epithelium DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwRptecWig RPTEC Sg RPTEC renal proximal tubule epithelium DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwH7hescDiffprota14dWig H7-ES diff 14d Sg H7-hESC embryonic stem cell (diff 14d) DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwH7hescDiffprota5dWig H7-ES diff 5d Sg H7-hESC embryonic stem cell (diff 5d) DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwH7hescWig H7-ES Sg H7-hESC embryonic stem cell DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwNb4Wig NB4 Sg NB4 acute promyelocytic leukemia (APL) cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHl60Wig HL-60 Sg HL-60 acute promyelocytic leukemia (APL) cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwMonocytescd14ro01746Wig Monocyte-CD14+ Sg Monocytes-CD14+_RO01746 monocyte, CD14+ DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwGm12865Wig GM12865 Sg GM12865 B-lymphocyte, lymphoblastoid cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwGm12878Wig GM12878 Sg GM12878 B-lymphocyte, lymphoblastoid cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwJurkatWig Jurkat Sg Jurkat T-lymphocyte acute leukemia cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwTh1wb54553204Wig Th1_Wb54553204 Sg Th1_Wb54553204 T-lymphocyte, helper type 1 DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwTh2Wig Th2 Sg Th2 T-lymphocyte, helper type 2 DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwTh1Wig Th1 Sg Th1 T-lymphocyte, helper type 1 DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwCd20ro01778Wig CD20+_RO01778 Sg CD20+_RO01778 B-lymphocyte, CD20+ DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwSknshraWig SK-N-SH_RA Sg SK-N-SH_RA neuroblastoma cell line, RA treated DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwCaco2Wig Caco-2 Sg Caco-2 colon adenocarcinoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHepg2Wig HepG2 Sg HepG2 hepatocellular carcinoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwGm06990Wig GM06990 Sg GM06990 B-lymphocyte, lymphoblastoid cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHeepicWig HEEpiC Sg HEEpiC esophageal epithelium DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwPrecWig PrEC Sg PrEC prostate epithelium DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwSaecWig SAEC Sg SAEC small airway epithelium DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwNhekWig NHEK Sg NHEK epidermal keratinocyte DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHreWig HRE Sg HRE renal epithelium DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHrcepicWig HRCEpiC Sg HRCEpiC renal cortical epithelium DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdadWig HMVEC-dAd Sg HMVEC-dAd dermal microvascular endothelial cell DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdneoWig HMVEC-dNeo Sg HMVEC-dNeo dermal MV endothelial cell, neonate DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecllyWig HMVEC-LLy Sg HMVEC-LLy lung microvascular endothelial cell, lymph DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHrgecWig HRGEC Sg HRGEC renal glomerular endothelial cell DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdblneoWig HMVEC-dBl-Neo Sg HMVEC-dBl-Neo dermal MV endo cell, neonate blood DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdlyneoWig HMVEC-dLy-Neo Sg HMVEC-dLy-Neo dermal MV endo cell, neonate lymph DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdbladWig HMVEC-dBl-Ad Sg HMVEC-dBl-Ad dermal MV endothelial cell, blood DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHmveclblWig HMVEC-LBl Sg HMVEC-LBl lung microvascular epithelium. blood DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHuvecWig HUVEC Sg HUVEC umbilical vein endothelial cell DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHsmmtubeWig HSMMtube Sg HSMMtube skeletal muscle myotube DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwLhcnm2Diff4dWig LHCN-M2 diff4d Sg LHCN-M2 skeletal myoblast (diff 4d) DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwLhcnm2Wig LHCN-M2 Sg LHCN-M2 skeletal myoblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHsmmWig HSMM Sg HSMM skeletal muscle myoblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwNhdfadWig NHDF-Ad Sg NHDF-Ad dermal fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwWi384ohtam20nm72hrWig WI-38 40HTAM Sg WI-38 embryonic lung fibroblast cell line (40HTAM) DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHcfaaWig HCFaa Sg HCFaa cardiac fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHaspWig HA-sp Sg HA-sp spinal cord astrocyte DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwRpmi7951Wig RPMI-7951 Sg RPMI-7951 melanoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwM059jWig M059J Sg M059J glioblastoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHahWig HA-h Sg HA-h hippocampal astrocyte DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwAg04450Wig AG04450 Sg AG04450 fetal lung fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwAg04449Wig AG04449 Sg AG04449 fetal skin fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHbvsmcWig HBVSMC Sg HBVSMC brain vascular smooth muscle DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwSkmcWig SKMC Sg SKMC skeletal muscle cell DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHaepicWig HAEpiC Sg HAEpiC amniotic epithelium (AEC) DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwNhdfneoWig NHDF-neo Sg NHDF-neo dermal fibroblast, neonate DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHgfWig HGF Sg HGF gingival fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHmfWig HMF Sg HMF mammary fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwAg10803Wig AG10803 Sg AG10803 skin fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwBonemarrowmscWig bonemarrow_MSC Sg bone_marrow_MSC bone marrow fibroblastoid DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHipepicWig HIPEpiC Sg HIPEpiC iris pigment epithelium DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHvmfWig HVMF Sg HVMF villous mesenchymal fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHacWig HAc Sg HAc cerebellar astrocyte DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHconfWig HConF Sg HConF conjunctival fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHpfWig HPF Sg HPF pulmonary fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHcpepicWig HCPEpiC Sg HCPEpiC choroid plexus epithelium DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwAoafWig AoAF Sg AoAF aorta fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHpafWig HPAF Sg HPAF pulmonary artery fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHcmWig HCM Sg HCM cardiac myocyte DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHcfWig HCF Sg HCF cardiac fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHpdlfWig HPdLF Sg HPdLF periodontal ligament fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwAg09319Wig AG09319 Sg AG09319 gingival fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHbmecWig HBMEC Sg HBMEC brain microvascular endothelial cell (MEC) DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwNhaWig NH-A Sg NH-A astrocyte DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwNhlfWig NHLF Sg NHLF lung fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwGm04504Wig GM04504 Sg GM04504 skin fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwGm04503Wig GM04503 Sg GM04503 skin fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwWi38Wig WI-38 Sg WI-38 embryonic lung fibroblast cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHnpcepicWig HNPCEpiC Sg HNPCEpiC non-pigmented ciliary epithelium (NPCEC) DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwAg09309Wig AG09309 Sg AG09309 skin fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwBjWig BJ Sg BJ foreskin fibroblast cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwNt2d1Wig NT2-D1 Sg NT2-D1 embryonal carcinoma (NTera2) cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHffmycWig HFF-Myc Sg HFF-Myc foreskin fibroblast cell line, cMyc DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHffWig HFF Sg HFF foreskin fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwNhberaWig NHBE_RA Sg NHBE_RA bronchial epithelium, RA treated DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHct116Wig HCT-116 Sg HCT-116 colorectal carcinoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwPanc1Wig PANC-1 Sg PANC-1 pancreatic carcinoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwT47dWig T-47D Sg T-47D mammary ductal carcinoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHmecWig HMEC Sg HMEC mammary epithelium DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwLncapWig LNCaP Sg LNCaP prostate adenocarcinoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwA549Wig A549 Sg A549 lung adenocarcinoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwK562Wig K562 Sg K562 lymphoblast chronic myeloid leukemia cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnase DNase HS DNase I Hypersensitivity in 95 cell types from ENCODE Regulation Description These tracks contain the results of DNase I hypersensitivity experiments performed by the John Stamatoyannapoulos lab at the University of Washington from September 2007 to January 2011, as part of the ENCODE project first production phase. Colors were assigned to cell types based on similarity of signal. Other views of this data (along with additional documentation) are available from the hg19 ENCODE UW DNaseI HS track. Display Conventions and Configuration This track is a composite annotation track containing multiple subtracks, one for each cell type. The display mode and filtering of each subtrack can be individually controlled. For more information about track configuration, see Configuring Multi-View Tracks. Methods Raw sequence data files were processed by the UCSC ENCODE DNase analysis pipeline (July 2014 specification), diagrammed here: Credit: Qian Alvin Qin, X. Liu lab Briefly, sequence files were aligned to the hg38 (GRCh38) genome assembly augmented with 'sponge' sequence (ref). Multi-mapped reads were removed, as were reads that aligned to 'sponge' or mitochondiral sequence. Results from all replicates were pooled, and further processed by the Hotspot program to call peaks as well as broader regions of activity ('hotspots'), and to create signal density graphs. Signal graphs were normalized so the average value genome-wide is 1. The cell types were clustered into a binary tree, a rainbow was cast to the leaf nodes providing coloring based on similarity. Credit: Chris Eisenhart, J. Kent lab (Please note there is different coloring on the ENCODE hg38 Transcription track, Layered H3K4Me1 track, Layered H3K4Me3 track, and Layered H3K27Ac track, which match the coloring used in their previous versions lifted from the hg19 assembly). Credits The processed data for this track were produced by UCSC. Credits for the primary data underlying this track are included in the ENCODE UW DNaseI HS track description. References Miga KH, Eisenhart C, Kent WJ. Utilizing mapping targets of sequences underrepresented in the reference assembly to reduce false positive alignments. Nucleic Acids Res. 2015 Nov 16;43(20):e133. PMID: 26163063 Thurman RE, Rynes E, Humbert R, Vierstra J, Maurano MT, Haugen E, Sheffield NC, Stergachis AB, Wang H, Vernot B et al. The accessible chromatin landscape of the human genome. Nature. 2012 Sep 6;489(7414):75-82. PMID: 22955617; PMC: PMC3721348 See also the references in the ENCODE UW DNaseI HS track. 
wgEncodeRegDnaseSignal Signal HotSpot5 signal on BWA. Dupe, sponge and mitochondria filtered Regulation Description This track provides an integrated display of DNase hypersensitivity in multiple cell types using overlapping colored graphs of signal density with graph colors assigned to cell types based on similarity of signal. The track is based on results of experiments performed by the John Stamatoyannapoulos lab at the University of Washington from September 2007 to January 2011 as part of the ENCODE project first production phase. The signal graphs displayed here are also included in the comprehensive DNaseI HS track, which also provides peak and region calls and uses the same coloring based on similiarity of cell types (please note there is different coloring on the ENCODE hg38 Transcription track, Layered H3K4Me1 track, Layered H3K4Me3 track, and Layered H3K27Ac track, which match the coloring used in their previous versions lifted from the hg19 assembly). Methods Raw sequence data files were processed by the UCSC ENCODE DNase analysis pipeline described in the DNaseI HS track description. Signal graphs were normalized so the average value genome-wide is 1. Colors for the signal graphs were assigned by the UCSC BigWigCluster tool. The cell types were clustered into a binary tree, a rainbow was cast to the leaf nodes providing coloring based on similarity. Credit: Chris Eisenhart, J. Kent lab Credits The processed data for this track were generated at UCSC. Credits for the primary data underlying this track are included in the DNaseI HS track description. References Miga KH, Eisenhart C, Kent WJ. Utilizing mapping targets of sequences underrepresented in the reference assembly to reduce false positive alignments. Nucleic Acids Res. 2015 Nov 16;43(20):e133. PMID: 26163063 Thurman RE, Rynes E, Humbert R, Vierstra J, Maurano MT, Haugen E, Sheffield NC, Stergachis AB, Wang H, Vernot B et al. The accessible chromatin landscape of the human genome. Nature. 2012 Sep 6;489(7414):75-82. PMID: 22955617; PMC: PMC3721348 See also the references in the DNaseI HS track. 
wgEncodeRegDnaseUwBe2cSignal BE2_C Sg BE2_C neuroblastoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwWerirb1Signal WERI-Rb-1 Sg WERI-Rb-1 retinoblastoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwMcf7Estradiol100nm1hrSignal MCF-7 estr 1h Sg MCF-7 mammary adenocarcinoma cell line (estradi 1h) DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwMcf7Estradiolctrl0hrSignal MCF-7 estr 0h Sg MCF-7 mammary adenocarcinoma cell line (estradi 0h) DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwMcf7Signal MCF-7 Sg MCF-7 mammary adenocarcinoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwSknmcSignal SK-N-MC Sg SK-N-MC neuroepithelioma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHelas3Signal HeLa-S3 Sg HeLa-S3 cervical epithelial adenocarcinoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdlyadSignal HMVEC-dLy-Ad Sg HMVEC-dLy-Ad dermal MV endothelial cell, lymph DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHrpepicSignal HRPEpiC Sg HRPEpiC retinal pigment epithelium DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwRptecSignal RPTEC Sg RPTEC renal proximal tubule epithelium DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwH7hescDiffprota14dSignal H7-ES diff 14d Sg H7-hESC embryonic stem cell (diff 14d) DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwH7hescDiffprota5dSignal H7-ES diff 5d Sg H7-hESC embryonic stem cell (diff 5d) DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwH7hescSignal H7-ES Sg H7-hESC embryonic stem cell DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwNb4Signal NB4 Sg NB4 acute promyelocytic leukemia (APL) cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHl60Signal HL-60 Sg HL-60 acute promyelocytic leukemia (APL) cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwMonocytescd14ro01746Signal Monocyte-CD14+ Sg Monocytes-CD14+_RO01746 monocyte, CD14+ DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwGm12865Signal GM12865 Sg GM12865 B-lymphocyte, lymphoblastoid cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwGm12878Signal GM12878 Sg GM12878 B-lymphocyte, lymphoblastoid cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwJurkatSignal Jurkat Sg Jurkat T-lymphocyte acute leukemia cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwTh1wb54553204Signal Th1_Wb54553204 Sg Th1_Wb54553204 T-lymphocyte, helper type 1 DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwTh2Signal Th2 Sg Th2 T-lymphocyte, helper type 2 DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwTh1Signal Th1 Sg Th1 T-lymphocyte, helper type 1 DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwCd20ro01778Signal CD20+_RO01778 Sg CD20+_RO01778 B-lymphocyte, CD20+ DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwSknshraSignal SK-N-SH_RA Sg SK-N-SH_RA neuroblastoma cell line, RA treated DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwCaco2Signal Caco-2 Sg Caco-2 colon adenocarcinoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHepg2Signal HepG2 Sg HepG2 hepatocellular carcinoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwGm06990Signal GM06990 Sg GM06990 B-lymphocyte, lymphoblastoid cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHeepicSignal HEEpiC Sg HEEpiC esophageal epithelium DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwPrecSignal PrEC Sg PrEC prostate epithelium DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwSaecSignal SAEC Sg SAEC small airway epithelium DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwNhekSignal NHEK Sg NHEK epidermal keratinocyte DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHreSignal HRE Sg HRE renal epithelium DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHrcepicSignal HRCEpiC Sg HRCEpiC renal cortical epithelium DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdadSignal HMVEC-dAd Sg HMVEC-dAd dermal microvascular endothelial cell DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdneoSignal HMVEC-dNeo Sg HMVEC-dNeo dermal MV endothelial cell, neonate DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecllySignal HMVEC-LLy Sg HMVEC-LLy lung microvascular endothelial cell, lymph DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHrgecSignal HRGEC Sg HRGEC renal glomerular endothelial cell DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdblneoSignal HMVEC-dBl-Neo Sg HMVEC-dBl-Neo dermal MV endo cell, neonate blood DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdlyneoSignal HMVEC-dLy-Neo Sg HMVEC-dLy-Neo dermal MV end cell, neonate lymph DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdbladSignal HMVEC-dBl-Ad Sg HMVEC-dBl-Ad dermal MV endothelial cell, blood DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHmveclblSignal HMVEC-LBl Sg HMVEC-LBl lung microvascular epithelium. blood DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHuvecSignal HUVEC Sg HUVEC umbilical vein endothelial cell DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHsmmtubeSignal HSMMtube Sg HSMMtube skeletal muscle myotube DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwLhcnm2Diff4dSignal LHCN-M2 diff4d Sg LHCN-M2 skeletal myoblast (diff 4d) DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwLhcnm2Signal LHCN-M2 Sg LHCN-M2 skeletal myoblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHsmmSignal HSMM Sg HSMM skeletal muscle myoblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwNhdfadSignal NHDF-Ad Sg NHDF-Ad dermal fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwWi384ohtam20nm72hrSignal WI-38 40HTAM Sg WI-38 embryonic lung fibroblast cell line (40HTAM) DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHcfaaSignal HCFaa Sg HCFaa cardiac fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHaspSignal HA-sp Sg HA-sp spinal cord astrocyte DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwRpmi7951Signal RPMI-7951 Sg RPMI-7951 melanoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwM059jSignal M059J Sg M059J glioblastoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHahSignal HA-h Sg HA-h hippocampal astrocyte DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwAg04450Signal AG04450 Sg AG04450 fetal lung fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwAg04449Signal AG04449 Sg AG04449 fetal skin fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHbvsmcSignal HBVSMC Sg HBVSMC brain vascular smooth muscle DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwSkmcSignal SKMC Sg SKMC skeletal muscle cell DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHaepicSignal HAEpiC Sg HAEpiC amniotic epithelium (AEC) DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwNhdfneoSignal NHDF-neo Sg NHDF-neo dermal fibroblast, neonate DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHgfSignal HGF Sg HGF gingival fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHmfSignal HMF Sg HMF mammary fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwAg10803Signal AG10803 Sg AG10803 skin fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwBonemarrowmscSignal bonemarrow_MSC Sg bone_marrow_MSC bone marrow fibroblastoid DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHipepicSignal HIPEpiC Sg HIPEpiC iris pigment epithelium DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHvmfSignal HVMF Sg HVMF villous mesenchymal fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHacSignal HAc Sg HAc cerebellar astrocyte DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHconfSignal HConF Sg HConF conjunctival fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHpfSignal HPF Sg HPF pulmonary fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHcpepicSignal HCPEpiC Sg HCPEpiC choroid plexus epithelium DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwAoafSignal AoAF Sg AoAF aorta fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHpafSignal HPAF Sg HPAF pulmonary artery fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHcmSignal HCM Sg HCM cardiac myocyte DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHcfSignal HCF Sg HCF cardiac fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHpdlfSignal HPdLF Sg HPdLF periodontal ligament fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwAg09319Signal AG09319 Sg AG09319 gingival fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHbmecSignal HBMEC Sg HBMEC brain microvascular endothelial cell (MEC) DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwNhaSignal NH-A Sg NH-A astrocyte DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwNhlfSignal NHLF Sg NHLF lung fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwGm04504Signal GM04504 Sg GM04504 skin fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwGm04503Signal GM04503 Sg GM04503 skin fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwWi38Signal WI-38 Sg WI-38 embryonic lung fibroblast cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHnpcepicSignal HNPCEpiC Sg HNPCEpiC non-pigmented ciliary epithelium (NPCEC) DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwAg09309Signal AG09309 Sg AG09309 skin fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwBjSignal BJ Sg BJ foreskin fibroblast cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwNt2d1Signal NT2-D1 Sg NT2-D1 embryonal carcinoma (NTera2) cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHffmycSignal HFF-Myc Sg HFF-Myc foreskin fibroblast cell line, cMyc DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHffSignal HFF Sg HFF foreskin fibroblast DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwNhberaSignal NHBE_RA Sg NHBE_RA bronchial epithelium, RA treated DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHct116Signal HCT-116 Sg HCT-116 colorectal carcinoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwPanc1Signal PANC-1 Sg PANC-1 pancreatic carcinoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwT47dSignal T-47D Sg T-47D mammary ductal carcinoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwHmecSignal HMEC Sg HMEC mammary epithelium DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwLncapSignal LNCaP Sg LNCaP prostate adenocarcinoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwA549Signal A549 Sg A549 lung adenocarcinoma cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnaseUwK562Signal K562 Sg K562 lymphoblast chronic myeloid leukemia  cell line DNaseI Signal from ENCODE Regulation 
wgEncodeRegDnasePeak Peaks HotSpot5 peak calls on BWA. Dupe, sponge and mitochondria filtered Regulation 
wgEncodeRegDnaseUwBe2cPeak BE2_C Pk BE2_C neuroblastoma cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwWerirb1Peak WERI-Rb-1 Pk WERI-Rb-1 retinoblastoma cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwMcf7Estradiol100nm1hrPeak MCF-7 estr 1h Pk MCF-7 mammary adenocarcinoma cell line (estradi 1h) DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwMcf7Estradiolctrl0hrPeak MCF-7 estr 0h Pk MCF-7 mammary adenocarcinoma cell line (estradi 0h) DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwMcf7Peak MCF-7 Pk MCF-7 mammary adenocarcinoma cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwSknmcPeak SK-N-MC Pk SK-N-MC neuroepithelioma cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHelas3Peak HeLa-S3 Pk HeLa-S3 cervical epithelial adenocarcinoma cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdlyadPeak HMVEC-dLy-Ad Pk HMVEC-dLy-Ad dermal MV endothelial cell, lymph DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHrpepicPeak HRPEpiC Pk HRPEpiC retinal pigment epithelium DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwRptecPeak RPTEC Pk RPTEC renal proximal tubule epithelium DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwH7hescDiffprota14dPeak H7-ES diff 14d Pk H7-hESC embryonic stem cell (diff 14d) DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwH7hescDiffprota5dPeak H7-ES diff 5d Pk H7-hESC embryonic stem cell (diff 5d) DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwH7hescPeak H7-ES Pk H7-hESC embryonic stem cell DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwNb4Peak NB4 Pk NB4 acute promyelocytic leukemia (APL) cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHl60Peak HL-60 Pk HL-60 acute promyelocytic leukemia (APL) cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwMonocytescd14ro01746Peak Monocyte-CD14+ Pk Monocytes-CD14+_RO01746 monocyte, CD14+ DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwGm12865Peak GM12865 Pk GM12865 B-lymphocyte, lymphoblastoid cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwGm12878Peak GM12878 Pk GM12878 B-lymphocyte, lymphoblastoid cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwJurkatPeak Jurkat Pk Jurkat T-lymphocyte acute leukemia cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwTh1wb54553204Peak Th1_Wb54553204 Pk Th1_Wb54553204 T-lymphocyte, helper type 1 DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwTh2Peak Th2 Pk Th2 T-lymphocyte, helper type 2 DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwTh1Peak Th1 Pk Th1 T-lymphocyte, helper type 1 DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwCd20ro01778Peak CD20+_RO01778 Pk CD20+_RO01778 B-lymphocyte, CD20+ DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwSknshraPeak SK-N-SH_RA Pk SK-N-SH_RA neuroblastoma cell line, RA treated DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwCaco2Peak Caco-2 Pk Caco-2 colon adenocarcinoma cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHepg2Peak HepG2 Pk HepG2 hepatocellular carcinoma cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwGm06990Peak GM06990 Pk GM06990 B-lymphocyte, lymphoblastoid cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHeepicPeak HEEpiC Pk HEEpiC esophageal epithelium DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwPrecPeak PrEC Pk PrEC prostate epithelium DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwSaecPeak SAEC Pk SAEC small airway epithelium DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwNhekPeak NHEK Pk NHEK epidermal keratinocyte DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHrePeak HRE Pk HRE renal epithelium DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHrcepicPeak HRCEpiC Pk HRCEpiC renal cortical epithelium DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdadPeak HMVEC-dAd Pk HMVEC-dAd dermal microvascular endothelial cell DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdneoPeak HMVEC-dNeo Pk HMVEC-dNeo dermal MV endothelial cell, neonate DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecllyPeak HMVEC-LLy Pk HMVEC-LLy lung microvascular endothelial cell, lymph DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHrgecPeak HRGEC Pk HRGEC renal glomerular endothelial cell DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdblneoPeak HMVEC-dBl-Neo Pk HMVEC-dBl-Neo dermal MV endothelial cell, neonate blood DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdlyneoPeak HMVEC-dLy-Neo Pk HMVEC-dLy-Neo dermal MV endothelial cell, neonate lymph DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdbladPeak HMVEC-dBl-Ad Pk HMVEC-dBl-Ad dermal MV endothelial cell, blood DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHmveclblPeak HMVEC-LBl Pk HMVEC-LBl lung microvascular epithelium. blood DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHuvecPeak HUVEC Pk HUVEC umbilical vein endothelial cell DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHsmmtubePeak HSMMtube Pk HSMMtube skeletal muscle myotube DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwLhcnm2Diff4dPeak LHCN-M2 diff4d Pk LHCN-M2 skeletal myoblast (diff 4d) DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwLhcnm2Peak LHCN-M2 Pk LHCN-M2 skeletal myoblast DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHsmmPeak HSMM Pk HSMM skeletal muscle myoblast DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwNhdfadPeak NHDF-Ad Pk NHDF-Ad dermal fibroblast DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwWi384ohtam20nm72hrPeak WI-38 40HTAM Pk WI-38 embryonic lung fibroblast cell line (40HTAM) DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHcfaaPeak HCFaa Pk HCFaa cardiac fibroblast DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHaspPeak HA-sp Pk HA-sp spinal cord astrocyte DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwRpmi7951Peak RPMI-7951 Pk RPMI-7951 melanoma cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwM059jPeak M059J Pk M059J glioblastoma cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHahPeak HA-h Pk HA-h hippocampal astrocyte DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwAg04450Peak AG04450 Pk AG04450 fetal lung fibroblast DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwAg04449Peak AG04449 Pk AG04449 fetal skin fibroblast DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHbvsmcPeak HBVSMC Pk HBVSMC brain vascular smooth muscle DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwSkmcPeak SKMC Pk SKMC skeletal muscle cell DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHaepicPeak HAEpiC Pk HAEpiC amniotic epithelium (AEC) DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwNhdfneoPeak NHDF-neo Pk NHDF-neo dermal fibroblast, neonate DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHgfPeak HGF Pk HGF gingival fibroblast DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHmfPeak HMF Pk HMF mammary fibroblast DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwAg10803Peak AG10803 Pk AG10803 skin fibroblast DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwBonemarrowmscPeak bonemarrow_MSC Pk bone_marrow_MSC bone marrow fibroblastoid DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHipepicPeak HIPEpiC Pk HIPEpiC iris pigment epithelium DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHvmfPeak HVMF Pk HVMF villous mesenchymal fibroblast DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHacPeak HAc Pk HAc cerebellar astrocyte DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHconfPeak HConF Pk HConF conjunctival fibroblast DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHpfPeak HPF Pk HPF pulmonary fibroblast DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHcpepicPeak HCPEpiC Pk HCPEpiC choroid plexus epithelium DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwAoafPeak AoAF Pk AoAF aorta fibroblast DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHpafPeak HPAF Pk HPAF pulmonary artery fibroblast DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHcmPeak HCM Pk HCM cardiac myocyte DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHcfPeak HCF Pk HCF cardiac fibroblast DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHpdlfPeak HPdLF Pk HPdLF periodontal ligament fibroblast DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwAg09319Peak AG09319 Pk AG09319 gingival fibroblast DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHbmecPeak HBMEC Pk HBMEC brain microvascular endothelial cell (MEC) DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwNhaPeak NH-A Pk NH-A astrocyte DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwNhlfPeak NHLF Pk NHLF lung fibroblast DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwGm04504Peak GM04504 Pk GM04504 skin fibroblast DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwGm04503Peak GM04503 Pk GM04503 skin fibroblast DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwWi38Peak WI-38 Pk WI-38 embryonic lung fibroblast cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHnpcepicPeak HNPCEpiC Pk HNPCEpiC non-pigmented ciliary epithelium (NPCEC) DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwAg09309Peak AG09309 Pk AG09309 skin fibroblast DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwBjPeak BJ Pk BJ foreskin fibroblast cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwNt2d1Peak NT2-D1 Pk NT2-D1 embryonal carcinoma (NTera2) cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHffmycPeak HFF-Myc Pk HFF-Myc foreskin fibroblast cell line, cMyc DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHffPeak HFF Pk HFF foreskin fibroblast DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwNhberaPeak NHBE_RA Pk NHBE_RA bronchial epithelium, RA treated DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHct116Peak HCT-116 Pk HCT-116 colorectal carcinoma cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwPanc1Peak PANC-1 Pk PANC-1 pancreatic carcinoma cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwT47dPeak T-47D Pk T-47D mammary ductal carcinoma cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwHmecPeak HMEC Pk HMEC mammary epithelium DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwLncapPeak LNCaP Pk LNCaP prostate adenocarcinoma cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwA549Peak A549 Pk A549 lung adenocarcinoma cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseUwK562Peak K562 Pk K562 lymphoblast chronic myeloid leukemia cell line DNaseI Peaks from ENCODE Regulation 
wgEncodeRegDnaseHotspot Hotspots Hotspot5 hotspot calls on BWA. Dupe, sponge and mitochondria filtered Regulation 
wgEncodeRegDnaseUwBe2cHotspot BE2_C Ht BE2_C neuroblastoma cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwWerirb1Hotspot WERI-Rb-1 Ht WERI-Rb-1 retinoblastoma cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwMcf7Estradiol100nm1hrHotspot MCF-7 estr 1h Ht MCF-7 mammary adenocarcinoma cell line (estradi 1h) DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwMcf7Estradiolctrl0hrHotspot MCF-7 estr 0h Ht MCF-7 mammary adenocarcinoma cell line (estradi 0h) DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwMcf7Hotspot MCF-7 Ht MCF-7 mammary adenocarcinoma cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwSknmcHotspot SK-N-MC Ht SK-N-MC neuroepithelioma cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHelas3Hotspot HeLa-S3 Ht HeLa-S3 cervical epithelial adenocarcinoma cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdlyadHotspot HMVEC-dLy-Ad Ht HMVEC-dLy-Ad dermal MV endothelial cell, lymph DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHrpepicHotspot HRPEpiC Ht HRPEpiC retinal pigment epithelium DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwRptecHotspot RPTEC Ht RPTEC renal proximal tubule epithelium DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwH7hescDiffprota14dHotspot H7-ES diff 14d Ht H7-hESC embryonic stem cell (diff 14d) DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwH7hescDiffprota5dHotspot H7-ES diff 5d Ht H7-hESC embryonic stem cell (diff 5d) DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwH7hescHotspot H7-ES Ht H7-hESC embryonic stem cell DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwNb4Hotspot NB4 Ht NB4 acute promyelocytic leukemia (APL) cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHl60Hotspot HL-60 Ht HL-60 acute promyelocytic leukemia (APL) cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwMonocytescd14ro01746Hotspot Monocyte-CD14+ Ht Monocytes-CD14+_RO01746 monocyte, CD14+ DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwGm12865Hotspot GM12865 Ht GM12865 B-lymphocyte, lymphoblastoid cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwGm12878Hotspot GM12878 Ht GM12878 B-lymphocyte, lymphoblastoid cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwJurkatHotspot Jurkat Ht Jurkat T-lymphocyte acute leukemia cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwTh1wb54553204Hotspot Th1_Wb54553204 Ht Th1_Wb54553204 T-lymphocyte, helper type 1 DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwTh2Hotspot Th2 Ht Th2 T-lymphocyte, helper type 2 DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwTh1Hotspot Th1 Ht Th1 T-lymphocyte, helper type 1 DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwCd20ro01778Hotspot CD20+_RO01778 Ht CD20+_RO01778 B-lymphocyte, CD20+ DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwSknshraHotspot SK-N-SH_RA Ht SK-N-SH_RA neuroblastoma cell line, RA treated DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwCaco2Hotspot Caco-2 Ht Caco-2 colon adenocarcinoma cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHepg2Hotspot HepG2 Ht HepG2 hepatocellular carcinoma cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwGm06990Hotspot GM06990 Ht GM06990 B-lymphocyte, lymphoblastoid cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHeepicHotspot HEEpiC Ht HEEpiC esophageal epithelium DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwPrecHotspot PrEC Ht PrEC prostate epithelium DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwSaecHotspot SAEC Ht SAEC small airway epithelium DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwNhekHotspot NHEK Ht NHEK epidermal keratinocyte DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHreHotspot HRE Ht HRE renal epithelium DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHrcepicHotspot HRCEpiC Ht HRCEpiC renal cortical epithelium DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdadHotspot HMVEC-dAd Ht HMVEC-dAd dermal microvascular endothelial cell DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdneoHotspot HMVEC-dNeo Ht HMVEC-dNeo dermal microvascular endo cell, neonate DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecllyHotspot HMVEC-LLy Ht HMVEC-LLy lung microvascular endothelial cell, lymph DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHrgecHotspot HRGEC Ht HRGEC renal glomerular endothelial cell DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdblneoHotspot HMVEC-dBl-Neo Ht HMVEC-dBl-Neo dermal MV endo cell, neonate blood DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdlyneoHotspot HMVEC-dLy-Neo Ht HMVEC-dLy-Neo dermal MV endo cell, neonate lymph DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHmvecdbladHotspot HMVEC-dBl-Ad Ht HMVEC-dBl-Ad dermal MV endothelial cell, blood DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHmveclblHotspot HMVEC-LBl Ht HMVEC-LBl lung microvascular epithelium. blood DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHuvecHotspot HUVEC Ht HUVEC umbilical vein endothelial cell DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHsmmtubeHotspot HSMMtube Ht HSMMtube skeletal muscle myotube DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwLhcnm2Diff4dHotspot LHCN-M2 diff4d Ht LHCN-M2 skeletal myoblast (diff 4d) DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwLhcnm2Hotspot LHCN-M2 Ht LHCN-M2 skeletal myoblast DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHsmmHotspot HSMM Ht HSMM skeletal muscle myoblast DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwNhdfadHotspot NHDF-Ad Ht NHDF-Ad dermal fibroblast DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwWi384ohtam20nm72hrHotspot WI-38 40HTAM Ht WI-38 embryonic lung fibroblast cell line (40HTAM) DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHcfaaHotspot HCFaa Ht HCFaa cardiac fibroblast DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHaspHotspot HA-sp Ht HA-sp spinal cord astrocyte DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwRpmi7951Hotspot RPMI-7951 Ht RPMI-7951 melanoma cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwM059jHotspot M059J Ht M059J glioblastoma cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHahHotspot HA-h Ht HA-h hippocampal astrocyte DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwAg04450Hotspot AG04450 Ht AG04450 fetal lung fibroblast DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwAg04449Hotspot AG04449 Ht AG04449 fetal skin fibroblast DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHbvsmcHotspot HBVSMC Ht HBVSMC brain vascular smooth muscle DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwSkmcHotspot SKMC Ht SKMC skeletal muscle cell DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHaepicHotspot HAEpiC Ht HAEpiC amniotic epithelium (AEC) DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwNhdfneoHotspot NHDF-neo Ht NHDF-neo dermal fibroblast, neonate DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHgfHotspot HGF Ht HGF gingival fibroblast DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHmfHotspot HMF Ht HMF mammary fibroblast DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwAg10803Hotspot AG10803 Ht AG10803 skin fibroblast DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwBonemarrowmscHotspot bonemarrow_MSC Ht bone_marrow_MSC bone marrow fibroblastoid DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHipepicHotspot HIPEpiC Ht HIPEpiC iris pigment epithelium DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHvmfHotspot HVMF Ht HVMF villous mesenchymal fibroblast DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHacHotspot HAc Ht HAc cerebellar astrocyte DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHconfHotspot HConF Ht HConF conjunctival fibroblast DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHpfHotspot HPF Ht HPF pulmonary fibroblast DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHcpepicHotspot HCPEpiC Ht HCPEpiC choroid plexus epithelium DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwAoafHotspot AoAF Ht AoAF aorta fibroblast DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHpafHotspot HPAF Ht HPAF pulmonary artery fibroblast DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHcmHotspot HCM Ht HCM cardiac myocyte DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHcfHotspot HCF Ht HCF cardiac fibroblast DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHpdlfHotspot HPdLF Ht HPdLF periodontal ligament fibroblast DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwAg09319Hotspot AG09319 Ht AG09319 gingival fibroblast DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHbmecHotspot HBMEC Ht HBMEC brain microvascular endothelial cell (MEC) DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwNhaHotspot NH-A Ht NH-A astrocyte DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwNhlfHotspot NHLF Ht NHLF lung fibroblast DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwGm04504Hotspot GM04504 Ht GM04504 skin fibroblast DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwGm04503Hotspot GM04503 Ht GM04503 skin fibroblast DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwWi38Hotspot WI-38 Ht WI-38 embryonic lung fibroblast cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHnpcepicHotspot HNPCEpiC Ht HNPCEpiC non-pigmented ciliary epithelium (NPCEC) DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwAg09309Hotspot AG09309 Ht AG09309 skin fibroblast DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwBjHotspot BJ Ht BJ foreskin fibroblast cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwNt2d1Hotspot NT2-D1 Ht NT2-D1 embryonal carcinoma (NTera2) cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHffmycHotspot HFF-Myc Ht HFF-Myc foreskin fibroblast cell line, cMyc DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHffHotspot HFF Ht HFF foreskin fibroblast DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwNhberaHotspot NHBE_RA Ht NHBE_RA bronchial epithelium, RA treated DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHct116Hotspot HCT-116 Ht HCT-116 colorectal carcinoma cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwPanc1Hotspot PANC-1 Ht PANC-1 pancreatic carcinoma cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwT47dHotspot T-47D Ht T-47D mammary ductal carcinoma cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwHmecHotspot HMEC Ht HMEC mammary epithelium DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwLncapHotspot LNCaP Ht LNCaP prostate adenocarcinoma cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwA549Hotspot A549 Ht A549 lung adenocarcinoma cell line DNaseI Hotspots from ENCODE Regulation 
wgEncodeRegDnaseUwK562Hotspot K562 Ht K562 lymphoblast chronic myeloid leukemia cell line DNaseI Hotspots from ENCODE Regulation 
encRegTfbsClustered TF Clusters Transcription Factor ChIP-seq Clusters (340 factors, 129 cell types) from ENCODE 3 Regulation Description This track shows regions of transcription factor binding derived from a large collection of ChIP-seq experiments performed by the ENCODE project between February 2011 and November 2018, spanning the first production phase of ENCODE ("ENCODE 2") through the second full production phase ("ENCODE 3"). Transcription factors (TFs) are proteins that bind to DNA and interact with RNA polymerases to regulate gene expression. Some TFs contain a DNA binding domain and can bind directly to specific short DNA sequences ('motifs'); others bind to DNA indirectly through interactions with TFs containing a DNA binding domain. High-throughput antibody capture and sequencing methods (e.g. chromatin immunoprecipitation followed by sequencing, or 'ChIP-seq') can be used to identify regions of TF binding genome-wide. These regions are commonly called ChIP-seq peaks. ENCODE TF ChIP-seq data were processed using the ENCODE Transcription Factor ChIP-seq Processing Pipeline to generate peaks of TF binding. Peaks from 1264 experiments (1256 in hg38) representing 338 transcription factors (340 in hg38) in 130 cell types (129 in hg38) are combined here into clusters to produce a summary display showing occupancy regions for each factor. The underlying ChIP-seq peak data are available from the ENCODE 3 TF ChIP Peaks tracks ( hg19, hg38) Display Conventions A gray box encloses each peak cluster of transcription factor occupancy, with the darkness of the box being proportional to the maximum signal strength observed in any cell type contributing to the cluster. The HGNC gene name for the transcription factor is shown to the left of each cluster. To the right of the cluster a configurable label can optionally display information about the cell types contributing to the cluster and how many cell types were assayed for the factor (count where detected / count where assayed). For brevity in the display, each cell type is abbreviated to a single letter. The darkness of the letter is proportional to the signal strength observed in the cell line. Abbreviations starting with capital letters designate ENCODE cell types initially identified for intensive study, while those starting with lowercase letters designate cell lines added later in the project. Click on a peak cluster to see more information about the TF/cell assays contributing to the cluster and the cell line abbreviation table. Methods Peaks of transcription factor occupancy ("optimal peak set") from ENCODE ChIP-seq datasets were clustered using the UCSC hgBedsToBedExps tool. Scores were assigned to peaks by multiplying the input signal values by a normalization factor calculated as the ratio of the maximum score value (1000) to the signal value at one standard deviation from the mean, with values exceeding 1000 capped at 1000. This has the effect of distributing scores up to mean plus one 1 standard deviation across the score range, but assigning all above to the maximum score. The cluster score is the highest score for any peak contributing to the cluster. Data Access The raw data for the ENCODE3 TF Clusters track can be accessed from the Table Browser or combined with other datasets through the Data Integrator. This data is stored internally as a BED5+3 MySQL table with additional metadata tables. For automated analysis and download, the encRegTfbsClusteredWithCells.hg38.bed.gz track data file can be downloaded from our downloads server, which has 5 fields of BED data followed by a comma-separated list of cell types. The data can also be queried using the JSON API or the Public SQL server. Credits Thanks to the ENCODE Consortium, the ENCODE ChIP-seq production laboratories, and the ENCODE Data Coordination Center for generating and processing the TF ChIP-seq datasets used here. The ENCODE accession numbers of the constituent datasets are available from the peak details page. Special thanks to Henry Pratt, Jill Moore, Michael Purcaro, and Zhiping Weng, PI, at the ENCODE Data Analysis Center (ZLab at UMass Medical Center) for providing the peak datasets, metadata, and guidance developing this track. Please check the ZLab ENCODE Public Hubs for the most updated data. The integrative view presented here was developed by Jim Kent at UCSC. References ENCODE Project Consortium. A user's guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol. 2011 Apr;9(4):e1001046. PMID: 21526222; PMCID: PMC3079585 ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012 Sep 6;489(7414):57-74. PMID: 22955616; PMCID: PMC3439153 Sloan CA, Chan ET, Davidson JM, Malladi VS, Strattan JS, Hitz BC, Gabdank I, Narayanan AK, Ho M, Lee BT et al. ENCODE data at the ENCODE portal. Nucleic Acids Res. 2016 Jan 4;44(D1):D726-32. PMID: 26527727; PMC: PMC4702836 Gerstein MB, Kundaje A, Hariharan M, Landt SG, Yan KK, Cheng C, Mu XJ, Khurana E, Rozowsky J, Alexander R et al. Architecture of the human regulatory network derived from ENCODE data. Nature. 2012 Sep 6;489(7414):91-100. PMID: 22955619 Wang J, Zhuang J, Iyer S, Lin X, Whitfield TW, Greven MC, Pierce BG, Dong X, Kundaje A, Cheng Y et al. Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors. Genome Res. 2012 Sep;22(9):1798-812. PMID: 22955990; PMC: PMC3431495 Wang J, Zhuang J, Iyer S, Lin XY, Greven MC, Kim BH, Moore J, Pierce BG, Dong X, Virgil D et al. Factorbook.org: a Wiki-based database for transcription factor-binding data generated by the ENCODE consortium. Nucleic Acids Res. 2013 Jan;41(Database issue):D171-6. PMID: 23203885; PMC: PMC3531197 Data Use Policy Users may freely download, analyze and publish results based on any ENCODE data without restrictions. Researchers using unpublished ENCODE data are encouraged to contact the data producers to discuss possible coordinated publications; however, this is optional. Users of ENCODE datasets are requested to cite the ENCODE Consortium and ENCODE production laboratory(s) that generated the datasets used, as described in Citing ENCODE. 
encTfChipPk TF ChIP Transcription Factor ChIP-seq Peaks (340 factors in 129 cell types) from ENCODE 3 Regulation Description This track represents a comprehensive set of human transcription factor binding sites based on ChIP-seq experiments generated by production groups in the ENCODE Consortium between February 2011 and November 2018. Transcription factors (TFs) are proteins that bind to DNA and interact with RNA polymerases to regulate gene expression. Some TFs contain a DNA binding domain and can bind directly to specific short DNA sequences ('motifs'); others bind to DNA indirectly through interactions with TFs containing a DNA binding domain. High-throughput antibody capture and sequencing methods (e.g. chromatin immunoprecipitation followed by sequencing, or 'ChIP-seq') can be used to identify regions of TF binding genome-wide. These regions are commonly called ChIP-seq peaks. The related Transcription Factor ChIP-seq Clusters tracks (hg19, hg38) provide summary views of this data. Display and File Conventions and Configuration The display for this track shows site location with the point-source of the peak marked with a colored vertical bar and the level of enrichment at the site indicated by the darkness of the item. The subtracks are colored by UCSC ENCODE 2 cell type color conventions on the hg19 assembly, and by similarity of cell types in DNaseI hypersensitivity assays (as in the DNase Signal) track in the hg38 assembly. The display can be filtered to higher valued items, using the Score range: configuration item. The score values were computed at UCSC based on signal values assigned by the ENCODE pipeline. The input signal values were multiplied by a normalization factor calculated as the ratio of the maximum score value (1000) to the signal value at 1 standard deviation from the mean, with values exceeding 1000 capped at 1000. This has the effect of distributing scores up to mean + 1std across the score range, but assigning all above to the maximum score. Methods The ChIP-seq peaks in this track were generated by the the ENCODE Transcription Factor ChIP-seq Processing Pipeline. Methods documentation and full metadata for each track can be found at the ENCODE project portal, using The ENCODE file accession (ENCFF*) listed in the track label. Credits Thanks to the ENCODE Consortium, the ENCODE ChIP-seq production laboratories, and the ENCODE Data Coordination Center for generating and processing the datasets used here. Special thanks to Henry Pratt, Jill Moore, Michael Purcaro, and Zhiping Weng, PI, at the ENCODE Data Analysis Center (ZLab at UMass Medical Center) for providing the peak datasets, metadata, and guidance developing this track. Please check the ZLab ENCODE Public Hubs for the most updated data. References ENCODE Project Consortium. A user's guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol. 2011 Apr;9(4):e1001046. PMID: 21526222; PMCID: PMC3079585 ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012 Sep 6;489(7414):57-74. PMID: 22955616; PMCID: PMC3439153 Sloan CA, Chan ET, Davidson JM, Malladi VS, Strattan JS, Hitz BC, Gabdank I, Narayanan AK, Ho M, Lee BT et al. ENCODE data at the ENCODE portal. Nucleic Acids Res. 2016 Jan 4;44(D1):D726-32. PMID: 26527727; PMC: PMC4702836 Gerstein MB, Kundaje A, Hariharan M, Landt SG, Yan KK, Cheng C, Mu XJ, Khurana E, Rozowsky J, Alexander R et al. Architecture of the human regulatory network derived from ENCODE data. Nature. 2012 Sep 6;489(7414):91-100. PMID: 22955619 Wang J, Zhuang J, Iyer S, Lin X, Whitfield TW, Greven MC, Pierce BG, Dong X, Kundaje A, Cheng Y et al. Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors. Genome Res. 2012 Sep;22(9):1798-812. PMID: 22955990; PMC: PMC3431495 Wang J, Zhuang J, Iyer S, Lin XY, Greven MC, Kim BH, Moore J, Pierce BG, Dong X, Virgil D et al. Factorbook.org: a Wiki-based database for transcription factor-binding data generated by the ENCODE consortium. Nucleic Acids Res. 2013 Jan;41(Database issue):D171-6. PMID: 23203885; PMC: PMC3531197 Data Use Policy Users may freely download, analyze and publish results based on any ENCODE data without restrictions. Researchers using unpublished ENCODE data are encouraged to contact the data producers to discuss possible coordinated publications; however, this is optional. Users of ENCODE datasets are requested to cite the ENCODE Consortium and ENCODE production laboratory(s) that generated the datasets used, as described in Citing ENCODE. 
encTfChipPkENCFF635MUK vagina POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in vagina from ENCODE 3 (ENCFF635MUK) Regulation 
encTfChipPkENCFF865QLX vagina POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in vagina from ENCODE 3 (ENCFF865QLX) Regulation 
encTfChipPkENCFF242HMY vagina EP300 2 Transcription Factor ChIP-seq Peaks of EP300 in vagina from ENCODE 3 (ENCFF242HMY) Regulation 
encTfChipPkENCFF116VEG vagina EP300 1 Transcription Factor ChIP-seq Peaks of EP300 in vagina from ENCODE 3 (ENCFF116VEG) Regulation 
encTfChipPkENCFF508LRF vagina CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in vagina from ENCODE 3 (ENCFF508LRF) Regulation 
encTfChipPkENCFF579GUD vagina CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in vagina from ENCODE 3 (ENCFF579GUD) Regulation 
encTfChipPkENCFF198EUQ uterus POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in uterus from ENCODE 3 (ENCFF198EUQ) Regulation 
encTfChipPkENCFF236XBY uterus POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in uterus from ENCODE 3 (ENCFF236XBY) Regulation 
encTfChipPkENCFF179YWB uterus CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in uterus from ENCODE 3 (ENCFF179YWB) Regulation 
encTfChipPkENCFF866EIC uterus CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in uterus from ENCODE 3 (ENCFF866EIC) Regulation 
encTfChipPkENCFF834DID lungUpLb POLR2A 4 Transcription Factor ChIP-seq Peaks of POLR2A in upper_lobe_of_left_lung from ENCODE 3 (ENCFF834DID) Regulation 
encTfChipPkENCFF626AFW lungUpLb POLR2A 3 Transcription Factor ChIP-seq Peaks of POLR2A in upper_lobe_of_left_lung from ENCODE 3 (ENCFF626AFW) Regulation 
encTfChipPkENCFF468AEV lungUpLb POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in upper_lobe_of_left_lung from ENCODE 3 (ENCFF468AEV) Regulation 
encTfChipPkENCFF665TLS lungUpLb POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in upper_lobe_of_left_lung from ENCODE 3 (ENCFF665TLS) Regulation 
encTfChipPkENCFF567XKZ lungUpLbe EP300 4 Transcription Factor ChIP-seq Peaks of EP300 in upper_lobe_of_left_lung from ENCODE 3 (ENCFF567XKZ) Regulation 
encTfChipPkENCFF833NHM lungUpLbe EP300 3 Transcription Factor ChIP-seq Peaks of EP300 in upper_lobe_of_left_lung from ENCODE 3 (ENCFF833NHM) Regulation 
encTfChipPkENCFF676WYA lungUpLbe EP300 2 Transcription Factor ChIP-seq Peaks of EP300 in upper_lobe_of_left_lung from ENCODE 3 (ENCFF676WYA) Regulation 
encTfChipPkENCFF348MWL lungUpLbe EP300 1 Transcription Factor ChIP-seq Peaks of EP300 in upper_lobe_of_left_lung from ENCODE 3 (ENCFF348MWL) Regulation 
encTfChipPkENCFF716XFO lungUpLobe CTCF 4 Transcription Factor ChIP-seq Peaks of CTCF in upper_lobe_of_left_lung from ENCODE 3 (ENCFF716XFO) Regulation 
encTfChipPkENCFF254NYT lungUpLobe CTCF 3 Transcription Factor ChIP-seq Peaks of CTCF in upper_lobe_of_left_lung from ENCODE 3 (ENCFF254NYT) Regulation 
encTfChipPkENCFF749CMN lungUpLobe CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in upper_lobe_of_left_lung from ENCODE 3 (ENCFF749CMN) Regulation 
encTfChipPkENCFF869YGK lungUpLobe CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in upper_lobe_of_left_lung from ENCODE 3 (ENCFF869YGK) Regulation 
encTfChipPkENCFF028RZP trnsvCln POLR2A 4 Transcription Factor ChIP-seq Peaks of POLR2A in transverse_colon from ENCODE 3 (ENCFF028RZP) Regulation 
encTfChipPkENCFF228NVN trnsvCln POLR2A 3 Transcription Factor ChIP-seq Peaks of POLR2A in transverse_colon from ENCODE 3 (ENCFF228NVN) Regulation 
encTfChipPkENCFF211VGU trnsvCln POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in transverse_colon from ENCODE 3 (ENCFF211VGU) Regulation 
encTfChipPkENCFF185LTG trnsvCln POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in transverse_colon from ENCODE 3 (ENCFF185LTG) Regulation 
encTfChipPkENCFF580NSJ transvCln EP300 3 Transcription Factor ChIP-seq Peaks of EP300 in transverse_colon from ENCODE 3 (ENCFF580NSJ) Regulation 
encTfChipPkENCFF079CRY transvCln EP300 2 Transcription Factor ChIP-seq Peaks of EP300 in transverse_colon from ENCODE 3 (ENCFF079CRY) Regulation 
encTfChipPkENCFF244FQD transvCln EP300 1 Transcription Factor ChIP-seq Peaks of EP300 in transverse_colon from ENCODE 3 (ENCFF244FQD) Regulation 
encTfChipPkENCFF693TBO trnsvColon CTCF 4 Transcription Factor ChIP-seq Peaks of CTCF in transverse_colon from ENCODE 3 (ENCFF693TBO) Regulation 
encTfChipPkENCFF538QPY trnsvColon CTCF 3 Transcription Factor ChIP-seq Peaks of CTCF in transverse_colon from ENCODE 3 (ENCFF538QPY) Regulation 
encTfChipPkENCFF607VAP trnsvColon CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in transverse_colon from ENCODE 3 (ENCFF607VAP) Regulation 
encTfChipPkENCFF907KEJ trnsvColon CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in transverse_colon from ENCODE 3 (ENCFF907KEJ) Regulation 
encTfChipPkENCFF663DIG tblNerve POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in tibial_nerve from ENCODE 3 (ENCFF663DIG) Regulation 
encTfChipPkENCFF355SDI tblNerve POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in tibial_nerve from ENCODE 3 (ENCFF355SDI) Regulation 
encTfChipPkENCFF049JNK tbialNerv EP300 2 Transcription Factor ChIP-seq Peaks of EP300 in tibial_nerve from ENCODE 3 (ENCFF049JNK) Regulation 
encTfChipPkENCFF848EEE tbialNerv EP300 1 Transcription Factor ChIP-seq Peaks of EP300 in tibial_nerve from ENCODE 3 (ENCFF848EEE) Regulation 
encTfChipPkENCFF691IPU tibialNerve CTCF Transcription Factor ChIP-seq Peaks of CTCF in tibial_nerve from ENCODE 3 (ENCFF691IPU) Regulation 
encTfChipPkENCFF611MLV tbialNerve CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in tibial_nerve from ENCODE 3 (ENCFF611MLV) Regulation 
encTfChipPkENCFF237VLQ tbialNerve CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in tibial_nerve from ENCODE 3 (ENCFF237VLQ) Regulation 
encTfChipPkENCFF960TOX tibialArtery CTCF Transcription Factor ChIP-seq Peaks of CTCF in tibial_artery from ENCODE 3 (ENCFF960TOX) Regulation 
encTfChipPkENCFF445NPR thyroid POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in thyroid_gland from ENCODE 3 (ENCFF445NPR) Regulation 
encTfChipPkENCFF710ZQC thyroid POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in thyroid_gland from ENCODE 3 (ENCFF710ZQC) Regulation 
encTfChipPkENCFF989JUA thyroid CTCF 3 Transcription Factor ChIP-seq Peaks of CTCF in thyroid_gland from ENCODE 3 (ENCFF989JUA) Regulation 
encTfChipPkENCFF026ZWL thyroid CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in thyroid_gland from ENCODE 3 (ENCFF026ZWL) Regulation 
encTfChipPkENCFF728IYI thyroid CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in thyroid_gland from ENCODE 3 (ENCFF728IYI) Regulation 
encTfChipPkENCFF535DHF testis POLR2A Transcription Factor ChIP-seq Peaks of POLR2A in testis from ENCODE 3 (ENCFF535DHF) Regulation 
encTfChipPkENCFF046VTZ testis EP300 Transcription Factor ChIP-seq Peaks of EP300 in testis from ENCODE 3 (ENCFF046VTZ) Regulation 
encTfChipPkENCFF644JKD testis CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in testis from ENCODE 3 (ENCFF644JKD) Regulation 
encTfChipPkENCFF788RFY testis CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in testis from ENCODE 3 (ENCFF788RFY) Regulation 
encTfChipPkENCFF480OTT sprpSkin POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in suprapubic_skin from ENCODE 3 (ENCFF480OTT) Regulation 
encTfChipPkENCFF401DJJ sprpSkin POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in suprapubic_skin from ENCODE 3 (ENCFF401DJJ) Regulation 
encTfChipPkENCFF266KJH suprpSkin EP300 2 Transcription Factor ChIP-seq Peaks of EP300 in suprapubic_skin from ENCODE 3 (ENCFF266KJH) Regulation 
encTfChipPkENCFF104UOC suprpSkin EP300 1 Transcription Factor ChIP-seq Peaks of EP300 in suprapubic_skin from ENCODE 3 (ENCFF104UOC) Regulation 
encTfChipPkENCFF079BIZ suprpSkin EP300 Transcription Factor ChIP-seq Peaks of EP300 in suprapubic_skin from ENCODE 3 (ENCFF079BIZ) Regulation 
encTfChipPkENCFF783HDF suprpbSkin EP300 4 Transcription Factor ChIP-seq Peaks of EP300 in suprapubic_skin from ENCODE 3 (ENCFF783HDF) Regulation 
encTfChipPkENCFF102XCU suprpbSkin CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in suprapubic_skin from ENCODE 3 (ENCFF102XCU) Regulation 
encTfChipPkENCFF687WWO suprpbSkin CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in suprapubic_skin from ENCODE 3 (ENCFF687WWO) Regulation 
encTfChipPkENCFF085MWN subcutAdp EP300 4 Transcription Factor ChIP-seq Peaks of EP300 in subcutaneous_adipose_tissue from ENCODE 3 (ENCFF085MWN) Regulation 
encTfChipPkENCFF434OJH subcutAdp EP300 3 Transcription Factor ChIP-seq Peaks of EP300 in subcutaneous_adipose_tissue from ENCODE 3 (ENCFF434OJH) Regulation 
encTfChipPkENCFF191VCL subcutAdp EP300 2 Transcription Factor ChIP-seq Peaks of EP300 in subcutaneous_adipose_tissue from ENCODE 3 (ENCFF191VCL) Regulation 
encTfChipPkENCFF042DNR subcutAdp EP300 1 Transcription Factor ChIP-seq Peaks of EP300 in subcutaneous_adipose_tissue from ENCODE 3 (ENCFF042DNR) Regulation 
encTfChipPkENCFF688KFE subcutAdip CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in subcutaneous_adipose_tissue from ENCODE 3 (ENCFF688KFE) Regulation 
encTfChipPkENCFF719VDM subcutAdip CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in subcutaneous_adipose_tissue from ENCODE 3 (ENCFF719VDM) Regulation 
encTfChipPkENCFF280GHS stomach POLR2A 4 Transcription Factor ChIP-seq Peaks of POLR2A in stomach from ENCODE 3 (ENCFF280GHS) Regulation 
encTfChipPkENCFF905CUU stomach POLR2A 3 Transcription Factor ChIP-seq Peaks of POLR2A in stomach from ENCODE 3 (ENCFF905CUU) Regulation 
encTfChipPkENCFF880FUR stomach POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in stomach from ENCODE 3 (ENCFF880FUR) Regulation 
encTfChipPkENCFF827SHP stomach POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in stomach from ENCODE 3 (ENCFF827SHP) Regulation 
encTfChipPkENCFF469SGL stomach EP300 3 Transcription Factor ChIP-seq Peaks of EP300 in stomach from ENCODE 3 (ENCFF469SGL) Regulation 
encTfChipPkENCFF904COM stomach EP300 2 Transcription Factor ChIP-seq Peaks of EP300 in stomach from ENCODE 3 (ENCFF904COM) Regulation 
encTfChipPkENCFF856BRS stomach EP300 1 Transcription Factor ChIP-seq Peaks of EP300 in stomach from ENCODE 3 (ENCFF856BRS) Regulation 
encTfChipPkENCFF831BFL stomach CTCF 4 Transcription Factor ChIP-seq Peaks of CTCF in stomach from ENCODE 3 (ENCFF831BFL) Regulation 
encTfChipPkENCFF220VAH stomach CTCF 3 Transcription Factor ChIP-seq Peaks of CTCF in stomach from ENCODE 3 (ENCFF220VAH) Regulation 
encTfChipPkENCFF825XAC stomach CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in stomach from ENCODE 3 (ENCFF825XAC) Regulation 
encTfChipPkENCFF481CNC stomach CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in stomach from ENCODE 3 (ENCFF481CNC) Regulation 
encTfChipPkENCFF379SGB spleen POLR2A 4 Transcription Factor ChIP-seq Peaks of POLR2A in spleen from ENCODE 3 (ENCFF379SGB) Regulation 
encTfChipPkENCFF323FPP spleen POLR2A 3 Transcription Factor ChIP-seq Peaks of POLR2A in spleen from ENCODE 3 (ENCFF323FPP) Regulation 
encTfChipPkENCFF128AIK spleen POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in spleen from ENCODE 3 (ENCFF128AIK) Regulation 
encTfChipPkENCFF290BOD spleen POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in spleen from ENCODE 3 (ENCFF290BOD) Regulation 
encTfChipPkENCFF068YLN spleen CTCF 5 Transcription Factor ChIP-seq Peaks of CTCF in spleen from ENCODE 3 (ENCFF068YLN) Regulation 
encTfChipPkENCFF340BQM spleen CTCF 4 Transcription Factor ChIP-seq Peaks of CTCF in spleen from ENCODE 3 (ENCFF340BQM) Regulation 
encTfChipPkENCFF248QUD spleen CTCF 3 Transcription Factor ChIP-seq Peaks of CTCF in spleen from ENCODE 3 (ENCFF248QUD) Regulation 
encTfChipPkENCFF234VTM spleen CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in spleen from ENCODE 3 (ENCFF234VTM) Regulation 
encTfChipPkENCFF540DVR spleen CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in spleen from ENCODE 3 (ENCFF540DVR) Regulation 
encTfChipPkENCFF141MTA smoothMuscle CTCF Transcription Factor ChIP-seq Peaks of CTCF in smooth_muscle_cell from ENCODE 3 (ENCFF141MTA) Regulation 
encTfChipPkENCFF928ZSB sigmdCln POLR2A 5 Transcription Factor ChIP-seq Peaks of POLR2A in sigmoid_colon from ENCODE 3 (ENCFF928ZSB) Regulation 
encTfChipPkENCFF191BTJ sigmdCln POLR2A 4 Transcription Factor ChIP-seq Peaks of POLR2A in sigmoid_colon from ENCODE 3 (ENCFF191BTJ) Regulation 
encTfChipPkENCFF680JEG sigmdCln POLR2A 3 Transcription Factor ChIP-seq Peaks of POLR2A in sigmoid_colon from ENCODE 3 (ENCFF680JEG) Regulation 
encTfChipPkENCFF182ETN sigmdCln POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in sigmoid_colon from ENCODE 3 (ENCFF182ETN) Regulation 
encTfChipPkENCFF328BTO sigmdCln POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in sigmoid_colon from ENCODE 3 (ENCFF328BTO) Regulation 
encTfChipPkENCFF091KSY sigmdCln EP300 4 Transcription Factor ChIP-seq Peaks of EP300 in sigmoid_colon from ENCODE 3 (ENCFF091KSY) Regulation 
encTfChipPkENCFF169FFA sigmdCln EP300 3 Transcription Factor ChIP-seq Peaks of EP300 in sigmoid_colon from ENCODE 3 (ENCFF169FFA) Regulation 
encTfChipPkENCFF231LOU sigmdCln EP300 2 Transcription Factor ChIP-seq Peaks of EP300 in sigmoid_colon from ENCODE 3 (ENCFF231LOU) Regulation 
encTfChipPkENCFF616YFR sigmdCln EP300 1 Transcription Factor ChIP-seq Peaks of EP300 in sigmoid_colon from ENCODE 3 (ENCFF616YFR) Regulation 
encTfChipPkENCFF615AFS sigmdColon CTCF 4 Transcription Factor ChIP-seq Peaks of CTCF in sigmoid_colon from ENCODE 3 (ENCFF615AFS) Regulation 
encTfChipPkENCFF782FTD sigmdColon CTCF 3 Transcription Factor ChIP-seq Peaks of CTCF in sigmoid_colon from ENCODE 3 (ENCFF782FTD) Regulation 
encTfChipPkENCFF668SIT sigmdColon CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in sigmoid_colon from ENCODE 3 (ENCFF668SIT) Regulation 
encTfChipPkENCFF070ILT sigmdColon CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in sigmoid_colon from ENCODE 3 (ENCFF070ILT) Regulation 
encTfChipPkENCFF113NNM liverRLobe CTCF 1 Transcription Factor ChIP-seq Peaks of POLR2A in right_lobe_of_liver from ENCODE 3 (ENCFF113NNM) Regulation 
encTfChipPkENCFF136LAP liverRLobe CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in right_lobe_of_liver from ENCODE 3 (ENCFF136LAP) Regulation 
encTfChipPkENCFF409DTL retinPgmtEpi CTCF Transcription Factor ChIP-seq Peaks of CTCF in retinal_pigment_epithelial_cell from ENCODE 3 (ENCFF409DTL) Regulation 
encTfChipPkENCFF674MDG prostate POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in prostate_gland from ENCODE 3 (ENCFF674MDG) Regulation 
encTfChipPkENCFF160SYU prostate POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in prostate_gland from ENCODE 3 (ENCFF160SYU) Regulation 
encTfChipPkENCFF341UHT prostate CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in prostate_gland from ENCODE 3 (ENCFF341UHT) Regulation 
encTfChipPkENCFF142JXX prostate CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in prostate_gland from ENCODE 3 (ENCFF142JXX) Regulation 
encTfChipPkENCFF016APK ovary POLR2A Transcription Factor ChIP-seq Peaks of POLR2A in ovary from ENCODE 3 (ENCFF016APK) Regulation 
encTfChipPkENCFF353CLB ovary EP300 2 Transcription Factor ChIP-seq Peaks of EP300 in ovary from ENCODE 3 (ENCFF353CLB) Regulation 
encTfChipPkENCFF970XBE ovary EP300 1 Transcription Factor ChIP-seq Peaks of EP300 in ovary from ENCODE 3 (ENCFF970XBE) Regulation 
encTfChipPkENCFF886WWT ovary CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in ovary from ENCODE 3 (ENCFF886WWT) Regulation 
encTfChipPkENCFF006YGI ovary CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in ovary from ENCODE 3 (ENCFF006YGI) Regulation 
encTfChipPkENCFF454DZP omntalFat EP300 4 Transcription Factor ChIP-seq Peaks of EP300 in omental_fat_pad from ENCODE 3 (ENCFF454DZP) Regulation 
encTfChipPkENCFF102IIP omntalFat EP300 3 Transcription Factor ChIP-seq Peaks of EP300 in omental_fat_pad from ENCODE 3 (ENCFF102IIP) Regulation 
encTfChipPkENCFF895RTD omntalFat EP300 2 Transcription Factor ChIP-seq Peaks of EP300 in omental_fat_pad from ENCODE 3 (ENCFF895RTD) Regulation 
encTfChipPkENCFF199FCD omntalFat EP300 1 Transcription Factor ChIP-seq Peaks of EP300 in omental_fat_pad from ENCODE 3 (ENCFF199FCD) Regulation 
encTfChipPkENCFF157OEN omentalFat CTCF 3 Transcription Factor ChIP-seq Peaks of CTCF in omental_fat_pad from ENCODE 3 (ENCFF157OEN) Regulation 
encTfChipPkENCFF399NTP omentalFat CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in omental_fat_pad from ENCODE 3 (ENCFF399NTP) Regulation 
encTfChipPkENCFF668UDC omentalFat CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in omental_fat_pad from ENCODE 3 (ENCFF668UDC) Regulation 
encTfChipPkENCFF122IMV neutrophil CTCF Transcription Factor ChIP-seq Peaks of CTCF in neutrophil from ENCODE 3 (ENCFF122IMV) Regulation 
encTfChipPkENCFF295HQJ neurlProgntr EZH2 Transcription Factor ChIP-seq Peaks of EZH2 in neural_progenitor_cell from ENCODE 3 (ENCFF295HQJ) Regulation 
encTfChipPkENCFF560GGY neurlProgntr CTCF Transcription Factor ChIP-seq Peaks of CTCF in neural_progenitor_cell from ENCODE 3 (ENCFF560GGY) Regulation 
encTfChipPkENCFF944KJO neuralCell SMC3 Transcription Factor ChIP-seq Peaks of SMC3 in neural_cell from ENCODE 3 (ENCFF944KJO) Regulation 
encTfChipPkENCFF454TRL neuralCell RAD21 Transcription Factor ChIP-seq Peaks of RAD21 in neural_cell from ENCODE 3 (ENCFF454TRL) Regulation 
encTfChipPkENCFF255WJM neuralCell MXI1 Transcription Factor ChIP-seq Peaks of MXI1 in neural_cell from ENCODE 3 (ENCFF255WJM) Regulation 
encTfChipPkENCFF108BSU neuralCell EZH2 Transcription Factor ChIP-seq Peaks of EZH2 in neural_cell from ENCODE 3 (ENCFF108BSU) Regulation 
encTfChipPkENCFF459ARL neuralCell EP300 Transcription Factor ChIP-seq Peaks of EP300 in neural_cell from ENCODE 3 (ENCFF459ARL) Regulation 
encTfChipPkENCFF372JOV neuralCell CTCF Transcription Factor ChIP-seq Peaks of CTCF in neural_cell from ENCODE 3 (ENCFF372JOV) Regulation 
encTfChipPkENCFF719TNH myotube CTCF Transcription Factor ChIP-seq Peaks of CTCF in myotube from ENCODE 3 (ENCFF719TNH) Regulation 
encTfChipPkENCFF845NAG medlblastoma CTCF Transcription Factor ChIP-seq Peaks of CTCF in medulloblastoma from ENCODE 3 (ENCFF845NAG) Regulation 
encTfChipPkENCFF493HJH mammaryEpith CTCF Transcription Factor ChIP-seq Peaks of CTCF in mammary_epithelial_cell from ENCODE 3 (ENCFF493HJH) Regulation 
encTfChipPkENCFF072MPX lwrLgSkn POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in lower_leg_skin from ENCODE 3 (ENCFF072MPX) Regulation 
encTfChipPkENCFF818GNJ lwrLgSkn POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in lower_leg_skin from ENCODE 3 (ENCFF818GNJ) Regulation 
encTfChipPkENCFF916FGF lwrLegSkin CTCF 4 Transcription Factor ChIP-seq Peaks of CTCF in lower_leg_skin from ENCODE 3 (ENCFF916FGF) Regulation 
encTfChipPkENCFF992DNN lwrLegSkin CTCF 3 Transcription Factor ChIP-seq Peaks of CTCF in lower_leg_skin from ENCODE 3 (ENCFF992DNN) Regulation 
encTfChipPkENCFF846VQK lwrLegSkin CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in lower_leg_skin from ENCODE 3 (ENCFF846VQK) Regulation 
encTfChipPkENCFF912XIE lwrLegSkin CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in lower_leg_skin from ENCODE 3 (ENCFF912XIE) Regulation 
encTfChipPkENCFF727ZIT liver ZBTB33 2 Transcription Factor ChIP-seq Peaks of ZBTB33 in liver from ENCODE 3 (ENCFF727ZIT) Regulation 
encTfChipPkENCFF882UHR liver ZBTB33 1 Transcription Factor ChIP-seq Peaks of ZBTB33 in liver from ENCODE 3 (ENCFF882UHR) Regulation 
encTfChipPkENCFF459TWF liver YY1 2 Transcription Factor ChIP-seq Peaks of YY1 in liver from ENCODE 3 (ENCFF459TWF) Regulation 
encTfChipPkENCFF838VFX liver YY1 1 Transcription Factor ChIP-seq Peaks of YY1 in liver from ENCODE 3 (ENCFF838VFX) Regulation 
encTfChipPkENCFF214OJW liver TAF1 Transcription Factor ChIP-seq Peaks of TAF1 in liver from ENCODE 3 (ENCFF214OJW) Regulation 
encTfChipPkENCFF978TMH liver SP1 2 Transcription Factor ChIP-seq Peaks of SP1 in liver from ENCODE 3 (ENCFF978TMH) Regulation 
encTfChipPkENCFF433EFF liver SP1 1 Transcription Factor ChIP-seq Peaks of SP1 in liver from ENCODE 3 (ENCFF433EFF) Regulation 
encTfChipPkENCFF572MCI liver RXRA 2 Transcription Factor ChIP-seq Peaks of RXRA in liver from ENCODE 3 (ENCFF572MCI) Regulation 
encTfChipPkENCFF201KGJ liver RXRA 1 Transcription Factor ChIP-seq Peaks of RXRA in liver from ENCODE 3 (ENCFF201KGJ) Regulation 
encTfChipPkENCFF288XHG liver REST 2 Transcription Factor ChIP-seq Peaks of REST in liver from ENCODE 3 (ENCFF288XHG) Regulation 
encTfChipPkENCFF178WRO liver REST 1 Transcription Factor ChIP-seq Peaks of REST in liver from ENCODE 3 (ENCFF178WRO) Regulation 
encTfChipPkENCFF315BSV liver RAD21 3 Transcription Factor ChIP-seq Peaks of RAD21 in liver from ENCODE 3 (ENCFF315BSV) Regulation 
encTfChipPkENCFF295GOD liver RAD21 2 Transcription Factor ChIP-seq Peaks of RAD21 in liver from ENCODE 3 (ENCFF295GOD) Regulation 
encTfChipPkENCFF229WFR liver RAD21 1 Transcription Factor ChIP-seq Peaks of RAD21 in liver from ENCODE 3 (ENCFF229WFR) Regulation 
encTfChipPkENCFF819WNB liver NR2F2 2 Transcription Factor ChIP-seq Peaks of NR2F2 in liver from ENCODE 3 (ENCFF819WNB) Regulation 
encTfChipPkENCFF379TVQ liver NR2F2 1 Transcription Factor ChIP-seq Peaks of NR2F2 in liver from ENCODE 3 (ENCFF379TVQ) Regulation 
encTfChipPkENCFF669BQN liver MAX 2 Transcription Factor ChIP-seq Peaks of MAX in liver from ENCODE 3 (ENCFF669BQN) Regulation 
encTfChipPkENCFF493ZMX liver MAX 1 Transcription Factor ChIP-seq Peaks of MAX in liver from ENCODE 3 (ENCFF493ZMX) Regulation 
encTfChipPkENCFF229COM liver JUND 2 Transcription Factor ChIP-seq Peaks of JUND in liver from ENCODE 3 (ENCFF229COM) Regulation 
encTfChipPkENCFF420PED liver JUND 1 Transcription Factor ChIP-seq Peaks of JUND in liver from ENCODE 3 (ENCFF420PED) Regulation 
encTfChipPkENCFF497MUF liver HNF4G Transcription Factor ChIP-seq Peaks of HNF4G in liver from ENCODE 3 (ENCFF497MUF) Regulation 
encTfChipPkENCFF905JAC liver HNF4A 2 Transcription Factor ChIP-seq Peaks of HNF4A in liver from ENCODE 3 (ENCFF905JAC) Regulation 
encTfChipPkENCFF837QHJ liver HNF4A 1 Transcription Factor ChIP-seq Peaks of HNF4A in liver from ENCODE 3 (ENCFF837QHJ) Regulation 
encTfChipPkENCFF280YAF liver GABPA 2 Transcription Factor ChIP-seq Peaks of GABPA in liver from ENCODE 3 (ENCFF280YAF) Regulation 
encTfChipPkENCFF344XWK liver GABPA 1 Transcription Factor ChIP-seq Peaks of GABPA in liver from ENCODE 3 (ENCFF344XWK) Regulation 
encTfChipPkENCFF293LRQ liver FOXA2 2 Transcription Factor ChIP-seq Peaks of FOXA2 in liver from ENCODE 3 (ENCFF293LRQ) Regulation 
encTfChipPkENCFF168JLI liver FOXA2 1 Transcription Factor ChIP-seq Peaks of FOXA2 in liver from ENCODE 3 (ENCFF168JLI) Regulation 
encTfChipPkENCFF324QGE liver FOXA1 2 Transcription Factor ChIP-seq Peaks of FOXA1 in liver from ENCODE 3 (ENCFF324QGE) Regulation 
encTfChipPkENCFF951VPZ liver FOXA1 1 Transcription Factor ChIP-seq Peaks of FOXA1 in liver from ENCODE 3 (ENCFF951VPZ) Regulation 
encTfChipPkENCFF617JQS liver EGR1 2 Transcription Factor ChIP-seq Peaks of EGR1 in liver from ENCODE 3 (ENCFF617JQS) Regulation 
encTfChipPkENCFF808WST liver EGR1 1 Transcription Factor ChIP-seq Peaks of EGR1 in liver from ENCODE 3 (ENCFF808WST) Regulation 
encTfChipPkENCFF143HEE liver CTCF Transcription Factor ChIP-seq Peaks of CTCF in liver from ENCODE 3 (ENCFF143HEE) Regulation 
encTfChipPkENCFF146URA liver ATF3 2 Transcription Factor ChIP-seq Peaks of ATF3 in liver from ENCODE 3 (ENCFF146URA) Regulation 
encTfChipPkENCFF782SGI liver ATF3 1 Transcription Factor ChIP-seq Peaks of ATF3 in liver from ENCODE 3 (ENCFF782SGI) Regulation 
encTfChipPkENCFF674KUN kidneyEpith CTCF Transcription Factor ChIP-seq Peaks of CTCF in kidney_epithelial_cell from ENCODE 3 (ENCFF674KUN) Regulation 
encTfChipPkENCFF028IIR keratinocyte CTCF Transcription Factor ChIP-seq Peaks of CTCF in keratinocyte from ENCODE 3 (ENCFF028IIR) Regulation 
encTfChipPkENCFF324UNA hepatocyte EZH2 Transcription Factor ChIP-seq Peaks of EZH2 in hepatocyte from ENCODE 3 (ENCFF324UNA) Regulation 
encTfChipPkENCFF846FYU hepatocyte CTCF Transcription Factor ChIP-seq Peaks of CTCF in hepatocyte from ENCODE 3 (ENCFF846FYU) Regulation 
encTfChipPkENCFF226GKH hrtLfVnt POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in heart_left_ventricle from ENCODE 3 (ENCFF226GKH) Regulation 
encTfChipPkENCFF156SPI hrtLfVnt POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in heart_left_ventricle from ENCODE 3 (ENCFF156SPI) Regulation 
encTfChipPkENCFF552XDP heartLftVent CTCF Transcription Factor ChIP-seq Peaks of CTCF in heart_left_ventricle from ENCODE 3 (ENCFF552XDP) Regulation 
encTfChipPkENCFF530FGP gEsphSph POLR2A 3 Transcription Factor ChIP-seq Peaks of POLR2A in gastroesophageal_sphincter from ENCODE 3 (ENCFF530FGP) Regulation 
encTfChipPkENCFF128UUT gEsphSph POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in gastroesophageal_sphincter from ENCODE 3 (ENCFF128UUT) Regulation 
encTfChipPkENCFF835VAP gEsphSph POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in gastroesophageal_sphincter from ENCODE 3 (ENCFF835VAP) Regulation 
encTfChipPkENCFF291RDN gsEsphSph EP300 3 Transcription Factor ChIP-seq Peaks of EP300 in gastroesophageal_sphincter from ENCODE 3 (ENCFF291RDN) Regulation 
encTfChipPkENCFF481USU gsEsphSph EP300 2 Transcription Factor ChIP-seq Peaks of EP300 in gastroesophageal_sphincter from ENCODE 3 (ENCFF481USU) Regulation 
encTfChipPkENCFF992XPI gsEsphSph EP300 1 Transcription Factor ChIP-seq Peaks of EP300 in gastroesophageal_sphincter from ENCODE 3 (ENCFF992XPI) Regulation 
encTfChipPkENCFF951SRP gstEsphSph CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in gastroesophageal_sphincter from ENCODE 3 (ENCFF951SRP) Regulation 
encTfChipPkENCFF973KKY gstEsphSph CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in gastroesophageal_sphincter from ENCODE 3 (ENCFF973KKY) Regulation 
encTfChipPkENCFF227YCI gstrcMed POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in gastrocnemius_medialis from ENCODE 3 (ENCFF227YCI) Regulation 
encTfChipPkENCFF089XKW gstrcMed POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in gastrocnemius_medialis from ENCODE 3 (ENCFF089XKW) Regulation 
encTfChipPkENCFF100SKI gastrocMed CTCF 3 Transcription Factor ChIP-seq Peaks of CTCF in gastrocnemius_medialis from ENCODE 3 (ENCFF100SKI) Regulation 
encTfChipPkENCFF016OGE gastrocMed CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in gastrocnemius_medialis from ENCODE 3 (ENCFF016OGE) Regulation 
encTfChipPkENCFF281XHU gastrocMed CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in gastrocnemius_medialis from ENCODE 3 (ENCFF281XHU) Regulation 
encTfChipPkENCFF060WTK frskinKrtn CTCF 3 Transcription Factor ChIP-seq Peaks of CTCF in foreskin_keratinocyte from ENCODE 3 (ENCFF060WTK) Regulation 
encTfChipPkENCFF236RJT frskinKrtn CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in foreskin_keratinocyte from ENCODE 3 (ENCFF236RJT) Regulation 
encTfChipPkENCFF349RNE frskinKrtn CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in foreskin_keratinocyte from ENCODE 3 (ENCFF349RNE) Regulation 
encTfChipPkENCFF273NIW frsknFibro CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in foreskin_fibroblast from ENCODE 3 (ENCFF273NIW) Regulation 
encTfChipPkENCFF178FRI frsknFibro CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in foreskin_fibroblast from ENCODE 3 (ENCFF178FRI) Regulation 
encTfChipPkENCFF032BJW vlMesenFibro CTCF Transcription Factor ChIP-seq Peaks of CTCF in fibroblast_of_villous_mesenchyme from ENCODE 3 (ENCFF032BJW) Regulation 
encTfChipPkENCFF322FBH aortaAdFibro CTCF Transcription Factor ChIP-seq Peaks of CTCF in fibroblast_of_the_aortic_adventitia from ENCODE 3 (ENCFF322FBH) Regulation 
encTfChipPkENCFF093QTY plArtryFibro CTCF Transcription Factor ChIP-seq Peaks of CTCF in fibroblast_of_pulmonary_artery from ENCODE 3 (ENCFF093QTY) Regulation 
encTfChipPkENCFF196CRQ mamryGlFibro CTCF Transcription Factor ChIP-seq Peaks of CTCF in fibroblast_of_mammary_gland from ENCODE 3 (ENCFF196CRQ) Regulation 
encTfChipPkENCFF218LOB lungFibro CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in fibroblast_of_lung from ENCODE 3 (ENCFF218LOB) Regulation 
encTfChipPkENCFF777ODE lungFibro CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in fibroblast_of_lung from ENCODE 3 (ENCFF777ODE) Regulation 
encTfChipPkENCFF930NQQ esphSqEp POLR2A 4 Transcription Factor ChIP-seq Peaks of POLR2A in esophagus_squamous_epithelium from ENCODE 3 (ENCFF930NQQ) Regulation 
encTfChipPkENCFF691ARB esphSqEp POLR2A 3 Transcription Factor ChIP-seq Peaks of POLR2A in esophagus_squamous_epithelium from ENCODE 3 (ENCFF691ARB) Regulation 
encTfChipPkENCFF542QLV esphSqEp POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in esophagus_squamous_epithelium from ENCODE 3 (ENCFF542QLV) Regulation 
encTfChipPkENCFF157FXA esphSqEp POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in esophagus_squamous_epithelium from ENCODE 3 (ENCFF157FXA) Regulation 
encTfChipPkENCFF505VMB esphSquEpi CTCF 4 Transcription Factor ChIP-seq Peaks of CTCF in esophagus_squamous_epithelium from ENCODE 3 (ENCFF505VMB) Regulation 
encTfChipPkENCFF661IIS esphSquEpi CTCF 3 Transcription Factor ChIP-seq Peaks of CTCF in esophagus_squamous_epithelium from ENCODE 3 (ENCFF661IIS) Regulation 
encTfChipPkENCFF350AMQ esphSquEpi CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in esophagus_squamous_epithelium from ENCODE 3 (ENCFF350AMQ) Regulation 
encTfChipPkENCFF898JJD esphSquEpi CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in esophagus_squamous_epithelium from ENCODE 3 (ENCFF898JJD) Regulation 
encTfChipPkENCFF906CSG esophMscMc POLR2A Transcription Factor ChIP-seq Peaks of POLR2A in esophagus_muscularis_mucosa from ENCODE 3 (ENCFF906CSG) Regulation 
encTfChipPkENCFF087RBS esphMscMc EP300 4 Transcription Factor ChIP-seq Peaks of EP300 in esophagus_muscularis_mucosa from ENCODE 3 (ENCFF087RBS) Regulation 
encTfChipPkENCFF287SLI esphMscMc EP300 3 Transcription Factor ChIP-seq Peaks of EP300 in esophagus_muscularis_mucosa from ENCODE 3 (ENCFF287SLI) Regulation 
encTfChipPkENCFF261OWX esphMscMc EP300 2 Transcription Factor ChIP-seq Peaks of EP300 in esophagus_muscularis_mucosa from ENCODE 3 (ENCFF261OWX) Regulation 
encTfChipPkENCFF081YBG esphMscMc EP300 1 Transcription Factor ChIP-seq Peaks of EP300 in esophagus_muscularis_mucosa from ENCODE 3 (ENCFF081YBG) Regulation 
encTfChipPkENCFF725FJK esphMscMuc CTCF 3 Transcription Factor ChIP-seq Peaks of CTCF in esophagus_muscularis_mucosa from ENCODE 3 (ENCFF725FJK) Regulation 
encTfChipPkENCFF373DVN esphMscMuc CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in esophagus_muscularis_mucosa from ENCODE 3 (ENCFF373DVN) Regulation 
encTfChipPkENCFF897UFD esphMscMuc CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in esophagus_muscularis_mucosa from ENCODE 3 (ENCFF897UFD) Regulation 
encTfChipPkENCFF180BYN erythblst GATA1 2 Transcription Factor ChIP-seq Peaks of GATA1 in erythroblast from ENCODE 3 (ENCFF180BYN) Regulation 
encTfChipPkENCFF789ZAT erythblst GATA1 1 Transcription Factor ChIP-seq Peaks of GATA1 in erythroblast from ENCODE 3 (ENCFF789ZAT) Regulation 
encTfChipPkENCFF712LFQ prostateEpi CTCF Transcription Factor ChIP-seq Peaks of CTCF in epithelial_cell_of_prostate from ENCODE 3 (ENCFF712LFQ) Regulation 
encTfChipPkENCFF796AAX esophagEpi CTCF Transcription Factor ChIP-seq Peaks of CTCF in epithelial_cell_of_esophagus from ENCODE 3 (ENCFF796AAX) Regulation 
encTfChipPkENCFF387VGY umbilVein POLR2A Transcription Factor ChIP-seq Peaks of POLR2A in endothelial_cell_of_umbilical_vein from ENCODE 3 (ENCFF387VGY) Regulation 
encTfChipPkENCFF987YIJ umbilVein GATA2 Transcription Factor ChIP-seq Peaks of GATA2 in endothelial_cell_of_umbilical_vein from ENCODE 3 (ENCFF987YIJ) Regulation 
encTfChipPkENCFF327GZX umbilVeinEndo FOS Transcription Factor ChIP-seq Peaks of FOS in endothelial_cell_of_umbilical_vein from ENCODE 3 (ENCFF327GZX) Regulation 
encTfChipPkENCFF522JCV umbilVenEndo CTCF Transcription Factor ChIP-seq Peaks of CTCF in endothelial_cell_of_umbilical_vein from ENCODE 3 (ENCFF522JCV) Regulation 
encTfChipPkENCFF136ZAK chorPlexEpi CTCF Transcription Factor ChIP-seq Peaks of CTCF in choroid_plexus_epithelial_cell from ENCODE 3 (ENCFF136ZAK) Regulation 
encTfChipPkENCFF863ZIN heartMuscl CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in cardiac_muscle_cell from ENCODE 3 (ENCFF863ZIN) Regulation 
encTfChipPkENCFF301YXM heartMuscl CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in cardiac_muscle_cell from ENCODE 3 (ENCFF301YXM) Regulation 
encTfChipPkENCFF243AGG heartFibro CTCF Transcription Factor ChIP-seq Peaks of CTCF in cardiac_fibroblast from ENCODE 3 (ENCFF243AGG) Regulation 
encTfChipPkENCFF607YLT brestEpi POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in breast_epithelium from ENCODE 3 (ENCFF607YLT) Regulation 
encTfChipPkENCFF294TAI brestEpi POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in breast_epithelium from ENCODE 3 (ENCFF294TAI) Regulation 
encTfChipPkENCFF906VTL breastEpi EP300 4 Transcription Factor ChIP-seq Peaks of EP300 in breast_epithelium from ENCODE 3 (ENCFF906VTL) Regulation 
encTfChipPkENCFF614VFU breastEpi EP300 3 Transcription Factor ChIP-seq Peaks of EP300 in breast_epithelium from ENCODE 3 (ENCFF614VFU) Regulation 
encTfChipPkENCFF757KZD breastEpi EP300 2 Transcription Factor ChIP-seq Peaks of EP300 in breast_epithelium from ENCODE 3 (ENCFF757KZD) Regulation 
encTfChipPkENCFF978RPI breastEpi EP300 1 Transcription Factor ChIP-seq Peaks of EP300 in breast_epithelium from ENCODE 3 (ENCFF978RPI) Regulation 
encTfChipPkENCFF113XGW breastEpi CTCF 3 Transcription Factor ChIP-seq Peaks of CTCF in breast_epithelium from ENCODE 3 (ENCFF113XGW) Regulation 
encTfChipPkENCFF167SCX breastEpi CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in breast_epithelium from ENCODE 3 (ENCFF167SCX) Regulation 
encTfChipPkENCFF338TGS breastEpi CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in breast_epithelium from ENCODE 3 (ENCFF338TGS) Regulation 
encTfChipPkENCFF427RYJ brainMicEndo CTCF Transcription Factor ChIP-seq Peaks of CTCF in brain_microvascular_endothelial_cell from ENCODE 3 (ENCFF427RYJ) Regulation 
encTfChipPkENCFF371GSC pancreas POLR2A 4 Transcription Factor ChIP-seq Peaks of POLR2A in body_of_pancreas from ENCODE 3 (ENCFF371GSC) Regulation 
encTfChipPkENCFF296AFJ pancreas POLR2A 3 Transcription Factor ChIP-seq Peaks of POLR2A in body_of_pancreas from ENCODE 3 (ENCFF296AFJ) Regulation 
encTfChipPkENCFF306CZZ pancreas POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in body_of_pancreas from ENCODE 3 (ENCFF306CZZ) Regulation 
encTfChipPkENCFF389ULP pancreas POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in body_of_pancreas from ENCODE 3 (ENCFF389ULP) Regulation 
encTfChipPkENCFF900GKE pancreas CTCF 4 Transcription Factor ChIP-seq Peaks of CTCF in body_of_pancreas from ENCODE 3 (ENCFF900GKE) Regulation 
encTfChipPkENCFF153EBU pancreas CTCF 3 Transcription Factor ChIP-seq Peaks of CTCF in body_of_pancreas from ENCODE 3 (ENCFF153EBU) Regulation 
encTfChipPkENCFF610UCL pancreas CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in body_of_pancreas from ENCODE 3 (ENCFF610UCL) Regulation 
encTfChipPkENCFF872XQU pancreas CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in body_of_pancreas from ENCODE 3 (ENCFF872XQU) Regulation 
encTfChipPkENCFF984VPB biplNeuron ZEB1 Transcription Factor ChIP-seq Peaks of ZEB1 in bipolar_neuron from ENCODE 3 (ENCFF984VPB) Regulation 
encTfChipPkENCFF482JUI bipNeuron SMARCA4 Transcription Factor ChIP-seq Peaks of SMARCA4 in bipolar_neuron from ENCODE 3 (ENCFF482JUI) Regulation 
encTfChipPkENCFF203ZIS biplNeuron CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in bipolar_neuron from ENCODE 3 (ENCFF203ZIS) Regulation 
encTfChipPkENCFF904CNB biplNeuron CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in bipolar_neuron from ENCODE 3 (ENCFF904CNB) Regulation 
encTfChipPkENCFF600CYD spinlAstrcyt CTCF Transcription Factor ChIP-seq Peaks of CTCF in astrocyte_of_the_spinal_cord from ENCODE 3 (ENCFF600CYD) Regulation 
encTfChipPkENCFF515KNI cerebAstrcyt CTCF Transcription Factor ChIP-seq Peaks of CTCF in astrocyte_of_the_cerebellum from ENCODE 3 (ENCFF515KNI) Regulation 
encTfChipPkENCFF148BSH astrocyte CTCF Transcription Factor ChIP-seq Peaks of CTCF in astrocyte from ENCODE 3 (ENCFF148BSH) Regulation 
encTfChipPkENCFF374MIO ascendAorta CTCF Transcription Factor ChIP-seq Peaks of CTCF in ascending_aorta from ENCODE 3 (ENCFF374MIO) Regulation 
encTfChipPkENCFF967EOL adrnlGld POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in adrenal_gland from ENCODE 3 (ENCFF967EOL) Regulation 
encTfChipPkENCFF363GNR adrnlGld POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in adrenal_gland from ENCODE 3 (ENCFF363GNR) Regulation 
encTfChipPkENCFF412TMX adrenlGlnd CTCF 4 Transcription Factor ChIP-seq Peaks of CTCF in adrenal_gland from ENCODE 3 (ENCFF412TMX) Regulation 
encTfChipPkENCFF574FIL adrenlGlnd CTCF 3 Transcription Factor ChIP-seq Peaks of CTCF in adrenal_gland from ENCODE 3 (ENCFF574FIL) Regulation 
encTfChipPkENCFF174CEI adrenlGlnd CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in adrenal_gland from ENCODE 3 (ENCFF174CEI) Regulation 
encTfChipPkENCFF114FNT adrenlGlnd CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in adrenal_gland from ENCODE 3 (ENCFF114FNT) Regulation 
encTfChipPkENCFF447UZC 22Rv1 ZFX Transcription Factor ChIP-seq Peaks of ZFX in 22Rv1 from ENCODE 3 (ENCFF447UZC) Regulation 
encTfChipPkENCFF147YCW 22Rv1 CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in 22Rv1 from ENCODE 3 (ENCFF147YCW) Regulation 
encTfChipPkENCFF730MQM 22Rv1 CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in 22Rv1 from ENCODE 3 (ENCFF730MQM) Regulation 
encTfChipPkENCFF695MEK WI38 CTCF Transcription Factor ChIP-seq Peaks of CTCF in WI38 from ENCODE 3 (ENCFF695MEK) Regulation 
encTfChipPkENCFF262ZOT WERI-Rb-1 CTCF Transcription Factor ChIP-seq Peaks of CTCF in WERI-Rb-1 from ENCODE 3 (ENCFF262ZOT) Regulation 
encTfChipPkENCFF078XBU VCaP CTCF Transcription Factor ChIP-seq Peaks of CTCF in VCaP from ENCODE 3 (ENCFF078XBU) Regulation 
encTfChipPkENCFF946YUA T47D JUND Transcription Factor ChIP-seq Peaks of JUND in T47D from ENCODE 3 (ENCFF946YUA) Regulation 
encTfChipPkENCFF574HSR T47D GATA3 Transcription Factor ChIP-seq Peaks of GATA3 in T47D from ENCODE 3 (ENCFF574HSR) Regulation 
encTfChipPkENCFF420MLJ T47D FOXA1 Transcription Factor ChIP-seq Peaks of FOXA1 in T47D from ENCODE 3 (ENCFF420MLJ) Regulation 
encTfChipPkENCFF396TFS T47D ESR1 3 Transcription Factor ChIP-seq Peaks of ESR1 in T47D from ENCODE 3 (ENCFF396TFS) Regulation 
encTfChipPkENCFF637WCT T47D ESR1 2 Transcription Factor ChIP-seq Peaks of ESR1 in T47D from ENCODE 3 (ENCFF637WCT) Regulation 
encTfChipPkENCFF433NIE T47D ESR1 1 Transcription Factor ChIP-seq Peaks of ESR1 in T47D from ENCODE 3 (ENCFF433NIE) Regulation 
encTfChipPkENCFF938CRS SU-DHL-6 CTCF Transcription Factor ChIP-seq Peaks of CTCF in SU-DHL-6 from ENCODE 3 (ENCFF938CRS) Regulation 
encTfChipPkENCFF363UWP SK-N-SH YY1 Transcription Factor ChIP-seq Peaks of YY1 in SK-N-SH from ENCODE 3 (ENCFF363UWP) Regulation 
encTfChipPkENCFF261PAC SK-N-SH USF2 Transcription Factor ChIP-seq Peaks of USF2 in SK-N-SH from ENCODE 3 (ENCFF261PAC) Regulation 
encTfChipPkENCFF452RZW SK-N-SH USF1 Transcription Factor ChIP-seq Peaks of USF1 in SK-N-SH from ENCODE 3 (ENCFF452RZW) Regulation 
encTfChipPkENCFF423CTO SK-N-SH TAF1 Transcription Factor ChIP-seq Peaks of TAF1 in SK-N-SH from ENCODE 3 (ENCFF423CTO) Regulation 
encTfChipPkENCFF663RUS SK-N-SH SIN3A Transcription Factor ChIP-seq Peaks of SIN3A in SK-N-SH from ENCODE 3 (ENCFF663RUS) Regulation 
encTfChipPkENCFF502JJJ SK-N-SH RFX5 Transcription Factor ChIP-seq Peaks of RFX5 in SK-N-SH from ENCODE 3 (ENCFF502JJJ) Regulation 
encTfChipPkENCFF796YFZ SK-N-SH REST 2 Transcription Factor ChIP-seq Peaks of REST in SK-N-SH from ENCODE 3 (ENCFF796YFZ) Regulation 
encTfChipPkENCFF540FXB SK-N-SH REST 1 Transcription Factor ChIP-seq Peaks of REST in SK-N-SH from ENCODE 3 (ENCFF540FXB) Regulation 
encTfChipPkENCFF073ADA SK-N-SH RCOR1 Transcription Factor ChIP-seq Peaks of RCOR1 in SK-N-SH from ENCODE 3 (ENCFF073ADA) Regulation 
encTfChipPkENCFF557OCR SK-N-SH RAD21 Transcription Factor ChIP-seq Peaks of RAD21 in SK-N-SH from ENCODE 3 (ENCFF557OCR) Regulation 
encTfChipPkENCFF116RCK SK-N-SH MXI1 Transcription Factor ChIP-seq Peaks of MXI1 in SK-N-SH from ENCODE 3 (ENCFF116RCK) Regulation 
encTfChipPkENCFF187QQB SK-N-SH JUND 2 Transcription Factor ChIP-seq Peaks of JUND in SK-N-SH from ENCODE 3 (ENCFF187QQB) Regulation 
encTfChipPkENCFF246HKM SK-N-SH JUND 1 Transcription Factor ChIP-seq Peaks of JUND in SK-N-SH from ENCODE 3 (ENCFF246HKM) Regulation 
encTfChipPkENCFF917TPE SK-N-SH IRF3 Transcription Factor ChIP-seq Peaks of IRF3 in SK-N-SH from ENCODE 3 (ENCFF917TPE) Regulation 
encTfChipPkENCFF540DWT SK-N-SH CTCF 3 Transcription Factor ChIP-seq Peaks of CTCF in SK-N-SH from ENCODE 3 (ENCFF540DWT) Regulation 
encTfChipPkENCFF685KTA SK-N-SH CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in SK-N-SH from ENCODE 3 (ENCFF685KTA) Regulation 
encTfChipPkENCFF049UCF SK-N-SH CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in SK-N-SH from ENCODE 3 (ENCFF049UCF) Regulation 
encTfChipPkENCFF035WFT SK-N-MC EZH2 Transcription Factor ChIP-seq Peaks of EZH2 in SK-N-MC from ENCODE 3 (ENCFF035WFT) Regulation 
encTfChipPkENCFF626MUS SH-SY5Y GATA3 Transcription Factor ChIP-seq Peaks of GATA3 in SH-SY5Y from ENCODE 3 (ENCFF626MUS) Regulation 
encTfChipPkENCFF064YWN SH-SY5Y GATA2 Transcription Factor ChIP-seq Peaks of GATA2 in SH-SY5Y from ENCODE 3 (ENCFF064YWN) Regulation 
encTfChipPkENCFF798HCA Raji POLR2A Transcription Factor ChIP-seq Peaks of POLR2A in Raji from ENCODE 3 (ENCFF798HCA) Regulation 
encTfChipPkENCFF855KNL RWPE2 CTCF Transcription Factor ChIP-seq Peaks of CTCF in RWPE2 from ENCODE 3 (ENCFF855KNL) Regulation 
encTfChipPkENCFF273HTX RWPE1 CTCF Transcription Factor ChIP-seq Peaks of CTCF in RWPE1 from ENCODE 3 (ENCFF273HTX) Regulation 
encTfChipPkENCFF563GSK PeyrPtch POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in Peyer's_patch from ENCODE 3 (ENCFF563GSK) Regulation 
encTfChipPkENCFF797OLU PeyrPtch POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in Peyer's_patch from ENCODE 3 (ENCFF797OLU) Regulation 
encTfChipPkENCFF486UBE PeyerPatch CTCF 4 Transcription Factor ChIP-seq Peaks of CTCF in Peyer's_patch from ENCODE 3 (ENCFF486UBE) Regulation 
encTfChipPkENCFF072UWP PeyerPatch CTCF 3 Transcription Factor ChIP-seq Peaks of CTCF in Peyer's_patch from ENCODE 3 (ENCFF072UWP) Regulation 
encTfChipPkENCFF579XTC PeyerPatch CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in Peyer's_patch from ENCODE 3 (ENCFF579XTC) Regulation 
encTfChipPkENCFF805FIF PeyerPatch CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in Peyer's_patch from ENCODE 3 (ENCFF805FIF) Regulation 
encTfChipPkENCFF177UJN parathyAdn CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in Parathyroid_adenoma from ENCODE 3 (ENCFF177UJN) Regulation 
encTfChipPkENCFF509NRY parathyAdn CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in Parathyroid_adenoma from ENCODE 3 (ENCFF509NRY) Regulation 
encTfChipPkENCFF171XUS Panc1 TCF7L2 Transcription Factor ChIP-seq Peaks of TCF7L2 in Panc1 from ENCODE 3 (ENCFF171XUS) Regulation 
encTfChipPkENCFF713ZPE Panc1 REST Transcription Factor ChIP-seq Peaks of REST in Panc1 from ENCODE 3 (ENCFF713ZPE) Regulation 
encTfChipPkENCFF753HNR Panc1 CTCF Transcription Factor ChIP-seq Peaks of CTCF in Panc1 from ENCODE 3 (ENCFF753HNR) Regulation 
encTfChipPkENCFF213CYP PFSK-1 TAF1 Transcription Factor ChIP-seq Peaks of TAF1 in PFSK-1 from ENCODE 3 (ENCFF213CYP) Regulation 
encTfChipPkENCFF896RCP PFSK-1 REST Transcription Factor ChIP-seq Peaks of REST in PFSK-1 from ENCODE 3 (ENCFF896RCP) Regulation 
encTfChipPkENCFF476NAK PC-9 EZH2 Transcription Factor ChIP-seq Peaks of EZH2 in PC-9 from ENCODE 3 (ENCFF476NAK) Regulation 
encTfChipPkENCFF616KNI PC-9 CTCF Transcription Factor ChIP-seq Peaks of CTCF in PC-9 from ENCODE 3 (ENCFF616KNI) Regulation 
encTfChipPkENCFF702LEL PC-3 EZH2 Transcription Factor ChIP-seq Peaks of EZH2 in PC-3 from ENCODE 3 (ENCFF702LEL) Regulation 
encTfChipPkENCFF232FXZ PC-3 CTCF Transcription Factor ChIP-seq Peaks of CTCF in PC-3 from ENCODE 3 (ENCFF232FXZ) Regulation 
encTfChipPkENCFF186NOM OCI-LY7 CTCF Transcription Factor ChIP-seq Peaks of CTCF in OCI-LY7 from ENCODE 3 (ENCFF186NOM) Regulation 
encTfChipPkENCFF588MSD OCI-LY3 CTCF Transcription Factor ChIP-seq Peaks of CTCF in OCI-LY3 from ENCODE 3 (ENCFF588MSD) Regulation 
encTfChipPkENCFF520VKN OCI-LY1 EZH2 Transcription Factor ChIP-seq Peaks of EZH2 in OCI-LY1 from ENCODE 3 (ENCFF520VKN) Regulation 
encTfChipPkENCFF713PIC OCI-LY1 CTCF Transcription Factor ChIP-seq Peaks of CTCF in OCI-LY1 from ENCODE 3 (ENCFF713PIC) Regulation 
encTfChipPkENCFF597KMH NT2/D1 ZNF274 Transcription Factor ChIP-seq Peaks of ZNF274 in NT2/D1 from ENCODE 3 (ENCFF597KMH) Regulation 
encTfChipPkENCFF226OCL NT2/D1 YY1 Transcription Factor ChIP-seq Peaks of YY1 in NT2/D1 from ENCODE 3 (ENCFF226OCL) Regulation 
encTfChipPkENCFF259KAD NCI-H929 CTCF Transcription Factor ChIP-seq Peaks of CTCF in NCI-H929 from ENCODE 3 (ENCFF259KAD) Regulation 
encTfChipPkENCFF456PDQ NB4 CTCF Transcription Factor ChIP-seq Peaks of CTCF in NB4 from ENCODE 3 (ENCFF456PDQ) Regulation 
encTfChipPkENCFF253WCQ MM.1S EZH2 Transcription Factor ChIP-seq Peaks of EZH2 in MM.1S from ENCODE 3 (ENCFF253WCQ) Regulation 
encTfChipPkENCFF825ZYC MM.1S CTCF Transcription Factor ChIP-seq Peaks of CTCF in MM.1S from ENCODE 3 (ENCFF825ZYC) Regulation 
encTfChipPkENCFF014OJI MCF_10A STAT3 3 Transcription Factor ChIP-seq Peaks of STAT3 in MCF_10A from ENCODE 3 (ENCFF014OJI) Regulation 
encTfChipPkENCFF199CQN MCF_10A STAT3 2 Transcription Factor ChIP-seq Peaks of STAT3 in MCF_10A from ENCODE 3 (ENCFF199CQN) Regulation 
encTfChipPkENCFF854RVF MCF_10A STAT3 1 Transcription Factor ChIP-seq Peaks of STAT3 in MCF_10A from ENCODE 3 (ENCFF854RVF) Regulation 
encTfChipPkENCFF875HHT MCF_10A POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in MCF_10A from ENCODE 3 (ENCFF875HHT) Regulation 
encTfChipPkENCFF326DTU MCF_10A POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in MCF_10A from ENCODE 3 (ENCFF326DTU) Regulation 
encTfChipPkENCFF443CEL MCF_10A MYC Transcription Factor ChIP-seq Peaks of MYC in MCF_10A from ENCODE 3 (ENCFF443CEL) Regulation 
encTfChipPkENCFF558PJH MCF_10A FOS 4 Transcription Factor ChIP-seq Peaks of FOS in MCF_10A from ENCODE 3 (ENCFF558PJH) Regulation 
encTfChipPkENCFF222ZHH MCF_10A FOS 3 Transcription Factor ChIP-seq Peaks of FOS in MCF_10A from ENCODE 3 (ENCFF222ZHH) Regulation 
encTfChipPkENCFF353OBA MCF_10A FOS 2 Transcription Factor ChIP-seq Peaks of FOS in MCF_10A from ENCODE 3 (ENCFF353OBA) Regulation 
encTfChipPkENCFF436WHK MCF_10A FOS 1 Transcription Factor ChIP-seq Peaks of FOS in MCF_10A from ENCODE 3 (ENCFF436WHK) Regulation 
encTfChipPkENCFF525RRP MCF-7 ZNF8 Transcription Factor ChIP-seq Peaks of ZNF8 in MCF-7 from ENCODE 3 (ENCFF525RRP) Regulation 
encTfChipPkENCFF329QYZ MCF-7 ZNF687 Transcription Factor ChIP-seq Peaks of ZNF687 in MCF-7 from ENCODE 3 (ENCFF329QYZ) Regulation 
encTfChipPkENCFF541HRT MCF-7 ZNF592 2 Transcription Factor ChIP-seq Peaks of ZNF592 in MCF-7 from ENCODE 3 (ENCFF541HRT) Regulation 
encTfChipPkENCFF720PZA MCF-7 ZNF592 1 Transcription Factor ChIP-seq Peaks of ZNF592 in MCF-7 from ENCODE 3 (ENCFF720PZA) Regulation 
encTfChipPkENCFF306PBX MCF-7 ZNF579 Transcription Factor ChIP-seq Peaks of ZNF579 in MCF-7 from ENCODE 3 (ENCFF306PBX) Regulation 
encTfChipPkENCFF290LSS MCF-7 ZNF574 Transcription Factor ChIP-seq Peaks of ZNF574 in MCF-7 from ENCODE 3 (ENCFF290LSS) Regulation 
encTfChipPkENCFF414EYO MCF-7 ZNF512B 2 Transcription Factor ChIP-seq Peaks of ZNF512B in MCF-7 from ENCODE 3 (ENCFF414EYO) Regulation 
encTfChipPkENCFF209TEF MCF-7 ZNF512B 1 Transcription Factor ChIP-seq Peaks of ZNF512B in MCF-7 from ENCODE 3 (ENCFF209TEF) Regulation 
encTfChipPkENCFF675SAG MCF-7 ZNF507 Transcription Factor ChIP-seq Peaks of ZNF507 in MCF-7 from ENCODE 3 (ENCFF675SAG) Regulation 
encTfChipPkENCFF786XJV MCF-7 ZNF444 Transcription Factor ChIP-seq Peaks of ZNF444 in MCF-7 from ENCODE 3 (ENCFF786XJV) Regulation 
encTfChipPkENCFF619BFO MCF-7 ZNF24 Transcription Factor ChIP-seq Peaks of ZNF24 in MCF-7 from ENCODE 3 (ENCFF619BFO) Regulation 
encTfChipPkENCFF246ZMG MCF-7 ZNF217 2 Transcription Factor ChIP-seq Peaks of ZNF217 in MCF-7 from ENCODE 3 (ENCFF246ZMG) Regulation 
encTfChipPkENCFF620RPM MCF-7 ZNF217 1 Transcription Factor ChIP-seq Peaks of ZNF217 in MCF-7 from ENCODE 3 (ENCFF620RPM) Regulation 
encTfChipPkENCFF621ZSK MCF-7 ZNF207 Transcription Factor ChIP-seq Peaks of ZNF207 in MCF-7 from ENCODE 3 (ENCFF621ZSK) Regulation 
encTfChipPkENCFF687REM MCF-7 ZKSCAN1 Transcription Factor ChIP-seq Peaks of ZKSCAN1 in MCF-7 from ENCODE 3 (ENCFF687REM) Regulation 
encTfChipPkENCFF694ZRC MCF-7 ZHX2 Transcription Factor ChIP-seq Peaks of ZHX2 in MCF-7 from ENCODE 3 (ENCFF694ZRC) Regulation 
encTfChipPkENCFF775BWJ MCF-7 ZFX Transcription Factor ChIP-seq Peaks of ZFX in MCF-7 from ENCODE 3 (ENCFF775BWJ) Regulation 
encTfChipPkENCFF794UEM MCF-7 ZBTB7B Transcription Factor ChIP-seq Peaks of ZBTB7B in MCF-7 from ENCODE 3 (ENCFF794UEM) Regulation 
encTfChipPkENCFF932XEU MCF-7 ZBTB40 Transcription Factor ChIP-seq Peaks of ZBTB40 in MCF-7 from ENCODE 3 (ENCFF932XEU) Regulation 
encTfChipPkENCFF780WLS MCF-7 ZBTB33 Transcription Factor ChIP-seq Peaks of ZBTB33 in MCF-7 from ENCODE 3 (ENCFF780WLS) Regulation 
encTfChipPkENCFF496RVC MCF-7 ZBTB11 Transcription Factor ChIP-seq Peaks of ZBTB11 in MCF-7 from ENCODE 3 (ENCFF496RVC) Regulation 
encTfChipPkENCFF589MVU MCF-7 ZBTB1 Transcription Factor ChIP-seq Peaks of ZBTB1 in MCF-7 from ENCODE 3 (ENCFF589MVU) Regulation 
encTfChipPkENCFF452VLA MCF-7 TRIM22 Transcription Factor ChIP-seq Peaks of TRIM22 in MCF-7 from ENCODE 3 (ENCFF452VLA) Regulation 
encTfChipPkENCFF762MGC MCF-7 TAF1 Transcription Factor ChIP-seq Peaks of TAF1 in MCF-7 from ENCODE 3 (ENCFF762MGC) Regulation 
encTfChipPkENCFF258ZVN MCF-7 SUZ12 Transcription Factor ChIP-seq Peaks of SUZ12 in MCF-7 from ENCODE 3 (ENCFF258ZVN) Regulation 
encTfChipPkENCFF275WAD MCF-7 SREBF1 Transcription Factor ChIP-seq Peaks of SREBF1 in MCF-7 from ENCODE 3 (ENCFF275WAD) Regulation 
encTfChipPkENCFF577EMC MCF-7 SP1 Transcription Factor ChIP-seq Peaks of SP1 in MCF-7 from ENCODE 3 (ENCFF577EMC) Regulation 
encTfChipPkENCFF761NKP MCF-7 SMARCE1 Transcription Factor ChIP-seq Peaks of SMARCE1 in MCF-7 from ENCODE 3 (ENCFF761NKP) Regulation 
encTfChipPkENCFF618JNX MCF-7 SMARCA5 Transcription Factor ChIP-seq Peaks of SMARCA5 in MCF-7 from ENCODE 3 (ENCFF618JNX) Regulation 
encTfChipPkENCFF441UHA MCF-7 SIX4 Transcription Factor ChIP-seq Peaks of SIX4 in MCF-7 from ENCODE 3 (ENCFF441UHA) Regulation 
encTfChipPkENCFF220RUS MCF-7 SIN3A Transcription Factor ChIP-seq Peaks of SIN3A in MCF-7 from ENCODE 3 (ENCFF220RUS) Regulation 
encTfChipPkENCFF103MPW MCF-7 RFX5 Transcription Factor ChIP-seq Peaks of RFX5 in MCF-7 from ENCODE 3 (ENCFF103MPW) Regulation 
encTfChipPkENCFF150PTQ MCF-7 RFX1 2 Transcription Factor ChIP-seq Peaks of RFX1 in MCF-7 from ENCODE 3 (ENCFF150PTQ) Regulation 
encTfChipPkENCFF928YTD MCF-7 RFX1 1 Transcription Factor ChIP-seq Peaks of RFX1 in MCF-7 from ENCODE 3 (ENCFF928YTD) Regulation 
encTfChipPkENCFF838LXI MCF-7 RCOR1 Transcription Factor ChIP-seq Peaks of RCOR1 in MCF-7 from ENCODE 3 (ENCFF838LXI) Regulation 
encTfChipPkENCFF091AYX MCF-7 RAD51 Transcription Factor ChIP-seq Peaks of RAD51 in MCF-7 from ENCODE 3 (ENCFF091AYX) Regulation 
encTfChipPkENCFF964EVA MCF-7 POLR2A Transcription Factor ChIP-seq Peaks of POLR2A in MCF-7 from ENCODE 3 (ENCFF964EVA) Regulation 
encTfChipPkENCFF105PFS MCF-7 PKNOX1 Transcription Factor ChIP-seq Peaks of PKNOX1 in MCF-7 from ENCODE 3 (ENCFF105PFS) Regulation 
encTfChipPkENCFF473UHQ MCF-7 PAX8 Transcription Factor ChIP-seq Peaks of PAX8 in MCF-7 from ENCODE 3 (ENCFF473UHQ) Regulation 
encTfChipPkENCFF269RME MCF-7 NRF1 Transcription Factor ChIP-seq Peaks of NRF1 in MCF-7 from ENCODE 3 (ENCFF269RME) Regulation 
encTfChipPkENCFF927DIO MCF-7 NFXL1 Transcription Factor ChIP-seq Peaks of NFXL1 in MCF-7 from ENCODE 3 (ENCFF927DIO) Regulation 
encTfChipPkENCFF895MJB MCF-7 NFRKB Transcription Factor ChIP-seq Peaks of NFRKB in MCF-7 from ENCODE 3 (ENCFF895MJB) Regulation 
encTfChipPkENCFF385WUL MCF-7 NFIB 2 Transcription Factor ChIP-seq Peaks of NFIB in MCF-7 from ENCODE 3 (ENCFF385WUL) Regulation 
encTfChipPkENCFF519XTN MCF-7 NFIB 1 Transcription Factor ChIP-seq Peaks of NFIB in MCF-7 from ENCODE 3 (ENCFF519XTN) Regulation 
encTfChipPkENCFF059LJD MCF-7 NEUROD1 Transcription Factor ChIP-seq Peaks of NEUROD1 in MCF-7 from ENCODE 3 (ENCFF059LJD) Regulation 
encTfChipPkENCFF510UNI MCF-7 NCOA3 2 Transcription Factor ChIP-seq Peaks of NCOA3 in MCF-7 from ENCODE 3 (ENCFF510UNI) Regulation 
encTfChipPkENCFF320TAN MCF-7 NCOA3 1 Transcription Factor ChIP-seq Peaks of NCOA3 in MCF-7 from ENCODE 3 (ENCFF320TAN) Regulation 
encTfChipPkENCFF209WRW MCF-7 NBN Transcription Factor ChIP-seq Peaks of NBN in MCF-7 from ENCODE 3 (ENCFF209WRW) Regulation 
encTfChipPkENCFF370EQJ MCF-7 MYC 3 Transcription Factor ChIP-seq Peaks of MYC in MCF-7 from ENCODE 3 (ENCFF370EQJ) Regulation 
encTfChipPkENCFF658XME MCF-7 MYC 2 Transcription Factor ChIP-seq Peaks of MYC in MCF-7 from ENCODE 3 (ENCFF658XME) Regulation 
encTfChipPkENCFF300OKR MCF-7 MYC 1 Transcription Factor ChIP-seq Peaks of MYC in MCF-7 from ENCODE 3 (ENCFF300OKR) Regulation 
encTfChipPkENCFF083AZM MCF-7 MTA3 Transcription Factor ChIP-seq Peaks of MTA3 in MCF-7 from ENCODE 3 (ENCFF083AZM) Regulation 
encTfChipPkENCFF180XXZ MCF-7 MTA2 Transcription Factor ChIP-seq Peaks of MTA2 in MCF-7 from ENCODE 3 (ENCFF180XXZ) Regulation 
encTfChipPkENCFF225VFR MCF-7 MTA1 Transcription Factor ChIP-seq Peaks of MTA1 in MCF-7 from ENCODE 3 (ENCFF225VFR) Regulation 
encTfChipPkENCFF432GSK MCF-7 MNT 2 Transcription Factor ChIP-seq Peaks of MNT in MCF-7 from ENCODE 3 (ENCFF432GSK) Regulation 
encTfChipPkENCFF403BWK MCF-7 MNT 1 Transcription Factor ChIP-seq Peaks of MNT in MCF-7 from ENCODE 3 (ENCFF403BWK) Regulation 
encTfChipPkENCFF578NMN MCF-7 MLLT1 Transcription Factor ChIP-seq Peaks of MLLT1 in MCF-7 from ENCODE 3 (ENCFF578NMN) Regulation 
encTfChipPkENCFF464QAL MCF-7 MBD2 Transcription Factor ChIP-seq Peaks of MBD2 in MCF-7 from ENCODE 3 (ENCFF464QAL) Regulation 
encTfChipPkENCFF873SVI MCF-7 MAFK Transcription Factor ChIP-seq Peaks of MAFK in MCF-7 from ENCODE 3 (ENCFF873SVI) Regulation 
encTfChipPkENCFF569ZCY MCF-7 JUND Transcription Factor ChIP-seq Peaks of JUND in MCF-7 from ENCODE 3 (ENCFF569ZCY) Regulation 
encTfChipPkENCFF907UNK MCF-7 JUN Transcription Factor ChIP-seq Peaks of JUN in MCF-7 from ENCODE 3 (ENCFF907UNK) Regulation 
encTfChipPkENCFF708ACK MCF-7 HSF1 Transcription Factor ChIP-seq Peaks of HSF1 in MCF-7 from ENCODE 3 (ENCFF708ACK) Regulation 
encTfChipPkENCFF144OPN MCF-7 HES1 Transcription Factor ChIP-seq Peaks of HES1 in MCF-7 from ENCODE 3 (ENCFF144OPN) Regulation 
encTfChipPkENCFF401IAI MCF-7 HCFC1 Transcription Factor ChIP-seq Peaks of HCFC1 in MCF-7 from ENCODE 3 (ENCFF401IAI) Regulation 
encTfChipPkENCFF046BRP MCF-7 GATAD2B 2 Transcription Factor ChIP-seq Peaks of GATAD2B in MCF-7 from ENCODE 3 (ENCFF046BRP) Regulation 
encTfChipPkENCFF191SBE MCF-7 GATAD2B 1 Transcription Factor ChIP-seq Peaks of GATAD2B in MCF-7 from ENCODE 3 (ENCFF191SBE) Regulation 
encTfChipPkENCFF625IUE MCF-7 GATA3 Transcription Factor ChIP-seq Peaks of GATA3 in MCF-7 from ENCODE 3 (ENCFF625IUE) Regulation 
encTfChipPkENCFF899MQW MCF-7 FOXK2 Transcription Factor ChIP-seq Peaks of FOXK2 in MCF-7 from ENCODE 3 (ENCFF899MQW) Regulation 
encTfChipPkENCFF160RLI MCF-7 FOXA1 Transcription Factor ChIP-seq Peaks of FOXA1 in MCF-7 from ENCODE 3 (ENCFF160RLI) Regulation 
encTfChipPkENCFF170POB MCF-7 FOS Transcription Factor ChIP-seq Peaks of FOS in MCF-7 from ENCODE 3 (ENCFF170POB) Regulation 
encTfChipPkENCFF541DRZ MCF-7 ESRRA 2 Transcription Factor ChIP-seq Peaks of ESRRA in MCF-7 from ENCODE 3 (ENCFF541DRZ) Regulation 
encTfChipPkENCFF519TRJ MCF-7 ESRRA 1 Transcription Factor ChIP-seq Peaks of ESRRA in MCF-7 from ENCODE 3 (ENCFF519TRJ) Regulation 
encTfChipPkENCFF408TWV MCF-7 ELK1 Transcription Factor ChIP-seq Peaks of ELK1 in MCF-7 from ENCODE 3 (ENCFF408TWV) Regulation 
encTfChipPkENCFF020UCD MCF-7 ELF1 Transcription Factor ChIP-seq Peaks of ELF1 in MCF-7 from ENCODE 3 (ENCFF020UCD) Regulation 
encTfChipPkENCFF347USC MCF-7 E4F1 Transcription Factor ChIP-seq Peaks of E4F1 in MCF-7 from ENCODE 3 (ENCFF347USC) Regulation 
encTfChipPkENCFF072VGV MCF-7 E2F8 Transcription Factor ChIP-seq Peaks of E2F8 in MCF-7 from ENCODE 3 (ENCFF072VGV) Regulation 
encTfChipPkENCFF042AWM MCF-7 DPF2 Transcription Factor ChIP-seq Peaks of DPF2 in MCF-7 from ENCODE 3 (ENCFF042AWM) Regulation 
encTfChipPkENCFF762CDY MCF-7 CUX1 Transcription Factor ChIP-seq Peaks of CUX1 in MCF-7 from ENCODE 3 (ENCFF762CDY) Regulation 
encTfChipPkENCFF785NTC MCF-7 CTCF 6 Transcription Factor ChIP-seq Peaks of CTCF in MCF-7 from ENCODE 3 (ENCFF785NTC) Regulation 
encTfChipPkENCFF628EUU MCF-7 CTCF 5 Transcription Factor ChIP-seq Peaks of CTCF in MCF-7 from ENCODE 3 (ENCFF628EUU) Regulation 
encTfChipPkENCFF685HMV MCF-7 CTCF 4 Transcription Factor ChIP-seq Peaks of CTCF in MCF-7 from ENCODE 3 (ENCFF685HMV) Regulation 
encTfChipPkENCFF942TCG MCF-7 CTCF 3 Transcription Factor ChIP-seq Peaks of CTCF in MCF-7 from ENCODE 3 (ENCFF942TCG) Regulation 
encTfChipPkENCFF867BUQ MCF-7 CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in MCF-7 from ENCODE 3 (ENCFF867BUQ) Regulation 
encTfChipPkENCFF476DVJ MCF-7 CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in MCF-7 from ENCODE 3 (ENCFF476DVJ) Regulation 
encTfChipPkENCFF456MGR MCF-7 CTBP1 Transcription Factor ChIP-seq Peaks of CTBP1 in MCF-7 from ENCODE 3 (ENCFF456MGR) Regulation 
encTfChipPkENCFF883LRJ MCF-7 CREB1 2 Transcription Factor ChIP-seq Peaks of CREB1 in MCF-7 from ENCODE 3 (ENCFF883LRJ) Regulation 
encTfChipPkENCFF495PCJ MCF-7 CREB1 1 Transcription Factor ChIP-seq Peaks of CREB1 in MCF-7 from ENCODE 3 (ENCFF495PCJ) Regulation 
encTfChipPkENCFF682WFF MCF-7 COPS2 Transcription Factor ChIP-seq Peaks of COPS2 in MCF-7 from ENCODE 3 (ENCFF682WFF) Regulation 
encTfChipPkENCFF305CRL MCF-7 CLOCK 2 Transcription Factor ChIP-seq Peaks of CLOCK in MCF-7 from ENCODE 3 (ENCFF305CRL) Regulation 
encTfChipPkENCFF025SMR MCF-7 CLOCK 1 Transcription Factor ChIP-seq Peaks of CLOCK in MCF-7 from ENCODE 3 (ENCFF025SMR) Regulation 
encTfChipPkENCFF730UAD MCF-7 CHD1 Transcription Factor ChIP-seq Peaks of CHD1 in MCF-7 from ENCODE 3 (ENCFF730UAD) Regulation 
encTfChipPkENCFF414LXZ MCF-7 BMI1 Transcription Factor ChIP-seq Peaks of BMI1 in MCF-7 from ENCODE 3 (ENCFF414LXZ) Regulation 
encTfChipPkENCFF760ZVI MCF-7 ATF7 Transcription Factor ChIP-seq Peaks of ATF7 in MCF-7 from ENCODE 3 (ENCFF760ZVI) Regulation 
encTfChipPkENCFF618NVV MCF-7 ARID3A Transcription Factor ChIP-seq Peaks of ARID3A in MCF-7 from ENCODE 3 (ENCFF618NVV) Regulation 
encTfChipPkENCFF707BQD Loucy CTCF Transcription Factor ChIP-seq Peaks of CTCF in Loucy from ENCODE 3 (ENCFF707BQD) Regulation 
encTfChipPkENCFF670NSE LNCaP_FGC CTCF Transcription Factor ChIP-seq Peaks of CTCF in LNCaP_clone_FGC from ENCODE 3 (ENCFF670NSE) Regulation 
encTfChipPkENCFF501SHB LNCAP CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in LNCAP from ENCODE 3 (ENCFF501SHB) Regulation 
encTfChipPkENCFF850DQJ LNCAP CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in LNCAP from ENCODE 3 (ENCFF850DQJ) Regulation 
encTfChipPkENCFF649QKE KMS-11 CTCF Transcription Factor ChIP-seq Peaks of CTCF in KMS-11 from ENCODE 3 (ENCFF649QKE) Regulation 
encTfChipPkENCFF945HJR K562 ZZZ3 Transcription Factor ChIP-seq Peaks of ZZZ3 in K562 from ENCODE 3 (ENCFF945HJR) Regulation 
encTfChipPkENCFF979GFF K562 ZSCAN29 2 Transcription Factor ChIP-seq Peaks of ZSCAN29 in K562 from ENCODE 3 (ENCFF979GFF) Regulation 
encTfChipPkENCFF908ZLN K562 ZSCAN29 1 Transcription Factor ChIP-seq Peaks of ZSCAN29 in K562 from ENCODE 3 (ENCFF908ZLN) Regulation 
encTfChipPkENCFF979NKM K562 ZNF830 2 Transcription Factor ChIP-seq Peaks of ZNF830 in K562 from ENCODE 3 (ENCFF979NKM) Regulation 
encTfChipPkENCFF951OSW K562 ZNF830 1 Transcription Factor ChIP-seq Peaks of ZNF830 in K562 from ENCODE 3 (ENCFF951OSW) Regulation 
encTfChipPkENCFF008JJE K562 ZNF639 2 Transcription Factor ChIP-seq Peaks of ZNF639 in K562 from ENCODE 3 (ENCFF008JJE) Regulation 
encTfChipPkENCFF404EVY K562 ZNF639 1 Transcription Factor ChIP-seq Peaks of ZNF639 in K562 from ENCODE 3 (ENCFF404EVY) Regulation 
encTfChipPkENCFF972UGK K562 ZNF592 Transcription Factor ChIP-seq Peaks of ZNF592 in K562 from ENCODE 3 (ENCFF972UGK) Regulation 
encTfChipPkENCFF538GSS K562 ZNF407 2 Transcription Factor ChIP-seq Peaks of ZNF407 in K562 from ENCODE 3 (ENCFF538GSS) Regulation 
encTfChipPkENCFF644XES K562 ZNF407 1 Transcription Factor ChIP-seq Peaks of ZNF407 in K562 from ENCODE 3 (ENCFF644XES) Regulation 
encTfChipPkENCFF106YXG K562 ZNF384 Transcription Factor ChIP-seq Peaks of ZNF384 in K562 from ENCODE 3 (ENCFF106YXG) Regulation 
encTfChipPkENCFF082RIZ K562 ZNF318 2 Transcription Factor ChIP-seq Peaks of ZNF318 in K562 from ENCODE 3 (ENCFF082RIZ) Regulation 
encTfChipPkENCFF577LQR K562 ZNF318 1 Transcription Factor ChIP-seq Peaks of ZNF318 in K562 from ENCODE 3 (ENCFF577LQR) Regulation 
encTfChipPkENCFF056SEM K562 ZNF316 2 Transcription Factor ChIP-seq Peaks of ZNF316 in K562 from ENCODE 3 (ENCFF056SEM) Regulation 
encTfChipPkENCFF806GUF K562 ZNF316 1 Transcription Factor ChIP-seq Peaks of ZNF316 in K562 from ENCODE 3 (ENCFF806GUF) Regulation 
encTfChipPkENCFF596JDS K562 ZNF282 Transcription Factor ChIP-seq Peaks of ZNF282 in K562 from ENCODE 3 (ENCFF596JDS) Regulation 
encTfChipPkENCFF074WRG K562 ZNF280A Transcription Factor ChIP-seq Peaks of ZNF280A in K562 from ENCODE 3 (ENCFF074WRG) Regulation 
encTfChipPkENCFF498VQZ K562 ZNF274 2 Transcription Factor ChIP-seq Peaks of ZNF274 in K562 from ENCODE 3 (ENCFF498VQZ) Regulation 
encTfChipPkENCFF323AWS K562 ZNF274 1 Transcription Factor ChIP-seq Peaks of ZNF274 in K562 from ENCODE 3 (ENCFF323AWS) Regulation 
encTfChipPkENCFF260CBQ K562 ZNF24 3 Transcription Factor ChIP-seq Peaks of ZNF24 in K562 from ENCODE 3 (ENCFF260CBQ) Regulation 
encTfChipPkENCFF723JDW K562 ZNF24 2 Transcription Factor ChIP-seq Peaks of ZNF24 in K562 from ENCODE 3 (ENCFF723JDW) Regulation 
encTfChipPkENCFF007EEV K562 ZNF24 1 Transcription Factor ChIP-seq Peaks of ZNF24 in K562 from ENCODE 3 (ENCFF007EEV) Regulation 
encTfChipPkENCFF760EPB K562 ZNF184 2 Transcription Factor ChIP-seq Peaks of ZNF184 in K562 from ENCODE 3 (ENCFF760EPB) Regulation 
encTfChipPkENCFF855CUN K562 ZNF184 1 Transcription Factor ChIP-seq Peaks of ZNF184 in K562 from ENCODE 3 (ENCFF855CUN) Regulation 
encTfChipPkENCFF700GZI K562 ZNF143 Transcription Factor ChIP-seq Peaks of ZNF143 in K562 from ENCODE 3 (ENCFF700GZI) Regulation 
encTfChipPkENCFF195IFB K562 ZMYM3 Transcription Factor ChIP-seq Peaks of ZMYM3 in K562 from ENCODE 3 (ENCFF195IFB) Regulation 
encTfChipPkENCFF526PMI K562 ZMIZ1 Transcription Factor ChIP-seq Peaks of ZMIZ1 in K562 from ENCODE 3 (ENCFF526PMI) Regulation 
encTfChipPkENCFF704VDI K562 ZKSCAN1 Transcription Factor ChIP-seq Peaks of ZKSCAN1 in K562 from ENCODE 3 (ENCFF704VDI) Regulation 
encTfChipPkENCFF495BPY K562 ZHX1 Transcription Factor ChIP-seq Peaks of ZHX1 in K562 from ENCODE 3 (ENCFF495BPY) Regulation 
encTfChipPkENCFF150ZBH K562 ZFP91 Transcription Factor ChIP-seq Peaks of ZFP91 in K562 from ENCODE 3 (ENCFF150ZBH) Regulation 
encTfChipPkENCFF553KIK K562 ZEB2 2 Transcription Factor ChIP-seq Peaks of ZEB2 in K562 from ENCODE 3 (ENCFF553KIK) Regulation 
encTfChipPkENCFF808NWU K562 ZEB2 1 Transcription Factor ChIP-seq Peaks of ZEB2 in K562 from ENCODE 3 (ENCFF808NWU) Regulation 
encTfChipPkENCFF328SSL K562 ZBTB8A Transcription Factor ChIP-seq Peaks of ZBTB8A in K562 from ENCODE 3 (ENCFF328SSL) Regulation 
encTfChipPkENCFF245LRG K562 ZBTB7A Transcription Factor ChIP-seq Peaks of ZBTB7A in K562 from ENCODE 3 (ENCFF245LRG) Regulation 
encTfChipPkENCFF813GMP K562 ZBTB5 2 Transcription Factor ChIP-seq Peaks of ZBTB5 in K562 from ENCODE 3 (ENCFF813GMP) Regulation 
encTfChipPkENCFF014KUI K562 ZBTB5 1 Transcription Factor ChIP-seq Peaks of ZBTB5 in K562 from ENCODE 3 (ENCFF014KUI) Regulation 
encTfChipPkENCFF088LZZ K562 ZBTB40 Transcription Factor ChIP-seq Peaks of ZBTB40 in K562 from ENCODE 3 (ENCFF088LZZ) Regulation 
encTfChipPkENCFF556STK K562 ZBTB33 Transcription Factor ChIP-seq Peaks of ZBTB33 in K562 from ENCODE 3 (ENCFF556STK) Regulation 
encTfChipPkENCFF189WAO K562 ZBTB2 Transcription Factor ChIP-seq Peaks of ZBTB2 in K562 from ENCODE 3 (ENCFF189WAO) Regulation 
encTfChipPkENCFF913HCQ K562 ZBTB11 Transcription Factor ChIP-seq Peaks of ZBTB11 in K562 from ENCODE 3 (ENCFF913HCQ) Regulation 
encTfChipPkENCFF388TYU K562 ZBED1 Transcription Factor ChIP-seq Peaks of ZBED1 in K562 from ENCODE 3 (ENCFF388TYU) Regulation 
encTfChipPkENCFF635XCI K562 YY1 2 Transcription Factor ChIP-seq Peaks of YY1 in K562 from ENCODE 3 (ENCFF635XCI) Regulation 
encTfChipPkENCFF024TJO K562 YY1 1 Transcription Factor ChIP-seq Peaks of YY1 in K562 from ENCODE 3 (ENCFF024TJO) Regulation 
encTfChipPkENCFF929TWP K562 XRCC5 Transcription Factor ChIP-seq Peaks of XRCC5 in K562 from ENCODE 3 (ENCFF929TWP) Regulation 
encTfChipPkENCFF115PGE K562 XRCC3 Transcription Factor ChIP-seq Peaks of XRCC3 in K562 from ENCODE 3 (ENCFF115PGE) Regulation 
encTfChipPkENCFF157ZQI K562 WHSC1 Transcription Factor ChIP-seq Peaks of WHSC1 in K562 from ENCODE 3 (ENCFF157ZQI) Regulation 
encTfChipPkENCFF425FVY K562 USF2 Transcription Factor ChIP-seq Peaks of USF2 in K562 from ENCODE 3 (ENCFF425FVY) Regulation 
encTfChipPkENCFF403TAF K562 UBTF 2 Transcription Factor ChIP-seq Peaks of UBTF in K562 from ENCODE 3 (ENCFF403TAF) Regulation 
encTfChipPkENCFF345RRM K562 UBTF 1 Transcription Factor ChIP-seq Peaks of UBTF in K562 from ENCODE 3 (ENCFF345RRM) Regulation 
encTfChipPkENCFF134HBP K562 U2AF2 Transcription Factor ChIP-seq Peaks of U2AF2 in K562 from ENCODE 3 (ENCFF134HBP) Regulation 
encTfChipPkENCFF482DRO K562 U2AF1 Transcription Factor ChIP-seq Peaks of U2AF1 in K562 from ENCODE 3 (ENCFF482DRO) Regulation 
encTfChipPkENCFF534VQL K562 TRIP13 Transcription Factor ChIP-seq Peaks of TRIP13 in K562 from ENCODE 3 (ENCFF534VQL) Regulation 
encTfChipPkENCFF623ELO K562 TRIM28 3 Transcription Factor ChIP-seq Peaks of TRIM28 in K562 from ENCODE 3 (ENCFF623ELO) Regulation 
encTfChipPkENCFF996AMX K562 TRIM28 2 Transcription Factor ChIP-seq Peaks of TRIM28 in K562 from ENCODE 3 (ENCFF996AMX) Regulation 
encTfChipPkENCFF168KHS K562 TRIM28 1 Transcription Factor ChIP-seq Peaks of TRIM28 in K562 from ENCODE 3 (ENCFF168KHS) Regulation 
encTfChipPkENCFF950TOJ K562 TRIM24 2 Transcription Factor ChIP-seq Peaks of TRIM24 in K562 from ENCODE 3 (ENCFF950TOJ) Regulation 
encTfChipPkENCFF063NXI K562 TRIM24 1 Transcription Factor ChIP-seq Peaks of TRIM24 in K562 from ENCODE 3 (ENCFF063NXI) Regulation 
encTfChipPkENCFF309DMZ K562 THRA Transcription Factor ChIP-seq Peaks of THRA in K562 from ENCODE 3 (ENCFF309DMZ) Regulation 
encTfChipPkENCFF130TPD K562 THAP1 Transcription Factor ChIP-seq Peaks of THAP1 in K562 from ENCODE 3 (ENCFF130TPD) Regulation 
encTfChipPkENCFF547MLB K562 TEAD4 Transcription Factor ChIP-seq Peaks of TEAD4 in K562 from ENCODE 3 (ENCFF547MLB) Regulation 
encTfChipPkENCFF512IAI K562 TCF7 Transcription Factor ChIP-seq Peaks of TCF7 in K562 from ENCODE 3 (ENCFF512IAI) Regulation 
encTfChipPkENCFF912LXU K562 TCF12 2 Transcription Factor ChIP-seq Peaks of TCF12 in K562 from ENCODE 3 (ENCFF912LXU) Regulation 
encTfChipPkENCFF952JIK K562 TCF12 1 Transcription Factor ChIP-seq Peaks of TCF12 in K562 from ENCODE 3 (ENCFF952JIK) Regulation 
encTfChipPkENCFF370YGS K562 TBP Transcription Factor ChIP-seq Peaks of TBP in K562 from ENCODE 3 (ENCFF370YGS) Regulation 
encTfChipPkENCFF239WFN K562 TBL1XR1 2 Transcription Factor ChIP-seq Peaks of TBL1XR1 in K562 from ENCODE 3 (ENCFF239WFN) Regulation 
encTfChipPkENCFF868SWL K562 TBL1XR1 1 Transcription Factor ChIP-seq Peaks of TBL1XR1 in K562 from ENCODE 3 (ENCFF868SWL) Regulation 
encTfChipPkENCFF475LFH K562 TAL1 2 Transcription Factor ChIP-seq Peaks of TAL1 in K562 from ENCODE 3 (ENCFF475LFH) Regulation 
encTfChipPkENCFF078OUD K562 TAL1 1 Transcription Factor ChIP-seq Peaks of TAL1 in K562 from ENCODE 3 (ENCFF078OUD) Regulation 
encTfChipPkENCFF223HDM K562 TAF9B Transcription Factor ChIP-seq Peaks of TAF9B in K562 from ENCODE 3 (ENCFF223HDM) Regulation 
encTfChipPkENCFF852NOL K562 TAF7 Transcription Factor ChIP-seq Peaks of TAF7 in K562 from ENCODE 3 (ENCFF852NOL) Regulation 
encTfChipPkENCFF710LLF K562 TAF15 Transcription Factor ChIP-seq Peaks of TAF15 in K562 from ENCODE 3 (ENCFF710LLF) Regulation 
encTfChipPkENCFF856HYC K562 SUZ12 Transcription Factor ChIP-seq Peaks of SUZ12 in K562 from ENCODE 3 (ENCFF856HYC) Regulation 
encTfChipPkENCFF517IXK K562 STAT5A Transcription Factor ChIP-seq Peaks of STAT5A in K562 from ENCODE 3 (ENCFF517IXK) Regulation 
encTfChipPkENCFF204VQS K562 STAT2 Transcription Factor ChIP-seq Peaks of STAT2 in K562 from ENCODE 3 (ENCFF204VQS) Regulation 
encTfChipPkENCFF431NLF K562 STAT1 3 Transcription Factor ChIP-seq Peaks of STAT1 in K562 from ENCODE 3 (ENCFF431NLF) Regulation 
encTfChipPkENCFF747ICD K562 STAT1 2 Transcription Factor ChIP-seq Peaks of STAT1 in K562 from ENCODE 3 (ENCFF747ICD) Regulation 
encTfChipPkENCFF646MXG K562 STAT1 1 Transcription Factor ChIP-seq Peaks of STAT1 in K562 from ENCODE 3 (ENCFF646MXG) Regulation 
encTfChipPkENCFF217HAW K562 SRSF9 Transcription Factor ChIP-seq Peaks of SRSF9 in K562 from ENCODE 3 (ENCFF217HAW) Regulation 
encTfChipPkENCFF550VUN K562 SRSF7 Transcription Factor ChIP-seq Peaks of SRSF7 in K562 from ENCODE 3 (ENCFF550VUN) Regulation 
encTfChipPkENCFF777MYW K562 SREBF1 Transcription Factor ChIP-seq Peaks of SREBF1 in K562 from ENCODE 3 (ENCFF777MYW) Regulation 
encTfChipPkENCFF452LDK K562 SP1 Transcription Factor ChIP-seq Peaks of SP1 in K562 from ENCODE 3 (ENCFF452LDK) Regulation 
encTfChipPkENCFF431STY K562 SOX6 Transcription Factor ChIP-seq Peaks of SOX6 in K562 from ENCODE 3 (ENCFF431STY) Regulation 
encTfChipPkENCFF206MJS K562 SNRNP70 Transcription Factor ChIP-seq Peaks of SNRNP70 in K562 from ENCODE 3 (ENCFF206MJS) Regulation 
encTfChipPkENCFF175UEE K562 SMC3 Transcription Factor ChIP-seq Peaks of SMC3 in K562 from ENCODE 3 (ENCFF175UEE) Regulation 
encTfChipPkENCFF435SZS K562 SMARCE1 Transcription Factor ChIP-seq Peaks of SMARCE1 in K562 from ENCODE 3 (ENCFF435SZS) Regulation 
encTfChipPkENCFF751ZVX K562 SMARCC2 Transcription Factor ChIP-seq Peaks of SMARCC2 in K562 from ENCODE 3 (ENCFF751ZVX) Regulation 
encTfChipPkENCFF308QHX K562 SMARCB1 Transcription Factor ChIP-seq Peaks of SMARCB1 in K562 from ENCODE 3 (ENCFF308QHX) Regulation 
encTfChipPkENCFF481TNF K562 SMARCA5 Transcription Factor ChIP-seq Peaks of SMARCA5 in K562 from ENCODE 3 (ENCFF481TNF) Regulation 
encTfChipPkENCFF361RWX K562 SMARCA4 3 Transcription Factor ChIP-seq Peaks of SMARCA4 in K562 from ENCODE 3 (ENCFF361RWX) Regulation 
encTfChipPkENCFF868UOJ K562 SMARCA4 2 Transcription Factor ChIP-seq Peaks of SMARCA4 in K562 from ENCODE 3 (ENCFF868UOJ) Regulation 
encTfChipPkENCFF703NAE K562 SMARCA4 1 Transcription Factor ChIP-seq Peaks of SMARCA4 in K562 from ENCODE 3 (ENCFF703NAE) Regulation 
encTfChipPkENCFF069AAY K562 SMAD5 Transcription Factor ChIP-seq Peaks of SMAD5 in K562 from ENCODE 3 (ENCFF069AAY) Regulation 
encTfChipPkENCFF186MFI K562 SMAD2 Transcription Factor ChIP-seq Peaks of SMAD2 in K562 from ENCODE 3 (ENCFF186MFI) Regulation 
encTfChipPkENCFF084BUP K562 SMAD1 Transcription Factor ChIP-seq Peaks of SMAD1 in K562 from ENCODE 3 (ENCFF084BUP) Regulation 
encTfChipPkENCFF254QDM K562 SKIL Transcription Factor ChIP-seq Peaks of SKIL in K562 from ENCODE 3 (ENCFF254QDM) Regulation 
encTfChipPkENCFF247LOF K562 SIX5 Transcription Factor ChIP-seq Peaks of SIX5 in K562 from ENCODE 3 (ENCFF247LOF) Regulation 
encTfChipPkENCFF747XDN K562 SIRT6 Transcription Factor ChIP-seq Peaks of SIRT6 in K562 from ENCODE 3 (ENCFF747XDN) Regulation 
encTfChipPkENCFF543INR K562 SIN3B Transcription Factor ChIP-seq Peaks of SIN3B in K562 from ENCODE 3 (ENCFF543INR) Regulation 
encTfChipPkENCFF802JAN K562 SIN3A Transcription Factor ChIP-seq Peaks of SIN3A in K562 from ENCODE 3 (ENCFF802JAN) Regulation 
encTfChipPkENCFF690WNQ K562 SETDB1 Transcription Factor ChIP-seq Peaks of SETDB1 in K562 from ENCODE 3 (ENCFF690WNQ) Regulation 
encTfChipPkENCFF103RHL K562 SAP30 Transcription Factor ChIP-seq Peaks of SAP30 in K562 from ENCODE 3 (ENCFF103RHL) Regulation 
encTfChipPkENCFF087DKT K562 SAFB2 Transcription Factor ChIP-seq Peaks of SAFB2 in K562 from ENCODE 3 (ENCFF087DKT) Regulation 
encTfChipPkENCFF411YVY K562 SAFB Transcription Factor ChIP-seq Peaks of SAFB in K562 from ENCODE 3 (ENCFF411YVY) Regulation 
encTfChipPkENCFF091MQJ K562 RUNX1 2 Transcription Factor ChIP-seq Peaks of RUNX1 in K562 from ENCODE 3 (ENCFF091MQJ) Regulation 
encTfChipPkENCFF545WXN K562 RUNX1 1 Transcription Factor ChIP-seq Peaks of RUNX1 in K562 from ENCODE 3 (ENCFF545WXN) Regulation 
encTfChipPkENCFF462AZY K562 RNF2 4 Transcription Factor ChIP-seq Peaks of RNF2 in K562 from ENCODE 3 (ENCFF462AZY) Regulation 
encTfChipPkENCFF741CLJ K562 RNF2 3 Transcription Factor ChIP-seq Peaks of RNF2 in K562 from ENCODE 3 (ENCFF741CLJ) Regulation 
encTfChipPkENCFF820LKT K562 RNF2 2 Transcription Factor ChIP-seq Peaks of RNF2 in K562 from ENCODE 3 (ENCFF820LKT) Regulation 
encTfChipPkENCFF349MSP K562 RNF2 1 Transcription Factor ChIP-seq Peaks of RNF2 in K562 from ENCODE 3 (ENCFF349MSP) Regulation 
encTfChipPkENCFF599CBB K562 RLF Transcription Factor ChIP-seq Peaks of RLF in K562 from ENCODE 3 (ENCFF599CBB) Regulation 
encTfChipPkENCFF201YKU K562 RFX5 Transcription Factor ChIP-seq Peaks of RFX5 in K562 from ENCODE 3 (ENCFF201YKU) Regulation 
encTfChipPkENCFF193PVX K562 RFX1 2 Transcription Factor ChIP-seq Peaks of RFX1 in K562 from ENCODE 3 (ENCFF193PVX) Regulation 
encTfChipPkENCFF905GXS K562 RFX1 1 Transcription Factor ChIP-seq Peaks of RFX1 in K562 from ENCODE 3 (ENCFF905GXS) Regulation 
encTfChipPkENCFF023ZUW K562 REST 2 Transcription Factor ChIP-seq Peaks of REST in K562 from ENCODE 3 (ENCFF023ZUW) Regulation 
encTfChipPkENCFF290ESJ K562 REST 1 Transcription Factor ChIP-seq Peaks of REST in K562 from ENCODE 3 (ENCFF290ESJ) Regulation 
encTfChipPkENCFF968SUH K562 RCOR1 Transcription Factor ChIP-seq Peaks of RCOR1 in K562 from ENCODE 3 (ENCFF968SUH) Regulation 
encTfChipPkENCFF503DIK K562 RBM39 Transcription Factor ChIP-seq Peaks of RBM39 in K562 from ENCODE 3 (ENCFF503DIK) Regulation 
encTfChipPkENCFF670ILH K562 RBM34 Transcription Factor ChIP-seq Peaks of RBM34 in K562 from ENCODE 3 (ENCFF670ILH) Regulation 
encTfChipPkENCFF102XVH K562 RBM25 Transcription Factor ChIP-seq Peaks of RBM25 in K562 from ENCODE 3 (ENCFF102XVH) Regulation 
encTfChipPkENCFF420IBN K562 RBM22 Transcription Factor ChIP-seq Peaks of RBM22 in K562 from ENCODE 3 (ENCFF420IBN) Regulation 
encTfChipPkENCFF056OIG K562 RBM17 Transcription Factor ChIP-seq Peaks of RBM17 in K562 from ENCODE 3 (ENCFF056OIG) Regulation 
encTfChipPkENCFF563WDZ K562 RBM15 Transcription Factor ChIP-seq Peaks of RBM15 in K562 from ENCODE 3 (ENCFF563WDZ) Regulation 
encTfChipPkENCFF320YOI K562 RBM14 Transcription Factor ChIP-seq Peaks of RBM14 in K562 from ENCODE 3 (ENCFF320YOI) Regulation 
encTfChipPkENCFF232ASB K562 RBFOX2 Transcription Factor ChIP-seq Peaks of RBFOX2 in K562 from ENCODE 3 (ENCFF232ASB) Regulation 
encTfChipPkENCFF328QZM K562 RB1 Transcription Factor ChIP-seq Peaks of RB1 in K562 from ENCODE 3 (ENCFF328QZM) Regulation 
encTfChipPkENCFF740OPF K562 RAD51 Transcription Factor ChIP-seq Peaks of RAD51 in K562 from ENCODE 3 (ENCFF740OPF) Regulation 
encTfChipPkENCFF442XXV K562 PYGO2 Transcription Factor ChIP-seq Peaks of PYGO2 in K562 from ENCODE 3 (ENCFF442XXV) Regulation 
encTfChipPkENCFF917HXV K562 PTBP1 Transcription Factor ChIP-seq Peaks of PTBP1 in K562 from ENCODE 3 (ENCFF917HXV) Regulation 
encTfChipPkENCFF417RQZ K562 PRPF4 Transcription Factor ChIP-seq Peaks of PRPF4 in K562 from ENCODE 3 (ENCFF417RQZ) Regulation 
encTfChipPkENCFF600HPZ K562 PRDM10 Transcription Factor ChIP-seq Peaks of PRDM10 in K562 from ENCODE 3 (ENCFF600HPZ) Regulation 
encTfChipPkENCFF283CUY K562 POLR2G Transcription Factor ChIP-seq Peaks of POLR2G in K562 from ENCODE 3 (ENCFF283CUY) Regulation 
encTfChipPkENCFF285MBX K562 POLR2A 7 Transcription Factor ChIP-seq Peaks of POLR2A in K562 from ENCODE 3 (ENCFF285MBX) Regulation 
encTfChipPkENCFF668VIK K562 POLR2A 6 Transcription Factor ChIP-seq Peaks of POLR2A in K562 from ENCODE 3 (ENCFF668VIK) Regulation 
encTfChipPkENCFF881ONC K562 POLR2A 5 Transcription Factor ChIP-seq Peaks of POLR2A in K562 from ENCODE 3 (ENCFF881ONC) Regulation 
encTfChipPkENCFF099NYA K562 POLR2A 4 Transcription Factor ChIP-seq Peaks of POLR2A in K562 from ENCODE 3 (ENCFF099NYA) Regulation 
encTfChipPkENCFF730DLS K562 POLR2A 3 Transcription Factor ChIP-seq Peaks of POLR2A in K562 from ENCODE 3 (ENCFF730DLS) Regulation 
encTfChipPkENCFF741JES K562 POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in K562 from ENCODE 3 (ENCFF741JES) Regulation 
encTfChipPkENCFF182YZG K562 POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in K562 from ENCODE 3 (ENCFF182YZG) Regulation 
encTfChipPkENCFF800QDU K562 PML Transcription Factor ChIP-seq Peaks of PML in K562 from ENCODE 3 (ENCFF800QDU) Regulation 
encTfChipPkENCFF062VBB K562 PKNOX1 Transcription Factor ChIP-seq Peaks of PKNOX1 in K562 from ENCODE 3 (ENCFF062VBB) Regulation 
encTfChipPkENCFF952YDR K562 PHF8 Transcription Factor ChIP-seq Peaks of PHF8 in K562 from ENCODE 3 (ENCFF952YDR) Regulation 
encTfChipPkENCFF657UVA K562 PHF21A Transcription Factor ChIP-seq Peaks of PHF21A in K562 from ENCODE 3 (ENCFF657UVA) Regulation 
encTfChipPkENCFF259HUS K562 PHF20 Transcription Factor ChIP-seq Peaks of PHF20 in K562 from ENCODE 3 (ENCFF259HUS) Regulation 
encTfChipPkENCFF988OXX K562 PHB2 Transcription Factor ChIP-seq Peaks of PHB2 in K562 from ENCODE 3 (ENCFF988OXX) Regulation 
encTfChipPkENCFF941XZW K562 PCBP2 Transcription Factor ChIP-seq Peaks of PCBP2 in K562 from ENCODE 3 (ENCFF941XZW) Regulation 
encTfChipPkENCFF467RYH K562 PCBP1 Transcription Factor ChIP-seq Peaks of PCBP1 in K562 from ENCODE 3 (ENCFF467RYH) Regulation 
encTfChipPkENCFF885JMZ K562 NUFIP1 Transcription Factor ChIP-seq Peaks of NUFIP1 in K562 from ENCODE 3 (ENCFF885JMZ) Regulation 
encTfChipPkENCFF782YFS K562 NRF1 3 Transcription Factor ChIP-seq Peaks of NRF1 in K562 from ENCODE 3 (ENCFF782YFS) Regulation 
encTfChipPkENCFF626VDA K562 NRF1 2 Transcription Factor ChIP-seq Peaks of NRF1 in K562 from ENCODE 3 (ENCFF626VDA) Regulation 
encTfChipPkENCFF543STN K562 NRF1 1 Transcription Factor ChIP-seq Peaks of NRF1 in K562 from ENCODE 3 (ENCFF543STN) Regulation 
encTfChipPkENCFF315MUH K562 NR3C1 2 Transcription Factor ChIP-seq Peaks of NR3C1 in K562 from ENCODE 3 (ENCFF315MUH) Regulation 
encTfChipPkENCFF821YMC K562 NR3C1 1 Transcription Factor ChIP-seq Peaks of NR3C1 in K562 from ENCODE 3 (ENCFF821YMC) Regulation 
encTfChipPkENCFF194VBK K562 NR2F6 Transcription Factor ChIP-seq Peaks of NR2F6 in K562 from ENCODE 3 (ENCFF194VBK) Regulation 
encTfChipPkENCFF118HUH K562 NR2F2 Transcription Factor ChIP-seq Peaks of NR2F2 in K562 from ENCODE 3 (ENCFF118HUH) Regulation 
encTfChipPkENCFF363IQN K562 NR2F1 Transcription Factor ChIP-seq Peaks of NR2F1 in K562 from ENCODE 3 (ENCFF363IQN) Regulation 
encTfChipPkENCFF791ZPU K562 NR2C2 Transcription Factor ChIP-seq Peaks of NR2C2 in K562 from ENCODE 3 (ENCFF791ZPU) Regulation 
encTfChipPkENCFF023XHV K562 NR2C1 Transcription Factor ChIP-seq Peaks of NR2C1 in K562 from ENCODE 3 (ENCFF023XHV) Regulation 
encTfChipPkENCFF305OOU K562 NR0B1 Transcription Factor ChIP-seq Peaks of NR0B1 in K562 from ENCODE 3 (ENCFF305OOU) Regulation 
encTfChipPkENCFF329STX K562 NFXL1 Transcription Factor ChIP-seq Peaks of NFXL1 in K562 from ENCODE 3 (ENCFF329STX) Regulation 
encTfChipPkENCFF779KIS K562 NFRKB 2 Transcription Factor ChIP-seq Peaks of NFRKB in K562 from ENCODE 3 (ENCFF779KIS) Regulation 
encTfChipPkENCFF158FUG K562 NFRKB 1 Transcription Factor ChIP-seq Peaks of NFRKB in K562 from ENCODE 3 (ENCFF158FUG) Regulation 
encTfChipPkENCFF092TVM K562 NFIC Transcription Factor ChIP-seq Peaks of NFIC in K562 from ENCODE 3 (ENCFF092TVM) Regulation 
encTfChipPkENCFF312XHI K562 NFE2 Transcription Factor ChIP-seq Peaks of NFE2 in K562 from ENCODE 3 (ENCFF312XHI) Regulation 
encTfChipPkENCFF430JFH K562 NFATC3 2 Transcription Factor ChIP-seq Peaks of NFATC3 in K562 from ENCODE 3 (ENCFF430JFH) Regulation 
encTfChipPkENCFF082EPO K562 NFATC3 1 Transcription Factor ChIP-seq Peaks of NFATC3 in K562 from ENCODE 3 (ENCFF082EPO) Regulation 
encTfChipPkENCFF755APC K562 NEUROD1 Transcription Factor ChIP-seq Peaks of NEUROD1 in K562 from ENCODE 3 (ENCFF755APC) Regulation 
encTfChipPkENCFF638IIC K562 NCOR1 3 Transcription Factor ChIP-seq Peaks of NCOR1 in K562 from ENCODE 3 (ENCFF638IIC) Regulation 
encTfChipPkENCFF007ZUL K562 NCOR1 2 Transcription Factor ChIP-seq Peaks of NCOR1 in K562 from ENCODE 3 (ENCFF007ZUL) Regulation 
encTfChipPkENCFF856HUK K562 NCOR1 1 Transcription Factor ChIP-seq Peaks of NCOR1 in K562 from ENCODE 3 (ENCFF856HUK) Regulation 
encTfChipPkENCFF438BWN K562 NCOA6 Transcription Factor ChIP-seq Peaks of NCOA6 in K562 from ENCODE 3 (ENCFF438BWN) Regulation 
encTfChipPkENCFF749HKV K562 NCOA4 Transcription Factor ChIP-seq Peaks of NCOA4 in K562 from ENCODE 3 (ENCFF749HKV) Regulation 
encTfChipPkENCFF584SNZ K562 NCOA2 2 Transcription Factor ChIP-seq Peaks of NCOA2 in K562 from ENCODE 3 (ENCFF584SNZ) Regulation 
encTfChipPkENCFF071SOH K562 NCOA2 1 Transcription Factor ChIP-seq Peaks of NCOA2 in K562 from ENCODE 3 (ENCFF071SOH) Regulation 
encTfChipPkENCFF382RFJ K562 NCOA1 3 Transcription Factor ChIP-seq Peaks of NCOA1 in K562 from ENCODE 3 (ENCFF382RFJ) Regulation 
encTfChipPkENCFF474QDS K562 NCOA1 2 Transcription Factor ChIP-seq Peaks of NCOA1 in K562 from ENCODE 3 (ENCFF474QDS) Regulation 
encTfChipPkENCFF589OOF K562 NCOA1 1 Transcription Factor ChIP-seq Peaks of NCOA1 in K562 from ENCODE 3 (ENCFF589OOF) Regulation 
encTfChipPkENCFF728KKP K562 NBN Transcription Factor ChIP-seq Peaks of NBN in K562 from ENCODE 3 (ENCFF728KKP) Regulation 
encTfChipPkENCFF272LLG K562 MYNN Transcription Factor ChIP-seq Peaks of MYNN in K562 from ENCODE 3 (ENCFF272LLG) Regulation 
encTfChipPkENCFF605WXD K562 MYC 5 Transcription Factor ChIP-seq Peaks of MYC in K562 from ENCODE 3 (ENCFF605WXD) Regulation 
encTfChipPkENCFF527EGF K562 MYC 4 Transcription Factor ChIP-seq Peaks of MYC in K562 from ENCODE 3 (ENCFF527EGF) Regulation 
encTfChipPkENCFF492XUU K562 MYC 3 Transcription Factor ChIP-seq Peaks of MYC in K562 from ENCODE 3 (ENCFF492XUU) Regulation 
encTfChipPkENCFF339AQP K562 MYC 2 Transcription Factor ChIP-seq Peaks of MYC in K562 from ENCODE 3 (ENCFF339AQP) Regulation 
encTfChipPkENCFF700TLG K562 MYC 1 Transcription Factor ChIP-seq Peaks of MYC in K562 from ENCODE 3 (ENCFF700TLG) Regulation 
encTfChipPkENCFF905KOD K562 MYBL2 Transcription Factor ChIP-seq Peaks of MYBL2 in K562 from ENCODE 3 (ENCFF905KOD) Regulation 
encTfChipPkENCFF243QTL K562 MXI1 Transcription Factor ChIP-seq Peaks of MXI1 in K562 from ENCODE 3 (ENCFF243QTL) Regulation 
encTfChipPkENCFF459XLR K562 MTA3 Transcription Factor ChIP-seq Peaks of MTA3 in K562 from ENCODE 3 (ENCFF459XLR) Regulation 
encTfChipPkENCFF713ZVD K562 MTA2 2 Transcription Factor ChIP-seq Peaks of MTA2 in K562 from ENCODE 3 (ENCFF713ZVD) Regulation 
encTfChipPkENCFF558XIL K562 MTA2 1 Transcription Factor ChIP-seq Peaks of MTA2 in K562 from ENCODE 3 (ENCFF558XIL) Regulation 
encTfChipPkENCFF801KEW K562 MTA1 Transcription Factor ChIP-seq Peaks of MTA1 in K562 from ENCODE 3 (ENCFF801KEW) Regulation 
encTfChipPkENCFF459DYU K562 MNT 3 Transcription Factor ChIP-seq Peaks of MNT in K562 from ENCODE 3 (ENCFF459DYU) Regulation 
encTfChipPkENCFF454QQD K562 MNT 2 Transcription Factor ChIP-seq Peaks of MNT in K562 from ENCODE 3 (ENCFF454QQD) Regulation 
encTfChipPkENCFF926CRV K562 MNT 1 Transcription Factor ChIP-seq Peaks of MNT in K562 from ENCODE 3 (ENCFF926CRV) Regulation 
encTfChipPkENCFF388LUX K562 MLLT1 2 Transcription Factor ChIP-seq Peaks of MLLT1 in K562 from ENCODE 3 (ENCFF388LUX) Regulation 
encTfChipPkENCFF010AIG K562 MLLT1 1 Transcription Factor ChIP-seq Peaks of MLLT1 in K562 from ENCODE 3 (ENCFF010AIG) Regulation 
encTfChipPkENCFF071NYD K562 MITF 2 Transcription Factor ChIP-seq Peaks of MITF in K562 from ENCODE 3 (ENCFF071NYD) Regulation 
encTfChipPkENCFF262TMM K562 MITF 1 Transcription Factor ChIP-seq Peaks of MITF in K562 from ENCODE 3 (ENCFF262TMM) Regulation 
encTfChipPkENCFF163YZB K562 MIER1 Transcription Factor ChIP-seq Peaks of MIER1 in K562 from ENCODE 3 (ENCFF163YZB) Regulation 
encTfChipPkENCFF525MPI K562 MGA Transcription Factor ChIP-seq Peaks of MGA in K562 from ENCODE 3 (ENCFF525MPI) Regulation 
encTfChipPkENCFF937UEE K562 MEIS2 Transcription Factor ChIP-seq Peaks of MEIS2 in K562 from ENCODE 3 (ENCFF937UEE) Regulation 
encTfChipPkENCFF310SMW K562 MEF2A Transcription Factor ChIP-seq Peaks of MEF2A in K562 from ENCODE 3 (ENCFF310SMW) Regulation 
encTfChipPkENCFF288ZRD K562 MCM7 3 Transcription Factor ChIP-seq Peaks of MCM7 in K562 from ENCODE 3 (ENCFF288ZRD) Regulation 
encTfChipPkENCFF914ELA K562 MCM7 2 Transcription Factor ChIP-seq Peaks of MCM7 in K562 from ENCODE 3 (ENCFF914ELA) Regulation 
encTfChipPkENCFF159MQI K562 MCM7 1 Transcription Factor ChIP-seq Peaks of MCM7 in K562 from ENCODE 3 (ENCFF159MQI) Regulation 
encTfChipPkENCFF658SJY K562 MCM5 2 Transcription Factor ChIP-seq Peaks of MCM5 in K562 from ENCODE 3 (ENCFF658SJY) Regulation 
encTfChipPkENCFF603SXI K562 MCM5 1 Transcription Factor ChIP-seq Peaks of MCM5 in K562 from ENCODE 3 (ENCFF603SXI) Regulation 
encTfChipPkENCFF672PYP K562 MCM3 Transcription Factor ChIP-seq Peaks of MCM3 in K562 from ENCODE 3 (ENCFF672PYP) Regulation 
encTfChipPkENCFF571REC K562 MCM2 2 Transcription Factor ChIP-seq Peaks of MCM2 in K562 from ENCODE 3 (ENCFF571REC) Regulation 
encTfChipPkENCFF043HHG K562 MCM2 1 Transcription Factor ChIP-seq Peaks of MCM2 in K562 from ENCODE 3 (ENCFF043HHG) Regulation 
encTfChipPkENCFF617QSK K562 MBD2 Transcription Factor ChIP-seq Peaks of MBD2 in K562 from ENCODE 3 (ENCFF617QSK) Regulation 
encTfChipPkENCFF900NVQ K562 MAX 2 Transcription Factor ChIP-seq Peaks of MAX in K562 from ENCODE 3 (ENCFF900NVQ) Regulation 
encTfChipPkENCFF618VMC K562 MAX 1 Transcription Factor ChIP-seq Peaks of MAX in K562 from ENCODE 3 (ENCFF618VMC) Regulation 
encTfChipPkENCFF893SCL K562 MAFK Transcription Factor ChIP-seq Peaks of MAFK in K562 from ENCODE 3 (ENCFF893SCL) Regulation 
encTfChipPkENCFF498MGH K562 MAFF Transcription Factor ChIP-seq Peaks of MAFF in K562 from ENCODE 3 (ENCFF498MGH) Regulation 
encTfChipPkENCFF697VRJ K562 LEF1 2 Transcription Factor ChIP-seq Peaks of LEF1 in K562 from ENCODE 3 (ENCFF697VRJ) Regulation 
encTfChipPkENCFF134HQP K562 LEF1 1 Transcription Factor ChIP-seq Peaks of LEF1 in K562 from ENCODE 3 (ENCFF134HQP) Regulation 
encTfChipPkENCFF423LPW K562 L3MBTL2 Transcription Factor ChIP-seq Peaks of L3MBTL2 in K562 from ENCODE 3 (ENCFF423LPW) Regulation 
encTfChipPkENCFF379LKE K562 KLF16 Transcription Factor ChIP-seq Peaks of KLF16 in K562 from ENCODE 3 (ENCFF379LKE) Regulation 
encTfChipPkENCFF668XLN K562 KDM5B Transcription Factor ChIP-seq Peaks of KDM5B in K562 from ENCODE 3 (ENCFF668XLN) Regulation 
encTfChipPkENCFF955AOD K562 KDM4B 2 Transcription Factor ChIP-seq Peaks of KDM4B in K562 from ENCODE 3 (ENCFF955AOD) Regulation 
encTfChipPkENCFF470RHZ K562 KDM4B 1 Transcription Factor ChIP-seq Peaks of KDM4B in K562 from ENCODE 3 (ENCFF470RHZ) Regulation 
encTfChipPkENCFF483BRD K562 KDM1A 2 Transcription Factor ChIP-seq Peaks of KDM1A in K562 from ENCODE 3 (ENCFF483BRD) Regulation 
encTfChipPkENCFF796VMI K562 KDM1A 1 Transcription Factor ChIP-seq Peaks of KDM1A in K562 from ENCODE 3 (ENCFF796VMI) Regulation 
encTfChipPkENCFF207ZEK K562 KAT8 Transcription Factor ChIP-seq Peaks of KAT8 in K562 from ENCODE 3 (ENCFF207ZEK) Regulation 
encTfChipPkENCFF556XQQ K562 KAT2B Transcription Factor ChIP-seq Peaks of KAT2B in K562 from ENCODE 3 (ENCFF556XQQ) Regulation 
encTfChipPkENCFF213EYD K562 JUND Transcription Factor ChIP-seq Peaks of JUND in K562 from ENCODE 3 (ENCFF213EYD) Regulation 
encTfChipPkENCFF739XTO K562 JUNB Transcription Factor ChIP-seq Peaks of JUNB in K562 from ENCODE 3 (ENCFF739XTO) Regulation 
encTfChipPkENCFF032UMW K562 JUN 5 Transcription Factor ChIP-seq Peaks of JUN in K562 from ENCODE 3 (ENCFF032UMW) Regulation 
encTfChipPkENCFF394CEC K562 JUN 4 Transcription Factor ChIP-seq Peaks of JUN in K562 from ENCODE 3 (ENCFF394CEC) Regulation 
encTfChipPkENCFF167WUZ K562 JUN 3 Transcription Factor ChIP-seq Peaks of JUN in K562 from ENCODE 3 (ENCFF167WUZ) Regulation 
encTfChipPkENCFF672LKE K562 JUN 2 Transcription Factor ChIP-seq Peaks of JUN in K562 from ENCODE 3 (ENCFF672LKE) Regulation 
encTfChipPkENCFF881AVX K562 JUN 1 Transcription Factor ChIP-seq Peaks of JUN in K562 from ENCODE 3 (ENCFF881AVX) Regulation 
encTfChipPkENCFF886EVL K562 IRF2 Transcription Factor ChIP-seq Peaks of IRF2 in K562 from ENCODE 3 (ENCFF886EVL) Regulation 
encTfChipPkENCFF346LMY K562 IRF1 4 Transcription Factor ChIP-seq Peaks of IRF1 in K562 from ENCODE 3 (ENCFF346LMY) Regulation 
encTfChipPkENCFF938NBD K562 IRF1 3 Transcription Factor ChIP-seq Peaks of IRF1 in K562 from ENCODE 3 (ENCFF938NBD) Regulation 
encTfChipPkENCFF688XON K562 IRF1 2 Transcription Factor ChIP-seq Peaks of IRF1 in K562 from ENCODE 3 (ENCFF688XON) Regulation 
encTfChipPkENCFF978BBL K562 IRF1 1 Transcription Factor ChIP-seq Peaks of IRF1 in K562 from ENCODE 3 (ENCFF978BBL) Regulation 
encTfChipPkENCFF994OQH K562 IKZF1 2 Transcription Factor ChIP-seq Peaks of IKZF1 in K562 from ENCODE 3 (ENCFF994OQH) Regulation 
encTfChipPkENCFF785BTP K562 IKZF1 1 Transcription Factor ChIP-seq Peaks of IKZF1 in K562 from ENCODE 3 (ENCFF785BTP) Regulation 
encTfChipPkENCFF991ZSC K562 HNRNPUL1 Transcription Factor ChIP-seq Peaks of HNRNPUL1 in K562 from ENCODE 3 (ENCFF991ZSC) Regulation 
encTfChipPkENCFF662WPN K562 HNRNPLL Transcription Factor ChIP-seq Peaks of HNRNPLL in K562 from ENCODE 3 (ENCFF662WPN) Regulation 
encTfChipPkENCFF984ESZ K562 HNRNPL Transcription Factor ChIP-seq Peaks of HNRNPL in K562 from ENCODE 3 (ENCFF984ESZ) Regulation 
encTfChipPkENCFF984QUV K562 HNRNPK Transcription Factor ChIP-seq Peaks of HNRNPK in K562 from ENCODE 3 (ENCFF984QUV) Regulation 
encTfChipPkENCFF844QFF K562 HNRNPH1 Transcription Factor ChIP-seq Peaks of HNRNPH1 in K562 from ENCODE 3 (ENCFF844QFF) Regulation 
encTfChipPkENCFF718DFX K562 HMBOX1 Transcription Factor ChIP-seq Peaks of HMBOX1 in K562 from ENCODE 3 (ENCFF718DFX) Regulation 
encTfChipPkENCFF010OOE K562 HES1 Transcription Factor ChIP-seq Peaks of HES1 in K562 from ENCODE 3 (ENCFF010OOE) Regulation 
encTfChipPkENCFF295GBP K562 HDAC6 Transcription Factor ChIP-seq Peaks of HDAC6 in K562 from ENCODE 3 (ENCFF295GBP) Regulation 
encTfChipPkENCFF742LSD K562 HDAC3 Transcription Factor ChIP-seq Peaks of HDAC3 in K562 from ENCODE 3 (ENCFF742LSD) Regulation 
encTfChipPkENCFF618YRQ K562 HDAC2 3 Transcription Factor ChIP-seq Peaks of HDAC2 in K562 from ENCODE 3 (ENCFF618YRQ) Regulation 
encTfChipPkENCFF519RWJ K562 HDAC2 2 Transcription Factor ChIP-seq Peaks of HDAC2 in K562 from ENCODE 3 (ENCFF519RWJ) Regulation 
encTfChipPkENCFF363GSV K562 HDAC2 1 Transcription Factor ChIP-seq Peaks of HDAC2 in K562 from ENCODE 3 (ENCFF363GSV) Regulation 
encTfChipPkENCFF557WXK K562 HDAC1 4 Transcription Factor ChIP-seq Peaks of HDAC1 in K562 from ENCODE 3 (ENCFF557WXK) Regulation 
encTfChipPkENCFF188TBM K562 HDAC1 3 Transcription Factor ChIP-seq Peaks of HDAC1 in K562 from ENCODE 3 (ENCFF188TBM) Regulation 
encTfChipPkENCFF661VOO K562 HDAC1 2 Transcription Factor ChIP-seq Peaks of HDAC1 in K562 from ENCODE 3 (ENCFF661VOO) Regulation 
encTfChipPkENCFF758PGF K562 HDAC1 1 Transcription Factor ChIP-seq Peaks of HDAC1 in K562 from ENCODE 3 (ENCFF758PGF) Regulation 
encTfChipPkENCFF167RXK K562 HCFC1 Transcription Factor ChIP-seq Peaks of HCFC1 in K562 from ENCODE 3 (ENCFF167RXK) Regulation 
encTfChipPkENCFF678VPQ K562 GMEB1 Transcription Factor ChIP-seq Peaks of GMEB1 in K562 from ENCODE 3 (ENCFF678VPQ) Regulation 
encTfChipPkENCFF569CMJ K562 GATAD2B Transcription Factor ChIP-seq Peaks of GATAD2B in K562 from ENCODE 3 (ENCFF569CMJ) Regulation 
encTfChipPkENCFF950ZWP K562 GATAD2A Transcription Factor ChIP-seq Peaks of GATAD2A in K562 from ENCODE 3 (ENCFF950ZWP) Regulation 
encTfChipPkENCFF173TXA K562 GATA2 Transcription Factor ChIP-seq Peaks of GATA2 in K562 from ENCODE 3 (ENCFF173TXA) Regulation 
encTfChipPkENCFF148JKK K562 GATA1 Transcription Factor ChIP-seq Peaks of GATA1 in K562 from ENCODE 3 (ENCFF148JKK) Regulation 
encTfChipPkENCFF700DXR K562 GABPB1 Transcription Factor ChIP-seq Peaks of GABPB1 in K562 from ENCODE 3 (ENCFF700DXR) Regulation 
encTfChipPkENCFF124HAC K562 GABPA Transcription Factor ChIP-seq Peaks of GABPA in K562 from ENCODE 3 (ENCFF124HAC) Regulation 
encTfChipPkENCFF688ARM K562 FUS Transcription Factor ChIP-seq Peaks of FUS in K562 from ENCODE 3 (ENCFF688ARM) Regulation 
encTfChipPkENCFF778PWE K562 FOXM1 Transcription Factor ChIP-seq Peaks of FOXM1 in K562 from ENCODE 3 (ENCFF778PWE) Regulation 
encTfChipPkENCFF490EQR K562 FOXK2 2 Transcription Factor ChIP-seq Peaks of FOXK2 in K562 from ENCODE 3 (ENCFF490EQR) Regulation 
encTfChipPkENCFF066CWG K562 FOXK2 1 Transcription Factor ChIP-seq Peaks of FOXK2 in K562 from ENCODE 3 (ENCFF066CWG) Regulation 
encTfChipPkENCFF765NAN K562 FOXA1 Transcription Factor ChIP-seq Peaks of FOXA1 in K562 from ENCODE 3 (ENCFF765NAN) Regulation 
encTfChipPkENCFF087MFG K562 FOSL1 Transcription Factor ChIP-seq Peaks of FOSL1 in K562 from ENCODE 3 (ENCFF087MFG) Regulation 
encTfChipPkENCFF084DTV K562 FIP1L1 Transcription Factor ChIP-seq Peaks of FIP1L1 in K562 from ENCODE 3 (ENCFF084DTV) Regulation 
encTfChipPkENCFF560CYG K562 EWSR1 Transcription Factor ChIP-seq Peaks of EWSR1 in K562 from ENCODE 3 (ENCFF560CYG) Regulation 
encTfChipPkENCFF658SGJ K562 ETV6 2 Transcription Factor ChIP-seq Peaks of ETV6 in K562 from ENCODE 3 (ENCFF658SGJ) Regulation 
encTfChipPkENCFF426GSY K562 ETV6 1 Transcription Factor ChIP-seq Peaks of ETV6 in K562 from ENCODE 3 (ENCFF426GSY) Regulation 
encTfChipPkENCFF461PRP K562 ETS1 Transcription Factor ChIP-seq Peaks of ETS1 in K562 from ENCODE 3 (ENCFF461PRP) Regulation 
encTfChipPkENCFF592GWM K562 ESRRA Transcription Factor ChIP-seq Peaks of ESRRA in K562 from ENCODE 3 (ENCFF592GWM) Regulation 
encTfChipPkENCFF225BXA K562 EP400 Transcription Factor ChIP-seq Peaks of EP400 in K562 from ENCODE 3 (ENCFF225BXA) Regulation 
encTfChipPkENCFF755HCK K562 EP300 Transcription Factor ChIP-seq Peaks of EP300 in K562 from ENCODE 3 (ENCFF755HCK) Regulation 
encTfChipPkENCFF119SCQ K562 ELK1 Transcription Factor ChIP-seq Peaks of ELK1 in K562 from ENCODE 3 (ENCFF119SCQ) Regulation 
encTfChipPkENCFF539SXG K562 ELF4 Transcription Factor ChIP-seq Peaks of ELF4 in K562 from ENCODE 3 (ENCFF539SXG) Regulation 
encTfChipPkENCFF617ZLL K562 ELF1 Transcription Factor ChIP-seq Peaks of ELF1 in K562 from ENCODE 3 (ENCFF617ZLL) Regulation 
encTfChipPkENCFF682XPD K562 EHMT2 Transcription Factor ChIP-seq Peaks of EHMT2 in K562 from ENCODE 3 (ENCFF682XPD) Regulation 
encTfChipPkENCFF561OGS K562 EGR1 3 Transcription Factor ChIP-seq Peaks of EGR1 in K562 from ENCODE 3 (ENCFF561OGS) Regulation 
encTfChipPkENCFF175VSS K562 EGR1 2 Transcription Factor ChIP-seq Peaks of EGR1 in K562 from ENCODE 3 (ENCFF175VSS) Regulation 
encTfChipPkENCFF375RDB K562 EGR1 1 Transcription Factor ChIP-seq Peaks of EGR1 in K562 from ENCODE 3 (ENCFF375RDB) Regulation 
encTfChipPkENCFF752KNU K562 E4F1 Transcription Factor ChIP-seq Peaks of E4F1 in K562 from ENCODE 3 (ENCFF752KNU) Regulation 
encTfChipPkENCFF171WWF K562 E2F8 Transcription Factor ChIP-seq Peaks of E2F8 in K562 from ENCODE 3 (ENCFF171WWF) Regulation 
encTfChipPkENCFF013EHI K562 E2F7 Transcription Factor ChIP-seq Peaks of E2F7 in K562 from ENCODE 3 (ENCFF013EHI) Regulation 
encTfChipPkENCFF533GSH K562 E2F6 Transcription Factor ChIP-seq Peaks of E2F6 in K562 from ENCODE 3 (ENCFF533GSH) Regulation 
encTfChipPkENCFF445VTT K562 E2F1 2 Transcription Factor ChIP-seq Peaks of E2F1 in K562 from ENCODE 3 (ENCFF445VTT) Regulation 
encTfChipPkENCFF134JLR K562 E2F1 1 Transcription Factor ChIP-seq Peaks of E2F1 in K562 from ENCODE 3 (ENCFF134JLR) Regulation 
encTfChipPkENCFF217ZTP K562 DPF2 2 Transcription Factor ChIP-seq Peaks of DPF2 in K562 from ENCODE 3 (ENCFF217ZTP) Regulation 
encTfChipPkENCFF537VKZ K562 DPF2 1 Transcription Factor ChIP-seq Peaks of DPF2 in K562 from ENCODE 3 (ENCFF537VKZ) Regulation 
encTfChipPkENCFF549TVW K562 DNMT1 Transcription Factor ChIP-seq Peaks of DNMT1 in K562 from ENCODE 3 (ENCFF549TVW) Regulation 
encTfChipPkENCFF532HCE K562 DEAF1 Transcription Factor ChIP-seq Peaks of DEAF1 in K562 from ENCODE 3 (ENCFF532HCE) Regulation 
encTfChipPkENCFF870LJV K562 DACH1 Transcription Factor ChIP-seq Peaks of DACH1 in K562 from ENCODE 3 (ENCFF870LJV) Regulation 
encTfChipPkENCFF556HMX K562 CUX1 Transcription Factor ChIP-seq Peaks of CUX1 in K562 from ENCODE 3 (ENCFF556HMX) Regulation 
encTfChipPkENCFF396BZQ K562 CTCF 4 Transcription Factor ChIP-seq Peaks of CTCF in K562 from ENCODE 3 (ENCFF396BZQ) Regulation 
encTfChipPkENCFF119XFJ K562 CTCF 3 Transcription Factor ChIP-seq Peaks of CTCF in K562 from ENCODE 3 (ENCFF119XFJ) Regulation 
encTfChipPkENCFF519CXF K562 CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in K562 from ENCODE 3 (ENCFF519CXF) Regulation 
encTfChipPkENCFF843VHC K562 CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in K562 from ENCODE 3 (ENCFF843VHC) Regulation 
encTfChipPkENCFF349UTF K562 CTBP1 Transcription Factor ChIP-seq Peaks of CTBP1 in K562 from ENCODE 3 (ENCFF349UTF) Regulation 
encTfChipPkENCFF021XJN K562 CREM Transcription Factor ChIP-seq Peaks of CREM in K562 from ENCODE 3 (ENCFF021XJN) Regulation 
encTfChipPkENCFF678FRK K562 CREBBP Transcription Factor ChIP-seq Peaks of CREBBP in K562 from ENCODE 3 (ENCFF678FRK) Regulation 
encTfChipPkENCFF566HGU K562 CREB3L1 Transcription Factor ChIP-seq Peaks of CREB3L1 in K562 from ENCODE 3 (ENCFF566HGU) Regulation 
encTfChipPkENCFF552EBC K562 COPS2 Transcription Factor ChIP-seq Peaks of COPS2 in K562 from ENCODE 3 (ENCFF552EBC) Regulation 
encTfChipPkENCFF919KNQ K562 CHAMP1 2 Transcription Factor ChIP-seq Peaks of CHAMP1 in K562 from ENCODE 3 (ENCFF919KNQ) Regulation 
encTfChipPkENCFF646MEF K562 CHAMP1 1 Transcription Factor ChIP-seq Peaks of CHAMP1 in K562 from ENCODE 3 (ENCFF646MEF) Regulation 
encTfChipPkENCFF321KQD K562 CEBPB Transcription Factor ChIP-seq Peaks of CEBPB in K562 from ENCODE 3 (ENCFF321KQD) Regulation 
encTfChipPkENCFF384ALH K562 CDC5L Transcription Factor ChIP-seq Peaks of CDC5L in K562 from ENCODE 3 (ENCFF384ALH) Regulation 
encTfChipPkENCFF704PGT K562 CCAR2 Transcription Factor ChIP-seq Peaks of CCAR2 in K562 from ENCODE 3 (ENCFF704PGT) Regulation 
encTfChipPkENCFF180TUM K562 CC2D1A Transcription Factor ChIP-seq Peaks of CC2D1A in K562 from ENCODE 3 (ENCFF180TUM) Regulation 
encTfChipPkENCFF403TAE K562 CBX5 Transcription Factor ChIP-seq Peaks of CBX5 in K562 from ENCODE 3 (ENCFF403TAE) Regulation 
encTfChipPkENCFF951BQB K562 CBX3 2 Transcription Factor ChIP-seq Peaks of CBX3 in K562 from ENCODE 3 (ENCFF951BQB) Regulation 
encTfChipPkENCFF378YKS K562 CBX3 1 Transcription Factor ChIP-seq Peaks of CBX3 in K562 from ENCODE 3 (ENCFF378YKS) Regulation 
encTfChipPkENCFF163FLA K562 CBX1 Transcription Factor ChIP-seq Peaks of CBX1 in K562 from ENCODE 3 (ENCFF163FLA) Regulation 
encTfChipPkENCFF153IFH K562 CBFA2T3 Transcription Factor ChIP-seq Peaks of CBFA2T3 in K562 from ENCODE 3 (ENCFF153IFH) Regulation 
encTfChipPkENCFF419PEK K562 CBFA2T2 Transcription Factor ChIP-seq Peaks of CBFA2T2 in K562 from ENCODE 3 (ENCFF419PEK) Regulation 
encTfChipPkENCFF104MXG K562 C11orf30 Transcription Factor ChIP-seq Peaks of C11orf30 in K562 from ENCODE 3 (ENCFF104MXG) Regulation 
encTfChipPkENCFF411RMT K562 BRD9 Transcription Factor ChIP-seq Peaks of BRD9 in K562 from ENCODE 3 (ENCFF411RMT) Regulation 
encTfChipPkENCFF806CQB K562 BRD4 Transcription Factor ChIP-seq Peaks of BRD4 in K562 from ENCODE 3 (ENCFF806CQB) Regulation 
encTfChipPkENCFF652NES K562 BRCA1 Transcription Factor ChIP-seq Peaks of BRCA1 in K562 from ENCODE 3 (ENCFF652NES) Regulation 
encTfChipPkENCFF352DRR K562 BMI1 Transcription Factor ChIP-seq Peaks of BMI1 in K562 from ENCODE 3 (ENCFF352DRR) Regulation 
encTfChipPkENCFF477JTV K562 BHLHE40 Transcription Factor ChIP-seq Peaks of BHLHE40 in K562 from ENCODE 3 (ENCFF477JTV) Regulation 
encTfChipPkENCFF186JKG K562 BCOR Transcription Factor ChIP-seq Peaks of BCOR in K562 from ENCODE 3 (ENCFF186JKG) Regulation 
encTfChipPkENCFF543FNN K562 BACH1 Transcription Factor ChIP-seq Peaks of BACH1 in K562 from ENCODE 3 (ENCFF543FNN) Regulation 
encTfChipPkENCFF371SJR K562 ATF7 Transcription Factor ChIP-seq Peaks of ATF7 in K562 from ENCODE 3 (ENCFF371SJR) Regulation 
encTfChipPkENCFF182MNO K562 ATF4 Transcription Factor ChIP-seq Peaks of ATF4 in K562 from ENCODE 3 (ENCFF182MNO) Regulation 
encTfChipPkENCFF937OKC K562 ATF3 2 Transcription Factor ChIP-seq Peaks of ATF3 in K562 from ENCODE 3 (ENCFF937OKC) Regulation 
encTfChipPkENCFF467WOR K562 ATF3 1 Transcription Factor ChIP-seq Peaks of ATF3 in K562 from ENCODE 3 (ENCFF467WOR) Regulation 
encTfChipPkENCFF803FHN K562 ATF2 Transcription Factor ChIP-seq Peaks of ATF2 in K562 from ENCODE 3 (ENCFF803FHN) Regulation 
encTfChipPkENCFF958YSG K562 ASH1L Transcription Factor ChIP-seq Peaks of ASH1L in K562 from ENCODE 3 (ENCFF958YSG) Regulation 
encTfChipPkENCFF913AQF K562 ARNT 3 Transcription Factor ChIP-seq Peaks of ARNT in K562 from ENCODE 3 (ENCFF913AQF) Regulation 
encTfChipPkENCFF447FIO K562 ARNT 2 Transcription Factor ChIP-seq Peaks of ARNT in K562 from ENCODE 3 (ENCFF447FIO) Regulation 
encTfChipPkENCFF655EFA K562 ARNT 1 Transcription Factor ChIP-seq Peaks of ARNT in K562 from ENCODE 3 (ENCFF655EFA) Regulation 
encTfChipPkENCFF757OML K562 ARID3A Transcription Factor ChIP-seq Peaks of ARID3A in K562 from ENCODE 3 (ENCFF757OML) Regulation 
encTfChipPkENCFF344MKI K562 ARID2 Transcription Factor ChIP-seq Peaks of ARID2 in K562 from ENCODE 3 (ENCFF344MKI) Regulation 
encTfChipPkENCFF249TYS K562 ARID1B Transcription Factor ChIP-seq Peaks of ARID1B in K562 from ENCODE 3 (ENCFF249TYS) Regulation 
encTfChipPkENCFF089PKE K562 ARHGAP35 Transcription Factor ChIP-seq Peaks of ARHGAP35 in K562 from ENCODE 3 (ENCFF089PKE) Regulation 
encTfChipPkENCFF100VYA K562 AGO1 Transcription Factor ChIP-seq Peaks of AGO1 in K562 from ENCODE 3 (ENCFF100VYA) Regulation 
encTfChipPkENCFF489SKQ K562 AFF1 2 Transcription Factor ChIP-seq Peaks of AFF1 in K562 from ENCODE 3 (ENCFF489SKQ) Regulation 
encTfChipPkENCFF869BYK K562 AFF1 1 Transcription Factor ChIP-seq Peaks of AFF1 in K562 from ENCODE 3 (ENCFF869BYK) Regulation 
encTfChipPkENCFF085HJD Ishikawa POLR2A Transcription Factor ChIP-seq Peaks of POLR2A in Ishikawa from ENCODE 3 (ENCFF085HJD) Regulation 
encTfChipPkENCFF293OHT Ishikawa NR3C1 2 Transcription Factor ChIP-seq Peaks of NR3C1 in Ishikawa from ENCODE 3 (ENCFF293OHT) Regulation 
encTfChipPkENCFF519BOO Ishikawa NR3C1 1 Transcription Factor ChIP-seq Peaks of NR3C1 in Ishikawa from ENCODE 3 (ENCFF519BOO) Regulation 
encTfChipPkENCFF778BLL Ishikawa ESR1 3 Transcription Factor ChIP-seq Peaks of ESR1 in Ishikawa from ENCODE 3 (ENCFF778BLL) Regulation 
encTfChipPkENCFF279JGE Ishikawa ESR1 2 Transcription Factor ChIP-seq Peaks of ESR1 in Ishikawa from ENCODE 3 (ENCFF279JGE) Regulation 
encTfChipPkENCFF076OFH Ishikawa ESR1 1 Transcription Factor ChIP-seq Peaks of ESR1 in Ishikawa from ENCODE 3 (ENCFF076OFH) Regulation 
encTfChipPkENCFF675JJV Ishikawa CTCF Transcription Factor ChIP-seq Peaks of CTCF in Ishikawa from ENCODE 3 (ENCFF675JJV) Regulation 
encTfChipPkENCFF938BOJ IMR-90 USF2 Transcription Factor ChIP-seq Peaks of USF2 in IMR-90 from ENCODE 3 (ENCFF938BOJ) Regulation 
encTfChipPkENCFF380ZXB IMR-90 SMC3 Transcription Factor ChIP-seq Peaks of SMC3 in IMR-90 from ENCODE 3 (ENCFF380ZXB) Regulation 
encTfChipPkENCFF139EBY IMR-90 RCOR1 Transcription Factor ChIP-seq Peaks of RCOR1 in IMR-90 from ENCODE 3 (ENCFF139EBY) Regulation 
encTfChipPkENCFF895JAW IMR-90 RAD21 Transcription Factor ChIP-seq Peaks of RAD21 in IMR-90 from ENCODE 3 (ENCFF895JAW) Regulation 
encTfChipPkENCFF474PPT IMR-90 NFE2L2 Transcription Factor ChIP-seq Peaks of NFE2L2 in IMR-90 from ENCODE 3 (ENCFF474PPT) Regulation 
encTfChipPkENCFF351VGZ IMR-90 MAFK Transcription Factor ChIP-seq Peaks of MAFK in IMR-90 from ENCODE 3 (ENCFF351VGZ) Regulation 
encTfChipPkENCFF217ZMF IMR-90 FOS Transcription Factor ChIP-seq Peaks of FOS in IMR-90 from ENCODE 3 (ENCFF217ZMF) Regulation 
encTfChipPkENCFF687IUD IMR-90 ELK1 Transcription Factor ChIP-seq Peaks of ELK1 in IMR-90 from ENCODE 3 (ENCFF687IUD) Regulation 
encTfChipPkENCFF307XFM IMR-90 CTCF Transcription Factor ChIP-seq Peaks of CTCF in IMR-90 from ENCODE 3 (ENCFF307XFM) Regulation 
encTfChipPkENCFF510QXG IMR-90 CHD1 Transcription Factor ChIP-seq Peaks of CHD1 in IMR-90 from ENCODE 3 (ENCFF510QXG) Regulation 
encTfChipPkENCFF757KYL IMR-90 CEBPB Transcription Factor ChIP-seq Peaks of CEBPB in IMR-90 from ENCODE 3 (ENCFF757KYL) Regulation 
encTfChipPkENCFF567GON IMR-90 BHLHE40 Transcription Factor ChIP-seq Peaks of BHLHE40 in IMR-90 from ENCODE 3 (ENCFF567GON) Regulation 
encTfChipPkENCFF950VAR HepG2 ZNF384 Transcription Factor ChIP-seq Peaks of ZNF384 in HepG2 from ENCODE 3 (ENCFF950VAR) Regulation 
encTfChipPkENCFF482XNG HepG2 ZNF282 Transcription Factor ChIP-seq Peaks of ZNF282 in HepG2 from ENCODE 3 (ENCFF482XNG) Regulation 
encTfChipPkENCFF858WPR HepG2 ZNF24 2 Transcription Factor ChIP-seq Peaks of ZNF24 in HepG2 from ENCODE 3 (ENCFF858WPR) Regulation 
encTfChipPkENCFF904QAD HepG2 ZNF24 1 Transcription Factor ChIP-seq Peaks of ZNF24 in HepG2 from ENCODE 3 (ENCFF904QAD) Regulation 
encTfChipPkENCFF657ZXY HepG2 ZNF207 Transcription Factor ChIP-seq Peaks of ZNF207 in HepG2 from ENCODE 3 (ENCFF657ZXY) Regulation 
encTfChipPkENCFF769SEZ HepG2 ZMYM3 Transcription Factor ChIP-seq Peaks of ZMYM3 in HepG2 from ENCODE 3 (ENCFF769SEZ) Regulation 
encTfChipPkENCFF721NEC HepG2 ZKSCAN1 Transcription Factor ChIP-seq Peaks of ZKSCAN1 in HepG2 from ENCODE 3 (ENCFF721NEC) Regulation 
encTfChipPkENCFF964KDQ HepG2 ZHX2 Transcription Factor ChIP-seq Peaks of ZHX2 in HepG2 from ENCODE 3 (ENCFF964KDQ) Regulation 
encTfChipPkENCFF953JQD HepG2 ZBTB7A Transcription Factor ChIP-seq Peaks of ZBTB7A in HepG2 from ENCODE 3 (ENCFF953JQD) Regulation 
encTfChipPkENCFF624WDI HepG2 ZBTB40 Transcription Factor ChIP-seq Peaks of ZBTB40 in HepG2 from ENCODE 3 (ENCFF624WDI) Regulation 
encTfChipPkENCFF943WRA HepG2 ZBTB33 Transcription Factor ChIP-seq Peaks of ZBTB33 in HepG2 from ENCODE 3 (ENCFF943WRA) Regulation 
encTfChipPkENCFF177YDT HepG2 YY1 Transcription Factor ChIP-seq Peaks of YY1 in HepG2 from ENCODE 3 (ENCFF177YDT) Regulation 
encTfChipPkENCFF790ZAQ HepG2 XRCC5 Transcription Factor ChIP-seq Peaks of XRCC5 in HepG2 from ENCODE 3 (ENCFF790ZAQ) Regulation 
encTfChipPkENCFF914IFQ HepG2 USF1 Transcription Factor ChIP-seq Peaks of USF1 in HepG2 from ENCODE 3 (ENCFF914IFQ) Regulation 
encTfChipPkENCFF562ADR HepG2 U2AF2 Transcription Factor ChIP-seq Peaks of U2AF2 in HepG2 from ENCODE 3 (ENCFF562ADR) Regulation 
encTfChipPkENCFF034KUO HepG2 U2AF1 Transcription Factor ChIP-seq Peaks of U2AF1 in HepG2 from ENCODE 3 (ENCFF034KUO) Regulation 
encTfChipPkENCFF063GDN HepG2 TRIM22 Transcription Factor ChIP-seq Peaks of TRIM22 in HepG2 from ENCODE 3 (ENCFF063GDN) Regulation 
encTfChipPkENCFF912SQI HepG2 TFAP4 Transcription Factor ChIP-seq Peaks of TFAP4 in HepG2 from ENCODE 3 (ENCFF912SQI) Regulation 
encTfChipPkENCFF928MIN HepG2 TCF7 Transcription Factor ChIP-seq Peaks of TCF7 in HepG2 from ENCODE 3 (ENCFF928MIN) Regulation 
encTfChipPkENCFF299JYV HepG2 TCF12 2 Transcription Factor ChIP-seq Peaks of TCF12 in HepG2 from ENCODE 3 (ENCFF299JYV) Regulation 
encTfChipPkENCFF820PHL HepG2 TCF12 1 Transcription Factor ChIP-seq Peaks of TCF12 in HepG2 from ENCODE 3 (ENCFF820PHL) Regulation 
encTfChipPkENCFF887DUY HepG2 TBX3 2 Transcription Factor ChIP-seq Peaks of TBX3 in HepG2 from ENCODE 3 (ENCFF887DUY) Regulation 
encTfChipPkENCFF654KVO HepG2 TBX3 1 Transcription Factor ChIP-seq Peaks of TBX3 in HepG2 from ENCODE 3 (ENCFF654KVO) Regulation 
encTfChipPkENCFF534GKQ HepG2 TBP Transcription Factor ChIP-seq Peaks of TBP in HepG2 from ENCODE 3 (ENCFF534GKQ) Regulation 
encTfChipPkENCFF126KGW HepG2 TBL1XR1 Transcription Factor ChIP-seq Peaks of TBL1XR1 in HepG2 from ENCODE 3 (ENCFF126KGW) Regulation 
encTfChipPkENCFF718RXL HepG2 TAF15 Transcription Factor ChIP-seq Peaks of TAF15 in HepG2 from ENCODE 3 (ENCFF718RXL) Regulation 
encTfChipPkENCFF234TBW HepG2 TAF1 Transcription Factor ChIP-seq Peaks of TAF1 in HepG2 from ENCODE 3 (ENCFF234TBW) Regulation 
encTfChipPkENCFF239LRW HepG2 SUZ12 Transcription Factor ChIP-seq Peaks of SUZ12 in HepG2 from ENCODE 3 (ENCFF239LRW) Regulation 
encTfChipPkENCFF105XWO HepG2 SRSF9 Transcription Factor ChIP-seq Peaks of SRSF9 in HepG2 from ENCODE 3 (ENCFF105XWO) Regulation 
encTfChipPkENCFF122FVR HepG2 SRSF4 Transcription Factor ChIP-seq Peaks of SRSF4 in HepG2 from ENCODE 3 (ENCFF122FVR) Regulation 
encTfChipPkENCFF735WMX HepG2 SP1 2 Transcription Factor ChIP-seq Peaks of SP1 in HepG2 from ENCODE 3 (ENCFF735WMX) Regulation 
encTfChipPkENCFF175VXL HepG2 SP1 1 Transcription Factor ChIP-seq Peaks of SP1 in HepG2 from ENCODE 3 (ENCFF175VXL) Regulation 
encTfChipPkENCFF944LNI HepG2 SOX6 Transcription Factor ChIP-seq Peaks of SOX6 in HepG2 from ENCODE 3 (ENCFF944LNI) Regulation 
encTfChipPkENCFF257QND HepG2 SOX13 Transcription Factor ChIP-seq Peaks of SOX13 in HepG2 from ENCODE 3 (ENCFF257QND) Regulation 
encTfChipPkENCFF858FBZ HepG2 SNRNP70 Transcription Factor ChIP-seq Peaks of SNRNP70 in HepG2 from ENCODE 3 (ENCFF858FBZ) Regulation 
encTfChipPkENCFF035YWE HepG2 SMC3 Transcription Factor ChIP-seq Peaks of SMC3 in HepG2 from ENCODE 3 (ENCFF035YWE) Regulation 
encTfChipPkENCFF210HAA HepG2 SMARCE1 Transcription Factor ChIP-seq Peaks of SMARCE1 in HepG2 from ENCODE 3 (ENCFF210HAA) Regulation 
encTfChipPkENCFF150NHK HepG2 SMARCC2 Transcription Factor ChIP-seq Peaks of SMARCC2 in HepG2 from ENCODE 3 (ENCFF150NHK) Regulation 
encTfChipPkENCFF035ZFO HepG2 SKI Transcription Factor ChIP-seq Peaks of SKI in HepG2 from ENCODE 3 (ENCFF035ZFO) Regulation 
encTfChipPkENCFF193DQZ HepG2 SIN3B Transcription Factor ChIP-seq Peaks of SIN3B in HepG2 from ENCODE 3 (ENCFF193DQZ) Regulation 
encTfChipPkENCFF635YMI HepG2 SIN3A Transcription Factor ChIP-seq Peaks of SIN3A in HepG2 from ENCODE 3 (ENCFF635YMI) Regulation 
encTfChipPkENCFF105TFM HepG2 RXRA Transcription Factor ChIP-seq Peaks of RXRA in HepG2 from ENCODE 3 (ENCFF105TFM) Regulation 
encTfChipPkENCFF380SYL HepG2 RNF2 Transcription Factor ChIP-seq Peaks of RNF2 in HepG2 from ENCODE 3 (ENCFF380SYL) Regulation 
encTfChipPkENCFF059GWW HepG2 RFX5 Transcription Factor ChIP-seq Peaks of RFX5 in HepG2 from ENCODE 3 (ENCFF059GWW) Regulation 
encTfChipPkENCFF788CJF HepG2 RFX1 Transcription Factor ChIP-seq Peaks of RFX1 in HepG2 from ENCODE 3 (ENCFF788CJF) Regulation 
encTfChipPkENCFF986RRJ HepG2 REST 2 Transcription Factor ChIP-seq Peaks of REST in HepG2 from ENCODE 3 (ENCFF986RRJ) Regulation 
encTfChipPkENCFF669XCW HepG2 REST 1 Transcription Factor ChIP-seq Peaks of REST in HepG2 from ENCODE 3 (ENCFF669XCW) Regulation 
encTfChipPkENCFF987VKU HepG2 RCOR1 Transcription Factor ChIP-seq Peaks of RCOR1 in HepG2 from ENCODE 3 (ENCFF987VKU) Regulation 
encTfChipPkENCFF420ALF HepG2 RBM39 Transcription Factor ChIP-seq Peaks of RBM39 in HepG2 from ENCODE 3 (ENCFF420ALF) Regulation 
encTfChipPkENCFF305WYD HepG2 RBM22 Transcription Factor ChIP-seq Peaks of RBM22 in HepG2 from ENCODE 3 (ENCFF305WYD) Regulation 
encTfChipPkENCFF871YRG HepG2 RBFOX2 Transcription Factor ChIP-seq Peaks of RBFOX2 in HepG2 from ENCODE 3 (ENCFF871YRG) Regulation 
encTfChipPkENCFF859MBC HepG2 RAD51 Transcription Factor ChIP-seq Peaks of RAD51 in HepG2 from ENCODE 3 (ENCFF859MBC) Regulation 
encTfChipPkENCFF874VFZ HepG2 RAD21 2 Transcription Factor ChIP-seq Peaks of RAD21 in HepG2 from ENCODE 3 (ENCFF874VFZ) Regulation 
encTfChipPkENCFF093XOJ HepG2 RAD21 1 Transcription Factor ChIP-seq Peaks of RAD21 in HepG2 from ENCODE 3 (ENCFF093XOJ) Regulation 
encTfChipPkENCFF875ZPV HepG2 PTBP1 Transcription Factor ChIP-seq Peaks of PTBP1 in HepG2 from ENCODE 3 (ENCFF875ZPV) Regulation 
encTfChipPkENCFF908QCS HepG2 PRPF4 Transcription Factor ChIP-seq Peaks of PRPF4 in HepG2 from ENCODE 3 (ENCFF908QCS) Regulation 
encTfChipPkENCFF551IJP HepG2 POLR2G Transcription Factor ChIP-seq Peaks of POLR2G in HepG2 from ENCODE 3 (ENCFF551IJP) Regulation 
encTfChipPkENCFF565SUC HepG2 POLR2A Transcription Factor ChIP-seq Peaks of POLR2A in HepG2 from ENCODE 3 (ENCFF565SUC) Regulation 
encTfChipPkENCFF873OHG HepG2 PLRG1 Transcription Factor ChIP-seq Peaks of PLRG1 in HepG2 from ENCODE 3 (ENCFF873OHG) Regulation 
encTfChipPkENCFF202WIO HepG2 PHF8 Transcription Factor ChIP-seq Peaks of PHF8 in HepG2 from ENCODE 3 (ENCFF202WIO) Regulation 
encTfChipPkENCFF882RPA HepG2 PHB2 Transcription Factor ChIP-seq Peaks of PHB2 in HepG2 from ENCODE 3 (ENCFF882RPA) Regulation 
encTfChipPkENCFF642XRH HepG2 PCBP2 Transcription Factor ChIP-seq Peaks of PCBP2 in HepG2 from ENCODE 3 (ENCFF642XRH) Regulation 
encTfChipPkENCFF487WAN HepG2 PCBP1 Transcription Factor ChIP-seq Peaks of PCBP1 in HepG2 from ENCODE 3 (ENCFF487WAN) Regulation 
encTfChipPkENCFF313RFR HepG2 NRF1 2 Transcription Factor ChIP-seq Peaks of NRF1 in HepG2 from ENCODE 3 (ENCFF313RFR) Regulation 
encTfChipPkENCFF418DKQ HepG2 NRF1 1 Transcription Factor ChIP-seq Peaks of NRF1 in HepG2 from ENCODE 3 (ENCFF418DKQ) Regulation 
encTfChipPkENCFF350CKI HepG2 NR2F6 Transcription Factor ChIP-seq Peaks of NR2F6 in HepG2 from ENCODE 3 (ENCFF350CKI) Regulation 
encTfChipPkENCFF162TPR HepG2 NFRKB Transcription Factor ChIP-seq Peaks of NFRKB in HepG2 from ENCODE 3 (ENCFF162TPR) Regulation 
encTfChipPkENCFF882YLO HepG2 NFE2L2 Transcription Factor ChIP-seq Peaks of NFE2L2 in HepG2 from ENCODE 3 (ENCFF882YLO) Regulation 
encTfChipPkENCFF616RSZ HepG2 NCOR1 Transcription Factor ChIP-seq Peaks of NCOR1 in HepG2 from ENCODE 3 (ENCFF616RSZ) Regulation 
encTfChipPkENCFF516UWH HepG2 NBN Transcription Factor ChIP-seq Peaks of NBN in HepG2 from ENCODE 3 (ENCFF516UWH) Regulation 
encTfChipPkENCFF482JSR HepG2 MNT 2 Transcription Factor ChIP-seq Peaks of MNT in HepG2 from ENCODE 3 (ENCFF482JSR) Regulation 
encTfChipPkENCFF562FMQ HepG2 MNT 1 Transcription Factor ChIP-seq Peaks of MNT in HepG2 from ENCODE 3 (ENCFF562FMQ) Regulation 
encTfChipPkENCFF140PUO HepG2 MAX Transcription Factor ChIP-seq Peaks of MAX in HepG2 from ENCODE 3 (ENCFF140PUO) Regulation 
encTfChipPkENCFF171OJF HepG2 MAFK 2 Transcription Factor ChIP-seq Peaks of MAFK in HepG2 from ENCODE 3 (ENCFF171OJF) Regulation 
encTfChipPkENCFF770TZL HepG2 MAFK 1 Transcription Factor ChIP-seq Peaks of MAFK in HepG2 from ENCODE 3 (ENCFF770TZL) Regulation 
encTfChipPkENCFF493TIR HepG2 MAFF Transcription Factor ChIP-seq Peaks of MAFF in HepG2 from ENCODE 3 (ENCFF493TIR) Regulation 
encTfChipPkENCFF611PIO HepG2 LCORL Transcription Factor ChIP-seq Peaks of LCORL in HepG2 from ENCODE 3 (ENCFF611PIO) Regulation 
encTfChipPkENCFF334HKG HepG2 KDM5A Transcription Factor ChIP-seq Peaks of KDM5A in HepG2 from ENCODE 3 (ENCFF334HKG) Regulation 
encTfChipPkENCFF768FGG HepG2 KDM1A Transcription Factor ChIP-seq Peaks of KDM1A in HepG2 from ENCODE 3 (ENCFF768FGG) Regulation 
encTfChipPkENCFF091BEK HepG2 KAT2B Transcription Factor ChIP-seq Peaks of KAT2B in HepG2 from ENCODE 3 (ENCFF091BEK) Regulation 
encTfChipPkENCFF539GRW HepG2 JUND 2 Transcription Factor ChIP-seq Peaks of JUND in HepG2 from ENCODE 3 (ENCFF539GRW) Regulation 
encTfChipPkENCFF430PEI HepG2 JUND 1 Transcription Factor ChIP-seq Peaks of JUND in HepG2 from ENCODE 3 (ENCFF430PEI) Regulation 
encTfChipPkENCFF969BZA HepG2 IKZF1 Transcription Factor ChIP-seq Peaks of IKZF1 in HepG2 from ENCODE 3 (ENCFF969BZA) Regulation 
encTfChipPkENCFF509YFF HepG2 HNRNPUL1 Transcription Factor ChIP-seq Peaks of HNRNPUL1 in HepG2 from ENCODE 3 (ENCFF509YFF) Regulation 
encTfChipPkENCFF890KTX HepG2 HNRNPLL Transcription Factor ChIP-seq Peaks of HNRNPLL in HepG2 from ENCODE 3 (ENCFF890KTX) Regulation 
encTfChipPkENCFF039CUI HepG2 HNRNPL Transcription Factor ChIP-seq Peaks of HNRNPL in HepG2 from ENCODE 3 (ENCFF039CUI) Regulation 
encTfChipPkENCFF828KXG HepG2 HNRNPK Transcription Factor ChIP-seq Peaks of HNRNPK in HepG2 from ENCODE 3 (ENCFF828KXG) Regulation 
encTfChipPkENCFF046NUR HepG2 HNRNPH1 Transcription Factor ChIP-seq Peaks of HNRNPH1 in HepG2 from ENCODE 3 (ENCFF046NUR) Regulation 
encTfChipPkENCFF086CTA HepG2 HNF4G Transcription Factor ChIP-seq Peaks of HNF4G in HepG2 from ENCODE 3 (ENCFF086CTA) Regulation 
encTfChipPkENCFF072CXB HepG2 HNF4A Transcription Factor ChIP-seq Peaks of HNF4A in HepG2 from ENCODE 3 (ENCFF072CXB) Regulation 
encTfChipPkENCFF800QTO HepG2 HNF1A Transcription Factor ChIP-seq Peaks of HNF1A in HepG2 from ENCODE 3 (ENCFF800QTO) Regulation 
encTfChipPkENCFF109EXK HepG2 HDAC6 Transcription Factor ChIP-seq Peaks of HDAC6 in HepG2 from ENCODE 3 (ENCFF109EXK) Regulation 
encTfChipPkENCFF182XZZ HepG2 HDAC2 2 Transcription Factor ChIP-seq Peaks of HDAC2 in HepG2 from ENCODE 3 (ENCFF182XZZ) Regulation 
encTfChipPkENCFF589GSN HepG2 HDAC2 1 Transcription Factor ChIP-seq Peaks of HDAC2 in HepG2 from ENCODE 3 (ENCFF589GSN) Regulation 
encTfChipPkENCFF069KPS HepG2 HDAC1 Transcription Factor ChIP-seq Peaks of HDAC1 in HepG2 from ENCODE 3 (ENCFF069KPS) Regulation 
encTfChipPkENCFF485SRU HepG2 HCFC1 Transcription Factor ChIP-seq Peaks of HCFC1 in HepG2 from ENCODE 3 (ENCFF485SRU) Regulation 
encTfChipPkENCFF097OXR HepG2 GATA4 Transcription Factor ChIP-seq Peaks of GATA4 in HepG2 from ENCODE 3 (ENCFF097OXR) Regulation 
encTfChipPkENCFF054HJA HepG2 GABPA Transcription Factor ChIP-seq Peaks of GABPA in HepG2 from ENCODE 3 (ENCFF054HJA) Regulation 
encTfChipPkENCFF216YZI HepG2 FUS Transcription Factor ChIP-seq Peaks of FUS in HepG2 from ENCODE 3 (ENCFF216YZI) Regulation 
encTfChipPkENCFF029UJC HepG2 FOXP1 Transcription Factor ChIP-seq Peaks of FOXP1 in HepG2 from ENCODE 3 (ENCFF029UJC) Regulation 
encTfChipPkENCFF315CHX HepG2 FOXK2 Transcription Factor ChIP-seq Peaks of FOXK2 in HepG2 from ENCODE 3 (ENCFF315CHX) Regulation 
encTfChipPkENCFF259BJR HepG2 FOXA2 2 Transcription Factor ChIP-seq Peaks of FOXA2 in HepG2 from ENCODE 3 (ENCFF259BJR) Regulation 
encTfChipPkENCFF184NAC HepG2 FOXA2 1 Transcription Factor ChIP-seq Peaks of FOXA2 in HepG2 from ENCODE 3 (ENCFF184NAC) Regulation 
encTfChipPkENCFF367TQC HepG2 FOXA1 3 Transcription Factor ChIP-seq Peaks of FOXA1 in HepG2 from ENCODE 3 (ENCFF367TQC) Regulation 
encTfChipPkENCFF872MGU HepG2 FOXA1 2 Transcription Factor ChIP-seq Peaks of FOXA1 in HepG2 from ENCODE 3 (ENCFF872MGU) Regulation 
encTfChipPkENCFF152BOT HepG2 FOXA1 1 Transcription Factor ChIP-seq Peaks of FOXA1 in HepG2 from ENCODE 3 (ENCFF152BOT) Regulation 
encTfChipPkENCFF054ESU HepG2 FOSL2 Transcription Factor ChIP-seq Peaks of FOSL2 in HepG2 from ENCODE 3 (ENCFF054ESU) Regulation 
encTfChipPkENCFF031LBW HepG2 FIP1L1 Transcription Factor ChIP-seq Peaks of FIP1L1 in HepG2 from ENCODE 3 (ENCFF031LBW) Regulation 
encTfChipPkENCFF504QZJ HepG2 EZH2 Transcription Factor ChIP-seq Peaks of EZH2 in HepG2 from ENCODE 3 (ENCFF504QZJ) Regulation 
encTfChipPkENCFF710CRT HepG2 ETV4 Transcription Factor ChIP-seq Peaks of ETV4 in HepG2 from ENCODE 3 (ENCFF710CRT) Regulation 
encTfChipPkENCFF128TUP HepG2 ETS1 Transcription Factor ChIP-seq Peaks of ETS1 in HepG2 from ENCODE 3 (ENCFF128TUP) Regulation 
encTfChipPkENCFF674QCU HepG2 EP300 2 Transcription Factor ChIP-seq Peaks of EP300 in HepG2 from ENCODE 3 (ENCFF674QCU) Regulation 
encTfChipPkENCFF806JJS HepG2 EP300 1 Transcription Factor ChIP-seq Peaks of EP300 in HepG2 from ENCODE 3 (ENCFF806JJS) Regulation 
encTfChipPkENCFF840RWO HepG2 ELF1 Transcription Factor ChIP-seq Peaks of ELF1 in HepG2 from ENCODE 3 (ENCFF840RWO) Regulation 
encTfChipPkENCFF413RQL HepG2 EHMT2 Transcription Factor ChIP-seq Peaks of EHMT2 in HepG2 from ENCODE 3 (ENCFF413RQL) Regulation 
encTfChipPkENCFF543WTP HepG2 CTCF Transcription Factor ChIP-seq Peaks of CTCF in HepG2 from ENCODE 3 (ENCFF543WTP) Regulation 
encTfChipPkENCFF290UGF HepG2 CREM Transcription Factor ChIP-seq Peaks of CREM in HepG2 from ENCODE 3 (ENCFF290UGF) Regulation 
encTfChipPkENCFF550TXR HepG2 CREB1 Transcription Factor ChIP-seq Peaks of CREB1 in HepG2 from ENCODE 3 (ENCFF550TXR) Regulation 
encTfChipPkENCFF148ABR HepG2 CHD4 Transcription Factor ChIP-seq Peaks of CHD4 in HepG2 from ENCODE 3 (ENCFF148ABR) Regulation 
encTfChipPkENCFF915ZYE HepG2 CEBPB 2 Transcription Factor ChIP-seq Peaks of CEBPB in HepG2 from ENCODE 3 (ENCFF915ZYE) Regulation 
encTfChipPkENCFF862DXR HepG2 CEBPB 1 Transcription Factor ChIP-seq Peaks of CEBPB in HepG2 from ENCODE 3 (ENCFF862DXR) Regulation 
encTfChipPkENCFF039LHY HepG2 CCAR2 Transcription Factor ChIP-seq Peaks of CCAR2 in HepG2 from ENCODE 3 (ENCFF039LHY) Regulation 
encTfChipPkENCFF501QII HepG2 CBX2 Transcription Factor ChIP-seq Peaks of CBX2 in HepG2 from ENCODE 3 (ENCFF501QII) Regulation 
encTfChipPkENCFF736GHL HepG2 BRD4 Transcription Factor ChIP-seq Peaks of BRD4 in HepG2 from ENCODE 3 (ENCFF736GHL) Regulation 
encTfChipPkENCFF897ETK HepG2 BRCA1 Transcription Factor ChIP-seq Peaks of BRCA1 in HepG2 from ENCODE 3 (ENCFF897ETK) Regulation 
encTfChipPkENCFF361YXC HepG2 BHLHE40 2 Transcription Factor ChIP-seq Peaks of BHLHE40 in HepG2 from ENCODE 3 (ENCFF361YXC) Regulation 
encTfChipPkENCFF863ATX HepG2 BHLHE40 1 Transcription Factor ChIP-seq Peaks of BHLHE40 in HepG2 from ENCODE 3 (ENCFF863ATX) Regulation 
encTfChipPkENCFF906FVB HepG2 ATM Transcription Factor ChIP-seq Peaks of ATM in HepG2 from ENCODE 3 (ENCFF906FVB) Regulation 
encTfChipPkENCFF498YGH HepG2 ATF7 Transcription Factor ChIP-seq Peaks of ATF7 in HepG2 from ENCODE 3 (ENCFF498YGH) Regulation 
encTfChipPkENCFF137OEY HepG2 ATF3 Transcription Factor ChIP-seq Peaks of ATF3 in HepG2 from ENCODE 3 (ENCFF137OEY) Regulation 
encTfChipPkENCFF089BQU HepG2 ATF2 Transcription Factor ChIP-seq Peaks of ATF2 in HepG2 from ENCODE 3 (ENCFF089BQU) Regulation 
encTfChipPkENCFF638IUM HepG2 ASH2L Transcription Factor ChIP-seq Peaks of ASH2L in HepG2 from ENCODE 3 (ENCFF638IUM) Regulation 
encTfChipPkENCFF616WXJ HepG2 ARNT Transcription Factor ChIP-seq Peaks of ARNT in HepG2 from ENCODE 3 (ENCFF616WXJ) Regulation 
encTfChipPkENCFF247GXE HepG2 ARID3A Transcription Factor ChIP-seq Peaks of ARID3A in HepG2 from ENCODE 3 (ENCFF247GXE) Regulation 
encTfChipPkENCFF465FII HepG2 AGO2 Transcription Factor ChIP-seq Peaks of AGO2 in HepG2 from ENCODE 3 (ENCFF465FII) Regulation 
encTfChipPkENCFF627BHP HepG2 AGO1 Transcription Factor ChIP-seq Peaks of AGO1 in HepG2 from ENCODE 3 (ENCFF627BHP) Regulation 
encTfChipPkENCFF267DZF HeLa-S3 ZHX1 Transcription Factor ChIP-seq Peaks of ZHX1 in HeLa-S3 from ENCODE 3 (ENCFF267DZF) Regulation 
encTfChipPkENCFF834LQR HeLa-S3 UBTF Transcription Factor ChIP-seq Peaks of UBTF in HeLa-S3 from ENCODE 3 (ENCFF834LQR) Regulation 
encTfChipPkENCFF302RQH HeLa-S3 TBP Transcription Factor ChIP-seq Peaks of TBP in HeLa-S3 from ENCODE 3 (ENCFF302RQH) Regulation 
encTfChipPkENCFF044DFE HeLa-S3 SUPT20H Transcription Factor ChIP-seq Peaks of SUPT20H in HeLa-S3 from ENCODE 3 (ENCFF044DFE) Regulation 
encTfChipPkENCFF785YII HeLa-S3 SREBF2 Transcription Factor ChIP-seq Peaks of SREBF2 in HeLa-S3 from ENCODE 3 (ENCFF785YII) Regulation 
encTfChipPkENCFF208NUB HeLa-S3 REST Transcription Factor ChIP-seq Peaks of REST in HeLa-S3 from ENCODE 3 (ENCFF208NUB) Regulation 
encTfChipPkENCFF246QVY HeLa-S3 POLR2A Transcription Factor ChIP-seq Peaks of POLR2A in HeLa-S3 from ENCODE 3 (ENCFF246QVY) Regulation 
encTfChipPkENCFF305KIK HeLa-S3 NFE2L2 Transcription Factor ChIP-seq Peaks of NFE2L2 in HeLa-S3 from ENCODE 3 (ENCFF305KIK) Regulation 
encTfChipPkENCFF328IZQ HeLa-S3 MAFK Transcription Factor ChIP-seq Peaks of MAFK in HeLa-S3 from ENCODE 3 (ENCFF328IZQ) Regulation 
encTfChipPkENCFF672LKL HeLa-S3 MAFF Transcription Factor ChIP-seq Peaks of MAFF in HeLa-S3 from ENCODE 3 (ENCFF672LKL) Regulation 
encTfChipPkENCFF091UDB HeLa-S3 GABPA Transcription Factor ChIP-seq Peaks of GABPA in HeLa-S3 from ENCODE 3 (ENCFF091UDB) Regulation 
encTfChipPkENCFF260KLJ HeLa-S3 EZH2 Transcription Factor ChIP-seq Peaks of EZH2 in HeLa-S3 from ENCODE 3 (ENCFF260KLJ) Regulation 
encTfChipPkENCFF797QGP HL-60 SPI1 Transcription Factor ChIP-seq Peaks of SPI1 in HL-60 from ENCODE 3 (ENCFF797QGP) Regulation 
encTfChipPkENCFF839LPE HL-60 REST Transcription Factor ChIP-seq Peaks of REST in HL-60 from ENCODE 3 (ENCFF839LPE) Regulation 
encTfChipPkENCFF564YAP HL-60 GABPA Transcription Factor ChIP-seq Peaks of GABPA in HL-60 from ENCODE 3 (ENCFF564YAP) Regulation 
encTfChipPkENCFF152JZK HL-60 CTCF Transcription Factor ChIP-seq Peaks of CTCF in HL-60 from ENCODE 3 (ENCFF152JZK) Regulation 
encTfChipPkENCFF750KVF HFF-Myc CTCF Transcription Factor ChIP-seq Peaks of CTCF in HFF-Myc from ENCODE 3 (ENCFF750KVF) Regulation 
encTfChipPkENCFF817UEX HEK293T ZNF384 Transcription Factor ChIP-seq Peaks of ZNF384 in HEK293T from ENCODE 3 (ENCFF817UEX) Regulation 
encTfChipPkENCFF829NNC HEK293T ZFX Transcription Factor ChIP-seq Peaks of ZFX in HEK293T from ENCODE 3 (ENCFF829NNC) Regulation 
encTfChipPkENCFF708RSP HEK293T SUZ12 Transcription Factor ChIP-seq Peaks of SUZ12 in HEK293T from ENCODE 3 (ENCFF708RSP) Regulation 
encTfChipPkENCFF532KPP HEK293T SP1 Transcription Factor ChIP-seq Peaks of SP1 in HEK293T from ENCODE 3 (ENCFF532KPP) Regulation 
encTfChipPkENCFF234WZT HEK293T PKNOX1 Transcription Factor ChIP-seq Peaks of PKNOX1 in HEK293T from ENCODE 3 (ENCFF234WZT) Regulation 
encTfChipPkENCFF421INQ HEK293T NFRKB Transcription Factor ChIP-seq Peaks of NFRKB in HEK293T from ENCODE 3 (ENCFF421INQ) Regulation 
encTfChipPkENCFF939UTN HEK293T LEF1 Transcription Factor ChIP-seq Peaks of LEF1 in HEK293T from ENCODE 3 (ENCFF939UTN) Regulation 
encTfChipPkENCFF156RLT HEK293T L3MBTL2 Transcription Factor ChIP-seq Peaks of L3MBTL2 in HEK293T from ENCODE 3 (ENCFF156RLT) Regulation 
encTfChipPkENCFF685TME HEK293T FOXM1 Transcription Factor ChIP-seq Peaks of FOXM1 in HEK293T from ENCODE 3 (ENCFF685TME) Regulation 
encTfChipPkENCFF959TZW HEK293T FOXK2 Transcription Factor ChIP-seq Peaks of FOXK2 in HEK293T from ENCODE 3 (ENCFF959TZW) Regulation 
encTfChipPkENCFF514ZNN HEK293T FOXA1 Transcription Factor ChIP-seq Peaks of FOXA1 in HEK293T from ENCODE 3 (ENCFF514ZNN) Regulation 
encTfChipPkENCFF919JTO HEK293T ELF4 Transcription Factor ChIP-seq Peaks of ELF4 in HEK293T from ENCODE 3 (ENCFF919JTO) Regulation 
encTfChipPkENCFF867WWZ HEK293T CTBP1 Transcription Factor ChIP-seq Peaks of CTBP1 in HEK293T from ENCODE 3 (ENCFF867WWZ) Regulation 
encTfChipPkENCFF104NYV HEK293T BHLHE40 Transcription Factor ChIP-seq Peaks of BHLHE40 in HEK293T from ENCODE 3 (ENCFF104NYV) Regulation 
encTfChipPkENCFF694XWV HEK293T ARNT Transcription Factor ChIP-seq Peaks of ARNT in HEK293T from ENCODE 3 (ENCFF694XWV) Regulation 
encTfChipPkENCFF827SZZ HEK293 ZNF263 Transcription Factor ChIP-seq Peaks of ZNF263 in HEK293 from ENCODE 3 (ENCFF827SZZ) Regulation 
encTfChipPkENCFF860DHS HEK293 TRIM28 Transcription Factor ChIP-seq Peaks of TRIM28 in HEK293 from ENCODE 3 (ENCFF860DHS) Regulation 
encTfChipPkENCFF215SIC HCT116 ZFX Transcription Factor ChIP-seq Peaks of ZFX in HCT116 from ENCODE 3 (ENCFF215SIC) Regulation 
encTfChipPkENCFF998KDQ HCT116 JUND Transcription Factor ChIP-seq Peaks of JUND in HCT116 from ENCODE 3 (ENCFF998KDQ) Regulation 
encTfChipPkENCFF926EZW HCT116 EZH2 Transcription Factor ChIP-seq Peaks of EZH2 in HCT116 from ENCODE 3 (ENCFF926EZW) Regulation 
encTfChipPkENCFF171SNH HCT116 CTCF 3 Transcription Factor ChIP-seq Peaks of CTCF in HCT116 from ENCODE 3 (ENCFF171SNH) Regulation 
encTfChipPkENCFF518MQA HCT116 CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in HCT116 from ENCODE 3 (ENCFF518MQA) Regulation 
encTfChipPkENCFF549PGC HCT116 CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in HCT116 from ENCODE 3 (ENCFF549PGC) Regulation 
encTfChipPkENCFF723LVE H54 CTCF Transcription Factor ChIP-seq Peaks of CTCF in H54 from ENCODE 3 (ENCFF723LVE) Regulation 
encTfChipPkENCFF933WSP H1-hESC ZNF143 Transcription Factor ChIP-seq Peaks of ZNF143 in H1-hESC from ENCODE 3 (ENCFF933WSP) Regulation 
encTfChipPkENCFF509GYP H1-hESC YY1 Transcription Factor ChIP-seq Peaks of YY1 in H1-hESC from ENCODE 3 (ENCFF509GYP) Regulation 
encTfChipPkENCFF710JBU H1-hESC USF2 Transcription Factor ChIP-seq Peaks of USF2 in H1-hESC from ENCODE 3 (ENCFF710JBU) Regulation 
encTfChipPkENCFF699HXL H1-hESC USF1 Transcription Factor ChIP-seq Peaks of USF1 in H1-hESC from ENCODE 3 (ENCFF699HXL) Regulation 
encTfChipPkENCFF740HPV H1-hESC TCF12 Transcription Factor ChIP-seq Peaks of TCF12 in H1-hESC from ENCODE 3 (ENCFF740HPV) Regulation 
encTfChipPkENCFF748YXF H1-hESC TBP Transcription Factor ChIP-seq Peaks of TBP in H1-hESC from ENCODE 3 (ENCFF748YXF) Regulation 
encTfChipPkENCFF243PSJ H1-hESC TAF7 Transcription Factor ChIP-seq Peaks of TAF7 in H1-hESC from ENCODE 3 (ENCFF243PSJ) Regulation 
encTfChipPkENCFF870SFJ H1-hESC TAF1 Transcription Factor ChIP-seq Peaks of TAF1 in H1-hESC from ENCODE 3 (ENCFF870SFJ) Regulation 
encTfChipPkENCFF671SZQ H1-hESC SUZ12 Transcription Factor ChIP-seq Peaks of SUZ12 in H1-hESC from ENCODE 3 (ENCFF671SZQ) Regulation 
encTfChipPkENCFF345IDL H1-hESC SRF Transcription Factor ChIP-seq Peaks of SRF in H1-hESC from ENCODE 3 (ENCFF345IDL) Regulation 
encTfChipPkENCFF500JFI H1-hESC SP1 Transcription Factor ChIP-seq Peaks of SP1 in H1-hESC from ENCODE 3 (ENCFF500JFI) Regulation 
encTfChipPkENCFF644BNN H1-hESC SIX5 Transcription Factor ChIP-seq Peaks of SIX5 in H1-hESC from ENCODE 3 (ENCFF644BNN) Regulation 
encTfChipPkENCFF539KSF H1-hESC SIRT6 Transcription Factor ChIP-seq Peaks of SIRT6 in H1-hESC from ENCODE 3 (ENCFF539KSF) Regulation 
encTfChipPkENCFF514BGQ H1-hESC SIN3A 2 Transcription Factor ChIP-seq Peaks of SIN3A in H1-hESC from ENCODE 3 (ENCFF514BGQ) Regulation 
encTfChipPkENCFF905VZD H1-hESC SIN3A 1 Transcription Factor ChIP-seq Peaks of SIN3A in H1-hESC from ENCODE 3 (ENCFF905VZD) Regulation 
encTfChipPkENCFF193TFR H1-hESC SAP30 Transcription Factor ChIP-seq Peaks of SAP30 in H1-hESC from ENCODE 3 (ENCFF193TFR) Regulation 
encTfChipPkENCFF430SIE H1-hESC RXRA Transcription Factor ChIP-seq Peaks of RXRA in H1-hESC from ENCODE 3 (ENCFF430SIE) Regulation 
encTfChipPkENCFF283MNG H1-hESC RNF2 Transcription Factor ChIP-seq Peaks of RNF2 in H1-hESC from ENCODE 3 (ENCFF283MNG) Regulation 
encTfChipPkENCFF062WBN H1-hESC RFX5 Transcription Factor ChIP-seq Peaks of RFX5 in H1-hESC from ENCODE 3 (ENCFF062WBN) Regulation 
encTfChipPkENCFF403CAJ H1-hESC REST 2 Transcription Factor ChIP-seq Peaks of REST in H1-hESC from ENCODE 3 (ENCFF403CAJ) Regulation 
encTfChipPkENCFF779CWH H1-hESC REST 1 Transcription Factor ChIP-seq Peaks of REST in H1-hESC from ENCODE 3 (ENCFF779CWH) Regulation 
encTfChipPkENCFF607WCG H1-hESC RBBP5 Transcription Factor ChIP-seq Peaks of RBBP5 in H1-hESC from ENCODE 3 (ENCFF607WCG) Regulation 
encTfChipPkENCFF060IVS H1-hESC RAD21 2 Transcription Factor ChIP-seq Peaks of RAD21 in H1-hESC from ENCODE 3 (ENCFF060IVS) Regulation 
encTfChipPkENCFF255FRL H1-hESC RAD21 1 Transcription Factor ChIP-seq Peaks of RAD21 in H1-hESC from ENCODE 3 (ENCFF255FRL) Regulation 
encTfChipPkENCFF422HDN H1-hESC POLR2A Transcription Factor ChIP-seq Peaks of POLR2A in H1-hESC from ENCODE 3 (ENCFF422HDN) Regulation 
encTfChipPkENCFF651QOL H1-hESC PHF8 Transcription Factor ChIP-seq Peaks of PHF8 in H1-hESC from ENCODE 3 (ENCFF651QOL) Regulation 
encTfChipPkENCFF407IVS H1-hESC NRF1 Transcription Factor ChIP-seq Peaks of NRF1 in H1-hESC from ENCODE 3 (ENCFF407IVS) Regulation 
encTfChipPkENCFF794GVQ H1-hESC NANOG Transcription Factor ChIP-seq Peaks of NANOG in H1-hESC from ENCODE 3 (ENCFF794GVQ) Regulation 
encTfChipPkENCFF392JJN H1-hESC MYC Transcription Factor ChIP-seq Peaks of MYC in H1-hESC from ENCODE 3 (ENCFF392JJN) Regulation 
encTfChipPkENCFF712RIS H1-hESC MAFK Transcription Factor ChIP-seq Peaks of MAFK in H1-hESC from ENCODE 3 (ENCFF712RIS) Regulation 
encTfChipPkENCFF342EEV H1-hESC KDM5A Transcription Factor ChIP-seq Peaks of KDM5A in H1-hESC from ENCODE 3 (ENCFF342EEV) Regulation 
encTfChipPkENCFF205WRX H1-hESC KDM4A Transcription Factor ChIP-seq Peaks of KDM4A in H1-hESC from ENCODE 3 (ENCFF205WRX) Regulation 
encTfChipPkENCFF562OAN H1-hESC KDM1A Transcription Factor ChIP-seq Peaks of KDM1A in H1-hESC from ENCODE 3 (ENCFF562OAN) Regulation 
encTfChipPkENCFF646IUA H1-hESC JUND 2 Transcription Factor ChIP-seq Peaks of JUND in H1-hESC from ENCODE 3 (ENCFF646IUA) Regulation 
encTfChipPkENCFF443HNU H1-hESC JUND 1 Transcription Factor ChIP-seq Peaks of JUND in H1-hESC from ENCODE 3 (ENCFF443HNU) Regulation 
encTfChipPkENCFF312GEN H1-hESC JUN Transcription Factor ChIP-seq Peaks of JUN in H1-hESC from ENCODE 3 (ENCFF312GEN) Regulation 
encTfChipPkENCFF129WNO H1-hESC HDAC6 Transcription Factor ChIP-seq Peaks of HDAC6 in H1-hESC from ENCODE 3 (ENCFF129WNO) Regulation 
encTfChipPkENCFF497YNJ H1-hESC HDAC2 2 Transcription Factor ChIP-seq Peaks of HDAC2 in H1-hESC from ENCODE 3 (ENCFF497YNJ) Regulation 
encTfChipPkENCFF009IVJ H1-hESC HDAC2 1 Transcription Factor ChIP-seq Peaks of HDAC2 in H1-hESC from ENCODE 3 (ENCFF009IVJ) Regulation 
encTfChipPkENCFF225GFQ H1-hESC GABPA Transcription Factor ChIP-seq Peaks of GABPA in H1-hESC from ENCODE 3 (ENCFF225GFQ) Regulation 
encTfChipPkENCFF063OKB H1-hESC FOSL1 Transcription Factor ChIP-seq Peaks of FOSL1 in H1-hESC from ENCODE 3 (ENCFF063OKB) Regulation 
encTfChipPkENCFF483HNU H1-hESC EP300 2 Transcription Factor ChIP-seq Peaks of EP300 in H1-hESC from ENCODE 3 (ENCFF483HNU) Regulation 
encTfChipPkENCFF834UVX H1-hESC EP300 1 Transcription Factor ChIP-seq Peaks of EP300 in H1-hESC from ENCODE 3 (ENCFF834UVX) Regulation 
encTfChipPkENCFF477ANT H1-hESC EGR1 Transcription Factor ChIP-seq Peaks of EGR1 in H1-hESC from ENCODE 3 (ENCFF477ANT) Regulation 
encTfChipPkENCFF821AQO H1-hESC CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in H1-hESC from ENCODE 3 (ENCFF821AQO) Regulation 
encTfChipPkENCFF368LWM H1-hESC CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in H1-hESC from ENCODE 3 (ENCFF368LWM) Regulation 
encTfChipPkENCFF658SXI H1-hESC CHD7 Transcription Factor ChIP-seq Peaks of CHD7 in H1-hESC from ENCODE 3 (ENCFF658SXI) Regulation 
encTfChipPkENCFF806HXY H1-hESC CHD1 2 Transcription Factor ChIP-seq Peaks of CHD1 in H1-hESC from ENCODE 3 (ENCFF806HXY) Regulation 
encTfChipPkENCFF549ODQ H1-hESC CHD1 1 Transcription Factor ChIP-seq Peaks of CHD1 in H1-hESC from ENCODE 3 (ENCFF549ODQ) Regulation 
encTfChipPkENCFF962YTC H1-hESC BRCA1 Transcription Factor ChIP-seq Peaks of BRCA1 in H1-hESC from ENCODE 3 (ENCFF962YTC) Regulation 
encTfChipPkENCFF087VWX H1-hESC BCL11A 2 Transcription Factor ChIP-seq Peaks of BCL11A in H1-hESC from ENCODE 3 (ENCFF087VWX) Regulation 
encTfChipPkENCFF533KIC H1-hESC BCL11A 1 Transcription Factor ChIP-seq Peaks of BCL11A in H1-hESC from ENCODE 3 (ENCFF533KIC) Regulation 
encTfChipPkENCFF851YHG H1-hESC BACH1 Transcription Factor ChIP-seq Peaks of BACH1 in H1-hESC from ENCODE 3 (ENCFF851YHG) Regulation 
encTfChipPkENCFF487GLV H1-hESC ATF3 Transcription Factor ChIP-seq Peaks of ATF3 in H1-hESC from ENCODE 3 (ENCFF487GLV) Regulation 
encTfChipPkENCFF777DCR H1-hESC ASH2L Transcription Factor ChIP-seq Peaks of ASH2L in H1-hESC from ENCODE 3 (ENCFF777DCR) Regulation 
encTfChipPkENCFF904USP GM23338 REST Transcription Factor ChIP-seq Peaks of REST in GM23338 from ENCODE 3 (ENCFF904USP) Regulation 
encTfChipPkENCFF621PFM GM23338 NANOG Transcription Factor ChIP-seq Peaks of NANOG in GM23338 from ENCODE 3 (ENCFF621PFM) Regulation 
encTfChipPkENCFF097WNJ GM23338 EZH2 Transcription Factor ChIP-seq Peaks of EZH2 in GM23338 from ENCODE 3 (ENCFF097WNJ) Regulation 
encTfChipPkENCFF511AZU GM23338 ETS1 Transcription Factor ChIP-seq Peaks of ETS1 in GM23338 from ENCODE 3 (ENCFF511AZU) Regulation 
encTfChipPkENCFF960XTR GM23338 CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in GM23338 from ENCODE 3 (ENCFF960XTR) Regulation 
encTfChipPkENCFF322WKG GM23338 CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in GM23338 from ENCODE 3 (ENCFF322WKG) Regulation 
encTfChipPkENCFF976SAN GM23248 EZH2 Transcription Factor ChIP-seq Peaks of EZH2 in GM23248 from ENCODE 3 (ENCFF976SAN) Regulation 
encTfChipPkENCFF419PTP GM20000 CTCF Transcription Factor ChIP-seq Peaks of CTCF in GM20000 from ENCODE 3 (ENCFF419PTP) Regulation 
encTfChipPkENCFF084FRB GM13977 CTCF Transcription Factor ChIP-seq Peaks of CTCF in GM13977 from ENCODE 3 (ENCFF084FRB) Regulation 
encTfChipPkENCFF072IHJ GM12892 YY1 Transcription Factor ChIP-seq Peaks of YY1 in GM12892 from ENCODE 3 (ENCFF072IHJ) Regulation 
encTfChipPkENCFF033PLJ GM12892 TAF1 Transcription Factor ChIP-seq Peaks of TAF1 in GM12892 from ENCODE 3 (ENCFF033PLJ) Regulation 
encTfChipPkENCFF403ZEO GM12892 POLR2A Transcription Factor ChIP-seq Peaks of POLR2A in GM12892 from ENCODE 3 (ENCFF403ZEO) Regulation 
encTfChipPkENCFF538VYU GM12891 YY1 Transcription Factor ChIP-seq Peaks of YY1 in GM12891 from ENCODE 3 (ENCFF538VYU) Regulation 
encTfChipPkENCFF471NIK GM12891 TAF1 Transcription Factor ChIP-seq Peaks of TAF1 in GM12891 from ENCODE 3 (ENCFF471NIK) Regulation 
encTfChipPkENCFF744AGB GM12891 SPI1 Transcription Factor ChIP-seq Peaks of SPI1 in GM12891 from ENCODE 3 (ENCFF744AGB) Regulation 
encTfChipPkENCFF113EFE GM12891 POU2F2 Transcription Factor ChIP-seq Peaks of POU2F2 in GM12891 from ENCODE 3 (ENCFF113EFE) Regulation 
encTfChipPkENCFF021HUZ GM12891 POLR2A Transcription Factor ChIP-seq Peaks of POLR2A in GM12891 from ENCODE 3 (ENCFF021HUZ) Regulation 
encTfChipPkENCFF987CQF GM12891 PAX5 Transcription Factor ChIP-seq Peaks of PAX5 in GM12891 from ENCODE 3 (ENCFF987CQF) Regulation 
encTfChipPkENCFF260NAX GM12878 ZZZ3 Transcription Factor ChIP-seq Peaks of ZZZ3 in GM12878 from ENCODE 3 (ENCFF260NAX) Regulation 
encTfChipPkENCFF214NJL GM12878 ZSCAN29 Transcription Factor ChIP-seq Peaks of ZSCAN29 in GM12878 from ENCODE 3 (ENCFF214NJL) Regulation 
encTfChipPkENCFF137BRA GM12878 ZNF687 Transcription Factor ChIP-seq Peaks of ZNF687 in GM12878 from ENCODE 3 (ENCFF137BRA) Regulation 
encTfChipPkENCFF615DTQ GM12878 ZNF592 Transcription Factor ChIP-seq Peaks of ZNF592 in GM12878 from ENCODE 3 (ENCFF615DTQ) Regulation 
encTfChipPkENCFF942MDT GM12878 ZNF384 Transcription Factor ChIP-seq Peaks of ZNF384 in GM12878 from ENCODE 3 (ENCFF942MDT) Regulation 
encTfChipPkENCFF200SLC GM12878 ZNF217 Transcription Factor ChIP-seq Peaks of ZNF217 in GM12878 from ENCODE 3 (ENCFF200SLC) Regulation 
encTfChipPkENCFF676BIG GM12878 ZNF207 Transcription Factor ChIP-seq Peaks of ZNF207 in GM12878 from ENCODE 3 (ENCFF676BIG) Regulation 
encTfChipPkENCFF193POQ GM12878 ZNF143 2 Transcription Factor ChIP-seq Peaks of ZNF143 in GM12878 from ENCODE 3 (ENCFF193POQ) Regulation 
encTfChipPkENCFF153TQR GM12878 ZNF143 1 Transcription Factor ChIP-seq Peaks of ZNF143 in GM12878 from ENCODE 3 (ENCFF153TQR) Regulation 
encTfChipPkENCFF084IUW GM12878 ZBTB40 Transcription Factor ChIP-seq Peaks of ZBTB40 in GM12878 from ENCODE 3 (ENCFF084IUW) Regulation 
encTfChipPkENCFF475DID GM12878 ZBTB33 2 Transcription Factor ChIP-seq Peaks of ZBTB33 in GM12878 from ENCODE 3 (ENCFF475DID) Regulation 
encTfChipPkENCFF773OQL GM12878 ZBTB33 1 Transcription Factor ChIP-seq Peaks of ZBTB33 in GM12878 from ENCODE 3 (ENCFF773OQL) Regulation 
encTfChipPkENCFF630FLK GM12878 ZBED1 Transcription Factor ChIP-seq Peaks of ZBED1 in GM12878 from ENCODE 3 (ENCFF630FLK) Regulation 
encTfChipPkENCFF752IXD GM12878 YY1 2 Transcription Factor ChIP-seq Peaks of YY1 in GM12878 from ENCODE 3 (ENCFF752IXD) Regulation 
encTfChipPkENCFF223MUF GM12878 YY1 1 Transcription Factor ChIP-seq Peaks of YY1 in GM12878 from ENCODE 3 (ENCFF223MUF) Regulation 
encTfChipPkENCFF514DDI GM12878 WRNIP1 Transcription Factor ChIP-seq Peaks of WRNIP1 in GM12878 from ENCODE 3 (ENCFF514DDI) Regulation 
encTfChipPkENCFF514SWA GM12878 USF2 Transcription Factor ChIP-seq Peaks of USF2 in GM12878 from ENCODE 3 (ENCFF514SWA) Regulation 
encTfChipPkENCFF295ZLM GM12878 UBTF Transcription Factor ChIP-seq Peaks of UBTF in GM12878 from ENCODE 3 (ENCFF295ZLM) Regulation 
encTfChipPkENCFF552WAH GM12878 TRIM22 2 Transcription Factor ChIP-seq Peaks of TRIM22 in GM12878 from ENCODE 3 (ENCFF552WAH) Regulation 
encTfChipPkENCFF830TFU GM12878 TRIM22 1 Transcription Factor ChIP-seq Peaks of TRIM22 in GM12878 from ENCODE 3 (ENCFF830TFU) Regulation 
encTfChipPkENCFF152RNE GM12878 TCF7 Transcription Factor ChIP-seq Peaks of TCF7 in GM12878 from ENCODE 3 (ENCFF152RNE) Regulation 
encTfChipPkENCFF897RYA GM12878 TCF12 2 Transcription Factor ChIP-seq Peaks of TCF12 in GM12878 from ENCODE 3 (ENCFF897RYA) Regulation 
encTfChipPkENCFF768VSH GM12878 TCF12 1 Transcription Factor ChIP-seq Peaks of TCF12 in GM12878 from ENCODE 3 (ENCFF768VSH) Regulation 
encTfChipPkENCFF971VHK GM12878 TBX21 Transcription Factor ChIP-seq Peaks of TBX21 in GM12878 from ENCODE 3 (ENCFF971VHK) Regulation 
encTfChipPkENCFF896UZB GM12878 TBP Transcription Factor ChIP-seq Peaks of TBP in GM12878 from ENCODE 3 (ENCFF896UZB) Regulation 
encTfChipPkENCFF392JWA GM12878 TBL1XR1 Transcription Factor ChIP-seq Peaks of TBL1XR1 in GM12878 from ENCODE 3 (ENCFF392JWA) Regulation 
encTfChipPkENCFF540AAP GM12878 TAF1 Transcription Factor ChIP-seq Peaks of TAF1 in GM12878 from ENCODE 3 (ENCFF540AAP) Regulation 
encTfChipPkENCFF547FUI GM12878 SUZ12 Transcription Factor ChIP-seq Peaks of SUZ12 in GM12878 from ENCODE 3 (ENCFF547FUI) Regulation 
encTfChipPkENCFF069YVD GM12878 SUPT20H Transcription Factor ChIP-seq Peaks of SUPT20H in GM12878 from ENCODE 3 (ENCFF069YVD) Regulation 
encTfChipPkENCFF383YEA GM12878 STAT5A Transcription Factor ChIP-seq Peaks of STAT5A in GM12878 from ENCODE 3 (ENCFF383YEA) Regulation 
encTfChipPkENCFF923CHO GM12878 STAT3 Transcription Factor ChIP-seq Peaks of STAT3 in GM12878 from ENCODE 3 (ENCFF923CHO) Regulation 
encTfChipPkENCFF323QQU GM12878 STAT1 Transcription Factor ChIP-seq Peaks of STAT1 in GM12878 from ENCODE 3 (ENCFF323QQU) Regulation 
encTfChipPkENCFF182IFE GM12878 SRF 3 Transcription Factor ChIP-seq Peaks of SRF in GM12878 from ENCODE 3 (ENCFF182IFE) Regulation 
encTfChipPkENCFF829SEJ GM12878 SRF 2 Transcription Factor ChIP-seq Peaks of SRF in GM12878 from ENCODE 3 (ENCFF829SEJ) Regulation 
encTfChipPkENCFF766WWB GM12878 SRF 1 Transcription Factor ChIP-seq Peaks of SRF in GM12878 from ENCODE 3 (ENCFF766WWB) Regulation 
encTfChipPkENCFF572RPI GM12878 SMC3 Transcription Factor ChIP-seq Peaks of SMC3 in GM12878 from ENCODE 3 (ENCFF572RPI) Regulation 
encTfChipPkENCFF052STI GM12878 SMARCA5 Transcription Factor ChIP-seq Peaks of SMARCA5 in GM12878 from ENCODE 3 (ENCFF052STI) Regulation 
encTfChipPkENCFF855SJG GM12878 SMAD5 Transcription Factor ChIP-seq Peaks of SMAD5 in GM12878 from ENCODE 3 (ENCFF855SJG) Regulation 
encTfChipPkENCFF987PGY GM12878 SMAD1 Transcription Factor ChIP-seq Peaks of SMAD1 in GM12878 from ENCODE 3 (ENCFF987PGY) Regulation 
encTfChipPkENCFF903KEI GM12878 SKIL Transcription Factor ChIP-seq Peaks of SKIL in GM12878 from ENCODE 3 (ENCFF903KEI) Regulation 
encTfChipPkENCFF864TFH GM12878 SIX5 Transcription Factor ChIP-seq Peaks of SIX5 in GM12878 from ENCODE 3 (ENCFF864TFH) Regulation 
encTfChipPkENCFF050CYK GM12878 SIN3A Transcription Factor ChIP-seq Peaks of SIN3A in GM12878 from ENCODE 3 (ENCFF050CYK) Regulation 
encTfChipPkENCFF313BDA GM12878 RXRA Transcription Factor ChIP-seq Peaks of RXRA in GM12878 from ENCODE 3 (ENCFF313BDA) Regulation 
encTfChipPkENCFF677QUK GM12878 RUNX3 Transcription Factor ChIP-seq Peaks of RUNX3 in GM12878 from ENCODE 3 (ENCFF677QUK) Regulation 
encTfChipPkENCFF259LNG GM12878 RFX5 Transcription Factor ChIP-seq Peaks of RFX5 in GM12878 from ENCODE 3 (ENCFF259LNG) Regulation 
encTfChipPkENCFF313CII GM12878 REST Transcription Factor ChIP-seq Peaks of REST in GM12878 from ENCODE 3 (ENCFF313CII) Regulation 
encTfChipPkENCFF105YDI GM12878 RELB Transcription Factor ChIP-seq Peaks of RELB in GM12878 from ENCODE 3 (ENCFF105YDI) Regulation 
encTfChipPkENCFF470ZMK GM12878 RCOR1 Transcription Factor ChIP-seq Peaks of RCOR1 in GM12878 from ENCODE 3 (ENCFF470ZMK) Regulation 
encTfChipPkENCFF687SSY GM12878 RBBP5 Transcription Factor ChIP-seq Peaks of RBBP5 in GM12878 from ENCODE 3 (ENCFF687SSY) Regulation 
encTfChipPkENCFF034OSV GM12878 RB1 Transcription Factor ChIP-seq Peaks of RB1 in GM12878 from ENCODE 3 (ENCFF034OSV) Regulation 
encTfChipPkENCFF996NBR GM12878 RAD51 Transcription Factor ChIP-seq Peaks of RAD51 in GM12878 from ENCODE 3 (ENCFF996NBR) Regulation 
encTfChipPkENCFF654EGO GM12878 RAD21 Transcription Factor ChIP-seq Peaks of RAD21 in GM12878 from ENCODE 3 (ENCFF654EGO) Regulation 
encTfChipPkENCFF455ZLJ GM12878 POLR2A Transcription Factor ChIP-seq Peaks of POLR2A in GM12878 from ENCODE 3 (ENCFF455ZLJ) Regulation 
encTfChipPkENCFF335ADU GM12878 PKNOX1 Transcription Factor ChIP-seq Peaks of PKNOX1 in GM12878 from ENCODE 3 (ENCFF335ADU) Regulation 
encTfChipPkENCFF926LHG GM12878 PBX3 Transcription Factor ChIP-seq Peaks of PBX3 in GM12878 from ENCODE 3 (ENCFF926LHG) Regulation 
encTfChipPkENCFF992JWY GM12878 PAX8 Transcription Factor ChIP-seq Peaks of PAX8 in GM12878 from ENCODE 3 (ENCFF992JWY) Regulation 
encTfChipPkENCFF946SAG GM12878 PAX5 Transcription Factor ChIP-seq Peaks of PAX5 in GM12878 from ENCODE 3 (ENCFF946SAG) Regulation 
encTfChipPkENCFF652BRY GM12878 NRF1 Transcription Factor ChIP-seq Peaks of NRF1 in GM12878 from ENCODE 3 (ENCFF652BRY) Regulation 
encTfChipPkENCFF434HVY GM12878 NR2C2 Transcription Factor ChIP-seq Peaks of NR2C2 in GM12878 from ENCODE 3 (ENCFF434HVY) Regulation 
encTfChipPkENCFF510NDO GM12878 NFYB Transcription Factor ChIP-seq Peaks of NFYB in GM12878 from ENCODE 3 (ENCFF510NDO) Regulation 
encTfChipPkENCFF278GJK GM12878 NFYA Transcription Factor ChIP-seq Peaks of NFYA in GM12878 from ENCODE 3 (ENCFF278GJK) Regulation 
encTfChipPkENCFF860IXB GM12878 NFXL1 Transcription Factor ChIP-seq Peaks of NFXL1 in GM12878 from ENCODE 3 (ENCFF860IXB) Regulation 
encTfChipPkENCFF480WDX GM12878 NFIC Transcription Factor ChIP-seq Peaks of NFIC in GM12878 from ENCODE 3 (ENCFF480WDX) Regulation 
encTfChipPkENCFF743UMZ GM12878 NFE2 Transcription Factor ChIP-seq Peaks of NFE2 in GM12878 from ENCODE 3 (ENCFF743UMZ) Regulation 
encTfChipPkENCFF704PDA GM12878 NFATC3 Transcription Factor ChIP-seq Peaks of NFATC3 in GM12878 from ENCODE 3 (ENCFF704PDA) Regulation 
encTfChipPkENCFF138ZBJ GM12878 NFATC1 Transcription Factor ChIP-seq Peaks of NFATC1 in GM12878 from ENCODE 3 (ENCFF138ZBJ) Regulation 
encTfChipPkENCFF811VEN GM12878 NBN Transcription Factor ChIP-seq Peaks of NBN in GM12878 from ENCODE 3 (ENCFF811VEN) Regulation 
encTfChipPkENCFF402TSJ GM12878 MYB Transcription Factor ChIP-seq Peaks of MYB in GM12878 from ENCODE 3 (ENCFF402TSJ) Regulation 
encTfChipPkENCFF199HGX GM12878 MXI1 Transcription Factor ChIP-seq Peaks of MXI1 in GM12878 from ENCODE 3 (ENCFF199HGX) Regulation 
encTfChipPkENCFF661FMB GM12878 MTA3 Transcription Factor ChIP-seq Peaks of MTA3 in GM12878 from ENCODE 3 (ENCFF661FMB) Regulation 
encTfChipPkENCFF587POH GM12878 MTA2 Transcription Factor ChIP-seq Peaks of MTA2 in GM12878 from ENCODE 3 (ENCFF587POH) Regulation 
encTfChipPkENCFF125MEN GM12878 MLLT1 Transcription Factor ChIP-seq Peaks of MLLT1 in GM12878 from ENCODE 3 (ENCFF125MEN) Regulation 
encTfChipPkENCFF830BRO GM12878 MEF2C Transcription Factor ChIP-seq Peaks of MEF2C in GM12878 from ENCODE 3 (ENCFF830BRO) Regulation 
encTfChipPkENCFF623FAW GM12878 MEF2B Transcription Factor ChIP-seq Peaks of MEF2B in GM12878 from ENCODE 3 (ENCFF623FAW) Regulation 
encTfChipPkENCFF958GXF GM12878 MEF2A Transcription Factor ChIP-seq Peaks of MEF2A in GM12878 from ENCODE 3 (ENCFF958GXF) Regulation 
encTfChipPkENCFF270NAL GM12878 MAX Transcription Factor ChIP-seq Peaks of MAX in GM12878 from ENCODE 3 (ENCFF270NAL) Regulation 
encTfChipPkENCFF186AWV GM12878 MAFK Transcription Factor ChIP-seq Peaks of MAFK in GM12878 from ENCODE 3 (ENCFF186AWV) Regulation 
encTfChipPkENCFF417WPC GM12878 KLF5 Transcription Factor ChIP-seq Peaks of KLF5 in GM12878 from ENCODE 3 (ENCFF417WPC) Regulation 
encTfChipPkENCFF799KZP GM12878 KDM1A Transcription Factor ChIP-seq Peaks of KDM1A in GM12878 from ENCODE 3 (ENCFF799KZP) Regulation 
encTfChipPkENCFF710ROZ GM12878 KAT2A Transcription Factor ChIP-seq Peaks of KAT2A in GM12878 from ENCODE 3 (ENCFF710ROZ) Regulation 
encTfChipPkENCFF873DJD GM12878 JUND Transcription Factor ChIP-seq Peaks of JUND in GM12878 from ENCODE 3 (ENCFF873DJD) Regulation 
encTfChipPkENCFF478XNA GM12878 JUNB Transcription Factor ChIP-seq Peaks of JUNB in GM12878 from ENCODE 3 (ENCFF478XNA) Regulation 
encTfChipPkENCFF843HDK GM12878 IRF5 Transcription Factor ChIP-seq Peaks of IRF5 in GM12878 from ENCODE 3 (ENCFF843HDK) Regulation 
encTfChipPkENCFF720YMW GM12878 IRF4 Transcription Factor ChIP-seq Peaks of IRF4 in GM12878 from ENCODE 3 (ENCFF720YMW) Regulation 
encTfChipPkENCFF719MXF GM12878 IRF3 2 Transcription Factor ChIP-seq Peaks of IRF3 in GM12878 from ENCODE 3 (ENCFF719MXF) Regulation 
encTfChipPkENCFF604AZX GM12878 IRF3 1 Transcription Factor ChIP-seq Peaks of IRF3 in GM12878 from ENCODE 3 (ENCFF604AZX) Regulation 
encTfChipPkENCFF088OLI GM12878 IKZF2 2 Transcription Factor ChIP-seq Peaks of IKZF2 in GM12878 from ENCODE 3 (ENCFF088OLI) Regulation 
encTfChipPkENCFF526WVH GM12878 IKZF2 1 Transcription Factor ChIP-seq Peaks of IKZF2 in GM12878 from ENCODE 3 (ENCFF526WVH) Regulation 
encTfChipPkENCFF018NNF GM12878 IKZF1 3 Transcription Factor ChIP-seq Peaks of IKZF1 in GM12878 from ENCODE 3 (ENCFF018NNF) Regulation 
encTfChipPkENCFF968NOG GM12878 IKZF1 2 Transcription Factor ChIP-seq Peaks of IKZF1 in GM12878 from ENCODE 3 (ENCFF968NOG) Regulation 
encTfChipPkENCFF197ABX GM12878 IKZF1 1 Transcription Factor ChIP-seq Peaks of IKZF1 in GM12878 from ENCODE 3 (ENCFF197ABX) Regulation 
encTfChipPkENCFF603BID GM12878 HSF1 Transcription Factor ChIP-seq Peaks of HSF1 in GM12878 from ENCODE 3 (ENCFF603BID) Regulation 
encTfChipPkENCFF248JAL GM12878 HDAC6 Transcription Factor ChIP-seq Peaks of HDAC6 in GM12878 from ENCODE 3 (ENCFF248JAL) Regulation 
encTfChipPkENCFF299UPZ GM12878 HDAC2 Transcription Factor ChIP-seq Peaks of HDAC2 in GM12878 from ENCODE 3 (ENCFF299UPZ) Regulation 
encTfChipPkENCFF722QBB GM12878 HCFC1 Transcription Factor ChIP-seq Peaks of HCFC1 in GM12878 from ENCODE 3 (ENCFF722QBB) Regulation 
encTfChipPkENCFF298AIX GM12878 GATAD2B Transcription Factor ChIP-seq Peaks of GATAD2B in GM12878 from ENCODE 3 (ENCFF298AIX) Regulation 
encTfChipPkENCFF946ACA GM12878 GABPA Transcription Factor ChIP-seq Peaks of GABPA in GM12878 from ENCODE 3 (ENCFF946ACA) Regulation 
encTfChipPkENCFF990MTR GM12878 FOXK2 Transcription Factor ChIP-seq Peaks of FOXK2 in GM12878 from ENCODE 3 (ENCFF990MTR) Regulation 
encTfChipPkENCFF615NYO GM12878 EZH2 Transcription Factor ChIP-seq Peaks of EZH2 in GM12878 from ENCODE 3 (ENCFF615NYO) Regulation 
encTfChipPkENCFF745ANU GM12878 ETV6 2 Transcription Factor ChIP-seq Peaks of ETV6 in GM12878 from ENCODE 3 (ENCFF745ANU) Regulation 
encTfChipPkENCFF116AMK GM12878 ETV6 1 Transcription Factor ChIP-seq Peaks of ETV6 in GM12878 from ENCODE 3 (ENCFF116AMK) Regulation 
encTfChipPkENCFF980VOD GM12878 ETS1 Transcription Factor ChIP-seq Peaks of ETS1 in GM12878 from ENCODE 3 (ENCFF980VOD) Regulation 
encTfChipPkENCFF722LJP GM12878 ESRRA Transcription Factor ChIP-seq Peaks of ESRRA in GM12878 from ENCODE 3 (ENCFF722LJP) Regulation 
encTfChipPkENCFF510FUM GM12878 EP300 3 Transcription Factor ChIP-seq Peaks of EP300 in GM12878 from ENCODE 3 (ENCFF510FUM) Regulation 
encTfChipPkENCFF080HJX GM12878 EP300 2 Transcription Factor ChIP-seq Peaks of EP300 in GM12878 from ENCODE 3 (ENCFF080HJX) Regulation 
encTfChipPkENCFF865UDD GM12878 EP300 1 Transcription Factor ChIP-seq Peaks of EP300 in GM12878 from ENCODE 3 (ENCFF865UDD) Regulation 
encTfChipPkENCFF432AQP GM12878 ELK1 Transcription Factor ChIP-seq Peaks of ELK1 in GM12878 from ENCODE 3 (ENCFF432AQP) Regulation 
encTfChipPkENCFF948CPI GM12878 ELF1 Transcription Factor ChIP-seq Peaks of ELF1 in GM12878 from ENCODE 3 (ENCFF948CPI) Regulation 
encTfChipPkENCFF341EJT GM12878 EGR1 Transcription Factor ChIP-seq Peaks of EGR1 in GM12878 from ENCODE 3 (ENCFF341EJT) Regulation 
encTfChipPkENCFF023ALY GM12878 EED Transcription Factor ChIP-seq Peaks of EED in GM12878 from ENCODE 3 (ENCFF023ALY) Regulation 
encTfChipPkENCFF249SVT GM12878 EBF1 Transcription Factor ChIP-seq Peaks of EBF1 in GM12878 from ENCODE 3 (ENCFF249SVT) Regulation 
encTfChipPkENCFF035GFS GM12878 E4F1 Transcription Factor ChIP-seq Peaks of E4F1 in GM12878 from ENCODE 3 (ENCFF035GFS) Regulation 
encTfChipPkENCFF412GFI GM12878 E2F8 Transcription Factor ChIP-seq Peaks of E2F8 in GM12878 from ENCODE 3 (ENCFF412GFI) Regulation 
encTfChipPkENCFF687SFB GM12878 E2F4 Transcription Factor ChIP-seq Peaks of E2F4 in GM12878 from ENCODE 3 (ENCFF687SFB) Regulation 
encTfChipPkENCFF771IAW GM12878 DPF2 Transcription Factor ChIP-seq Peaks of DPF2 in GM12878 from ENCODE 3 (ENCFF771IAW) Regulation 
encTfChipPkENCFF567NFS GM12878 CUX1 Transcription Factor ChIP-seq Peaks of CUX1 in GM12878 from ENCODE 3 (ENCFF567NFS) Regulation 
encTfChipPkENCFF960ZGP GM12878 CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in GM12878 from ENCODE 3 (ENCFF960ZGP) Regulation 
encTfChipPkENCFF356LIU GM12878 CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in GM12878 from ENCODE 3 (ENCFF356LIU) Regulation 
encTfChipPkENCFF091YID GM12878 CREM Transcription Factor ChIP-seq Peaks of CREM in GM12878 from ENCODE 3 (ENCFF091YID) Regulation 
encTfChipPkENCFF249SIN GM12878 CHD4 Transcription Factor ChIP-seq Peaks of CHD4 in GM12878 from ENCODE 3 (ENCFF249SIN) Regulation 
encTfChipPkENCFF863CTN GM12878 CHD1 Transcription Factor ChIP-seq Peaks of CHD1 in GM12878 from ENCODE 3 (ENCFF863CTN) Regulation 
encTfChipPkENCFF786YYI GM12878 CEBPB Transcription Factor ChIP-seq Peaks of CEBPB in GM12878 from ENCODE 3 (ENCFF786YYI) Regulation 
encTfChipPkENCFF417SVR GM12878 CBX5 Transcription Factor ChIP-seq Peaks of CBX5 in GM12878 from ENCODE 3 (ENCFF417SVR) Regulation 
encTfChipPkENCFF552QOA GM12878 CBX3 Transcription Factor ChIP-seq Peaks of CBX3 in GM12878 from ENCODE 3 (ENCFF552QOA) Regulation 
encTfChipPkENCFF070SOX GM12878 CBFB Transcription Factor ChIP-seq Peaks of CBFB in GM12878 from ENCODE 3 (ENCFF070SOX) Regulation 
encTfChipPkENCFF005JKU GM12878 BRCA1 Transcription Factor ChIP-seq Peaks of BRCA1 in GM12878 from ENCODE 3 (ENCFF005JKU) Regulation 
encTfChipPkENCFF592LPO GM12878 BMI1 Transcription Factor ChIP-seq Peaks of BMI1 in GM12878 from ENCODE 3 (ENCFF592LPO) Regulation 
encTfChipPkENCFF370ZNL GM12878 BHLHE40 2 Transcription Factor ChIP-seq Peaks of BHLHE40 in GM12878 from ENCODE 3 (ENCFF370ZNL) Regulation 
encTfChipPkENCFF622HGF GM12878 BHLHE40 1 Transcription Factor ChIP-seq Peaks of BHLHE40 in GM12878 from ENCODE 3 (ENCFF622HGF) Regulation 
encTfChipPkENCFF247MHT GM12878 BCL3 Transcription Factor ChIP-seq Peaks of BCL3 in GM12878 from ENCODE 3 (ENCFF247MHT) Regulation 
encTfChipPkENCFF383HAY GM12878 BCL11A Transcription Factor ChIP-seq Peaks of BCL11A in GM12878 from ENCODE 3 (ENCFF383HAY) Regulation 
encTfChipPkENCFF832YIE GM12878 BATF Transcription Factor ChIP-seq Peaks of BATF in GM12878 from ENCODE 3 (ENCFF832YIE) Regulation 
encTfChipPkENCFF725YZH GM12878 BACH1 Transcription Factor ChIP-seq Peaks of BACH1 in GM12878 from ENCODE 3 (ENCFF725YZH) Regulation 
encTfChipPkENCFF495PWL GM12878 ATF7 Transcription Factor ChIP-seq Peaks of ATF7 in GM12878 from ENCODE 3 (ENCFF495PWL) Regulation 
encTfChipPkENCFF806KKM GM12878 ATF2 2 Transcription Factor ChIP-seq Peaks of ATF2 in GM12878 from ENCODE 3 (ENCFF806KKM) Regulation 
encTfChipPkENCFF210HTZ GM12878 ATF2 1 Transcription Factor ChIP-seq Peaks of ATF2 in GM12878 from ENCODE 3 (ENCFF210HTZ) Regulation 
encTfChipPkENCFF096XRG GM12878 ASH2L Transcription Factor ChIP-seq Peaks of ASH2L in GM12878 from ENCODE 3 (ENCFF096XRG) Regulation 
encTfChipPkENCFF758RQJ GM12878 ARNT Transcription Factor ChIP-seq Peaks of ARNT in GM12878 from ENCODE 3 (ENCFF758RQJ) Regulation 
encTfChipPkENCFF003VDB GM12878 ARID3A Transcription Factor ChIP-seq Peaks of ARID3A in GM12878 from ENCODE 3 (ENCFF003VDB) Regulation 
encTfChipPkENCFF834WWA GM12874 CTCF Transcription Factor ChIP-seq Peaks of CTCF in GM12874 from ENCODE 3 (ENCFF834WWA) Regulation 
encTfChipPkENCFF913EEI GM12873 CTCF Transcription Factor ChIP-seq Peaks of CTCF in GM12873 from ENCODE 3 (ENCFF913EEI) Regulation 
encTfChipPkENCFF965YZI GM12865 CTCF Transcription Factor ChIP-seq Peaks of CTCF in GM12865 from ENCODE 3 (ENCFF965YZI) Regulation 
encTfChipPkENCFF751IKT GM12864 CTCF Transcription Factor ChIP-seq Peaks of CTCF in GM12864 from ENCODE 3 (ENCFF751IKT) Regulation 
encTfChipPkENCFF178PUI GM10266 CTCF Transcription Factor ChIP-seq Peaks of CTCF in GM10266 from ENCODE 3 (ENCFF178PUI) Regulation 
encTfChipPkENCFF329TZO GM08714 ZNF274 Transcription Factor ChIP-seq Peaks of ZNF274 in GM08714 from ENCODE 3 (ENCFF329TZO) Regulation 
encTfChipPkENCFF897RQN GM06990 CTCF Transcription Factor ChIP-seq Peaks of CTCF in GM06990 from ENCODE 3 (ENCFF897RQN) Regulation 
encTfChipPkENCFF837RIT DOHH2 CTCF Transcription Factor ChIP-seq Peaks of CTCF in DOHH2 from ENCODE 3 (ENCFF837RIT) Regulation 
encTfChipPkENCFF990ZZT Caco-2 CTCF Transcription Factor ChIP-seq Peaks of CTCF in Caco-2 from ENCODE 3 (ENCFF990ZZT) Regulation 
encTfChipPkENCFF300XXC CD14+monocyte CTCF Transcription Factor ChIP-seq Peaks of CTCF in CD14-positive_monocyte from ENCODE 3 (ENCFF300XXC) Regulation 
encTfChipPkENCFF856AUX C4-2B ZFX Transcription Factor ChIP-seq Peaks of ZFX in C4-2B from ENCODE 3 (ENCFF856AUX) Regulation 
encTfChipPkENCFF675JFN C4-2B CTCF Transcription Factor ChIP-seq Peaks of CTCF in C4-2B from ENCODE 3 (ENCFF675JFN) Regulation 
encTfChipPkENCFF910TER B_cell CTCF Transcription Factor ChIP-seq Peaks of CTCF in B_cell from ENCODE 3 (ENCFF910TER) Regulation 
encTfChipPkENCFF704JHR BJ CTCF Transcription Factor ChIP-seq Peaks of CTCF in BJ from ENCODE 3 (ENCFF704JHR) Regulation 
encTfChipPkENCFF594OZI BE2C CTCF Transcription Factor ChIP-seq Peaks of CTCF in BE2C from ENCODE 3 (ENCFF594OZI) Regulation 
encTfChipPkENCFF100IYW AG10803 CTCF Transcription Factor ChIP-seq Peaks of CTCF in AG10803 from ENCODE 3 (ENCFF100IYW) Regulation 
encTfChipPkENCFF119XBW AG09319 CTCF Transcription Factor ChIP-seq Peaks of CTCF in AG09319 from ENCODE 3 (ENCFF119XBW) Regulation 
encTfChipPkENCFF826NCK AG09309 CTCF Transcription Factor ChIP-seq Peaks of CTCF in AG09309 from ENCODE 3 (ENCFF826NCK) Regulation 
encTfChipPkENCFF788LNG AG04450 CTCF Transcription Factor ChIP-seq Peaks of CTCF in AG04450 from ENCODE 3 (ENCFF788LNG) Regulation 
encTfChipPkENCFF652LEH AG04449 CTCF Transcription Factor ChIP-seq Peaks of CTCF in AG04449 from ENCODE 3 (ENCFF652LEH) Regulation 
encTfChipPkENCFF807XMX A673 EZH2 Transcription Factor ChIP-seq Peaks of EZH2 in A673 from ENCODE 3 (ENCFF807XMX) Regulation 
encTfChipPkENCFF695QMG A673 CTCF Transcription Factor ChIP-seq Peaks of CTCF in A673 from ENCODE 3 (ENCFF695QMG) Regulation 
encTfChipPkENCFF593ZJA A549 ZBTB33 Transcription Factor ChIP-seq Peaks of ZBTB33 in A549 from ENCODE 3 (ENCFF593ZJA) Regulation 
encTfChipPkENCFF613DTQ A549 YY1 Transcription Factor ChIP-seq Peaks of YY1 in A549 from ENCODE 3 (ENCFF613DTQ) Regulation 
encTfChipPkENCFF593EOW A549 USF2 Transcription Factor ChIP-seq Peaks of USF2 in A549 from ENCODE 3 (ENCFF593EOW) Regulation 
encTfChipPkENCFF228CDD A549 TCF12 Transcription Factor ChIP-seq Peaks of TCF12 in A549 from ENCODE 3 (ENCFF228CDD) Regulation 
encTfChipPkENCFF886KDK A549 TAF1 Transcription Factor ChIP-seq Peaks of TAF1 in A549 from ENCODE 3 (ENCFF886KDK) Regulation 
encTfChipPkENCFF483YCC A549 SREBF2 Transcription Factor ChIP-seq Peaks of SREBF2 in A549 from ENCODE 3 (ENCFF483YCC) Regulation 
encTfChipPkENCFF624DDK A549 SREBF1 Transcription Factor ChIP-seq Peaks of SREBF1 in A549 from ENCODE 3 (ENCFF624DDK) Regulation 
encTfChipPkENCFF404OSB A549 SP1 Transcription Factor ChIP-seq Peaks of SP1 in A549 from ENCODE 3 (ENCFF404OSB) Regulation 
encTfChipPkENCFF256LDD A549 SMC3 Transcription Factor ChIP-seq Peaks of SMC3 in A549 from ENCODE 3 (ENCFF256LDD) Regulation 
encTfChipPkENCFF189NMX A549 SIX5 Transcription Factor ChIP-seq Peaks of SIX5 in A549 from ENCODE 3 (ENCFF189NMX) Regulation 
encTfChipPkENCFF708HTR A549 SIN3A 2 Transcription Factor ChIP-seq Peaks of SIN3A in A549 from ENCODE 3 (ENCFF708HTR) Regulation 
encTfChipPkENCFF567BJI A549 SIN3A 1 Transcription Factor ChIP-seq Peaks of SIN3A in A549 from ENCODE 3 (ENCFF567BJI) Regulation 
encTfChipPkENCFF110EOX A549 RNF2 Transcription Factor ChIP-seq Peaks of RNF2 in A549 from ENCODE 3 (ENCFF110EOX) Regulation 
encTfChipPkENCFF179WDI A549 RFX5 Transcription Factor ChIP-seq Peaks of RFX5 in A549 from ENCODE 3 (ENCFF179WDI) Regulation 
encTfChipPkENCFF706DRE A549 REST 2 Transcription Factor ChIP-seq Peaks of REST in A549 from ENCODE 3 (ENCFF706DRE) Regulation 
encTfChipPkENCFF107EWI A549 REST 1 Transcription Factor ChIP-seq Peaks of REST in A549 from ENCODE 3 (ENCFF107EWI) Regulation 
encTfChipPkENCFF993WZP A549 RCOR1 Transcription Factor ChIP-seq Peaks of RCOR1 in A549 from ENCODE 3 (ENCFF993WZP) Regulation 
encTfChipPkENCFF897QCA A549 RAD21 Transcription Factor ChIP-seq Peaks of RAD21 in A549 from ENCODE 3 (ENCFF897QCA) Regulation 
encTfChipPkENCFF664KTN A549 POLR2A 2 Transcription Factor ChIP-seq Peaks of POLR2A in A549 from ENCODE 3 (ENCFF664KTN) Regulation 
encTfChipPkENCFF915LKZ A549 POLR2A 1 Transcription Factor ChIP-seq Peaks of POLR2A in A549 from ENCODE 3 (ENCFF915LKZ) Regulation 
encTfChipPkENCFF907WHF A549 PHF8 Transcription Factor ChIP-seq Peaks of PHF8 in A549 from ENCODE 3 (ENCFF907WHF) Regulation 
encTfChipPkENCFF463DJO A549 NR3C1 5 Transcription Factor ChIP-seq Peaks of NR3C1 in A549 from ENCODE 3 (ENCFF463DJO) Regulation 
encTfChipPkENCFF114SRD A549 NR3C1 4 Transcription Factor ChIP-seq Peaks of NR3C1 in A549 from ENCODE 3 (ENCFF114SRD) Regulation 
encTfChipPkENCFF963CGV A549 NR3C1 3 Transcription Factor ChIP-seq Peaks of NR3C1 in A549 from ENCODE 3 (ENCFF963CGV) Regulation 
encTfChipPkENCFF514IGC A549 NR3C1 2 Transcription Factor ChIP-seq Peaks of NR3C1 in A549 from ENCODE 3 (ENCFF514IGC) Regulation 
encTfChipPkENCFF714KXI A549 NR3C1 1 Transcription Factor ChIP-seq Peaks of NR3C1 in A549 from ENCODE 3 (ENCFF714KXI) Regulation 
encTfChipPkENCFF418TUX A549 NFE2L2 Transcription Factor ChIP-seq Peaks of NFE2L2 in A549 from ENCODE 3 (ENCFF418TUX) Regulation 
encTfChipPkENCFF542GMN A549 MYC Transcription Factor ChIP-seq Peaks of MYC in A549 from ENCODE 3 (ENCFF542GMN) Regulation 
encTfChipPkENCFF813WJW A549 MAFK Transcription Factor ChIP-seq Peaks of MAFK in A549 from ENCODE 3 (ENCFF813WJW) Regulation 
encTfChipPkENCFF149INM A549 KDM5A Transcription Factor ChIP-seq Peaks of KDM5A in A549 from ENCODE 3 (ENCFF149INM) Regulation 
encTfChipPkENCFF316CBQ A549 KDM1A Transcription Factor ChIP-seq Peaks of KDM1A in A549 from ENCODE 3 (ENCFF316CBQ) Regulation 
encTfChipPkENCFF587VEY A549 JUND Transcription Factor ChIP-seq Peaks of JUND in A549 from ENCODE 3 (ENCFF587VEY) Regulation 
encTfChipPkENCFF127HJG A549 JUN Transcription Factor ChIP-seq Peaks of JUN in A549 from ENCODE 3 (ENCFF127HJG) Regulation 
encTfChipPkENCFF814DAF A549 HDAC2 Transcription Factor ChIP-seq Peaks of HDAC2 in A549 from ENCODE 3 (ENCFF814DAF) Regulation 
encTfChipPkENCFF520GJC A549 GABPA Transcription Factor ChIP-seq Peaks of GABPA in A549 from ENCODE 3 (ENCFF520GJC) Regulation 
encTfChipPkENCFF167BKY A549 FOXA1 2 Transcription Factor ChIP-seq Peaks of FOXA1 in A549 from ENCODE 3 (ENCFF167BKY) Regulation 
encTfChipPkENCFF297HAX A549 FOXA1 1 Transcription Factor ChIP-seq Peaks of FOXA1 in A549 from ENCODE 3 (ENCFF297HAX) Regulation 
encTfChipPkENCFF808RWZ A549 FOSL2 Transcription Factor ChIP-seq Peaks of FOSL2 in A549 from ENCODE 3 (ENCFF808RWZ) Regulation 
encTfChipPkENCFF896WFR A549 ETS1 Transcription Factor ChIP-seq Peaks of ETS1 in A549 from ENCODE 3 (ENCFF896WFR) Regulation 
encTfChipPkENCFF558UWY A549 ESRRA Transcription Factor ChIP-seq Peaks of ESRRA in A549 from ENCODE 3 (ENCFF558UWY) Regulation 
encTfChipPkENCFF605JXG A549 ELK1 Transcription Factor ChIP-seq Peaks of ELK1 in A549 from ENCODE 3 (ENCFF605JXG) Regulation 
encTfChipPkENCFF935ZUW A549 ELF1 Transcription Factor ChIP-seq Peaks of ELF1 in A549 from ENCODE 3 (ENCFF935ZUW) Regulation 
encTfChipPkENCFF199OOU A549 EHMT2 Transcription Factor ChIP-seq Peaks of EHMT2 in A549 from ENCODE 3 (ENCFF199OOU) Regulation 
encTfChipPkENCFF646TUX A549 CTCF 3 Transcription Factor ChIP-seq Peaks of CTCF in A549 from ENCODE 3 (ENCFF646TUX) Regulation 
encTfChipPkENCFF615GTV A549 CTCF 2 Transcription Factor ChIP-seq Peaks of CTCF in A549 from ENCODE 3 (ENCFF615GTV) Regulation 
encTfChipPkENCFF535MZG A549 CTCF 1 Transcription Factor ChIP-seq Peaks of CTCF in A549 from ENCODE 3 (ENCFF535MZG) Regulation 
encTfChipPkENCFF186ZET A549 CREB1 2 Transcription Factor ChIP-seq Peaks of CREB1 in A549 from ENCODE 3 (ENCFF186ZET) Regulation 
encTfChipPkENCFF576PUH A549 CREB1 1 Transcription Factor ChIP-seq Peaks of CREB1 in A549 from ENCODE 3 (ENCFF576PUH) Regulation 
encTfChipPkENCFF766YPH A549 CHD4 Transcription Factor ChIP-seq Peaks of CHD4 in A549 from ENCODE 3 (ENCFF766YPH) Regulation 
encTfChipPkENCFF047UIF A549 CEBPB Transcription Factor ChIP-seq Peaks of CEBPB in A549 from ENCODE 3 (ENCFF047UIF) Regulation 
encTfChipPkENCFF330OCU A549 CBX8 Transcription Factor ChIP-seq Peaks of CBX8 in A549 from ENCODE 3 (ENCFF330OCU) Regulation 
encTfChipPkENCFF208AXT A549 CBX2 Transcription Factor ChIP-seq Peaks of CBX2 in A549 from ENCODE 3 (ENCFF208AXT) Regulation 
encTfChipPkENCFF093ZAB A549 BCL3 Transcription Factor ChIP-seq Peaks of BCL3 in A549 from ENCODE 3 (ENCFF093ZAB) Regulation 
encTfChipPkENCFF851UTY A549 ATF3 Transcription Factor ChIP-seq Peaks of ATF3 in A549 from ENCODE 3 (ENCFF851UTY) Regulation 
cons241way Cactus 241-way Cactus Alignment & Conservation of Zoonomia Placental Mammals (241 Species) Comparative Genomics Downloads for data in this track are available: Cactus alignments (MAF format), and phylogenetic trees, and PhyloP conservation (WIG and bigWig format) Description Warning: Unlike other alignment tracks on the genome browser, this one does not show insertions in the query genomes. Also, all other alignment tracks show one query genome sequence for target target genome sequence, but in this track, each target genome sequence can be aligned to multiple query genome sequences. Only the first sequence is shown on the genome browser itself, the others are shown on the details page, when one clicks on the alignment. If you are interested in this track and want these shortcomings to be fixed, please contact us. This track shows multiple alignments of 241 vertebrate species and measurements of evolutionary conservation from the Zoonomia Project. The multiple alignments were generated using the Cactus comparative genomics alignment system. Cactus produces reference-free, whole-genome multiple alignments. The base-wise conservation scores are computed using phyloP from the PHAST package, for all species. This version was prepared by Michael Dong (Uppsala U) with an improved neutral model incorporating better versions of ancestral repeats. For genome assemblies not available in the genome browser, there are alternative assembly hub genome browsers. Missing sequence in any assembly is highlighted in the track display by regions of yellow when zoomed out and by Ns when displayed at base level (see Gap Annotation, below). count commonname CLADE group scientificname sequencingsource NCBIassembly speciesstatus 1 Cape golden mole AFROSORICIDA Chrysochloridae Chrysochloris asiatica 1. Zoonomia GCA_004027935.1 LC 2 Small madagascar hedgehog AFROSORICIDA Tenrecidae Echinops telfairi 2. Existing assembly GCF_000313985.1 LC 3 Talazac's shrew tenrec AFROSORICIDA Tenrecidae Microgale talazaci 1. Zoonomia GCA_004026705.1 LC 4 Cheetah CARNIVORA Felidae Acinonyx jubatus 2. Existing assembly GCF_001443585.1 CR 5 Giant panda CARNIVORA Ursidae Ailuropoda melanoleuca 2. Existing assembly GCA_002007445.1 VU 6 Lesser panda CARNIVORA Ailuridae Ailurus fulgens 2. Existing assembly GCA_002007465.1 EN 7 Domestic dog CARNIVORA Canidae Canis lupus familiaris 2. Existing assembly GCF_000002285.3 LC 8 Domestic dog (village dog) CARNIVORA Canidae Canis lupus familiaris 1. Zoonomia GCA_004027395.1 LC 9 Fossa CARNIVORA Eupleridae Cryptoprocta ferox 1. Zoonomia GCA_004023885.1 VU 10 Sea otter CARNIVORA Mustelidae Enhydra lutris 2. Existing assembly GCF_002288905.1 EN 11 Domestic cat CARNIVORA Felidae Felis catus 2. Existing assembly GCF_000181335.2 LC 12 Black-footed cat CARNIVORA Felidae Felis nigripes 1. Zoonomia GCA_004023925.1 VU 13 Dwarf mongoose CARNIVORA Herpestidae Helogale parvula 1. Zoonomia GCA_004023845.1 LC 14 Striped hyena CARNIVORA Hyaenidae Hyaena hyaena 1. Zoonomia GCA_004023945.1 NT 15 Weddell seal CARNIVORA Phocidae Leptonychotes weddellii 2. Existing assembly GCF_000349705.1 LC 16 African hunting dog CARNIVORA Canidae Lycaon pictus 2. Existing assembly GCA_001887905.1 EN 17 Honey badger CARNIVORA Mustelidae Mellivora capensis 1. Zoonomia GCA_004024625.1 LC 18 Northern elephant seal CARNIVORA Phocidae Mirounga angustirostris 1. Zoonomia GCA_004023865.1 LC 19 South African banded mongoose CARNIVORA Herpestidae Mungos mungo 1. Zoonomia GCA_004023785.1 LC 20 Domestic ferret CARNIVORA Mustelidae Mustela putorius 2. Existing assembly GCF_000239315.1 LC 21 Hawaiian monk seal CARNIVORA Phocidae Neomonachus schauinslandi 2. Existing assembly GCA_002201575.1 EN 22 Pacific walrus CARNIVORA Odobenidae Odobenus rosmarus 2. Existing assembly GCF_000321225.1 DD 23 Jaguar CARNIVORA Felidae Panthera onca 1. Zoonomia GCA_004023805.1 NT 24 Leopard CARNIVORA Felidae Panthera pardus 2. Existing assembly GCA_001857705.1 VU 25 Amur tiger CARNIVORA Felidae Panthera tigris 2. Existing assembly GCF_000464555.1 EN 26 Asian palm civet CARNIVORA Viverridae Paradoxurus hermaphroditus 1. Zoonomia GCA_004024585.1 LC 27 Giant otter CARNIVORA Mustelidae Pteronura brasiliensis 1. Zoonomia GCA_004024605.1 EN 28 Puma CARNIVORA Felidae Puma concolor 2. Existing assembly GCF_003327715.1 LC 29 Western spotted skunk CARNIVORA Mephitidae Spilogale gracilis 1. Zoonomia GCA_004023965.1 LC 30 Meerkat CARNIVORA Herpestidae Suricata suricatta 1. Zoonomia GCA_004023905.1 LC 31 Polar bear CARNIVORA Ursidae Ursus maritimus 2. Existing assembly GCF_000687225.1 VU 32 Arctic fox CARNIVORA Canidae Vulpes lagopus 1. Zoonomia GCA_004023825.1 LC 33 California sea lion CARNIVORA Otariidae Zalophus californianus 1. Zoonomia GCA_004024565.1 LC 34 Aoudad CETARTIODACTYLA Bovidae Ammotragus lervia 2. Existing assembly GCA_002201775.1 VU 35 Pronghorn CETARTIODACTYLA Antilocapridae Antilocapra americana 1. Zoonomia GCA_004027515.1 LC 36 Minke whale CETARTIODACTYLA Balaenopteridae Balaenoptera acutorostrata 2. Existing assembly GCF_000493695.1 LC 37 Antarctic minke whale CETARTIODACTYLA Balaenopteridae Balaenoptera bonaerensis 2. Existing assembly GCA_000978805.1 DD 38 Hirola CETARTIODACTYLA Bovidae Beatragus hunteri 1. Zoonomia GCA_004027495.1 CR 39 American bison CETARTIODACTYLA Bovidae Bison bison 2. Existing assembly GCF_000754665.1 NT 40 Zebu cattle CETARTIODACTYLA Bovidae Bos indicus 2. Existing assembly GCA_000247795.2 LC 41 Wild yak CETARTIODACTYLA Bovidae Bos mutus 2. Existing assembly GCF_000298355.1 VU 42 Cattle CETARTIODACTYLA Bovidae Bos taurus 2. Existing assembly GCF_000003205.7 LC 43 Water buffalo CETARTIODACTYLA Bovidae Bubalus bubalis 2. Existing assembly GCF_000471725.1 LC 44 Bactrian camel CETARTIODACTYLA Camelidae Camelus bactrianus 2. Existing assembly GCF_000767855.1 LC 45 Arabian camel CETARTIODACTYLA Camelidae Camelus dromedarius 2. Existing assembly GCF_000767585.1 LC 46 Wild bactrian camel CETARTIODACTYLA Camelidae Camelus ferus 2. Existing assembly GCF_000311805.1 CR 47 Wild goat CETARTIODACTYLA Bovidae Capra aegagrus 2. Existing assembly GCA_000978405.1 VU 48 Goat CETARTIODACTYLA Bovidae Capra hircus 2. Existing assembly GCF_001704415.1 LC 49 Chacoan peccary CETARTIODACTYLA Tayassuidae Catagonus wagneri 1. Zoonomia GCA_004024745.1 EN 50 Beluga whale CETARTIODACTYLA Monodontidae Delphinapterus leucas 2. Existing assembly GCF_002288925.1 LC 51 Pere david's deer CETARTIODACTYLA Cervidae Elaphurus davidianus 2. Existing assembly GCA_002443075.1 CR 52 Grey whale CETARTIODACTYLA Eschrichtiidae Eschrichtius robustus 1. Zoonomia GCA_004363415.1 LC 53 North Pacific right whale CETARTIODACTYLA Balaenidae Eubalaena japonica 1. Zoonomia GCA_004363455.1 EN 54 Giraffe CETARTIODACTYLA Giraffidae Giraffa tippelskirchi 2. Existing assembly GCA_001651235.1 VU 55 Nilgiri tahr CETARTIODACTYLA Bovidae Hemitragus hylocrius 1. Zoonomia GCA_004026825.1 EN 56 Hippopotamus CETARTIODACTYLA Hippopotamidae Hippopotamus amphibius 1. Zoonomia GCA_004027065.1 VU 57 Amazon river dolphin CETARTIODACTYLA Iniidae Inia geoffrensis 1. Zoonomia GCA_004363515.1 DD 58 Pygmy sperm whale CETARTIODACTYLA Physeteridae Kogia breviceps 1. Zoonomia GCA_004363705.1 DD 59 Yangtze river dolphin CETARTIODACTYLA Iniidae Lipotes vexillifer 2. Existing assembly GCF_000442215.1 CR 60 Sowerby's beaked whale CETARTIODACTYLA Ziphiidae Mesoplodon bidens 1. Zoonomia GCA_004027085.1 DD 61 Narwhal CETARTIODACTYLA Monodontidae Monodon monoceros 1. Zoonomia GCA_004026685.1 LC 62 Siberian musk deer CETARTIODACTYLA Moschidae Moschus moschiferus 1. Zoonomia GCA_004024705.1 VU 63 Yangtze finless porpoise CETARTIODACTYLA Phocoenidae Neophocaena asiaeorientalis 2. Existing assembly GCA_003031525.1 EN 64 White-tailed deer CETARTIODACTYLA Cervidae Odocoileus virginianus 2. Existing assembly GCA_002102435.1 LC 65 Okapi CETARTIODACTYLA Giraffidae Okapia johnstoni 2. Existing assembly GCA_001660835.1 EN 66 Killer whale CETARTIODACTYLA Delphinidae Orcinus orca 2. Existing assembly GCF_000331955.2 DD 67 Sheep CETARTIODACTYLA Bovidae Ovis aries 2. Existing assembly GCF_000298735.2 LC 68 Peninsular bighorn sheep CETARTIODACTYLA Bovidae Ovis canadensis cremnobates 1. Zoonomia GCA_004026945.1 EN 69 Chiru CETARTIODACTYLA Bovidae Pantholops hodgsonii 2. Existing assembly GCF_000400835.1 NT 70 Harbor porpoise CETARTIODACTYLA Phocoenidae Phocoena phocoena 1. Zoonomia GCA_004363495.1 LC 71 Indus river dolphin CETARTIODACTYLA Platanistidae Platanista gangetica minor 1. Zoonomia GCA_004363435.1 EN 72 Siberian reindeer CETARTIODACTYLA Cervidae Rangifer tarandus 1. Zoonomia GCA_004026565.1 VU 73 Russian saiga CETARTIODACTYLA Bovidae Saiga tatarica tatarica 1. Zoonomia GCA_004024985.1 CR 74 Pig CETARTIODACTYLA Suidae Sus scrofa 2. Existing assembly GCF_000003025.5 LC 75 Java lesser chevrotain CETARTIODACTYLA Tragulidae Tragulus javanicus 1. Zoonomia GCA_004024965.1 DD 76 Bottlenose dolphin CETARTIODACTYLA Delphinidae Tursiops truncatus 2. Existing assembly GCA_001922835.1 LC 77 Alpaca CETARTIODACTYLA Camelidae Vicugna pacos 2. Existing assembly GCA_000767525.1 LC 78 Cuvier's beaked whale CETARTIODACTYLA Ziphiidae Ziphius cavirostris 1. Zoonomia GCA_004364475.1 LC 79 Tailed tailless bat CHIROPTERA Phyllostomidae Anoura caudifer 1. Zoonomia GCA_004027475.1 LC 80 Jamacian fruit-eating bat CHIROPTERA Phyllostomidae Artibeus jamaicensis 1. Zoonomia GCA_004027435.1 LC 81 Seba's short-tailed bat CHIROPTERA Phyllostomidae Carollia perspicillata 1. Zoonomia GCA_004027735.1 LC 82 Bumblebee bat CHIROPTERA Craseonycteridae Craseonycteris thonglongyai 1. Zoonomia GCA_004027555.1 VU 83 Common vampire bat CHIROPTERA Phyllostomidae Desmodus rotundus 2. Existing assembly GCA_002940915.2 LC 84 Straw-colored fruit bat CHIROPTERA Pteropodidae Eidolon helvum 2. Existing assembly GCA_000465285.1 NT 85 Big brown bat CHIROPTERA Vespertilionidae Eptesicus fuscus 2. Existing assembly GCF_000308155.1 LC 86 Great roundleaf bat CHIROPTERA Hipposideridae Hipposideros armiger 2. Existing assembly GCA_001890085.1 LC 87 Cantor's leaf-nosed bat CHIROPTERA Hipposideridae Hipposideros galeritus 1. Zoonomia GCA_004027415.1 LC 88 Eastern red bat CHIROPTERA Vespertilionidae Lasiurus borealis 1. Zoonomia GCA_004026805.1 LC 89 Long-tongued fruit bat CHIROPTERA Pteropodidae Macroglossus sobrinus 1. Zoonomia GCA_004027375.1 LC 90 Greater false vampire bat CHIROPTERA Megadermatidae Megaderma lyra 1. Zoonomia GCA_004026885.1 LC 91 Hairy big-eared bat CHIROPTERA Phyllostomidae Micronycteris hirsuta 1. Zoonomia GCA_004026765.1 LC 92 Natal long-fingered bat CHIROPTERA Vespertilionidae Miniopterus natalensis 2. Existing assembly GCF_001595765.1 LC 93 Common bent-wing bat CHIROPTERA Vespertilionidae Miniopterus schreibersii 1. Zoonomia GCA_004026525.1 NT 94 Ghost-faced bat CHIROPTERA Mormoopidae Mormoops blainvillei 1. Zoonomia GCA_004026545.1 LC 95 Ashy-gray tube-nosed bat CHIROPTERA Vespertilionidae Murina feae 1. Zoonomia GCA_004026665.1 LC 96 Brandt's bat CHIROPTERA Vespertilionidae Myotis brandtii 2. Existing assembly GCF_000412655.1 LC 97 David's myotis bat CHIROPTERA Vespertilionidae Myotis davidii 2. Existing assembly GCF_000327345.1 LC 98 Little brown bat CHIROPTERA Vespertilionidae Myotis lucifugus 2. Existing assembly GCF_000147115.1 LC 99 Greater mouse-eared bat CHIROPTERA Vespertilionidae Myotis myotis 1. Zoonomia GCA_004026985.1 LC 100 Greater bulldog bat CHIROPTERA Noctilionidae Noctilio leporinus 1. Zoonomia GCA_004026585.1 LC 101 Common pipistrelle CHIROPTERA Vespertilionidae Pipistrellus pipistrellus 1. Zoonomia GCA_004026625.1 LC 102 Parnell's mustached bat CHIROPTERA Mormoopidae Pteronotus parnellii 2. Existing assembly GCA_000465405.1 LC 103 Black flying fox CHIROPTERA Pteropodidae Pteropus alecto 2. Existing assembly GCF_000325575.1 LC 104 Large flying fox CHIROPTERA Pteropodidae Pteropus vampyrus 2. Existing assembly GCF_000151845.1 NT 105 Chinese rufous horseshoe bat CHIROPTERA Rhinolophidae Rhinolophus sinicus 2. Existing assembly GCA_001888835.1 LC 106 Egyptian fruit bat CHIROPTERA Pteropodidae Rousettus aegyptiacus 1. Zoonomia GCA_004024865.1 LC 107 Mexican free-tailed bat CHIROPTERA Molossidae Tadarida brasiliensis 1. Zoonomia GCA_004025005.1 LC 108 Stripe-headed round-eared bat CHIROPTERA Phyllostomidae Tonatia saurophila 1. Zoonomia GCA_004024845.1 LC 109 Screaming hairy armadillo CINGULATA Dasypodidae Chaetophractus vellerosus 1. Zoonomia GCA_004027955.1 LC 110 Nine-banded armadillo CINGULATA Dasypodidae Dasypus novemcinctus 2. Existing assembly GCF_000208655.1 LC 111 Southern three-banded armadillo CINGULATA Dasypodidae Tolypeutes matacus 1. Zoonomia GCA_004025125.1 NT 112 Sunda flying lemur DERMOPTERA Cynocephalidae Galeopterus variegatus 1. Zoonomia GCA_004027255.1 LC 113 Star-nosed mole EULIPOTYPHLA Talpidae Condylura cristata 2. Existing assembly GCF_000260355.1 LC 114 Indochinese shrew EULIPOTYPHLA Soricidae Crocidura indochinensis 1. Zoonomia GCA_004027635.1 LC 115 Western european hedgehog EULIPOTYPHLA Erinaceidae Erinaceus europaeus 2. Existing assembly GCF_000296755.1 LC 116 Eastern mole EULIPOTYPHLA Talpidae Scalopus aquaticus 1. Zoonomia GCA_004024925.1 LC 117 Hispaniolan solenodon EULIPOTYPHLA Solenodontidae Solenodon paradoxus 1. Zoonomia GCA_004363575.1 EN 118 European shrew EULIPOTYPHLA Soricidae Sorex araneus 2. Existing assembly GCF_000181275.1 LC 119 Gracile shrew-like mole EULIPOTYPHLA Talpidae Uropsilus gracilis 1. Zoonomia GCA_004024945.1 LC 120 African yellow-spotted rock hyrax HYRACOIDEA Procaviidae Heterohyrax brucei 1. Zoonomia GCA_004026845.1 LC 121 South African rock hyrax HYRACOIDEA Procaviidae Procavia capensis 1. Zoonomia GCA_004026925.1 LC 122 Snowshoe hare LAGOMORPHA Leporidae Lepus americanus 1. Zoonomia GCA_004026855.1 LC 123 American pika LAGOMORPHA Ochotonidae Ochotona princeps 2. Existing assembly GCF_000292845.1 LC 124 Rabbit LAGOMORPHA Leporidae Oryctolagus cuniculus 2. Existing assembly GCF_000003625.3 NT 125 Cape elephant shrew MACROSCELIDEA Macroscelididae Elephantulus edwardii 1. Zoonomia GCA_004027355.1 LC 126 Southern white rhinoceros PERISSODACTYLA Rhinocerotidae Ceratotherium simum 2. Existing assembly GCF_000283155.1 NT 127 Northern white rhino PERISSODACTYLA Rhinocerotidae Ceratotherium simum cottoni 1. Zoonomia GCA_004027795.1 CR 128 Sumatran rhinoceros PERISSODACTYLA Rhinocerotidae Dicerorhinus sumatrensis 2. Existing assembly GCA_002844835.1 CR 129 Black rhinocerous PERISSODACTYLA Rhinocerotidae Diceros bicornis 1. Zoonomia GCA_004027315.1 CR 130 Ass PERISSODACTYLA Equidae Equus asinus 2. Existing assembly GCF_001305755.1 LC 131 Horse PERISSODACTYLA Equidae Equus caballus 2. Existing assembly GCF_000002305.2 LC 132 Przewalski's horse PERISSODACTYLA Equidae Equus przewalskii 2. Existing assembly GCF_000696695.1 EN 133 Malayan tapir PERISSODACTYLA Tapiridae Tapirus indicus 1. Zoonomia GCA_004024905.1 EN 134 South American tapir PERISSODACTYLA Tapiridae Tapirus terrestris 1. Zoonomia GCA_004025025.1 VU 135 Malayan pangolin PHOLIDOTA Manidae Manis javanica 2. Existing assembly GCF_001685135.1 CR 136 Chinese pangolin PHOLIDOTA Manidae Manis pentadactyla 2. Existing assembly GCA_000738955.1 CR 137 Linnaeus's two toed sloth PILOSA Megalonychidae Choloepus didactylus 1. Zoonomia GCA_004027855.1 LC 138 Hoffmann's two-fingered sloth PILOSA Megalonychidae Choloepus hoffmanni 2. Existing assembly GCA_000164785.2 LC 139 Giant anteater PILOSA Myrmecophagidae Myrmecophaga tridactyla 1. Zoonomia GCA_004026745.1 VU 140 Southern tamandua PILOSA Myrmecophagidae Tamandua tetradactyla 1. Zoonomia GCA_004025105.1 LC 141 Mexican howler monkey PRIMATES Atelidae Alouatta palliata mexicana 1. Zoonomia GCA_004027835.1 CR 142 Ma's night monkey PRIMATES Aotidae Aotus nancymaae 2. Existing assembly GCA_000952055.2 VU 143 Geoffroy's spider monkey PRIMATES Atelidae Ateles geoffroyi 1. Zoonomia GCA_004024785.1 EN 144 White-eared titi PRIMATES Pitheciidae Callicebus donacophilus 1. Zoonomia GCA_004027715.1 LC 145 White-tufted-ear marmoset PRIMATES Cebidae Callithrix jacchus 2. Existing assembly GCA_002754865.1 LC 146 White-fronted capuchin PRIMATES Cebidae Cebus albifrons 1. Zoonomia GCA_004027755.1 LC 147 White-faced sapajou PRIMATES Cebidae Cebus capucinus 2. Existing assembly GCF_001604975.1 LC 148 Sooty mangabey PRIMATES Cercopithecidae Cercocebus atys 2. Existing assembly GCF_000955945.1 NT 149 De brazza's monkey PRIMATES Cercopithecidae Cercopithecus neglectus 1. Zoonomia GCA_004027615.1 LC 150 Fat-tailed dwarf lemur PRIMATES Cheirogaleidae Cheirogaleus medius 1. Zoonomia GCA_004024725.1 LC 151 Green monkey PRIMATES Cercopithecidae Chlorocebus sabaeus 2. Existing assembly GCF_000409795.2 LC 152 Angolan colobus PRIMATES Cercopithecidae Colobus angolensis 2. Existing assembly GCF_000951035.1 VU 153 Aye-aye PRIMATES Daubentoniidae Daubentonia madagascariensis 1. Zoonomia GCA_004027145.1 EN 154 Patas monkey PRIMATES Cercopithecidae Erythrocebus patas 1. Zoonomia GCA_004027335.1 LC 155 Sclater's lemur PRIMATES Lemuridae Eulemur flavifrons 2. Existing assembly GCA_001262665.1 CR 156 Common brown lemur PRIMATES Lemuridae Eulemur fulvus 1. Zoonomia GCA_004027275.1 NT 157 Western lowland gorilla PRIMATES Hominidae Gorilla gorilla 2. Existing assembly GCA_900006655.3 CR 158 Human PRIMATES Hominidae Homo sapiens 2. Existing assembly GCA_000001405.27 LC 159 Indri PRIMATES Indridae Indri indri 1. Zoonomia GCA_004363605.1 CR 160 Ring tailed lemur PRIMATES Lemuridae Lemur catta 1. Zoonomia GCA_004024665.1 EN 161 Crab-eating macaque PRIMATES Cercopithecidae Macaca fascicularis 2. Existing assembly GCF_000364345.1 DD 162 Rhesus monkey PRIMATES Cercopithecidae Macaca mulatta 2. Existing assembly GCF_000772875.2 LC 163 Pig-tailed macaque PRIMATES Cercopithecidae Macaca nemestrina 2. Existing assembly GCF_000956065.1 VU 164 Drill PRIMATES Cercopithecidae Mandrillus leucophaeus 2. Existing assembly GCF_000951045.1 EN 165 Gray mouse lemur PRIMATES Cheirogaleidae Microcebus murinus 2. Existing assembly GCA_000165445.3 LC 166 Coquerel's giant mouse lemur PRIMATES Cheirogaleidae Mirza coquereli 1. Zoonomia GCA_004024645.1 EN 167 Proboscis monkey PRIMATES Cercopithecidae Nasalis larvatus 1. Zoonomia GCA_004027105.1 EN 168 Northern white-cheeked gibbon PRIMATES Hylobatidae Nomascus leucogenys 2. Existing assembly GCF_000146795.2 CR 169 Sunda slow loris PRIMATES Lorisidae Nycticebus coucang 1. Zoonomia GCA_004027815.1 VU 170 Small-eared galago PRIMATES Galagidae Otolemur garnettii 2. Existing assembly GCF_000181295.1 LC 171 Pygmy chimpanzee PRIMATES Hominidae Pan paniscus 2. Existing assembly GCF_000258655.2 EN 172 Chimpanzee PRIMATES Hominidae Pan troglodytes 2. Existing assembly GCA_002880755.3 EN 173 Olive baboon PRIMATES Cercopithecidae Papio anubis 2. Existing assembly GCA_000264685.2 LC 174 Ugandan red colobus PRIMATES Cercopithecidae Piliocolobus tephrosceles 2. Existing assembly GCA_002776525.1 EN 175 White-faced saki PRIMATES Pitheciidae Pithecia pithecia 1. Zoonomia GCA_004026645.1 LC 176 Sumatran orangutan PRIMATES Hominidae Pongo abelii 2. Existing assembly GCA_002880775.3 CR 177 Coquerel's sifaka PRIMATES Indridae Propithecus coquereli 2. Existing assembly GCF_000956105.1 EN 178 Red-shanked douc PRIMATES Cercopithecidae Pygathrix nemaeus 1. Zoonomia GCA_004024825.1 EN 179 Black snub-nosed monkey PRIMATES Cercopithecidae Rhinopithecus bieti 2. Existing assembly GCF_001698545.1 EN 180 Golden snub-nosed monkey PRIMATES Cercopithecidae Rhinopithecus roxellana 2. Existing assembly GCF_000769185.1 EN 181 Emperor tamarin PRIMATES Cebidae Saguinus imperator 1. Zoonomia GCA_004024885.1 LC 182 Bolivian squirrel monkey PRIMATES Cebidae Saimiri boliviensis 2. Existing assembly GCF_000235385.1 LC 183 Northern Plains gray langur PRIMATES Cercopithecidae Semnopithecus entellus 1. Zoonomia GCA_004025065.1 LC 184 African savanna elephant PROBOSCIDEA Elephantidae Loxodonta Africana 2. Existing assembly GCF_000001905.1 VU 185 Cairo spiny mouse RODENTIA Muridae Acomys cahirinus 1. Zoonomia GCA_004027535.1 LC 186 Gobi jerboa RODENTIA Dipodidae Allactaga bullata 1. Zoonomia GCA_004027895.1 LC 187 Mountain beaver RODENTIA Aplodontiidae Aplodontia rufa 1. Zoonomia GCA_004027875.1 LC 188 Desmarest's hutia RODENTIA Capromyidae Capromys pilorides 1. Zoonomia GCA_004027915.1 LC 189 North American beaver RODENTIA Castoridae Castor canadensis 1. Zoonomia GCA_004027675.1 LC 190 Brazilian guinea pig RODENTIA Caviidae Cavia aperea 2. Existing assembly GCA_000688575.1 LC 191 Domestic guinea pig RODENTIA Caviidae Cavia porcellus 2. Existing assembly GCF_000151735.1 LC 192 Montane guinea pig RODENTIA Caviidae Cavia tschudii 1. Zoonomia GCA_004027695.1 LC 193 Long-tailed chinchilla RODENTIA Chinchillidae Chinchilla lanigera 2. Existing assembly GCF_000276665.1 EN 194 Gambian pouched rat RODENTIA Nesomyidae Cricetomys gambianus 1. Zoonomia GCA_004027575.1 LC 195 Chinese hamster RODENTIA Nesomyidae Cricetulus griseus 2. Existing assembly GCA_900186095.1 LC 196 Common gundi RODENTIA Ctenodactylidae Ctenodactylus gundi 1. Zoonomia GCA_004027205.1 LC 197 Social tuco-tuco RODENTIA Ctenomyidae Ctenomys sociabilis 1. Zoonomia GCA_004027165.1 CR 198 Lowland paca RODENTIA Cuniculidae Cuniculus paca 1. Zoonomia GCA_004365215.1 LC 199 Central American agouti RODENTIA Dasyproctidae Dasyprocta punctata 1. Zoonomia GCA_004363535.1 LC 200 Pacarana RODENTIA Dinomyidae Dinomys branickii 1. Zoonomia GCA_004027595.1 LC 201 Ord's kangaroo rat RODENTIA Heteromyidae Dipodomys ordii 2. Existing assembly GCF_000151885.1 LC 202 Stephen's kangaroo rat RODENTIA Heteromyidae Dipodomys stephensi 1. Zoonomia GCA_004024685.1 VU 203 Patagonian mara RODENTIA Caviidae Dolichotis patagonum 1. Zoonomia GCA_004027295.1 NT 204 Transcaucasian mole vole RODENTIA Cricetidae Ellobius lutescens 2. Existing assembly GCA_001685075.1 LC 205 Northern mole vole RODENTIA Cricetidae Ellobius talpinus 2. Existing assembly GCA_001685095.1 LC 206 Damara mole-rat RODENTIA Bathyergidae Fukomys damarensis 2. Existing assembly GCF_000743615.1 LC 207 Edible dormouse RODENTIA Gliridae Glis glis 1. Zoonomia GCA_004027185.1 LC 208 Woodland doormouse RODENTIA Gliridae Graphiurus murinus 1. Zoonomia GCA_004027655.1 LC 209 Naked mole-rat RODENTIA Bathyergidae Heterocephalus glaber 2. Existing assembly GCF_000247695.1 LC 210 Capybara RODENTIA Caviidae Hydrochoerus hydrochaeris 1. Zoonomia GCA_004027455.1 LC 211 Northern crested porcupine RODENTIA Hystricidae Hystrix cristata 1. Zoonomia GCA_004026905.1 LC 212 Thirteen-lined ground squirrel RODENTIA Sciuridae Ictidomys tridecemlineatus 2. Existing assembly GCF_000236235.1 LC 213 Lesser egyptian jerboa RODENTIA Dipodidae Jaculus jaculus 2. Existing assembly GCF_000280705.1 LC 214 Alpine marmot RODENTIA Sciuridae Marmota marmota 2. Existing assembly GCF_001458135.1 LC 215 Mongolian jird RODENTIA Muridae Meriones unguiculatus 1. Zoonomia GCA_004026785.1 LC 216 Golden hamster RODENTIA Cricetidae Mesocricetus auratus 2. Existing assembly GCF_000349665.1 VU 217 Prairie vole RODENTIA Cricetidae Microtus ochrogaster 2. Existing assembly GCF_000317375.1 LC 218 Ryukyu mouse RODENTIA Muridae Mus caroli 2. Existing assembly GCA_900094665.2 LC 219 House mouse RODENTIA Muridae Mus musculus 2. Existing assembly GCF_000001635.26 LC 220 Shrew mouse RODENTIA Muridae Mus pahari 2. Existing assembly GCA_900095145.2 LC 221 Western wild mouse RODENTIA Muridae Mus spretus 2. Existing assembly GCA_001624865.1 LC 222 Hazel dormouse RODENTIA Gliridae Muscardinus avellanarius 1. Zoonomia GCA_004027005.1 LC 223 Coypu RODENTIA Myocastoridae Myocastor coypus 1. Zoonomia GCA_004027025.1 LC 224 Upper galilee mountains blind mole rat RODENTIA Spalacidae Nannospalax galili 2. Existing assembly GCF_000622305.1 DD 225 Degu RODENTIA Octodontidae Octodon degus 2. Existing assembly GCF_000260255.1 LC 226 Muskrat RODENTIA Cricetidae Ondatra zibethicus 1. Zoonomia GCA_004026605.1 LC 227 Scorpion mouse RODENTIA Cricetidae Onychomys torridus 1. Zoonomia GCA_004026725.1 LC 228 Pacific pocket mouse RODENTIA Heteromyidae Perognathus longimembris pacificus 1. Zoonomia GCA_004363475.1 LC 229 Prairie deer mouse RODENTIA Cricetidae Peromyscus maniculatus 2. Existing assembly GCF_000500345.1 LC 230 Dassie rat RODENTIA Petromuridae Petromus typicus 1. Zoonomia GCA_004026965.1 LC 231 Fat sand rat RODENTIA Muridae Psammomys obesus 2. Existing assembly GCA_002215935.1 LC 232 Norway rat RODENTIA Muridae Rattus norvegicus 2. Existing assembly GCF_000001895.5 LC 233 Hispid cotton rat RODENTIA Cricetidae Sigmodon hispidus 1. Zoonomia GCA_004025045.1 LC 234 Daurian ground squirrel RODENTIA Sciuridae Spermophilus dauricus 2. Existing assembly GCA_002406435.1 LC 235 Greater cane rat RODENTIA Thryonomyidae Thryonomys swinderianus 1. Zoonomia GCA_004025085.1 LC 236 Cape ground squirrel RODENTIA Sciuridae Xerus inauris 1. Zoonomia GCA_004024805.1 LC 237 Meadow jumping mouse RODENTIA Dipodidae Zapus hudsonius 1. Zoonomia GCA_004024765.1 LC 238 Northern tree shrew SCANDENTIA Tupaiidae Tupaia belangeri chinensis 2. Existing assembly GCF_000334495.1 LC 239 Large treeshrew SCANDENTIA Tupaiidae Tupaia tana 1. Zoonomia GCA_004365275.1 LC 240 Florida manatee SIRENIA Trichechidae Trichechus manatus 2. Existing assembly GCF_000243295.1 EN 241 Aardvark TUBULIDENTATA Orycteropodidae Orycteropus afer 1. Zoonomia GCA_004365145.1 LC Table 1. Genome assemblies included in the 241-way Conservation track. Species status:LC = Least Concern; NT = Near threatened; VU = Vulnerable; EN = Endangered; CR = Critically endangered Display Conventions and Configuration In full and pack display modes, conservation scores are displayed as a wiggle track (histogram) in which the height reflects the size of the score. The conservation wiggles can be configured in a variety of ways to highlight different aspects of the displayed information. Click the Graph configuration help link for an explanation of the configuration options. Pairwise alignments of each species to the human genome are displayed below the conservation histogram as a grayscale density plot (in pack mode) or as a wiggle (in full mode) that indicates alignment quality. In dense display mode, conservation is shown in grayscale using darker values to indicate higher levels of overall conservation as scored by phastCons. Checkboxes on the track configuration page allow selection of the species to include in the pairwise display. Note that excluding species from the pairwise display does not alter the the conservation score display. To view detailed information about the alignments at a specific position, zoom the display in to 30,000 or fewer bases, then click on the alignment. Gap Annotation The Display chains between alignments configuration option enables display of gaps between alignment blocks in the pairwise alignments in a manner similar to the Chain track display. The following conventions are used: Single line: No bases in the aligned species. Possibly due to a lineage-specific insertion between the aligned blocks in the human genome or a lineage-specific deletion between the aligned blocks in the aligning species. Double line: Aligning species has one or more unalignable bases in the gap region. Possibly due to excessive evolutionary distance between species or independent indels in the region between the aligned blocks in both species. Pale yellow coloring: Aligning species has Ns in the gap region. Reflects uncertainty in the relationship between the DNA of both species, due to lack of sequence in relevant portions of the aligning species. Genomic Breaks Discontinuities in the genomic context (chromosome, scaffold or region) of the aligned DNA in the aligning species are shown as follows: Vertical blue bar: Represents a discontinuity that persists indefinitely on either side, e.g. a large region of DNA on either side of the bar comes from a different chromosome in the aligned species due to a large scale rearrangement. Green square brackets: Enclose shorter alignments consisting of DNA from one genomic context in the aligned species nested inside a larger chain of alignments from a different genomic context. The alignment within the brackets may represent a short misalignment, a lineage-specific insertion of a transposon in the human genome that aligns to a paralogous copy somewhere else in the aligned species, or other similar occurrence. Base Level When zoomed-in to the base-level display, the track shows the base composition of each alignment. The numbers and symbols on the Gaps line indicate the lengths of gaps in the human sequence at those alignment positions relative to the longest non-human sequence. If there is sufficient space in the display, the size of the gap is shown. If the space is insufficient and the gap size is a multiple of 3, a &quot;*&quot; is displayed; other gap sizes are indicated by &quot;+&quot;. Codon translation is available in base-level display mode if the displayed region is identified as a coding segment. To display this annotation, select the species for translation from the pull-down menu in the Codon Translation configuration section at the top of the page. Then, select one of the following modes: No codon translation: The gene annotation is not used; the bases are displayed without translation. Use default species reading frames for translation: The annotations from the genome displayed in the Default species to establish reading frame pull-down menu are used to translate all the aligned species present in the alignment. Use reading frames for species if available, otherwise no translation: Codon translation is performed only for those species where the region is annotated as protein coding. Use reading frames for species if available, otherwise use default species: Codon translation is done on those species that are annotated as being protein coding over the aligned region using species-specific annotation; the remaining species are translated using the default species annotation. Codon translation uses the following gene tracks as the basis for translation: Gene TrackSpecies UCSC GenesHuman Ensembl Genes v104Brazilian guinea pig, gibbon RefSeq GenesAngolan colobus, Balaenoptera acutorostrata, Bison bison, Black flying-fox, Brandt's myotis (bat), Bushbaby, Camelus bactrianus, Camelus ferus, Canis lupus familiaris, Cape elephant shrew, Capra hircus, Cavia porcellus, Ceratotherium simum, Cercocebus atys, Chinchilla, Chinese tree shrew, Chlorocebus sabaeus, Condylura cristata, Damara mole rat, Dasypus novemcinctus, David's myotis (bat), Delphinapterus leucas, Echinops telfairi, Enhydra lutris, Eptesicus fuscus, Equus asinus, Equus przewalskii, Erinaceus europaeus, Felis catus, Heterocephalus glaber, Jaculus jaculus, Kangaroo rat, Killer whale, Leptonychotes weddellii, Lipotes vexillifer, Little brown bat, Loxodonta africana, Macaca fascicularis, Macaca nemestrina, Mandrillus leucophaeus, Manis javanica, Marmota marmota, Mesocricetus auratus, Miniopterus natalensis, Mus musculus, Nannospalax galili, Ochotona princeps, Octodon degus, Oryctolagus cuniculus, Pacific walrus, Pan paniscus, Panthera tigris, Peromyscus maniculatus, Prairie vole, Propithecus coquereli, Pteropus vampyrus, Puma concolor, Rattus norvegicus, Rhinopithecus bieti, Shrew, Squirrel monkey, Squirrel, Sus scrofa, Trichechus manatus, Ursus maritimus, White-faced sapajou, Wild yak no annotationAcinonyx jubatus, Acomys cahirinus, Ailuropoda melanoleuca, Ailurus fulgens, Allactaga bullata, Alouatta palliata, Ammotragus lervia, Anoura caudifer, Antilocapra americana, Aotus nancymaae, Aplodontia rufa, Artibeus jamaicensis, Ateles geoffroyi, Balaenoptera bonaerensis, Beatragus hunteri, Bos indicus, Bos taurus, Bubalus bubalis, Callicebus donacophilus, Callithrix jacchus, Camelus dromedarius, Canis lupus, Capra aegagrus, Capromys pilorides, Carollia perspicillata, Castor canadensis, Catagonus wagneri, Cavia tschudii, Cebus albifrons, Ceratotherium simum cottoni, Cercopithecus neglectus, Chaetophractus vellerosus, Cheirogaleus medius, Choloepus didactylus, Choloepus hoffmanni, Chrysochloris asiatica, Craseonycteris thonglongyai, Cricetomys gambianus, Cricetulus griseus, Crocidura indochinensis, Cryptoprocta ferox, Ctenodactylus gundi, Ctenomys sociabilis, Cuniculus paca, Dasyprocta punctata, Daubentonia madagascariensis, Desmodus rotundus, Dicerorhinus sumatrensis, Diceros bicornis, Dinomys branickii, Dipodomys stephensi, Dolichotis patagonum, Elaphurus davidianus, Ellobius lutescens, Ellobius talpinus, Equus caballus, Erythrocebus patas, Eschrichtius robustus, Eubalaena japonica, Eulemur flavifrons, Eulemur fulvus, Felis nigripes, Galeopterus variegatus, Giraffa tippelskirchi, Glis glis, Gorilla gorilla, Graphiurus murinus, Helogale parvula, Hemitragus hylocrius, Heterohyrax brucei, Hippopotamus amphibius, Hipposideros armiger, Hipposideros galeritus, Hyaena hyaena, Hydrochoerus hydrochaeris, Hystrix cristata, Indri indri, Inia geoffrensis, Kogia breviceps, Lasiurus borealis, Lemur catta, Lepus americanus, Lycaon pictus, Macaca mulatta, Macroglossus sobrinus, Manis pentadactyla, Megaderma lyra, Mellivora capensis, Meriones unguiculatus, Mesoplodon bidens, Microcebus murinus, Microgale talazaci, Micronycteris hirsuta, Miniopterus schreibersii, Mirounga angustirostris, Mirza coquereli, Monodon monoceros, Mormoops blainvillei, Moschus moschiferus, Mungos mungo, Murina feae, Mus caroli, Mus pahari, Mus spretus, Muscardinus avellanarius, Mustela putorius, Myocastor coypus, Myotis myotis, Myrmecophaga tridactyla, Nasalis larvatus, Neomonachus schauinslandi, Neophocaena asiaeorientalis, Noctilio leporinus, Nycticebus coucang, Odocoileus virginianus, Okapia johnstoni, Ondatra zibethicus, Onychomys torridus, Orycteropus afer, Ovis aries, Ovis canadensis, Pan troglodytes, Panthera onca, Panthera pardus, Pantholops hodgsonii, Papio anubis, Paradoxurus hermaphroditus Table 2. Gene tracks used for codon translation. Methods The Zoonomia alignment was composed of two sets of mammalian genomes: newly assembled DISCOVAR assemblies and GenBank assemblies. The DISCOVAR genomes were masked with RepeatMasker (commit 2d947604), using Repbase version 20170127 as the repeat library and CrossMatch as the alignment engine. The pipeline used is available at repeatMaskerPipeline (commit a6ad966). The guide-tree topology was taken from the TimeTree database (using release current in October 2018), and the branch lengths were estimated using the least-squares-fit mode of PHYLIP, version 3.695. The distance matrix used was largely based on distances from the 4d site trees from the UCSC browser. To add those species not present in the UCSC tree, approximate distances estimated by Mash (commit 541971b) to the closest UCSC species were added to the distance between the two closest UCSC species. We used the HAL package (commit 68db41d) produce the HAL file. Phylogenetic Tree Model The phyloP are phylogenetic methods that rely on a tree model containing the tree topology, branch lengths representing evolutionary distance at neutrally evolving sites, the background distribution of nucleotides, and a substitution rate matrix. The all-species tree model for this track was generated using the phyloFit program from the PHAST package (REV model, EM algorithm, medium precision) using multiple alignments of 4-fold degenerate sites extracted from the 241-way alignment (msa_view). The 4d sites were derived from the RefSeq (Reviewed+Coding) gene set, filtered to select single-coverage long transcripts. This same tree model was used in the phyloP calculations; however, the background frequencies were modified to maintain reversibility. The resulting tree model: all species. PhyloP Conservation The phyloP program supports several different methods for computing p-values of conservation or acceleration, for individual nucleotides or larger elements ( http://compgen.cshl.edu/phast/). Here it was used to produce separate scores at each base (--wig-scores option), considering all branches of the phylogeny rather than a particular subtree or lineage (i.e., the --subtree option was not used). The scores were computed by performing a likelihood ratio test at each alignment column (--method LRT), and scores for both conservation and acceleration were produced (--mode CONACC). References Zoonomia: Zoonomia Consortium.. A comparative genomics multitool for scientific discovery and conservation. Nature. 2020 Nov;587(7833):240-245. PMID: 33177664; PMC: PMC7759459; DOI: 10.1038/s41586-020-2876-6 Cactus: Armstrong J, Hickey G, Diekhans M, Fiddes IT, Novak AM, Deran A, Fang Q, Xie D, Feng S, Stiller J et al. Progressive Cactus is a multiple-genome aligner for the thousand-genome era. Nature. 2020 Nov;587(7833):246-251. PMID: 33177663; PMC: PMC7673649; DOI: 10.1038/s41586-020-2871-y Paten B, Earl D, Nguyen N, Diekhans M, Zerbino D, Haussler D. Cactus: Algorithms for genome multiple sequence alignment. Genome Res. 2011 Sep;21(9):1512-28. PMID: 21665927; PMC: PMC3166836; DOI: 10.1101/gr.123356.111 Harris RS. Improved pairwise alignment of genomic DNA. Ph.D. Thesis. Pennsylvania State University, USA. 2007. PhyloP: Cooper GM, Stone EA, Asimenos G, NISC Comparative Sequencing Program., Green ED, Batzoglou S, Sidow A. Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. 2005 Jul;15(7):901-13. PMID: 15965027; PMC: PMC1172034; DOI: 10.1101/gr.3577405 Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 2010 Jan;20(1):110-21. PMID: 19858363; PMC: PMC2798823 Siepel A, Haussler D. Phylogenetic Hidden Markov Models. In: Nielsen R, editor. Statistical Methods in Molecular Evolution. New York: Springer; 2005. pp. 325-351. DOI: 10.1007/0-387-27733-1_12 Siepel A, Pollard KS, and Haussler D. New methods for detecting lineage-specific selection. In Proceedings of the 10th International Conference on Research in Computational Molecular Biology (RECOMB 2006), pp. 190-205. DOI: 10.1007/11732990_17 
cons241wayViewalign Cactus Alignments Cactus Alignment & Conservation of Zoonomia Placental Mammals (241 Species) Comparative Genomics 
cactus241wayBM Cactus Align Cactus Alignments of Zoonomia 241 Placental Mammals Comparative Genomics 
cons241wayViewphyloP Basewise Conservation (phyloP) Cactus Alignment & Conservation of Zoonomia Placental Mammals (241 Species) Comparative Genomics 
phyloP241wayBW Basewise Cons PhyloP Basewise Conservation of Zoonomia 241 Placental Mammals Comparative Genomics 
covidHgiGwasR4Pval COVID GWAS v4 COVID risk variants from GWAS meta-analyses by the COVID-19 Host Genetics Initiative (Rel 4, Oct 2020) Phenotype and Literature Description This track set shows the results of the GWAS Data Release 4 (October 2020) from the COVID-19 Host Genetics Initiative (HGI): a collaborative effort to facilitate the generation of meta-analysis across multiple studies contributed by partners world-wide to identify the genetic determinants of SARS-CoV-2 infection susceptibility, disease severity and outcomes. The COVID-19 HGI also aims to provide a platform for study partners to share analytical results in the form of summary statistics and/or individual level data of COVID-19 host genetics research. At the time of this release, a total of 137 studies were registered with this effort. The specific phenotypes studied by the COVID-19 HGI are those that benefit from maximal sample size: primary analysis on disease severity. For the Data Release 4 the number of cases have increased by nearly ten-fold (more than 30,000 COVID-19 cases and 1.47 million controls) by combining data from 34 studies across 16 countries. The four tracks here are based on data from HGI meta-analyses A2, B2, C1, and C2, described here: Severe COVID vars (A2): Cases with very severe respiratory failure confirmed for COVID-19 vs. population (i.e. everybody that is not a case). The increased sample size resulted in strong evidence of seven genomic regions associated with severe COVID-19 and one additional signal associated with COVID-19 partial-susceptibility. Many of these regions were identified by the Genetics of Mortality in Critical Care (GenOMICC) study and are shown below (table adapted from Pairo-Castineira et. al.). SNP Human GRCh37/hg19 Assembly Human GRCh38/hg38 Assembly Risk Allele Alternative Gene nearest to SNP rs73064425 chr3:45901089-45901089 chr3:45859597-45859597 T C LZTFL1 rs9380142 chr6:29798794-29798794 chr6:29831017-29831017 A G HLA-G rs143334143 chr6:31121426-31121426 chr6:31153649-31153649 A G CCHCR1 rs10735079 chr12:113380008-113380008 chr12:112942203-112942203 A G OAS3 rs74956615 chr19:10427721-10427721 chr19:10317045-10317045 A T ICAM5/TYK2 rs2109069 chr19:4719443-4719443 chr19:4719431-4719431 A G DPP9 rs2236757 chr21:34624917-34624917 chr21:33252612-33252612 A G IFNAR2 Hosp COVID vars (B2): Cases hospitalized and confirmed for COVID-19 vs. population (i.e. everybody that is not a case) Tested COVID vars (C1): Cases with laboratory confirmed SARS-CoV-2 infection, or health record/physician-confirmed COVID-19, or self-reported COVID-19 via questionare vs. laboratory /self-reported negative cases All COVID vars (C2): Cases with laboratory confirmed SARS-CoV-2 infection, or health record/physician-confirmed COVID-19, or self-reported COVID-19 vs. population (i.e. everybody that is not a case) Due to privacy concerns, these browser tracks exclude data provided by 23andMe contributed studies in the full analysis results. The actual study and case and control counts for the individual browser tracks are listed in the track labels. Details on all studies can be found here. Display Conventions Displayed items are colored by GWAS effect: red for positive (harmful) effect, blue for negative (protective) effect. The height ('lollipop stem') of the item is based on statistical significance (p-value). For better visualization of the data, only SNPs with p-values smaller than 1e-3 are displayed by default. The color saturation indicates effect size (beta coefficient): values over the median of effect size are brightly colored (bright red &nbsp;&nbsp; , bright blue &nbsp;&nbsp; ), those below the median are paler (light red &nbsp;&nbsp; , light blue &nbsp;&nbsp; ). Each track has separate display controls and data can be filtered according to the number of studies, minimum -log10 p-value, and the effect size (beta coefficient), using the track Configure options. Mouseover on items shows the rs ID (or chrom:pos if none assigned), both the non-effect and effect alleles, the effect size (beta coefficient), the p-value, and the number of studies. Additional information on each variant can be found on the details page by clicking on the item. Methods COVID-19 Host Genetics Initiative (HGI) GWAS meta-analysis round 4 (October 2020) results were used in this study. Each participating study partner submitted GWAS summary statistics for up to four of the COVID-19 phenotype definitions. Data were generated from genome-wide SNP array and whole exome and genome sequencing, leveraging the impact of both common and rare variants. The statistical analysis performed takes into account differences between sex, ancestry, and date of sample collection. Alleles were harmonized across studies and reported allele frequencies are based on gnomAD version 3.0 reference data. Most study partners used the SAIGE GWAS pipeline in order to generate summary statistics used for the COVID-19 HGI meta-analysis. The summary statistics of individual studies were manually examined for inflation, deflation, and excessive number of false positives. Qualifying summary statistics were filtered for INFO > 0.6 and MAF > 0.0001 prior to meta-analyzing the entirety of the data. The meta-analysis was performed using fixed effects inverse variance weighting. The meta-analysis software and workflow are available here. More information about the prospective studies, processing pipeline, results and data sharing can be found here. Data Access The data underlying these tracks and summary statistics results are publicly available in COVID19-hg Release 4 (October 2020). The raw data can be explored interactively with the Table Browser, or the Data Integrator. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits Thanks to the COVID-19 Host Genetics Initiative contributors and project leads for making these data available, and in particular to Rachel Liao, Juha Karjalainen, and Kumar Veerapen at the Broad Institute for their review and input during browser track development. References COVID-19 Host Genetics Initiative. The COVID-19 Host Genetics Initiative, a global initiative to elucidate the role of host genetic factors in susceptibility and severity of the SARS-CoV-2 virus pandemic. Eur J Hum Genet. 2020 Jun;28(6):715-718. PMID: 32404885; PMC: PMC7220587 Pairo-Castineira E, Clohisey S, Klaric L, Bretherick AD, Rawlik K, Pasko D, Walker S, Parkinson N, Fourman MH, Russell CD et al. Genetic mechanisms of critical illness in Covid-19. Nature. 2020 Dec 11;. PMID: 33307546 
covid COVID Data Container of SARS-CoV-2 data Phenotype and Literature Description This is a container track for all data related to SARS-CoV-2 for hg38 in the UCSC Genome Browser. Click into any of the sub-tracks to see information details on the specific annotations. 
covidHgiGwasR4PvalC2 All COVID vars COVID risk variants from the COVID-19 HGI GWAS Analysis C2 (17965 cases, 33 studies, Rel 4: Oct 2020) Phenotype and Literature 
covidHgiGwasR4PvalC1 Tested COVID vars Tested COVID risk variants from the COVID-19 HGI GWAS Analyis C1 (11085 cases, 20 studies, Rel 4: Oct 2020) Phenotype and Literature 
covidHgiGwasR4PvalB2 Hosp COVID vars Hospitalized COVID risk variants from the COVID-19 HGI GWAS Analysis B2 (7885 cases, 21 studies, Rel 4: Oct 2020) Phenotype and Literature 
covidHgiGwasR4PvalA2 Severe COVID vars Severe respiratory COVID risk variants from the COVID-19 HGI GWAS Analysis A2 (4336 cases, 12 studies, Rel 4: Oct 2020) Phenotype and Literature 
crossTissueMapsFullDetails Cross Tissue Details Cross tissue nuclei full details Single Cell RNA-seq Description This track collection shows data from Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. The dataset covers ~200,000 single nuclei from a total of 16 human donors across 25 samples, using 4 different sample preparation protocols followed by droplet based single-cell RNA-seq. The samples were obtained from frozen tissue as part of the Genotype-Tissue Expression (GTEx) project. Samples were taken from the esophagus, skeletal muscle, heart, lung, prostate, breast, and skin. The dataset includes 43 broad cell classes, some specific to certain tissues and some shared across all tissue types. This track collection contains three bar chart tracks of RNA expression. The first track, Cross Tissue Nuclei, allows cells to be grouped together and faceted on up to 4 categories: tissue, cell class, cell subclass, and cell type. The second track, Cross Tissue Details, allows cells to be grouped together and faceted on up to 7 categories: tissue, cell class, cell subclass, cell type, granular cell type, sex, and donor. The third track, GTEx Immune Atlas, allows cells to be grouped together and faceted on up to 5 categories: tissue, cell type, cell class, sex, and donor. Please see the GTEx portal for further interactive displays and additional data. Display Conventions and Configuration Tissue-cell type combinations in the Full and Combined tracks are colored by which cell type they belong to in the below table: Color Cell Type Endothelial Epithelial Glia Immune Neuron Stromal Other Tissue-cell type combinations in the Immune Atlas track are shaded according to the below table: Color Cell Type Inflammatory Macrophage Lung Macrophage Monocyte/Macrophage FCGR3A High Monocyte/Macrophage FCGR3A Low Macrophage HLAII High Macrophage LYVE1 High Proliferating Macrophage Dendritic Cell 1 Dendritic Cell 2 Mature Dendritic Cell Langerhans CD14+ Monocyte CD16+ Monocyte LAM-like Other Methods Using the previously collected tissue samples from the Genotype-Tissue Expression project, nuclei were isolated using four different protocols and sequenced using droplet based single cell RNA-seq. CellBender v2.1 and other standard quality control techniques were applied, resulting in 209,126 nuclei profiles across eight tissues, with a mean of 918 genes and 1519 transcripts per profile. Data from all samples was integrated with a conditional variation autoencoder in order to correct for multiple sources of variation like sex, and protocol while preserving tissue and cell type specific effects. For detailed methods, please refer to Eraslan et al, or the GTEx portal website. UCSC Methods The gene expression files were downloaded from the GTEx portal. The UCSC command line utilities matrixClusterColumns, matrixToBarChartBed, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions or our Data Access FAQ for more information. Credits Thanks to the GTEx Consortium for creating and analyzing these data. References Eraslan G, Drokhlyansky E, Anand S, Fiskin E, Subramanian A, Slyper M, Wang J, Van Wittenberghe N, Rouhana JM, Waldman J et al. Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. Science. 2022 May 13;376(6594):eabl4290. PMID: 35549429; PMC: PMC9383269 
knownGeneV46 GENCODE V46 GENCODE V46 Genes and Gene Predictions Description The GENCODE Genes track (version 46, May 2024) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. By default, only the basic gene set is displayed, which is a subset of the comprehensive gene set. The basic set represents transcripts that GENCODE believes will be useful to the majority of users. The track includes protein-coding genes, non-coding RNA genes, and pseudo-genes, though pseudo-genes are not displayed by default. It contains annotations on the reference chromosomes as well as assembly patches and alternative loci (haplotypes). The v46 release was derived from the GTF file that contains annotations only on the main chromosomes. Statistics for this build and information on how they were generated can be found on the GENCODE site. For more information on the different gene tracks, see our Genes FAQ. Display Conventions and Configuration By default, this track displays only the basic GENCODE set, splice variants, and non-coding genes. It includes options to display the entire GENCODE set and pseudogenes. To customize these options, the respective boxes can be checked or unchecked at the top of this description page. This track also includes a variety of labels which identify the transcripts when visibility is set to &quot;full&quot; or &quot;pack&quot;. Gene symbols (e.g. NIPA1) are displayed by default, but additional options include GENCODE Transcript ID (ENST00000561183.5), UCSC Known Gene ID (uc001yve.4), UniProt Display ID (Q7RTP0). Additional information about gene and transcript names can be found in our FAQ. This track, in general, follows the display conventions for gene prediction tracks. The exons for putative non-coding genes and untranslated regions are represented by relatively thin blocks, while those for coding open reading frames are thicker. Coloring for the gene annotations is mostly based on the annotation type: MANE: MANE Select Plus Clinical transcripts. For non-MANE transcripts, the following conventions apply. coding: protein coding transcripts, including polymorphic pseudogenes non-coding: non-protein coding transcripts pseudogene: pseudogene transcript annotations problem: problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) This track contains an optional codon coloring feature that allows users to quickly validate and compare gene predictions. There is also an option to display the data as a density graph, which can be helpful for visualizing the distribution of items over a region. Squishy-pack Display Within a gene using the pack display mode, transcripts below a specified rank will be condensed into a view similar to squish mode. The transcript ranking approach is preliminary and will change in future releases. The transcripts rankings are defined by the following criteria for protein-coding and non-coding genes: Protein_coding genes MANE or Ensembl canonical 1st: MANE Select / Ensembl canonical 2nd: MANE Plus Clinical Coding biotypes 1st: protein_coding and protein_coding_LoF 2nd: NMDs and NSDs 3rd: retained intron and protein_coding_CDS_not_defined Completeness 1st: full length 2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype 1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Methods The GENCODE v46 track was built from the GENCODE downloads file gencode.v46.chr_patch_hapl_scaff.annotation.gff3.gz. Data from other sources were correlated with the GENCODE data to build association tables. Related Data The GENCODE Genes transcripts are annotated in numerous tables, each of which is also available as a downloadable file. One can see a full list of the associated tables in the Table Browser by selecting GENCODE Genes from the track menu; this list is then available on the table menu. Data access GENCODE Genes and its associated tables can be explored interactively using the REST API, the Table Browser or the Data Integrator. The genePred format files for hg38 are available from our downloads directory or in our GTF download directory. All the tables can also be queried directly from our public MySQL servers, with more information available on our help page as well as on our blog. Credits The GENCODE Genes track was produced at UCSC from the GENCODE comprehensive gene set using a computational pipeline developed by Jim Kent and Brian Raney. This version of the track was generated by Jonathan Casper. References Frankish A, Carbonell-Sala S, Diekhans M, Jungreis I, Loveland JE, Mudge JM, Sisu C, Wright JC, Arnan C, Barnes I et al. GENCODE: reference annotation for the human and mouse genomes in 2023. Nucleic Acids Res. 2023 Jan 6;51(D1):D942-D949. PMID: 36420896; PMC: PMC9825462 A full list of GENCODE publications is available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
knownGeneArchive GENCODE Archive GENCODE Archive Genes and Gene Predictions Description This super track contains previous versions of the GENCODE primary gene set. 
highlyReproducible Highly Reproducible Regions Highly Reproducible genomic regions for sequencing Mapping and Sequencing Description This container track helps call out sections of the genome that often cause problems or confusion when working with the genome. The hg19 genome has a track with the same name, but with many more subtracks, as the GeT-RM and Genome-in-a-Bottle artifact variants do not exist yet for hg38, to our knowledge. If you are missing a track here that you know from hg19 and have an idea how to add it hg38, do not hesitate to contact us. Problematic Regions The Problematic Regions track contains the following subtracks: The UCSC Unusual Regions subtrack contains annotations collected at UCSC, put together from other tracks, our experiences and support email list requests over the years. For example, it contains the most well-known gene clusters (IGH, IGL, PAR1/2, TCRA, TCRB, etc) and annotations for the GRC fixed sequences, alternate haplotypes, unplaced contigs, pseudo-autosomal regions, and mitochondria. These loci can yield alignments with low-quality mapping scores and discordant read pairs, especially for short-read sequencing data. This data set was manually curated, based on the Genome Browser's assembly description, the FAQs about assembly, and the NCBI RefSeq &quot;other&quot; annotations track data. The ENCODE Blacklist subtrack contains a comprehensive set of regions which are troublesome for high-throughput Next-Generation Sequencing (NGS) aligners. These regions tend to have a very high ratio of multi-mapping to unique mapping reads and high variance in mappability due to repetitive elements such as satellite, centromeric and telomeric repeats. The GRC Exclusions subtrack contains a set of regions that have been flagged by the GRC to contain false duplications or contamination sequences. The GRC has now removed these sequences from the files that it uses to generate the reference assembly, however, removing the sequences from the GRCh38/hg38 assembly would trigger the next major release of the human assembly. In order to help users recognize these regions and avoid them in their analyses, the GRC have produced a masking file to be used as a companion to GRCh38, and the BED file is available from the GenBank FTP site. Highly Reproducible Regions The Highly Reproducible Regions track highlights regions and variants from eight samples that can be used to assess variant detection pipelines. The &quot;Highly Reproducible Regions&quot; subtrack comprises the intersection of the reproducible regions across all eight samples, while the &quot;Variants&quot; subtracks contain the reproducible variants from each assayed sample. Both tracks contain data from the following samples: a Chinese Quartet, samples CQ-5, CQ-6, CQ-7, CQ-8 a HapMap Trio, samples NA10385, NA12248, NA12249 a Genome in a Bottle sample, NA12878s Please refer to the Pan et al reference for more information on how these regions were defined. GIAB Problematic Regions The Genome in a Bottle (GIAB) Problematic Regions tracks provide stratifications of the genome to evaluate variant calls in complex regions. It is designed for use with Global Alliance for Genomic Health (GA4GH) benchmarking tools like hap.py and includes regions with low complexity, segmental duplications, functional regions, and difficult-to-sequence areas. Developed in collaboration with GA4GH, the Genome in a Bottle (GIAB) consortium, and the Telomere-to-Telomere Consortium (T2T), the dataset aims to standardize the analysis of genetic variation by offering pre-defined BED files for stratifying true and false positives in genomic studies, facilitating accurate assessments in complex areas of the genome. The creation of the GIAB Problematic Regions tracks involves using a pipeline and configuration to generate stratification BED files that categorize genomic regions based on specific challenges, such as low complexity or difficult mapping, to facilitate accurate benchmarking of variant calls. For more information on the pipeline and configuration used, please visit the following webpage: https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/genome-stratifications/v3.5/README.md. If you have questions or comments, please write to Justin Zook (jzook@nist.gov). Display Conventions and Configuration Each track contains a set of regions of varying length with no special configuration options. The UCSC Unusual Regions track has a mouse-over description, all other tracks have at most a name field, which can be shown in pack mode. The tracks are usually kept in dense mode. The Hide empty subtracks control hides subtracks with no data in the browser window. Changing the browser window by zooming or scrolling may result in the display of a different selection of tracks. Data access The raw data can be explored interactively with the Table Browser or the Data Integrator. For automated download and analysis, the genome annotation is stored in bigBed files that can be downloaded from our download server. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/problematic/comments.bb -chrom=chr21 -start=0 -end=100000000 stdout Methods Files were downloaded from the respective databases and converted to bigBed format. The procedure is documented in our hg38 makeDoc file. Credits Thanks to Anna Benet-Pag&egrave;s, Max Haeussler, Angie Hinrichs, Daniel Schmelter, and Jairo Navarro at the UCSC Genome Browser for planning, building, and testing these tracks. The underlying data comes from the ENCODE Blacklist and some parts were copied manually from the HGNC and NCBI RefSeq tracks. References Amemiya HM, Kundaje A, Boyle AP. The ENCODE Blacklist: Identification of Problematic Regions of the Genome. Sci Rep. 2019 Jun 27;9(1):9354. PMID: 31249361; PMC: PMC6597582 Dwarshuis N, Kalra D, McDaniel J, Sanio P, Alvarez Jerez P, Jadhav B, Huang WE, Mondal R, Busby B, Olson ND et al. The GIAB genomic stratifications resource for human reference genomes. Nat Commun. 2024 Oct 19;15(1):9029. PMID: 39424793; PMC: PMC11489684 Krusche P, Trigg L, Boutros PC, Mason CE, De La Vega FM, Moore BL, Gonzalez-Porta M, Eberle MA, Tezak Z, Lababidi S et al. Best practices for benchmarking germline small-variant calls in human genomes. Nat Biotechnol. 2019 May;37(5):555-560. PMID: 30858580; PMC: PMC6699627 Pan B, Ren L, Onuchic V, Guan M, Kusko R, Bruinsma S, Trigg L, Scherer A, Ning B, Zhang C et al. Assessing reproducibility of inherited variants detected with short-read whole genome sequencing. Genome Biol. 2022 Jan 3;23(1):2. PMID: 34980216; PMC: PMC8722114 
highReproVcfs Highly Reproducible Variants Highly Reproducible Variants Mapping and Sequencing 
hr_na12878Vcf HR_NA12878 Variants HR_NA12878 Variants Mapping and Sequencing 
hr_na12249Vcf HR_NA12249 Variants HR_NA12249 Variants Mapping and Sequencing 
hr_na12248Vcf HR_NA12248 Variants HR_NA12248 Variants Mapping and Sequencing 
hr_na10835Vcf HR_NA10835 Variants HR_NA10835 Variants Mapping and Sequencing 
cq8Vcf CQ-8 Variants CQ-8 Variants Mapping and Sequencing 
cq7Vcf CQ-7 Variants CQ-7 Variants Mapping and Sequencing 
cq56Vcf CQ-56 Variants CQ-56 Variants Mapping and Sequencing 
highReproBeds Highly Reproducible Regions Highly Reproducible Regions Mapping and Sequencing 
highReproRegions Highly Reproducible Regions Highly Reproducible Regions Mapping and Sequencing 
hmc HMC HMC - Homologous Missense Constraint Score on PFAM domains Phenotype and Literature Description The "Constraint scores" container track includes several subtracks showing the results of constraint prediction algorithms. These try to find regions of negative selection, where variations likely have functional impact. The algorithms do not use multi-species alignments to derive evolutionary constraint, but use primarily human variation, usually from variants collected by gnomAD (see the gnomAD V2 or V3 tracks on hg19 and hg38) or TOPMED (contained in our dbSNP tracks and available as a filter). One of the subtracks is based on UK Biobank variants, which are not available publicly, so we have no track with the raw data. The number of human genomes that are used as the input for these scores are 76k, 53k and 110k for gnomAD, TOPMED and UK Biobank, respectively. Note that another important constraint score, gnomAD constraint, is not part of this container track but can be found in the hg38 gnomAD track. The algorithms included in this track are: JARVIS - "Junk" Annotation genome-wide Residual Variation Intolerance Score: JARVIS scores were created by first scanning the entire genome with a sliding-window approach (using a 1-nucleotide step), recording the number of all TOPMED variants and common variants, irrespective of their predicted effect, within each window, to eventually calculate a single-nucleotide resolution genome-wide residual variation intolerance score (gwRVIS). That score, gwRVIS was then combined with primary genomic sequence context, and additional genomic annotations with a multi-module deep learning framework to infer pathogenicity of noncoding regions that still remains naive to existing phylogenetic conservation metrics. The higher the score, the more deleterious the prediction. This score covers the entire genome, except the gaps. HMC - Homologous Missense Constraint: Homologous Missense Constraint (HMC) is a amino acid level measure of genetic intolerance of missense variants within human populations. For all assessable amino-acid positions in Pfam domains, the number of missense substitutions directly observed in gnomAD (Observed) was counted and compared to the expected value under a neutral evolution model (Expected). The upper limit of a 95% confidence interval for the Observed/Expected ratio is defined as the HMC score. Missense variants disrupting the amino-acid positions with HMC&lt;0.8 are predicted to be likely deleterious. This score only covers PFAM domains within coding regions. MetaDome - Tolerance Landscape Score (hg19 only): MetaDome Tolerance Landscape scores are computed as a missense over synonymous variant count ratio, which is calculated in a sliding window (with a size of 21 codons/residues) to provide a per-position indication of regional tolerance to missense variation. The variant database was gnomAD and the score corrected for codon composition. Scores &lt;0.7 are considered intolerant. This score covers only coding regions. MTR - Missense Tolerance Ratio (hg19 only): Missense Tolerance Ratio (MTR) scores aim to quantify the amount of purifying selection acting specifically on missense variants in a given window of protein-coding sequence. It is estimated across sliding windows of 31 codons (default) and uses observed standing variation data from the WES component of gnomAD / the Exome Aggregation Consortium Database (ExAC), version 2.0. Scores were computed using Ensembl v95 release. The number of gnomAD 2 exomes used here is higher than the number of gnomAD 3 samples (125 exoms versus 76k full genomes), but this score only covers coding regions. UK Biobank depletion rank score (hg38 only): Halldorsson et al. tabulated the number of UK Biobank variants in each 500bp window of the genome and compared this number to an expected number given the heptamer nucleotide composition of the window and the fraction of heptamers with a sequence variant across the genome and their mutational classes. A variant depletion score was computed for every overlapping set of 500-bp windows in the genome with a 50-bp step size. They then assigned a rank (depletion rank (DR)) from 0 (most depletion) to 100 (least depletion) for each 500-bp window. Since the windows are overlapping, we plot the value only in the central 50bp of the 500bp window, following advice from the author of the score, Hakon Jonsson, deCODE Genetics. He suggested that the value of the central window, rather than the worst possible score of all overlapping windows, is the most informative for a position. This score covers almost the entire genome, only very few regions were excluded, where the genome sequence had too many gap characters. Display Conventions and Configuration JARVIS JARVIS scores are shown as a signal ("wiggle") track, with one score per genome position. Mousing over the bars displays the exact values. The scores were downloaded and converted to a single bigWig file. Move the mouse over the bars to display the exact values. A horizontal line is shown at the 0.733 value which signifies the 90th percentile. See hg19 makeDoc and hg38 makeDoc. Interpretation: The authors offer a suggested guideline of > 0.9998 for identifying higher confidence calls and minimizing false positives. In addition to that strict threshold, the following two more relaxed cutoffs can be used to explore additional hits. Note that these thresholds are offered as guidelines and are not necessarily representative of pathogenicity. PercentileJARVIS score threshold 99th0.9998 95th0.9826 90th0.7338 HMC HMC scores are displayed as a signal ("wiggle") track, with one score per genome position. Mousing over the bars displays the exact values. The highly-constrained cutoff of 0.8 is indicated with a line. Interpretation: A protein residue with HMC score &lt;1 indicates that missense variants affecting the homologous residues are significantly under negative selection (P-value &lt; 0.05) and likely to be deleterious. A more stringent score threshold of HMC&lt;0.8 is recommended to prioritize predicted disease-associated variants. MetaDome MetaDome data can be found on two tracks, MetaDome and MetaDome All Data. The MetaDome track should be used by default for data exploration. In this track the raw data containing the MetaDome tolerance scores were converted into a signal ("wiggle") track. Since this data was computed on the proteome, there was a small amount of coordinate overlap, roughly 0.42%. In these regions the lowest possible score was chosen for display in the track to maintain sensitivity. For this reason, if a protein variant is being evaluated, the MetaDome All Data track can be used to validate the score. More information on this data can be found in the MetaDome FAQ. Interpretation: The authors suggest the following guidelines for evaluating intolerance. By default, the MetaDome track displays a horizontal line at 0.7 which signifies the first intolerant bin. For more information see the MetaDome publication. ClassificationMetaDome Tolerance Score Highly intolerant&le; 0.175 Intolerant&le; 0.525 Slightly intolerant&le; 0.7 MTR MTR data can be found on two tracks, MTR All data and MTR Scores. In the MTR Scores track the data has been converted into 4 separate signal tracks representing each base pair mutation, with the lowest possible score shown when multiple transcripts overlap at a position. Overlaps can happen since this score is derived from transcripts and multiple transcripts can overlap. A horizontal line is drawn on the 0.8 score line to roughly represent the 25th percentile, meaning the items below may be of particular interest. It is recommended that the data be explored using this version of the track, as it condenses the information substantially while retaining the magnitude of the data. Any specific point mutations of interest can then be researched in the MTR All data track. This track contains all of the information from MTRV2 including more than 3 possible scores per base when transcripts overlap. A mouse-over on this track shows the ref and alt allele, as well as the MTR score and the MTR score percentile. Filters are available for MTR score, False Discovery Rate (FDR), MTR percentile, and variant consequence. By default, only items in the bottom 25 percentile are shown. Items in the track are colored according to their MTR percentile: Green items MTR percentiles over 75 Black items MTR percentiles between 25 and 75 Red items MTR percentiles below 25 Blue items No MTR score Interpretation: Regions with low MTR scores were seen to be enriched with pathogenic variants. For example, ClinVar pathogenic variants were seen to have an average score of 0.77 whereas ClinVar benign variants had an average score of 0.92. Further validation using the FATHMM cancer-associated training dataset saw that scores less than 0.5 contained 8.6% of the pathogenic variants while only containing 0.9% of neutral variants. In summary, lower scores are more likely to represent pathogenic variants whereas higher scores could be pathogenic, but have a higher chance to be a false positive. For more information see the MTR-Viewer publication. Methods JARVIS Scores were downloaded and converted to a single bigWig file. See the hg19 makeDoc and the hg38 makeDoc for more info. HMC Scores were downloaded and converted to .bedGraph files with a custom Python script. The bedGraph files were then converted to bigWig files, as documented in our makeDoc hg19 build log. MetaDome The authors provided a bed file containing codon coordinates along with the scores. This file was parsed with a python script to create the two tracks. For the first track the scores were aggregated for each coordinate, then the lowest score chosen for any overlaps and the result written out to bedGraph format. The file was then converted to bigWig with the bedGraphToBigWig utility. For the second track the file was reorganized into a bed 4+3 and conveted to bigBed with the bedToBigBed utility. See the hg19 makeDoc for details including the build script. The raw MetaDome data can also be accessed via their Zenodo handle. MTR V2 file was downloaded and columns were reshuffled as well as itemRgb added for the MTR All data track. For the MTR Scores track the file was parsed with a python script to pull out the highest possible MTR score for each of the 3 possible mutations at each base pair and 4 tracks built out of these values representing each mutation. See the hg19 makeDoc entry on MTR for more info. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated access, this track, like all others, is available via our API. However, for bulk processing, it is recommended to download the dataset. For automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed files that can be downloaded from our download server. Individual regions or the whole genome annotation can be obtained using our tools bigWigToWig or bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tools can also be used to obtain features confined to a given range, e.g., bigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/hmc/hmc.bw stdout Please refer to our Data Access FAQ for more information. Credits Thanks to Jean-Madeleine Desainteagathe (APHP Paris, France) for suggesting the JARVIS, MTR, HMC tracks. Thanks to Xialei Zhang for providing the HMC data file and to Dimitrios Vitsios and Slave Petrovski for helping clean up the hg38 JARVIS files for providing guidance on interpretation. Additional thanks to Laurens van de Wiel for providing the MetaDome data as well as guidance on the track development and interpretation. References Vitsios D, Dhindsa RS, Middleton L, Gussow AB, Petrovski S. Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning. Nat Commun. 2021 Mar 8;12(1):1504. PMID: 33686085; PMC: PMC7940646 Xiaolei Zhang, Pantazis I. Theotokis, Nicholas Li, the SHaRe Investigators, Caroline F. Wright, Kaitlin E. Samocha, Nicola Whiffin, James S. Ware Genetic constraint at single amino acid resolution improves missense variant prioritisation and gene discovery. Medrxiv 2022.02.16.22271023 Wiel L, Baakman C, Gilissen D, Veltman JA, Vriend G, Gilissen C. MetaDome: Pathogenicity analysis of genetic variants through aggregation of homologous human protein domains. Hum Mutat. 2019 Aug;40(8):1030-1038. PMID: 31116477; PMC: PMC6772141 Silk M, Petrovski S, Ascher DB. MTR-Viewer: identifying regions within genes under purifying selection. Nucleic Acids Res. 2019 Jul 2;47(W1):W121-W126. PMID: 31170280; PMC: PMC6602522 Halldorsson BV, Eggertsson HP, Moore KHS, Hauswedell H, Eiriksson O, Ulfarsson MO, Palsson G, Hardarson MT, Oddsson A, Jensson BO et al. The sequences of 150,119 genomes in the UK Biobank. Nature. 2022 Jul;607(7920):732-740. PMID: 35859178; PMC: PMC9329122 
nestedRepeats Interrupted Rpts Fragments of Interrupted Repeats Joined by RepeatMasker ID Repeats Description This track shows joined fragments of interrupted repeats extracted from the output of the RepeatMasker program which screens DNA sequences for interspersed repeats and low complexity DNA sequences using the Repbase Update library of repeats from the Genetic Information Research Institute (GIRI). Repbase Update is described in Jurka (2000) in the References section below. The detailed annotations from RepeatMasker are in the RepeatMasker track. This track shows fragments of original repeat insertions which have been interrupted by insertions of younger repeats or through local rearrangements. The fragments are joined using the ID column of RepeatMasker output. Display Conventions and Configuration In pack or full mode, each interrupted repeat is displayed as boxes (fragments) joined by horizontal lines, labeled with the repeat name. If all fragments are on the same strand, arrows are added to the horizontal line to indicate the strand. In dense or squish mode, labels and arrows are omitted and in dense mode, all items are collapsed to fit on a single row. Items are shaded according to the average identity score of their fragments. Usually, the shade of an item is similar to the shades of its fragments unless some fragments are much more diverged than others. The score displayed above is the average identity score, clipped to a range of 50% - 100% and then mapped to the range 0 - 1000 for shading in the browser. Methods UCSC has used the most current versions of the RepeatMasker software and repeat libraries available to generate these data. Note that these versions may be newer than those that are publicly available on the Internet. Data are generated using the RepeatMasker -s flag. Additional flags may be used for certain organisms. See the FAQ for more information. Credits Thanks to Arian Smit, Robert Hubley and GIRI for providing the tools and repeat libraries used to generate this track. References Smit AFA, Hubley R, Green P. RepeatMasker Open-3.0. http://www.repeatmasker.org. 1996-2010. Repbase Update is described in: Jurka J. Repbase Update: a database and an electronic journal of repetitive elements. Trends Genet. 2000 Sep;16(9):418-420. PMID: 10973072 For a discussion of repeats in mammalian genomes, see: Smit AF. Interspersed repeats and other mementos of transposable elements in mammalian genomes. Curr Opin Genet Dev. 1999 Dec;9(6):657-63. PMID: 10607616 Smit AF. The origin of interspersed repeats in the human genome. Curr Opin Genet Dev. 1996 Dec;6(6):743-8. PMID: 8994846 
refSeqComposite NCBI RefSeq RefSeq genes from NCBI Genes and Gene Predictions Description The NCBI RefSeq Genes composite track shows human protein-coding and non-protein-coding genes taken from the NCBI RNA reference sequences collection (RefSeq). All subtracks use coordinates provided by RefSeq, except for the UCSC RefSeq track, which UCSC produces by realigning the RefSeq RNAs to the genome. This realignment may result in occasional differences between the annotation coordinates provided by UCSC and NCBI. For RNA-seq analysis, we advise using NCBI aligned tables like RefSeq All or RefSeq Curated. See the Methods section for more details about how the different tracks were created. Please visit NCBI's Feedback for Gene and Reference Sequences (RefSeq) page to make suggestions, submit additions and corrections, or ask for help concerning RefSeq records. For more information on the different gene tracks, see our Genes FAQ. Display Conventions and Configuration This track is a composite track that contains differing data sets. To show only a selected set of subtracks, uncheck the boxes next to the tracks that you wish to hide. Note: Not all subtracts are available on all assemblies. The possible subtracks include: RefSeq aligned annotations and UCSC alignment of RefSeq annotations RefSeq All &ndash; all curated and predicted annotations provided by RefSeq. RefSeq Curated &ndash; subset of RefSeq All that includes only those annotations whose accessions begin with NM, NR, NP or YP. (NP and YP are used only for protein-coding genes on the mitochondrion; YP is used for human only.) RefSeq Predicted &ndash; subset of RefSeq All that includes those annotations whose accessions begin with XM or XR. RefSeq Other &ndash; all other annotations produced by the RefSeq group that do not fit the requirements for inclusion in the RefSeq Curated or the RefSeq Predicted tracks, as they do not have a product and therefore no RefSeq accession. More than 90% are pseudogenes, T-cell receptor or immunoglobulin segments. The few remaining entries are gene clusters (e.g. protocadherin). RefSeq Alignments &ndash; alignments of RefSeq RNAs to the human genome provided by the RefSeq group, following the display conventions for PSL tracks. RefSeq Diffs &ndash; alignment differences between the human reference genome(s) and RefSeq transcripts. (Track not currently available for every assembly.) UCSC RefSeq &ndash; annotations generated from UCSC's realignment of RNAs with NM and NR accessions to the human genome. This track was previously known as the &quot;RefSeq Genes&quot; track. RefSeq Select+MANE (subset) &ndash; Subset of RefSeq Curated, transcripts marked as RefSeq Select or MANE Select. A single Select transcript is chosen as representative for each protein-coding gene. This track includes transcripts categorized as MANE, which are further agreed upon as representative by both NCBI RefSeq and Ensembl/GENCODE, and have a 100% identical match to a transcript in the Ensembl annotation. See NCBI RefSeq Select. Note that we provide a separate track, MANE (hg38), which contains only the MANE transcripts. RefSeq HGMD (subset) &ndash; Subset of RefSeq Curated, transcripts annotated by the Human Gene Mutation Database. This track is only available on the human genomes hg19 and hg38. It is the most restricted RefSeq subset, targeting clinical diagnostics. The RefSeq All, RefSeq Curated, RefSeq Predicted, RefSeq HGMD, RefSeq Select/MANE and UCSC RefSeq tracks follow the display conventions for gene prediction tracks. The color shading indicates the level of review the RefSeq record has undergone: predicted (light), provisional (medium), or reviewed (dark), as defined by RefSeq. Color Level of review Reviewed: the RefSeq record has been reviewed by NCBI staff or by a collaborator. The NCBI review process includes assessing available sequence data and the literature. Some RefSeq records may incorporate expanded sequence and annotation information. Provisional: the RefSeq record has not yet been subject to individual review. The initial sequence-to-gene association has been established by outside collaborators or NCBI staff. Predicted: the RefSeq record has not yet been subject to individual review, and some aspect of the RefSeq record is predicted. The item labels and codon display properties for features within this track can be configured through the check-box controls at the top of the track description page. To adjust the settings for an individual subtrack, click the wrench icon next to the track name in the subtrack list . Label: By default, items are labeled by gene name. Click the appropriate Label option to display the accession name or OMIM identifier instead of the gene name, show all or a subset of these labels including the gene name, OMIM identifier and accession names, or turn off the label completely. Codon coloring: This track has an optional codon coloring feature that allows users to quickly validate and compare gene predictions. To display codon colors, select the genomic codons option from the Color track by codons pull-down menu. For more information about this feature, go to the Coloring Gene Predictions and Annotations by Codon page. The RefSeq Diffs track contains five different types of inconsistency between the reference genome sequence and the RefSeq transcript sequences. The five types of differences are as follows: mismatch &ndash; aligned but mismatching bases, plus HGVS g. to show the genomic change required to match the transcript and HGVS c./n. to show the transcript change required to match the genome. short gap &ndash; genomic gaps that are too small to be introns (arbitrary cutoff of &lt; 45 bp), most likely insertions/deletion variants or errors, with HGVS g. and c./n. showing differences. shift gap &ndash; shortGap items whose placement could be shifted left and/or right on the genome due to repetitive sequence, with HGVS c./n. position range of ambiguous region in transcript. Here, thin and thick lines are used -- the thin line shows the span of the repetitive sequence, and the thick line shows the rightmost shifted gap. double gap &ndash; genomic gaps that are long enough to be introns but that skip over transcript sequence (invisible in default setting), with HGVS c./n. deletion. skipped &ndash; sequence at the beginning or end of a transcript that is not aligned to the genome (invisible in default setting), with HGVS c./n. deletion HGVS Terminology (Human Genome Variation Society): g. = genomic sequence ; c. = coding DNA sequence ; n. = non-coding RNA reference sequence. When reporting HGVS with RefSeq sequences, to make sure that results from research articles can be mapped to the genome unambiguously, please specify the RefSeq annotation release displayed on the transcript's Genome Browser details page and also the RefSeq transcript ID with version (e.g. NM_012309.4 not NM_012309). Methods Tracks contained in the RefSeq annotation and RefSeq RNA alignment tracks were created at UCSC using data from the NCBI RefSeq project. Data files were downloaded from RefSeq in GFF file format and converted to the genePred and PSL table formats for display in the Genome Browser. Information about the NCBI annotation pipeline can be found here. The RefSeq Diffs track is generated by UCSC using NCBI's RefSeq RNA alignments. The UCSC RefSeq Genes track is constructed using the same methods as previous RefSeq Genes tracks. RefSeq RNAs were aligned against the human genome using BLAT. Those with an alignment of less than 15% were discarded. When a single RNA aligned in multiple places, the alignment having the highest base identity was identified. Only alignments having a base identity level within 0.1% of the best and at least 96% base identity with the genomic sequence were kept. Data Access The raw data for these tracks can be accessed in multiple ways. It can be explored interactively using the REST API, Table Browser or Data Integrator. The tables can also be accessed programmatically through our public MySQL server or downloaded from our downloads server for local processing. The previous track versions are available in the archives of our downloads server. You can also access any RefSeq table entries in JSON format through our JSON API. The data in the RefSeq Other and RefSeq Diffs tracks are organized in bigBed file format; more information about accessing the information in this bigBed file can be found below. The other subtracks are associated with database tables as follows: genePred format: RefSeq All - ncbiRefSeq RefSeq Curated - ncbiRefSeqCurated RefSeq Predicted - ncbiRefSeqPredicted RefSeq HGMD - ncbiRefSeqHgmd RefSeq Select+MANE - ncbiRefSeqSelect UCSC RefSeq - refGene PSL format: RefSeq Alignments - ncbiRefSeqPsl The first column of each of these tables is &quot;bin&quot;. This column is designed to speed up access for display in the Genome Browser, but can be safely ignored in downstream analysis. You can read more about the bin indexing system here. The annotations in the RefSeqOther and RefSeqDiffs tracks are stored in bigBed files, which can be obtained from our downloads server here, ncbiRefSeqOther.bb and ncbiRefSeqDiffs.bb. Individual regions or the whole set of genome-wide annotations can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system from the utilities directory linked below. For example, to extract only annotations in a given region, you could use the following command: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/ncbiRefSeq/ncbiRefSeqOther.bb -chrom=chr16 -start=34990190 -end=36727467 stdout You can download a GTF format version of the RefSeq All table from the GTF downloads directory. The genePred format tracks can also be converted to GTF format using the genePredToGtf utility, available from the utilities directory on the UCSC downloads server. The utility can be run from the command line like so: genePredToGtf hg38 ncbiRefSeqPredicted ncbiRefSeqPredicted.gtf Note that using genePredToGtf in this manner accesses our public MySQL server, and you therefore must set up your hg.conf as described on the MySQL page linked near the beginning of the Data Access section. A file containing the RNA sequences in FASTA format for all items in the RefSeq All, RefSeq Curated, and RefSeq Predicted tracks can be found on our downloads server here. Please refer to our mailing list archives for questions. Previous versions of the ncbiRefSeq set of tracks can be found on our archive download server. Credits This track was produced at UCSC from data generated by scientists worldwide and curated by the NCBI RefSeq project. References Kent WJ. BLAT - the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 Pruitt KD, Brown GR, Hiatt SM, Thibaud-Nissen F, Astashyn A, Ermolaeva O, Farrell CM, Hart J, Landrum MJ, McGarvey KM et al. RefSeq: an update on mammalian reference sequences. Nucleic Acids Res. 2014 Jan;42(Database issue):D756-63. PMID: 24259432; PMC: PMC3965018 Pruitt KD, Tatusova T, Maglott DR. NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2005 Jan 1;33(Database issue):D501-4. PMID: 15608248; PMC: PMC539979 
ncbiRefSeqHistorical RefSeq Historical NCBI RefSeq Historical Transcript Versions Genes and Gene Predictions 
ncbiRefSeqHgmd RefSeq HGMD NCBI RefSeq HGMD subset: transcripts with clinical variants in HGMD Genes and Gene Predictions 
ncbiRefSeqSelect RefSeq Select and MANE NCBI RefSeq Select and MANE subset: A single representative transcript Genes and Gene Predictions 
refGene UCSC RefSeq UCSC annotations of RefSeq RNAs (NM_* and NR_*) Genes and Gene Predictions Description The RefSeq Genes track shows known human protein-coding and non-protein-coding genes taken from the NCBI RNA reference sequences collection (RefSeq). The data underlying this track are updated weekly. Please visit the Feedback for Gene and Reference Sequences (RefSeq) page to make suggestions, submit additions and corrections, or ask for help concerning RefSeq records. For more information on the different gene tracks, see our Genes FAQ. Display Conventions and Configuration This track follows the display conventions for gene prediction tracks. The color shading indicates the level of review the RefSeq record has undergone: predicted (light), provisional (medium), reviewed (dark). The item labels and display colors of features within this track can be configured through the controls at the top of the track description page. Label: By default, items are labeled by gene name. Click the appropriate Label option to display the accession name instead of the gene name, show both the gene and accession names, or turn off the label completely. Codon coloring: This track contains an optional codon coloring feature that allows users to quickly validate and compare gene predictions. To display codon colors, select the genomic codons option from the Color track by codons pull-down menu. For more information about this feature, go to the Coloring Gene Predictions and Annotations by Codon page. Hide non-coding genes: By default, both the protein-coding and non-protein-coding genes are displayed. If you wish to see only the coding genes, click this box. Methods RefSeq RNAs were aligned against the human genome using BLAT. Those with an alignment of less than 15% were discarded. When a single RNA aligned in multiple places, the alignment having the highest base identity was identified. Only alignments having a base identity level within 0.1% of the best and at least 96% base identity with the genomic sequence were kept. Credits This track was produced at UCSC from RNA sequence data generated by scientists worldwide and curated by the NCBI RefSeq project. References Kent WJ. BLAT - the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 Pruitt KD, Brown GR, Hiatt SM, Thibaud-Nissen F, Astashyn A, Ermolaeva O, Farrell CM, Hart J, Landrum MJ, McGarvey KM et al. RefSeq: an update on mammalian reference sequences. Nucleic Acids Res. 2014 Jan;42(Database issue):D756-63. PMID: 24259432; PMC: PMC3965018 Pruitt KD, Tatusova T, Maglott DR. NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2005 Jan 1;33(Database issue):D501-4. PMID: 15608248; PMC: PMC539979 
ncbiRefSeqGenomicDiff RefSeq Diffs Differences between NCBI RefSeq Transcripts and the Reference Genome Genes and Gene Predictions 
ncbiRefSeqPsl RefSeq Alignments RefSeq Alignments of RNAs Genes and Gene Predictions 
ncbiRefSeqOther RefSeq Other NCBI RefSeq Other Annotations (not NM_*, NR_*, XM_*, XR_*, NP_* or YP_*) Genes and Gene Predictions 
ncbiRefSeqPredicted RefSeq Predicted NCBI RefSeq genes, predicted subset (XM_* or XR_*) Genes and Gene Predictions 
ncbiRefSeqCurated RefSeq Curated NCBI RefSeq genes, curated subset (NM_*, NR_*, NP_* or YP_*) Genes and Gene Predictions 
ncbiRefSeq RefSeq All NCBI RefSeq genes, curated and predicted (NM_*, XM_*, NR_*, XR_*, NP_*, YP_*) Genes and Gene Predictions 
omimGene2 OMIM Genes OMIM Gene Phenotypes - Dark Green Can Be Disease-causing Phenotype and Literature Description NOTE: OMIM is intended for use primarily by physicians and other professionals concerned with genetic disorders, by genetics researchers, and by advanced students in science and medicine. While the OMIM database is open to the public, users seeking information about a personal medical or genetic condition are urged to consult with a qualified physician for diagnosis and for answers to personal questions. Further, please be sure to click through to omim.org for the very latest, as they are continually updating data. NOTE ABOUT DOWNLOADS: OMIM is the property of Johns Hopkins University and is not available for download or mirroring by any third party without their permission. Please see OMIM for downloads. OMIM is a compendium of human genes and genetic phenotypes. The full-text, referenced overviews in OMIM contain information on all known Mendelian disorders and over 12,000 genes. OMIM is authored and edited at the McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, under the direction of Dr. Ada Hamosh. This database was initiated in the early 1960s by Dr. Victor A. McKusick as a catalog of Mendelian traits and disorders, entitled Mendelian Inheritance in Man (MIM). The OMIM data are separated into three separate tracks: OMIM Alleles &nbsp;&nbsp;&nbsp;&nbsp;Variants in the OMIM database that have associated dbSNP identifiers. This track is currently unavailable on the hg38 assembly, as it depends on dbSNP data that has not been released yet. OMIM Genes &nbsp;&nbsp;&nbsp;&nbsp;The genomic positions of gene entries in the OMIM database. The coloring indicates the associated OMIM phenotype map key. OMIM Phenotypes - Gene Unknown &nbsp;&nbsp;&nbsp;&nbsp;Regions known to be associated with a phenotype, but for which no specific gene is known to be causative. This track also includes known multi-gene syndromes. This track shows the genomic positions of all gene entries in the Online Mendelian Inheritance in Man (OMIM) database. Display Conventions and Configuration Genomic locations of OMIM gene entries are displayed as solid blocks. The entries are colored according to the associated OMIM phenotype map key (if any): Lighter Green for phenotype map key 1 OMIM records - the disorder has been placed on the map based on its association with a gene, but the underlying defect is not known. Light Green for phenotype map key 2 OMIM records - the disorder has been placed on the map by linkage; no mutation has been found. Dark Green for phenotype map key 3 OMIM records - the molecular basis for the disorder is known; a mutation has been found in the gene. Purple for phenotype map key 4 OMIM records - a contiguous gene deletion or duplication syndrome; multiple genes are deleted or duplicated causing the phenotype. Light Gray for Others - no associated OMIM phenotype map key info available. Gene symbol and disease information, when available, are displayed on the details page for an item, and links to related RefSeq Genes and UCSC Genes are given. The descriptions of the OMIM entries are shown on the main browser display when Full display mode is chosen. In Pack mode, the descriptions are shown when mousing over each entry. Items displayed can be filtered according to phenotype map key on the track controls page. Methods The mappings displayed in this track are based on OMIM gene entries, their Entrez Gene IDs, and the corresponding RefSeq Gene locations: The data file genemap.txt from OMIM was loaded into the MySQL table omimGeneMap. The data file mim2gene.txt from OMIM was processed and loaded into the MySQL table omim2gene. Entries in genemap.txt having disorder info were parsed and loaded into the omimPhenotype table. For each OMIM gene in the omim2gene table, the Entrez Gene ID was used to get the corresponding RefSeq Gene ID via the ncbiRefLink table, and the RefSeq ID was used to get the genomic location from the ncbiRefSeq table. The OMIM gene IDs and corresponding RefSeq Gene locations were loaded into the omimGene2 table, the primary table for this track. Data Access Because OMIM has only allowed Data queries within individual chromosomes, no download files are available from the Genome Browser. Full genome datasets can be downloaded directly from the OMIM Downloads page. All genome-wide downloads are freely available from OMIM after registration. If you need the OMIM data in exactly the format of the UCSC Genome Browser, for example if you are running a UCSC Genome Browser local installation (a partial &quot;mirror&quot;), please create a user account on omim.org and contact OMIM via https://omim.org/contact. Send them your OMIM account name and request access to the UCSC Genome Browser &quot;entitlement&quot;. They will then grant you access to a MySQL/MariaDB data dump that contains all UCSC Genome Browser OMIM tables. UCSC offers queries within chromosomes from Table Browser that include a variety of filtering options and cross-referencing other datasets using our Data Integrator tool. UCSC also has an API that can be used to retrieve data in JSON format from a particular chromosome range. Please refer to our searchable mailing list archives for more questions and example queries, or our Data Access FAQ for more information. Example: Retrieve phenotype, Mode of Inheritance, and other OMIM data within a range Go to Table Browser, make sure the right dataset is selected: group: Phenotype and Literature, track: OMIM Genes, table: omimGene2. Define region of interest by entering coordinates or a gene symbol into the &quot;Position&quot; textbox, such as chr1:11,106,535-11,262,551 or MTOR, or upload a list. Format your data by setting the &quot;Output format&quot; dropdown to &quot;selected fields from primary and related Tables&quot; and click get output. This brings up the data field and linked table selection page. Select chrom, chromStart, chromEnd, and name from omimGene2 table. Then select the related tables omim2gene and omimPhenotype and click allow selection from check tables. This brings up the fields of the linked tables, where you can select approvedGeneSymbol, omimID, description, omimPhenotypeMapKey, and inhMode. Click on the get output to proceed to the results page: chr1 11106534 11262551 MTOR 601231, Smith-Kingsmore syndrome,Focal cortical dysplasia, type II, somatic, 3, Autosomal dominant For a quick link to pre-fill these options, click this session link. Credits Thanks to OMIM and NCBI for the use of their data. This track was constructed by Fan Hsu, Robert Kuhn, and Brooke Rhead of the UCSC Genome Bioinformatics Group. References Amberger J, Bocchini CA, Scott AF, Hamosh A. McKusick's Online Mendelian Inheritance in Man (OMIM&#174;). Nucleic Acids Res. 2009 Jan;37(Database issue):D793-6. Epub 2008 Oct 8. Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005 Jan 1;33(Database issue):D514-7. 
recombPat Recomb. deCODE Pat Recombination rate: deCODE Genetics, paternal Mapping and Sequencing Description The recombination rate track represents calculated rates of recombination based on the genetic maps from deCODE (Halldorsson et al., 2019) and 1000 Genomes (2013 Phase 3 release, lifted from hg19). The deCODE map is more recent, has a higher resolution and was natively created on hg38 and therefore recommended. For the Recomb. deCODE average track, the recombination rates for chrX represent the female rate. This track also includes a subtrack with all the individual deCODE recombination events and another subtrack with several thousand de-novo mutations found in the deCODE sequencing data. These two tracks are hidden by default and have to be switched on explicitly on the configuration page. Display Conventions and Configuration This is a super track that contains different subtracks, three with the deCODE recombination rates (paternal, maternal and average) and one with the 1000 Genomes recombination rate (average). These tracks are in signal graph (wiggle) format. By default, to show most recombination hotspots, their maximum value is set to 100 cM, even though many regions have values higher than 100. The maximum value can be changed on the configuration pages of the tracks. There are two more tracks that show additional details provided by deCODE: one subtrack with the raw data of all cross-overs tagged with their proband ID and another one with around 8000 human de-novo mutation variants that are linked to cross-over changes. Methods The deCODE genetic map was created at deCODE Genetics. It is based on microarrays assaying 626,828 SNP markers that allowed to identify 1,476,140 crossovers in 56,321 paternal meioses and 3,055,395 crossovers in 70,086 maternal meioses. In total, the data is based on 4,531,535 crossovers in 126,427 meioses. By using WGS data with 9,305,070 SNPs, the boundaries for 761,981 crossovers were refined: 247,942 crossovers in 9423 paternal meioses and 514,039 crossovers in 11,750 maternal meioses. The average resolution of the genetic map is 682 base pairs (bp): 655 and 708 bp for the paternal and maternal maps, respectively. The 1000 Genomes genetic map is based on the IMPUTE genetic map based on 1000 Genomes Phase 3, on hg19 coordinates. It was converted to hg38 by Po-Ru Loh at the Broad Institute. After a run of liftOver, he post-processed the data to deal with situations in which consecutive map locations became much closer/farther after lifting. The heuristic used is sufficient for statistical phasing but may not be optimal for other analyses. For this reason, and because of its higher resolution, the DeCODE map is therefore recommended for hg38. As with all other tracks, the data conversion commands and pointers to the original data files are documented in the makeDoc file of this track. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated access, this track, like all others, is available via our API. However, for bulk processing, it is recommended to download the dataset. For automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed files that can be downloaded from our download server. Individual regions or the whole genome annotation can be obtained using our tools bigWigToWig or bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tools can also be used to obtain features confined to a given range, e.g., bigWigToBedGraph -chrom=chr17 -start=45941345 -end=45942345 http://hgdownload.soe.ucsc.edu/gbdb/hg38/recombRate/recombAvg.bw stdout Please refer to our Data Access FAQ for more information. Credits This track was produced at UCSC using data that are freely available for the deCODE and 1000 Genomes genetic maps. Thanks to Po-Ru Loh at the Broad Institute for providing the code to lift the hg19 1000 Genomes map data to hg38. References 1000 Genomes Project Consortium., Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, Hurles ME, McVean GA. A map of human genome variation from population-scale sequencing. Nature. 2010 Oct 28;467(7319):1061-73. PMID: 20981092; PMC: PMC3042601 Halldorsson BV, Palsson G, Stefansson OA, Jonsson H, Hardarson MT, Eggertsson HP, Gunnarsson B, Oddsson A, Halldorsson GH, Zink F et al. Characterizing mutagenic effects of recombination through a sequence-level genetic map. Science. 2019 Jan 25;363(6425). PMID: 30679340 
TSS_activity_read_counts TSS activity - read counts FANTOM5: TSS activity per sample read counts Regulation Description The FANTOM5 track shows mapped transcription start sites (TSS) and their usage in primary cells, cell lines, and tissues to produce a comprehensive overview of gene expression across the human body by using single molecule sequencing. Display Conventions and Configuration Items in this track are colored according to their strand orientation. Blue indicates alignment to the negative strand, and red indicates alignment to the positive strand. Methods Protocol Individual biological states are profiled by HeliScopeCAGE, which is a variation of the CAGE (Cap Analysis Gene Expression) protocol based on a single molecule sequencer. The standard protocol requiring 5 &micro;g of total RNA as a starting material is referred to as hCAGE, and an optimized version for a lower quantity (~ 100 ng) is referred to as LQhCAGE (Kanamori-Katyama et al. 2011). hCAGE LQhCAGE Samples Transcription start sites (TSSs) were mapped and their usage in human and mouse primary cells, cell lines, and tissues was to produce a comprehensive overview of mammalian gene expression across the human body. 5&prime;-end of the mapped CAGE reads are counted at a single base pair resolution (CTSS, CAGE tag starting sites) on the genomic coordinates, which represent TSS activities in the sample. Individual samples shown in "TSS activity" tracks are grouped as below. Primary cell Tissue Cell Line Time course Fractionation TSS peaks TSS (CAGE) peaks across the panel of the biological states (samples) are identified by DPI (decomposition based peak identification, Forrest et al. 2014), where each of the peaks consists of neighboring and related TSSs. The peaks are used as anchors to define promoters and units of promoter-level expression analysis. Two subsets of the peaks are defined based on evidence of read counts, depending on scopes of subsequent analyses, and the first subset (referred as a robust set of the peaks, thresholded for expression analysis is shown as TSS peaks. They are named "p#@GENE_SYMBOL" if associated with 5'-end of known genes, or "p@CHROM:START..END,STRAND" otherwise. The summary tracks consist of the TSS (CAGE) peaks and summary profiles of TSS activities (total and maximum values). The summary track consists of the following tracks. TSS (CAGE) peaks the robust peaks TSS summary profiles Total counts and TPM (tags per million) in all the samples Maximum counts and TPM among the samples TSS activity 5&prime;-end of the mapped CAGE reads are counted at a single base pair resolution (CTSS, CAGE tag starting sites) on the genomic coordinates, which represent TSS activities in the sample. The read counts tracks indicate raw counts of CAGE reads, and the TPM tracks indicate normalized counts as TPM (tags per million). Categories of individual samples - Cell Line hCAGE - Cell Line LQhCAGE - fractionation hCAGE - Primary cell hCAGE - Primary cell LQhCAGE - Time course hCAGE - Tissue hCAGE Data Access FANTOM5 data can be explored interactively with the Table Browser and cross-referenced with the Data Integrator. For programmatic access, the track can be accessed using the Genome Browser&apos;s REST API. ReMap annotations can be downloaded from the Genome Browser's download server as a bigBed file. This compressed binary format can be remotely queried through command line utilities. Please note that some of the download files can be quite large. The FANTOM5 reprocessed data can be found and downloaded on the FANTOM website. Credits Thanks to the FANTOM5 consortium, the Large Scale Data Managing Unit and Preventive Medicine and Applied Genomics Unit, the Center for Integrative Medical Sciences (IMS), and RIKEN for providing this data and its analysis. References FANTOM Consortium and the RIKEN PMI and CLST (DGT), Forrest AR, Kawaji H, Rehli M, Baillie JK, de Hoon MJ, Haberle V, Lassmann T, Kulakovskiy IV, Lizio M et al. A promoter-level mammalian expression atlas. Nature. 2014 Mar 27;507(7493):462-70. PMID: 24670764; PMC: PMC4529748 Kanamori-Katayama M, Itoh M, Kawaji H, Lassmann T, Katayama S, Kojima M, Bertin N, Kaiho A, Ninomiya N, Daub CO et al. Unamplified cap analysis of gene expression on a single-molecule sequencer. Genome Res. 2011 Jul;21(7):1150-9. PMID: 21596820; PMC: PMC3129257 Lizio M, Harshbarger J, Shimoji H, Severin J, Kasukawa T, Sahin S, Abugessaisa I, Fukuda S, Hori F, Ishikawa-Kato S et al. Gateways to the FANTOM5 promoter level mammalian expression atlas. Genome Biol. 2015 Jan 5;16(1):22. PMID: 25723102; PMC: PMC4310165 
VeinAdult_CNhs12844_ctss_rev VeinAdult- vein, adult_CNhs12844_10191-103E2_reverse Regulation 
VeinAdult_CNhs12844_ctss_fwd VeinAdult+ vein, adult_CNhs12844_10191-103E2_forward Regulation 
VaginaAdult_CNhs12854_ctss_rev VaginaAdult- vagina, adult_CNhs12854_10204-103F6_reverse Regulation 
VaginaAdult_CNhs12854_ctss_fwd VaginaAdult+ vagina, adult_CNhs12854_10204-103F6_forward Regulation 
UterusFetalDonor1_CNhs11763_ctss_rev UterusFetalD1- uterus, fetal, donor1_CNhs11763_10055-101H1_reverse Regulation 
UterusFetalDonor1_CNhs11763_ctss_fwd UterusFetalD1+ uterus, fetal, donor1_CNhs11763_10055-101H1_forward Regulation 
UterusAdultPool1_CNhs11676_ctss_rev UterusAdultPl1- uterus, adult, pool1_CNhs11676_10100-102D1_reverse Regulation 
UterusAdultPool1_CNhs11676_ctss_fwd UterusAdultPl1+ uterus, adult, pool1_CNhs11676_10100-102D1_forward Regulation 
UrethraDonor2_CNhs13464_ctss_rev UrethraD2- Urethra, donor2_CNhs13464_10319-105A4_reverse Regulation 
UrethraDonor2_CNhs13464_ctss_fwd UrethraD2+ Urethra, donor2_CNhs13464_10319-105A4_forward Regulation 
UniversalRNAHumanNormalTissuesBiochainPool1_CNhs10612_ctss_rev UniversalRnaNormalTissuesBiochainPl1- Universal RNA - Human Normal Tissues Biochain, pool1_CNhs10612_10007-101B4_reverse Regulation 
UniversalRNAHumanNormalTissuesBiochainPool1_CNhs10612_ctss_fwd UniversalRnaNormalTissuesBiochainPl1+ Universal RNA - Human Normal Tissues Biochain, pool1_CNhs10612_10007-101B4_forward Regulation 
UmbilicalCordFetalDonor1_CNhs11765_ctss_rev UmbilicalCordFetalD1- umbilical cord, fetal, donor1_CNhs11765_10057-101H3_reverse Regulation 
UmbilicalCordFetalDonor1_CNhs11765_ctss_fwd UmbilicalCordFetalD1+ umbilical cord, fetal, donor1_CNhs11765_10057-101H3_forward Regulation 
TracheaFetalDonor1_CNhs11766_ctss_rev TracheaFetalD1- trachea, fetal, donor1_CNhs11766_10058-101H4_reverse Regulation 
TracheaFetalDonor1_CNhs11766_ctss_fwd TracheaFetalD1+ trachea, fetal, donor1_CNhs11766_10058-101H4_forward Regulation 
TracheaAdultPool1_CNhs10635_ctss_rev TracheaAdultPl1- trachea, adult, pool1_CNhs10635_10029-101E2_reverse Regulation 
TracheaAdultPool1_CNhs10635_ctss_fwd TracheaAdultPl1+ trachea, adult, pool1_CNhs10635_10029-101E2_forward Regulation 
TonsilAdultPool1_CNhs10654_ctss_rev TonsilAdultPl1- tonsil, adult, pool1_CNhs10654_10047-101G2_reverse Regulation 
TonsilAdultPool1_CNhs10654_ctss_fwd TonsilAdultPl1+ tonsil, adult, pool1_CNhs10654_10047-101G2_forward Regulation 
TongueFetalDonor1_CNhs11768_ctss_rev TongueFetalD1- tongue, fetal, donor1_CNhs11768_10059-101H5_reverse Regulation 
TongueFetalDonor1_CNhs11768_ctss_fwd TongueFetalD1+ tongue, fetal, donor1_CNhs11768_10059-101H5_forward Regulation 
TongueEpidermisFungiformPapillaeDonor1_CNhs13460_ctss_rev TongueEpidermisD1- tongue epidermis (fungiform papillae), donor1_CNhs13460_10288-104F9_reverse Regulation 
TongueEpidermisFungiformPapillaeDonor1_CNhs13460_ctss_fwd TongueEpidermisD1+ tongue epidermis (fungiform papillae), donor1_CNhs13460_10288-104F9_forward Regulation 
TongueAdult_CNhs12853_ctss_rev TongueAdult- tongue, adult_CNhs12853_10203-103F5_reverse Regulation 
TongueAdult_CNhs12853_ctss_fwd TongueAdult+ tongue, adult_CNhs12853_10203-103F5_forward Regulation 
ThyroidFetalDonor1_CNhs11769_ctss_rev ThyroidFetalD1- thyroid, fetal, donor1_CNhs11769_10060-101H6_reverse Regulation 
ThyroidFetalDonor1_CNhs11769_ctss_fwd ThyroidFetalD1+ thyroid, fetal, donor1_CNhs11769_10060-101H6_forward Regulation 
ThyroidAdultPool1_CNhs10634_ctss_rev ThyroidAdultPl1- thyroid, adult, pool1_CNhs10634_10028-101E1_reverse Regulation 
ThyroidAdultPool1_CNhs10634_ctss_fwd ThyroidAdultPl1+ thyroid, adult, pool1_CNhs10634_10028-101E1_forward Regulation 
ThymusFetalPool1_CNhs10650_ctss_rev ThymusFetalPl1- thymus, fetal, pool1_CNhs10650_10043-101F7_reverse Regulation 
ThymusFetalPool1_CNhs10650_ctss_fwd ThymusFetalPl1+ thymus, fetal, pool1_CNhs10650_10043-101F7_forward Regulation 
ThymusAdultPool1_CNhs10633_ctss_rev ThymusAdultPl1- thymus, adult, pool1_CNhs10633_10027-101D9_reverse Regulation 
ThymusAdultPool1_CNhs10633_ctss_fwd ThymusAdultPl1+ thymus, adult, pool1_CNhs10633_10027-101D9_forward Regulation 
ThroatFetalDonor1_CNhs11770_ctss_rev ThroatFetalD1- throat, fetal, donor1_CNhs11770_10061-101H7_reverse Regulation 
ThroatFetalDonor1_CNhs11770_ctss_fwd ThroatFetalD1+ throat, fetal, donor1_CNhs11770_10061-101H7_forward Regulation 
ThroatAdult_CNhs12858_ctss_rev ThroatAdult- throat, adult_CNhs12858_10209-103G2_reverse Regulation 
ThroatAdult_CNhs12858_ctss_fwd ThroatAdult+ throat, adult_CNhs12858_10209-103G2_forward Regulation 
ThalamusNewbornDonor10223_CNhs14084_ctss_rev ThalamusNbD10223- thalamus, newborn, donor10223_CNhs14084_10366-105F6_reverse Regulation 
ThalamusNewbornDonor10223_CNhs14084_ctss_fwd ThalamusNbD10223+ thalamus, newborn, donor10223_CNhs14084_10366-105F6_forward Regulation 
ThalamusAdultDonor10258TechRep2_CNhs14551_ctss_rev ThalamusAdultD10258Tr2- thalamus, adult, donor10258, tech_rep2_CNhs14551_10370-105G1_reverse Regulation 
ThalamusAdultDonor10258TechRep2_CNhs14551_ctss_fwd ThalamusAdultD10258Tr2+ thalamus, adult, donor10258, tech_rep2_CNhs14551_10370-105G1_forward Regulation 
ThalamusAdultDonor10258TechRep1_CNhs14223_ctss_rev ThalamusAdultD10258Tr1- thalamus, adult, donor10258, tech_rep1_CNhs14223_10370-105G1_reverse Regulation 
ThalamusAdultDonor10258TechRep1_CNhs14223_ctss_fwd ThalamusAdultD10258Tr1+ thalamus, adult, donor10258, tech_rep1_CNhs14223_10370-105G1_forward Regulation 
ThalamusAdultDonor10252_CNhs12314_ctss_rev ThalamusAdultD10252- thalamus, adult, donor10252_CNhs12314_10154-103A1_reverse Regulation 
ThalamusAdultDonor10252_CNhs12314_ctss_fwd ThalamusAdultD10252+ thalamus, adult, donor10252_CNhs12314_10154-103A1_forward Regulation 
ThalamusAdultDonor10196_CNhs13794_ctss_rev ThalamusAdultD10196- thalamus - adult, donor10196_CNhs13794_10168-103B6_reverse Regulation 
ThalamusAdultDonor10196_CNhs13794_ctss_fwd ThalamusAdultD10196+ thalamus - adult, donor10196_CNhs13794_10168-103B6_forward Regulation 
TestisAdultPool2_CNhs12998_ctss_rev TestisAdultPl2- testis, adult, pool2_CNhs12998_10096-102C6_reverse Regulation 
TestisAdultPool2_CNhs12998_ctss_fwd TestisAdultPl2+ testis, adult, pool2_CNhs12998_10096-102C6_forward Regulation 
TestisAdultPool1_CNhs10632_ctss_rev TestisAdultPl1- testis, adult, pool1_CNhs10632_10026-101D8_reverse Regulation 
TestisAdultPool1_CNhs10632_ctss_fwd TestisAdultPl1+ testis, adult, pool1_CNhs10632_10026-101D8_forward Regulation 
TemporalLobeFetalDonor1TechRep2_CNhs12996_ctss_rev TemporalLobeFetalD1Tr2- temporal lobe, fetal, donor1, tech_rep2_CNhs12996_10063-101H9_reverse Regulation 
TemporalLobeFetalDonor1TechRep2_CNhs12996_ctss_fwd TemporalLobeFetalD1Tr2+ temporal lobe, fetal, donor1, tech_rep2_CNhs12996_10063-101H9_forward Regulation 
TemporalLobeFetalDonor1TechRep1_CNhs11772_ctss_rev TemporalLobeFetalD1Tr1- temporal lobe, fetal, donor1, tech_rep1_CNhs11772_10063-101H9_reverse Regulation 
TemporalLobeFetalDonor1TechRep1_CNhs11772_ctss_fwd TemporalLobeFetalD1Tr1+ temporal lobe, fetal, donor1, tech_rep1_CNhs11772_10063-101H9_forward Regulation 
TemporalLobeAdultPool1_CNhs10637_ctss_rev TemporalLobeAdultPl1- temporal lobe, adult, pool1_CNhs10637_10031-101E4_reverse Regulation 
TemporalLobeAdultPool1_CNhs10637_ctss_fwd TemporalLobeAdultPl1+ temporal lobe, adult, pool1_CNhs10637_10031-101E4_forward Regulation 
SubstantiaNigraNewbornDonor10223_CNhs14076_ctss_rev SubstantiaNigraNbD10223- substantia nigra, newborn, donor10223_CNhs14076_10358-105E7_reverse Regulation 
SubstantiaNigraNewbornDonor10223_CNhs14076_ctss_fwd SubstantiaNigraNbD10223+ substantia nigra, newborn, donor10223_CNhs14076_10358-105E7_forward Regulation 
SubstantiaNigraAdultDonor10258_CNhs14224_ctss_rev SubstantiaNigraAdultD10258- substantia nigra, adult, donor10258_CNhs14224_10371-105G2_reverse Regulation 
SubstantiaNigraAdultDonor10258_CNhs14224_ctss_fwd SubstantiaNigraAdultD10258+ substantia nigra, adult, donor10258_CNhs14224_10371-105G2_forward Regulation 
SubstantiaNigraAdultDonor10252_CNhs12318_ctss_rev SubstantiaNigraAdultD10252- substantia nigra, adult, donor10252_CNhs12318_10158-103A5_reverse Regulation 
SubstantiaNigraAdultDonor10252_CNhs12318_ctss_fwd SubstantiaNigraAdultD10252+ substantia nigra, adult, donor10252_CNhs12318_10158-103A5_forward Regulation 
SubstantiaNigraAdultDonor10196_CNhs13803_ctss_rev SubstantiaNigraAdultD10196- substantia nigra - adult, donor10196_CNhs13803_10178-103C7_reverse Regulation 
SubstantiaNigraAdultDonor10196_CNhs13803_ctss_fwd SubstantiaNigraAdultD10196+ substantia nigra - adult, donor10196_CNhs13803_10178-103C7_forward Regulation 
SubmaxillaryGlandAdult_CNhs12852_ctss_rev SubmaxillaryGlandAdult- submaxillary gland, adult_CNhs12852_10202-103F4_reverse Regulation 
SubmaxillaryGlandAdult_CNhs12852_ctss_fwd SubmaxillaryGlandAdult+ submaxillary gland, adult_CNhs12852_10202-103F4_forward Regulation 
StomachFetalDonor1_CNhs11771_ctss_rev StomachFetalD1- stomach, fetal, donor1_CNhs11771_10062-101H8_reverse Regulation 
StomachFetalDonor1_CNhs11771_ctss_fwd StomachFetalD1+ stomach, fetal, donor1_CNhs11771_10062-101H8_forward Regulation 
SpleenFetalPool1_CNhs10651_ctss_rev SpleenFetalPl1- spleen, fetal, pool1_CNhs10651_10044-101F8_reverse Regulation 
SpleenFetalPool1_CNhs10651_ctss_fwd SpleenFetalPl1+ spleen, fetal, pool1_CNhs10651_10044-101F8_forward Regulation 
SpleenAdultPool1_CNhs10631_ctss_rev SpleenAdultPl1- spleen, adult, pool1_CNhs10631_10025-101D7_reverse Regulation 
SpleenAdultPool1_CNhs10631_ctss_fwd SpleenAdultPl1+ spleen, adult, pool1_CNhs10631_10025-101D7_forward Regulation 
SpinalCordNewbornDonor10223_CNhs14077_ctss_rev SpinalCordNbD10223- spinal cord, newborn, donor10223_CNhs14077_10359-105E8_reverse Regulation 
SpinalCordNewbornDonor10223_CNhs14077_ctss_fwd SpinalCordNbD10223+ spinal cord, newborn, donor10223_CNhs14077_10359-105E8_forward Regulation 
SpinalCordFetalDonor1_CNhs11764_ctss_rev SpinalCordFetalD1- spinal cord, fetal, donor1_CNhs11764_10056-101H2_reverse Regulation 
SpinalCordFetalDonor1_CNhs11764_ctss_fwd SpinalCordFetalD1+ spinal cord, fetal, donor1_CNhs11764_10056-101H2_forward Regulation 
SpinalCordAdultDonor10258_CNhs14222_ctss_rev SpinalCordAdultD10258- spinal cord, adult, donor10258_CNhs14222_10369-105F9_reverse Regulation 
SpinalCordAdultDonor10258_CNhs14222_ctss_fwd SpinalCordAdultD10258+ spinal cord, adult, donor10258_CNhs14222_10369-105F9_forward Regulation 
SpinalCordAdultDonor10252_CNhs12227_ctss_rev SpinalCordAdultD10252- spinal cord, adult, donor10252_CNhs12227_10159-103A6_reverse Regulation 
SpinalCordAdultDonor10252_CNhs12227_ctss_fwd SpinalCordAdultD10252+ spinal cord, adult, donor10252_CNhs12227_10159-103A6_forward Regulation 
SpinalCordAdultDonor10196_CNhs13807_ctss_rev SpinalCordAdultD10196- spinal cord - adult, donor10196_CNhs13807_10181-103D1_reverse Regulation 
SpinalCordAdultDonor10196_CNhs13807_ctss_fwd SpinalCordAdultD10196+ spinal cord - adult, donor10196_CNhs13807_10181-103D1_forward Regulation 
SmoothMuscleAdultPool1_CNhs11755_ctss_rev SmoothMuscleAdultPl1- smooth muscle, adult, pool1_CNhs11755_10048-101G3_reverse Regulation 
SmoothMuscleAdultPool1_CNhs11755_ctss_fwd SmoothMuscleAdultPl1+ smooth muscle, adult, pool1_CNhs11755_10048-101G3_forward Regulation 
SmallIntestineFetalDonor1_CNhs11773_ctss_rev SmallIntestineFetalD1- small intestine, fetal, donor1_CNhs11773_10064-101I1_reverse Regulation 
SmallIntestineFetalDonor1_CNhs11773_ctss_fwd SmallIntestineFetalD1+ small intestine, fetal, donor1_CNhs11773_10064-101I1_forward Regulation 
SmallIntestineAdultPool1_CNhs10630_ctss_rev SmallIntestineAdultPl1- small intestine, adult, pool1_CNhs10630_10024-101D6_reverse Regulation 
SmallIntestineAdultPool1_CNhs10630_ctss_fwd SmallIntestineAdultPl1+ small intestine, adult, pool1_CNhs10630_10024-101D6_forward Regulation 
SkinPalmDonor1_CNhs13458_ctss_rev SkinPalmD1- Skin - palm, donor1_CNhs13458_10286-104F7_reverse Regulation 
SkinPalmDonor1_CNhs13458_ctss_fwd SkinPalmD1+ Skin - palm, donor1_CNhs13458_10286-104F7_forward Regulation 
SkinFetalDonor1_CNhs11774_ctss_rev SkinFetalD1- skin, fetal, donor1_CNhs11774_10065-101I2_reverse Regulation 
SkinFetalDonor1_CNhs11774_ctss_fwd SkinFetalD1+ skin, fetal, donor1_CNhs11774_10065-101I2_forward Regulation 
SkinAdultDonor1_CNhs11785_ctss_rev SkinAdultD1- skin, adult, donor1_CNhs11785_10074-102A2_reverse Regulation 
SkinAdultDonor1_CNhs11785_ctss_fwd SkinAdultD1+ skin, adult, donor1_CNhs11785_10074-102A2_forward Regulation 
SkeletalMuscleSoleusMuscleDonor1_CNhs13454_ctss_rev SkeletalMuscleSoleusMuscleD1- skeletal muscle - soleus muscle, donor1_CNhs13454_10282-104F3_reverse Regulation 
SkeletalMuscleSoleusMuscleDonor1_CNhs13454_ctss_fwd SkeletalMuscleSoleusMuscleD1+ skeletal muscle - soleus muscle, donor1_CNhs13454_10282-104F3_forward Regulation 
SkeletalMuscleFetalDonor1_CNhs11776_ctss_rev SkeletalMuscleFetalD1- skeletal muscle, fetal, donor1_CNhs11776_10066-101I3_reverse Regulation 
SkeletalMuscleFetalDonor1_CNhs11776_ctss_fwd SkeletalMuscleFetalD1+ skeletal muscle, fetal, donor1_CNhs11776_10066-101I3_forward Regulation 
SkeletalMuscleAdultPool1_CNhs10629_ctss_rev SkeletalMuscleAdultPl1- skeletal muscle, adult, pool1_CNhs10629_10023-101D5_reverse Regulation 
SkeletalMuscleAdultPool1_CNhs10629_ctss_fwd SkeletalMuscleAdultPl1+ skeletal muscle, adult, pool1_CNhs10629_10023-101D5_forward Regulation 
SeminalVesicleAdult_CNhs12851_ctss_rev SeminalVesicleAdult- seminal vesicle, adult_CNhs12851_10201-103F3_reverse Regulation 
SeminalVesicleAdult_CNhs12851_ctss_fwd SeminalVesicleAdult+ seminal vesicle, adult_CNhs12851_10201-103F3_forward Regulation 
SalivaryGlandAdultPool1_CNhs11677_ctss_rev SalivaryGlandAdultPl1- salivary gland, adult, pool1_CNhs11677_10093-102C3_reverse Regulation 
SalivaryGlandAdultPool1_CNhs11677_ctss_fwd SalivaryGlandAdultPl1+ salivary gland, adult, pool1_CNhs11677_10093-102C3_forward Regulation 
SABiosciencesXpressRefHumanUniversalTotalRNAPool1_CNhs10610_ctss_rev SabiosciencesXpressrefUniversalPl1- SABiosciences XpressRef Human Universal Total RNA, pool1_CNhs10610_10002-101A5_reverse Regulation 
SABiosciencesXpressRefHumanUniversalTotalRNAPool1_CNhs10610_ctss_fwd SabiosciencesXpressrefUniversalPl1+ SABiosciences XpressRef Human Universal Total RNA, pool1_CNhs10610_10002-101A5_forward Regulation 
RetinaAdultPool1_CNhs10636_ctss_rev RetinaAdultPl1- retina, adult, pool1_CNhs10636_10030-101E3_reverse Regulation 
RetinaAdultPool1_CNhs10636_ctss_fwd RetinaAdultPl1+ retina, adult, pool1_CNhs10636_10030-101E3_forward Regulation 
RectumFetalDonor1_CNhs11777_ctss_rev RectumFetalD1- rectum, fetal, donor1_CNhs11777_10067-101I4_reverse Regulation 
RectumFetalDonor1_CNhs11777_ctss_fwd RectumFetalD1+ rectum, fetal, donor1_CNhs11777_10067-101I4_forward Regulation 
PutamenNewbornDonor10223_CNhs14083_ctss_rev PutamenNbD10223- putamen, newborn, donor10223_CNhs14083_10365-105F5_reverse Regulation 
PutamenNewbornDonor10223_CNhs14083_ctss_fwd PutamenNbD10223+ putamen, newborn, donor10223_CNhs14083_10365-105F5_forward Regulation 
PutamenAdultDonor10258TechRep2_CNhs14618_ctss_rev PutamenAdultD10258Tr2- putamen, adult, donor10258, tech_rep2_CNhs14618_10372-105G3_reverse Regulation 
PutamenAdultDonor10258TechRep2_CNhs14618_ctss_fwd PutamenAdultD10258Tr2+ putamen, adult, donor10258, tech_rep2_CNhs14618_10372-105G3_forward Regulation 
PutamenAdultDonor10258TechRep1_CNhs14225_ctss_rev PutamenAdultD10258Tr1- putamen, adult, donor10258, tech_rep1_CNhs14225_10372-105G3_reverse Regulation 
PutamenAdultDonor10258TechRep1_CNhs14225_ctss_fwd PutamenAdultD10258Tr1+ putamen, adult, donor10258, tech_rep1_CNhs14225_10372-105G3_forward Regulation 
PutamenAdultDonor10252_CNhs13912_ctss_rev PutamenAdultD10252- putamen, adult, donor10252_CNhs13912_10152-102I8_reverse Regulation 
PutamenAdultDonor10252_CNhs13912_ctss_fwd PutamenAdultD10252+ putamen, adult, donor10252_CNhs13912_10152-102I8_forward Regulation 
PutamenAdultDonor10196_CNhs12324_ctss_rev PutamenAdultD10196- putamen, adult, donor10196_CNhs12324_10176-103C5_reverse Regulation 
PutamenAdultDonor10196_CNhs12324_ctss_fwd PutamenAdultD10196+ putamen, adult, donor10196_CNhs12324_10176-103C5_forward Regulation 
ProstateAdultPool1_CNhs10628_ctss_rev ProstateAdultPl1- prostate, adult, pool1_CNhs10628_10022-101D4_reverse Regulation 
ProstateAdultPool1_CNhs10628_ctss_fwd ProstateAdultPl1+ prostate, adult, pool1_CNhs10628_10022-101D4_forward Regulation 
PostcentralGyrusAdultPool1_CNhs10638_ctss_rev PostcentralGyrusAdultPl1- postcentral gyrus, adult, pool1_CNhs10638_10032-101E5_reverse Regulation 
PostcentralGyrusAdultPool1_CNhs10638_ctss_fwd PostcentralGyrusAdultPl1+ postcentral gyrus, adult, pool1_CNhs10638_10032-101E5_forward Regulation 
PonsAdultPool1_CNhs10640_ctss_rev PonsAdultPl1- pons, adult, pool1_CNhs10640_10033-101E6_reverse Regulation 
PonsAdultPool1_CNhs10640_ctss_fwd PonsAdultPl1+ pons, adult, pool1_CNhs10640_10033-101E6_forward Regulation 
PlacentaAdultPool1_CNhs10627_ctss_rev PlacentaAdultPl1- placenta, adult, pool1_CNhs10627_10021-101D3_reverse Regulation 
PlacentaAdultPool1_CNhs10627_ctss_fwd PlacentaAdultPl1+ placenta, adult, pool1_CNhs10627_10021-101D3_forward Regulation 
PituitaryGlandAdultDonor10258_CNhs14231_ctss_rev PituitaryGlandAdultD10258- pituitary gland, adult, donor10258_CNhs14231_10378-105G9_reverse Regulation 
PituitaryGlandAdultDonor10258_CNhs14231_ctss_fwd PituitaryGlandAdultD10258+ pituitary gland, adult, donor10258_CNhs14231_10378-105G9_forward Regulation 
PituitaryGlandAdultDonor10252_CNhs12229_ctss_rev PituitaryGlandAdultD10252- pituitary gland, adult, donor10252_CNhs12229_10162-103A9_reverse Regulation 
PituitaryGlandAdultDonor10252_CNhs12229_ctss_fwd PituitaryGlandAdultD10252+ pituitary gland, adult, donor10252_CNhs12229_10162-103A9_forward Regulation 
PituitaryGlandAdultDonor10196_CNhs13805_ctss_rev PituitaryGlandAdultD10196- pituitary gland - adult, donor10196_CNhs13805_10180-103C9_reverse Regulation 
PituitaryGlandAdultDonor10196_CNhs13805_ctss_fwd PituitaryGlandAdultD10196+ pituitary gland - adult, donor10196_CNhs13805_10180-103C9_forward Regulation 
PinealGlandAdultDonor10258_CNhs14230_ctss_rev PinealGlandAdultD10258- pineal gland, adult, donor10258_CNhs14230_10377-105G8_reverse Regulation 
PinealGlandAdultDonor10258_CNhs14230_ctss_fwd PinealGlandAdultD10258+ pineal gland, adult, donor10258_CNhs14230_10377-105G8_forward Regulation 
PinealGlandAdultDonor10252_CNhs12228_ctss_rev PinealGlandAdultD10252- pineal gland, adult, donor10252_CNhs12228_10160-103A7_reverse Regulation 
PinealGlandAdultDonor10252_CNhs12228_ctss_fwd PinealGlandAdultD10252+ pineal gland, adult, donor10252_CNhs12228_10160-103A7_forward Regulation 
PinealGlandAdultDonor10196_CNhs13804_ctss_rev PinealGlandAdultD10196- pineal gland - adult, donor10196_CNhs13804_10179-103C8_reverse Regulation 
PinealGlandAdultDonor10196_CNhs13804_ctss_fwd PinealGlandAdultD10196+ pineal gland - adult, donor10196_CNhs13804_10179-103C8_forward Regulation 
PenisAdult_CNhs12850_ctss_rev PenisAdult- penis, adult_CNhs12850_10200-103F2_reverse Regulation 
PenisAdult_CNhs12850_ctss_fwd PenisAdult+ penis, adult_CNhs12850_10200-103F2_forward Regulation 
ParotidGlandAdult_CNhs12849_ctss_rev ParotidGlandAdult- parotid gland, adult_CNhs12849_10199-103F1_reverse Regulation 
ParotidGlandAdult_CNhs12849_ctss_fwd ParotidGlandAdult+ parotid gland, adult_CNhs12849_10199-103F1_forward Regulation 
ParietalLobeNewbornDonor10223_CNhs14074_ctss_rev ParietalLobeNbD10223- parietal lobe, newborn, donor10223_CNhs14074_10356-105E5_reverse Regulation 
ParietalLobeNewbornDonor10223_CNhs14074_ctss_fwd ParietalLobeNbD10223+ parietal lobe, newborn, donor10223_CNhs14074_10356-105E5_forward Regulation 
ParietalLobeFetalDonor1_CNhs11782_ctss_rev ParietalLobeFetalD1- parietal lobe, fetal, donor1_CNhs11782_10072-101I9_reverse Regulation 
ParietalLobeFetalDonor1_CNhs11782_ctss_fwd ParietalLobeFetalD1+ parietal lobe, fetal, donor1_CNhs11782_10072-101I9_forward Regulation 
ParietalLobeAdultPool1_CNhs10641_ctss_rev ParietalLobeAdultPl1- parietal lobe, adult, pool1_CNhs10641_10034-101E7_reverse Regulation 
ParietalLobeAdultPool1_CNhs10641_ctss_fwd ParietalLobeAdultPl1+ parietal lobe, adult, pool1_CNhs10641_10034-101E7_forward Regulation 
ParietalLobeAdultDonor10252_CNhs12317_ctss_rev ParietalLobeAdultD10252- parietal lobe, adult, donor10252_CNhs12317_10157-103A4_reverse Regulation 
ParietalLobeAdultDonor10252_CNhs12317_ctss_fwd ParietalLobeAdultD10252+ parietal lobe, adult, donor10252_CNhs12317_10157-103A4_forward Regulation 
ParietalLobeAdultDonor10196_CNhs13797_ctss_rev ParietalLobeAdultD10196- parietal lobe - adult, donor10196_CNhs13797_10171-103B9_reverse Regulation 
ParietalLobeAdultDonor10196_CNhs13797_ctss_fwd ParietalLobeAdultD10196+ parietal lobe - adult, donor10196_CNhs13797_10171-103B9_forward Regulation 
ParietalCortexAdultDonor10258_CNhs14226_ctss_rev ParietalCortexAdultD10258- parietal cortex, adult, donor10258_CNhs14226_10373-105G4_reverse Regulation 
ParietalCortexAdultDonor10258_CNhs14226_ctss_fwd ParietalCortexAdultD10258+ parietal cortex, adult, donor10258_CNhs14226_10373-105G4_forward Regulation 
ParacentralGyrusAdultPool1_CNhs10642_ctss_rev ParacentralGyrusAdultPl1- paracentral gyrus, adult, pool1_CNhs10642_10035-101E8_reverse Regulation 
ParacentralGyrusAdultPool1_CNhs10642_ctss_fwd ParacentralGyrusAdultPl1+ paracentral gyrus, adult, pool1_CNhs10642_10035-101E8_forward Regulation 
PancreasAdultDonor1_CNhs11756_ctss_rev PancreasAdultD1- pancreas, adult, donor1_CNhs11756_10049-101G4_reverse Regulation 
PancreasAdultDonor1_CNhs11756_ctss_fwd PancreasAdultD1+ pancreas, adult, donor1_CNhs11756_10049-101G4_forward Regulation 
OvaryAdultPool1_CNhs10626_ctss_rev OvaryAdultPl1- ovary, adult, pool1_CNhs10626_10020-101D2_reverse Regulation 
OvaryAdultPool1_CNhs10626_ctss_fwd OvaryAdultPl1+ ovary, adult, pool1_CNhs10626_10020-101D2_forward Regulation 
OpticNerveDonor1_CNhs13449_ctss_rev OpticNerveD1- optic nerve, donor1_CNhs13449_10277-104E7_reverse Regulation 
OpticNerveDonor1_CNhs13449_ctss_fwd OpticNerveD1+ optic nerve, donor1_CNhs13449_10277-104E7_forward Regulation 
OlfactoryRegionAdult_CNhs12611_ctss_rev OlfactoryRegionAdult- olfactory region, adult_CNhs12611_10195-103E6_reverse Regulation 
OlfactoryRegionAdult_CNhs12611_ctss_fwd OlfactoryRegionAdult+ olfactory region, adult_CNhs12611_10195-103E6_forward Regulation 
OccipitalPoleAdultPool1_CNhs10643_ctss_rev OccipitalPoleAdultPl1- occipital pole, adult, pool1_CNhs10643_10036-101E9_reverse Regulation 
OccipitalPoleAdultPool1_CNhs10643_ctss_fwd OccipitalPoleAdultPl1+ occipital pole, adult, pool1_CNhs10643_10036-101E9_forward Regulation 
OccipitalLobeFetalDonor1_CNhs11784_ctss_rev OccipitalLobeFetalD1- occipital lobe, fetal, donor1_CNhs11784_10073-102A1_reverse Regulation 
OccipitalLobeFetalDonor1_CNhs11784_ctss_fwd OccipitalLobeFetalD1+ occipital lobe, fetal, donor1_CNhs11784_10073-102A1_forward Regulation 
OccipitalLobeAdultDonor1_CNhs11787_ctss_rev OccipitalLobeAdultD1- occipital lobe, adult, donor1_CNhs11787_10076-102A4_reverse Regulation 
OccipitalLobeAdultDonor1_CNhs11787_ctss_fwd OccipitalLobeAdultD1+ occipital lobe, adult, donor1_CNhs11787_10076-102A4_forward Regulation 
OccipitalCortexNewbornDonor10223_CNhs14073_ctss_rev OccipitalCortexNbD10223- occipital cortex, newborn, donor10223_CNhs14073_10355-105E4_reverse Regulation 
OccipitalCortexNewbornDonor10223_CNhs14073_ctss_fwd OccipitalCortexNbD10223+ occipital cortex, newborn, donor10223_CNhs14073_10355-105E4_forward Regulation 
OccipitalCortexAdultDonor10252_CNhs12320_ctss_rev OccipitalCortexAdultD10252- occipital cortex, adult, donor10252_CNhs12320_10163-103B1_reverse Regulation 
OccipitalCortexAdultDonor10252_CNhs12320_ctss_fwd OccipitalCortexAdultD10252+ occipital cortex, adult, donor10252_CNhs12320_10163-103B1_forward Regulation 
OccipitalCortexAdultDonor10196_CNhs13798_ctss_rev OccipitalCortexAdultD10196- occipital cortex - adult, donor10196_CNhs13798_10172-103C1_reverse Regulation 
OccipitalCortexAdultDonor10196_CNhs13798_ctss_fwd OccipitalCortexAdultD10196+ occipital cortex - adult, donor10196_CNhs13798_10172-103C1_forward Regulation 
NucleusAccumbensAdultPool1_CNhs10644_ctss_rev NucleusAccumbensAdultPl1- nucleus accumbens, adult, pool1_CNhs10644_10037-101F1_reverse Regulation 
NucleusAccumbensAdultPool1_CNhs10644_ctss_fwd NucleusAccumbensAdultPl1+ nucleus accumbens, adult, pool1_CNhs10644_10037-101F1_forward Regulation 
MedullaOblongataNewbornDonor10223_CNhs14079_ctss_rev MedullaOblongataNbD10223- medulla oblongata, newborn, donor10223_CNhs14079_10361-105F1_reverse Regulation 
MedullaOblongataNewbornDonor10223_CNhs14079_ctss_fwd MedullaOblongataNbD10223+ medulla oblongata, newborn, donor10223_CNhs14079_10361-105F1_forward Regulation 
MedullaOblongataAdultPool1_CNhs10645_ctss_rev MedullaOblongataAdultPl1- medulla oblongata, adult, pool1_CNhs10645_10038-101F2_reverse Regulation 
MedullaOblongataAdultPool1_CNhs10645_ctss_fwd MedullaOblongataAdultPl1+ medulla oblongata, adult, pool1_CNhs10645_10038-101F2_forward Regulation 
MedullaOblongataAdultDonor10252_CNhs12315_ctss_rev MedullaOblongataAdultD10252- medulla oblongata, adult, donor10252_CNhs12315_10155-103A2_reverse Regulation 
MedullaOblongataAdultDonor10252_CNhs12315_ctss_fwd MedullaOblongataAdultD10252+ medulla oblongata, adult, donor10252_CNhs12315_10155-103A2_forward Regulation 
MedullaOblongataAdultDonor10196_CNhs13800_ctss_rev MedullaOblongataAdultD10196- medulla oblongata - adult, donor10196_CNhs13800_10174-103C3_reverse Regulation 
MedullaOblongataAdultDonor10196_CNhs13800_ctss_fwd MedullaOblongataAdultD10196+ medulla oblongata - adult, donor10196_CNhs13800_10174-103C3_forward Regulation 
MedialTemporalGyrusNewbornDonor10223_CNhs14070_ctss_rev MedialTemporalGyrusNbD10223- medial temporal gyrus, newborn, donor10223_CNhs14070_10353-105E2_reverse Regulation 
MedialTemporalGyrusNewbornDonor10223_CNhs14070_ctss_fwd MedialTemporalGyrusNbD10223+ medial temporal gyrus, newborn, donor10223_CNhs14070_10353-105E2_forward Regulation 
MedialTemporalGyrusAdultDonor10258TechRep2_CNhs14552_ctss_rev MedialTemporalGyrusAdultD10258Tr2- medial temporal gyrus, adult, donor10258, tech_rep2_CNhs14552_10376-105G7_reverse Regulation 
MedialTemporalGyrusAdultDonor10258TechRep2_CNhs14552_ctss_fwd MedialTemporalGyrusAdultD10258Tr2+ medial temporal gyrus, adult, donor10258, tech_rep2_CNhs14552_10376-105G7_forward Regulation 
MedialTemporalGyrusAdultDonor10258TechRep1_CNhs14229_ctss_rev MedialTemporalGyrusAdultD10258Tr1- medial temporal gyrus, adult, donor10258, tech_rep1_CNhs14229_10376-105G7_reverse Regulation 
MedialTemporalGyrusAdultDonor10258TechRep1_CNhs14229_ctss_fwd MedialTemporalGyrusAdultD10258Tr1+ medial temporal gyrus, adult, donor10258, tech_rep1_CNhs14229_10376-105G7_forward Regulation 
MedialTemporalGyrusAdultDonor10252_CNhs12316_ctss_rev MedialTemporalGyrusAdultD10252- medial temporal gyrus, adult, donor10252_CNhs12316_10156-103A3_reverse Regulation 
MedialTemporalGyrusAdultDonor10252_CNhs12316_ctss_fwd MedialTemporalGyrusAdultD10252+ medial temporal gyrus, adult, donor10252_CNhs12316_10156-103A3_forward Regulation 
MedialTemporalGyrusAdultDonor10196_CNhs13809_ctss_rev MedialTemporalGyrusAdultD10196- medial temporal gyrus - adult, donor10196_CNhs13809_10183-103D3_reverse Regulation 
MedialTemporalGyrusAdultDonor10196_CNhs13809_ctss_fwd MedialTemporalGyrusAdultD10196+ medial temporal gyrus - adult, donor10196_CNhs13809_10183-103D3_forward Regulation 
MedialFrontalGyrusNewbornDonor10223_CNhs14069_ctss_rev MedialFrontalGyrusNbD10223- medial frontal gyrus, newborn, donor10223_CNhs14069_10352-105E1_reverse Regulation 
MedialFrontalGyrusNewbornDonor10223_CNhs14069_ctss_fwd MedialFrontalGyrusNbD10223+ medial frontal gyrus, newborn, donor10223_CNhs14069_10352-105E1_forward Regulation 
MedialFrontalGyrusAdultDonor10258_CNhs14221_ctss_rev MedialFrontalGyrusAdultD10258- medial frontal gyrus, adult, donor10258_CNhs14221_10368-105F8_reverse Regulation 
MedialFrontalGyrusAdultDonor10258_CNhs14221_ctss_fwd MedialFrontalGyrusAdultD10258+ medial frontal gyrus, adult, donor10258_CNhs14221_10368-105F8_forward Regulation 
MedialFrontalGyrusAdultDonor10252_CNhs12310_ctss_rev MedialFrontalGyrusAdultD10252- medial frontal gyrus, adult, donor10252_CNhs12310_10150-102I6_reverse Regulation 
MedialFrontalGyrusAdultDonor10252_CNhs12310_ctss_fwd MedialFrontalGyrusAdultD10252+ medial frontal gyrus, adult, donor10252_CNhs12310_10150-102I6_forward Regulation 
MedialFrontalGyrusAdultDonor10196_CNhs13796_ctss_rev MedialFrontalGyrusAdultD10196- medial frontal gyrus - adult, donor10196_CNhs13796_10170-103B8_reverse Regulation 
MedialFrontalGyrusAdultDonor10196_CNhs13796_ctss_fwd MedialFrontalGyrusAdultD10196+ medial frontal gyrus - adult, donor10196_CNhs13796_10170-103B8_forward Regulation 
LymphNodeAdultDonor1_CNhs11788_ctss_rev LymphNodeAdultD1- lymph node, adult, donor1_CNhs11788_10077-102A5_reverse Regulation 
LymphNodeAdultDonor1_CNhs11788_ctss_fwd LymphNodeAdultD1+ lymph node, adult, donor1_CNhs11788_10077-102A5_forward Regulation 
LungRightLowerLobeAdultDonor1_CNhs11786_ctss_rev LungRightLowerLobeAdultD1- lung, right lower lobe, adult, donor1_CNhs11786_10075-102A3_reverse Regulation 
LungRightLowerLobeAdultDonor1_CNhs11786_ctss_fwd LungRightLowerLobeAdultD1+ lung, right lower lobe, adult, donor1_CNhs11786_10075-102A3_forward Regulation 
LungFetalDonor1_CNhs11680_ctss_rev LungFetalD1- lung, fetal, donor1_CNhs11680_10068-101I5_reverse Regulation 
LungFetalDonor1_CNhs11680_ctss_fwd LungFetalD1+ lung, fetal, donor1_CNhs11680_10068-101I5_forward Regulation 
LungAdultPool1_CNhs10625_ctss_rev LungAdultPl1- lung, adult, pool1_CNhs10625_10019-101D1_reverse Regulation 
LungAdultPool1_CNhs10625_ctss_fwd LungAdultPl1+ lung, adult, pool1_CNhs10625_10019-101D1_forward Regulation 
LocusCoeruleusNewbornDonor10223_CNhs14080_ctss_rev LocusCoeruleusNbD10223- locus coeruleus, newborn, donor10223_CNhs14080_10362-105F2_reverse Regulation 
LocusCoeruleusNewbornDonor10223_CNhs14080_ctss_fwd LocusCoeruleusNbD10223+ locus coeruleus, newborn, donor10223_CNhs14080_10362-105F2_forward Regulation 
LocusCoeruleusAdultDonor10258_CNhs14550_ctss_rev LocusCoeruleusAdultD10258- locus coeruleus, adult, donor10258_CNhs14550_10375-105G6_reverse Regulation 
LocusCoeruleusAdultDonor10258_CNhs14550_ctss_fwd LocusCoeruleusAdultD10258+ locus coeruleus, adult, donor10258_CNhs14550_10375-105G6_forward Regulation 
LocusCoeruleusAdultDonor10252_CNhs12322_ctss_rev LocusCoeruleusAdultD10252- locus coeruleus, adult, donor10252_CNhs12322_10165-103B3_reverse Regulation 
LocusCoeruleusAdultDonor10252_CNhs12322_ctss_fwd LocusCoeruleusAdultD10252+ locus coeruleus, adult, donor10252_CNhs12322_10165-103B3_forward Regulation 
LocusCoeruleusAdultDonor10196_CNhs13808_ctss_rev LocusCoeruleusAdultD10196- locus coeruleus - adult, donor10196_CNhs13808_10182-103D2_reverse Regulation 
LocusCoeruleusAdultDonor10196_CNhs13808_ctss_fwd LocusCoeruleusAdultD10196+ locus coeruleus - adult, donor10196_CNhs13808_10182-103D2_forward Regulation 
LiverFetalPool1_CNhs11798_ctss_rev LiverFetalPl1- liver, fetal, pool1_CNhs11798_10086-102B5_reverse Regulation 
LiverFetalPool1_CNhs11798_ctss_fwd LiverFetalPl1+ liver, fetal, pool1_CNhs11798_10086-102B5_forward Regulation 
LiverAdultPool1_CNhs10624_ctss_rev LiverAdultPl1- liver, adult, pool1_CNhs10624_10018-101C9_reverse Regulation 
LiverAdultPool1_CNhs10624_ctss_fwd LiverAdultPl1+ liver, adult, pool1_CNhs10624_10018-101C9_forward Regulation 
LeftVentricleAdultDonor1_CNhs11789_ctss_rev LeftVentricleAdultD1- left ventricle, adult, donor1_CNhs11789_10078-102A6_reverse Regulation 
LeftVentricleAdultDonor1_CNhs11789_ctss_fwd LeftVentricleAdultD1+ left ventricle, adult, donor1_CNhs11789_10078-102A6_forward Regulation 
LeftAtriumAdultDonor1_CNhs11790_ctss_rev LeftAtriumAdultD1- left atrium, adult, donor1_CNhs11790_10079-102A7_reverse Regulation 
LeftAtriumAdultDonor1_CNhs11790_ctss_fwd LeftAtriumAdultD1+ left atrium, adult, donor1_CNhs11790_10079-102A7_forward Regulation 
KidneyFetalPool1_CNhs10652_ctss_rev KidneyFetalPl1- kidney, fetal, pool1_CNhs10652_10045-101F9_reverse Regulation 
KidneyFetalPool1_CNhs10652_ctss_fwd KidneyFetalPl1+ kidney, fetal, pool1_CNhs10652_10045-101F9_forward Regulation 
KidneyAdultPool1_CNhs10622_ctss_rev KidneyAdultPl1- kidney, adult, pool1_CNhs10622_10017-101C8_reverse Regulation 
KidneyAdultPool1_CNhs10622_ctss_fwd KidneyAdultPl1+ kidney, adult, pool1_CNhs10622_10017-101C8_forward Regulation 
InsulaAdultPool1_CNhs10646_ctss_rev InsulaAdultPl1- insula, adult, pool1_CNhs10646_10039-101F3_reverse Regulation 
InsulaAdultPool1_CNhs10646_ctss_fwd InsulaAdultPl1+ insula, adult, pool1_CNhs10646_10039-101F3_forward Regulation 
HippocampusNewbornDonor10223_CNhs14081_ctss_rev HippocampusNbD10223- hippocampus, newborn, donor10223_CNhs14081_10363-105F3_reverse Regulation 
HippocampusNewbornDonor10223_CNhs14081_ctss_fwd HippocampusNbD10223+ hippocampus, newborn, donor10223_CNhs14081_10363-105F3_forward Regulation 
HippocampusAdultDonor10258_CNhs14227_ctss_rev HippocampusAdultD10258- hippocampus, adult, donor10258_CNhs14227_10374-105G5_reverse Regulation 
HippocampusAdultDonor10258_CNhs14227_ctss_fwd HippocampusAdultD10258+ hippocampus, adult, donor10258_CNhs14227_10374-105G5_forward Regulation 
HippocampusAdultDonor10252_CNhs12312_ctss_rev HippocampusAdultD10252- hippocampus, adult, donor10252_CNhs12312_10153-102I9_reverse Regulation 
HippocampusAdultDonor10252_CNhs12312_ctss_fwd HippocampusAdultD10252+ hippocampus, adult, donor10252_CNhs12312_10153-102I9_forward Regulation 
HippocampusAdultDonor10196_CNhs13795_ctss_rev HippocampusAdultD10196- hippocampus - adult, donor10196_CNhs13795_10169-103B7_reverse Regulation 
HippocampusAdultDonor10196_CNhs13795_ctss_fwd HippocampusAdultD10196+ hippocampus - adult, donor10196_CNhs13795_10169-103B7_forward Regulation 
HeartTricuspidValveAdult_CNhs12857_ctss_rev HeartTricuspidValveAdult- heart - tricuspid valve, adult_CNhs12857_10207-103F9_reverse Regulation 
HeartTricuspidValveAdult_CNhs12857_ctss_fwd HeartTricuspidValveAdult+ heart - tricuspid valve, adult_CNhs12857_10207-103F9_forward Regulation 
HeartPulmonicValveAdult_CNhs12856_ctss_rev HeartPulmonicValveAdult- heart - pulmonic valve, adult_CNhs12856_10206-103F8_reverse Regulation 
HeartPulmonicValveAdult_CNhs12856_ctss_fwd HeartPulmonicValveAdult+ heart - pulmonic valve, adult_CNhs12856_10206-103F8_forward Regulation 
HeartMitralValveAdult_CNhs12855_ctss_rev HeartMitralValveAdult- heart - mitral valve, adult_CNhs12855_10205-103F7_reverse Regulation 
HeartMitralValveAdult_CNhs12855_ctss_fwd HeartMitralValveAdult+ heart - mitral valve, adult_CNhs12855_10205-103F7_forward Regulation 
HeartFetalPool1_CNhs10653_ctss_rev HeartFetalPl1- heart, fetal, pool1_CNhs10653_10046-101G1_reverse Regulation 
HeartFetalPool1_CNhs10653_ctss_fwd HeartFetalPl1+ heart, fetal, pool1_CNhs10653_10046-101G1_forward Regulation 
HeartAdultPool1_CNhs10621_ctss_rev HeartAdultPl1- heart, adult, pool1_CNhs10621_10016-101C7_reverse Regulation 
HeartAdultPool1_CNhs10621_ctss_fwd HeartAdultPl1+ heart, adult, pool1_CNhs10621_10016-101C7_forward Regulation 
HeartAdultDiseasedPostinfarctionDonor1_CNhs11757_ctss_rev HeartAdultDiseasedPost-infarctionD1- heart, adult, diseased post-infarction, donor1_CNhs11757_10050-101G5_reverse Regulation 
HeartAdultDiseasedPostinfarctionDonor1_CNhs11757_ctss_fwd HeartAdultDiseasedPost-infarctionD1+ heart, adult, diseased post-infarction, donor1_CNhs11757_10050-101G5_forward Regulation 
HeartAdultDiseasedDonor1_CNhs11758_ctss_rev HeartAdultDiseasedD1- heart, adult, diseased, donor1_CNhs11758_10051-101G6_reverse Regulation 
HeartAdultDiseasedDonor1_CNhs11758_ctss_fwd HeartAdultDiseasedD1+ heart, adult, diseased, donor1_CNhs11758_10051-101G6_forward Regulation 
GlobusPallidusNewbornDonor10223_CNhs14082_ctss_rev GlobusPallidusNbD10223- globus pallidus, newborn, donor10223_CNhs14082_10364-105F4_reverse Regulation 
GlobusPallidusNewbornDonor10223_CNhs14082_ctss_fwd GlobusPallidusNbD10223+ globus pallidus, newborn, donor10223_CNhs14082_10364-105F4_forward Regulation 
GlobusPallidusAdultDonor10258_CNhs14549_ctss_rev GlobusPallidusAdultD10258- globus pallidus, adult, donor10258_CNhs14549_10367-105F7_reverse Regulation 
GlobusPallidusAdultDonor10258_CNhs14549_ctss_fwd GlobusPallidusAdultD10258+ globus pallidus, adult, donor10258_CNhs14549_10367-105F7_forward Regulation 
GlobusPallidusAdultDonor10252_CNhs12319_ctss_rev GlobusPallidusAdultD10252- globus pallidus, adult, donor10252_CNhs12319_10161-103A8_reverse Regulation 
GlobusPallidusAdultDonor10252_CNhs12319_ctss_fwd GlobusPallidusAdultD10252+ globus pallidus, adult, donor10252_CNhs12319_10161-103A8_forward Regulation 
GlobusPallidusAdultDonor10196_CNhs13801_ctss_rev GlobusPallidusAdultD10196- globus pallidus - adult, donor10196_CNhs13801_10175-103C4_reverse Regulation 
GlobusPallidusAdultDonor10196_CNhs13801_ctss_fwd GlobusPallidusAdultD10196+ globus pallidus - adult, donor10196_CNhs13801_10175-103C4_forward Regulation 
GallBladderAdult_CNhs12848_ctss_rev GallBladderAdult- gall bladder, adult_CNhs12848_10198-103E9_reverse Regulation 
GallBladderAdult_CNhs12848_ctss_fwd GallBladderAdult+ gall bladder, adult_CNhs12848_10198-103E9_forward Regulation 
FrontalLobeAdultPool1_CNhs10647_ctss_rev FrontalLobeAdultPl1- frontal lobe, adult, pool1_CNhs10647_10040-101F4_reverse Regulation 
FrontalLobeAdultPool1_CNhs10647_ctss_fwd FrontalLobeAdultPl1+ frontal lobe, adult, pool1_CNhs10647_10040-101F4_forward Regulation 
FingernailIncludingNailPlateEponychiumAndHyponychiumDonor2_CNhs13445_ctss_rev FingernailD2- Fingernail (including nail plate, eponychium and hyponychium), donor2_CNhs13445_10301-104H4_reverse Regulation 
FingernailIncludingNailPlateEponychiumAndHyponychiumDonor2_CNhs13445_ctss_fwd FingernailD2+ Fingernail (including nail plate, eponychium and hyponychium), donor2_CNhs13445_10301-104H4_forward Regulation 
EyeVitreousHumorDonor1_CNhs13440_ctss_rev EyeVitreousHumorD1- eye - vitreous humor, donor1_CNhs13440_10268-104D7_reverse Regulation 
EyeVitreousHumorDonor1_CNhs13440_ctss_fwd EyeVitreousHumorD1+ eye - vitreous humor, donor1_CNhs13440_10268-104D7_forward Regulation 
EyeMuscleSuperiorDonor2_CNhs13441_ctss_rev EyeMuscleSuperiorD2- eye - muscle superior, donor2_CNhs13441_10297-104G9_reverse Regulation 
EyeMuscleSuperiorDonor2_CNhs13441_ctss_fwd EyeMuscleSuperiorD2+ eye - muscle superior, donor2_CNhs13441_10297-104G9_forward Regulation 
EyeMuscleMedialDonor2_CNhs13443_ctss_rev EyeMuscleMedialD2- eye - muscle medial, donor2_CNhs13443_10299-104H2_reverse Regulation 
EyeMuscleMedialDonor2_CNhs13443_ctss_fwd EyeMuscleMedialD2+ eye - muscle medial, donor2_CNhs13443_10299-104H2_forward Regulation 
EyeMuscleLateralDonor2_CNhs13442_ctss_rev EyeMuscleLateralD2- eye - muscle lateral, donor2_CNhs13442_10298-104H1_reverse Regulation 
EyeMuscleLateralDonor2_CNhs13442_ctss_fwd EyeMuscleLateralD2+ eye - muscle lateral, donor2_CNhs13442_10298-104H1_forward Regulation 
EyeMuscleInferiorRectusDonor1_CNhs13444_ctss_rev EyeMuscleInferiorRectusD1- eye - muscle inferior rectus, donor1_CNhs13444_10272-104E2_reverse Regulation 
EyeMuscleInferiorRectusDonor1_CNhs13444_ctss_fwd EyeMuscleInferiorRectusD1+ eye - muscle inferior rectus, donor1_CNhs13444_10272-104E2_forward Regulation 
EyeFetalDonor1_CNhs11762_ctss_rev EyeFetalD1- eye, fetal, donor1_CNhs11762_10054-101G9_reverse Regulation 
EyeFetalDonor1_CNhs11762_ctss_fwd EyeFetalD1+ eye, fetal, donor1_CNhs11762_10054-101G9_forward Regulation 
EsophagusAdultPool1_CNhs10620_ctss_rev EsophagusAdultPl1- esophagus, adult, pool1_CNhs10620_10015-101C6_reverse Regulation 
EsophagusAdultPool1_CNhs10620_ctss_fwd EsophagusAdultPl1+ esophagus, adult, pool1_CNhs10620_10015-101C6_forward Regulation 
EpididymisAdult_CNhs12847_ctss_rev EpididymisAdult- epididymis, adult_CNhs12847_10197-103E8_reverse Regulation 
EpididymisAdult_CNhs12847_ctss_fwd EpididymisAdult+ epididymis, adult_CNhs12847_10197-103E8_forward Regulation 
DuraMaterAdultDonor1_CNhs10648_ctss_rev DuraMaterAdultD1- dura mater, adult, donor1_CNhs10648_10041-101F5_reverse Regulation 
DuraMaterAdultDonor1_CNhs10648_ctss_fwd DuraMaterAdultD1+ dura mater, adult, donor1_CNhs10648_10041-101F5_forward Regulation 
DuodenumFetalDonor1TechRep2_CNhs12997_ctss_rev DuodenumFetalD1Tr2- duodenum, fetal, donor1, tech_rep2_CNhs12997_10071-101I8_reverse Regulation 
DuodenumFetalDonor1TechRep2_CNhs12997_ctss_fwd DuodenumFetalD1Tr2+ duodenum, fetal, donor1, tech_rep2_CNhs12997_10071-101I8_forward Regulation 
DuodenumFetalDonor1TechRep1_CNhs11781_ctss_rev DuodenumFetalD1Tr1- duodenum, fetal, donor1, tech_rep1_CNhs11781_10071-101I8_reverse Regulation 
DuodenumFetalDonor1TechRep1_CNhs11781_ctss_fwd DuodenumFetalD1Tr1+ duodenum, fetal, donor1, tech_rep1_CNhs11781_10071-101I8_forward Regulation 
DuctusDeferensAdult_CNhs12846_ctss_rev DuctusDeferensAdult- ductus deferens, adult_CNhs12846_10196-103E7_reverse Regulation 
DuctusDeferensAdult_CNhs12846_ctss_fwd DuctusDeferensAdult+ ductus deferens, adult_CNhs12846_10196-103E7_forward Regulation 
DiencephalonAdult_CNhs12610_ctss_rev DiencephalonAdult- diencephalon, adult_CNhs12610_10193-103E4_reverse Regulation 
DiencephalonAdult_CNhs12610_ctss_fwd DiencephalonAdult+ diencephalon, adult_CNhs12610_10193-103E4_forward Regulation 
DiaphragmFetalDonor1_CNhs11779_ctss_rev DiaphragmFetalD1- diaphragm, fetal, donor1_CNhs11779_10069-101I6_reverse Regulation 
DiaphragmFetalDonor1_CNhs11779_ctss_fwd DiaphragmFetalD1+ diaphragm, fetal, donor1_CNhs11779_10069-101I6_forward Regulation 
CruciateLigamentDonor2_CNhs13439_ctss_rev CruciateLigamentD2- cruciate ligament, donor2_CNhs13439_10295-104G7_reverse Regulation 
CruciateLigamentDonor2_CNhs13439_ctss_fwd CruciateLigamentD2+ cruciate ligament, donor2_CNhs13439_10295-104G7_forward Regulation 
CorpusCallosumAdultPool1_CNhs10649_ctss_rev CorpusCallosumAdultPl1- corpus callosum, adult, pool1_CNhs10649_10042-101F6_reverse Regulation 
CorpusCallosumAdultPool1_CNhs10649_ctss_fwd CorpusCallosumAdultPl1+ corpus callosum, adult, pool1_CNhs10649_10042-101F6_forward Regulation 
ColonFetalDonor1_CNhs11780_ctss_rev ColonFetalD1- colon, fetal, donor1_CNhs11780_10070-101I7_reverse Regulation 
ColonFetalDonor1_CNhs11780_ctss_fwd ColonFetalD1+ colon, fetal, donor1_CNhs11780_10070-101I7_forward Regulation 
ColonAdultPool1_CNhs10619_ctss_rev ColonAdultPl1- colon, adult, pool1_CNhs10619_10014-101C5_reverse Regulation 
ColonAdultPool1_CNhs10619_ctss_fwd ColonAdultPl1+ colon, adult, pool1_CNhs10619_10014-101C5_forward Regulation 
ColonAdultDonor1_CNhs11794_ctss_rev ColonAdultD1- colon, adult, donor1_CNhs11794_10082-102B1_reverse Regulation 
ColonAdultDonor1_CNhs11794_ctss_fwd ColonAdultD1+ colon, adult, donor1_CNhs11794_10082-102B1_forward Regulation 
ClontechHumanUniversalReferenceTotalRNAPool1_CNhs10608_ctss_rev ClontechUniversalReferencePl1- Clontech Human Universal Reference Total RNA, pool1_CNhs10608_10000-101A1_reverse Regulation 
ClontechHumanUniversalReferenceTotalRNAPool1_CNhs10608_ctss_fwd ClontechUniversalReferencePl1+ Clontech Human Universal Reference Total RNA, pool1_CNhs10608_10000-101A1_forward Regulation 
CervixAdultPool1_CNhs10618_ctss_rev CervixAdultPl1- cervix, adult, pool1_CNhs10618_10013-101C4_reverse Regulation 
CervixAdultPool1_CNhs10618_ctss_fwd CervixAdultPl1+ cervix, adult, pool1_CNhs10618_10013-101C4_forward Regulation 
CerebrospinalFluidDonor2_CNhs13437_ctss_rev CerebrospinalFluidD2- cerebrospinal fluid, donor2_CNhs13437_10294-104G6_reverse Regulation 
CerebrospinalFluidDonor2_CNhs13437_ctss_fwd CerebrospinalFluidD2+ cerebrospinal fluid, donor2_CNhs13437_10294-104G6_forward Regulation 
CerebralMeningesAdult_CNhs12840_ctss_rev CerebralMeningesAdult- cerebral meninges, adult_CNhs12840_10188-103D8_reverse Regulation 
CerebralMeningesAdult_CNhs12840_ctss_fwd CerebralMeningesAdult+ cerebral meninges, adult_CNhs12840_10188-103D8_forward Regulation 
CerebellumNewbornDonor10223_CNhs14075_ctss_rev CerebellumNbD10223- cerebellum, newborn, donor10223_CNhs14075_10357-105E6_reverse Regulation 
CerebellumNewbornDonor10223_CNhs14075_ctss_fwd CerebellumNbD10223+ cerebellum, newborn, donor10223_CNhs14075_10357-105E6_forward Regulation 
CerebellumAdultPool1_CNhs11795_ctss_rev CerebellumAdultPl1- cerebellum, adult, pool1_CNhs11795_10083-102B2_reverse Regulation 
CerebellumAdultPool1_CNhs11795_ctss_fwd CerebellumAdultPl1+ cerebellum, adult, pool1_CNhs11795_10083-102B2_forward Regulation 
CerebellumAdultDonor10252_CNhs12323_ctss_rev CerebellumAdultD10252- cerebellum, adult, donor10252_CNhs12323_10166-103B4_reverse Regulation 
CerebellumAdultDonor10252_CNhs12323_ctss_fwd CerebellumAdultD10252+ cerebellum, adult, donor10252_CNhs12323_10166-103B4_forward Regulation 
CerebellumAdultDonor10196_CNhs13799_ctss_rev CerebellumAdultD10196- cerebellum - adult, donor10196_CNhs13799_10173-103C2_reverse Regulation 
CerebellumAdultDonor10196_CNhs13799_ctss_fwd CerebellumAdultD10196+ cerebellum - adult, donor10196_CNhs13799_10173-103C2_forward Regulation 
CaudateNucleusNewbornDonor10223_CNhs14071_ctss_rev CaudateNucleusNbD10223- caudate nucleus, newborn, donor10223_CNhs14071_10354-105E3_reverse Regulation 
CaudateNucleusNewbornDonor10223_CNhs14071_ctss_fwd CaudateNucleusNbD10223+ caudate nucleus, newborn, donor10223_CNhs14071_10354-105E3_forward Regulation 
CaudateNucleusAdultDonor10258_CNhs14232_ctss_rev CaudateNucleusAdultD10258- caudate nucleus, adult, donor10258_CNhs14232_10379-105H1_reverse Regulation 
CaudateNucleusAdultDonor10258_CNhs14232_ctss_fwd CaudateNucleusAdultD10258+ caudate nucleus, adult, donor10258_CNhs14232_10379-105H1_forward Regulation 
CaudateNucleusAdultDonor10252_CNhs12321_ctss_rev CaudateNucleusAdultD10252- caudate nucleus, adult, donor10252_CNhs12321_10164-103B2_reverse Regulation 
CaudateNucleusAdultDonor10252_CNhs12321_ctss_fwd CaudateNucleusAdultD10252+ caudate nucleus, adult, donor10252_CNhs12321_10164-103B2_forward Regulation 
CaudateNucleusAdultDonor10196_CNhs13802_ctss_rev CaudateNucleusAdultD10196- caudate nucleus - adult, donor10196_CNhs13802_10177-103C6_reverse Regulation 
CaudateNucleusAdultDonor10196_CNhs13802_ctss_fwd CaudateNucleusAdultD10196+ caudate nucleus - adult, donor10196_CNhs13802_10177-103C6_forward Regulation 
BreastAdultDonor1_CNhs11792_ctss_rev BreastAdultD1- breast, adult, donor1_CNhs11792_10080-102A8_reverse Regulation 
BreastAdultDonor1_CNhs11792_ctss_fwd BreastAdultD1+ breast, adult, donor1_CNhs11792_10080-102A8_forward Regulation 
BrainFetalPool1_CNhs11797_ctss_rev BrainFetalPl1- brain, fetal, pool1_CNhs11797_10085-102B4_reverse Regulation 
BrainFetalPool1_CNhs11797_ctss_fwd BrainFetalPl1+ brain, fetal, pool1_CNhs11797_10085-102B4_forward Regulation 
BrainAdultPool1_CNhs10617_ctss_rev BrainAdultPl1- brain, adult, pool1_CNhs10617_10012-101C3_reverse Regulation 
BrainAdultPool1_CNhs10617_ctss_fwd BrainAdultPl1+ brain, adult, pool1_CNhs10617_10012-101C3_forward Regulation 
BrainAdultDonor1_CNhs11796_ctss_rev BrainAdultD1- brain, adult, donor1_CNhs11796_10084-102B3_reverse Regulation 
BrainAdultDonor1_CNhs11796_ctss_fwd BrainAdultD1+ brain, adult, donor1_CNhs11796_10084-102B3_forward Regulation 
BoneMarrowAdult_CNhs12845_ctss_rev BoneMarrowAdult- bone marrow, adult_CNhs12845_10192-103E3_reverse Regulation 
BoneMarrowAdult_CNhs12845_ctss_fwd BoneMarrowAdult+ bone marrow, adult_CNhs12845_10192-103E3_forward Regulation 
BloodAdultPool1_CNhs11761_ctss_rev BloodAdultPl1- blood, adult, pool1_CNhs11761_10053-101G8_reverse Regulation 
BloodAdultPool1_CNhs11761_ctss_fwd BloodAdultPl1+ blood, adult, pool1_CNhs11761_10053-101G8_forward Regulation 
BladderAdultPool1_CNhs10616_ctss_rev BladderAdultPl1- bladder, adult, pool1_CNhs10616_10011-101C2_reverse Regulation 
BladderAdultPool1_CNhs10616_ctss_fwd BladderAdultPl1+ bladder, adult, pool1_CNhs10616_10011-101C2_forward Regulation 
ArteryAdult_CNhs12843_ctss_rev ArteryAdult- artery, adult_CNhs12843_10190-103E1_reverse Regulation 
ArteryAdult_CNhs12843_ctss_fwd ArteryAdult+ artery, adult_CNhs12843_10190-103E1_forward Regulation 
AppendixAdult_CNhs12842_ctss_rev AppendixAdult- appendix, adult_CNhs12842_10189-103D9_reverse Regulation 
AppendixAdult_CNhs12842_ctss_fwd AppendixAdult+ appendix, adult_CNhs12842_10189-103D9_forward Regulation 
AortaAdultPool1_CNhs11760_ctss_rev AortaAdultPl1- aorta, adult, pool1_CNhs11760_10052-101G7_reverse Regulation 
AortaAdultPool1_CNhs11760_ctss_fwd AortaAdultPl1+ aorta, adult, pool1_CNhs11760_10052-101G7_forward Regulation 
AmygdalaNewbornDonor10223_CNhs14078_ctss_rev AmygdalaNbD1D10223- amygdala, newborn, donor10223_CNhs14078_10360-105E9_reverse Regulation 
AmygdalaNewbornDonor10223_CNhs14078_ctss_fwd AmygdalaNbD1D10223+ amygdala, newborn, donor10223_CNhs14078_10360-105E9_forward Regulation 
AmygdalaAdultDonor10252_CNhs12311_ctss_rev AmygdalaAdultD10252- amygdala, adult, donor10252_CNhs12311_10151-102I7_reverse Regulation 
AmygdalaAdultDonor10252_CNhs12311_ctss_fwd AmygdalaAdultD10252+ amygdala, adult, donor10252_CNhs12311_10151-102I7_forward Regulation 
AmygdalaAdultDonor10196_CNhs13793_ctss_rev AmygdalaAdultD10196- amygdala - adult, donor10196_CNhs13793_10167-103B5_reverse Regulation 
AmygdalaAdultDonor10196_CNhs13793_ctss_fwd AmygdalaAdultD10196+ amygdala - adult, donor10196_CNhs13793_10167-103B5_forward Regulation 
AdrenalGlandAdultPool1_CNhs11793_ctss_rev AdrenalGlandAdultPl1- adrenal gland, adult, pool1_CNhs11793_10081-102A9_reverse Regulation 
AdrenalGlandAdultPool1_CNhs11793_ctss_fwd AdrenalGlandAdultPl1+ adrenal gland, adult, pool1_CNhs11793_10081-102A9_forward Regulation 
AdiposeTissueAdultPool1_CNhs10615_ctss_rev AdiposeTissueAdultPl1- adipose tissue, adult, pool1_CNhs10615_10010-101C1_reverse Regulation 
AdiposeTissueAdultPool1_CNhs10615_ctss_fwd AdiposeTissueAdultPl1+ adipose tissue, adult, pool1_CNhs10615_10010-101C1_forward Regulation 
AdiposeDonor4_CNhs13975_ctss_rev AdiposeD4- adipose, donor4_CNhs13975_10187-103D7_reverse Regulation 
AdiposeDonor4_CNhs13975_ctss_fwd AdiposeD4+ adipose, donor4_CNhs13975_10187-103D7_forward Regulation 
AdiposeDonor3_CNhs13974_ctss_rev AdiposeD3- adipose, donor3_CNhs13974_10186-103D6_reverse Regulation 
AdiposeDonor3_CNhs13974_ctss_fwd AdiposeD3+ adipose, donor3_CNhs13974_10186-103D6_forward Regulation 
AdiposeDonor2_CNhs13973_ctss_rev AdiposeD2- adipose, donor2_CNhs13973_10185-103D5_reverse Regulation 
AdiposeDonor2_CNhs13973_ctss_fwd AdiposeD2+ adipose, donor2_CNhs13973_10185-103D5_forward Regulation 
AdiposeDonor1_CNhs13972_ctss_rev AdiposeD1- adipose, donor1_CNhs13972_10184-103D4_reverse Regulation 
AdiposeDonor1_CNhs13972_ctss_fwd AdiposeD1+ adipose, donor1_CNhs13972_10184-103D4_forward Regulation 
AchillesTendonDonor2_CNhs13435_ctss_rev AchillesTendonD2- achilles tendon, donor2_CNhs13435_10292-104G4_reverse Regulation 
AchillesTendonDonor2_CNhs13435_ctss_fwd AchillesTendonD2+ achilles tendon, donor2_CNhs13435_10292-104G4_forward Regulation 
Saos2OsteosarcomaCellLineUntreatedControlDay28BiolRep3B3T17_CNhs14196_ctss_rev Tc:Saos-2Untreated_Day28Br3- Saos-2 osteosarcoma cell line, untreated control, day28, biol_rep3 (B3 T17)_CNhs14196_12893-137H4_reverse Regulation 
Saos2OsteosarcomaCellLineUntreatedControlDay28BiolRep3B3T17_CNhs14196_ctss_fwd Tc:Saos-2Untreated_Day28Br3+ Saos-2 osteosarcoma cell line, untreated control, day28, biol_rep3 (B3 T17)_CNhs14196_12893-137H4_forward Regulation 
Saos2OsteosarcomaCellLineUntreatedControlDay28BiolRep2B2T17_CNhs14195_ctss_rev Tc:Saos-2Untreated_Day28Br2- Saos-2 osteosarcoma cell line, untreated control, day28, biol_rep2 (B2 T17)_CNhs14195_12795-136F5_reverse Regulation 
Saos2OsteosarcomaCellLineUntreatedControlDay28BiolRep2B2T17_CNhs14195_ctss_fwd Tc:Saos-2Untreated_Day28Br2+ Saos-2 osteosarcoma cell line, untreated control, day28, biol_rep2 (B2 T17)_CNhs14195_12795-136F5_forward Regulation 
Saos2OsteosarcomaCellLineUntreatedControlDay28BiolRep1B1T17_CNhs14194_ctss_rev Tc:Saos-2Untreated_Day28Br1- Saos-2 osteosarcoma cell line, untreated control, day28, biol_rep1 (B1 T17)_CNhs14194_12697-135D6_reverse Regulation 
Saos2OsteosarcomaCellLineUntreatedControlDay28BiolRep1B1T17_CNhs14194_ctss_fwd Tc:Saos-2Untreated_Day28Br1+ Saos-2 osteosarcoma cell line, untreated control, day28, biol_rep1 (B1 T17)_CNhs14194_12697-135D6_forward Regulation 
MesenchymalStemCellsAdiposeDerivedUndifferentiatedControlBiolRep3_CNhs13634_ctss_rev Tc:MscToAdiposeUndiffBr3- mesenchymal stem cells (adipose derived), undifferentiated control, biol_rep3_CNhs13634_13282-142F6_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedUndifferentiatedControlBiolRep3_CNhs13634_ctss_fwd Tc:MscToAdiposeUndiffBr3+ mesenchymal stem cells (adipose derived), undifferentiated control, biol_rep3_CNhs13634_13282-142F6_forward Regulation 
MesenchymalStemCellsAdiposeDerivedUndifferentiatedControlBiolRep2_CNhs13633_ctss_rev Tc:MscToAdiposeUndiffBr2- mesenchymal stem cells (adipose derived), undifferentiated control, biol_rep2_CNhs13633_13281-142F5_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedUndifferentiatedControlBiolRep2_CNhs13633_ctss_fwd Tc:MscToAdiposeUndiffBr2+ mesenchymal stem cells (adipose derived), undifferentiated control, biol_rep2_CNhs13633_13281-142F5_forward Regulation 
MesenchymalStemCellsAdiposeDerivedUndifferentiatedControlBiolRep1_CNhs13692_ctss_rev Tc:MscToAdiposeUndiffBr1- mesenchymal stem cells (adipose derived), undifferentiated control, biol_rep1_CNhs13692_13280-142F4_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedUndifferentiatedControlBiolRep1_CNhs13692_ctss_fwd Tc:MscToAdiposeUndiffBr1+ mesenchymal stem cells (adipose derived), undifferentiated control, biol_rep1_CNhs13692_13280-142F4_forward Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection00hr00minDonor1868_121MI_0h_CNhs13637_ctss_rev Tc:MdmToMock_00hr00minD1- Monocyte-derived macrophages response to mock influenza infection, 00hr00min, donor1 (868_121:MI_0h)_CNhs13637_13304-142I1_reverse Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection00hr00minDonor1868_121MI_0h_CNhs13637_ctss_fwd Tc:MdmToMock_00hr00minD1+ Monocyte-derived macrophages response to mock influenza infection, 00hr00min, donor1 (868_121:MI_0h)_CNhs13637_13304-142I1_forward Regulation 
MonocytederivedMacrophagesResponseToLPS16hrDonor1T20Subject1_CNhs12930_ctss_rev Tc:MdmToLps_16hrD1- Monocyte-derived macrophages response to LPS, 16hr, donor1 (t20 Subject1)_CNhs12930_12717-135F8_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS16hrDonor1T20Subject1_CNhs12930_ctss_fwd Tc:MdmToLps_16hrD1+ Monocyte-derived macrophages response to LPS, 16hr, donor1 (t20 Subject1)_CNhs12930_12717-135F8_forward Regulation 
MonocytederivedMacrophagesResponseToLPS10hrDonor1T17Subject1_CNhs12928_ctss_rev Tc:MdmToLps_10hrD1- Monocyte-derived macrophages response to LPS, 10hr, donor1 (t17 Subject1)_CNhs12928_12714-135F5_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS10hrDonor1T17Subject1_CNhs12928_ctss_fwd Tc:MdmToLps_10hrD1+ Monocyte-derived macrophages response to LPS, 10hr, donor1 (t17 Subject1)_CNhs12928_12714-135F5_forward Regulation 
MonocytederivedMacrophagesResponseToLPS07hrDonor3T15Subject3_CNhs13325_ctss_rev Tc:MdmToLps_07hrD3- Monocyte-derived macrophages response to LPS, 07hr, donor3 (t15 Subject3)_CNhs13325_12908-138A1_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS07hrDonor3T15Subject3_CNhs13325_ctss_fwd Tc:MdmToLps_07hrD3+ Monocyte-derived macrophages response to LPS, 07hr, donor3 (t15 Subject3)_CNhs13325_12908-138A1_forward Regulation 
MonocytederivedMacrophagesResponseToLPS07hrDonor2T15Subject2_CNhs13394_ctss_rev Tc:MdmToLps_07hrD2- Monocyte-derived macrophages response to LPS, 07hr, donor2 (t15 Subject2)_CNhs13394_12810-136H2_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS07hrDonor2T15Subject2_CNhs13394_ctss_fwd Tc:MdmToLps_07hrD2+ Monocyte-derived macrophages response to LPS, 07hr, donor2 (t15 Subject2)_CNhs13394_12810-136H2_forward Regulation 
MonocytederivedMacrophagesResponseToLPS07hrDonor1T15Subject1_CNhs12926_ctss_rev Tc:MdmToLps_07hrD1- Monocyte-derived macrophages response to LPS, 07hr, donor1 (t15 Subject1)_CNhs12926_12712-135F3_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS07hrDonor1T15Subject1_CNhs12926_ctss_fwd Tc:MdmToLps_07hrD1+ Monocyte-derived macrophages response to LPS, 07hr, donor1 (t15 Subject1)_CNhs12926_12712-135F3_forward Regulation 
MonocytederivedMacrophagesResponseToLPS06hrDonor3T14Subject3_CNhs13187_ctss_rev Tc:MdmToLps_06hrD3- Monocyte-derived macrophages response to LPS, 06hr, donor3 (t14 Subject3)_CNhs13187_12907-137I9_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS06hrDonor3T14Subject3_CNhs13187_ctss_fwd Tc:MdmToLps_06hrD3+ Monocyte-derived macrophages response to LPS, 06hr, donor3 (t14 Subject3)_CNhs13187_12907-137I9_forward Regulation 
MonocytederivedMacrophagesResponseToLPS06hrDonor2T14Subject2_CNhs13393_ctss_rev Tc:MdmToLps_06hrD2- Monocyte-derived macrophages response to LPS, 06hr, donor2 (t14 Subject2)_CNhs13393_12809-136H1_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS06hrDonor2T14Subject2_CNhs13393_ctss_fwd Tc:MdmToLps_06hrD2+ Monocyte-derived macrophages response to LPS, 06hr, donor2 (t14 Subject2)_CNhs13393_12809-136H1_forward Regulation 
MonocytederivedMacrophagesResponseToLPS06hrDonor1T14Subject1_CNhs12925_ctss_rev Tc:MdmToLps_06hrD1- Monocyte-derived macrophages response to LPS, 06hr, donor1 (t14 Subject1)_CNhs12925_12711-135F2_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS06hrDonor1T14Subject1_CNhs12925_ctss_fwd Tc:MdmToLps_06hrD1+ Monocyte-derived macrophages response to LPS, 06hr, donor1 (t14 Subject1)_CNhs12925_12711-135F2_forward Regulation 
MonocytederivedMacrophagesResponseToLPS04hrDonor1T12Subject1_CNhs13154_ctss_rev Tc:MdmToLps_04hrD1- Monocyte-derived macrophages response to LPS, 04hr, donor1 (t12 Subject1)_CNhs13154_12709-135E9_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS04hrDonor1T12Subject1_CNhs13154_ctss_fwd Tc:MdmToLps_04hrD1+ Monocyte-derived macrophages response to LPS, 04hr, donor1 (t12 Subject1)_CNhs13154_12709-135E9_forward Regulation 
MonocytederivedMacrophagesResponseToLPS03hr30minDonor1T11Subject1_CNhs13153_ctss_rev Tc:MdmToLps_03hr30minD1- Monocyte-derived macrophages response to LPS, 03hr30min, donor1 (t11 Subject1)_CNhs13153_12708-135E8_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS03hr30minDonor1T11Subject1_CNhs13153_ctss_fwd Tc:MdmToLps_03hr30minD1+ Monocyte-derived macrophages response to LPS, 03hr30min, donor1 (t11 Subject1)_CNhs13153_12708-135E8_forward Regulation 
MonocytederivedMacrophagesResponseToLPS02hr00minDonor1T8Subject1_CNhs13151_ctss_rev Tc:MdmToLps_02hr00minD1- Monocyte-derived macrophages response to LPS, 02hr00min, donor1 (t8 Subject1)_CNhs13151_12705-135E5_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS02hr00minDonor1T8Subject1_CNhs13151_ctss_fwd Tc:MdmToLps_02hr00minD1+ Monocyte-derived macrophages response to LPS, 02hr00min, donor1 (t8 Subject1)_CNhs13151_12705-135E5_forward Regulation 
MonocytederivedMacrophagesResponseToLPS01hr40minDonor3T7Subject3_CNhs13180_ctss_rev Tc:MdmToLps_01hr40minD3- Monocyte-derived macrophages response to LPS, 01hr40min, donor3 (t7 Subject3)_CNhs13180_12900-137I2_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS01hr40minDonor3T7Subject3_CNhs13180_ctss_fwd Tc:MdmToLps_01hr40minD3+ Monocyte-derived macrophages response to LPS, 01hr40min, donor3 (t7 Subject3)_CNhs13180_12900-137I2_forward Regulation 
MonocytederivedMacrophagesResponseToLPS01hr40minDonor2T7Subject2_CNhs13385_ctss_rev Tc:MdmToLps_01hr40minD2- Monocyte-derived macrophages response to LPS, 01hr40min, donor2 (t7 Subject2)_CNhs13385_12802-136G3_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS01hr40minDonor2T7Subject2_CNhs13385_ctss_fwd Tc:MdmToLps_01hr40minD2+ Monocyte-derived macrophages response to LPS, 01hr40min, donor2 (t7 Subject2)_CNhs13385_12802-136G3_forward Regulation 
MonocytederivedMacrophagesResponseToLPS01hr40minDonor1T7Subject1_CNhs13150_ctss_rev Tc:MdmToLps_01hr40minD1- Monocyte-derived macrophages response to LPS, 01hr40min, donor1 (t7 Subject1)_CNhs13150_12704-135E4_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS01hr40minDonor1T7Subject1_CNhs13150_ctss_fwd Tc:MdmToLps_01hr40minD1+ Monocyte-derived macrophages response to LPS, 01hr40min, donor1 (t7 Subject1)_CNhs13150_12704-135E4_forward Regulation 
MonocytederivedMacrophagesResponseToLPS01hr20minDonor1T6Subject1_CNhs13149_ctss_rev Tc:MdmToLps_01hr20minD1- Monocyte-derived macrophages response to LPS, 01hr20min, donor1 (t6 Subject1)_CNhs13149_12703-135E3_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS01hr20minDonor1T6Subject1_CNhs13149_ctss_fwd Tc:MdmToLps_01hr20minD1+ Monocyte-derived macrophages response to LPS, 01hr20min, donor1 (t6 Subject1)_CNhs13149_12703-135E3_forward Regulation 
MonocytederivedMacrophagesResponseToLPS01hr00minDonor1T5Subject1_CNhs13148_ctss_rev Tc:MdmToLps_01hr00minD1- Monocyte-derived macrophages response to LPS, 01hr00min, donor1 (t5 Subject1)_CNhs13148_12702-135E2_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS01hr00minDonor1T5Subject1_CNhs13148_ctss_fwd Tc:MdmToLps_01hr00minD1+ Monocyte-derived macrophages response to LPS, 01hr00min, donor1 (t5 Subject1)_CNhs13148_12702-135E2_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr45minDonor1T4Subject1_CNhs13147_ctss_rev Tc:MdmToLps_00hr45minD1- Monocyte-derived macrophages response to LPS, 00hr45min, donor1 (t4 Subject1)_CNhs13147_12701-135E1_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr45minDonor1T4Subject1_CNhs13147_ctss_fwd Tc:MdmToLps_00hr45minD1+ Monocyte-derived macrophages response to LPS, 00hr45min, donor1 (t4 Subject1)_CNhs13147_12701-135E1_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr30minDonor1T3Subject1_CNhs13146_ctss_rev Tc:MdmToLps_00hr30minD1- Monocyte-derived macrophages response to LPS, 00hr30min, donor1 (t3 Subject1)_CNhs13146_12700-135D9_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr30minDonor1T3Subject1_CNhs13146_ctss_fwd Tc:MdmToLps_00hr30minD1+ Monocyte-derived macrophages response to LPS, 00hr30min, donor1 (t3 Subject1)_CNhs13146_12700-135D9_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr15minDonor1T2Subject1_CNhs13145_ctss_rev Tc:MdmToLps_00hr15minD1- Monocyte-derived macrophages response to LPS, 00hr15min, donor1 (t2 Subject1)_CNhs13145_12699-135D8_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr15minDonor1T2Subject1_CNhs13145_ctss_fwd Tc:MdmToLps_00hr15minD1+ Monocyte-derived macrophages response to LPS, 00hr15min, donor1 (t2 Subject1)_CNhs13145_12699-135D8_forward Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay04BiolRep3_CNhs12804_ctss_rev Tc:K562ToHemin_Day04Br3- K562 erythroblastic leukemia response to hemin, day04, biol_rep3_CNhs12804_13228-141I6_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay04BiolRep3_CNhs12804_ctss_fwd Tc:K562ToHemin_Day04Br3+ K562 erythroblastic leukemia response to hemin, day04, biol_rep3_CNhs12804_13228-141I6_forward Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay04BiolRep2_CNhs12702_ctss_rev Tc:K562ToHemin_Day04Br2- K562 erythroblastic leukemia response to hemin, day04, biol_rep2_CNhs12702_13162-141B3_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay04BiolRep2_CNhs12702_ctss_fwd Tc:K562ToHemin_Day04Br2+ K562 erythroblastic leukemia response to hemin, day04, biol_rep2_CNhs12702_13162-141B3_forward Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay04BiolRep1_CNhs12474_ctss_rev Tc:K562ToHemin_Day04Br1- K562 erythroblastic leukemia response to hemin, day04, biol_rep1_CNhs12474_13096-140C9_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay04BiolRep1_CNhs12474_ctss_fwd Tc:K562ToHemin_Day04Br1+ K562 erythroblastic leukemia response to hemin, day04, biol_rep1_CNhs12474_13096-140C9_forward Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay03BiolRep3_CNhs12803_ctss_rev Tc:K562ToHemin_Day03Br3- K562 erythroblastic leukemia response to hemin, day03, biol_rep3_CNhs12803_13227-141I5_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay03BiolRep3_CNhs12803_ctss_fwd Tc:K562ToHemin_Day03Br3+ K562 erythroblastic leukemia response to hemin, day03, biol_rep3_CNhs12803_13227-141I5_forward Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay03BiolRep2_CNhs12701_ctss_rev Tc:K562ToHemin_Day03Br2- K562 erythroblastic leukemia response to hemin, day03, biol_rep2_CNhs12701_13161-141B2_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay03BiolRep2_CNhs12701_ctss_fwd Tc:K562ToHemin_Day03Br2+ K562 erythroblastic leukemia response to hemin, day03, biol_rep2_CNhs12701_13161-141B2_forward Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay03BiolRep1_CNhs12473_ctss_rev Tc:K562ToHemin_Day03Br1- K562 erythroblastic leukemia response to hemin, day03, biol_rep1_CNhs12473_13095-140C8_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay03BiolRep1_CNhs12473_ctss_fwd Tc:K562ToHemin_Day03Br1+ K562 erythroblastic leukemia response to hemin, day03, biol_rep1_CNhs12473_13095-140C8_forward Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay02BiolRep3_CNhs12802_ctss_rev Tc:K562ToHemin_Day02Br3- K562 erythroblastic leukemia response to hemin, day02, biol_rep3_CNhs12802_13226-141I4_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay02BiolRep3_CNhs12802_ctss_fwd Tc:K562ToHemin_Day02Br3+ K562 erythroblastic leukemia response to hemin, day02, biol_rep3_CNhs12802_13226-141I4_forward Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay02BiolRep2_CNhs12700_ctss_rev Tc:K562ToHemin_Day02Br2- K562 erythroblastic leukemia response to hemin, day02, biol_rep2_CNhs12700_13160-141B1_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay02BiolRep2_CNhs12700_ctss_fwd Tc:K562ToHemin_Day02Br2+ K562 erythroblastic leukemia response to hemin, day02, biol_rep2_CNhs12700_13160-141B1_forward Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay02BiolRep1_CNhs12472_ctss_rev Tc:K562ToHemin_Day02Br1- K562 erythroblastic leukemia response to hemin, day02, biol_rep1_CNhs12472_13094-140C7_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay02BiolRep1_CNhs12472_ctss_fwd Tc:K562ToHemin_Day02Br1+ K562 erythroblastic leukemia response to hemin, day02, biol_rep1_CNhs12472_13094-140C7_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin24hrBiolRep3_CNhs12801_ctss_rev Tc:K562ToHemin_24hrBr3- K562 erythroblastic leukemia response to hemin, 24hr, biol_rep3_CNhs12801_13225-141I3_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin24hrBiolRep3_CNhs12801_ctss_fwd Tc:K562ToHemin_24hrBr3+ K562 erythroblastic leukemia response to hemin, 24hr, biol_rep3_CNhs12801_13225-141I3_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin24hrBiolRep2_CNhs12699_ctss_rev Tc:K562ToHemin_24hrBr2- K562 erythroblastic leukemia response to hemin, 24hr, biol_rep2_CNhs12699_13159-141A9_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin24hrBiolRep2_CNhs12699_ctss_fwd Tc:K562ToHemin_24hrBr2+ K562 erythroblastic leukemia response to hemin, 24hr, biol_rep2_CNhs12699_13159-141A9_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin24hrBiolRep1_CNhs12471_ctss_rev Tc:K562ToHemin_24hrBr1- K562 erythroblastic leukemia response to hemin, 24hr, biol_rep1_CNhs12471_13093-140C6_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin24hrBiolRep1_CNhs12471_ctss_fwd Tc:K562ToHemin_24hrBr1+ K562 erythroblastic leukemia response to hemin, 24hr, biol_rep1_CNhs12471_13093-140C6_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin12hrBiolRep3_CNhs12800_ctss_rev Tc:K562ToHemin_12hrBr3- K562 erythroblastic leukemia response to hemin, 12hr, biol_rep3_CNhs12800_13224-141I2_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin12hrBiolRep3_CNhs12800_ctss_fwd Tc:K562ToHemin_12hrBr3+ K562 erythroblastic leukemia response to hemin, 12hr, biol_rep3_CNhs12800_13224-141I2_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin12hrBiolRep2_CNhs12698_ctss_rev Tc:K562ToHemin_12hrBr2- K562 erythroblastic leukemia response to hemin, 12hr, biol_rep2_CNhs12698_13158-141A8_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin12hrBiolRep2_CNhs12698_ctss_fwd Tc:K562ToHemin_12hrBr2+ K562 erythroblastic leukemia response to hemin, 12hr, biol_rep2_CNhs12698_13158-141A8_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin12hrBiolRep1_CNhs12470_ctss_rev Tc:K562ToHemin_12hrBr1- K562 erythroblastic leukemia response to hemin, 12hr, biol_rep1_CNhs12470_13092-140C5_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin12hrBiolRep1_CNhs12470_ctss_fwd Tc:K562ToHemin_12hrBr1+ K562 erythroblastic leukemia response to hemin, 12hr, biol_rep1_CNhs12470_13092-140C5_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin06hrBiolRep3_CNhs12799_ctss_rev Tc:K562ToHemin_06hrBr3- K562 erythroblastic leukemia response to hemin, 06hr, biol_rep3_CNhs12799_13223-141I1_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin06hrBiolRep3_CNhs12799_ctss_fwd Tc:K562ToHemin_06hrBr3+ K562 erythroblastic leukemia response to hemin, 06hr, biol_rep3_CNhs12799_13223-141I1_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin06hrBiolRep2_CNhs12697_ctss_rev Tc:K562ToHemin_06hrBr2- K562 erythroblastic leukemia response to hemin, 06hr, biol_rep2_CNhs12697_13157-141A7_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin06hrBiolRep2_CNhs12697_ctss_fwd Tc:K562ToHemin_06hrBr2+ K562 erythroblastic leukemia response to hemin, 06hr, biol_rep2_CNhs12697_13157-141A7_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin06hrBiolRep1_CNhs12469_ctss_rev Tc:K562ToHemin_06hrBr1- K562 erythroblastic leukemia response to hemin, 06hr, biol_rep1_CNhs12469_13091-140C4_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin06hrBiolRep1_CNhs12469_ctss_fwd Tc:K562ToHemin_06hrBr1+ K562 erythroblastic leukemia response to hemin, 06hr, biol_rep1_CNhs12469_13091-140C4_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin04hrBiolRep3_CNhs12798_ctss_rev Tc:K562ToHemin_04hrBr3- K562 erythroblastic leukemia response to hemin, 04hr, biol_rep3_CNhs12798_13222-141H9_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin04hrBiolRep3_CNhs12798_ctss_fwd Tc:K562ToHemin_04hrBr3+ K562 erythroblastic leukemia response to hemin, 04hr, biol_rep3_CNhs12798_13222-141H9_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin04hrBiolRep2_CNhs12696_ctss_rev Tc:K562ToHemin_04hrBr2- K562 erythroblastic leukemia response to hemin, 04hr, biol_rep2_CNhs12696_13156-141A6_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin04hrBiolRep2_CNhs12696_ctss_fwd Tc:K562ToHemin_04hrBr2+ K562 erythroblastic leukemia response to hemin, 04hr, biol_rep2_CNhs12696_13156-141A6_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin04hrBiolRep1_CNhs12468_ctss_rev Tc:K562ToHemin_04hrBr1- K562 erythroblastic leukemia response to hemin, 04hr, biol_rep1_CNhs12468_13090-140C3_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin04hrBiolRep1_CNhs12468_ctss_fwd Tc:K562ToHemin_04hrBr1+ K562 erythroblastic leukemia response to hemin, 04hr, biol_rep1_CNhs12468_13090-140C3_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr30minBiolRep3_CNhs12797_ctss_rev Tc:K562ToHemin_03hr30minBr3- K562 erythroblastic leukemia response to hemin, 03hr30min, biol_rep3_CNhs12797_13221-141H8_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr30minBiolRep3_CNhs12797_ctss_fwd Tc:K562ToHemin_03hr30minBr3+ K562 erythroblastic leukemia response to hemin, 03hr30min, biol_rep3_CNhs12797_13221-141H8_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr30minBiolRep2_CNhs12695_ctss_rev Tc:K562ToHemin_03hr30minBr2- K562 erythroblastic leukemia response to hemin, 03hr30min, biol_rep2_CNhs12695_13155-141A5_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr30minBiolRep2_CNhs12695_ctss_fwd Tc:K562ToHemin_03hr30minBr2+ K562 erythroblastic leukemia response to hemin, 03hr30min, biol_rep2_CNhs12695_13155-141A5_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr30minBiolRep1_CNhs12467_ctss_rev Tc:K562ToHemin_03hr30minBr1- K562 erythroblastic leukemia response to hemin, 03hr30min, biol_rep1_CNhs12467_13089-140C2_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr30minBiolRep1_CNhs12467_ctss_fwd Tc:K562ToHemin_03hr30minBr1+ K562 erythroblastic leukemia response to hemin, 03hr30min, biol_rep1_CNhs12467_13089-140C2_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr00minBiolRep3_CNhs12796_ctss_rev Tc:K562ToHemin_03hr00minBr3- K562 erythroblastic leukemia response to hemin, 03hr00min, biol_rep3_CNhs12796_13220-141H7_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr00minBiolRep3_CNhs12796_ctss_fwd Tc:K562ToHemin_03hr00minBr3+ K562 erythroblastic leukemia response to hemin, 03hr00min, biol_rep3_CNhs12796_13220-141H7_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr00minBiolRep2_CNhs12694_ctss_rev Tc:K562ToHemin_03hr00minBr2- K562 erythroblastic leukemia response to hemin, 03hr00min, biol_rep2_CNhs12694_13154-141A4_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr00minBiolRep2_CNhs12694_ctss_fwd Tc:K562ToHemin_03hr00minBr2+ K562 erythroblastic leukemia response to hemin, 03hr00min, biol_rep2_CNhs12694_13154-141A4_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr00minBiolRep1_CNhs12466_ctss_rev Tc:K562ToHemin_03hr00minBr1- K562 erythroblastic leukemia response to hemin, 03hr00min, biol_rep1_CNhs12466_13088-140C1_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr00minBiolRep1_CNhs12466_ctss_fwd Tc:K562ToHemin_03hr00minBr1+ K562 erythroblastic leukemia response to hemin, 03hr00min, biol_rep1_CNhs12466_13088-140C1_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr30minBiolRep3_CNhs12795_ctss_rev Tc:K562ToHemin_02hr30minBr3- K562 erythroblastic leukemia response to hemin, 02hr30min, biol_rep3_CNhs12795_13219-141H6_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr30minBiolRep3_CNhs12795_ctss_fwd Tc:K562ToHemin_02hr30minBr3+ K562 erythroblastic leukemia response to hemin, 02hr30min, biol_rep3_CNhs12795_13219-141H6_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr30minBiolRep2_CNhs12693_ctss_rev Tc:K562ToHemin_02hr30minBr2- K562 erythroblastic leukemia response to hemin, 02hr30min, biol_rep2_CNhs12693_13153-141A3_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr30minBiolRep2_CNhs12693_ctss_fwd Tc:K562ToHemin_02hr30minBr2+ K562 erythroblastic leukemia response to hemin, 02hr30min, biol_rep2_CNhs12693_13153-141A3_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr30minBiolRep1_CNhs12465_ctss_rev Tc:K562ToHemin_02hr30minBr1- K562 erythroblastic leukemia response to hemin, 02hr30min, biol_rep1_CNhs12465_13087-140B9_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr30minBiolRep1_CNhs12465_ctss_fwd Tc:K562ToHemin_02hr30minBr1+ K562 erythroblastic leukemia response to hemin, 02hr30min, biol_rep1_CNhs12465_13087-140B9_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr00minBiolRep3_CNhs12794_ctss_rev Tc:K562ToHemin_02hr00minBr3- K562 erythroblastic leukemia response to hemin, 02hr00min, biol_rep3_CNhs12794_13218-141H5_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr00minBiolRep3_CNhs12794_ctss_fwd Tc:K562ToHemin_02hr00minBr3+ K562 erythroblastic leukemia response to hemin, 02hr00min, biol_rep3_CNhs12794_13218-141H5_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr00minBiolRep2_CNhs12692_ctss_rev Tc:K562ToHemin_02hr00minBr2- K562 erythroblastic leukemia response to hemin, 02hr00min, biol_rep2_CNhs12692_13152-141A2_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr00minBiolRep2_CNhs12692_ctss_fwd Tc:K562ToHemin_02hr00minBr2+ K562 erythroblastic leukemia response to hemin, 02hr00min, biol_rep2_CNhs12692_13152-141A2_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr00minBiolRep1_CNhs12737_ctss_rev Tc:K562ToHemin_02hr00minBr1- K562 erythroblastic leukemia response to hemin, 02hr00min, biol_rep1_CNhs12737_13086-140B8_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr00minBiolRep1_CNhs12737_ctss_fwd Tc:K562ToHemin_02hr00minBr1+ K562 erythroblastic leukemia response to hemin, 02hr00min, biol_rep1_CNhs12737_13086-140B8_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr40minBiolRep3_CNhs12792_ctss_rev Tc:K562ToHemin_01hr40minBr3- K562 erythroblastic leukemia response to hemin, 01hr40min, biol_rep3_CNhs12792_13217-141H4_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr40minBiolRep3_CNhs12792_ctss_fwd Tc:K562ToHemin_01hr40minBr3+ K562 erythroblastic leukemia response to hemin, 01hr40min, biol_rep3_CNhs12792_13217-141H4_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr40minBiolRep2_CNhs12691_ctss_rev Tc:K562ToHemin_01hr40minBr2- K562 erythroblastic leukemia response to hemin, 01hr40min, biol_rep2_CNhs12691_13151-141A1_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr40minBiolRep2_CNhs12691_ctss_fwd Tc:K562ToHemin_01hr40minBr2+ K562 erythroblastic leukemia response to hemin, 01hr40min, biol_rep2_CNhs12691_13151-141A1_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr40minBiolRep1_CNhs12464_ctss_rev Tc:K562ToHemin_01hr40minBr1- K562 erythroblastic leukemia response to hemin, 01hr40min, biol_rep1_CNhs12464_13085-140B7_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr40minBiolRep1_CNhs12464_ctss_fwd Tc:K562ToHemin_01hr40minBr1+ K562 erythroblastic leukemia response to hemin, 01hr40min, biol_rep1_CNhs12464_13085-140B7_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr20minBiolRep3_CNhs12791_ctss_rev Tc:K562ToHemin_01hr20minBr3- K562 erythroblastic leukemia response to hemin, 01hr20min, biol_rep3_CNhs12791_13216-141H3_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr20minBiolRep3_CNhs12791_ctss_fwd Tc:K562ToHemin_01hr20minBr3+ K562 erythroblastic leukemia response to hemin, 01hr20min, biol_rep3_CNhs12791_13216-141H3_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr20minBiolRep2_CNhs12690_ctss_rev Tc:K562ToHemin_01hr20minBr2- K562 erythroblastic leukemia response to hemin, 01hr20min, biol_rep2_CNhs12690_13150-140I9_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr20minBiolRep2_CNhs12690_ctss_fwd Tc:K562ToHemin_01hr20minBr2+ K562 erythroblastic leukemia response to hemin, 01hr20min, biol_rep2_CNhs12690_13150-140I9_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr20minBiolRep1_CNhs12463_ctss_rev Tc:K562ToHemin_01hr20minBr1- K562 erythroblastic leukemia response to hemin, 01hr20min, biol_rep1_CNhs12463_13084-140B6_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr20minBiolRep1_CNhs12463_ctss_fwd Tc:K562ToHemin_01hr20minBr1+ K562 erythroblastic leukemia response to hemin, 01hr20min, biol_rep1_CNhs12463_13084-140B6_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr00minBiolRep3_CNhs12790_ctss_rev Tc:K562ToHemin_01hr00minBr3- K562 erythroblastic leukemia response to hemin, 01hr00min, biol_rep3_CNhs12790_13215-141H2_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr00minBiolRep3_CNhs12790_ctss_fwd Tc:K562ToHemin_01hr00minBr3+ K562 erythroblastic leukemia response to hemin, 01hr00min, biol_rep3_CNhs12790_13215-141H2_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr00minBiolRep2_CNhs12689_ctss_rev Tc:K562ToHemin_01hr00minBr2- K562 erythroblastic leukemia response to hemin, 01hr00min, biol_rep2_CNhs12689_13149-140I8_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr00minBiolRep2_CNhs12689_ctss_fwd Tc:K562ToHemin_01hr00minBr2+ K562 erythroblastic leukemia response to hemin, 01hr00min, biol_rep2_CNhs12689_13149-140I8_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr00minBiolRep1_CNhs12462_ctss_rev Tc:K562ToHemin_01hr00minBr1- K562 erythroblastic leukemia response to hemin, 01hr00min, biol_rep1_CNhs12462_13083-140B5_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr00minBiolRep1_CNhs12462_ctss_fwd Tc:K562ToHemin_01hr00minBr1+ K562 erythroblastic leukemia response to hemin, 01hr00min, biol_rep1_CNhs12462_13083-140B5_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr45minBiolRep3_CNhs12789_ctss_rev Tc:K562ToHemin_00hr45minBr3- K562 erythroblastic leukemia response to hemin, 00hr45min, biol_rep3_CNhs12789_13214-141H1_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr45minBiolRep3_CNhs12789_ctss_fwd Tc:K562ToHemin_00hr45minBr3+ K562 erythroblastic leukemia response to hemin, 00hr45min, biol_rep3_CNhs12789_13214-141H1_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr45minBiolRep2_CNhs12688_ctss_rev Tc:K562ToHemin_00hr45minBr2- K562 erythroblastic leukemia response to hemin, 00hr45min, biol_rep2_CNhs12688_13148-140I7_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr45minBiolRep2_CNhs12688_ctss_fwd Tc:K562ToHemin_00hr45minBr2+ K562 erythroblastic leukemia response to hemin, 00hr45min, biol_rep2_CNhs12688_13148-140I7_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr45minBiolRep1_CNhs12461_ctss_rev Tc:K562ToHemin_00hr45minBr1- K562 erythroblastic leukemia response to hemin, 00hr45min, biol_rep1_CNhs12461_13082-140B4_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr45minBiolRep1_CNhs12461_ctss_fwd Tc:K562ToHemin_00hr45minBr1+ K562 erythroblastic leukemia response to hemin, 00hr45min, biol_rep1_CNhs12461_13082-140B4_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr30minBiolRep3_CNhs12788_ctss_rev Tc:K562ToHemin_00hr30minBr3- K562 erythroblastic leukemia response to hemin, 00hr30min, biol_rep3_CNhs12788_13213-141G9_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr30minBiolRep3_CNhs12788_ctss_fwd Tc:K562ToHemin_00hr30minBr3+ K562 erythroblastic leukemia response to hemin, 00hr30min, biol_rep3_CNhs12788_13213-141G9_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr30minBiolRep2_CNhs12687_ctss_rev Tc:K562ToHemin_00hr30minBr2- K562 erythroblastic leukemia response to hemin, 00hr30min, biol_rep2_CNhs12687_13147-140I6_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr30minBiolRep2_CNhs12687_ctss_fwd Tc:K562ToHemin_00hr30minBr2+ K562 erythroblastic leukemia response to hemin, 00hr30min, biol_rep2_CNhs12687_13147-140I6_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr30minBiolRep1_CNhs12460_ctss_rev Tc:K562ToHemin_00hr30minBr1- K562 erythroblastic leukemia response to hemin, 00hr30min, biol_rep1_CNhs12460_13081-140B3_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr30minBiolRep1_CNhs12460_ctss_fwd Tc:K562ToHemin_00hr30minBr1+ K562 erythroblastic leukemia response to hemin, 00hr30min, biol_rep1_CNhs12460_13081-140B3_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr15minBiolRep3_CNhs12787_ctss_rev Tc:K562ToHemin_00hr15minBr3- K562 erythroblastic leukemia response to hemin, 00hr15min, biol_rep3_CNhs12787_13212-141G8_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr15minBiolRep3_CNhs12787_ctss_fwd Tc:K562ToHemin_00hr15minBr3+ K562 erythroblastic leukemia response to hemin, 00hr15min, biol_rep3_CNhs12787_13212-141G8_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr15minBiolRep2_CNhs12686_ctss_rev Tc:K562ToHemin_00hr15minBr2- K562 erythroblastic leukemia response to hemin, 00hr15min, biol_rep2_CNhs12686_13146-140I5_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr15minBiolRep2_CNhs12686_ctss_fwd Tc:K562ToHemin_00hr15minBr2+ K562 erythroblastic leukemia response to hemin, 00hr15min, biol_rep2_CNhs12686_13146-140I5_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr15minBiolRep1_CNhs12459_ctss_rev Tc:K562ToHemin_00hr15minBr1- K562 erythroblastic leukemia response to hemin, 00hr15min, biol_rep1_CNhs12459_13080-140B2_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr15minBiolRep1_CNhs12459_ctss_fwd Tc:K562ToHemin_00hr15minBr1+ K562 erythroblastic leukemia response to hemin, 00hr15min, biol_rep1_CNhs12459_13080-140B2_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr00minBiolRep3_CNhs12786_ctss_rev Tc:K562ToHemin_00hr00minBr3- K562 erythroblastic leukemia response to hemin, 00hr00min, biol_rep3_CNhs12786_13211-141G7_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr00minBiolRep3_CNhs12786_ctss_fwd Tc:K562ToHemin_00hr00minBr3+ K562 erythroblastic leukemia response to hemin, 00hr00min, biol_rep3_CNhs12786_13211-141G7_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr00minBiolRep2_CNhs12684_ctss_rev Tc:K562ToHemin_00hr00minBr2- K562 erythroblastic leukemia response to hemin, 00hr00min, biol_rep2_CNhs12684_13145-140I4_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr00minBiolRep2_CNhs12684_ctss_fwd Tc:K562ToHemin_00hr00minBr2+ K562 erythroblastic leukemia response to hemin, 00hr00min, biol_rep2_CNhs12684_13145-140I4_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr00minBiolRep1_CNhs12458_ctss_rev Tc:K562ToHemin_00hr00minBr1- K562 erythroblastic leukemia response to hemin, 00hr00min, biol_rep1_CNhs12458_13079-140B1_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr00minBiolRep1_CNhs12458_ctss_fwd Tc:K562ToHemin_00hr00minBr1+ K562 erythroblastic leukemia response to hemin, 00hr00min, biol_rep1_CNhs12458_13079-140B1_forward Regulation 
HIPSBiolRep3_CNhs14216_ctss_rev Tc:hIPSBr3- hIPS, biol_rep3_CNhs14216_14382-156B8_reverse Regulation 
HIPSBiolRep3_CNhs14216_ctss_fwd Tc:hIPSBr3+ hIPS, biol_rep3_CNhs14216_14382-156B8_forward Regulation 
HIPSBiolRep2_CNhs14215_ctss_rev Tc:hIPSBr2- hIPS, biol_rep2_CNhs14215_14381-156B7_reverse Regulation 
HIPSBiolRep2_CNhs14215_ctss_fwd Tc:hIPSBr2+ hIPS, biol_rep2_CNhs14215_14381-156B7_forward Regulation 
HIPSBiolRep1_CNhs14214_ctss_rev Tc:hIPSBr1- hIPS, biol_rep1_CNhs14214_14380-156B6_reverse Regulation 
HIPSBiolRep1_CNhs14214_ctss_fwd Tc:hIPSBr1+ hIPS, biol_rep1_CNhs14214_14380-156B6_forward Regulation 
HIPSCCl2BiolRep3_CNhs14219_ctss_rev Tc:hIPS+CCl2Br3- hIPS +CCl2, biol_rep3_CNhs14219_14385-156C2_reverse Regulation 
HIPSCCl2BiolRep3_CNhs14219_ctss_fwd Tc:hIPS+CCl2Br3+ hIPS +CCl2, biol_rep3_CNhs14219_14385-156C2_forward Regulation 
HIPSCCl2BiolRep2_CNhs14218_ctss_rev Tc:hIPS+CCl2Br2- hIPS +CCl2, biol_rep2_CNhs14218_14384-156C1_reverse Regulation 
HIPSCCl2BiolRep2_CNhs14218_ctss_fwd Tc:hIPS+CCl2Br2+ hIPS +CCl2, biol_rep2_CNhs14218_14384-156C1_forward Regulation 
HIPSCCl2BiolRep1_CNhs14217_ctss_rev Tc:hIPS+CCl2Br1- hIPS +CCl2, biol_rep1_CNhs14217_14383-156B9_reverse Regulation 
HIPSCCl2BiolRep1_CNhs14217_ctss_fwd Tc:hIPS+CCl2Br1+ hIPS +CCl2, biol_rep1_CNhs14217_14383-156B9_forward Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay09BiolRep3_CNhs13971_ctss_rev Tc:H1ToHsc_Day09Br3- H1 embryonic stem cells differentiation to CD34+ HSC, day09, biol_rep3_CNhs13971_13531-145G3_reverse Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay09BiolRep3_CNhs13971_ctss_fwd Tc:H1ToHsc_Day09Br3+ H1 embryonic stem cells differentiation to CD34+ HSC, day09, biol_rep3_CNhs13971_13531-145G3_forward Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay09BiolRep2_CNhs13970_ctss_rev Tc:H1ToHsc_Day09Br2- H1 embryonic stem cells differentiation to CD34+ HSC, day09, biol_rep2_CNhs13970_13530-145G2_reverse Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay09BiolRep2_CNhs13970_ctss_fwd Tc:H1ToHsc_Day09Br2+ H1 embryonic stem cells differentiation to CD34+ HSC, day09, biol_rep2_CNhs13970_13530-145G2_forward Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay09BiolRep1_CNhs13969_ctss_rev Tc:H1ToHsc_Day09Br1- H1 embryonic stem cells differentiation to CD34+ HSC, day09, biol_rep1_CNhs13969_13529-145G1_reverse Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay09BiolRep1_CNhs13969_ctss_fwd Tc:H1ToHsc_Day09Br1+ H1 embryonic stem cells differentiation to CD34+ HSC, day09, biol_rep1_CNhs13969_13529-145G1_forward Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay03BiolRep3_CNhs13968_ctss_rev Tc:H1ToHsc_Day03Br3- H1 embryonic stem cells differentiation to CD34+ HSC, day03, biol_rep3_CNhs13968_13528-145F9_reverse Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay03BiolRep3_CNhs13968_ctss_fwd Tc:H1ToHsc_Day03Br3+ H1 embryonic stem cells differentiation to CD34+ HSC, day03, biol_rep3_CNhs13968_13528-145F9_forward Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay03BiolRep2_CNhs13966_ctss_rev Tc:H1ToHsc_Day03Br2- H1 embryonic stem cells differentiation to CD34+ HSC, day03, biol_rep2_CNhs13966_13527-145F8_reverse Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay03BiolRep2_CNhs13966_ctss_fwd Tc:H1ToHsc_Day03Br2+ H1 embryonic stem cells differentiation to CD34+ HSC, day03, biol_rep2_CNhs13966_13527-145F8_forward Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay03BiolRep1_CNhs13965_ctss_rev Tc:H1ToHsc_Day03Br1- H1 embryonic stem cells differentiation to CD34+ HSC, day03, biol_rep1_CNhs13965_13526-145F7_reverse Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay03BiolRep1_CNhs13965_ctss_fwd Tc:H1ToHsc_Day03Br1+ H1 embryonic stem cells differentiation to CD34+ HSC, day03, biol_rep1_CNhs13965_13526-145F7_forward Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay00BiolRep3_CNhs13964_ctss_rev Tc:H1ToHsc_Day00Br3- H1 embryonic stem cells differentiation to CD34+ HSC, day00, biol_rep3_CNhs13964_13525-145F6_reverse Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay00BiolRep3_CNhs13964_ctss_fwd Tc:H1ToHsc_Day00Br3+ H1 embryonic stem cells differentiation to CD34+ HSC, day00, biol_rep3_CNhs13964_13525-145F6_forward Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay00BiolRep2_CNhs14068_ctss_rev Tc:H1ToHsc_Day00Br2- H1 embryonic stem cells differentiation to CD34+ HSC, day00, biol_rep2_CNhs14068_13524-145F5_reverse Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay00BiolRep2_CNhs14068_ctss_fwd Tc:H1ToHsc_Day00Br2+ H1 embryonic stem cells differentiation to CD34+ HSC, day00, biol_rep2_CNhs14068_13524-145F5_forward Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay00BiolRep1_CNhs14067_ctss_rev Tc:H1ToHsc_Day00Br1- H1 embryonic stem cells differentiation to CD34+ HSC, day00, biol_rep1_CNhs14067_13523-145F4_reverse Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay00BiolRep1_CNhs14067_ctss_fwd Tc:H1ToHsc_Day00Br1+ H1 embryonic stem cells differentiation to CD34+ HSC, day00, biol_rep1_CNhs14067_13523-145F4_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha24hr00minBiolRep2_CNhs14536_ctss_rev Tc:ARPE-19Emt_24hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 24hr00min, biol_rep2_CNhs14536_13680-147E8_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha24hr00minBiolRep2_CNhs14536_ctss_fwd Tc:ARPE-19Emt_24hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 24hr00min, biol_rep2_CNhs14536_13680-147E8_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha06hr00minBiolRep2_CNhs14520_ctss_rev Tc:ARPE-19Emt_06hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 06hr00min, biol_rep2_CNhs14520_13665-147D2_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha06hr00minBiolRep2_CNhs14520_ctss_fwd Tc:ARPE-19Emt_06hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 06hr00min, biol_rep2_CNhs14520_13665-147D2_forward Regulation 
MyoblastDifferentiationToMyotubesDay10ControlDonor3_CNhs14584_ctss_rev MyoblastToMyotubes_Day10D3- Myoblast differentiation to myotubes, day10, control donor3_CNhs14584_13494-145C2_reverse Regulation 
MyoblastDifferentiationToMyotubesDay10ControlDonor3_CNhs14584_ctss_fwd MyoblastToMyotubes_Day10D3+ Myoblast differentiation to myotubes, day10, control donor3_CNhs14584_13494-145C2_forward Regulation 
MyoblastDifferentiationToMyotubesDay06DuchenneMuscularDystrophyDonor2_CNhs14601_ctss_rev MyoblastToMyotubes_Day06D2- Myoblast differentiation to myotubes, day06, Duchenne Muscular Dystrophy donor2_CNhs14601_13510-145D9_reverse Regulation 
MyoblastDifferentiationToMyotubesDay06DuchenneMuscularDystrophyDonor2_CNhs14601_ctss_fwd MyoblastToMyotubes_Day06D2+ Myoblast differentiation to myotubes, day06, Duchenne Muscular Dystrophy donor2_CNhs14601_13510-145D9_forward Regulation 
MyoblastDifferentiationToMyotubesDay01ControlDonor2_CNhs14568_ctss_rev MyoblastToMyotubes_Day01D2- Myoblast differentiation to myotubes, day01, control donor2_CNhs14568_13479-145A5_reverse Regulation 
MyoblastDifferentiationToMyotubesDay01ControlDonor2_CNhs14568_ctss_fwd MyoblastToMyotubes_Day01D2+ Myoblast differentiation to myotubes, day01, control donor2_CNhs14568_13479-145A5_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay14BiolRep2_CNhs13631_ctss_rev MscAdipogenicInduction_Day14Br2- mesenchymal stem cells (adipose derived), adipogenic induction, day14, biol_rep2_CNhs13631_13278-142F2_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay14BiolRep2_CNhs13631_ctss_fwd MscAdipogenicInduction_Day14Br2+ mesenchymal stem cells (adipose derived), adipogenic induction, day14, biol_rep2_CNhs13631_13278-142F2_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay04BiolRep2_CNhs13623_ctss_rev MscAdipogenicInduction_Day04Br2- mesenchymal stem cells (adipose derived), adipogenic induction, day04, biol_rep2_CNhs13623_13269-142E2_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay04BiolRep2_CNhs13623_ctss_fwd MscAdipogenicInduction_Day04Br2+ mesenchymal stem cells (adipose derived), adipogenic induction, day04, biol_rep2_CNhs13623_13269-142E2_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay01BiolRep1_CNhs13615_ctss_rev MscAdipogenicInduction_Day01Br1- mesenchymal stem cells (adipose derived), adipogenic induction, day01, biol_rep1_CNhs13615_13262-142D4_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay01BiolRep1_CNhs13615_ctss_fwd MscAdipogenicInduction_Day01Br1+ mesenchymal stem cells (adipose derived), adipogenic induction, day01, biol_rep1_CNhs13615_13262-142D4_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction12hr00minBiolRep3_CNhs13614_ctss_rev MscAdipogenicInduction_12hr00minBr3- mesenchymal stem cells (adipose derived), adipogenic induction, 12hr00min, biol_rep3_CNhs13614_13261-142D3_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction12hr00minBiolRep3_CNhs13614_ctss_fwd MscAdipogenicInduction_12hr00minBr3+ mesenchymal stem cells (adipose derived), adipogenic induction, 12hr00min, biol_rep3_CNhs13614_13261-142D3_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction12hr00minBiolRep2_CNhs13613_ctss_rev MscAdipogenicInduction_12hr00minBr2- mesenchymal stem cells (adipose derived), adipogenic induction, 12hr00min, biol_rep2_CNhs13613_13260-142D2_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction12hr00minBiolRep2_CNhs13613_ctss_fwd MscAdipogenicInduction_12hr00minBr2+ mesenchymal stem cells (adipose derived), adipogenic induction, 12hr00min, biol_rep2_CNhs13613_13260-142D2_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction12hr00minBiolRep1_CNhs13612_ctss_rev MscAdipogenicInduction_12hr00minBr1- mesenchymal stem cells (adipose derived), adipogenic induction, 12hr00min, biol_rep1_CNhs13612_13259-142D1_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction12hr00minBiolRep1_CNhs13612_ctss_fwd MscAdipogenicInduction_12hr00minBr1+ mesenchymal stem cells (adipose derived), adipogenic induction, 12hr00min, biol_rep1_CNhs13612_13259-142D1_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction03hr00minBiolRep2_CNhs13610_ctss_rev MscAdipogenicInduction_03hr00minBr2- mesenchymal stem cells (adipose derived), adipogenic induction, 03hr00min, biol_rep2_CNhs13610_13257-142C8_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction03hr00minBiolRep2_CNhs13610_ctss_fwd MscAdipogenicInduction_03hr00minBr2+ mesenchymal stem cells (adipose derived), adipogenic induction, 03hr00min, biol_rep2_CNhs13610_13257-142C8_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr30minBiolRep2_CNhs13607_ctss_rev MscAdipogenicInduction_02hr30minBr2- mesenchymal stem cells (adipose derived), adipogenic induction, 02hr30min, biol_rep2_CNhs13607_13254-142C5_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr30minBiolRep2_CNhs13607_ctss_fwd MscAdipogenicInduction_02hr30minBr2+ mesenchymal stem cells (adipose derived), adipogenic induction, 02hr30min, biol_rep2_CNhs13607_13254-142C5_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr00minBiolRep3_CNhs13605_ctss_rev MscAdipogenicInduction_02hr00minBr3- mesenchymal stem cells (adipose derived), adipogenic induction, 02hr00min, biol_rep3_CNhs13605_13252-142C3_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr00minBiolRep3_CNhs13605_ctss_fwd MscAdipogenicInduction_02hr00minBr3+ mesenchymal stem cells (adipose derived), adipogenic induction, 02hr00min, biol_rep3_CNhs13605_13252-142C3_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr20minBiolRep3_CNhs13599_ctss_rev MscAdipogenicInduction_01hr20minBr3- mesenchymal stem cells (adipose derived), adipogenic induction, 01hr20min, biol_rep3_CNhs13599_13246-142B6_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr20minBiolRep3_CNhs13599_ctss_fwd MscAdipogenicInduction_01hr20minBr3+ mesenchymal stem cells (adipose derived), adipogenic induction, 01hr20min, biol_rep3_CNhs13599_13246-142B6_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr20minBiolRep2_CNhs13598_ctss_rev MscAdipogenicInduction_01hr20minBr2- mesenchymal stem cells (adipose derived), adipogenic induction, 01hr20min, biol_rep2_CNhs13598_13245-142B5_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr20minBiolRep2_CNhs13598_ctss_fwd MscAdipogenicInduction_01hr20minBr2+ mesenchymal stem cells (adipose derived), adipogenic induction, 01hr20min, biol_rep2_CNhs13598_13245-142B5_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr20minBiolRep1_CNhs13434_ctss_rev MscAdipogenicInduction_01hr20minBr1- mesenchymal stem cells (adipose derived), adipogenic induction, 01hr20min, biol_rep1_CNhs13434_13244-142B4_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr20minBiolRep1_CNhs13434_ctss_fwd MscAdipogenicInduction_01hr20minBr1+ mesenchymal stem cells (adipose derived), adipogenic induction, 01hr20min, biol_rep1_CNhs13434_13244-142B4_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr30minBiolRep3_CNhs13427_ctss_rev MscAdipogenicInduction_00hr30minBr3- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr30min, biol_rep3_CNhs13427_13237-142A6_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr30minBiolRep3_CNhs13427_ctss_fwd MscAdipogenicInduction_00hr30minBr3+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr30min, biol_rep3_CNhs13427_13237-142A6_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection24hr00minDonor4227_121Ud_24h_CNhs13643_ctss_rev MonocyteMacrophageUdornInfluenza_24hr00minD4- Monocyte-derived macrophages response to udorn influenza infection, 24hr00min, donor4 (227_121:Ud_24h)_CNhs13643_13314-143A2_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection24hr00minDonor4227_121Ud_24h_CNhs13643_ctss_fwd MonocyteMacrophageUdornInfluenza_24hr00minD4+ Monocyte-derived macrophages response to udorn influenza infection, 24hr00min, donor4 (227_121:Ud_24h)_CNhs13643_13314-143A2_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection02hr00minDonor2150_120Ud_2h_CNhs13647_ctss_rev MonocyteMacrophageUdornInfluenza_02hr00minD2- Monocyte-derived macrophages response to udorn influenza infection, 02hr00min, donor2 (150_120:Ud_2h)_CNhs13647_13318-143A6_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection02hr00minDonor2150_120Ud_2h_CNhs13647_ctss_fwd MonocyteMacrophageUdornInfluenza_02hr00minD2+ Monocyte-derived macrophages response to udorn influenza infection, 02hr00min, donor2 (150_120:Ud_2h)_CNhs13647_13318-143A6_forward Regulation 
MelanocyteDonor3MC3_CNhs13406_ctss_rev MelanocyteD3- Melanocyte, donor3 (MC+3)_CNhs13406_12837-137B2_reverse Regulation 
MelanocyteDonor3MC3_CNhs13406_ctss_fwd MelanocyteD3+ Melanocyte, donor3 (MC+3)_CNhs13406_12837-137B2_forward Regulation 
MelanocyteDonor2MC2_CNhs13156_ctss_rev MelanocyteD2- Melanocyte, donor2 (MC+2)_CNhs13156_12739-135I3_reverse Regulation 
MelanocyteDonor2MC2_CNhs13156_ctss_fwd MelanocyteD2+ Melanocyte, donor2 (MC+2)_CNhs13156_12739-135I3_forward Regulation 
MelanocyteDonor1MC1_CNhs12816_ctss_rev MelanocyteD1- Melanocyte, donor1 (MC+1)_CNhs12816_12641-134G4_reverse Regulation 
MelanocyteDonor1MC1_CNhs12816_ctss_fwd MelanocyteD1+ Melanocyte, donor1 (MC+1)_CNhs12816_12641-134G4_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay07BiolRep1_CNhs13659_ctss_rev Hes3-gfpCardiomyocyticInduction_Day07Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day07, biol_rep1_CNhs13659_13334-143C4_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay07BiolRep1_CNhs13659_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day07Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day07, biol_rep1_CNhs13659_13334-143C4_forward Regulation 
H9EmbryonicStemCellsBiolRep3H9ES3_CNhs12837_ctss_rev H9EmbryonicStemCellsBr3- H9 Embryonic Stem cells, biol_rep3 (H9ES-3)_CNhs12837_12822-136I5_reverse Regulation 
H9EmbryonicStemCellsBiolRep3H9ES3_CNhs12837_ctss_fwd H9EmbryonicStemCellsBr3+ H9 Embryonic Stem cells, biol_rep3 (H9ES-3)_CNhs12837_12822-136I5_forward Regulation 
H9EmbryonicStemCellsBiolRep2H9ES2_CNhs12824_ctss_rev H9EmbryonicStemCellsBr2- H9 Embryonic Stem cells, biol_rep2 (H9ES-2)_CNhs12824_12724-135G6_reverse Regulation 
H9EmbryonicStemCellsBiolRep2H9ES2_CNhs12824_ctss_fwd H9EmbryonicStemCellsBr2+ H9 Embryonic Stem cells, biol_rep2 (H9ES-2)_CNhs12824_12724-135G6_forward Regulation 
H9EmbryonicStemCellsBiolRep1H9ES1_CNhs11917_ctss_rev H9EmbryonicStemCellsBr1- H9 Embryonic Stem cells, biol_rep1 (H9ES-1)_CNhs11917_12626-134E7_reverse Regulation 
H9EmbryonicStemCellsBiolRep1H9ES1_CNhs11917_ctss_fwd H9EmbryonicStemCellsBr1+ H9 Embryonic Stem cells, biol_rep1 (H9ES-1)_CNhs11917_12626-134E7_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b05hrBiolRep3LK57_CNhs13585_ctss_rev AorticSmsToIL1b_05hrBr3- Aortic smooth muscle cell response to IL1b, 05hr, biol_rep3 (LK57)_CNhs13585_12856-137D3_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b05hrBiolRep3LK57_CNhs13585_ctss_fwd AorticSmsToIL1b_05hrBr3+ Aortic smooth muscle cell response to IL1b, 05hr, biol_rep3 (LK57)_CNhs13585_12856-137D3_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b03hrBiolRep3LK51_CNhs13583_ctss_rev AorticSmsToIL1b_03hrBr3- Aortic smooth muscle cell response to IL1b, 03hr, biol_rep3 (LK51)_CNhs13583_12854-137D1_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b03hrBiolRep3LK51_CNhs13583_ctss_fwd AorticSmsToIL1b_03hrBr3+ Aortic smooth muscle cell response to IL1b, 03hr, biol_rep3 (LK51)_CNhs13583_12854-137D1_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b02hrBiolRep1LK46_CNhs13354_ctss_rev AorticSmsToIL1b_02hrBr1- Aortic smooth muscle cell response to IL1b, 02hr, biol_rep1 (LK46)_CNhs13354_12657-134I2_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b02hrBiolRep1LK46_CNhs13354_ctss_fwd AorticSmsToIL1b_02hrBr1+ Aortic smooth muscle cell response to IL1b, 02hr, biol_rep1 (LK46)_CNhs13354_12657-134I2_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b01hrBiolRep3LK45_CNhs13581_ctss_rev AorticSmsToIL1b_01hrBr3- Aortic smooth muscle cell response to IL1b, 01hr, biol_rep3 (LK45)_CNhs13581_12852-137C8_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b01hrBiolRep3LK45_CNhs13581_ctss_fwd AorticSmsToIL1b_01hrBr3+ Aortic smooth muscle cell response to IL1b, 01hr, biol_rep3 (LK45)_CNhs13581_12852-137C8_forward Regulation 
AorticSmoothMuscleCellResponseToFGF204hrBiolRep3LK24_CNhs13574_ctss_rev AorticSmsToFgf2_04hrBr3- Aortic smooth muscle cell response to FGF2, 04hr, biol_rep3 (LK24)_CNhs13574_12845-137C1_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF204hrBiolRep3LK24_CNhs13574_ctss_fwd AorticSmsToFgf2_04hrBr3+ Aortic smooth muscle cell response to FGF2, 04hr, biol_rep3 (LK24)_CNhs13574_12845-137C1_forward Regulation 
AorticSmoothMuscleCellResponseToFGF204hrBiolRep2LK23_CNhs13365_ctss_rev AorticSmsToFgf2_04hrBr2- Aortic smooth muscle cell response to FGF2, 04hr, biol_rep2 (LK23)_CNhs13365_12747-136A2_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF204hrBiolRep2LK23_CNhs13365_ctss_fwd AorticSmsToFgf2_04hrBr2+ Aortic smooth muscle cell response to FGF2, 04hr, biol_rep2 (LK23)_CNhs13365_12747-136A2_forward Regulation 
AorticSmoothMuscleCellResponseToFGF204hrBiolRep1LK22_CNhs13346_ctss_rev AorticSmsToFgf2_04hrBr1- Aortic smooth muscle cell response to FGF2, 04hr, biol_rep1 (LK22)_CNhs13346_12649-134H3_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF204hrBiolRep1LK22_CNhs13346_ctss_fwd AorticSmsToFgf2_04hrBr1+ Aortic smooth muscle cell response to FGF2, 04hr, biol_rep1 (LK22)_CNhs13346_12649-134H3_forward Regulation 
AorticSmoothMuscleCellResponseToFGF201hrBiolRep2LK14_CNhs13362_ctss_rev AorticSmsToFgf2_01hrBr2- Aortic smooth muscle cell response to FGF2, 01hr, biol_rep2 (LK14)_CNhs13362_12744-135I8_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF201hrBiolRep2LK14_CNhs13362_ctss_fwd AorticSmsToFgf2_01hrBr2+ Aortic smooth muscle cell response to FGF2, 01hr, biol_rep2 (LK14)_CNhs13362_12744-135I8_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr00minBiolRep3LK3_CNhs13567_ctss_rev AorticSmsToFgf2_00hr00minBr3- Aortic smooth muscle cell response to FGF2, 00hr00min, biol_rep3 (LK3)_CNhs13567_12838-137B3_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr00minBiolRep3LK3_CNhs13567_ctss_fwd AorticSmsToFgf2_00hr00minBr3+ Aortic smooth muscle cell response to FGF2, 00hr00min, biol_rep3 (LK3)_CNhs13567_12838-137B3_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr00minBiolRep1_CNhs12564_ctss_rev Mcf7ToEgf1_00hr00minBr1- MCF7 breast cancer cell line response to EGF1, 00hr00min, biol_rep1_CNhs12564_13031-139E7_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr00minBiolRep1_CNhs12564_ctss_fwd Mcf7ToEgf1_00hr00minBr1+ MCF7 breast cancer cell line response to EGF1, 00hr00min, biol_rep1_CNhs12564_13031-139E7_forward Regulation 
WholeBloodRibopureDonor090612Donation3_CNhs11949_ctss_rev WholeBloodD090612Dn3- Whole blood (ribopure), donor090612, donation3_CNhs11949_12184-129A6_reverse Regulation 
WholeBloodRibopureDonor090612Donation3_CNhs11949_ctss_fwd WholeBloodD090612Dn3+ Whole blood (ribopure), donor090612, donation3_CNhs11949_12184-129A6_forward Regulation 
WholeBloodRibopureDonor090612Donation2_CNhs11673_ctss_rev WholeBloodD090612Dn2- Whole blood (ribopure), donor090612, donation2_CNhs11673_12183-129A5_reverse Regulation 
WholeBloodRibopureDonor090612Donation2_CNhs11673_ctss_fwd WholeBloodD090612Dn2+ Whole blood (ribopure), donor090612, donation2_CNhs11673_12183-129A5_forward Regulation 
WholeBloodRibopureDonor090612Donation1_CNhs11672_ctss_rev WholeBloodD090612Dn1- Whole blood (ribopure), donor090612, donation1_CNhs11672_12182-129A4_reverse Regulation 
WholeBloodRibopureDonor090612Donation1_CNhs11672_ctss_fwd WholeBloodD090612Dn1+ Whole blood (ribopure), donor090612, donation1_CNhs11672_12182-129A4_forward Regulation 
WholeBloodRibopureDonor090325Donation2_CNhs11076_ctss_rev WholeBloodD090325Dn2- Whole blood (ribopure), donor090325, donation2_CNhs11076_12177-128I8_reverse Regulation 
WholeBloodRibopureDonor090325Donation2_CNhs11076_ctss_fwd WholeBloodD090325Dn2+ Whole blood (ribopure), donor090325, donation2_CNhs11076_12177-128I8_forward Regulation 
WholeBloodRibopureDonor090325Donation1_CNhs11075_ctss_rev WholeBloodD090325Dn1- Whole blood (ribopure), donor090325, donation1_CNhs11075_12176-128I7_reverse Regulation 
WholeBloodRibopureDonor090325Donation1_CNhs11075_ctss_fwd WholeBloodD090325Dn1+ Whole blood (ribopure), donor090325, donation1_CNhs11075_12176-128I7_forward Regulation 
WholeBloodRibopureDonor090309Donation3_CNhs11948_ctss_rev WholeBloodD090309Dn3- Whole blood (ribopure), donor090309, donation3_CNhs11948_12181-129A3_reverse Regulation 
WholeBloodRibopureDonor090309Donation3_CNhs11948_ctss_fwd WholeBloodD090309Dn3+ Whole blood (ribopure), donor090309, donation3_CNhs11948_12181-129A3_forward Regulation 
WholeBloodRibopureDonor090309Donation2_CNhs11671_ctss_rev WholeBloodD090309Dn2- Whole blood (ribopure), donor090309, donation2_CNhs11671_12180-129A2_reverse Regulation 
WholeBloodRibopureDonor090309Donation2_CNhs11671_ctss_fwd WholeBloodD090309Dn2+ Whole blood (ribopure), donor090309, donation2_CNhs11671_12180-129A2_forward Regulation 
WholeBloodRibopureDonor090309Donation1_CNhs11675_ctss_rev WholeBloodD090309Dn1- Whole blood (ribopure), donor090309, donation1_CNhs11675_12179-129A1_reverse Regulation 
WholeBloodRibopureDonor090309Donation1_CNhs11675_ctss_fwd WholeBloodD090309Dn1+ Whole blood (ribopure), donor090309, donation1_CNhs11675_12179-129A1_forward Regulation 
UrothelialCellsDonor3_CNhs12122_ctss_rev UrothelialCellsD3- Urothelial Cells, donor3_CNhs12122_11681-122H7_reverse Regulation 
UrothelialCellsDonor3_CNhs12122_ctss_fwd UrothelialCellsD3+ Urothelial Cells, donor3_CNhs12122_11681-122H7_forward Regulation 
UrothelialCellsDonor2_CNhs12091_ctss_rev UrothelialCellsD2- Urothelial Cells, donor2_CNhs12091_11600-120H7_reverse Regulation 
UrothelialCellsDonor2_CNhs12091_ctss_fwd UrothelialCellsD2+ Urothelial Cells, donor2_CNhs12091_11600-120H7_forward Regulation 
UrothelialCellsDonor1_CNhs11334_ctss_rev UrothelialCellsD1- Urothelial Cells, donor1_CNhs11334_11520-119H8_reverse Regulation 
UrothelialCellsDonor1_CNhs11334_ctss_fwd UrothelialCellsD1+ Urothelial Cells, donor1_CNhs11334_11520-119H8_forward Regulation 
UrothelialCellsDonor0_CNhs10843_ctss_rev UrothelialCellsD0- Urothelial cells, donor0_CNhs10843_11216-116B1_reverse Regulation 
UrothelialCellsDonor0_CNhs10843_ctss_fwd UrothelialCellsD0+ Urothelial cells, donor0_CNhs10843_11216-116B1_forward Regulation 
TrachealEpithelialCellsDonor3_CNhs12051_ctss_rev TrachealEpithelialCellsD3- Tracheal Epithelial Cells, donor3_CNhs12051_11441-118I1_reverse Regulation 
TrachealEpithelialCellsDonor3_CNhs12051_ctss_fwd TrachealEpithelialCellsD3+ Tracheal Epithelial Cells, donor3_CNhs12051_11441-118I1_forward Regulation 
TrachealEpithelialCellsDonor2_CNhs11993_ctss_rev TrachealEpithelialCellsD2- Tracheal Epithelial Cells, donor2_CNhs11993_11369-118A1_reverse Regulation 
TrachealEpithelialCellsDonor2_CNhs11993_ctss_fwd TrachealEpithelialCellsD2+ Tracheal Epithelial Cells, donor2_CNhs11993_11369-118A1_forward Regulation 
TrachealEpithelialCellsDonor1_CNhs11092_ctss_rev TrachealEpithelialCellsD1- Tracheal Epithelial Cells, donor1_CNhs11092_11292-117A5_reverse Regulation 
TrachealEpithelialCellsDonor1_CNhs11092_ctss_fwd TrachealEpithelialCellsD1+ Tracheal Epithelial Cells, donor1_CNhs11092_11292-117A5_forward Regulation 
TrabecularMeshworkCellsDonor3_CNhs12124_ctss_rev TrabecularMeshworkCellsD3- Trabecular Meshwork Cells, donor3_CNhs12124_11693-123A1_reverse Regulation 
TrabecularMeshworkCellsDonor3_CNhs12124_ctss_fwd TrabecularMeshworkCellsD3+ Trabecular Meshwork Cells, donor3_CNhs12124_11693-123A1_forward Regulation 
TrabecularMeshworkCellsDonor2_CNhs12097_ctss_rev TrabecularMeshworkCellsD2- Trabecular Meshwork Cells, donor2_CNhs12097_11612-122A1_reverse Regulation 
TrabecularMeshworkCellsDonor2_CNhs12097_ctss_fwd TrabecularMeshworkCellsD2+ Trabecular Meshwork Cells, donor2_CNhs12097_11612-122A1_forward Regulation 
TrabecularMeshworkCellsDonor1_CNhs11340_ctss_rev TrabecularMeshworkCellsD1- Trabecular Meshwork Cells, donor1_CNhs11340_11532-120A2_reverse Regulation 
TrabecularMeshworkCellsDonor1_CNhs11340_ctss_fwd TrabecularMeshworkCellsD1+ Trabecular Meshwork Cells, donor1_CNhs11340_11532-120A2_forward Regulation 
TenocyteDonor3_CNhs12641_ctss_rev TenocyteD3- tenocyte, donor3_CNhs12641_11768-123I4_reverse Regulation 
TenocyteDonor3_CNhs12641_ctss_fwd TenocyteD3+ tenocyte, donor3_CNhs12641_11768-123I4_forward Regulation 
TenocyteDonor2_CNhs12640_ctss_rev TenocyteD2- tenocyte, donor2_CNhs12640_11765-123I1_reverse Regulation 
TenocyteDonor2_CNhs12640_ctss_fwd TenocyteD2+ tenocyte, donor2_CNhs12640_11765-123I1_forward Regulation 
TenocyteDonor1_CNhs12639_ctss_rev TenocyteD1- tenocyte, donor1_CNhs12639_11763-123H8_reverse Regulation 
TenocyteDonor1_CNhs12639_ctss_fwd TenocyteD1+ tenocyte, donor1_CNhs12639_11763-123H8_forward Regulation 
SynoviocyteDonor3_CNhs12050_ctss_rev SynoviocyteD3- Synoviocyte, donor3_CNhs12050_11440-118H9_reverse Regulation 
SynoviocyteDonor3_CNhs12050_ctss_fwd SynoviocyteD3+ Synoviocyte, donor3_CNhs12050_11440-118H9_forward Regulation 
SynoviocyteDonor2_CNhs11992_ctss_rev SynoviocyteD2- Synoviocyte, donor2_CNhs11992_11368-117I9_reverse Regulation 
SynoviocyteDonor2_CNhs11992_ctss_fwd SynoviocyteD2+ Synoviocyte, donor2_CNhs11992_11368-117I9_forward Regulation 
SynoviocyteDonor1_CNhs11068_ctss_rev SynoviocyteD1- Synoviocyte, donor1_CNhs11068_11291-117A4_reverse Regulation 
SynoviocyteDonor1_CNhs11068_ctss_fwd SynoviocyteD1+ Synoviocyte, donor1_CNhs11068_11291-117A4_forward Regulation 
SmoothMuscleCellsUterineDonor3_CNhs11927_ctss_rev SmcUterineD3- Smooth Muscle Cells - Uterine, donor3_CNhs11927_11466-119B8_reverse Regulation 
SmoothMuscleCellsUterineDonor3_CNhs11927_ctss_fwd SmcUterineD3+ Smooth Muscle Cells - Uterine, donor3_CNhs11927_11466-119B8_forward Regulation 
SmoothMuscleCellsUterineDonor1_CNhs11921_ctss_rev SmcUterineD1- Smooth Muscle Cells - Uterine, donor1_CNhs11921_11258-116F7_reverse Regulation 
SmoothMuscleCellsUterineDonor1_CNhs11921_ctss_fwd SmcUterineD1+ Smooth Muscle Cells - Uterine, donor1_CNhs11921_11258-116F7_forward Regulation 
SmoothMuscleCellsUmbilicalVeinDonor3_CNhs13076_ctss_rev SmcUmbilicalVeinD3- Smooth Muscle Cells - Umbilical Vein, donor3_CNhs13076_11702-123B1_reverse Regulation 
SmoothMuscleCellsUmbilicalVeinDonor3_CNhs13076_ctss_fwd SmcUmbilicalVeinD3+ Smooth Muscle Cells - Umbilical Vein, donor3_CNhs13076_11702-123B1_forward Regulation 
SmoothMuscleCellsUmbilicalVeinDonor2_CNhs12569_ctss_rev SmcUmbilicalVeinD2- Smooth Muscle Cells - Umbilical Vein, donor2_CNhs12569_11621-122B1_reverse Regulation 
SmoothMuscleCellsUmbilicalVeinDonor2_CNhs12569_ctss_fwd SmcUmbilicalVeinD2+ Smooth Muscle Cells - Umbilical Vein, donor2_CNhs12569_11621-122B1_forward Regulation 
SmoothMuscleCellsUmbilicalVeinDonor1_CNhs12597_ctss_rev SmcUmbilicalVeinD1- Smooth Muscle Cells - Umbilical Vein, donor1_CNhs12597_11541-120B2_reverse Regulation 
SmoothMuscleCellsUmbilicalVeinDonor1_CNhs12597_ctss_fwd SmcUmbilicalVeinD1+ Smooth Muscle Cells - Umbilical Vein, donor1_CNhs12597_11541-120B2_forward Regulation 
SmoothMuscleCellsUmbilicalArteryDonor3_CNhs12049_ctss_rev SmcUmbilicalArteryD3- Smooth Muscle Cells - Umbilical Artery, donor3_CNhs12049_11439-118H8_reverse Regulation 
SmoothMuscleCellsUmbilicalArteryDonor3_CNhs12049_ctss_fwd SmcUmbilicalArteryD3+ Smooth Muscle Cells - Umbilical Artery, donor3_CNhs12049_11439-118H8_forward Regulation 
SmoothMuscleCellsUmbilicalArteryDonor2_CNhs11991_ctss_rev SmcUmbilicalArteryD2- Smooth Muscle Cells - Umbilical Artery, donor2_CNhs11991_11367-117I8_reverse Regulation 
SmoothMuscleCellsUmbilicalArteryDonor2_CNhs11991_ctss_fwd SmcUmbilicalArteryD2+ Smooth Muscle Cells - Umbilical Artery, donor2_CNhs11991_11367-117I8_forward Regulation 
SmoothMuscleCellsUmbilicalArteryDonor1_CNhs11091_ctss_rev SmcUmbilicalArteryD1- Smooth Muscle Cells - Umbilical Artery, donor1_CNhs11091_11290-117A3_reverse Regulation 
SmoothMuscleCellsUmbilicalArteryDonor1_CNhs11091_ctss_fwd SmcUmbilicalArteryD1+ Smooth Muscle Cells - Umbilical Artery, donor1_CNhs11091_11290-117A3_forward Regulation 
SmoothMuscleCellsUmbilicalArteryDonor0_CNhs10839_ctss_rev SmcUmbilicalArteryD0- Smooth Muscle Cells - Umbilical artery, donor0_CNhs10839_11212-116A6_reverse Regulation 
SmoothMuscleCellsUmbilicalArteryDonor0_CNhs10839_ctss_fwd SmcUmbilicalArteryD0+ Smooth Muscle Cells - Umbilical artery, donor0_CNhs10839_11212-116A6_forward Regulation 
SmoothMuscleCellsTrachealDonor3_CNhs12894_ctss_rev SmcTrachealD3- Smooth Muscle Cells - Tracheal, donor3_CNhs12894_11674-122G9_reverse Regulation 
SmoothMuscleCellsTrachealDonor3_CNhs12894_ctss_fwd SmcTrachealD3+ Smooth Muscle Cells - Tracheal, donor3_CNhs12894_11674-122G9_forward Regulation 
SmoothMuscleCellsTrachealDonor2_CNhs12567_ctss_rev SmcTrachealD2- Smooth Muscle Cells - Tracheal, donor2_CNhs12567_11593-120G9_reverse Regulation 
SmoothMuscleCellsTrachealDonor2_CNhs12567_ctss_fwd SmcTrachealD2+ Smooth Muscle Cells - Tracheal, donor2_CNhs12567_11593-120G9_forward Regulation 
SmoothMuscleCellsTrachealDonor1_CNhs11329_ctss_rev SmcTrachealD1- Smooth Muscle Cells - Tracheal, donor1_CNhs11329_11513-119H1_reverse Regulation 
SmoothMuscleCellsTrachealDonor1_CNhs11329_ctss_fwd SmcTrachealD1+ Smooth Muscle Cells - Tracheal, donor1_CNhs11329_11513-119H1_forward Regulation 
SmoothMuscleCellsSubclavianArteryDonor3_CNhs12048_ctss_rev SmcSubclavianArteryD3- Smooth Muscle Cells - Subclavian Artery, donor3_CNhs12048_11438-118H7_reverse Regulation 
SmoothMuscleCellsSubclavianArteryDonor3_CNhs12048_ctss_fwd SmcSubclavianArteryD3+ Smooth Muscle Cells - Subclavian Artery, donor3_CNhs12048_11438-118H7_forward Regulation 
SmoothMuscleCellsSubclavianArteryDonor2_CNhs11990_ctss_rev SmcSubclavianArteryD2- Smooth Muscle Cells - Subclavian Artery, donor2_CNhs11990_11366-117I7_reverse Regulation 
SmoothMuscleCellsSubclavianArteryDonor2_CNhs11990_ctss_fwd SmcSubclavianArteryD2+ Smooth Muscle Cells - Subclavian Artery, donor2_CNhs11990_11366-117I7_forward Regulation 
SmoothMuscleCellsSubclavianArteryDonor1_CNhs11090_ctss_rev SmcSubclavianArteryD1- Smooth Muscle Cells - Subclavian Artery, donor1_CNhs11090_11289-117A2_reverse Regulation 
SmoothMuscleCellsSubclavianArteryDonor1_CNhs11090_ctss_fwd SmcSubclavianArteryD1+ Smooth Muscle Cells - Subclavian Artery, donor1_CNhs11090_11289-117A2_forward Regulation 
SmoothMuscleCellsPulmonaryArteryDonor3_CNhs12047_ctss_rev SmcPulmonaryArteryD3- Smooth Muscle Cells - Pulmonary Artery, donor3_CNhs12047_11437-118H6_reverse Regulation 
SmoothMuscleCellsPulmonaryArteryDonor3_CNhs12047_ctss_fwd SmcPulmonaryArteryD3+ Smooth Muscle Cells - Pulmonary Artery, donor3_CNhs12047_11437-118H6_forward Regulation 
SmoothMuscleCellsPulmonaryArteryDonor2_CNhs11989_ctss_rev SmcPulmonaryArteryD2- Smooth Muscle Cells - Pulmonary Artery, donor2_CNhs11989_11365-117I6_reverse Regulation 
SmoothMuscleCellsPulmonaryArteryDonor2_CNhs11989_ctss_fwd SmcPulmonaryArteryD2+ Smooth Muscle Cells - Pulmonary Artery, donor2_CNhs11989_11365-117I6_forward Regulation 
SmoothMuscleCellsPulmonaryArteryDonor1_CNhs11089_ctss_rev SmcPulmonaryArteryD1- Smooth Muscle Cells - Pulmonary Artery, donor1_CNhs11089_11288-117A1_reverse Regulation 
SmoothMuscleCellsPulmonaryArteryDonor1_CNhs11089_ctss_fwd SmcPulmonaryArteryD1+ Smooth Muscle Cells - Pulmonary Artery, donor1_CNhs11089_11288-117A1_forward Regulation 
SmoothMuscleCellsProstateDonor3_CNhs11910_ctss_rev SmcProstateD3- Smooth Muscle Cells - Prostate, donor3_CNhs11910_11465-119B7_reverse Regulation 
SmoothMuscleCellsProstateDonor3_CNhs11910_ctss_fwd SmcProstateD3+ Smooth Muscle Cells - Prostate, donor3_CNhs11910_11465-119B7_forward Regulation 
SmoothMuscleCellsProstateDonor2_CNhs11976_ctss_rev SmcProstateD2- Smooth Muscle Cells - Prostate, donor2_CNhs11976_11335-117F3_reverse Regulation 
SmoothMuscleCellsProstateDonor2_CNhs11976_ctss_fwd SmcProstateD2+ Smooth Muscle Cells - Prostate, donor2_CNhs11976_11335-117F3_forward Regulation 
SmoothMuscleCellsProstateDonor1_CNhs11920_ctss_rev SmcProstateD1- Smooth Muscle Cells - Prostate, donor1_CNhs11920_11257-116F6_reverse Regulation 
SmoothMuscleCellsProstateDonor1_CNhs11920_ctss_fwd SmcProstateD1+ Smooth Muscle Cells - Prostate, donor1_CNhs11920_11257-116F6_forward Regulation 
SmoothMuscleCellsIntestinalDonor1_CNhs12595_ctss_rev SmcIntestinalD1- Smooth Muscle Cells - Intestinal, donor1_CNhs12595_11509-119G6_reverse Regulation 
SmoothMuscleCellsIntestinalDonor1_CNhs12595_ctss_fwd SmcIntestinalD1+ Smooth Muscle Cells - Intestinal, donor1_CNhs12595_11509-119G6_forward Regulation 
SmoothMuscleCellsInternalThoracicArteryDonor3_CNhs12046_ctss_rev SmcInternalThoracicArteryD3- Smooth Muscle Cells - Internal Thoracic Artery, donor3_CNhs12046_11436-118H5_reverse Regulation 
SmoothMuscleCellsInternalThoracicArteryDonor3_CNhs12046_ctss_fwd SmcInternalThoracicArteryD3+ Smooth Muscle Cells - Internal Thoracic Artery, donor3_CNhs12046_11436-118H5_forward Regulation 
SmoothMuscleCellsInternalThoracicArteryDonor2_CNhs11988_ctss_rev SmcInternalThoracicArteryD2- Smooth Muscle Cells - Internal Thoracic Artery, donor2_CNhs11988_11364-117I5_reverse Regulation 
SmoothMuscleCellsInternalThoracicArteryDonor2_CNhs11988_ctss_fwd SmcInternalThoracicArteryD2+ Smooth Muscle Cells - Internal Thoracic Artery, donor2_CNhs11988_11364-117I5_forward Regulation 
SmoothMuscleCellsInternalThoracicArteryDonor1_CNhs11067_ctss_rev SmcInternalThoracicArteryD1- Smooth Muscle Cells - Internal Thoracic Artery, donor1_CNhs11067_11287-116I9_reverse Regulation 
SmoothMuscleCellsInternalThoracicArteryDonor1_CNhs11067_ctss_fwd SmcInternalThoracicArteryD1+ Smooth Muscle Cells - Internal Thoracic Artery, donor1_CNhs11067_11287-116I9_forward Regulation 
SmoothMuscleCellsEsophagealDonor2_CNhs12727_ctss_rev SmcEsophagealD2- Smooth Muscle Cells - Esophageal, donor2_CNhs12727_11588-120G4_reverse Regulation 
SmoothMuscleCellsEsophagealDonor2_CNhs12727_ctss_fwd SmcEsophagealD2+ Smooth Muscle Cells - Esophageal, donor2_CNhs12727_11588-120G4_forward Regulation 
SmoothMuscleCellsEsophagealDonor1_CNhs11324_ctss_rev SmcEsophagealD1- Smooth Muscle Cells - Esophageal, donor1_CNhs11324_11508-119G5_reverse Regulation 
SmoothMuscleCellsEsophagealDonor1_CNhs11324_ctss_fwd SmcEsophagealD1+ Smooth Muscle Cells - Esophageal, donor1_CNhs11324_11508-119G5_forward Regulation 
SmoothMuscleCellsCoronaryArteryDonor3_CNhs12045_ctss_rev SmcCoronaryArteryD3- Smooth Muscle Cells - Coronary Artery, donor3_CNhs12045_11435-118H4_reverse Regulation 
SmoothMuscleCellsCoronaryArteryDonor3_CNhs12045_ctss_fwd SmcCoronaryArteryD3+ Smooth Muscle Cells - Coronary Artery, donor3_CNhs12045_11435-118H4_forward Regulation 
SmoothMuscleCellsCoronaryArteryDonor2_CNhs11987_ctss_rev SmcCoronaryArteryD2- Smooth Muscle Cells - Coronary Artery, donor2_CNhs11987_11363-117I4_reverse Regulation 
SmoothMuscleCellsCoronaryArteryDonor2_CNhs11987_ctss_fwd SmcCoronaryArteryD2+ Smooth Muscle Cells - Coronary Artery, donor2_CNhs11987_11363-117I4_forward Regulation 
SmoothMuscleCellsCoronaryArteryDonor1_CNhs11088_ctss_rev SmcCoronaryArteryD1- Smooth Muscle Cells - Coronary Artery, donor1_CNhs11088_11286-116I8_reverse Regulation 
SmoothMuscleCellsCoronaryArteryDonor1_CNhs11088_ctss_fwd SmcCoronaryArteryD1+ Smooth Muscle Cells - Coronary Artery, donor1_CNhs11088_11286-116I8_forward Regulation 
SmoothMuscleCellsColonicDonor3_CNhs12007_ctss_rev SmcColonicD3- Smooth Muscle Cells - Colonic, donor3_CNhs12007_11396-118D1_reverse Regulation 
SmoothMuscleCellsColonicDonor3_CNhs12007_ctss_fwd SmcColonicD3+ Smooth Muscle Cells - Colonic, donor3_CNhs12007_11396-118D1_forward Regulation 
SmoothMuscleCellsColonicDonor2_CNhs11963_ctss_rev SmcColonicD2- Smooth Muscle Cells - Colonic, donor2_CNhs11963_11320-117D6_reverse Regulation 
SmoothMuscleCellsColonicDonor2_CNhs11963_ctss_fwd SmcColonicD2+ Smooth Muscle Cells - Colonic, donor2_CNhs11963_11320-117D6_forward Regulation 
SmoothMuscleCellsColonicDonor1_CNhs10868_ctss_rev SmcColonicD1- Smooth Muscle Cells - Colonic, donor1_CNhs10868_11239-116D6_reverse Regulation 
SmoothMuscleCellsColonicDonor1_CNhs10868_ctss_fwd SmcColonicD1+ Smooth Muscle Cells - Colonic, donor1_CNhs10868_11239-116D6_forward Regulation 
SmoothMuscleCellsCarotidDonor3_CNhs12044_ctss_rev SmcCarotidD3- Smooth Muscle Cells - Carotid, donor3_CNhs12044_11434-118H3_reverse Regulation 
SmoothMuscleCellsCarotidDonor3_CNhs12044_ctss_fwd SmcCarotidD3+ Smooth Muscle Cells - Carotid, donor3_CNhs12044_11434-118H3_forward Regulation 
SmoothMuscleCellsCarotidDonor2_CNhs11986_ctss_rev SmcCarotidD2- Smooth Muscle Cells - Carotid, donor2_CNhs11986_11362-117I3_reverse Regulation 
SmoothMuscleCellsCarotidDonor2_CNhs11986_ctss_fwd SmcCarotidD2+ Smooth Muscle Cells - Carotid, donor2_CNhs11986_11362-117I3_forward Regulation 
SmoothMuscleCellsCarotidDonor1_CNhs11087_ctss_rev SmcCarotidD1- Smooth Muscle Cells - Carotid, donor1_CNhs11087_11285-116I7_reverse Regulation 
SmoothMuscleCellsCarotidDonor1_CNhs11087_ctss_fwd SmcCarotidD1+ Smooth Muscle Cells - Carotid, donor1_CNhs11087_11285-116I7_forward Regulation 
SmoothMuscleCellsBronchialDonor2_CNhs12348_ctss_rev SmcBronchialD2- Smooth Muscle Cells - Bronchial, donor2_CNhs12348_11592-120G8_reverse Regulation 
SmoothMuscleCellsBronchialDonor2_CNhs12348_ctss_fwd SmcBronchialD2+ Smooth Muscle Cells - Bronchial, donor2_CNhs12348_11592-120G8_forward Regulation 
SmoothMuscleCellsBronchialDonor1_CNhs11328_ctss_rev SmcBronchialD1- Smooth Muscle Cells - Bronchial, donor1_CNhs11328_11512-119G9_reverse Regulation 
SmoothMuscleCellsBronchialDonor1_CNhs11328_ctss_fwd SmcBronchialD1+ Smooth Muscle Cells - Bronchial, donor1_CNhs11328_11512-119G9_forward Regulation 
SmoothMuscleCellsBrainVascularDonor3_CNhs12004_ctss_rev SmcBrainVascularD3- Smooth Muscle Cells - Brain Vascular, donor3_CNhs12004_11391-118C5_reverse Regulation 
SmoothMuscleCellsBrainVascularDonor3_CNhs12004_ctss_fwd SmcBrainVascularD3+ Smooth Muscle Cells - Brain Vascular, donor3_CNhs12004_11391-118C5_forward Regulation 
SmoothMuscleCellsBrainVascularDonor2_CNhs11900_ctss_rev SmcBrainVascularD2- Smooth Muscle Cells - Brain Vascular, donor2_CNhs11900_11315-117D1_reverse Regulation 
SmoothMuscleCellsBrainVascularDonor2_CNhs11900_ctss_fwd SmcBrainVascularD2+ Smooth Muscle Cells - Brain Vascular, donor2_CNhs11900_11315-117D1_forward Regulation 
SmoothMuscleCellsBrainVascularDonor1_CNhs10863_ctss_rev SmcBrainVascularD1- Smooth Muscle Cells - Brain Vascular, donor1_CNhs10863_11234-116D1_reverse Regulation 
SmoothMuscleCellsBrainVascularDonor1_CNhs10863_ctss_fwd SmcBrainVascularD1+ Smooth Muscle Cells - Brain Vascular, donor1_CNhs10863_11234-116D1_forward Regulation 
SmoothMuscleCellsBrachiocephalicDonor3_CNhs12043_ctss_rev SmcBrachiocephalicD3- Smooth Muscle Cells - Brachiocephalic, donor3_CNhs12043_11433-118H2_reverse Regulation 
SmoothMuscleCellsBrachiocephalicDonor3_CNhs12043_ctss_fwd SmcBrachiocephalicD3+ Smooth Muscle Cells - Brachiocephalic, donor3_CNhs12043_11433-118H2_forward Regulation 
SmoothMuscleCellsBrachiocephalicDonor2_CNhs11985_ctss_rev SmcBrachiocephalicD2- Smooth Muscle Cells - Brachiocephalic, donor2_CNhs11985_11361-117I2_reverse Regulation 
SmoothMuscleCellsBrachiocephalicDonor2_CNhs11985_ctss_fwd SmcBrachiocephalicD2+ Smooth Muscle Cells - Brachiocephalic, donor2_CNhs11985_11361-117I2_forward Regulation 
SmoothMuscleCellsBrachiocephalicDonor1_CNhs11086_ctss_rev SmcBrachiocephalicD1- Smooth Muscle Cells - Brachiocephalic, donor1_CNhs11086_11284-116I6_reverse Regulation 
SmoothMuscleCellsBrachiocephalicDonor1_CNhs11086_ctss_fwd SmcBrachiocephalicD1+ Smooth Muscle Cells - Brachiocephalic, donor1_CNhs11086_11284-116I6_forward Regulation 
SmoothMuscleCellsBladderDonor1_CNhs12893_ctss_rev SmcBladderD1- Smooth Muscle Cells - Bladder, donor1_CNhs12893_11519-119H7_reverse Regulation 
SmoothMuscleCellsBladderDonor1_CNhs12893_ctss_fwd SmcBladderD1+ Smooth Muscle Cells - Bladder, donor1_CNhs12893_11519-119H7_forward Regulation 
SmoothMuscleCellsAorticDonor3_CNhs11309_ctss_rev SmcAorticCytofracD3- Smooth Muscle Cells - Aortic, donor3_CNhs11309_11432-118H1_reverse Regulation 
SmoothMuscleCellsAorticDonor3_CNhs11309_ctss_fwd SmcAorticCytofracD3+ Smooth Muscle Cells - Aortic, donor3_CNhs11309_11432-118H1_forward Regulation 
SmoothMuscleCellsAorticDonor2_CNhs11305_ctss_rev SmcAorticCytofracD2- Smooth Muscle Cells - Aortic, donor2_CNhs11305_11360-117I1_reverse Regulation 
SmoothMuscleCellsAorticDonor2_CNhs11305_ctss_fwd SmcAorticCytofracD2+ Smooth Muscle Cells - Aortic, donor2_CNhs11305_11360-117I1_forward Regulation 
SmoothMuscleCellsAorticDonor1_CNhs11085_ctss_rev SmcAorticCytofracD1- Smooth Muscle Cells - Aortic, donor1_CNhs11085_11283-116I5_reverse Regulation 
SmoothMuscleCellsAorticDonor1_CNhs11085_ctss_fwd SmcAorticCytofracD1+ Smooth Muscle Cells - Aortic, donor1_CNhs11085_11283-116I5_forward Regulation 
SmoothMuscleCellsAorticDonor0_CNhs10838_ctss_rev SmcAorticCytofracD0- Smooth Muscle Cells - Aortic, donor0_CNhs10838_11210-116A4_reverse Regulation 
SmoothMuscleCellsAorticDonor0_CNhs10838_ctss_fwd SmcAorticCytofracD0+ Smooth Muscle Cells - Aortic, donor0_CNhs10838_11210-116A4_forward Regulation 
SmoothMuscleCellsAirwayControlDonor4_CNhs14193_ctss_rev SmcAirwayControlD4- Smooth muscle cells - airway, control, donor4_CNhs14193_11969-126D7_reverse Regulation 
SmoothMuscleCellsAirwayControlDonor4_CNhs14193_ctss_fwd SmcAirwayControlD4+ Smooth muscle cells - airway, control, donor4_CNhs14193_11969-126D7_forward Regulation 
SmoothMuscleCellsAirwayControlDonor3_CNhs14192_ctss_rev SmcAirwayControlD3- Smooth muscle cells - airway, control, donor3_CNhs14192_11968-126D6_reverse Regulation 
SmoothMuscleCellsAirwayControlDonor3_CNhs14192_ctss_fwd SmcAirwayControlD3+ Smooth muscle cells - airway, control, donor3_CNhs14192_11968-126D6_forward Regulation 
SmoothMuscleCellsAirwayControlDonor2_CNhs14191_ctss_rev SmcAirwayControlD2- Smooth muscle cells - airway, control, donor2_CNhs14191_11967-126D5_reverse Regulation 
SmoothMuscleCellsAirwayControlDonor2_CNhs14191_ctss_fwd SmcAirwayControlD2+ Smooth muscle cells - airway, control, donor2_CNhs14191_11967-126D5_forward Regulation 
SmoothMuscleCellsAirwayControlDonor1_CNhs14190_ctss_rev SmcAirwayControlD1- Smooth muscle cells - airway, control, donor1_CNhs14190_11966-126D4_reverse Regulation 
SmoothMuscleCellsAirwayControlDonor1_CNhs14190_ctss_fwd SmcAirwayControlD1+ Smooth muscle cells - airway, control, donor1_CNhs14190_11966-126D4_forward Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor6_CNhs14189_ctss_rev SmcAirwayAsthmaD6- Smooth muscle cells - airway, asthmatic, donor6_CNhs14189_11965-126D3_reverse Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor6_CNhs14189_ctss_fwd SmcAirwayAsthmaD6+ Smooth muscle cells - airway, asthmatic, donor6_CNhs14189_11965-126D3_forward Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor5_CNhs14188_ctss_rev SmcAirwayAsthmaD5- Smooth muscle cells - airway, asthmatic, donor5_CNhs14188_11964-126D2_reverse Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor5_CNhs14188_ctss_fwd SmcAirwayAsthmaD5+ Smooth muscle cells - airway, asthmatic, donor5_CNhs14188_11964-126D2_forward Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor4_CNhs14187_ctss_rev SmcAirwayAsthmaD4- Smooth muscle cells - airway, asthmatic, donor4_CNhs14187_11963-126D1_reverse Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor4_CNhs14187_ctss_fwd SmcAirwayAsthmaD4+ Smooth muscle cells - airway, asthmatic, donor4_CNhs14187_11963-126D1_forward Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor3_CNhs14186_ctss_rev SmcAirwayAsthmaD3- Smooth muscle cells - airway, asthmatic, donor3_CNhs14186_11962-126C9_reverse Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor3_CNhs14186_ctss_fwd SmcAirwayAsthmaD3+ Smooth muscle cells - airway, asthmatic, donor3_CNhs14186_11962-126C9_forward Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor2_CNhs14184_ctss_rev SmcAirwayAsthmaD2- Smooth muscle cells - airway, asthmatic, donor2_CNhs14184_11961-126C8_reverse Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor2_CNhs14184_ctss_fwd SmcAirwayAsthmaD2+ Smooth muscle cells - airway, asthmatic, donor2_CNhs14184_11961-126C8_forward Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor1_CNhs14183_ctss_rev SmcAirwayAsthmaD1- Smooth muscle cells - airway, asthmatic, donor1_CNhs14183_11960-126C7_reverse Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor1_CNhs14183_ctss_fwd SmcAirwayAsthmaD1+ Smooth muscle cells - airway, asthmatic, donor1_CNhs14183_11960-126C7_forward Regulation 
SmallAirwayEpithelialCellsDonor3_CNhs12016_ctss_rev SmallAirwayEpithelialCellsD3- Small Airway Epithelial Cells, donor3_CNhs12016_11406-118E2_reverse Regulation 
SmallAirwayEpithelialCellsDonor3_CNhs12016_ctss_fwd SmallAirwayEpithelialCellsD3+ Small Airway Epithelial Cells, donor3_CNhs12016_11406-118E2_forward Regulation 
SmallAirwayEpithelialCellsDonor2_CNhs11975_ctss_rev SmallAirwayEpithelialCellsD2- Small Airway Epithelial Cells, donor2_CNhs11975_11334-117F2_reverse Regulation 
SmallAirwayEpithelialCellsDonor2_CNhs11975_ctss_fwd SmallAirwayEpithelialCellsD2+ Small Airway Epithelial Cells, donor2_CNhs11975_11334-117F2_forward Regulation 
SmallAirwayEpithelialCellsDonor1_CNhs10884_ctss_rev SmallAirwayEpithelialCellsD1- Small Airway Epithelial Cells, donor1_CNhs10884_11256-116F5_reverse Regulation 
SmallAirwayEpithelialCellsDonor1_CNhs10884_ctss_fwd SmallAirwayEpithelialCellsD1+ Small Airway Epithelial Cells, donor1_CNhs10884_11256-116F5_forward Regulation 
SkeletalMuscleSatelliteCellsDonor3_CNhs12008_ctss_rev SkeletalMuscleSatelliteCellsD3- Skeletal Muscle Satellite Cells, donor3_CNhs12008_11397-118D2_reverse Regulation 
SkeletalMuscleSatelliteCellsDonor3_CNhs12008_ctss_fwd SkeletalMuscleSatelliteCellsD3+ Skeletal Muscle Satellite Cells, donor3_CNhs12008_11397-118D2_forward Regulation 
SkeletalMuscleSatelliteCellsDonor2_CNhs11964_ctss_rev SkeletalMuscleSatelliteCellsD2- Skeletal Muscle Satellite Cells, donor2_CNhs11964_11321-117D7_reverse Regulation 
SkeletalMuscleSatelliteCellsDonor2_CNhs11964_ctss_fwd SkeletalMuscleSatelliteCellsD2+ Skeletal Muscle Satellite Cells, donor2_CNhs11964_11321-117D7_forward Regulation 
SkeletalMuscleSatelliteCellsDonor1_CNhs10869_ctss_rev SkeletalMuscleSatelliteCellsD1- Skeletal Muscle Satellite Cells, donor1_CNhs10869_11240-116D7_reverse Regulation 
SkeletalMuscleSatelliteCellsDonor1_CNhs10869_ctss_fwd SkeletalMuscleSatelliteCellsD1+ Skeletal Muscle Satellite Cells, donor1_CNhs10869_11240-116D7_forward Regulation 
SkeletalMuscleCellsDifferentiatedIntoMyotubesMultinucleatedDonor3_CNhs12041_ctss_rev SkeletalMuscleCellsIntoMyotubesD3- Skeletal muscle cells differentiated into Myotubes - multinucleated, donor3_CNhs12041_11431-118G9_reverse Regulation 
SkeletalMuscleCellsDifferentiatedIntoMyotubesMultinucleatedDonor3_CNhs12041_ctss_fwd SkeletalMuscleCellsIntoMyotubesD3+ Skeletal muscle cells differentiated into Myotubes - multinucleated, donor3_CNhs12041_11431-118G9_forward Regulation 
SkeletalMuscleCellsDifferentiatedIntoMyotubesMultinucleatedDonor2_CNhs11984_ctss_rev SkeletalMuscleCellsIntoMyotubesD2- Skeletal muscle cells differentiated into Myotubes - multinucleated, donor2_CNhs11984_11359-117H9_reverse Regulation 
SkeletalMuscleCellsDifferentiatedIntoMyotubesMultinucleatedDonor2_CNhs11984_ctss_fwd SkeletalMuscleCellsIntoMyotubesD2+ Skeletal muscle cells differentiated into Myotubes - multinucleated, donor2_CNhs11984_11359-117H9_forward Regulation 
SkeletalMuscleCellsDifferentiatedIntoMyotubesMultinucleatedDonor1_CNhs11084_ctss_rev SkeletalMuscleCellsIntoMyotubesD1- Skeletal muscle cells differentiated into Myotubes - multinucleated, donor1_CNhs11084_11282-116I4_reverse Regulation 
SkeletalMuscleCellsDifferentiatedIntoMyotubesMultinucleatedDonor1_CNhs11084_ctss_fwd SkeletalMuscleCellsIntoMyotubesD1+ Skeletal muscle cells differentiated into Myotubes - multinucleated, donor1_CNhs11084_11282-116I4_forward Regulation 
SkeletalMuscleCellsDonor6_CNhs12060_ctss_rev SkeletalMuscleCellsD6- Skeletal Muscle Cells, donor6_CNhs12060_11459-119B1_reverse Regulation 
SkeletalMuscleCellsDonor6_CNhs12060_ctss_fwd SkeletalMuscleCellsD6+ Skeletal Muscle Cells, donor6_CNhs12060_11459-119B1_forward Regulation 
SkeletalMuscleCellsDonor5_CNhs12056_ctss_rev SkeletalMuscleCellsD5- Skeletal Muscle Cells, donor5_CNhs12056_11455-119A6_reverse Regulation 
SkeletalMuscleCellsDonor5_CNhs12056_ctss_fwd SkeletalMuscleCellsD5+ Skeletal Muscle Cells, donor5_CNhs12056_11455-119A6_forward Regulation 
SkeletalMuscleCellsDonor4_CNhs12053_ctss_rev SkeletalMuscleCellsD4- Skeletal Muscle Cells, donor4_CNhs12053_11451-119A2_reverse Regulation 
SkeletalMuscleCellsDonor4_CNhs12053_ctss_fwd SkeletalMuscleCellsD4+ Skeletal Muscle Cells, donor4_CNhs12053_11451-119A2_forward Regulation 
SkeletalMuscleCellsDonor3_CNhs12040_ctss_rev SkeletalMuscleCellsD3- Skeletal Muscle Cells, donor3_CNhs12040_11430-118G8_reverse Regulation 
SkeletalMuscleCellsDonor3_CNhs12040_ctss_fwd SkeletalMuscleCellsD3+ Skeletal Muscle Cells, donor3_CNhs12040_11430-118G8_forward Regulation 
SkeletalMuscleCellsDonor2_CNhs11983_ctss_rev SkeletalMuscleCellsD2- Skeletal Muscle Cells, donor2_CNhs11983_11358-117H8_reverse Regulation 
SkeletalMuscleCellsDonor2_CNhs11983_ctss_fwd SkeletalMuscleCellsD2+ Skeletal Muscle Cells, donor2_CNhs11983_11358-117H8_forward Regulation 
SkeletalMuscleCellsDonor1_CNhs11083_ctss_rev SkeletalMuscleCellsD1- Skeletal Muscle Cells, donor1_CNhs11083_11281-116I3_reverse Regulation 
SkeletalMuscleCellsDonor1_CNhs11083_ctss_fwd SkeletalMuscleCellsD1+ Skeletal Muscle Cells, donor1_CNhs11083_11281-116I3_forward Regulation 
SertoliCellsDonor2_CNhs11974_ctss_rev SertoliCellsD2- Sertoli Cells, donor2_CNhs11974_11333-117F1_reverse Regulation 
SertoliCellsDonor2_CNhs11974_ctss_fwd SertoliCellsD2+ Sertoli Cells, donor2_CNhs11974_11333-117F1_forward Regulation 
SertoliCellsDonor1_CNhs10851_ctss_rev SertoliCellsD1- Sertoli Cells, donor1_CNhs10851_11255-116F4_reverse Regulation 
SertoliCellsDonor1_CNhs10851_ctss_fwd SertoliCellsD1+ Sertoli Cells, donor1_CNhs10851_11255-116F4_forward Regulation 
SebocyteDonor3_CNhs11995_ctss_rev SebocyteD3- Sebocyte, donor3_CNhs11995_11378-118B1_reverse Regulation 
SebocyteDonor3_CNhs11995_ctss_fwd SebocyteD3+ Sebocyte, donor3_CNhs11995_11378-118B1_forward Regulation 
SebocyteDonor2_CNhs11951_ctss_rev SebocyteD2- Sebocyte, donor2_CNhs11951_11301-117B5_reverse Regulation 
SebocyteDonor2_CNhs11951_ctss_fwd SebocyteD2+ Sebocyte, donor2_CNhs11951_11301-117B5_forward Regulation 
SebocyteDonor1_CNhs10847_ctss_rev SebocyteD1- Sebocyte, donor1_CNhs10847_11220-116B5_reverse Regulation 
SebocyteDonor1_CNhs10847_ctss_fwd SebocyteD1+ Sebocyte, donor1_CNhs10847_11220-116B5_forward Regulation 
SchwannCellsDonor3_CNhs12621_ctss_rev SchwannCellsD3- Schwann Cells, donor3_CNhs12621_11659-122F3_reverse Regulation 
SchwannCellsDonor3_CNhs12621_ctss_fwd SchwannCellsD3+ Schwann Cells, donor3_CNhs12621_11659-122F3_forward Regulation 
SchwannCellsDonor2_CNhs12345_ctss_rev SchwannCellsD2- Schwann Cells, donor2_CNhs12345_11578-120F3_reverse Regulation 
SchwannCellsDonor2_CNhs12345_ctss_fwd SchwannCellsD2+ Schwann Cells, donor2_CNhs12345_11578-120F3_forward Regulation 
SchwannCellsDonor1_CNhs12073_ctss_rev SchwannCellsD1- Schwann Cells, donor1_CNhs12073_11498-119F4_reverse Regulation 
SchwannCellsDonor1_CNhs12073_ctss_fwd SchwannCellsD1+ Schwann Cells, donor1_CNhs12073_11498-119F4_forward Regulation 
SalivaryAcinarCellsDonor3_CNhs12812_ctss_rev SalivaryAcinarCellsD3- salivary acinar cells, donor3_CNhs12812_11773-123I9_reverse Regulation 
SalivaryAcinarCellsDonor3_CNhs12812_ctss_fwd SalivaryAcinarCellsD3+ salivary acinar cells, donor3_CNhs12812_11773-123I9_forward Regulation 
SalivaryAcinarCellsDonor2_CNhs12811_ctss_rev SalivaryAcinarCellsD2- salivary acinar cells, donor2_CNhs12811_11772-123I8_reverse Regulation 
SalivaryAcinarCellsDonor2_CNhs12811_ctss_fwd SalivaryAcinarCellsD2+ salivary acinar cells, donor2_CNhs12811_11772-123I8_forward Regulation 
SalivaryAcinarCellsDonor1_CNhs12810_ctss_rev SalivaryAcinarCellsD1- salivary acinar cells, donor1_CNhs12810_11771-123I7_reverse Regulation 
SalivaryAcinarCellsDonor1_CNhs12810_ctss_fwd SalivaryAcinarCellsD1+ salivary acinar cells, donor1_CNhs12810_11771-123I7_forward Regulation 
RenalProximalTubularEpithelialCellDonor3_CNhs12120_ctss_rev RptecD3- Renal Proximal Tubular Epithelial Cell, donor3_CNhs12120_11676-122H2_reverse Regulation 
RenalProximalTubularEpithelialCellDonor3_CNhs12120_ctss_fwd RptecD3+ Renal Proximal Tubular Epithelial Cell, donor3_CNhs12120_11676-122H2_forward Regulation 
RenalProximalTubularEpithelialCellDonor2_CNhs12087_ctss_rev RptecD2- Renal Proximal Tubular Epithelial Cell, donor2_CNhs12087_11595-120H2_reverse Regulation 
RenalProximalTubularEpithelialCellDonor2_CNhs12087_ctss_fwd RptecD2+ Renal Proximal Tubular Epithelial Cell, donor2_CNhs12087_11595-120H2_forward Regulation 
RenalProximalTubularEpithelialCellDonor1_CNhs11330_ctss_rev RptecD1- Renal Proximal Tubular Epithelial Cell, donor1_CNhs11330_11515-119H3_reverse Regulation 
RenalProximalTubularEpithelialCellDonor1_CNhs11330_ctss_fwd RptecD1+ Renal Proximal Tubular Epithelial Cell, donor1_CNhs11330_11515-119H3_forward Regulation 
RetinalPigmentEpithelialCellsDonor3_CNhs12733_ctss_rev RpecD3- Retinal Pigment Epithelial Cells, donor3_CNhs12733_11689-122I6_reverse Regulation 
RetinalPigmentEpithelialCellsDonor3_CNhs12733_ctss_fwd RpecD3+ Retinal Pigment Epithelial Cells, donor3_CNhs12733_11689-122I6_forward Regulation 
RetinalPigmentEpithelialCellsDonor2_CNhs12096_ctss_rev RpecD2- Retinal Pigment Epithelial Cells, donor2_CNhs12096_11608-120I6_reverse Regulation 
RetinalPigmentEpithelialCellsDonor2_CNhs12096_ctss_fwd RpecD2+ Retinal Pigment Epithelial Cells, donor2_CNhs12096_11608-120I6_forward Regulation 
RetinalPigmentEpithelialCellsDonor1_CNhs11338_ctss_rev RpecD1- Retinal Pigment Epithelial Cells, donor1_CNhs11338_11528-119I7_reverse Regulation 
RetinalPigmentEpithelialCellsDonor1_CNhs11338_ctss_fwd RpecD1+ Retinal Pigment Epithelial Cells, donor1_CNhs11338_11528-119I7_forward Regulation 
RetinalPigmentEpithelialCellsDonor0_CNhs10842_ctss_rev RpecD0- Retinal Pigment Epithelial Cells, donor0_CNhs10842_11215-116A9_reverse Regulation 
RetinalPigmentEpithelialCellsDonor0_CNhs10842_ctss_fwd RpecD0+ Retinal Pigment Epithelial Cells, donor0_CNhs10842_11215-116A9_forward Regulation 
RenalGlomerularEndothelialCellsDonor4_CNhs13080_ctss_rev RgecD4- Renal Glomerular Endothelial Cells, donor4_CNhs13080_11783-124B1_reverse Regulation 
RenalGlomerularEndothelialCellsDonor4_CNhs13080_ctss_fwd RgecD4+ Renal Glomerular Endothelial Cells, donor4_CNhs13080_11783-124B1_forward Regulation 
RenalGlomerularEndothelialCellsDonor3_CNhs12624_ctss_rev RgecD3- Renal Glomerular Endothelial Cells, donor3_CNhs12624_11675-122H1_reverse Regulation 
RenalGlomerularEndothelialCellsDonor3_CNhs12624_ctss_fwd RgecD3+ Renal Glomerular Endothelial Cells, donor3_CNhs12624_11675-122H1_forward Regulation 
RenalGlomerularEndothelialCellsDonor2_CNhs12086_ctss_rev RgecD2- Renal Glomerular Endothelial Cells, donor2_CNhs12086_11594-120H1_reverse Regulation 
RenalGlomerularEndothelialCellsDonor2_CNhs12086_ctss_fwd RgecD2+ Renal Glomerular Endothelial Cells, donor2_CNhs12086_11594-120H1_forward Regulation 
RenalGlomerularEndothelialCellsDonor1_CNhs12074_ctss_rev RgecD1- Renal Glomerular Endothelial Cells, donor1_CNhs12074_11514-119H2_reverse Regulation 
RenalGlomerularEndothelialCellsDonor1_CNhs12074_ctss_fwd RgecD1+ Renal Glomerular Endothelial Cells, donor1_CNhs12074_11514-119H2_forward Regulation 
RenalMesangialCellsDonor3_CNhs12121_ctss_rev RenalMesangialCellsD3- Renal Mesangial Cells, donor3_CNhs12121_11679-122H5_reverse Regulation 
RenalMesangialCellsDonor3_CNhs12121_ctss_fwd RenalMesangialCellsD3+ Renal Mesangial Cells, donor3_CNhs12121_11679-122H5_forward Regulation 
RenalMesangialCellsDonor2_CNhs12089_ctss_rev RenalMesangialCellsD2- Renal Mesangial Cells, donor2_CNhs12089_11598-120H5_reverse Regulation 
RenalMesangialCellsDonor2_CNhs12089_ctss_fwd RenalMesangialCellsD2+ Renal Mesangial Cells, donor2_CNhs12089_11598-120H5_forward Regulation 
RenalMesangialCellsDonor1_CNhs11333_ctss_rev RenalMesangialCellsD1- Renal Mesangial Cells, donor1_CNhs11333_11518-119H6_reverse Regulation 
RenalMesangialCellsDonor1_CNhs11333_ctss_fwd RenalMesangialCellsD1+ Renal Mesangial Cells, donor1_CNhs11333_11518-119H6_forward Regulation 
RenalEpithelialCellsDonor3_CNhs12732_ctss_rev RenalEpithelialCellsD3- Renal Epithelial Cells, donor3_CNhs12732_11678-122H4_reverse Regulation 
RenalEpithelialCellsDonor3_CNhs12732_ctss_fwd RenalEpithelialCellsD3+ Renal Epithelial Cells, donor3_CNhs12732_11678-122H4_forward Regulation 
RenalEpithelialCellsDonor2_CNhs12088_ctss_rev RenalEpithelialCellsD2- Renal Epithelial Cells, donor2_CNhs12088_11597-120H4_reverse Regulation 
RenalEpithelialCellsDonor2_CNhs12088_ctss_fwd RenalEpithelialCellsD2+ Renal Epithelial Cells, donor2_CNhs12088_11597-120H4_forward Regulation 
RenalEpithelialCellsDonor1_CNhs11332_ctss_rev RenalEpithelialCellsD1- Renal Epithelial Cells, donor1_CNhs11332_11517-119H5_reverse Regulation 
RenalEpithelialCellsDonor1_CNhs11332_ctss_fwd RenalEpithelialCellsD1+ Renal Epithelial Cells, donor1_CNhs11332_11517-119H5_forward Regulation 
RenalCorticalEpithelialCellsDonor2_CNhs12728_ctss_rev RcecD2- Renal Cortical Epithelial Cells, donor2_CNhs12728_11596-120H3_reverse Regulation 
RenalCorticalEpithelialCellsDonor2_CNhs12728_ctss_fwd RcecD2+ Renal Cortical Epithelial Cells, donor2_CNhs12728_11596-120H3_forward Regulation 
RenalCorticalEpithelialCellsDonor1_CNhs11331_ctss_rev RcecD1- Renal Cortical Epithelial Cells, donor1_CNhs11331_11516-119H4_reverse Regulation 
RenalCorticalEpithelialCellsDonor1_CNhs11331_ctss_fwd RcecD1+ Renal Cortical Epithelial Cells, donor1_CNhs11331_11516-119H4_forward Regulation 
ProstateStromalCellsDonor3_CNhs12015_ctss_rev ProstateStromalCellsD3- Prostate Stromal Cells, donor3_CNhs12015_11405-118E1_reverse Regulation 
ProstateStromalCellsDonor3_CNhs12015_ctss_fwd ProstateStromalCellsD3+ Prostate Stromal Cells, donor3_CNhs12015_11405-118E1_forward Regulation 
ProstateStromalCellsDonor2_CNhs11973_ctss_rev ProstateStromalCellsD2- Prostate Stromal Cells, donor2_CNhs11973_11332-117E9_reverse Regulation 
ProstateStromalCellsDonor2_CNhs11973_ctss_fwd ProstateStromalCellsD2+ Prostate Stromal Cells, donor2_CNhs11973_11332-117E9_forward Regulation 
ProstateStromalCellsDonor1_CNhs10883_ctss_rev ProstateStromalCellsD1- Prostate Stromal Cells, donor1_CNhs10883_11254-116F3_reverse Regulation 
ProstateStromalCellsDonor1_CNhs10883_ctss_fwd ProstateStromalCellsD1+ Prostate Stromal Cells, donor1_CNhs10883_11254-116F3_forward Regulation 
ProstateEpithelialCellsDonor3_CNhs12014_ctss_rev ProstateEpithelialCellsD3- Prostate Epithelial Cells, donor3_CNhs12014_11404-118D9_reverse Regulation 
ProstateEpithelialCellsDonor3_CNhs12014_ctss_fwd ProstateEpithelialCellsD3+ Prostate Epithelial Cells, donor3_CNhs12014_11404-118D9_forward Regulation 
ProstateEpithelialCellsDonor2_CNhs11972_ctss_rev ProstateEpithelialCellsD2- Prostate Epithelial Cells, donor2_CNhs11972_11331-117E8_reverse Regulation 
ProstateEpithelialCellsDonor2_CNhs11972_ctss_fwd ProstateEpithelialCellsD2+ Prostate Epithelial Cells, donor2_CNhs11972_11331-117E8_forward Regulation 
ProstateEpithelialCellsPolarizedDonor1_CNhs10882_ctss_rev ProstateEpithelialCellsD1- Prostate Epithelial Cells (polarized), donor1_CNhs10882_11253-116F2_reverse Regulation 
ProstateEpithelialCellsPolarizedDonor1_CNhs10882_ctss_fwd ProstateEpithelialCellsD1+ Prostate Epithelial Cells (polarized), donor1_CNhs10882_11253-116F2_forward Regulation 
PreadipocyteVisceralDonor3_CNhs12039_ctss_rev PreadipocyteVisceralD3- Preadipocyte - visceral, donor3_CNhs12039_11429-118G7_reverse Regulation 
PreadipocyteVisceralDonor3_CNhs12039_ctss_fwd PreadipocyteVisceralD3+ Preadipocyte - visceral, donor3_CNhs12039_11429-118G7_forward Regulation 
PreadipocyteVisceralDonor2_CNhs11982_ctss_rev PreadipocyteVisceralD2- Preadipocyte - visceral, donor2_CNhs11982_11357-117H7_reverse Regulation 
PreadipocyteVisceralDonor2_CNhs11982_ctss_fwd PreadipocyteVisceralD2+ Preadipocyte - visceral, donor2_CNhs11982_11357-117H7_forward Regulation 
PreadipocyteVisceralDonor1_CNhs11082_ctss_rev PreadipocyteVisceralD1- Preadipocyte - visceral, donor1_CNhs11082_11280-116I2_reverse Regulation 
PreadipocyteVisceralDonor1_CNhs11082_ctss_fwd PreadipocyteVisceralD1+ Preadipocyte - visceral, donor1_CNhs11082_11280-116I2_forward Regulation 
PreadipocyteSubcutaneousDonor3_CNhs12038_ctss_rev PreadipocyteSubcutaneousD3- Preadipocyte - subcutaneous, donor3_CNhs12038_11428-118G6_reverse Regulation 
PreadipocyteSubcutaneousDonor3_CNhs12038_ctss_fwd PreadipocyteSubcutaneousD3+ Preadipocyte - subcutaneous, donor3_CNhs12038_11428-118G6_forward Regulation 
PreadipocyteSubcutaneousDonor2_CNhs11981_ctss_rev PreadipocyteSubcutaneousD2- Preadipocyte - subcutaneous, donor2_CNhs11981_11356-117H6_reverse Regulation 
PreadipocyteSubcutaneousDonor2_CNhs11981_ctss_fwd PreadipocyteSubcutaneousD2+ Preadipocyte - subcutaneous, donor2_CNhs11981_11356-117H6_forward Regulation 
PreadipocyteSubcutaneousDonor1_CNhs11081_ctss_rev PreadipocyteSubcutaneousD1- Preadipocyte - subcutaneous, donor1_CNhs11081_11279-116I1_reverse Regulation 
PreadipocyteSubcutaneousDonor1_CNhs11081_ctss_fwd PreadipocyteSubcutaneousD1+ Preadipocyte - subcutaneous, donor1_CNhs11081_11279-116I1_forward Regulation 
PreadipocytePerirenalDonor1_CNhs12065_ctss_rev PreadipocytePerirenalD1- Preadipocyte - perirenal, donor1_CNhs12065_11469-119C2_reverse Regulation 
PreadipocytePerirenalDonor1_CNhs12065_ctss_fwd PreadipocytePerirenalD1+ Preadipocyte - perirenal, donor1_CNhs12065_11469-119C2_forward Regulation 
PreadipocyteOmentalDonor3_CNhs12013_ctss_rev PreadipocyteOmentalD3- Preadipocyte - omental, donor3_CNhs12013_11403-118D8_reverse Regulation 
PreadipocyteOmentalDonor3_CNhs12013_ctss_fwd PreadipocyteOmentalD3+ Preadipocyte - omental, donor3_CNhs12013_11403-118D8_forward Regulation 
PreadipocyteOmentalDonor2_CNhs11902_ctss_rev PreadipocyteOmentalD2- Preadipocyte - omental, donor2_CNhs11902_11329-117E6_reverse Regulation 
PreadipocyteOmentalDonor2_CNhs11902_ctss_fwd PreadipocyteOmentalD2+ Preadipocyte - omental, donor2_CNhs11902_11329-117E6_forward Regulation 
PreadipocyteOmentalDonor1_CNhs11065_ctss_rev PreadipocyteOmentalD1- Preadipocyte - omental, donor1_CNhs11065_11468-119C1_reverse Regulation 
PreadipocyteOmentalDonor1_CNhs11065_ctss_fwd PreadipocyteOmentalD1+ Preadipocyte - omental, donor1_CNhs11065_11468-119C1_forward Regulation 
PreadipocyteBreastDonor2_CNhs11971_ctss_rev PreadipocyteBreastD2- Preadipocyte - breast, donor2_CNhs11971_11328-117E5_reverse Regulation 
PreadipocyteBreastDonor2_CNhs11971_ctss_fwd PreadipocyteBreastD2+ Preadipocyte - breast, donor2_CNhs11971_11328-117E5_forward Regulation 
PreadipocyteBreastDonor1_CNhs11052_ctss_rev PreadipocyteBreastD1- Preadipocyte - breast, donor1_CNhs11052_11467-119B9_reverse Regulation 
PreadipocyteBreastDonor1_CNhs11052_ctss_fwd PreadipocyteBreastD1+ Preadipocyte - breast, donor1_CNhs11052_11467-119B9_forward Regulation 
PlacentalEpithelialCellsDonor3_CNhs12037_ctss_rev PlacentalEpithelialCellsD3- Placental Epithelial Cells, donor3_CNhs12037_11427-118G5_reverse Regulation 
PlacentalEpithelialCellsDonor3_CNhs12037_ctss_fwd PlacentalEpithelialCellsD3+ Placental Epithelial Cells, donor3_CNhs12037_11427-118G5_forward Regulation 
PlacentalEpithelialCellsDonor2_CNhs11386_ctss_rev PlacentalEpithelialCellsD2- Placental Epithelial Cells, donor2_CNhs11386_11355-117H5_reverse Regulation 
PlacentalEpithelialCellsDonor2_CNhs11386_ctss_fwd PlacentalEpithelialCellsD2+ Placental Epithelial Cells, donor2_CNhs11386_11355-117H5_forward Regulation 
PlacentalEpithelialCellsDonor1_CNhs11079_ctss_rev PlacentalEpithelialCellsD1- Placental Epithelial Cells, donor1_CNhs11079_11278-116H9_reverse Regulation 
PlacentalEpithelialCellsDonor1_CNhs11079_ctss_fwd PlacentalEpithelialCellsD1+ Placental Epithelial Cells, donor1_CNhs11079_11278-116H9_forward Regulation 
PeripheralBloodMononuclearCellsDonor3_CNhs12002_ctss_rev PeripheralBloodMononuclearCellsD3- Peripheral Blood Mononuclear Cells, donor3_CNhs12002_11388-118C2_reverse Regulation 
PeripheralBloodMononuclearCellsDonor3_CNhs12002_ctss_fwd PeripheralBloodMononuclearCellsD3+ Peripheral Blood Mononuclear Cells, donor3_CNhs12002_11388-118C2_forward Regulation 
PeripheralBloodMononuclearCellsDonor2_CNhs11958_ctss_rev PeripheralBloodMononuclearCellsD2- Peripheral Blood Mononuclear Cells, donor2_CNhs11958_11312-117C7_reverse Regulation 
PeripheralBloodMononuclearCellsDonor2_CNhs11958_ctss_fwd PeripheralBloodMononuclearCellsD2+ Peripheral Blood Mononuclear Cells, donor2_CNhs11958_11312-117C7_forward Regulation 
PeripheralBloodMononuclearCellsDonor1_CNhs10860_ctss_rev PeripheralBloodMononuclearCellsD1- Peripheral Blood Mononuclear Cells, donor1_CNhs10860_11231-116C7_reverse Regulation 
PeripheralBloodMononuclearCellsDonor1_CNhs10860_ctss_fwd PeripheralBloodMononuclearCellsD1+ Peripheral Blood Mononuclear Cells, donor1_CNhs10860_11231-116C7_forward Regulation 
PerineurialCellsDonor2_CNhs12590_ctss_rev PerineurialCellsD2- Perineurial Cells, donor2_CNhs12590_11579-120F4_reverse Regulation 
PerineurialCellsDonor2_CNhs12590_ctss_fwd PerineurialCellsD2+ Perineurial Cells, donor2_CNhs12590_11579-120F4_forward Regulation 
PerineurialCellsDonor1_CNhs12587_ctss_rev PerineurialCellsD1- Perineurial Cells, donor1_CNhs12587_11499-119F5_reverse Regulation 
PerineurialCellsDonor1_CNhs12587_ctss_fwd PerineurialCellsD1+ Perineurial Cells, donor1_CNhs12587_11499-119F5_forward Regulation 
PericytesDonor3_CNhs12116_ctss_rev PericytesD3- Pericytes, donor3_CNhs12116_11652-122E5_reverse Regulation 
PericytesDonor3_CNhs12116_ctss_fwd PericytesD3+ Pericytes, donor3_CNhs12116_11652-122E5_forward Regulation 
PericytesDonor2_CNhs12079_ctss_rev PericytesD2- Pericytes, donor2_CNhs12079_11571-120E5_reverse Regulation 
PericytesDonor2_CNhs12079_ctss_fwd PericytesD2+ Pericytes, donor2_CNhs12079_11571-120E5_forward Regulation 
PericytesDonor1_CNhs11317_ctss_rev PericytesD1- Pericytes, donor1_CNhs11317_11491-119E6_reverse Regulation 
PericytesDonor1_CNhs11317_ctss_fwd PericytesD1+ Pericytes, donor1_CNhs11317_11491-119E6_forward Regulation 
PancreaticStromalCellsDonor1_CNhs10877_ctss_rev PancreaticStromalCellsD1- Pancreatic stromal cells, donor1_CNhs10877_11249-116E7_reverse Regulation 
PancreaticStromalCellsDonor1_CNhs10877_ctss_fwd PancreaticStromalCellsD1+ Pancreatic stromal cells, donor1_CNhs10877_11249-116E7_forward Regulation 
OsteoblastDifferentiatedDonor3_CNhs12035_ctss_rev OsteoblastDifferentiatedD3- Osteoblast - differentiated, donor3_CNhs12035_11425-118G3_reverse Regulation 
OsteoblastDifferentiatedDonor3_CNhs12035_ctss_fwd OsteoblastDifferentiatedD3+ Osteoblast - differentiated, donor3_CNhs12035_11425-118G3_forward Regulation 
OsteoblastDifferentiatedDonor2_CNhs11980_ctss_rev OsteoblastDifferentiatedD2- Osteoblast - differentiated, donor2_CNhs11980_11353-117H3_reverse Regulation 
OsteoblastDifferentiatedDonor2_CNhs11980_ctss_fwd OsteoblastDifferentiatedD2+ Osteoblast - differentiated, donor2_CNhs11980_11353-117H3_forward Regulation 
OsteoblastDifferentiatedDonor1_CNhs11311_ctss_rev OsteoblastDifferentiatedD1- Osteoblast - differentiated, donor1_CNhs11311_11276-116H7_reverse Regulation 
OsteoblastDifferentiatedDonor1_CNhs11311_ctss_fwd OsteoblastDifferentiatedD1+ Osteoblast - differentiated, donor1_CNhs11311_11276-116H7_forward Regulation 
OsteoblastDonor3_CNhs12036_ctss_rev OsteoblastD3- Osteoblast, donor3_CNhs12036_11426-118G4_reverse Regulation 
OsteoblastDonor3_CNhs12036_ctss_fwd OsteoblastD3+ Osteoblast, donor3_CNhs12036_11426-118G4_forward Regulation 
OsteoblastDonor2_CNhs11385_ctss_rev OsteoblastD2- Osteoblast, donor2_CNhs11385_11354-117H4_reverse Regulation 
OsteoblastDonor2_CNhs11385_ctss_fwd OsteoblastD2+ Osteoblast, donor2_CNhs11385_11354-117H4_forward Regulation 
OsteoblastDonor1_CNhs11078_ctss_rev OsteoblastD1- Osteoblast, donor1_CNhs11078_11277-116H8_reverse Regulation 
OsteoblastDonor1_CNhs11078_ctss_fwd OsteoblastD1+ Osteoblast, donor1_CNhs11078_11277-116H8_forward Regulation 
OligodendrocytePrecursorsDonor1_CNhs12586_ctss_rev OligodendrocytePrecursorsD1- Oligodendrocyte - precursors, donor1_CNhs12586_11496-119F2_reverse Regulation 
OligodendrocytePrecursorsDonor1_CNhs12586_ctss_fwd OligodendrocytePrecursorsD1+ Oligodendrocyte - precursors, donor1_CNhs12586_11496-119F2_forward Regulation 
OlfactoryEpithelialCellsDonor4_CNhs13819_ctss_rev OlfactoryEpithelialCellsD4- Olfactory epithelial cells, donor4_CNhs13819_11936-126A1_reverse Regulation 
OlfactoryEpithelialCellsDonor4_CNhs13819_ctss_fwd OlfactoryEpithelialCellsD4+ Olfactory epithelial cells, donor4_CNhs13819_11936-126A1_forward Regulation 
OlfactoryEpithelialCellsDonor3_CNhs13818_ctss_rev OlfactoryEpithelialCellsD3- Olfactory epithelial cells, donor3_CNhs13818_11935-125I9_reverse Regulation 
OlfactoryEpithelialCellsDonor3_CNhs13818_ctss_fwd OlfactoryEpithelialCellsD3+ Olfactory epithelial cells, donor3_CNhs13818_11935-125I9_forward Regulation 
OlfactoryEpithelialCellsDonor2_CNhs13817_ctss_rev OlfactoryEpithelialCellsD2- Olfactory epithelial cells, donor2_CNhs13817_11934-125I8_reverse Regulation 
OlfactoryEpithelialCellsDonor2_CNhs13817_ctss_fwd OlfactoryEpithelialCellsD2+ Olfactory epithelial cells, donor2_CNhs13817_11934-125I8_forward Regulation 
OlfactoryEpithelialCellsDonor1_CNhs13816_ctss_rev OlfactoryEpithelialCellsD1- Olfactory epithelial cells, donor1_CNhs13816_11933-125I7_reverse Regulation 
OlfactoryEpithelialCellsDonor1_CNhs13816_ctss_fwd OlfactoryEpithelialCellsD1+ Olfactory epithelial cells, donor1_CNhs13816_11933-125I7_forward Regulation 
NucleusPulposusCellDonor3_CNhs12063_ctss_rev NucleusPulposusCellD3- Nucleus Pulposus Cell, donor3_CNhs12063_11462-119B4_reverse Regulation 
NucleusPulposusCellDonor3_CNhs12063_ctss_fwd NucleusPulposusCellD3+ Nucleus Pulposus Cell, donor3_CNhs12063_11462-119B4_forward Regulation 
NucleusPulposusCellDonor2_CNhs12019_ctss_rev NucleusPulposusCellD2- Nucleus Pulposus Cell, donor2_CNhs12019_11409-118E5_reverse Regulation 
NucleusPulposusCellDonor2_CNhs12019_ctss_fwd NucleusPulposusCellD2+ Nucleus Pulposus Cell, donor2_CNhs12019_11409-118E5_forward Regulation 
NucleusPulposusCellDonor1_CNhs10881_ctss_rev NucleusPulposusCellD1- Nucleus Pulposus Cell, donor1_CNhs10881_11252-116F1_reverse Regulation 
NucleusPulposusCellDonor1_CNhs10881_ctss_fwd NucleusPulposusCellD1+ Nucleus Pulposus Cell, donor1_CNhs10881_11252-116F1_forward Regulation 
NeutrophilsDonor3_CNhs11905_ctss_rev NeutrophilsD3- Neutrophils, donor3_CNhs11905_11390-118C4_reverse Regulation 
NeutrophilsDonor3_CNhs11905_ctss_fwd NeutrophilsD3+ Neutrophils, donor3_CNhs11905_11390-118C4_forward Regulation 
NeutrophilsDonor2_CNhs11959_ctss_rev NeutrophilsD2- Neutrophils, donor2_CNhs11959_11314-117C9_reverse Regulation 
NeutrophilsDonor2_CNhs11959_ctss_fwd NeutrophilsD2+ Neutrophils, donor2_CNhs11959_11314-117C9_forward Regulation 
NeutrophilsDonor1_CNhs10862_ctss_rev NeutrophilsD1- Neutrophils, donor1_CNhs10862_11233-116C9_reverse Regulation 
NeutrophilsDonor1_CNhs10862_ctss_fwd NeutrophilsD1+ Neutrophils, donor1_CNhs10862_11233-116C9_forward Regulation 
NeuronsDonor3_CNhs13815_ctss_rev NeuronsD3- Neurons, donor3_CNhs13815_11655-122E8_reverse Regulation 
NeuronsDonor3_CNhs13815_ctss_fwd NeuronsD3+ Neurons, donor3_CNhs13815_11655-122E8_forward Regulation 
NeuronsDonor2_CNhs12726_ctss_rev NeuronsD2- Neurons, donor2_CNhs12726_11574-120E8_reverse Regulation 
NeuronsDonor2_CNhs12726_ctss_fwd NeuronsD2+ Neurons, donor2_CNhs12726_11574-120E8_forward Regulation 
NeuronsDonor1_CNhs12338_ctss_rev NeuronsD1- Neurons, donor1_CNhs12338_11494-119E9_reverse Regulation 
NeuronsDonor1_CNhs12338_ctss_fwd NeuronsD1+ Neurons, donor1_CNhs12338_11494-119E9_forward Regulation 
NeuralStemCellsDonor2_CNhs11384_ctss_rev NeuralStemCellsD2- Neural stem cells, donor2_CNhs11384_11352-117H2_reverse Regulation 
NeuralStemCellsDonor2_CNhs11384_ctss_fwd NeuralStemCellsD2+ Neural stem cells, donor2_CNhs11384_11352-117H2_forward Regulation 
NeuralStemCellsDonor1_CNhs11063_ctss_rev NeuralStemCellsD1- Neural stem cells, donor1_CNhs11063_11275-116H6_reverse Regulation 
NeuralStemCellsDonor1_CNhs11063_ctss_fwd NeuralStemCellsD1+ Neural stem cells, donor1_CNhs11063_11275-116H6_forward Regulation 
NaturalKillerCellsDonor3_CNhs12001_ctss_rev NaturalKillerCellsD3- Natural Killer Cells, donor3_CNhs12001_11387-118C1_reverse Regulation 
NaturalKillerCellsDonor3_CNhs12001_ctss_fwd NaturalKillerCellsD3+ Natural Killer Cells, donor3_CNhs12001_11387-118C1_forward Regulation 
NaturalKillerCellsDonor2_CNhs11957_ctss_rev NaturalKillerCellsD2- Natural Killer Cells, donor2_CNhs11957_11311-117C6_reverse Regulation 
NaturalKillerCellsDonor2_CNhs11957_ctss_fwd NaturalKillerCellsD2+ Natural Killer Cells, donor2_CNhs11957_11311-117C6_forward Regulation 
NaturalKillerCellsDonor1_CNhs10859_ctss_rev NaturalKillerCellsD1- Natural Killer Cells, donor1_CNhs10859_11230-116C6_reverse Regulation 
NaturalKillerCellsDonor1_CNhs10859_ctss_fwd NaturalKillerCellsD1+ Natural Killer Cells, donor1_CNhs10859_11230-116C6_forward Regulation 
NasalEpithelialCellsDonor2_CNhs12574_ctss_rev NasalEpithelialCellsD2- nasal epithelial cells, donor2_CNhs12574_12227-129F4_reverse Regulation 
NasalEpithelialCellsDonor2_CNhs12574_ctss_fwd NasalEpithelialCellsD2+ nasal epithelial cells, donor2_CNhs12574_12227-129F4_forward Regulation 
NasalEpithelialCellsDonor1TechRep1_CNhs12589_ctss_rev NasalEpithelialCellsD1Tr1- nasal epithelial cells, donor1, tech_rep1_CNhs12589_12226-129F3_reverse Regulation 
NasalEpithelialCellsDonor1TechRep1_CNhs12589_ctss_fwd NasalEpithelialCellsD1Tr1+ nasal epithelial cells, donor1, tech_rep1_CNhs12589_12226-129F3_forward Regulation 
MyoblastDonor3_CNhs11908_ctss_rev MyoblastD3- Myoblast, donor3_CNhs11908_11398-118D3_reverse Regulation 
MyoblastDonor3_CNhs11908_ctss_fwd MyoblastD3+ Myoblast, donor3_CNhs11908_11398-118D3_forward Regulation 
MyoblastDonor2_CNhs11965_ctss_rev MyoblastD2- Myoblast, donor2_CNhs11965_11322-117D8_reverse Regulation 
MyoblastDonor2_CNhs11965_ctss_fwd MyoblastD2+ Myoblast, donor2_CNhs11965_11322-117D8_forward Regulation 
MyoblastDonor1_CNhs10870_ctss_rev MyoblastD1- Myoblast, donor1_CNhs10870_11241-116D8_reverse Regulation 
MyoblastDonor1_CNhs10870_ctss_fwd MyoblastD1+ Myoblast, donor1_CNhs10870_11241-116D8_forward Regulation 
MesenchymalStemCellsWhartonsJellyDonor1_CNhs11057_ctss_rev MscWharton'sJellyD1- Mesenchymal Stem Cells - Wharton's Jelly, donor1_CNhs11057_11548-120B9_reverse Regulation 
MesenchymalStemCellsWhartonsJellyDonor1_CNhs11057_ctss_fwd MscWharton'sJellyD1+ Mesenchymal Stem Cells - Wharton's Jelly, donor1_CNhs11057_11548-120B9_forward Regulation 
MesenchymalStemCellsVertebralDonor1_CNhs10846_ctss_rev MscVertebralD1- Mesenchymal Stem Cells - Vertebral, donor1_CNhs10846_11219-116B4_reverse Regulation 
MesenchymalStemCellsVertebralDonor1_CNhs10846_ctss_fwd MscVertebralD1+ Mesenchymal Stem Cells - Vertebral, donor1_CNhs10846_11219-116B4_forward Regulation 
MesenchymalStemCellsUmbilicalDonor3_CNhs12127_ctss_rev MscUmbilicalD3- Mesenchymal Stem Cells - umbilical, donor3_CNhs12127_11700-123A8_reverse Regulation 
MesenchymalStemCellsUmbilicalDonor3_CNhs12127_ctss_fwd MscUmbilicalD3+ Mesenchymal Stem Cells - umbilical, donor3_CNhs12127_11700-123A8_forward Regulation 
MesenchymalStemCellsUmbilicalDonor2_CNhs12102_ctss_rev MscUmbilicalD2- Mesenchymal Stem Cells - umbilical, donor2_CNhs12102_11619-122A8_reverse Regulation 
MesenchymalStemCellsUmbilicalDonor2_CNhs12102_ctss_fwd MscUmbilicalD2+ Mesenchymal Stem Cells - umbilical, donor2_CNhs12102_11619-122A8_forward Regulation 
MesenchymalStemCellsUmbilicalDonor1_CNhs11347_ctss_rev MscUmbilicalD1- Mesenchymal Stem Cells - umbilical, donor1_CNhs11347_11539-120A9_reverse Regulation 
MesenchymalStemCellsUmbilicalDonor1_CNhs11347_ctss_fwd MscUmbilicalD1+ Mesenchymal Stem Cells - umbilical, donor1_CNhs11347_11539-120A9_forward Regulation 
MesenchymalStemCellsUmbilicalDonor0_CNhs12492_ctss_rev MscUmbilicalD0- Mesenchymal stem cells - umbilical, donor0_CNhs12492_11214-116A8_reverse Regulation 
MesenchymalStemCellsUmbilicalDonor0_CNhs12492_ctss_fwd MscUmbilicalD0+ Mesenchymal stem cells - umbilical, donor0_CNhs12492_11214-116A8_forward Regulation 
MesenchymalStemCellsHepaticDonor2_CNhs12730_ctss_rev MscHepaticD2- Mesenchymal Stem Cells - hepatic, donor2_CNhs12730_11618-122A7_reverse Regulation 
MesenchymalStemCellsHepaticDonor2_CNhs12730_ctss_fwd MscHepaticD2+ Mesenchymal Stem Cells - hepatic, donor2_CNhs12730_11618-122A7_forward Regulation 
MesenchymalStemCellsHepaticDonor1_CNhs11346_ctss_rev MscHepaticD1- Mesenchymal Stem Cells - hepatic, donor1_CNhs11346_11538-120A8_reverse Regulation 
MesenchymalStemCellsHepaticDonor1_CNhs11346_ctss_fwd MscHepaticD1+ Mesenchymal Stem Cells - hepatic, donor1_CNhs11346_11538-120A8_forward Regulation 
MesenchymalStemCellsHepaticDonor0_CNhs10845_ctss_rev MscHepaticD0- Mesenchymal stem cells - hepatic, donor0_CNhs10845_11218-116B3_reverse Regulation 
MesenchymalStemCellsHepaticDonor0_CNhs10845_ctss_fwd MscHepaticD0+ Mesenchymal stem cells - hepatic, donor0_CNhs10845_11218-116B3_forward Regulation 
MesenchymalStemCellsBoneMarrowDonor4_CNhs11316_ctss_rev MscBoneMarrowD4- Mesenchymal Stem Cells - bone marrow, donor4_CNhs11316_11464-119B6_reverse Regulation 
MesenchymalStemCellsBoneMarrowDonor4_CNhs11316_ctss_fwd MscBoneMarrowD4+ Mesenchymal Stem Cells - bone marrow, donor4_CNhs11316_11464-119B6_forward Regulation 
MesenchymalStemCellsBoneMarrowDonor3_CNhs12126_ctss_rev MscBoneMarrowD3- Mesenchymal Stem Cells - bone marrow, donor3_CNhs12126_11697-123A5_reverse Regulation 
MesenchymalStemCellsBoneMarrowDonor3_CNhs12126_ctss_fwd MscBoneMarrowD3+ Mesenchymal Stem Cells - bone marrow, donor3_CNhs12126_11697-123A5_forward Regulation 
MesenchymalStemCellsBoneMarrowDonor2_CNhs12100_ctss_rev MscBoneMarrowD2- Mesenchymal Stem Cells - bone marrow, donor2_CNhs12100_11616-122A5_reverse Regulation 
MesenchymalStemCellsBoneMarrowDonor2_CNhs12100_ctss_fwd MscBoneMarrowD2+ Mesenchymal Stem Cells - bone marrow, donor2_CNhs12100_11616-122A5_forward Regulation 
MesenchymalStemCellsBoneMarrowDonor1_CNhs11344_ctss_rev MscBoneMarrowD1- Mesenchymal Stem Cells - bone marrow, donor1_CNhs11344_11536-120A6_reverse Regulation 
MesenchymalStemCellsBoneMarrowDonor1_CNhs11344_ctss_fwd MscBoneMarrowD1+ Mesenchymal Stem Cells - bone marrow, donor1_CNhs11344_11536-120A6_forward Regulation 
MesenchymalStemCellsAmnioticMembraneDonor2_CNhs12104_ctss_rev MscAmnioticMembraneD2- Mesenchymal Stem Cells - amniotic membrane, donor2_CNhs12104_11627-122B7_reverse Regulation 
MesenchymalStemCellsAmnioticMembraneDonor2_CNhs12104_ctss_fwd MscAmnioticMembraneD2+ Mesenchymal Stem Cells - amniotic membrane, donor2_CNhs12104_11627-122B7_forward Regulation 
MesenchymalStemCellsAmnioticMembraneDonor1_CNhs11349_ctss_rev MscAmnioticMembraneD1- Mesenchymal Stem Cells - amniotic membrane, donor1_CNhs11349_11547-120B8_reverse Regulation 
MesenchymalStemCellsAmnioticMembraneDonor1_CNhs11349_ctss_fwd MscAmnioticMembraneD1+ Mesenchymal Stem Cells - amniotic membrane, donor1_CNhs11349_11547-120B8_forward Regulation 
MesenchymalStemCellsAdiposeDonor3_CNhs12922_ctss_rev MscAdiposeD3- Mesenchymal Stem Cells - adipose, donor3_CNhs12922_11698-123A6_reverse Regulation 
MesenchymalStemCellsAdiposeDonor3_CNhs12922_ctss_fwd MscAdiposeD3+ Mesenchymal Stem Cells - adipose, donor3_CNhs12922_11698-123A6_forward Regulation 
MesenchymalStemCellsAdiposeDonor2_CNhs12101_ctss_rev MscAdiposeD2- Mesenchymal Stem Cells - adipose, donor2_CNhs12101_11617-122A6_reverse Regulation 
MesenchymalStemCellsAdiposeDonor2_CNhs12101_ctss_fwd MscAdiposeD2+ Mesenchymal Stem Cells - adipose, donor2_CNhs12101_11617-122A6_forward Regulation 
MesenchymalStemCellsAdiposeDonor1_CNhs11345_ctss_rev MscAdiposeD1- Mesenchymal Stem Cells - adipose, donor1_CNhs11345_11537-120A7_reverse Regulation 
MesenchymalStemCellsAdiposeDonor1_CNhs11345_ctss_fwd MscAdiposeD1+ Mesenchymal Stem Cells - adipose, donor1_CNhs11345_11537-120A7_forward Regulation 
MesenchymalStemCellsAdiposeDonor0_CNhs10844_ctss_rev MscAdiposeD0- Mesenchymal stem cells - adipose, donor0_CNhs10844_11217-116B2_reverse Regulation 
MesenchymalStemCellsAdiposeDonor0_CNhs10844_ctss_fwd MscAdiposeD0+ Mesenchymal stem cells - adipose, donor0_CNhs10844_11217-116B2_forward Regulation 
MesenchymalPrecursorCellOvarianCancerRightOvaryDonor4_CNhs13096_ctss_rev MpcOvarianCancerRightOvaryD4- mesenchymal precursor cell - ovarian cancer right ovary, donor4_CNhs13096_11837-124H1_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerRightOvaryDonor4_CNhs13096_ctss_fwd MpcOvarianCancerRightOvaryD4+ mesenchymal precursor cell - ovarian cancer right ovary, donor4_CNhs13096_11837-124H1_forward Regulation 
MesenchymalPrecursorCellOvarianCancerRightOvaryDonor3SOC5702_CNhs12377_ctss_rev MpcOvarianCancerRightOvaryD3- mesenchymal precursor cell - ovarian cancer right ovary, donor3 (SOC-57-02)_CNhs12377_11761-123H6_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerRightOvaryDonor3SOC5702G_CNhs13507_ctss_rev MpcOvarianCancerRightOvaryD3- mesenchymal precursor cell - ovarian cancer right ovary, donor3 (SOC-57-02-G)_CNhs13507_11842-124H6_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerRightOvaryDonor3SOC5702_CNhs12377_ctss_fwd MpcOvarianCancerRightOvaryD3+ mesenchymal precursor cell - ovarian cancer right ovary, donor3 (SOC-57-02)_CNhs12377_11761-123H6_forward Regulation 
MesenchymalPrecursorCellOvarianCancerRightOvaryDonor3SOC5702G_CNhs13507_ctss_fwd MpcOvarianCancerRightOvaryD3+ mesenchymal precursor cell - ovarian cancer right ovary, donor3 (SOC-57-02-G)_CNhs13507_11842-124H6_forward Regulation 
MesenchymalPrecursorCellOvarianCancerRightOvaryDonor2_CNhs12375_ctss_rev MpcOvarianCancerRightOvaryD2- mesenchymal precursor cell - ovarian cancer right ovary, donor2_CNhs12375_11759-123H4_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerRightOvaryDonor2_CNhs12375_ctss_fwd MpcOvarianCancerRightOvaryD2+ mesenchymal precursor cell - ovarian cancer right ovary, donor2_CNhs12375_11759-123H4_forward Regulation 
MesenchymalPrecursorCellOvarianCancerRightOvaryDonor1_CNhs12373_ctss_rev MpcOvarianCancerRightOvaryD1- mesenchymal precursor cell - ovarian cancer right ovary, donor1_CNhs12373_11757-123H2_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerRightOvaryDonor1_CNhs12373_ctss_fwd MpcOvarianCancerRightOvaryD1+ mesenchymal precursor cell - ovarian cancer right ovary, donor1_CNhs12373_11757-123H2_forward Regulation 
MesenchymalPrecursorCellOvarianCancerMetastasisDonor4_CNhs13097_ctss_rev MpcOvarianCancerMetastasisD4- mesenchymal precursor cell - ovarian cancer metastasis, donor4_CNhs13097_11838-124H2_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerMetastasisDonor4_CNhs13097_ctss_fwd MpcOvarianCancerMetastasisD4+ mesenchymal precursor cell - ovarian cancer metastasis, donor4_CNhs13097_11838-124H2_forward Regulation 
MesenchymalPrecursorCellOvarianCancerMetastasisDonor3_CNhs12378_ctss_rev MpcOvarianCancerMetastasisD3- mesenchymal precursor cell - ovarian cancer metastasis, donor3_CNhs12378_11762-123H7_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerMetastasisDonor3_CNhs12378_ctss_fwd MpcOvarianCancerMetastasisD3+ mesenchymal precursor cell - ovarian cancer metastasis, donor3_CNhs12378_11762-123H7_forward Regulation 
MesenchymalPrecursorCellOvarianCancerMetastasisDonor2_CNhs13093_ctss_rev MpcOvarianCancerMetastasisD2- mesenchymal precursor cell - ovarian cancer metastasis, donor2_CNhs13093_11835-124G8_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerMetastasisDonor2_CNhs13093_ctss_fwd MpcOvarianCancerMetastasisD2+ mesenchymal precursor cell - ovarian cancer metastasis, donor2_CNhs13093_11835-124G8_forward Regulation 
MesenchymalPrecursorCellOvarianCancerMetastasisDonor1_CNhs12374_ctss_rev MpcOvarianCancerMetastasisD1- mesenchymal precursor cell - ovarian cancer metastasis, donor1_CNhs12374_11758-123H3_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerMetastasisDonor1_CNhs12374_ctss_fwd MpcOvarianCancerMetastasisD1+ mesenchymal precursor cell - ovarian cancer metastasis, donor1_CNhs12374_11758-123H3_forward Regulation 
MesenchymalPrecursorCellOvarianCancerLeftOvaryDonor4_CNhs13094_ctss_rev MpcOvarianCancerLeftOvaryD4- mesenchymal precursor cell - ovarian cancer left ovary, donor4_CNhs13094_11836-124G9_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerLeftOvaryDonor4_CNhs13094_ctss_fwd MpcOvarianCancerLeftOvaryD4+ mesenchymal precursor cell - ovarian cancer left ovary, donor4_CNhs13094_11836-124G9_forward Regulation 
MesenchymalPrecursorCellOvarianCancerLeftOvaryDonor3_CNhs12376_ctss_rev MpcOvarianCancerLeftOvaryD3- mesenchymal precursor cell - ovarian cancer left ovary, donor3_CNhs12376_11760-123H5_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerLeftOvaryDonor3_CNhs12376_ctss_fwd MpcOvarianCancerLeftOvaryD3+ mesenchymal precursor cell - ovarian cancer left ovary, donor3_CNhs12376_11760-123H5_forward Regulation 
MesenchymalPrecursorCellOvarianCancerLeftOvaryDonor2_CNhs13092_ctss_rev MpcOvarianCancerLeftOvaryD2- mesenchymal precursor cell - ovarian cancer left ovary, donor2_CNhs13092_11833-124G6_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerLeftOvaryDonor2_CNhs13092_ctss_fwd MpcOvarianCancerLeftOvaryD2+ mesenchymal precursor cell - ovarian cancer left ovary, donor2_CNhs13092_11833-124G6_forward Regulation 
MesenchymalPrecursorCellOvarianCancerLeftOvaryDonor1_CNhs12372_ctss_rev MpcOvarianCancerLeftOvaryD1- mesenchymal precursor cell - ovarian cancer left ovary, donor1_CNhs12372_11756-123H1_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerLeftOvaryDonor1_CNhs12372_ctss_fwd MpcOvarianCancerLeftOvaryD1+ mesenchymal precursor cell - ovarian cancer left ovary, donor1_CNhs12372_11756-123H1_forward Regulation 
MesenchymalPrecursorCellCardiacDonor4_CNhs12371_ctss_rev MpcCardiacD4- mesenchymal precursor cell - cardiac, donor4_CNhs12371_11755-123G9_reverse Regulation 
MesenchymalPrecursorCellCardiacDonor4_CNhs12371_ctss_fwd MpcCardiacD4+ mesenchymal precursor cell - cardiac, donor4_CNhs12371_11755-123G9_forward Regulation 
MesenchymalPrecursorCellCardiacDonor3_CNhs12370_ctss_rev MpcCardiacD3- mesenchymal precursor cell - cardiac, donor3_CNhs12370_11754-123G8_reverse Regulation 
MesenchymalPrecursorCellCardiacDonor3_CNhs12370_ctss_fwd MpcCardiacD3+ mesenchymal precursor cell - cardiac, donor3_CNhs12370_11754-123G8_forward Regulation 
MesenchymalPrecursorCellCardiacDonor2_CNhs12369_ctss_rev MpcCardiacD2- mesenchymal precursor cell - cardiac, donor2_CNhs12369_11753-123G7_reverse Regulation 
MesenchymalPrecursorCellCardiacDonor2_CNhs12369_ctss_fwd MpcCardiacD2+ mesenchymal precursor cell - cardiac, donor2_CNhs12369_11753-123G7_forward Regulation 
MesenchymalPrecursorCellCardiacDonor1_CNhs12368_ctss_rev MpcCardiacD1- mesenchymal precursor cell - cardiac, donor1_CNhs12368_11752-123G6_reverse Regulation 
MesenchymalPrecursorCellCardiacDonor1_CNhs12368_ctss_fwd MpcCardiacD1+ mesenchymal precursor cell - cardiac, donor1_CNhs12368_11752-123G6_forward Regulation 
MesenchymalPrecursorCellBoneMarrowDonor3_CNhs13098_ctss_rev MpcBoneMarrowD3- mesenchymal precursor cell - bone marrow, donor3_CNhs13098_11840-124H4_reverse Regulation 
MesenchymalPrecursorCellBoneMarrowDonor3_CNhs13098_ctss_fwd MpcBoneMarrowD3+ mesenchymal precursor cell - bone marrow, donor3_CNhs13098_11840-124H4_forward Regulation 
MesenchymalPrecursorCellBoneMarrowDonor2_CNhs12367_ctss_rev MpcBoneMarrowD2- mesenchymal precursor cell - bone marrow, donor2_CNhs12367_11751-123G5_reverse Regulation 
MesenchymalPrecursorCellBoneMarrowDonor2_CNhs12367_ctss_fwd MpcBoneMarrowD2+ mesenchymal precursor cell - bone marrow, donor2_CNhs12367_11751-123G5_forward Regulation 
MesenchymalPrecursorCellBoneMarrowDonor1_CNhs12366_ctss_rev MpcBoneMarrowD1- mesenchymal precursor cell - bone marrow, donor1_CNhs12366_11750-123G4_reverse Regulation 
MesenchymalPrecursorCellBoneMarrowDonor1_CNhs12366_ctss_fwd MpcBoneMarrowD1+ mesenchymal precursor cell - bone marrow, donor1_CNhs12366_11750-123G4_forward Regulation 
MesenchymalPrecursorCellAdiposeDonor3_CNhs12365_ctss_rev MpcAdiposeD3- mesenchymal precursor cell - adipose, donor3_CNhs12365_11749-123G3_reverse Regulation 
MesenchymalPrecursorCellAdiposeDonor3_CNhs12365_ctss_fwd MpcAdiposeD3+ mesenchymal precursor cell - adipose, donor3_CNhs12365_11749-123G3_forward Regulation 
MesenchymalPrecursorCellAdiposeDonor2_CNhs12364_ctss_rev MpcAdiposeD2- mesenchymal precursor cell - adipose, donor2_CNhs12364_11748-123G2_reverse Regulation 
MesenchymalPrecursorCellAdiposeDonor2_CNhs12364_ctss_fwd MpcAdiposeD2+ mesenchymal precursor cell - adipose, donor2_CNhs12364_11748-123G2_forward Regulation 
MesenchymalPrecursorCellAdiposeDonor1_CNhs12363_ctss_rev MpcAdiposeD1- mesenchymal precursor cell - adipose, donor1_CNhs12363_11747-123G1_reverse Regulation 
MesenchymalPrecursorCellAdiposeDonor1_CNhs12363_ctss_fwd MpcAdiposeD1+ mesenchymal precursor cell - adipose, donor1_CNhs12363_11747-123G1_forward Regulation 
MigratoryLangerhansCellsDonor3_CNhs13547_ctss_rev MigratoryLangerhansCellsD3- migratory langerhans cells, donor3_CNhs13547_11903-125F4_reverse Regulation 
MigratoryLangerhansCellsDonor3_CNhs13547_ctss_fwd MigratoryLangerhansCellsD3+ migratory langerhans cells, donor3_CNhs13547_11903-125F4_forward Regulation 
MigratoryLangerhansCellsDonor2_CNhs13536_ctss_rev MigratoryLangerhansCellsD2- migratory langerhans cells, donor2_CNhs13536_11902-125F3_reverse Regulation 
MigratoryLangerhansCellsDonor2_CNhs13536_ctss_fwd MigratoryLangerhansCellsD2+ migratory langerhans cells, donor2_CNhs13536_11902-125F3_forward Regulation 
MigratoryLangerhansCellsDonor1_CNhs13535_ctss_rev MigratoryLangerhansCellsD1- migratory langerhans cells, donor1_CNhs13535_11901-125F2_reverse Regulation 
MigratoryLangerhansCellsDonor1_CNhs13535_ctss_fwd MigratoryLangerhansCellsD1+ migratory langerhans cells, donor1_CNhs13535_11901-125F2_forward Regulation 
MesothelialCellsDonor3_CNhs12012_ctss_rev MesothelialCellsD3- Mesothelial Cells, donor3_CNhs12012_11402-118D7_reverse Regulation 
MesothelialCellsDonor3_CNhs12012_ctss_fwd MesothelialCellsD3+ Mesothelial Cells, donor3_CNhs12012_11402-118D7_forward Regulation 
MesothelialCellsDonor1_CNhs10850_ctss_rev MesothelialCellsD1- Mesothelial Cells, donor1_CNhs10850_11247-116E5_reverse Regulation 
MesothelialCellsDonor1_CNhs10850_ctss_fwd MesothelialCellsD1+ Mesothelial Cells, donor1_CNhs10850_11247-116E5_forward Regulation 
MeningealCellsDonor3_CNhs12731_ctss_rev MeningealCellsD3- Meningeal Cells, donor3_CNhs12731_11654-122E7_reverse Regulation 
MeningealCellsDonor3_CNhs12731_ctss_fwd MeningealCellsD3+ Meningeal Cells, donor3_CNhs12731_11654-122E7_forward Regulation 
MeningealCellsDonor2_CNhs12080_ctss_rev MeningealCellsD2- Meningeal Cells, donor2_CNhs12080_11573-120E7_reverse Regulation 
MeningealCellsDonor2_CNhs12080_ctss_fwd MeningealCellsD2+ Meningeal Cells, donor2_CNhs12080_11573-120E7_forward Regulation 
MeningealCellsDonor1_CNhs11320_ctss_rev MeningealCellsD1- Meningeal Cells, donor1_CNhs11320_11493-119E8_reverse Regulation 
MeningealCellsDonor1_CNhs11320_ctss_fwd MeningealCellsD1+ Meningeal Cells, donor1_CNhs11320_11493-119E8_forward Regulation 
MelanocyteLightDonor3_CNhs12033_ctss_rev MelanocyteLightD3- Melanocyte - light, donor3_CNhs12033_11423-118G1_reverse Regulation 
MelanocyteLightDonor3_CNhs12033_ctss_fwd MelanocyteLightD3+ Melanocyte - light, donor3_CNhs12033_11423-118G1_forward Regulation 
MelanocyteLightDonor2_CNhs11383_ctss_rev MelanocyteLightD2- Melanocyte - light, donor2_CNhs11383_11351-117H1_reverse Regulation 
MelanocyteLightDonor2_CNhs11383_ctss_fwd MelanocyteLightD2+ Melanocyte - light, donor2_CNhs11383_11351-117H1_forward Regulation 
MelanocyteLightDonor1_CNhs11303_ctss_rev MelanocyteLightD1- Melanocyte - light, donor1_CNhs11303_11274-116H5_reverse Regulation 
MelanocyteLightDonor1_CNhs11303_ctss_fwd MelanocyteLightD1+ Melanocyte - light, donor1_CNhs11303_11274-116H5_forward Regulation 
MelanocyteDarkDonor3_CNhs12570_ctss_rev MelanocyteDarkD3- Melanocyte - dark, donor3_CNhs12570_11663-122F7_reverse Regulation 
MelanocyteDarkDonor3_CNhs12570_ctss_fwd MelanocyteDarkD3+ Melanocyte - dark, donor3_CNhs12570_11663-122F7_forward Regulation 
MelanocyteDarkDonor2_CNhs12346_ctss_rev MelanocyteDarkD2- Melanocyte - dark, donor2_CNhs12346_11582-120F7_reverse Regulation 
MelanocyteDarkDonor2_CNhs12346_ctss_fwd MelanocyteDarkD2+ Melanocyte - dark, donor2_CNhs12346_11582-120F7_forward Regulation 
MelanocyteDarkDonor1_CNhs12591_ctss_rev MelanocyteDarkD1- Melanocyte - dark, donor1_CNhs12591_11502-119F8_reverse Regulation 
MelanocyteDarkDonor1_CNhs12591_ctss_fwd MelanocyteDarkD1+ Melanocyte - dark, donor1_CNhs12591_11502-119F8_forward Regulation 
MastCellStimulatedDonor1_CNhs11073_ctss_rev MastCellStimulatedD1- Mast cell - stimulated, donor1_CNhs11073_11487-119E2_reverse Regulation 
MastCellStimulatedDonor1_CNhs11073_ctss_fwd MastCellStimulatedD1+ Mast cell - stimulated, donor1_CNhs11073_11487-119E2_forward Regulation 
MastCellExpandedAndStimulatedDonor8_CNhs13927_ctss_rev MastCellExpD8- Mast cell, expanded and stimulated, donor8_CNhs13927_11942-126A7_reverse Regulation 
MastCellExpandedDonor8_CNhs13926_ctss_rev MastCellExpD8- Mast cell, expanded, donor8_CNhs13926_11941-126A6_reverse Regulation 
MastCellExpandedAndStimulatedDonor8_CNhs13927_ctss_fwd MastCellExpD8+ Mast cell, expanded and stimulated, donor8_CNhs13927_11942-126A7_forward Regulation 
MastCellExpandedDonor8_CNhs13926_ctss_fwd MastCellExpD8+ Mast cell, expanded, donor8_CNhs13926_11941-126A6_forward Regulation 
MastCellExpandedAndStimulatedDonor5_CNhs13925_ctss_rev MastCellExpD5- Mast cell, expanded and stimulated, donor5_CNhs13925_11940-126A5_reverse Regulation 
MastCellExpandedDonor5_CNhs13924_ctss_rev MastCellExpD5- Mast cell, expanded, donor5_CNhs13924_11939-126A4_reverse Regulation 
MastCellExpandedAndStimulatedDonor5_CNhs13925_ctss_fwd MastCellExpD5+ Mast cell, expanded and stimulated, donor5_CNhs13925_11940-126A5_forward Regulation 
MastCellExpandedDonor5_CNhs13924_ctss_fwd MastCellExpD5+ Mast cell, expanded, donor5_CNhs13924_11939-126A4_forward Regulation 
MastCellDonor4_CNhs12592_ctss_rev MastCellD4- Mast cell, donor4_CNhs12592_11567-120E1_reverse Regulation 
MastCellDonor4_CNhs12592_ctss_fwd MastCellD4+ Mast cell, donor4_CNhs12592_11567-120E1_forward Regulation 
MastCellDonor3_CNhs12593_ctss_rev MastCellD3- Mast cell, donor3_CNhs12593_11566-120D9_reverse Regulation 
MastCellDonor3_CNhs12593_ctss_fwd MastCellD3+ Mast cell, donor3_CNhs12593_11566-120D9_forward Regulation 
MastCellDonor2_CNhs12594_ctss_rev MastCellD2- Mast cell, donor2_CNhs12594_11565-120D8_reverse Regulation 
MastCellDonor2_CNhs12594_ctss_fwd MastCellD2+ Mast cell, donor2_CNhs12594_11565-120D8_forward Regulation 
MastCellDonor1_CNhs12566_ctss_rev MastCellD1- Mast cell, donor1_CNhs12566_11563-120D6_reverse Regulation 
MastCellDonor1_CNhs12566_ctss_fwd MastCellD1+ Mast cell, donor1_CNhs12566_11563-120D6_forward Regulation 
MammaryEpithelialCellDonor3_CNhs12032_ctss_rev MammaryEpithelialCellD3- Mammary Epithelial Cell, donor3_CNhs12032_11422-118F9_reverse Regulation 
MammaryEpithelialCellDonor3_CNhs12032_ctss_fwd MammaryEpithelialCellD3+ Mammary Epithelial Cell, donor3_CNhs12032_11422-118F9_forward Regulation 
MammaryEpithelialCellDonor2_CNhs11382_ctss_rev MammaryEpithelialCellD2- Mammary Epithelial Cell, donor2_CNhs11382_11350-117G9_reverse Regulation 
MammaryEpithelialCellDonor2_CNhs11382_ctss_fwd MammaryEpithelialCellD2+ Mammary Epithelial Cell, donor2_CNhs11382_11350-117G9_forward Regulation 
MammaryEpithelialCellDonor1_CNhs11077_ctss_rev MammaryEpithelialCellD1- Mammary Epithelial Cell, donor1_CNhs11077_11273-116H4_reverse Regulation 
MammaryEpithelialCellDonor1_CNhs11077_ctss_fwd MammaryEpithelialCellD1+ Mammary Epithelial Cell, donor1_CNhs11077_11273-116H4_forward Regulation 
MallassezderivedCellsDonor3_CNhs13551_ctss_rev MallassezCellsD3- Mallassez-derived cells, donor3_CNhs13551_11930-125I4_reverse Regulation 
MallassezderivedCellsDonor3_CNhs13551_ctss_fwd MallassezCellsD3+ Mallassez-derived cells, donor3_CNhs13551_11930-125I4_forward Regulation 
MallassezderivedCellsDonor2_CNhs13550_ctss_rev MallassezCellsD2- Mallassez-derived cells, donor2_CNhs13550_11929-125I3_reverse Regulation 
MallassezderivedCellsDonor2_CNhs13550_ctss_fwd MallassezCellsD2+ Mallassez-derived cells, donor2_CNhs13550_11929-125I3_forward Regulation 
MacrophageMonocyteDerivedDonor3_CNhs12003_ctss_rev MacrophageMonocyteD3- Macrophage - monocyte derived, donor3_CNhs12003_11389-118C3_reverse Regulation 
MacrophageMonocyteDerivedDonor3_CNhs12003_ctss_fwd MacrophageMonocyteD3+ Macrophage - monocyte derived, donor3_CNhs12003_11389-118C3_forward Regulation 
MacrophageMonocyteDerivedDonor2_CNhs11899_ctss_rev MacrophageMonocyteD2- Macrophage - monocyte derived, donor2_CNhs11899_11313-117C8_reverse Regulation 
MacrophageMonocyteDerivedDonor2_CNhs11899_ctss_fwd MacrophageMonocyteD2+ Macrophage - monocyte derived, donor2_CNhs11899_11313-117C8_forward Regulation 
MacrophageMonocyteDerivedDonor1_CNhs10861_ctss_rev MacrophageMonocyteD1- Macrophage - monocyte derived, donor1_CNhs10861_11232-116C8_reverse Regulation 
MacrophageMonocyteDerivedDonor1_CNhs10861_ctss_fwd MacrophageMonocyteD1+ Macrophage - monocyte derived, donor1_CNhs10861_11232-116C8_forward Regulation 
LensEpithelialCellsDonor3_CNhs12572_ctss_rev LensEpithelialCellsD3- Lens Epithelial Cells, donor3_CNhs12572_11690-122I7_reverse Regulation 
LensEpithelialCellsDonor3_CNhs12572_ctss_fwd LensEpithelialCellsD3+ Lens Epithelial Cells, donor3_CNhs12572_11690-122I7_forward Regulation 
LensEpithelialCellsDonor2_CNhs12568_ctss_rev LensEpithelialCellsD2- Lens Epithelial Cells, donor2_CNhs12568_11609-120I7_reverse Regulation 
LensEpithelialCellsDonor2_CNhs12568_ctss_fwd LensEpithelialCellsD2+ Lens Epithelial Cells, donor2_CNhs12568_11609-120I7_forward Regulation 
LensEpithelialCellsDonor1_CNhs12342_ctss_rev LensEpithelialCellsD1- Lens Epithelial Cells, donor1_CNhs12342_11529-119I8_reverse Regulation 
LensEpithelialCellsDonor1_CNhs12342_ctss_fwd LensEpithelialCellsD1+ Lens Epithelial Cells, donor1_CNhs12342_11529-119I8_forward Regulation 
KeratocytesDonor3_CNhs12921_ctss_rev KeratocytesD3- Keratocytes, donor3_CNhs12921_11688-122I5_reverse Regulation 
KeratocytesDonor3_CNhs12921_ctss_fwd KeratocytesD3+ Keratocytes, donor3_CNhs12921_11688-122I5_forward Regulation 
KeratocytesDonor2_CNhs12095_ctss_rev KeratocytesD2- Keratocytes, donor2_CNhs12095_11607-120I5_reverse Regulation 
KeratocytesDonor2_CNhs12095_ctss_fwd KeratocytesD2+ Keratocytes, donor2_CNhs12095_11607-120I5_forward Regulation 
KeratocytesDonor1_CNhs11337_ctss_rev KeratocytesD1- Keratocytes, donor1_CNhs11337_11527-119I6_reverse Regulation 
KeratocytesDonor1_CNhs11337_ctss_fwd KeratocytesD1+ Keratocytes, donor1_CNhs11337_11527-119I6_forward Regulation 
KeratinocyteOralDonor1_CNhs10879_ctss_rev KeratinocyteOralD1- Keratinocyte - oral, donor1_CNhs10879_11251-116E9_reverse Regulation 
KeratinocyteOralDonor1_CNhs10879_ctss_fwd KeratinocyteOralD1+ Keratinocyte - oral, donor1_CNhs10879_11251-116E9_forward Regulation 
KeratinocyteEpidermalDonor3_CNhs12031_ctss_rev KeratinocyteEpidermalD3- Keratinocyte - epidermal, donor3_CNhs12031_11421-118F8_reverse Regulation 
KeratinocyteEpidermalDonor3_CNhs12031_ctss_fwd KeratinocyteEpidermalD3+ Keratinocyte - epidermal, donor3_CNhs12031_11421-118F8_forward Regulation 
KeratinocyteEpidermalDonor2_CNhs11381_ctss_rev KeratinocyteEpidermalD2- Keratinocyte - epidermal, donor2_CNhs11381_11349-117G8_reverse Regulation 
KeratinocyteEpidermalDonor2_CNhs11381_ctss_fwd KeratinocyteEpidermalD2+ Keratinocyte - epidermal, donor2_CNhs11381_11349-117G8_forward Regulation 
KeratinocyteEpidermalDonor1_CNhs11064_ctss_rev KeratinocyteEpidermalD1- Keratinocyte - epidermal, donor1_CNhs11064_11272-116H3_reverse Regulation 
KeratinocyteEpidermalDonor1_CNhs11064_ctss_fwd KeratinocyteEpidermalD1+ Keratinocyte - epidermal, donor1_CNhs11064_11272-116H3_forward Regulation 
IrisPigmentEpithelialCellsDonor1_CNhs12596_ctss_rev IrisPigmentEpithelialCellsD1- Iris Pigment Epithelial Cells, donor1_CNhs12596_11530-119I9_reverse Regulation 
IrisPigmentEpithelialCellsDonor1_CNhs12596_ctss_fwd IrisPigmentEpithelialCellsD1+ Iris Pigment Epithelial Cells, donor1_CNhs12596_11530-119I9_forward Regulation 
IntestinalEpithelialCellsPolarizedDonor1_CNhs10875_ctss_rev IntestinalEpithelialCellsD1- Intestinal epithelial cells (polarized), donor1_CNhs10875_11246-116E4_reverse Regulation 
IntestinalEpithelialCellsPolarizedDonor1_CNhs10875_ctss_fwd IntestinalEpithelialCellsD1+ Intestinal epithelial cells (polarized), donor1_CNhs10875_11246-116E4_forward Regulation 
ImmatureLangerhansCellsDonor2_CNhs13480_ctss_rev ImmatureLangerhansCellsD2- immature langerhans cells, donor2_CNhs13480_11905-125F6_reverse Regulation 
ImmatureLangerhansCellsDonor2_CNhs13480_ctss_fwd ImmatureLangerhansCellsD2+ immature langerhans cells, donor2_CNhs13480_11905-125F6_forward Regulation 
ImmatureLangerhansCellsDonor1_CNhs13537_ctss_rev ImmatureLangerhansCellsD1- immature langerhans cells, donor1_CNhs13537_11904-125F5_reverse Regulation 
ImmatureLangerhansCellsDonor1_CNhs13537_ctss_fwd ImmatureLangerhansCellsD1+ immature langerhans cells, donor1_CNhs13537_11904-125F5_forward Regulation 
HepatocyteDonor3_CNhs12626_ctss_rev HepatocyteD3- Hepatocyte, donor3_CNhs12626_11684-122I1_reverse Regulation 
HepatocyteDonor3_CNhs12626_ctss_fwd HepatocyteD3+ Hepatocyte, donor3_CNhs12626_11684-122I1_forward Regulation 
HepatocyteDonor2_CNhs12349_ctss_rev HepatocyteD2- Hepatocyte, donor2_CNhs12349_11603-120I1_reverse Regulation 
HepatocyteDonor2_CNhs12349_ctss_fwd HepatocyteD2+ Hepatocyte, donor2_CNhs12349_11603-120I1_forward Regulation 
HepatocyteDonor1_CNhs12340_ctss_rev HepatocyteD1- Hepatocyte, donor1_CNhs12340_11523-119I2_reverse Regulation 
HepatocyteDonor1_CNhs12340_ctss_fwd HepatocyteD1+ Hepatocyte, donor1_CNhs12340_11523-119I2_forward Regulation 
HepaticStellateCellsLipocyteDonor3_CNhs12627_ctss_rev HepaticStellateCellsD3- Hepatic Stellate Cells (lipocyte), donor3_CNhs12627_11685-122I2_reverse Regulation 
HepaticStellateCellsLipocyteDonor3_CNhs12627_ctss_fwd HepaticStellateCellsD3+ Hepatic Stellate Cells (lipocyte), donor3_CNhs12627_11685-122I2_forward Regulation 
HepaticStellateCellsLipocyteDonor2_CNhs12093_ctss_rev HepaticStellateCellsD2- Hepatic Stellate Cells (lipocyte), donor2_CNhs12093_11604-120I2_reverse Regulation 
HepaticStellateCellsLipocyteDonor2_CNhs12093_ctss_fwd HepaticStellateCellsD2+ Hepatic Stellate Cells (lipocyte), donor2_CNhs12093_11604-120I2_forward Regulation 
HepaticStellateCellsLipocyteDonor1_CNhs11335_ctss_rev HepaticStellateCellsD1- Hepatic Stellate Cells (lipocyte), donor1_CNhs11335_11524-119I3_reverse Regulation 
HepaticStellateCellsLipocyteDonor1_CNhs11335_ctss_fwd HepaticStellateCellsD1+ Hepatic Stellate Cells (lipocyte), donor1_CNhs11335_11524-119I3_forward Regulation 
HepaticSinusoidalEndothelialCellsDonor3_CNhs12625_ctss_rev HepaticSinusoidalEndothelialCellsD3- Hepatic Sinusoidal Endothelial Cells, donor3_CNhs12625_11682-122H8_reverse Regulation 
HepaticSinusoidalEndothelialCellsDonor3_CNhs12625_ctss_fwd HepaticSinusoidalEndothelialCellsD3+ Hepatic Sinusoidal Endothelial Cells, donor3_CNhs12625_11682-122H8_forward Regulation 
HepaticSinusoidalEndothelialCellsDonor2_CNhs12092_ctss_rev HepaticSinusoidalEndothelialCellsD2- Hepatic Sinusoidal Endothelial Cells, donor2_CNhs12092_11601-120H8_reverse Regulation 
HepaticSinusoidalEndothelialCellsDonor2_CNhs12092_ctss_fwd HepaticSinusoidalEndothelialCellsD2+ Hepatic Sinusoidal Endothelial Cells, donor2_CNhs12092_11601-120H8_forward Regulation 
HepaticSinusoidalEndothelialCellsDonor1_CNhs12075_ctss_rev HepaticSinusoidalEndothelialCellsD1- Hepatic Sinusoidal Endothelial Cells, donor1_CNhs12075_11521-119H9_reverse Regulation 
HepaticSinusoidalEndothelialCellsDonor1_CNhs12075_ctss_fwd HepaticSinusoidalEndothelialCellsD1+ Hepatic Sinusoidal Endothelial Cells, donor1_CNhs12075_11521-119H9_forward Regulation 
HairFollicleOuterRootSheathCellsDonor2_CNhs12347_ctss_rev HairFollicleOuterRootSheathCellsD2- Hair Follicle Outer Root Sheath Cells, donor2_CNhs12347_11584-120F9_reverse Regulation 
HairFollicleOuterRootSheathCellsDonor2_CNhs12347_ctss_fwd HairFollicleOuterRootSheathCellsD2+ Hair Follicle Outer Root Sheath Cells, donor2_CNhs12347_11584-120F9_forward Regulation 
HairFollicleOuterRootSheathCellsDonor1_CNhs12339_ctss_rev HairFollicleOuterRootSheathCellsD1- Hair Follicle Outer Root Sheath Cells, donor1_CNhs12339_11504-119G1_reverse Regulation 
HairFollicleOuterRootSheathCellsDonor1_CNhs12339_ctss_fwd HairFollicleOuterRootSheathCellsD1+ Hair Follicle Outer Root Sheath Cells, donor1_CNhs12339_11504-119G1_forward Regulation 
HairFollicleDermalPapillaCellsDonor3_CNhs12030_ctss_rev HairFollicleDermalPapillaCellsD3- Hair Follicle Dermal Papilla Cells, donor3_CNhs12030_11420-118F7_reverse Regulation 
HairFollicleDermalPapillaCellsDonor3_CNhs12030_ctss_fwd HairFollicleDermalPapillaCellsD3+ Hair Follicle Dermal Papilla Cells, donor3_CNhs12030_11420-118F7_forward Regulation 
HairFollicleDermalPapillaCellsDonor2_CNhs11979_ctss_rev HairFollicleDermalPapillaCellsD2- Hair Follicle Dermal Papilla Cells, donor2_CNhs11979_11348-117G7_reverse Regulation 
HairFollicleDermalPapillaCellsDonor2_CNhs11979_ctss_fwd HairFollicleDermalPapillaCellsD2+ Hair Follicle Dermal Papilla Cells, donor2_CNhs11979_11348-117G7_forward Regulation 
HairFollicleDermalPapillaCellsDonor1_CNhs12501_ctss_rev HairFollicleDermalPapillaCellsD1- Hair Follicle Dermal Papilla Cells, donor1_CNhs12501_11271-116H2_reverse Regulation 
HairFollicleDermalPapillaCellsDonor1_CNhs12501_ctss_fwd HairFollicleDermalPapillaCellsD1+ Hair Follicle Dermal Papilla Cells, donor1_CNhs12501_11271-116H2_forward Regulation 
GingivalEpithelialCellsDonor3GEA15_CNhs11903_ctss_rev GingivalEpithelialCellsD3- Gingival epithelial cells, donor3 (GEA15)_CNhs11903_11379-118B2_reverse Regulation 
GingivalEpithelialCellsDonor3GEA15_CNhs11903_ctss_fwd GingivalEpithelialCellsD3+ Gingival epithelial cells, donor3 (GEA15)_CNhs11903_11379-118B2_forward Regulation 
GingivalEpithelialCellsDonor2GEA14_CNhs11896_ctss_rev GingivalEpithelialCellsD2- Gingival epithelial cells, donor2 (GEA14)_CNhs11896_11302-117B6_reverse Regulation 
GingivalEpithelialCellsDonor2GEA14_CNhs11896_ctss_fwd GingivalEpithelialCellsD2+ Gingival epithelial cells, donor2 (GEA14)_CNhs11896_11302-117B6_forward Regulation 
GingivalEpithelialCellsDonor1GEA11_CNhs11061_ctss_rev GingivalEpithelialCellsD1- Gingival epithelial cells, donor1 (GEA11)_CNhs11061_11221-116B6_reverse Regulation 
GingivalEpithelialCellsDonor1GEA11_CNhs11061_ctss_fwd GingivalEpithelialCellsD1+ Gingival epithelial cells, donor1 (GEA11)_CNhs11061_11221-116B6_forward Regulation 
GammaDeltaPositiveTCellsDonor2_CNhs13915_ctss_rev GammaDeltaTcellsD2- gamma delta positive T cells, donor2_CNhs13915_11938-126A3_reverse Regulation 
GammaDeltaPositiveTCellsDonor2_CNhs13915_ctss_fwd GammaDeltaTcellsD2+ gamma delta positive T cells, donor2_CNhs13915_11938-126A3_forward Regulation 
GammaDeltaPositiveTCellsDonor1_CNhs13914_ctss_rev GammaDeltaTcellsD1- gamma delta positive T cells, donor1_CNhs13914_11937-126A2_reverse Regulation 
GammaDeltaPositiveTCellsDonor1_CNhs13914_ctss_fwd GammaDeltaTcellsD1+ gamma delta positive T cells, donor1_CNhs13914_11937-126A2_forward Regulation 
FibroblastVillousMesenchymalDonor3_CNhs12920_ctss_rev FibroVillousMesenchymalD3- Fibroblast - Villous Mesenchymal, donor3_CNhs12920_11696-123A4_reverse Regulation 
FibroblastVillousMesenchymalDonor3_CNhs12920_ctss_fwd FibroVillousMesenchymalD3+ Fibroblast - Villous Mesenchymal, donor3_CNhs12920_11696-123A4_forward Regulation 
FibroblastVillousMesenchymalDonor2_CNhs12099_ctss_rev FibroVillousMesenchymalD2- Fibroblast - Villous Mesenchymal, donor2_CNhs12099_11615-122A4_reverse Regulation 
FibroblastVillousMesenchymalDonor2_CNhs12099_ctss_fwd FibroVillousMesenchymalD2+ Fibroblast - Villous Mesenchymal, donor2_CNhs12099_11615-122A4_forward Regulation 
FibroblastVillousMesenchymalDonor1_CNhs11343_ctss_rev FibroVillousMesenchymalD1- Fibroblast - Villous Mesenchymal, donor1_CNhs11343_11535-120A5_reverse Regulation 
FibroblastVillousMesenchymalDonor1_CNhs11343_ctss_fwd FibroVillousMesenchymalD1+ Fibroblast - Villous Mesenchymal, donor1_CNhs11343_11535-120A5_forward Regulation 
FibroblastSkinWalkerWarburgDonor1_CNhs11352_ctss_rev FibroSkinWalkerWarburgD1- Fibroblast - skin walker warburg, donor1_CNhs11352_11554-120C6_reverse Regulation 
FibroblastSkinWalkerWarburgDonor1_CNhs11352_ctss_fwd FibroSkinWalkerWarburgD1+ Fibroblast - skin walker warburg, donor1_CNhs11352_11554-120C6_forward Regulation 
FibroblastSkinSpinalMuscularAtrophyDonor3_CNhs11912_ctss_rev FibroSkinSpinalMuscularAtrophyNucfracD3- Fibroblast - skin spinal muscular atrophy, donor3_CNhs11912_11559-120D2_reverse Regulation 
FibroblastSkinSpinalMuscularAtrophyDonor3_CNhs11912_ctss_fwd FibroSkinSpinalMuscularAtrophyNucfracD3+ Fibroblast - skin spinal muscular atrophy, donor3_CNhs11912_11559-120D2_forward Regulation 
FibroblastSkinSpinalMuscularAtrophyDonor2_CNhs11911_ctss_rev FibroSkinSpinalMuscularAtrophyNucfracD2- Fibroblast - skin spinal muscular atrophy, donor2_CNhs11911_11558-120D1_reverse Regulation 
FibroblastSkinSpinalMuscularAtrophyDonor2_CNhs11911_ctss_fwd FibroSkinSpinalMuscularAtrophyNucfracD2+ Fibroblast - skin spinal muscular atrophy, donor2_CNhs11911_11558-120D1_forward Regulation 
FibroblastSkinSpinalMuscularAtrophyDonor1_CNhs11074_ctss_rev FibroSkinSpinalMuscularAtrophyNucfracD1- Fibroblast - skin spinal muscular atrophy, donor1_CNhs11074_11555-120C7_reverse Regulation 
FibroblastSkinSpinalMuscularAtrophyDonor1_CNhs11074_ctss_fwd FibroSkinSpinalMuscularAtrophyNucfracD1+ Fibroblast - skin spinal muscular atrophy, donor1_CNhs11074_11555-120C7_forward Regulation 
FibroblastSkinNormalDonor2_CNhs11914_ctss_rev FibroSkinNormalNucfracD2- Fibroblast - skin normal, donor2_CNhs11914_11561-120D4_reverse Regulation 
FibroblastSkinNormalDonor2_CNhs11914_ctss_fwd FibroSkinNormalNucfracD2+ Fibroblast - skin normal, donor2_CNhs11914_11561-120D4_forward Regulation 
FibroblastSkinNormalDonor1_CNhs11351_ctss_rev FibroSkinNormalNucfracD1- Fibroblast - skin normal, donor1_CNhs11351_11553-120C5_reverse Regulation 
FibroblastSkinNormalDonor1_CNhs11351_ctss_fwd FibroSkinNormalNucfracD1+ Fibroblast - skin normal, donor1_CNhs11351_11553-120C5_forward Regulation 
FibroblastSkinDystrophiaMyotonicaDonor3_CNhs11913_ctss_rev FibroSkinDystrophiaMyotonicaNucfracD3- Fibroblast - skin dystrophia myotonica, donor3_CNhs11913_11560-120D3_reverse Regulation 
FibroblastSkinDystrophiaMyotonicaDonor3_CNhs11913_ctss_fwd FibroSkinDystrophiaMyotonicaNucfracD3+ Fibroblast - skin dystrophia myotonica, donor3_CNhs11913_11560-120D3_forward Regulation 
FibroblastSkinDystrophiaMyotonicaDonor2_CNhs11354_ctss_rev FibroSkinDystrophiaMyotonicaNucfracD2- Fibroblast - skin dystrophia myotonica, donor2_CNhs11354_11557-120C9_reverse Regulation 
FibroblastSkinDystrophiaMyotonicaDonor2_CNhs11354_ctss_fwd FibroSkinDystrophiaMyotonicaNucfracD2+ Fibroblast - skin dystrophia myotonica, donor2_CNhs11354_11557-120C9_forward Regulation 
FibroblastSkinDystrophiaMyotonicaDonor1_CNhs11353_ctss_rev FibroSkinDystrophiaMyotonicaNucfracD1- Fibroblast - skin dystrophia myotonica, donor1_CNhs11353_11556-120C8_reverse Regulation 
FibroblastSkinDystrophiaMyotonicaDonor1_CNhs11353_ctss_fwd FibroSkinDystrophiaMyotonicaNucfracD1+ Fibroblast - skin dystrophia myotonica, donor1_CNhs11353_11556-120C8_forward Regulation 
FibroblastPulmonaryArteryDonor1_CNhs10878_ctss_rev FibroPulmonaryArteryD1- Fibroblast - Pulmonary Artery, donor1_CNhs10878_11250-116E8_reverse Regulation 
FibroblastPulmonaryArteryDonor1_CNhs10878_ctss_fwd FibroPulmonaryArteryD1+ Fibroblast - Pulmonary Artery, donor1_CNhs10878_11250-116E8_forward Regulation 
FibroblastPeriodontalLigamentDonor6PLH3_CNhs11996_ctss_rev FibroPeriodontalLigamentD6- Fibroblast - Periodontal Ligament, donor6 (PLH3)_CNhs11996_11380-118B3_reverse Regulation 
FibroblastPeriodontalLigamentDonor6PLH3_CNhs11996_ctss_fwd FibroPeriodontalLigamentD6+ Fibroblast - Periodontal Ligament, donor6 (PLH3)_CNhs11996_11380-118B3_forward Regulation 
FibroblastPeriodontalLigamentDonor5PL30_CNhs11953_ctss_rev FibroPeriodontalLigamentD5- Fibroblast - Periodontal Ligament, donor5 (PL30)_CNhs11953_11304-117B8_reverse Regulation 
FibroblastPeriodontalLigamentDonor5PL30_CNhs11953_ctss_fwd FibroPeriodontalLigamentD5+ Fibroblast - Periodontal Ligament, donor5 (PL30)_CNhs11953_11304-117B8_forward Regulation 
FibroblastPeriodontalLigamentDonor4PL29_CNhs12493_ctss_rev FibroPeriodontalLigamentD4- Fibroblast - Periodontal Ligament, donor4 (PL29)_CNhs12493_11223-116B8_reverse Regulation 
FibroblastPeriodontalLigamentDonor4PL29_CNhs12493_ctss_fwd FibroPeriodontalLigamentD4+ Fibroblast - Periodontal Ligament, donor4 (PL29)_CNhs12493_11223-116B8_forward Regulation 
FibroblastPeriodontalLigamentDonor3_CNhs11907_ctss_rev FibroPeriodontalLigamentD3- Fibroblast - Periodontal Ligament, donor3_CNhs11907_11395-118C9_reverse Regulation 
FibroblastPeriodontalLigamentDonor3_CNhs11907_ctss_fwd FibroPeriodontalLigamentD3+ Fibroblast - Periodontal Ligament, donor3_CNhs11907_11395-118C9_forward Regulation 
FibroblastPeriodontalLigamentDonor2_CNhs11962_ctss_rev FibroPeriodontalLigamentD2- Fibroblast - Periodontal Ligament, donor2_CNhs11962_11319-117D5_reverse Regulation 
FibroblastPeriodontalLigamentDonor2_CNhs11962_ctss_fwd FibroPeriodontalLigamentD2+ Fibroblast - Periodontal Ligament, donor2_CNhs11962_11319-117D5_forward Regulation 
FibroblastPeriodontalLigamentDonor1_CNhs10867_ctss_rev FibroPeriodontalLigamentD1- Fibroblast - Periodontal Ligament, donor1_CNhs10867_11238-116D5_reverse Regulation 
FibroblastPeriodontalLigamentDonor1_CNhs10867_ctss_fwd FibroPeriodontalLigamentD1+ Fibroblast - Periodontal Ligament, donor1_CNhs10867_11238-116D5_forward Regulation 
FibroblastMammaryDonor3_CNhs12128_ctss_rev FibroMammaryD3- Fibroblast - Mammary, donor3_CNhs12128_11701-123A9_reverse Regulation 
FibroblastMammaryDonor3_CNhs12128_ctss_fwd FibroMammaryD3+ Fibroblast - Mammary, donor3_CNhs12128_11701-123A9_forward Regulation 
FibroblastMammaryDonor2_CNhs12103_ctss_rev FibroMammaryD2- Fibroblast - Mammary, donor2_CNhs12103_11620-122A9_reverse Regulation 
FibroblastMammaryDonor2_CNhs12103_ctss_fwd FibroMammaryD2+ Fibroblast - Mammary, donor2_CNhs12103_11620-122A9_forward Regulation 
FibroblastMammaryDonor1_CNhs11348_ctss_rev FibroMammaryD1- Fibroblast - Mammary, donor1_CNhs11348_11540-120B1_reverse Regulation 
FibroblastMammaryDonor1_CNhs11348_ctss_fwd FibroMammaryD1+ Fibroblast - Mammary, donor1_CNhs11348_11540-120B1_forward Regulation 
FibroblastLymphaticDonor3_CNhs12118_ctss_rev FibroLymphaticD3- Fibroblast - Lymphatic, donor3_CNhs12118_11667-122G2_reverse Regulation 
FibroblastLymphaticDonor3_CNhs12118_ctss_fwd FibroLymphaticD3+ Fibroblast - Lymphatic, donor3_CNhs12118_11667-122G2_forward Regulation 
FibroblastLymphaticDonor2_CNhs12082_ctss_rev FibroLymphaticD2- Fibroblast - Lymphatic, donor2_CNhs12082_11586-120G2_reverse Regulation 
FibroblastLymphaticDonor2_CNhs12082_ctss_fwd FibroLymphaticD2+ Fibroblast - Lymphatic, donor2_CNhs12082_11586-120G2_forward Regulation 
FibroblastLymphaticDonor1_CNhs11322_ctss_rev FibroLymphaticD1- Fibroblast - Lymphatic, donor1_CNhs11322_11506-119G3_reverse Regulation 
FibroblastLymphaticDonor1_CNhs11322_ctss_fwd FibroLymphaticD1+ Fibroblast - Lymphatic, donor1_CNhs11322_11506-119G3_forward Regulation 
FibroblastLungDonor3_CNhs12029_ctss_rev FibroLungD3- Fibroblast - Lung, donor3_CNhs12029_11419-118F6_reverse Regulation 
FibroblastLungDonor3_CNhs12029_ctss_fwd FibroLungD3+ Fibroblast - Lung, donor3_CNhs12029_11419-118F6_forward Regulation 
FibroblastLungDonor2_CNhs11380_ctss_rev FibroLungD2- Fibroblast - Lung, donor2_CNhs11380_11347-117G6_reverse Regulation 
FibroblastLungDonor2_CNhs11380_ctss_fwd FibroLungD2+ Fibroblast - Lung, donor2_CNhs11380_11347-117G6_forward Regulation 
FibroblastLungDonor1_CNhs12500_ctss_rev FibroLungD1- Fibroblast - Lung, donor1_CNhs12500_11270-116H1_reverse Regulation 
FibroblastLungDonor1_CNhs12500_ctss_fwd FibroLungD1+ Fibroblast - Lung, donor1_CNhs12500_11270-116H1_forward Regulation 
FibroblastGingivalDonor9Control_CNhs14134_ctss_rev FibroGingivalD9- Fibroblast - Gingival, donor9 (control)_CNhs14134_11927-125I1_reverse Regulation 
FibroblastGingivalDonor9Control_CNhs14134_ctss_fwd FibroGingivalD9+ Fibroblast - Gingival, donor9 (control)_CNhs14134_11927-125I1_forward Regulation 
FibroblastGingivalDonor8Control_CNhs14133_ctss_rev FibroGingivalD8- Fibroblast - Gingival, donor8 (control)_CNhs14133_11926-125H9_reverse Regulation 
FibroblastGingivalDonor8ChronicPeriodontitis_CNhs14132_ctss_rev FibroGingivalD8- Fibroblast - Gingival, donor8 (chronic periodontitis)_CNhs14132_11925-125H8_reverse Regulation 
FibroblastGingivalDonor8Control_CNhs14133_ctss_fwd FibroGingivalD8+ Fibroblast - Gingival, donor8 (control)_CNhs14133_11926-125H9_forward Regulation 
FibroblastGingivalDonor8ChronicPeriodontitis_CNhs14132_ctss_fwd FibroGingivalD8+ Fibroblast - Gingival, donor8 (chronic periodontitis)_CNhs14132_11925-125H8_forward Regulation 
FibroblastGingivalDonor7Control_CNhs14131_ctss_rev FibroGingivalD7- Fibroblast - Gingival, donor7 (control)_CNhs14131_11924-125H7_reverse Regulation 
FibroblastGingivalDonor7AggressivePeriodontitis_CNhs14130_ctss_rev FibroGingivalD7- Fibroblast - Gingival, donor7 (aggressive periodontitis)_CNhs14130_11923-125H6_reverse Regulation 
FibroblastGingivalDonor7Control_CNhs14131_ctss_fwd FibroGingivalD7+ Fibroblast - Gingival, donor7 (control)_CNhs14131_11924-125H7_forward Regulation 
FibroblastGingivalDonor7AggressivePeriodontitis_CNhs14130_ctss_fwd FibroGingivalD7+ Fibroblast - Gingival, donor7 (aggressive periodontitis)_CNhs14130_11923-125H6_forward Regulation 
FibroblastGingivalDonor6AggressivePeriodontitis_CNhs14128_ctss_rev FibroGingivalD6- Fibroblast - Gingival, donor6 (aggressive periodontitis)_CNhs14128_11921-125H4_reverse Regulation 
FibroblastGingivalDonor6Control_CNhs14129_ctss_rev FibroGingivalD6- Fibroblast - Gingival, donor6 (control)_CNhs14129_11922-125H5_reverse Regulation 
FibroblastGingivalDonor6Control_CNhs14129_ctss_fwd FibroGingivalD6+ Fibroblast - Gingival, donor6 (control)_CNhs14129_11922-125H5_forward Regulation 
FibroblastGingivalDonor6AggressivePeriodontitis_CNhs14128_ctss_fwd FibroGingivalD6+ Fibroblast - Gingival, donor6 (aggressive periodontitis)_CNhs14128_11921-125H4_forward Regulation 
FibroblastGingivalDonor5GFH3_CNhs11952_ctss_rev FibroGingivalD5- Fibroblast - Gingival, donor5 (GFH3)_CNhs11952_11303-117B7_reverse Regulation 
FibroblastGingivalDonor5GFH3_CNhs11952_ctss_fwd FibroGingivalD5+ Fibroblast - Gingival, donor5 (GFH3)_CNhs11952_11303-117B7_forward Regulation 
FibroblastGingivalDonor4GFH2_CNhs10848_ctss_rev FibroGingivalD4- Fibroblast - Gingival, donor4 (GFH2)_CNhs10848_11222-116B7_reverse Regulation 
FibroblastGingivalDonor4GFH2_CNhs10848_ctss_fwd FibroGingivalD4+ Fibroblast - Gingival, donor4 (GFH2)_CNhs10848_11222-116B7_forward Regulation 
FibroblastGingivalDonor3_CNhs12006_ctss_rev FibroGingivalD3- Fibroblast - Gingival, donor3_CNhs12006_11394-118C8_reverse Regulation 
FibroblastGingivalDonor3_CNhs12006_ctss_fwd FibroGingivalD3+ Fibroblast - Gingival, donor3_CNhs12006_11394-118C8_forward Regulation 
FibroblastGingivalDonor2_CNhs11961_ctss_rev FibroGingivalD2- Fibroblast - Gingival, donor2_CNhs11961_11318-117D4_reverse Regulation 
FibroblastGingivalDonor2_CNhs11961_ctss_fwd FibroGingivalD2+ Fibroblast - Gingival, donor2_CNhs11961_11318-117D4_forward Regulation 
FibroblastGingivalDonor10Periodontitis_CNhs14135_ctss_rev FibroGingivalD10 (p- Fibroblast - Gingival, donor10 (periodontitis)_CNhs14135_11928-125I2_reverse Regulation 
FibroblastGingivalDonor10Periodontitis_CNhs14135_ctss_fwd FibroGingivalD10 (p+ Fibroblast - Gingival, donor10 (periodontitis)_CNhs14135_11928-125I2_forward Regulation 
FibroblastGingivalDonor1_CNhs10866_ctss_rev FibroGingivalD1- Fibroblast - Gingival, donor1_CNhs10866_11237-116D4_reverse Regulation 
FibroblastGingivalDonor1_CNhs10866_ctss_fwd FibroGingivalD1+ Fibroblast - Gingival, donor1_CNhs10866_11237-116D4_forward Regulation 
FibroblastDermalDonor6_CNhs12059_ctss_rev FibroDermalD6- Fibroblast - Dermal, donor6_CNhs12059_11458-119A9_reverse Regulation 
FibroblastDermalDonor6_CNhs12059_ctss_fwd FibroDermalD6+ Fibroblast - Dermal, donor6_CNhs12059_11458-119A9_forward Regulation 
FibroblastDermalDonor5_CNhs12055_ctss_rev FibroDermalD5- Fibroblast - Dermal, donor5_CNhs12055_11454-119A5_reverse Regulation 
FibroblastDermalDonor5_CNhs12055_ctss_fwd FibroDermalD5+ Fibroblast - Dermal, donor5_CNhs12055_11454-119A5_forward Regulation 
FibroblastDermalDonor4_CNhs12052_ctss_rev FibroDermalD4- Fibroblast - Dermal, donor4_CNhs12052_11450-119A1_reverse Regulation 
FibroblastDermalDonor4_CNhs12052_ctss_fwd FibroDermalD4+ Fibroblast - Dermal, donor4_CNhs12052_11450-119A1_forward Regulation 
FibroblastDermalDonor3_CNhs12028_ctss_rev FibroDermalD3- Fibroblast - Dermal, donor3_CNhs12028_11418-118F5_reverse Regulation 
FibroblastDermalDonor3_CNhs12028_ctss_fwd FibroDermalD3+ Fibroblast - Dermal, donor3_CNhs12028_11418-118F5_forward Regulation 
FibroblastDermalDonor2_CNhs11379_ctss_rev FibroDermalD2- Fibroblast - Dermal, donor2_CNhs11379_11346-117G5_reverse Regulation 
FibroblastDermalDonor2_CNhs11379_ctss_fwd FibroDermalD2+ Fibroblast - Dermal, donor2_CNhs11379_11346-117G5_forward Regulation 
FibroblastDermalDonor1_CNhs12499_ctss_rev FibroDermalD1- Fibroblast - Dermal, donor1_CNhs12499_11269-116G9_reverse Regulation 
FibroblastDermalDonor1_CNhs12499_ctss_fwd FibroDermalD1+ Fibroblast - Dermal, donor1_CNhs12499_11269-116G9_forward Regulation 
FibroblastConjunctivalDonor3_CNhs12734_ctss_rev FibroConjunctivalD3- Fibroblast - Conjunctival, donor3_CNhs12734_11692-122I9_reverse Regulation 
FibroblastConjunctivalDonor3_CNhs12734_ctss_fwd FibroConjunctivalD3+ Fibroblast - Conjunctival, donor3_CNhs12734_11692-122I9_forward Regulation 
FibroblastConjunctivalDonor1_CNhs11339_ctss_rev FibroConjunctivalD1- Fibroblast - Conjunctival, donor1_CNhs11339_11531-120A1_reverse Regulation 
FibroblastConjunctivalDonor1_CNhs11339_ctss_fwd FibroConjunctivalD1+ Fibroblast - Conjunctival, donor1_CNhs11339_11531-120A1_forward Regulation 
FibroblastChoroidPlexusDonor3_CNhs12620_ctss_rev FibroChoroidPlexusD3- Fibroblast - Choroid Plexus, donor3_CNhs12620_11653-122E6_reverse Regulation 
FibroblastChoroidPlexusDonor3_CNhs12620_ctss_fwd FibroChoroidPlexusD3+ Fibroblast - Choroid Plexus, donor3_CNhs12620_11653-122E6_forward Regulation 
FibroblastChoroidPlexusDonor2_CNhs12344_ctss_rev FibroChoroidPlexusD2- Fibroblast - Choroid Plexus, donor2_CNhs12344_11572-120E6_reverse Regulation 
FibroblastChoroidPlexusDonor2_CNhs12344_ctss_fwd FibroChoroidPlexusD2+ Fibroblast - Choroid Plexus, donor2_CNhs12344_11572-120E6_forward Regulation 
FibroblastChoroidPlexusDonor1_CNhs11319_ctss_rev FibroChoroidPlexusD1- Fibroblast - Choroid Plexus, donor1_CNhs11319_11492-119E7_reverse Regulation 
FibroblastChoroidPlexusDonor1_CNhs11319_ctss_fwd FibroChoroidPlexusD1+ Fibroblast - Choroid Plexus, donor1_CNhs11319_11492-119E7_forward Regulation 
FibroblastCardiacDonor6_CNhs12061_ctss_rev FibroCardiacD6- Fibroblast - Cardiac, donor6_CNhs12061_11460-119B2_reverse Regulation 
FibroblastCardiacDonor6_CNhs12061_ctss_fwd FibroCardiacD6+ Fibroblast - Cardiac, donor6_CNhs12061_11460-119B2_forward Regulation 
FibroblastCardiacDonor5_CNhs12057_ctss_rev FibroCardiacD5- Fibroblast - Cardiac, donor5_CNhs12057_11456-119A7_reverse Regulation 
FibroblastCardiacDonor5_CNhs12057_ctss_fwd FibroCardiacD5+ Fibroblast - Cardiac, donor5_CNhs12057_11456-119A7_forward Regulation 
FibroblastCardiacDonor4_CNhs11909_ctss_rev FibroCardiacD4- Fibroblast - Cardiac, donor4_CNhs11909_11452-119A3_reverse Regulation 
FibroblastCardiacDonor4_CNhs11909_ctss_fwd FibroCardiacD4+ Fibroblast - Cardiac, donor4_CNhs11909_11452-119A3_forward Regulation 
FibroblastCardiacDonor3_CNhs12027_ctss_rev FibroCardiacD3- Fibroblast - Cardiac, donor3_CNhs12027_11417-118F4_reverse Regulation 
FibroblastCardiacDonor3_CNhs12027_ctss_fwd FibroCardiacD3+ Fibroblast - Cardiac, donor3_CNhs12027_11417-118F4_forward Regulation 
FibroblastCardiacDonor2_CNhs11378_ctss_rev FibroCardiacD2- Fibroblast - Cardiac, donor2_CNhs11378_11345-117G4_reverse Regulation 
FibroblastCardiacDonor2_CNhs11378_ctss_fwd FibroCardiacD2+ Fibroblast - Cardiac, donor2_CNhs11378_11345-117G4_forward Regulation 
FibroblastCardiacDonor1_CNhs12498_ctss_rev FibroCardiacD1- Fibroblast - Cardiac, donor1_CNhs12498_11268-116G8_reverse Regulation 
FibroblastCardiacDonor1_CNhs12498_ctss_fwd FibroCardiacD1+ Fibroblast - Cardiac, donor1_CNhs12498_11268-116G8_forward Regulation 
FibroblastAorticAdventitialDonor3_CNhs12011_ctss_rev FibroAorticAdventitialD3- Fibroblast - Aortic Adventitial, donor3_CNhs12011_11401-118D6_reverse Regulation 
FibroblastAorticAdventitialDonor3_CNhs12011_ctss_fwd FibroAorticAdventitialD3+ Fibroblast - Aortic Adventitial, donor3_CNhs12011_11401-118D6_forward Regulation 
FibroblastAorticAdventitialDonor2_CNhs11968_ctss_rev FibroAorticAdventitialD2- Fibroblast - Aortic Adventitial, donor2_CNhs11968_11326-117E3_reverse Regulation 
FibroblastAorticAdventitialDonor2_CNhs11968_ctss_fwd FibroAorticAdventitialD2+ Fibroblast - Aortic Adventitial, donor2_CNhs11968_11326-117E3_forward Regulation 
FibroblastAorticAdventitialDonor1_CNhs10874_ctss_rev FibroAorticAdventitialD1- Fibroblast - Aortic Adventitial, donor1_CNhs10874_11245-116E3_reverse Regulation 
FibroblastAorticAdventitialDonor1_CNhs10874_ctss_fwd FibroAorticAdventitialD1+ Fibroblast - Aortic Adventitial, donor1_CNhs10874_11245-116E3_forward Regulation 
EsophagealEpithelialCellsDonor3_CNhs12622_ctss_rev EsophagealEpithelialCellsD3- Esophageal Epithelial Cells, donor3_CNhs12622_11668-122G3_reverse Regulation 
EsophagealEpithelialCellsDonor3_CNhs12622_ctss_fwd EsophagealEpithelialCellsD3+ Esophageal Epithelial Cells, donor3_CNhs12622_11668-122G3_forward Regulation 
EsophagealEpithelialCellsDonor2_CNhs12083_ctss_rev EsophagealEpithelialCellsD2- Esophageal Epithelial Cells, donor2_CNhs12083_11587-120G3_reverse Regulation 
EsophagealEpithelialCellsDonor2_CNhs12083_ctss_fwd EsophagealEpithelialCellsD2+ Esophageal Epithelial Cells, donor2_CNhs12083_11587-120G3_forward Regulation 
EsophagealEpithelialCellsDonor1_CNhs11323_ctss_rev EsophagealEpithelialCellsD1- Esophageal Epithelial Cells, donor1_CNhs11323_11507-119G4_reverse Regulation 
EsophagealEpithelialCellsDonor1_CNhs11323_ctss_fwd EsophagealEpithelialCellsD1+ Esophageal Epithelial Cells, donor1_CNhs11323_11507-119G4_forward Regulation 
EndothelialCellsVeinDonor3_CNhs12026_ctss_rev EndothelialCellsVeinD3- Endothelial Cells - Vein, donor3_CNhs12026_11416-118F3_reverse Regulation 
EndothelialCellsVeinDonor3_CNhs12026_ctss_fwd EndothelialCellsVeinD3+ Endothelial Cells - Vein, donor3_CNhs12026_11416-118F3_forward Regulation 
EndothelialCellsVeinDonor2_CNhs11377_ctss_rev EndothelialCellsVeinD2- Endothelial Cells - Vein, donor2_CNhs11377_11344-117G3_reverse Regulation 
EndothelialCellsVeinDonor2_CNhs11377_ctss_fwd EndothelialCellsVeinD2+ Endothelial Cells - Vein, donor2_CNhs11377_11344-117G3_forward Regulation 
EndothelialCellsVeinDonor1_CNhs12497_ctss_rev EndothelialCellsVeinD1- Endothelial Cells - Vein, donor1_CNhs12497_11267-116G7_reverse Regulation 
EndothelialCellsVeinDonor1_CNhs12497_ctss_fwd EndothelialCellsVeinD1+ Endothelial Cells - Vein, donor1_CNhs12497_11267-116G7_forward Regulation 
EndothelialCellsUmbilicalVeinDonor3_CNhs12010_ctss_rev EndothelialCellsUmbilicalVeinD3- Endothelial Cells - Umbilical vein, donor3_CNhs12010_11400-118D5_reverse Regulation 
EndothelialCellsUmbilicalVeinDonor3_CNhs12010_ctss_fwd EndothelialCellsUmbilicalVeinD3+ Endothelial Cells - Umbilical vein, donor3_CNhs12010_11400-118D5_forward Regulation 
EndothelialCellsUmbilicalVeinDonor2_CNhs11967_ctss_rev EndothelialCellsUmbilicalVeinD2- Endothelial Cells - Umbilical vein, donor2_CNhs11967_11324-117E1_reverse Regulation 
EndothelialCellsUmbilicalVeinDonor2_CNhs11967_ctss_fwd EndothelialCellsUmbilicalVeinD2+ Endothelial Cells - Umbilical vein, donor2_CNhs11967_11324-117E1_forward Regulation 
EndothelialCellsUmbilicalVeinDonor1_CNhs10872_ctss_rev EndothelialCellsUmbilicalVeinD1- Endothelial Cells - Umbilical vein, donor1_CNhs10872_11243-116E1_reverse Regulation 
EndothelialCellsUmbilicalVeinDonor1_CNhs10872_ctss_fwd EndothelialCellsUmbilicalVeinD1+ Endothelial Cells - Umbilical vein, donor1_CNhs10872_11243-116E1_forward Regulation 
EndothelialCellsThoracicDonor2_CNhs11978_ctss_rev EndothelialCellsThoracicD2- Endothelial Cells - Thoracic, donor2_CNhs11978_11343-117G2_reverse Regulation 
EndothelialCellsThoracicDonor2_CNhs11978_ctss_fwd EndothelialCellsThoracicD2+ Endothelial Cells - Thoracic, donor2_CNhs11978_11343-117G2_forward Regulation 
EndothelialCellsThoracicDonor1_CNhs11926_ctss_rev EndothelialCellsThoracicD1- Endothelial Cells - Thoracic, donor1_CNhs11926_11266-116G6_reverse Regulation 
EndothelialCellsThoracicDonor1_CNhs11926_ctss_fwd EndothelialCellsThoracicD1+ Endothelial Cells - Thoracic, donor1_CNhs11926_11266-116G6_forward Regulation 
EndothelialCellsMicrovascularDonor3_CNhs12024_ctss_rev EndothelialCellsMicrovascularD3- Endothelial Cells - Microvascular, donor3_CNhs12024_11414-118F1_reverse Regulation 
EndothelialCellsMicrovascularDonor3_CNhs12024_ctss_fwd EndothelialCellsMicrovascularD3+ Endothelial Cells - Microvascular, donor3_CNhs12024_11414-118F1_forward Regulation 
EndothelialCellsMicrovascularDonor2_CNhs11376_ctss_rev EndothelialCellsMicrovascularD2- Endothelial Cells - Microvascular, donor2_CNhs11376_11342-117G1_reverse Regulation 
EndothelialCellsMicrovascularDonor2_CNhs11376_ctss_fwd EndothelialCellsMicrovascularD2+ Endothelial Cells - Microvascular, donor2_CNhs11376_11342-117G1_forward Regulation 
EndothelialCellsMicrovascularDonor1_CNhs11925_ctss_rev EndothelialCellsMicrovascularD1- Endothelial Cells - Microvascular, donor1_CNhs11925_11265-116G5_reverse Regulation 
EndothelialCellsMicrovascularDonor1_CNhs11925_ctss_fwd EndothelialCellsMicrovascularD1+ Endothelial Cells - Microvascular, donor1_CNhs11925_11265-116G5_forward Regulation 
EndothelialCellsLymphaticDonor3_CNhs11906_ctss_rev EndothelialCellsLymphaticD3- Endothelial Cells - Lymphatic, donor3_CNhs11906_11393-118C7_reverse Regulation 
EndothelialCellsLymphaticDonor3_CNhs11906_ctss_fwd EndothelialCellsLymphaticD3+ Endothelial Cells - Lymphatic, donor3_CNhs11906_11393-118C7_forward Regulation 
EndothelialCellsLymphaticDonor2_CNhs11901_ctss_rev EndothelialCellsLymphaticD2- Endothelial Cells - Lymphatic, donor2_CNhs11901_11317-117D3_reverse Regulation 
EndothelialCellsLymphaticDonor2_CNhs11901_ctss_fwd EndothelialCellsLymphaticD2+ Endothelial Cells - Lymphatic, donor2_CNhs11901_11317-117D3_forward Regulation 
EndothelialCellsLymphaticDonor1_CNhs10865_ctss_rev EndothelialCellsLymphaticD1- Endothelial Cells - Lymphatic, donor1_CNhs10865_11236-116D3_reverse Regulation 
EndothelialCellsLymphaticDonor1_CNhs10865_ctss_fwd EndothelialCellsLymphaticD1+ Endothelial Cells - Lymphatic, donor1_CNhs10865_11236-116D3_forward Regulation 
EndothelialCellsArteryDonor3_CNhs12023_ctss_rev EndothelialCellsArteryD3- Endothelial Cells - Artery, donor3_CNhs12023_11413-118E9_reverse Regulation 
EndothelialCellsArteryDonor3_CNhs12023_ctss_fwd EndothelialCellsArteryD3+ Endothelial Cells - Artery, donor3_CNhs12023_11413-118E9_forward Regulation 
EndothelialCellsArteryDonor2_CNhs11977_ctss_rev EndothelialCellsArteryD2- Endothelial Cells - Artery, donor2_CNhs11977_11341-117F9_reverse Regulation 
EndothelialCellsArteryDonor2_CNhs11977_ctss_fwd EndothelialCellsArteryD2+ Endothelial Cells - Artery, donor2_CNhs11977_11341-117F9_forward Regulation 
EndothelialCellsArteryDonor1_CNhs12496_ctss_rev EndothelialCellsArteryD1- Endothelial Cells - Artery, donor1_CNhs12496_11264-116G4_reverse Regulation 
EndothelialCellsArteryDonor1_CNhs12496_ctss_fwd EndothelialCellsArteryD1+ Endothelial Cells - Artery, donor1_CNhs12496_11264-116G4_forward Regulation 
EndothelialCellsAorticDonor3_CNhs12022_ctss_rev EndothelialCellsAorticD3- Endothelial Cells - Aortic, donor3_CNhs12022_11412-118E8_reverse Regulation 
EndothelialCellsAorticDonor3_CNhs12022_ctss_fwd EndothelialCellsAorticD3+ Endothelial Cells - Aortic, donor3_CNhs12022_11412-118E8_forward Regulation 
EndothelialCellsAorticDonor2_CNhs11375_ctss_rev EndothelialCellsAorticD2- Endothelial Cells - Aortic, donor2_CNhs11375_11340-117F8_reverse Regulation 
EndothelialCellsAorticDonor2_CNhs11375_ctss_fwd EndothelialCellsAorticD2+ Endothelial Cells - Aortic, donor2_CNhs11375_11340-117F8_forward Regulation 
EndothelialCellsAorticDonor1_CNhs12495_ctss_rev EndothelialCellsAorticD1- Endothelial Cells - Aortic, donor1_CNhs12495_11263-116G3_reverse Regulation 
EndothelialCellsAorticDonor1_CNhs12495_ctss_fwd EndothelialCellsAorticD1+ Endothelial Cells - Aortic, donor1_CNhs12495_11263-116G3_forward Regulation 
EndothelialCellsAorticDonor0_CNhs10837_ctss_rev EndothelialCellsAorticD0- Endothelial Cells - Aortic, donor0_CNhs10837_11207-116A1_reverse Regulation 
EndothelialCellsAorticDonor0_CNhs10837_ctss_fwd EndothelialCellsAorticD0+ Endothelial Cells - Aortic, donor0_CNhs10837_11207-116A1_forward Regulation 
DendriticCellsPlasmacytoidDonor1_CNhs10857_ctss_rev DendriticCellsPlasmacytoidD1- Dendritic Cells - plasmacytoid, donor1_CNhs10857_11228-116C4_reverse Regulation 
DendriticCellsPlasmacytoidDonor1_CNhs10857_ctss_fwd DendriticCellsPlasmacytoidD1+ Dendritic Cells - plasmacytoid, donor1_CNhs10857_11228-116C4_forward Regulation 
DendriticCellsMonocyteImmatureDerivedDonor3_CNhs12000_ctss_rev DendriticCellsMonocyteImmatureD3- Dendritic Cells - monocyte immature derived, donor3_CNhs12000_11384-118B7_reverse Regulation 
DendriticCellsMonocyteImmatureDerivedDonor3_CNhs12000_ctss_fwd DendriticCellsMonocyteImmatureD3+ Dendritic Cells - monocyte immature derived, donor3_CNhs12000_11384-118B7_forward Regulation 
DendriticCellsMonocyteImmatureDerivedDonor1TechRep1_CNhs10855_ctss_rev DendriticCellsMonocyteImmatureD1Tr1- Dendritic Cells - monocyte immature derived, donor1, tech_rep1_CNhs10855_11227-116C3_reverse Regulation 
DendriticCellsMonocyteImmatureDerivedDonor1TechRep1_CNhs10855_ctss_fwd DendriticCellsMonocyteImmatureD1Tr1+ Dendritic Cells - monocyte immature derived, donor1, tech_rep1_CNhs10855_11227-116C3_forward Regulation 
CornealEpithelialCellsDonor3_CNhs12123_ctss_rev CornealEpithelialCellsD3- Corneal Epithelial Cells, donor3_CNhs12123_11687-122I4_reverse Regulation 
CornealEpithelialCellsDonor3_CNhs12123_ctss_fwd CornealEpithelialCellsD3+ Corneal Epithelial Cells, donor3_CNhs12123_11687-122I4_forward Regulation 
CornealEpithelialCellsDonor2_CNhs12094_ctss_rev CornealEpithelialCellsD2- Corneal Epithelial Cells, donor2_CNhs12094_11606-120I4_reverse Regulation 
CornealEpithelialCellsDonor2_CNhs12094_ctss_fwd CornealEpithelialCellsD2+ Corneal Epithelial Cells, donor2_CNhs12094_11606-120I4_forward Regulation 
CornealEpithelialCellsDonor1_CNhs11336_ctss_rev CornealEpithelialCellsD1- Corneal Epithelial Cells, donor1_CNhs11336_11526-119I5_reverse Regulation 
CornealEpithelialCellsDonor1_CNhs11336_ctss_fwd CornealEpithelialCellsD1+ Corneal Epithelial Cells, donor1_CNhs11336_11526-119I5_forward Regulation 
CiliaryEpithelialCellsDonor3_CNhs12009_ctss_rev CiliaryEpithelialCellsD3- Ciliary Epithelial Cells, donor3_CNhs12009_11399-118D4_reverse Regulation 
CiliaryEpithelialCellsDonor3_CNhs12009_ctss_fwd CiliaryEpithelialCellsD3+ Ciliary Epithelial Cells, donor3_CNhs12009_11399-118D4_forward Regulation 
CiliaryEpithelialCellsDonor2_CNhs11966_ctss_rev CiliaryEpithelialCellsD2- Ciliary Epithelial Cells, donor2_CNhs11966_11323-117D9_reverse Regulation 
CiliaryEpithelialCellsDonor2_CNhs11966_ctss_fwd CiliaryEpithelialCellsD2+ Ciliary Epithelial Cells, donor2_CNhs11966_11323-117D9_forward Regulation 
CiliaryEpithelialCellsDonor1_CNhs10871_ctss_rev CiliaryEpithelialCellsD1- Ciliary Epithelial Cells, donor1_CNhs10871_11242-116D9_reverse Regulation 
CiliaryEpithelialCellsDonor1_CNhs10871_ctss_fwd CiliaryEpithelialCellsD1+ Ciliary Epithelial Cells, donor1_CNhs10871_11242-116D9_forward Regulation 
ChorionicMembraneCellsDonor3_CNhs12380_ctss_rev ChorionicMembraneCellsD3- chorionic membrane cells, donor3_CNhs12380_12240-129G8_reverse Regulation 
ChorionicMembraneCellsDonor3_CNhs12380_ctss_fwd ChorionicMembraneCellsD3+ chorionic membrane cells, donor3_CNhs12380_12240-129G8_forward Regulation 
ChorionicMembraneCellsDonor2_CNhs12506_ctss_rev ChorionicMembraneCellsD2- chorionic membrane cells, donor2_CNhs12506_12239-129G7_reverse Regulation 
ChorionicMembraneCellsDonor2_CNhs12506_ctss_fwd ChorionicMembraneCellsD2+ chorionic membrane cells, donor2_CNhs12506_12239-129G7_forward Regulation 
ChorionicMembraneCellsDonor1_CNhs12504_ctss_rev ChorionicMembraneCellsD1- chorionic membrane cells, donor1_CNhs12504_12238-129G6_reverse Regulation 
ChorionicMembraneCellsDonor1_CNhs12504_ctss_fwd ChorionicMembraneCellsD1+ chorionic membrane cells, donor1_CNhs12504_12238-129G6_forward Regulation 
ChondrocyteReDiffDonor3_CNhs12021_ctss_rev ChondrocyteReDiffD3- Chondrocyte - re diff, donor3_CNhs12021_11411-118E7_reverse Regulation 
ChondrocyteReDiffDonor3_CNhs12021_ctss_fwd ChondrocyteReDiffD3+ Chondrocyte - re diff, donor3_CNhs12021_11411-118E7_forward Regulation 
ChondrocyteReDiffDonor2_CNhs11373_ctss_rev ChondrocyteReDiffD2- Chondrocyte - re diff, donor2_CNhs11373_11339-117F7_reverse Regulation 
ChondrocyteReDiffDonor2_CNhs11373_ctss_fwd ChondrocyteReDiffD2+ Chondrocyte - re diff, donor2_CNhs11373_11339-117F7_forward Regulation 
ChondrocyteDeDiffDonor3_CNhs12020_ctss_rev ChondrocyteDeDiffD3- Chondrocyte - de diff, donor3_CNhs12020_11410-118E6_reverse Regulation 
ChondrocyteDeDiffDonor3_CNhs12020_ctss_fwd ChondrocyteDeDiffD3+ Chondrocyte - de diff, donor3_CNhs12020_11410-118E6_forward Regulation 
ChondrocyteDeDiffDonor2_CNhs11372_ctss_rev ChondrocyteDeDiffD2- Chondrocyte - de diff, donor2_CNhs11372_11338-117F6_reverse Regulation 
ChondrocyteDeDiffDonor2_CNhs11372_ctss_fwd ChondrocyteDeDiffD2+ Chondrocyte - de diff, donor2_CNhs11372_11338-117F6_forward Regulation 
ChondrocyteDeDiffDonor1_CNhs11923_ctss_rev ChondrocyteDeDiffD1- Chondrocyte - de diff, donor1_CNhs11923_11261-116G1_reverse Regulation 
ChondrocyteDeDiffDonor1_CNhs11923_ctss_fwd ChondrocyteDeDiffD1+ Chondrocyte - de diff, donor1_CNhs11923_11261-116G1_forward Regulation 
CD8TCellsDonor3_CNhs11999_ctss_rev Cd8+TCellsD3- CD8+ T Cells, donor3_CNhs11999_11383-118B6_reverse Regulation 
CD8TCellsDonor3_CNhs11999_ctss_fwd Cd8+TCellsD3+ CD8+ T Cells, donor3_CNhs11999_11383-118B6_forward Regulation 
CD8TCellsDonor2_CNhs11956_ctss_rev Cd8+TCellsD2- CD8+ T Cells, donor2_CNhs11956_11307-117C2_reverse Regulation 
CD8TCellsDonor2_CNhs11956_ctss_fwd Cd8+TCellsD2+ CD8+ T Cells, donor2_CNhs11956_11307-117C2_forward Regulation 
CD8TCellsDonor1_CNhs10854_ctss_rev Cd8+TCellsD1- CD8+ T Cells, donor1_CNhs10854_11226-116C2_reverse Regulation 
CD8TCellsDonor1_CNhs10854_ctss_fwd Cd8+TCellsD1+ CD8+ T Cells, donor1_CNhs10854_11226-116C2_forward Regulation 
CD4TCellsDonor3_CNhs11998_ctss_rev Cd4+TCellsD3- CD4+ T Cells, donor3_CNhs11998_11382-118B5_reverse Regulation 
CD4TCellsDonor3_CNhs11998_ctss_fwd Cd4+TCellsD3+ CD4+ T Cells, donor3_CNhs11998_11382-118B5_forward Regulation 
CD4TCellsDonor2_CNhs11955_ctss_rev Cd4+TCellsD2- CD4+ T Cells, donor2_CNhs11955_11306-117C1_reverse Regulation 
CD4TCellsDonor2_CNhs11955_ctss_fwd Cd4+TCellsD2+ CD4+ T Cells, donor2_CNhs11955_11306-117C1_forward Regulation 
CD4TCellsDonor1_CNhs10853_ctss_rev Cd4+TCellsD1- CD4+ T Cells, donor1_CNhs10853_11225-116C1_reverse Regulation 
CD4TCellsDonor1_CNhs10853_ctss_fwd Cd4+TCellsD1+ CD4+ T Cells, donor1_CNhs10853_11225-116C1_forward Regulation 
CD4CD25CD45RAMemoryConventionalTCellsExpandedDonor3_CNhs13921_ctss_rev Cd4+cd25-cd45ra-ExpdD3- CD4+CD25-CD45RA- memory conventional T cells expanded, donor3_CNhs13921_11918-125H1_reverse Regulation 
CD4CD25CD45RAMemoryConventionalTCellsExpandedDonor3_CNhs13921_ctss_fwd Cd4+cd25-cd45ra-ExpdD3+ CD4+CD25-CD45RA- memory conventional T cells expanded, donor3_CNhs13921_11918-125H1_forward Regulation 
CD4CD25CD45RAMemoryConventionalTCellsExpandedDonor2_CNhs13920_ctss_rev Cd4+cd25-cd45ra-ExpdD2- CD4+CD25-CD45RA- memory conventional T cells expanded, donor2_CNhs13920_11914-125G6_reverse Regulation 
CD4CD25CD45RAMemoryConventionalTCellsExpandedDonor2_CNhs13920_ctss_fwd Cd4+cd25-cd45ra-ExpdD2+ CD4+CD25-CD45RA- memory conventional T cells expanded, donor2_CNhs13920_11914-125G6_forward Regulation 
CD4CD25CD45RAMemoryConventionalTCellsExpandedDonor1_CNhs13215_ctss_rev Cd4+cd25-cd45ra-ExpdD1- CD4+CD25-CD45RA- memory conventional T cells expanded, donor1_CNhs13215_11792-124C1_reverse Regulation 
CD4CD25CD45RAMemoryConventionalTCellsExpandedDonor1_CNhs13215_ctss_fwd Cd4+cd25-cd45ra-ExpdD1+ CD4+CD25-CD45RA- memory conventional T cells expanded, donor1_CNhs13215_11792-124C1_forward Regulation 
CD4CD25CD45RAMemoryConventionalTCellsDonor3_CNhs13539_ctss_rev Cd4+cd25-cd45ra-D3- CD4+CD25-CD45RA- memory conventional T cells, donor3_CNhs13539_11909-125G1_reverse Regulation 
CD4CD25CD45RAMemoryConventionalTCellsDonor3_CNhs13539_ctss_fwd Cd4+cd25-cd45ra-D3+ CD4+CD25-CD45RA- memory conventional T cells, donor3_CNhs13539_11909-125G1_forward Regulation 
CD4CD25CD45RANaiveConventionalTCellsExpandedDonor3_CNhs13814_ctss_rev Cd4+cd25-cd45ra+ExpdD3- CD4+CD25-CD45RA+ naive conventional T cells expanded, donor3_CNhs13814_11917-125G9_reverse Regulation 
CD4CD25CD45RANaiveConventionalTCellsExpandedDonor3_CNhs13814_ctss_fwd Cd4+cd25-cd45ra+ExpdD3+ CD4+CD25-CD45RA+ naive conventional T cells expanded, donor3_CNhs13814_11917-125G9_forward Regulation 
CD4CD25CD45RANaiveConventionalTCellsExpandedDonor2_CNhs13813_ctss_rev Cd4+cd25-cd45ra+ExpdD2- CD4+CD25-CD45RA+ naive conventional T cells expanded, donor2_CNhs13813_11913-125G5_reverse Regulation 
CD4CD25CD45RANaiveConventionalTCellsExpandedDonor2_CNhs13813_ctss_fwd Cd4+cd25-cd45ra+ExpdD2+ CD4+CD25-CD45RA+ naive conventional T cells expanded, donor2_CNhs13813_11913-125G5_forward Regulation 
CD4CD25CD45RANaiveConventionalTCellsExpandedDonor1_CNhs13202_ctss_rev Cd4+cd25-cd45ra+ExpdD1- CD4+CD25-CD45RA+ naive conventional T cells expanded, donor1_CNhs13202_11791-124B9_reverse Regulation 
CD4CD25CD45RANaiveConventionalTCellsExpandedDonor1_CNhs13202_ctss_fwd Cd4+cd25-cd45ra+ExpdD1+ CD4+CD25-CD45RA+ naive conventional T cells expanded, donor1_CNhs13202_11791-124B9_forward Regulation 
CD4CD25CD45RANaiveConventionalTCellsDonor3_CNhs13512_ctss_rev Cd4+cd25-cd45ra+D3- CD4+CD25-CD45RA+ naive conventional T cells, donor3_CNhs13512_11906-125F7_reverse Regulation 
CD4CD25CD45RANaiveConventionalTCellsDonor3_CNhs13512_ctss_fwd Cd4+cd25-cd45ra+D3+ CD4+CD25-CD45RA+ naive conventional T cells, donor3_CNhs13512_11906-125F7_forward Regulation 
CD4CD25CD45RANaiveConventionalTCellsDonor2_CNhs13205_ctss_rev Cd4+cd25-cd45ra+D2- CD4+CD25-CD45RA+ naive conventional T cells, donor2_CNhs13205_11795-124C4_reverse Regulation 
CD4CD25CD45RANaiveConventionalTCellsDonor2_CNhs13205_ctss_fwd Cd4+cd25-cd45ra+D2+ CD4+CD25-CD45RA+ naive conventional T cells, donor2_CNhs13205_11795-124C4_forward Regulation 
CD4CD25CD45RANaiveConventionalTCellsDonor1_CNhs13223_ctss_rev Cd4+cd25-cd45ra+D1- CD4+CD25-CD45RA+ naive conventional T cells, donor1_CNhs13223_11784-124B2_reverse Regulation 
CD4CD25CD45RANaiveConventionalTCellsDonor1_CNhs13223_ctss_fwd Cd4+cd25-cd45ra+D1+ CD4+CD25-CD45RA+ naive conventional T cells, donor1_CNhs13223_11784-124B2_forward Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsExpandedDonor3_CNhs13812_ctss_rev Cd4+cd25+cd45ra-ExpdD3- CD4+CD25+CD45RA- memory regulatory T cells expanded, donor3_CNhs13812_11920-125H3_reverse Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsExpandedDonor3_CNhs13812_ctss_fwd Cd4+cd25+cd45ra-ExpdD3+ CD4+CD25+CD45RA- memory regulatory T cells expanded, donor3_CNhs13812_11920-125H3_forward Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsExpandedDonor2_CNhs13811_ctss_rev Cd4+cd25+cd45ra-ExpdD2- CD4+CD25+CD45RA- memory regulatory T cells expanded, donor2_CNhs13811_11916-125G8_reverse Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsExpandedDonor2_CNhs13811_ctss_fwd Cd4+cd25+cd45ra-ExpdD2+ CD4+CD25+CD45RA- memory regulatory T cells expanded, donor2_CNhs13811_11916-125G8_forward Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsExpandedDonor1_CNhs13204_ctss_rev Cd4+cd25+cd45ra-ExpdD1- CD4+CD25+CD45RA- memory regulatory T cells expanded, donor1_CNhs13204_11794-124C3_reverse Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsExpandedDonor1_CNhs13204_ctss_fwd Cd4+cd25+cd45ra-ExpdD1+ CD4+CD25+CD45RA- memory regulatory T cells expanded, donor1_CNhs13204_11794-124C3_forward Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsDonor3_CNhs13538_ctss_rev Cd4+cd25+cd45ra-D3- CD4+CD25+CD45RA- memory regulatory T cells, donor3_CNhs13538_11908-125F9_reverse Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsDonor3_CNhs13538_ctss_fwd Cd4+cd25+cd45ra-D3+ CD4+CD25+CD45RA- memory regulatory T cells, donor3_CNhs13538_11908-125F9_forward Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsDonor2_CNhs13206_ctss_rev Cd4+cd25+cd45ra-D2- CD4+CD25+CD45RA- memory regulatory T cells, donor2_CNhs13206_11797-124C6_reverse Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsDonor2_CNhs13206_ctss_fwd Cd4+cd25+cd45ra-D2+ CD4+CD25+CD45RA- memory regulatory T cells, donor2_CNhs13206_11797-124C6_forward Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsDonor1_CNhs13195_ctss_rev Cd4+cd25+cd45ra-D1- CD4+CD25+CD45RA- memory regulatory T cells, donor1_CNhs13195_11782-124A9_reverse Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsDonor1_CNhs13195_ctss_fwd Cd4+cd25+cd45ra-D1+ CD4+CD25+CD45RA- memory regulatory T cells, donor1_CNhs13195_11782-124A9_forward Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsExpandedDonor3_CNhs13919_ctss_rev Cd4+cd25+cd45ra+ExpdD3- CD4+CD25+CD45RA+ naive regulatory T cells expanded, donor3_CNhs13919_11919-125H2_reverse Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsExpandedDonor3_CNhs13919_ctss_fwd Cd4+cd25+cd45ra+ExpdD3+ CD4+CD25+CD45RA+ naive regulatory T cells expanded, donor3_CNhs13919_11919-125H2_forward Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsExpandedDonor2_CNhs13918_ctss_rev Cd4+cd25+cd45ra+ExpdD2- CD4+CD25+CD45RA+ naive regulatory T cells expanded, donor2_CNhs13918_11915-125G7_reverse Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsExpandedDonor2_CNhs13918_ctss_fwd Cd4+cd25+cd45ra+ExpdD2+ CD4+CD25+CD45RA+ naive regulatory T cells expanded, donor2_CNhs13918_11915-125G7_forward Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsExpandedDonor1_CNhs13203_ctss_rev Cd4+cd25+cd45ra+ExpdD1- CD4+CD25+CD45RA+ naive regulatory T cells expanded, donor1_CNhs13203_11793-124C2_reverse Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsExpandedDonor1_CNhs13203_ctss_fwd Cd4+cd25+cd45ra+ExpdD1+ CD4+CD25+CD45RA+ naive regulatory T cells expanded, donor1_CNhs13203_11793-124C2_forward Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsDonor3_CNhs13513_ctss_rev Cd4+cd25+cd45ra+D3- CD4+CD25+CD45RA+ naive regulatory T cells, donor3_CNhs13513_11907-125F8_reverse Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsDonor3_CNhs13513_ctss_fwd Cd4+cd25+cd45ra+D3+ CD4+CD25+CD45RA+ naive regulatory T cells, donor3_CNhs13513_11907-125F8_forward Regulation 
CD34CellsDifferentiatedToErythrocyteLineageBiol_Rep2_CNhs13553_ctss_rev Cd34ErythrocyteBr2- CD34 cells differentiated to erythrocyte lineage, biol_ rep2_CNhs13553_11932-125I6_reverse Regulation 
CD34CellsDifferentiatedToErythrocyteLineageBiol_Rep2_CNhs13553_ctss_fwd Cd34ErythrocyteBr2+ CD34 cells differentiated to erythrocyte lineage, biol_ rep2_CNhs13553_11932-125I6_forward Regulation 
CD34CellsDifferentiatedToErythrocyteLineageBiol_Rep1_CNhs13552_ctss_rev Cd34ErythrocyteBr1- CD34 cells differentiated to erythrocyte lineage, biol_ rep1_CNhs13552_11931-125I5_reverse Regulation 
CD34CellsDifferentiatedToErythrocyteLineageBiol_Rep1_CNhs13552_ctss_fwd Cd34ErythrocyteBr1+ CD34 cells differentiated to erythrocyte lineage, biol_ rep1_CNhs13552_11931-125I5_forward Regulation 
CD34StemCellsAdultBoneMarrowDerivedDonor1TechRep1_CNhs12588_ctss_rev Cd34+StemCellsAdultBoneMarrowD1Tr1- CD34+ stem cells - adult bone marrow derived, donor1, tech_rep1_CNhs12588_12225-129F2_reverse Regulation 
CD34StemCellsAdultBoneMarrowDerivedDonor1TechRep1_CNhs12588_ctss_fwd Cd34+StemCellsAdultBoneMarrowD1Tr1+ CD34+ stem cells - adult bone marrow derived, donor1, tech_rep1_CNhs12588_12225-129F2_forward Regulation 
CD19BCellsDonor3_CNhs12354_ctss_rev Cd19+BCellsD3- CD19+ B Cells, donor3_CNhs12354_11705-123B4_reverse Regulation 
CD19BCellsDonor3_CNhs12354_ctss_fwd Cd19+BCellsD3+ CD19+ B Cells, donor3_CNhs12354_11705-123B4_forward Regulation 
CD19BCellsDonor2_CNhs12352_ctss_rev Cd19+BCellsD2- CD19+ B Cells, donor2_CNhs12352_11624-122B4_reverse Regulation 
CD19BCellsDonor2_CNhs12352_ctss_fwd Cd19+BCellsD2+ CD19+ B Cells, donor2_CNhs12352_11624-122B4_forward Regulation 
CD19BCellsDonor1_CNhs12343_ctss_rev Cd19+BCellsD1- CD19+ B Cells, donor1_CNhs12343_11544-120B5_reverse Regulation 
CD19BCellsDonor1_CNhs12343_ctss_fwd Cd19+BCellsD1+ CD19+ B Cells, donor1_CNhs12343_11544-120B5_forward Regulation 
CD14CD16MonocytesDonor3_CNhs13548_ctss_rev Cd14-cd16+MonocytesD3- CD14-CD16+ Monocytes, donor3_CNhs13548_11911-125G3_reverse Regulation 
CD14CD16MonocytesDonor3_CNhs13548_ctss_fwd Cd14-cd16+MonocytesD3+ CD14-CD16+ Monocytes, donor3_CNhs13548_11911-125G3_forward Regulation 
CD14CD16MonocytesDonor2_CNhs13207_ctss_rev Cd14-cd16+MonocytesD2- CD14-CD16+ Monocytes, donor2_CNhs13207_11800-124C9_reverse Regulation 
CD14CD16MonocytesDonor2_CNhs13207_ctss_fwd Cd14-cd16+MonocytesD2+ CD14-CD16+ Monocytes, donor2_CNhs13207_11800-124C9_forward Regulation 
CD14MonocytesTreatedWithTrehaloseDimycolateTDMDonor3_CNhs13544_ctss_rev Cd14+MoW/TrehaloseDimycolateD3- CD14+ monocytes - treated with Trehalose dimycolate (TDM), donor3_CNhs13544_11882-125D1_reverse Regulation 
CD14MonocytesTreatedWithTrehaloseDimycolateTDMDonor3_CNhs13544_ctss_fwd Cd14+MoW/TrehaloseDimycolateD3+ CD14+ monocytes - treated with Trehalose dimycolate (TDM), donor3_CNhs13544_11882-125D1_forward Regulation 
CD14MonocytesTreatedWithTrehaloseDimycolateTDMDonor2_CNhs13483_ctss_rev Cd14+MoW/TrehaloseDimycolateD2- CD14+ monocytes - treated with Trehalose dimycolate (TDM), donor2_CNhs13483_11872-125B9_reverse Regulation 
CD14MonocytesTreatedWithTrehaloseDimycolateTDMDonor2_CNhs13483_ctss_fwd Cd14+MoW/TrehaloseDimycolateD2+ CD14+ monocytes - treated with Trehalose dimycolate (TDM), donor2_CNhs13483_11872-125B9_forward Regulation 
CD14MonocytesTreatedWithTrehaloseDimycolateTDMDonor1_CNhs13467_ctss_rev Cd14+MoW/TrehaloseDimycolateD1- CD14+ monocytes - treated with Trehalose dimycolate (TDM), donor1_CNhs13467_11862-125A8_reverse Regulation 
CD14MonocytesTreatedWithTrehaloseDimycolateTDMDonor1_CNhs13467_ctss_fwd Cd14+MoW/TrehaloseDimycolateD1+ CD14+ monocytes - treated with Trehalose dimycolate (TDM), donor1_CNhs13467_11862-125A8_forward Regulation 
CD14MonocytesTreatedWithSalmonellaDonor3_CNhs13493_ctss_rev Cd14+MoW/SalmonellaD3- CD14+ monocytes - treated with Salmonella, donor3_CNhs13493_11886-125D5_reverse Regulation 
CD14MonocytesTreatedWithSalmonellaDonor3_CNhs13493_ctss_fwd Cd14+MoW/SalmonellaD3+ CD14+ monocytes - treated with Salmonella, donor3_CNhs13493_11886-125D5_forward Regulation 
CD14MonocytesTreatedWithSalmonellaDonor2_CNhs13485_ctss_rev Cd14+MoW/SalmonellaD2- CD14+ monocytes - treated with Salmonella, donor2_CNhs13485_11876-125C4_reverse Regulation 
CD14MonocytesTreatedWithSalmonellaDonor2_CNhs13485_ctss_fwd Cd14+MoW/SalmonellaD2+ CD14+ monocytes - treated with Salmonella, donor2_CNhs13485_11876-125C4_forward Regulation 
CD14MonocytesTreatedWithSalmonellaDonor1_CNhs13471_ctss_rev Cd14+MoW/SalmonellaD1- CD14+ monocytes - treated with Salmonella, donor1_CNhs13471_11866-125B3_reverse Regulation 
CD14MonocytesTreatedWithSalmonellaDonor1_CNhs13471_ctss_fwd Cd14+MoW/SalmonellaD1+ CD14+ monocytes - treated with Salmonella, donor1_CNhs13471_11866-125B3_forward Regulation 
CD14MonocytesTreatedWithLipopolysaccharideDonor3_CNhs13545_ctss_rev Cd14+MoW/LipopolysaccharideD3- CD14+ monocytes - treated with lipopolysaccharide, donor3_CNhs13545_11885-125D4_reverse Regulation 
CD14MonocytesTreatedWithLipopolysaccharideDonor3_CNhs13545_ctss_fwd Cd14+MoW/LipopolysaccharideD3+ CD14+ monocytes - treated with lipopolysaccharide, donor3_CNhs13545_11885-125D4_forward Regulation 
CD14MonocytesTreatedWithLipopolysaccharideDonor2_CNhs13533_ctss_rev Cd14+MoW/LipopolysaccharideD2- CD14+ monocytes - treated with lipopolysaccharide, donor2_CNhs13533_11875-125C3_reverse Regulation 
CD14MonocytesTreatedWithLipopolysaccharideDonor2_CNhs13533_ctss_fwd Cd14+MoW/LipopolysaccharideD2+ CD14+ monocytes - treated with lipopolysaccharide, donor2_CNhs13533_11875-125C3_forward Regulation 
CD14MonocytesTreatedWithLipopolysaccharideDonor1_CNhs13470_ctss_rev Cd14+MoW/LipopolysaccharideD1- CD14+ monocytes - treated with lipopolysaccharide, donor1_CNhs13470_11865-125B2_reverse Regulation 
CD14MonocytesTreatedWithLipopolysaccharideDonor1_CNhs13470_ctss_fwd Cd14+MoW/LipopolysaccharideD1+ CD14+ monocytes - treated with lipopolysaccharide, donor1_CNhs13470_11865-125B2_forward Regulation 
CD14MonocytesTreatedWithIFNNhexaneDonor3_CNhs13490_ctss_rev Cd14+MoW/Ifn+N-hexaneD3- CD14+ monocytes - treated with IFN + N-hexane, donor3_CNhs13490_11881-125C9_reverse Regulation 
CD14MonocytesTreatedWithIFNNhexaneDonor3_CNhs13490_ctss_fwd Cd14+MoW/Ifn+N-hexaneD3+ CD14+ monocytes - treated with IFN + N-hexane, donor3_CNhs13490_11881-125C9_forward Regulation 
CD14MonocytesTreatedWithIFNNhexaneDonor2_CNhs13476_ctss_rev Cd14+MoW/Ifn+N-hexaneD2- CD14+ monocytes - treated with IFN + N-hexane, donor2_CNhs13476_11871-125B8_reverse Regulation 
CD14MonocytesTreatedWithIFNNhexaneDonor2_CNhs13476_ctss_fwd Cd14+MoW/Ifn+N-hexaneD2+ CD14+ monocytes - treated with IFN + N-hexane, donor2_CNhs13476_11871-125B8_forward Regulation 
CD14MonocytesTreatedWithIFNNhexaneDonor1_CNhs13466_ctss_rev Cd14+MoW/Ifn+N-hexaneD1- CD14+ monocytes - treated with IFN + N-hexane, donor1_CNhs13466_11861-125A7_reverse Regulation 
CD14MonocytesTreatedWithIFNNhexaneDonor1_CNhs13466_ctss_fwd Cd14+MoW/Ifn+N-hexaneD1+ CD14+ monocytes - treated with IFN + N-hexane, donor1_CNhs13466_11861-125A7_forward Regulation 
CD14MonocytesTreatedWithGroupAStreptococciDonor3_CNhs13492_ctss_rev Cd14+MoW/GroupAStreptococciD3- CD14+ monocytes - treated with Group A streptococci, donor3_CNhs13492_11884-125D3_reverse Regulation 
CD14MonocytesTreatedWithGroupAStreptococciDonor3_CNhs13492_ctss_fwd Cd14+MoW/GroupAStreptococciD3+ CD14+ monocytes - treated with Group A streptococci, donor3_CNhs13492_11884-125D3_forward Regulation 
CD14MonocytesTreatedWithGroupAStreptococciDonor2_CNhs13532_ctss_rev Cd14+MoW/GroupAStreptococciD2- CD14+ monocytes - treated with Group A streptococci, donor2_CNhs13532_11874-125C2_reverse Regulation 
CD14MonocytesTreatedWithGroupAStreptococciDonor2_CNhs13532_ctss_fwd Cd14+MoW/GroupAStreptococciD2+ CD14+ monocytes - treated with Group A streptococci, donor2_CNhs13532_11874-125C2_forward Regulation 
CD14MonocytesTreatedWithGroupAStreptococciDonor1_CNhs13469_ctss_rev Cd14+MoW/GroupAStreptococciD1- CD14+ monocytes - treated with Group A streptococci, donor1_CNhs13469_11864-125B1_reverse Regulation 
CD14MonocytesTreatedWithGroupAStreptococciDonor1_CNhs13469_ctss_fwd Cd14+MoW/GroupAStreptococciD1+ CD14+ monocytes - treated with Group A streptococci, donor1_CNhs13469_11864-125B1_forward Regulation 
CD14MonocytesTreatedWithCryptococcusDonor3_CNhs13546_ctss_rev Cd14+MoW/CryptococcusD3- CD14+ monocytes - treated with Cryptococcus, donor3_CNhs13546_11887-125D6_reverse Regulation 
CD14MonocytesTreatedWithCryptococcusDonor3_CNhs13546_ctss_fwd Cd14+MoW/CryptococcusD3+ CD14+ monocytes - treated with Cryptococcus, donor3_CNhs13546_11887-125D6_forward Regulation 
CD14MonocytesTreatedWithCryptococcusDonor2_CNhs13487_ctss_rev Cd14+MoW/CryptococcusD2- CD14+ monocytes - treated with Cryptococcus, donor2_CNhs13487_11877-125C5_reverse Regulation 
CD14MonocytesTreatedWithCryptococcusDonor2_CNhs13487_ctss_fwd Cd14+MoW/CryptococcusD2+ CD14+ monocytes - treated with Cryptococcus, donor2_CNhs13487_11877-125C5_forward Regulation 
CD14MonocytesTreatedWithCryptococcusDonor1_CNhs13472_ctss_rev Cd14+MoW/CryptococcusD1- CD14+ monocytes - treated with Cryptococcus, donor1_CNhs13472_11867-125B4_reverse Regulation 
CD14MonocytesTreatedWithCryptococcusDonor1_CNhs13472_ctss_fwd Cd14+MoW/CryptococcusD1+ CD14+ monocytes - treated with Cryptococcus, donor1_CNhs13472_11867-125B4_forward Regulation 
CD14MonocytesTreatedWithCandidaDonor3_CNhs13494_ctss_rev Cd14+MoW/CandidaD3- CD14+ monocytes - treated with Candida, donor3_CNhs13494_11888-125D7_reverse Regulation 
CD14MonocytesTreatedWithCandidaDonor3_CNhs13494_ctss_fwd Cd14+MoW/CandidaD3+ CD14+ monocytes - treated with Candida, donor3_CNhs13494_11888-125D7_forward Regulation 
CD14MonocytesTreatedWithCandidaDonor2_CNhs13488_ctss_rev Cd14+MoW/CandidaD2- CD14+ monocytes - treated with Candida, donor2_CNhs13488_11878-125C6_reverse Regulation 
CD14MonocytesTreatedWithCandidaDonor2_CNhs13488_ctss_fwd Cd14+MoW/CandidaD2+ CD14+ monocytes - treated with Candida, donor2_CNhs13488_11878-125C6_forward Regulation 
CD14MonocytesTreatedWithCandidaDonor1_CNhs13473_ctss_rev Cd14+MoW/CandidaD1- CD14+ monocytes - treated with Candida, donor1_CNhs13473_11868-125B5_reverse Regulation 
CD14MonocytesTreatedWithCandidaDonor1_CNhs13473_ctss_fwd Cd14+MoW/CandidaD1+ CD14+ monocytes - treated with Candida, donor1_CNhs13473_11868-125B5_forward Regulation 
CD14MonocytesTreatedWithBCGDonor3_CNhs13543_ctss_rev Cd14+MoW/BcgD3- CD14+ monocytes - treated with BCG, donor3_CNhs13543_11880-125C8_reverse Regulation 
CD14MonocytesTreatedWithBCGDonor3_CNhs13543_ctss_fwd Cd14+MoW/BcgD3+ CD14+ monocytes - treated with BCG, donor3_CNhs13543_11880-125C8_forward Regulation 
CD14MonocytesTreatedWithBCGDonor2_CNhs13475_ctss_rev Cd14+MoW/BcgD2- CD14+ monocytes - treated with BCG, donor2_CNhs13475_11870-125B7_reverse Regulation 
CD14MonocytesTreatedWithBCGDonor2_CNhs13475_ctss_fwd Cd14+MoW/BcgD2+ CD14+ monocytes - treated with BCG, donor2_CNhs13475_11870-125B7_forward Regulation 
CD14MonocytesTreatedWithBCGDonor1_CNhs13465_ctss_rev Cd14+MoW/BcgD1- CD14+ monocytes - treated with BCG, donor1_CNhs13465_11860-125A6_reverse Regulation 
CD14MonocytesTreatedWithBCGDonor1_CNhs13465_ctss_fwd Cd14+MoW/BcgD1+ CD14+ monocytes - treated with BCG, donor1_CNhs13465_11860-125A6_forward Regulation 
CD14MonocytesTreatedWithBglucanDonor3_CNhs13495_ctss_rev Cd14+MoW/B-glucanD3- CD14+ monocytes - treated with B-glucan, donor3_CNhs13495_11889-125D8_reverse Regulation 
CD14MonocytesTreatedWithBglucanDonor3_CNhs13495_ctss_fwd Cd14+MoW/B-glucanD3+ CD14+ monocytes - treated with B-glucan, donor3_CNhs13495_11889-125D8_forward Regulation 
CD14MonocytesTreatedWithBglucanDonor2_CNhs13489_ctss_rev Cd14+MoW/B-glucanD2- CD14+ monocytes - treated with B-glucan, donor2_CNhs13489_11879-125C7_reverse Regulation 
CD14MonocytesTreatedWithBglucanDonor2_CNhs13489_ctss_fwd Cd14+MoW/B-glucanD2+ CD14+ monocytes - treated with B-glucan, donor2_CNhs13489_11879-125C7_forward Regulation 
CD14MonocytesTreatedWithBglucanDonor1_CNhs13474_ctss_rev Cd14+MoW/B-glucanD1- CD14+ monocytes - treated with B-glucan, donor1_CNhs13474_11869-125B6_reverse Regulation 
CD14MonocytesTreatedWithBglucanDonor1_CNhs13474_ctss_fwd Cd14+MoW/B-glucanD1+ CD14+ monocytes - treated with B-glucan, donor1_CNhs13474_11869-125B6_forward Regulation 
CD14MonocytesMockTreatedDonor3_CNhs13491_ctss_rev Cd14+MoMockTreatedD3- CD14+ monocytes - mock treated, donor3_CNhs13491_11883-125D2_reverse Regulation 
CD14MonocytesMockTreatedDonor3_CNhs13491_ctss_fwd Cd14+MoMockTreatedD3+ CD14+ monocytes - mock treated, donor3_CNhs13491_11883-125D2_forward Regulation 
CD14MonocytesMockTreatedDonor2_CNhs13484_ctss_rev Cd14+MoMockTreatedD2- CD14+ monocytes - mock treated, donor2_CNhs13484_11873-125C1_reverse Regulation 
CD14MonocytesMockTreatedDonor2_CNhs13484_ctss_fwd Cd14+MoMockTreatedD2+ CD14+ monocytes - mock treated, donor2_CNhs13484_11873-125C1_forward Regulation 
CD14MonocytesMockTreatedDonor1_CNhs13468_ctss_rev Cd14+MoMockTreatedD1- CD14+ monocytes - mock treated, donor1_CNhs13468_11863-125A9_reverse Regulation 
CD14MonocytesMockTreatedDonor1_CNhs13468_ctss_fwd Cd14+MoMockTreatedD1+ CD14+ monocytes - mock treated, donor1_CNhs13468_11863-125A9_forward Regulation 
CD14MonocyteDerivedEndothelialProgenitorCellsDonor3_CNhs11904_ctss_rev Cd14+MoEndothelialProgenitorCellsD3- CD14+ monocyte derived endothelial progenitor cells, donor3_CNhs11904_11386-118B9_reverse Regulation 
CD14MonocyteDerivedEndothelialProgenitorCellsDonor3_CNhs11904_ctss_fwd Cd14+MoEndothelialProgenitorCellsD3+ CD14+ monocyte derived endothelial progenitor cells, donor3_CNhs11904_11386-118B9_forward Regulation 
CD14MonocyteDerivedEndothelialProgenitorCellsDonor2_CNhs11897_ctss_rev Cd14+MoEndothelialProgenitorCellsD2- CD14+ monocyte derived endothelial progenitor cells, donor2_CNhs11897_11310-117C5_reverse Regulation 
CD14MonocyteDerivedEndothelialProgenitorCellsDonor2_CNhs11897_ctss_fwd Cd14+MoEndothelialProgenitorCellsD2+ CD14+ monocyte derived endothelial progenitor cells, donor2_CNhs11897_11310-117C5_forward Regulation 
CD14MonocyteDerivedEndothelialProgenitorCellsDonor1_CNhs10858_ctss_rev Cd14+MoEndothelialProgenitorCellsD1- CD14+ monocyte derived endothelial progenitor cells, donor1_CNhs10858_11229-116C5_reverse Regulation 
CD14MonocyteDerivedEndothelialProgenitorCellsDonor1_CNhs10858_ctss_fwd Cd14+MoEndothelialProgenitorCellsD1+ CD14+ monocyte derived endothelial progenitor cells, donor1_CNhs10858_11229-116C5_forward Regulation 
CD14MonocytesDonor3_CNhs11997_ctss_rev Cd14+MoD3- CD14+ Monocytes, donor3_CNhs11997_11381-118B4_reverse Regulation 
CD14MonocytesDonor3_CNhs11997_ctss_fwd Cd14+MoD3+ CD14+ Monocytes, donor3_CNhs11997_11381-118B4_forward Regulation 
CD14MonocytesDonor2_CNhs11954_ctss_rev Cd14+MoD2- CD14+ Monocytes, donor2_CNhs11954_11305-117B9_reverse Regulation 
CD14MonocytesDonor2_CNhs11954_ctss_fwd Cd14+MoD2+ CD14+ Monocytes, donor2_CNhs11954_11305-117B9_forward Regulation 
CD14MonocytesDonor1_CNhs10852_ctss_rev Cd14+MoD1- CD14+ Monocytes, donor1_CNhs10852_11224-116B9_reverse Regulation 
CD14MonocytesDonor1_CNhs10852_ctss_fwd Cd14+MoD1+ CD14+ Monocytes, donor1_CNhs10852_11224-116B9_forward Regulation 
CD14CD16MonocytesDonor3_CNhs13540_ctss_rev Cd14+cd16-MonocytesD3- CD14+CD16- Monocytes, donor3_CNhs13540_11910-125G2_reverse Regulation 
CD14CD16MonocytesDonor3_CNhs13540_ctss_fwd Cd14+cd16-MonocytesD3+ CD14+CD16- Monocytes, donor3_CNhs13540_11910-125G2_forward Regulation 
CD14CD16MonocytesDonor2_CNhs13216_ctss_rev Cd14+cd16-MonocytesD2- CD14+CD16- Monocytes, donor2_CNhs13216_11799-124C8_reverse Regulation 
CD14CD16MonocytesDonor2_CNhs13216_ctss_fwd Cd14+cd16-MonocytesD2+ CD14+CD16- Monocytes, donor2_CNhs13216_11799-124C8_forward Regulation 
CD14CD16MonocytesDonor1_CNhs13224_ctss_rev Cd14+cd16-MonocytesD1- CD14+CD16- Monocytes, donor1_CNhs13224_11788-124B6_reverse Regulation 
CD14CD16MonocytesDonor1_CNhs13224_ctss_fwd Cd14+cd16-MonocytesD1+ CD14+CD16- Monocytes, donor1_CNhs13224_11788-124B6_forward Regulation 
CD14CD16MonocytesDonor3_CNhs13549_ctss_rev Cd14+cd16+MonocytesD3- CD14+CD16+ Monocytes, donor3_CNhs13549_11912-125G4_reverse Regulation 
CD14CD16MonocytesDonor3_CNhs13549_ctss_fwd Cd14+cd16+MonocytesD3+ CD14+CD16+ Monocytes, donor3_CNhs13549_11912-125G4_forward Regulation 
CD14CD16MonocytesDonor2_CNhs13208_ctss_rev Cd14+cd16+MonocytesD2- CD14+CD16+ Monocytes, donor2_CNhs13208_11801-124D1_reverse Regulation 
CD14CD16MonocytesDonor2_CNhs13208_ctss_fwd Cd14+cd16+MonocytesD2+ CD14+CD16+ Monocytes, donor2_CNhs13208_11801-124D1_forward Regulation 
CD14CD16MonocytesDonor1_CNhs13541_ctss_rev Cd14+cd16+MonocytesD1- CD14+CD16+ Monocytes, donor1_CNhs13541_11789-124B7_reverse Regulation 
CD14CD16MonocytesDonor1_CNhs13541_ctss_fwd Cd14+cd16+MonocytesD1+ CD14+CD16+ Monocytes, donor1_CNhs13541_11789-124B7_forward Regulation 
MultipotentCordBloodUnrestrictedSomaticStemCellsDonor2_CNhs12105_ctss_rev CbStemCellsD2- Multipotent Cord Blood Unrestricted Somatic Stem Cells, donor2_CNhs12105_11629-122B9_reverse Regulation 
MultipotentCordBloodUnrestrictedSomaticStemCellsDonor2_CNhs12105_ctss_fwd CbStemCellsD2+ Multipotent Cord Blood Unrestricted Somatic Stem Cells, donor2_CNhs12105_11629-122B9_forward Regulation 
MultipotentCordBloodUnrestrictedSomaticStemCellsDonor1_CNhs11350_ctss_rev CbStemCellsD1- Multipotent Cord Blood Unrestricted Somatic Stem Cells, donor1_CNhs11350_11549-120C1_reverse Regulation 
MultipotentCordBloodUnrestrictedSomaticStemCellsDonor1_CNhs11350_ctss_fwd CbStemCellsD1+ Multipotent Cord Blood Unrestricted Somatic Stem Cells, donor1_CNhs11350_11549-120C1_forward Regulation 
CardiacMyocyteDonor3_CNhs12571_ctss_rev CardiacMyocyteD3- Cardiac Myocyte, donor3_CNhs12571_11686-122I3_reverse Regulation 
CardiacMyocyteDonor3_CNhs12571_ctss_fwd CardiacMyocyteD3+ Cardiac Myocyte, donor3_CNhs12571_11686-122I3_forward Regulation 
CardiacMyocyteDonor2_CNhs12350_ctss_rev CardiacMyocyteD2- Cardiac Myocyte, donor2_CNhs12350_11605-120I3_reverse Regulation 
CardiacMyocyteDonor2_CNhs12350_ctss_fwd CardiacMyocyteD2+ Cardiac Myocyte, donor2_CNhs12350_11605-120I3_forward Regulation 
CardiacMyocyteDonor1_CNhs12341_ctss_rev CardiacMyocyteD1- Cardiac Myocyte, donor1_CNhs12341_11525-119I4_reverse Regulation 
CardiacMyocyteDonor1_CNhs12341_ctss_fwd CardiacMyocyteD1+ Cardiac Myocyte, donor1_CNhs12341_11525-119I4_forward Regulation 
BronchialEpithelialCellDonor7_CNhs12642_ctss_rev BronchialEpithelialCellD7- Bronchial Epithelial Cell, donor7_CNhs12642_11769-123I5_reverse Regulation 
BronchialEpithelialCellDonor7_CNhs12642_ctss_fwd BronchialEpithelialCellD7+ Bronchial Epithelial Cell, donor7_CNhs12642_11769-123I5_forward Regulation 
BronchialEpithelialCellDonor6_CNhs12062_ctss_rev BronchialEpithelialCellD6- Bronchial Epithelial Cell, donor6_CNhs12062_11461-119B3_reverse Regulation 
BronchialEpithelialCellDonor6_CNhs12062_ctss_fwd BronchialEpithelialCellD6+ Bronchial Epithelial Cell, donor6_CNhs12062_11461-119B3_forward Regulation 
BronchialEpithelialCellDonor5_CNhs12058_ctss_rev BronchialEpithelialCellD5- Bronchial Epithelial Cell, donor5_CNhs12058_11457-119A8_reverse Regulation 
BronchialEpithelialCellDonor5_CNhs12058_ctss_fwd BronchialEpithelialCellD5+ Bronchial Epithelial Cell, donor5_CNhs12058_11457-119A8_forward Regulation 
BronchialEpithelialCellDonor4_CNhs12054_ctss_rev BronchialEpithelialCellD4- Bronchial Epithelial Cell, donor4_CNhs12054_11453-119A4_reverse Regulation 
BronchialEpithelialCellDonor4_CNhs12054_ctss_fwd BronchialEpithelialCellD4+ Bronchial Epithelial Cell, donor4_CNhs12054_11453-119A4_forward Regulation 
BronchialEpithelialCellDonor3_CNhs12623_ctss_rev BronchialEpithelialCellD3- Bronchial Epithelial Cell, donor3_CNhs12623_11672-122G7_reverse Regulation 
BronchialEpithelialCellDonor3_CNhs12623_ctss_fwd BronchialEpithelialCellD3+ Bronchial Epithelial Cell, donor3_CNhs12623_11672-122G7_forward Regulation 
BronchialEpithelialCellDonor2_CNhs12085_ctss_rev BronchialEpithelialCellD2- Bronchial Epithelial Cell, donor2_CNhs12085_11591-120G7_reverse Regulation 
BronchialEpithelialCellDonor2_CNhs12085_ctss_fwd BronchialEpithelialCellD2+ Bronchial Epithelial Cell, donor2_CNhs12085_11591-120G7_forward Regulation 
BronchialEpithelialCellDonor1_CNhs11327_ctss_rev BronchialEpithelialCellD1- Bronchial Epithelial Cell, donor1_CNhs11327_11511-119G8_reverse Regulation 
BronchialEpithelialCellDonor1_CNhs11327_ctss_fwd BronchialEpithelialCellD1+ Bronchial Epithelial Cell, donor1_CNhs11327_11511-119G8_forward Regulation 
BasophilsDonor3_CNhs12575_ctss_rev BasophilsD3- Basophils, donor3_CNhs12575_12243-129H2_reverse Regulation 
BasophilsDonor3_CNhs12575_ctss_fwd BasophilsD3+ Basophils, donor3_CNhs12575_12243-129H2_forward Regulation 
AstrocyteCerebralCortexDonor3_CNhs12005_ctss_rev AstrocyteCerebralCortexD3- Astrocyte - cerebral cortex, donor3_CNhs12005_11392-118C6_reverse Regulation 
AstrocyteCerebralCortexDonor3_CNhs12005_ctss_fwd AstrocyteCerebralCortexD3+ Astrocyte - cerebral cortex, donor3_CNhs12005_11392-118C6_forward Regulation 
AstrocyteCerebralCortexDonor2_CNhs11960_ctss_rev AstrocyteCerebralCortexD2- Astrocyte - cerebral cortex, donor2_CNhs11960_11316-117D2_reverse Regulation 
AstrocyteCerebralCortexDonor2_CNhs11960_ctss_fwd AstrocyteCerebralCortexD2+ Astrocyte - cerebral cortex, donor2_CNhs11960_11316-117D2_forward Regulation 
AstrocyteCerebralCortexDonor1_CNhs10864_ctss_rev AstrocyteCerebralCortexD1- Astrocyte - cerebral cortex, donor1_CNhs10864_11235-116D2_reverse Regulation 
AstrocyteCerebralCortexDonor1_CNhs10864_ctss_fwd AstrocyteCerebralCortexD1+ Astrocyte - cerebral cortex, donor1_CNhs10864_11235-116D2_forward Regulation 
AstrocyteCerebellumDonor3_CNhs12117_ctss_rev AstrocyteCerebellumD3- Astrocyte - cerebellum, donor3_CNhs12117_11661-122F5_reverse Regulation 
AstrocyteCerebellumDonor3_CNhs12117_ctss_fwd AstrocyteCerebellumD3+ Astrocyte - cerebellum, donor3_CNhs12117_11661-122F5_forward Regulation 
AstrocyteCerebellumDonor2_CNhs12081_ctss_rev AstrocyteCerebellumD2- Astrocyte - cerebellum, donor2_CNhs12081_11580-120F5_reverse Regulation 
AstrocyteCerebellumDonor2_CNhs12081_ctss_fwd AstrocyteCerebellumD2+ Astrocyte - cerebellum, donor2_CNhs12081_11580-120F5_forward Regulation 
AstrocyteCerebellumDonor1_CNhs11321_ctss_rev AstrocyteCerebellumD1- Astrocyte - cerebellum, donor1_CNhs11321_11500-119F6_reverse Regulation 
AstrocyteCerebellumDonor1_CNhs11321_ctss_fwd AstrocyteCerebellumD1+ Astrocyte - cerebellum, donor1_CNhs11321_11500-119F6_forward Regulation 
AnulusPulposusCellDonor2_CNhs12064_ctss_rev AnulusPulposusCellD2- Anulus Pulposus Cell, donor2_CNhs12064_11463-119B5_reverse Regulation 
AnulusPulposusCellDonor2_CNhs12064_ctss_fwd AnulusPulposusCellD2+ Anulus Pulposus Cell, donor2_CNhs12064_11463-119B5_forward Regulation 
AnulusPulposusCellDonor1_CNhs10876_ctss_rev AnulusPulposusCellD1- Anulus Pulposus Cell, donor1_CNhs10876_11248-116E6_reverse Regulation 
AnulusPulposusCellDonor1_CNhs10876_ctss_fwd AnulusPulposusCellD1+ Anulus Pulposus Cell, donor1_CNhs10876_11248-116E6_forward Regulation 
AmnioticMembraneCellsDonor3_CNhs12379_ctss_rev AmnioticMembraneCellsD3- amniotic membrane cells, donor3_CNhs12379_12237-129G5_reverse Regulation 
AmnioticMembraneCellsDonor3_CNhs12379_ctss_fwd AmnioticMembraneCellsD3+ amniotic membrane cells, donor3_CNhs12379_12237-129G5_forward Regulation 
AmnioticMembraneCellsDonor2_CNhs12503_ctss_rev AmnioticMembraneCellsD2- amniotic membrane cells, donor2_CNhs12503_12236-129G4_reverse Regulation 
AmnioticMembraneCellsDonor2_CNhs12503_ctss_fwd AmnioticMembraneCellsD2+ amniotic membrane cells, donor2_CNhs12503_12236-129G4_forward Regulation 
AmnioticMembraneCellsDonor1_CNhs12502_ctss_rev AmnioticMembraneCellsD1- amniotic membrane cells, donor1_CNhs12502_12235-129G3_reverse Regulation 
AmnioticMembraneCellsDonor1_CNhs12502_ctss_fwd AmnioticMembraneCellsD1+ amniotic membrane cells, donor1_CNhs12502_12235-129G3_forward Regulation 
AmnioticEpithelialCellsDonor3_CNhs12125_ctss_rev AmnioticEpithelialCellsD3- Amniotic Epithelial Cells, donor3_CNhs12125_11694-123A2_reverse Regulation 
AmnioticEpithelialCellsDonor3_CNhs12125_ctss_fwd AmnioticEpithelialCellsD3+ Amniotic Epithelial Cells, donor3_CNhs12125_11694-123A2_forward Regulation 
AmnioticEpithelialCellsDonor2_CNhs12098_ctss_rev AmnioticEpithelialCellsD2- Amniotic Epithelial Cells, donor2_CNhs12098_11613-122A2_reverse Regulation 
AmnioticEpithelialCellsDonor2_CNhs12098_ctss_fwd AmnioticEpithelialCellsD2+ Amniotic Epithelial Cells, donor2_CNhs12098_11613-122A2_forward Regulation 
AmnioticEpithelialCellsDonor1_CNhs11341_ctss_rev AmnioticEpithelialCellsD1- Amniotic Epithelial Cells, donor1_CNhs11341_11533-120A3_reverse Regulation 
AmnioticEpithelialCellsDonor1_CNhs11341_ctss_fwd AmnioticEpithelialCellsD1+ Amniotic Epithelial Cells, donor1_CNhs11341_11533-120A3_forward Regulation 
AlveolarEpithelialCellsDonor3_CNhs12119_ctss_rev AlveolarEpithelialCellsD3- Alveolar Epithelial Cells, donor3_CNhs12119_11671-122G6_reverse Regulation 
AlveolarEpithelialCellsDonor3_CNhs12119_ctss_fwd AlveolarEpithelialCellsD3+ Alveolar Epithelial Cells, donor3_CNhs12119_11671-122G6_forward Regulation 
AlveolarEpithelialCellsDonor2_CNhs12084_ctss_rev AlveolarEpithelialCellsD2- Alveolar Epithelial Cells, donor2_CNhs12084_11590-120G6_reverse Regulation 
AlveolarEpithelialCellsDonor2_CNhs12084_ctss_fwd AlveolarEpithelialCellsD2+ Alveolar Epithelial Cells, donor2_CNhs12084_11590-120G6_forward Regulation 
AlveolarEpithelialCellsDonor1_CNhs11325_ctss_rev AlveolarEpithelialCellsD1- Alveolar Epithelial Cells, donor1_CNhs11325_11510-119G7_reverse Regulation 
AlveolarEpithelialCellsDonor1_CNhs11325_ctss_fwd AlveolarEpithelialCellsD1+ Alveolar Epithelial Cells, donor1_CNhs11325_11510-119G7_forward Regulation 
AdipocyteSubcutaneousDonor3_CNhs12017_ctss_rev AdipocyteSubcutaneousD3- Adipocyte - subcutaneous, donor3_CNhs12017_11408-118E4_reverse Regulation 
AdipocyteSubcutaneousDonor3_CNhs12017_ctss_fwd AdipocyteSubcutaneousD3+ Adipocyte - subcutaneous, donor3_CNhs12017_11408-118E4_forward Regulation 
AdipocyteSubcutaneousDonor2_CNhs11371_ctss_rev AdipocyteSubcutaneousD2- Adipocyte - subcutaneous, donor2_CNhs11371_11336-117F4_reverse Regulation 
AdipocyteSubcutaneousDonor2_CNhs11371_ctss_fwd AdipocyteSubcutaneousD2+ Adipocyte - subcutaneous, donor2_CNhs11371_11336-117F4_forward Regulation 
AdipocyteSubcutaneousDonor1_CNhs12494_ctss_rev AdipocyteSubcutaneousD1- Adipocyte - subcutaneous, donor1_CNhs12494_11259-116F8_reverse Regulation 
AdipocyteSubcutaneousDonor1_CNhs12494_ctss_fwd AdipocyteSubcutaneousD1+ Adipocyte - subcutaneous, donor1_CNhs12494_11259-116F8_forward Regulation 
AdipocytePerirenalDonor1_CNhs12069_ctss_rev AdipocytePerirenalD1- Adipocyte - perirenal, donor1_CNhs12069_11476-119C9_reverse Regulation 
AdipocytePerirenalDonor1_CNhs12069_ctss_fwd AdipocytePerirenalD1+ Adipocyte - perirenal, donor1_CNhs12069_11476-119C9_forward Regulation 
AdipocyteOmentalDonor3_CNhs12068_ctss_rev AdipocyteOmentalD3- Adipocyte - omental, donor3_CNhs12068_11475-119C8_reverse Regulation 
AdipocyteOmentalDonor3_CNhs12068_ctss_fwd AdipocyteOmentalD3+ Adipocyte - omental, donor3_CNhs12068_11475-119C8_forward Regulation 
AdipocyteOmentalDonor2_CNhs12067_ctss_rev AdipocyteOmentalD2- Adipocyte - omental, donor2_CNhs12067_11474-119C7_reverse Regulation 
AdipocyteOmentalDonor2_CNhs12067_ctss_fwd AdipocyteOmentalD2+ Adipocyte - omental, donor2_CNhs12067_11474-119C7_forward Regulation 
AdipocyteOmentalDonor1_CNhs11054_ctss_rev AdipocyteOmentalD1- Adipocyte - omental, donor1_CNhs11054_11473-119C6_reverse Regulation 
AdipocyteOmentalDonor1_CNhs11054_ctss_fwd AdipocyteOmentalD1+ Adipocyte - omental, donor1_CNhs11054_11473-119C6_forward Regulation 
AdipocyteBreastDonor2_CNhs11969_ctss_rev AdipocyteBreastD2- Adipocyte - breast, donor2_CNhs11969_11327-117E4_reverse Regulation 
AdipocyteBreastDonor2_CNhs11969_ctss_fwd AdipocyteBreastD2+ Adipocyte - breast, donor2_CNhs11969_11327-117E4_forward Regulation 
AdipocyteBreastDonor1_CNhs11051_ctss_rev AdipocyteBreastD1- Adipocyte - breast, donor1_CNhs11051_11376-118A8_reverse Regulation 
AdipocyteBreastDonor1_CNhs11051_ctss_fwd AdipocyteBreastD1+ Adipocyte - breast, donor1_CNhs11051_11376-118A8_forward Regulation 
PromyelocytesmyelocytesPMCDonor3_CNhs12529_ctss_rev Promyelocytes/myelocytesPmcD3- promyelocytes/myelocytes PMC, donor3_CNhs12529_12140-128E7_reverse Regulation 
PromyelocytesmyelocytesPMCDonor3_CNhs12529_ctss_fwd Promyelocytes/myelocytesPmcD3+ promyelocytes/myelocytes PMC, donor3_CNhs12529_12140-128E7_forward Regulation 
PromyelocytesmyelocytesPMCDonor2_CNhs12525_ctss_rev Promyelocytes/myelocytesPmcD2- promyelocytes/myelocytes PMC, donor2_CNhs12525_12136-128E3_reverse Regulation 
PromyelocytesmyelocytesPMCDonor2_CNhs12525_ctss_fwd Promyelocytes/myelocytesPmcD2+ promyelocytes/myelocytes PMC, donor2_CNhs12525_12136-128E3_forward Regulation 
PromyelocytesmyelocytesPMCDonor1_CNhs12520_ctss_rev Promyelocytes/myelocytesPmcD1- promyelocytes/myelocytes PMC, donor1_CNhs12520_12132-128D8_reverse Regulation 
PromyelocytesmyelocytesPMCDonor1_CNhs12520_ctss_fwd Promyelocytes/myelocytesPmcD1+ promyelocytes/myelocytes PMC, donor1_CNhs12520_12132-128D8_forward Regulation 
NeutrophilPMNDonor3_CNhs12530_ctss_rev NeutrophilPmnD3- neutrophil PMN, donor3_CNhs12530_12141-128E8_reverse Regulation 
NeutrophilPMNDonor3_CNhs12530_ctss_fwd NeutrophilPmnD3+ neutrophil PMN, donor3_CNhs12530_12141-128E8_forward Regulation 
NeutrophilPMNDonor2_CNhs12526_ctss_rev NeutrophilPmnD2- neutrophil PMN, donor2_CNhs12526_12137-128E4_reverse Regulation 
NeutrophilPMNDonor2_CNhs12526_ctss_fwd NeutrophilPmnD2+ neutrophil PMN, donor2_CNhs12526_12137-128E4_forward Regulation 
NeutrophilPMNDonor1_CNhs12522_ctss_rev NeutrophilPmnD1- neutrophil PMN, donor1_CNhs12522_12133-128D9_reverse Regulation 
NeutrophilPMNDonor1_CNhs12522_ctss_fwd NeutrophilPmnD1+ neutrophil PMN, donor1_CNhs12522_12133-128D9_forward Regulation 
NasalEpithelialCellsDonor1TechRep2_CNhs12554_ctss_rev NasalEpithelialCellsD1Tr2- nasal epithelial cells, donor1, tech_rep2_CNhs12554_12226-129F3_reverse Regulation 
NasalEpithelialCellsDonor1TechRep2_CNhs12554_ctss_fwd NasalEpithelialCellsD1Tr2+ nasal epithelial cells, donor1, tech_rep2_CNhs12554_12226-129F3_forward Regulation 
MesothelialCellsDonor2_CNhs12197_ctss_rev MesothelialCellsD2- Mesothelial Cells, donor2_CNhs12197_12156-128G5_reverse Regulation 
MesothelialCellsDonor2_CNhs12197_ctss_fwd MesothelialCellsD2+ Mesothelial Cells, donor2_CNhs12197_12156-128G5_forward Regulation 
MatureAdipocyteDonor4_CNhs12562_ctss_rev MatureAdipocyteD4- mature adipocyte, donor4_CNhs12562_12234-129G2_reverse Regulation 
MatureAdipocyteDonor4_CNhs12562_ctss_fwd MatureAdipocyteD4+ mature adipocyte, donor4_CNhs12562_12234-129G2_forward Regulation 
MatureAdipocyteDonor3_CNhs12560_ctss_rev MatureAdipocyteD3- mature adipocyte, donor3_CNhs12560_12233-129G1_reverse Regulation 
MatureAdipocyteDonor3_CNhs12560_ctss_fwd MatureAdipocyteD3+ mature adipocyte, donor3_CNhs12560_12233-129G1_forward Regulation 
MatureAdipocyteDonor2_CNhs12559_ctss_rev MatureAdipocyteD2- mature adipocyte, donor2_CNhs12559_12232-129F9_reverse Regulation 
MatureAdipocyteDonor2_CNhs12559_ctss_fwd MatureAdipocyteD2+ mature adipocyte, donor2_CNhs12559_12232-129F9_forward Regulation 
MatureAdipocyteDonor1_CNhs12558_ctss_rev MatureAdipocyteD1- mature adipocyte, donor1_CNhs12558_12231-129F8_reverse Regulation 
MatureAdipocyteDonor1_CNhs12558_ctss_fwd MatureAdipocyteD1+ mature adipocyte, donor1_CNhs12558_12231-129F8_forward Regulation 
MallassezderivedCellsDonor1MZH3_CNhs12538_ctss_rev MallassezCellsD1- Mallassez-derived cells, donor1 (MZH3)_CNhs12538_12142-128E9_reverse Regulation 
MallassezderivedCellsDonor1MZH3_CNhs12538_ctss_fwd MallassezCellsD1+ Mallassez-derived cells, donor1 (MZH3)_CNhs12538_12142-128E9_forward Regulation 
GranulocyteMacrophageProgenitorDonor3_CNhs12528_ctss_rev GranulocyteMacrophageProgenitorD3- granulocyte macrophage progenitor, donor3_CNhs12528_12139-128E6_reverse Regulation 
GranulocyteMacrophageProgenitorDonor3_CNhs12528_ctss_fwd GranulocyteMacrophageProgenitorD3+ granulocyte macrophage progenitor, donor3_CNhs12528_12139-128E6_forward Regulation 
GranulocyteMacrophageProgenitorDonor2_CNhs12524_ctss_rev GranulocyteMacrophageProgenitorD2- granulocyte macrophage progenitor, donor2_CNhs12524_12135-128E2_reverse Regulation 
GranulocyteMacrophageProgenitorDonor2_CNhs12524_ctss_fwd GranulocyteMacrophageProgenitorD2+ granulocyte macrophage progenitor, donor2_CNhs12524_12135-128E2_forward Regulation 
GranulocyteMacrophageProgenitorDonor1_CNhs12519_ctss_rev GranulocyteMacrophageProgenitorD1- granulocyte macrophage progenitor, donor1_CNhs12519_12131-128D7_reverse Regulation 
GranulocyteMacrophageProgenitorDonor1_CNhs12519_ctss_fwd GranulocyteMacrophageProgenitorD1+ granulocyte macrophage progenitor, donor1_CNhs12519_12131-128D7_forward Regulation 
EosinophilsDonor3_CNhs12549_ctss_rev EosinophilsD3- Eosinophils, donor3_CNhs12549_12246-129H5_reverse Regulation 
EosinophilsDonor3_CNhs12549_ctss_fwd EosinophilsD3+ Eosinophils, donor3_CNhs12549_12246-129H5_forward Regulation 
EosinophilsDonor2_CNhs12548_ctss_rev EosinophilsD2- Eosinophils, donor2_CNhs12548_12245-129H4_reverse Regulation 
EosinophilsDonor2_CNhs12548_ctss_fwd EosinophilsD2+ Eosinophils, donor2_CNhs12548_12245-129H4_forward Regulation 
EosinophilsDonor1_CNhs12547_ctss_rev EosinophilsD1- Eosinophils, donor1_CNhs12547_12244-129H3_reverse Regulation 
EosinophilsDonor1_CNhs12547_ctss_fwd EosinophilsD1+ Eosinophils, donor1_CNhs12547_12244-129H3_forward Regulation 
DendriticCellsPlasmacytoidDonor3_CNhs12200_ctss_rev DendriticCellsPlasmacytoidD3- Dendritic Cells - plasmacytoid, donor3_CNhs12200_11385-118B8_reverse Regulation 
DendriticCellsPlasmacytoidDonor3_CNhs12200_ctss_fwd DendriticCellsPlasmacytoidD3+ Dendritic Cells - plasmacytoid, donor3_CNhs12200_11385-118B8_forward Regulation 
DendriticCellsPlasmacytoidDonor2_CNhs12196_ctss_rev DendriticCellsPlasmacytoidD2- Dendritic Cells - plasmacytoid, donor2_CNhs12196_11309-117C4_reverse Regulation 
DendriticCellsPlasmacytoidDonor2_CNhs12196_ctss_fwd DendriticCellsPlasmacytoidD2+ Dendritic Cells - plasmacytoid, donor2_CNhs12196_11309-117C4_forward Regulation 
DendriticCellsMonocyteImmatureDerivedDonor2_CNhs12195_ctss_rev DendriticCellsMonocyteImmatureD2- Dendritic Cells - monocyte immature derived, donor2_CNhs12195_11308-117C3_reverse Regulation 
DendriticCellsMonocyteImmatureDerivedDonor2_CNhs12195_ctss_fwd DendriticCellsMonocyteImmatureD2+ Dendritic Cells - monocyte immature derived, donor2_CNhs12195_11308-117C3_forward Regulation 
CommonMyeloidProgenitorCMPDonor2_CNhs12523_ctss_rev CommonMyeloidProgenitorCmpD2- common myeloid progenitor CMP, donor2_CNhs12523_12134-128E1_reverse Regulation 
CommonMyeloidProgenitorCMPDonor2_CNhs12523_ctss_fwd CommonMyeloidProgenitorCmpD2+ common myeloid progenitor CMP, donor2_CNhs12523_12134-128E1_forward Regulation 
CommonMyeloidProgenitorCMPDonor1_CNhs12518_ctss_rev CommonMyeloidProgenitorCmpD1- common myeloid progenitor CMP, donor1_CNhs12518_12130-128D6_reverse Regulation 
CommonMyeloidProgenitorCMPDonor1_CNhs12518_ctss_fwd CommonMyeloidProgenitorCmpD1+ common myeloid progenitor CMP, donor1_CNhs12518_12130-128D6_forward Regulation 
CD8TCellsPluriselectDonor090612Donation3_CNhs12187_ctss_rev Cd8+TCellsPluriD090612Dn3- CD8+ T Cells (pluriselect), donor090612, donation3_CNhs12187_12211-129D6_reverse Regulation 
CD8TCellsPluriselectDonor090612Donation3_CNhs12187_ctss_fwd Cd8+TCellsPluriD090612Dn3+ CD8+ T Cells (pluriselect), donor090612, donation3_CNhs12187_12211-129D6_forward Regulation 
CD8TCellsPluriselectDonor090612Donation2_CNhs12184_ctss_rev Cd8+TCellsPluriD090612Dn2- CD8+ T Cells (pluriselect), donor090612, donation2_CNhs12184_12206-129D1_reverse Regulation 
CD8TCellsPluriselectDonor090612Donation2_CNhs12184_ctss_fwd Cd8+TCellsPluriD090612Dn2+ CD8+ T Cells (pluriselect), donor090612, donation2_CNhs12184_12206-129D1_forward Regulation 
CD8TCellsPluriselectDonor090612Donation1_CNhs12182_ctss_rev Cd8+TCellsPluriD090612Dn1- CD8+ T Cells (pluriselect), donor090612, donation1_CNhs12182_12201-129C5_reverse Regulation 
CD8TCellsPluriselectDonor090612Donation1_CNhs12182_ctss_fwd Cd8+TCellsPluriD090612Dn1+ CD8+ T Cells (pluriselect), donor090612, donation1_CNhs12182_12201-129C5_forward Regulation 
CD8TCellsPluriselectDonor090325Donation2_CNhs12199_ctss_rev Cd8+TCellsPluriD090325Dn2- CD8+ T Cells (pluriselect), donor090325, donation2_CNhs12199_12171-128I2_reverse Regulation 
CD8TCellsPluriselectDonor090325Donation2_CNhs12199_ctss_fwd Cd8+TCellsPluriD090325Dn2+ CD8+ T Cells (pluriselect), donor090325, donation2_CNhs12199_12171-128I2_forward Regulation 
CD8TCellsPluriselectDonor090325Donation1_CNhs12201_ctss_rev Cd8+TCellsPluriD090325Dn1- CD8+ T Cells (pluriselect), donor090325, donation1_CNhs12201_12148-128F6_reverse Regulation 
CD8TCellsPluriselectDonor090325Donation1_CNhs12201_ctss_fwd Cd8+TCellsPluriD090325Dn1+ CD8+ T Cells (pluriselect), donor090325, donation1_CNhs12201_12148-128F6_forward Regulation 
CD8TCellsPluriselectDonor090309Donation3_CNhs12180_ctss_rev Cd8+TCellsPluriD090309Dn3- CD8+ T Cells (pluriselect), donor090309, donation3_CNhs12180_12196-129B9_reverse Regulation 
CD8TCellsPluriselectDonor090309Donation3_CNhs12180_ctss_fwd Cd8+TCellsPluriD090309Dn3+ CD8+ T Cells (pluriselect), donor090309, donation3_CNhs12180_12196-129B9_forward Regulation 
CD8TCellsPluriselectDonor090309Donation2_CNhs12178_ctss_rev Cd8+TCellsPluriD090309Dn2- CD8+ T Cells (pluriselect), donor090309, donation2_CNhs12178_12191-129B4_reverse Regulation 
CD8TCellsPluriselectDonor090309Donation2_CNhs12178_ctss_fwd Cd8+TCellsPluriD090309Dn2+ CD8+ T Cells (pluriselect), donor090309, donation2_CNhs12178_12191-129B4_forward Regulation 
CD8TCellsPluriselectDonor090309Donation1_CNhs12176_ctss_rev Cd8+TCellsPluriD090309Dn1- CD8+ T Cells (pluriselect), donor090309, donation1_CNhs12176_12186-129A8_reverse Regulation 
CD8TCellsPluriselectDonor090309Donation1_CNhs12176_ctss_fwd Cd8+TCellsPluriD090309Dn1+ CD8+ T Cells (pluriselect), donor090309, donation1_CNhs12176_12186-129A8_forward Regulation 
CD4CD25CD45RAMemoryConventionalTCellsDonor2_CNhs13237_ctss_rev Cd4+cd25-cd45ra-D2- CD4+CD25-CD45RA- memory conventional T cells, donor2_CNhs13237_11798-124C7_reverse Regulation 
CD4CD25CD45RAMemoryConventionalTCellsDonor2_CNhs13237_ctss_fwd Cd4+cd25-cd45ra-D2+ CD4+CD25-CD45RA- memory conventional T cells, donor2_CNhs13237_11798-124C7_forward Regulation 
CD4CD25CD45RAMemoryConventionalTCellsDonor1_CNhs13239_ctss_rev Cd4+cd25-cd45ra-D1- CD4+CD25-CD45RA- memory conventional T cells, donor1_CNhs13239_11786-124B4_reverse Regulation 
CD4CD25CD45RAMemoryConventionalTCellsDonor1_CNhs13239_ctss_fwd Cd4+cd25-cd45ra-D1+ CD4+CD25-CD45RA- memory conventional T cells, donor1_CNhs13239_11786-124B4_forward Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsDonor2_CNhs13235_ctss_rev Cd4+cd25+cd45ra+D2- CD4+CD25+CD45RA+ naive regulatory T cells, donor2_CNhs13235_11796-124C5_reverse Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsDonor2_CNhs13235_ctss_fwd Cd4+cd25+cd45ra+D2+ CD4+CD25+CD45RA+ naive regulatory T cells, donor2_CNhs13235_11796-124C5_forward Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsDonor1_CNhs13238_ctss_rev Cd4+cd25+cd45ra+D1- CD4+CD25+CD45RA+ naive regulatory T cells, donor1_CNhs13238_11780-124A7_reverse Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsDonor1_CNhs13238_ctss_fwd Cd4+cd25+cd45ra+D1+ CD4+CD25+CD45RA+ naive regulatory T cells, donor1_CNhs13238_11780-124A7_forward Regulation 
CD34StemCellsAdultBoneMarrowDerivedDonor1TechRep2_CNhs12553_ctss_rev Cd34+StemCellsAdultBoneMarrowD1Tr2- CD34+ stem cells - adult bone marrow derived, donor1, tech_rep2_CNhs12553_12225-129F2_reverse Regulation 
CD34StemCellsAdultBoneMarrowDerivedDonor1TechRep2_CNhs12553_ctss_fwd Cd34+StemCellsAdultBoneMarrowD1Tr2+ CD34+ stem cells - adult bone marrow derived, donor1, tech_rep2_CNhs12553_12225-129F2_forward Regulation 
CD34ProgenitorsDonor2_CNhs12205_ctss_rev Cd34+ProgenitorsD2- CD34+ Progenitors, donor2_CNhs12205_11625-122B5_reverse Regulation 
CD34ProgenitorsDonor2_CNhs12205_ctss_fwd Cd34+ProgenitorsD2+ CD34+ Progenitors, donor2_CNhs12205_11625-122B5_forward Regulation 
CD34ProgenitorsDonor1_CNhs13227_ctss_rev Cd34+ProgenitorsD1- CD34+ Progenitors, donor1_CNhs13227_11545-120B6_reverse Regulation 
CD34ProgenitorsDonor1_CNhs13227_ctss_fwd Cd34+ProgenitorsD1+ CD34+ Progenitors, donor1_CNhs13227_11545-120B6_forward Regulation 
CD19BCellsPluriselectDonor090612Donation3_CNhs12188_ctss_rev Cd19+BCellsPluriD090612Dn3- CD19+ B Cells (pluriselect), donor090612, donation3_CNhs12188_12214-129D9_reverse Regulation 
CD19BCellsPluriselectDonor090612Donation3_CNhs12188_ctss_fwd Cd19+BCellsPluriD090612Dn3+ CD19+ B Cells (pluriselect), donor090612, donation3_CNhs12188_12214-129D9_forward Regulation 
CD19BCellsPluriselectDonor090612Donation2_CNhs12185_ctss_rev Cd19+BCellsPluriD090612Dn2- CD19+ B Cells (pluriselect), donor090612, donation2_CNhs12185_12209-129D4_reverse Regulation 
CD19BCellsPluriselectDonor090612Donation2_CNhs12185_ctss_fwd Cd19+BCellsPluriD090612Dn2+ CD19+ B Cells (pluriselect), donor090612, donation2_CNhs12185_12209-129D4_forward Regulation 
CD19BCellsPluriselectDonor090612Donation1_CNhs12183_ctss_rev Cd19+BCellsPluriD090612Dn1- CD19+ B Cells (pluriselect), donor090612, donation1_CNhs12183_12204-129C8_reverse Regulation 
CD19BCellsPluriselectDonor090612Donation1_CNhs12183_ctss_fwd Cd19+BCellsPluriD090612Dn1+ CD19+ B Cells (pluriselect), donor090612, donation1_CNhs12183_12204-129C8_forward Regulation 
CD19BCellsPluriselectDonor090325Donation2_CNhs12175_ctss_rev Cd19+BCellsPluriD090325Dn2- CD19+ B Cells (pluriselect), donor090325, donation2_CNhs12175_12174-128I5_reverse Regulation 
CD19BCellsPluriselectDonor090325Donation2_CNhs12175_ctss_fwd Cd19+BCellsPluriD090325Dn2+ CD19+ B Cells (pluriselect), donor090325, donation2_CNhs12175_12174-128I5_forward Regulation 
CD19BCellsPluriselectDonor090325Donation1_CNhs12531_ctss_rev Cd19+BCellsPluriD090325Dn1- CD19+ B Cells (pluriselect), donor090325, donation1_CNhs12531_12151-128F9_reverse Regulation 
CD19BCellsPluriselectDonor090325Donation1_CNhs12531_ctss_fwd Cd19+BCellsPluriD090325Dn1+ CD19+ B Cells (pluriselect), donor090325, donation1_CNhs12531_12151-128F9_forward Regulation 
CD19BCellsPluriselectDonor090309Donation3_CNhs12181_ctss_rev Cd19+BCellsPluriD090309Dn3- CD19+ B Cells (pluriselect), donor090309, donation3_CNhs12181_12199-129C3_reverse Regulation 
CD19BCellsPluriselectDonor090309Donation3_CNhs12181_ctss_fwd Cd19+BCellsPluriD090309Dn3+ CD19+ B Cells (pluriselect), donor090309, donation3_CNhs12181_12199-129C3_forward Regulation 
CD19BCellsPluriselectDonor090309Donation2_CNhs12179_ctss_rev Cd19+BCellsPluriD090309Dn2- CD19+ B Cells (pluriselect), donor090309, donation2_CNhs12179_12194-129B7_reverse Regulation 
CD19BCellsPluriselectDonor090309Donation2_CNhs12179_ctss_fwd Cd19+BCellsPluriD090309Dn2+ CD19+ B Cells (pluriselect), donor090309, donation2_CNhs12179_12194-129B7_forward Regulation 
CD19BCellsPluriselectDonor090309Donation1_CNhs12177_ctss_rev Cd19+BCellsPluriD090309Dn1- CD19+ B Cells (pluriselect), donor090309, donation1_CNhs12177_12189-129B2_reverse Regulation 
CD19BCellsPluriselectDonor090309Donation1_CNhs12177_ctss_fwd Cd19+BCellsPluriD090309Dn1+ CD19+ B Cells (pluriselect), donor090309, donation1_CNhs12177_12189-129B2_forward Regulation 
CD14CD16MonocytesDonor1_CNhs13229_ctss_rev Cd14-cd16+MonocytesD1- CD14-CD16+ Monocytes, donor1_CNhs13229_11790-124B8_reverse Regulation 
CD14CD16MonocytesDonor1_CNhs13229_ctss_fwd Cd14-cd16+MonocytesD1+ CD14-CD16+ Monocytes, donor1_CNhs13229_11790-124B8_forward Regulation 
CD133StemCellsCordBloodDerivedPool1_CNhs12545_ctss_rev Cd133+StemCellsCordBloodPl1- CD133+ stem cells - cord blood derived, pool1_CNhs12545_12223-129E9_reverse Regulation 
CD133StemCellsCordBloodDerivedPool1_CNhs12545_ctss_fwd Cd133+StemCellsCordBloodPl1+ CD133+ stem cells - cord blood derived, pool1_CNhs12545_12223-129E9_forward Regulation 
CD133StemCellsAdultBoneMarrowDerivedPool1_CNhs12552_ctss_rev Cd133+StemCellsAdultBoneMarrowPl1- CD133+ stem cells - adult bone marrow derived, pool1_CNhs12552_12224-129F1_reverse Regulation 
CD133StemCellsAdultBoneMarrowDerivedPool1_CNhs12552_ctss_fwd Cd133+StemCellsAdultBoneMarrowPl1+ CD133+ stem cells - adult bone marrow derived, pool1_CNhs12552_12224-129F1_forward Regulation 
BasophilsDonor2_CNhs12563_ctss_rev BasophilsD2- Basophils, donor2_CNhs12563_12242-129H1_reverse Regulation 
BasophilsDonor2_CNhs12563_ctss_fwd BasophilsD2+ Basophils, donor2_CNhs12563_12242-129H1_forward Regulation 
BasophilsDonor1_CNhs12546_ctss_rev BasophilsD1- Basophils, donor1_CNhs12546_12241-129G9_reverse Regulation 
BasophilsDonor1_CNhs12546_ctss_fwd BasophilsD1+ Basophils, donor1_CNhs12546_12241-129G9_forward Regulation 
SmoothMuscleCellsAorticDonor0NuclearFraction_CNhs12402_ctss_rev SmcAorticCytofracD0- Smooth Muscle Cells - Aortic, donor0 (nuclear fraction)_CNhs12402_14314-155D3_reverse Regulation 
SmoothMuscleCellsAorticDonor0CytoplasmicFraction_CNhs12401_ctss_rev SmcAorticCytofracD0- Smooth Muscle Cells - Aortic, donor0 (cytoplasmic fraction)_CNhs12401_14313-155D2_reverse Regulation 
SmoothMuscleCellsAorticDonor0NuclearFraction_CNhs12402_ctss_fwd SmcAorticCytofracD0+ Smooth Muscle Cells - Aortic, donor0 (nuclear fraction)_CNhs12402_14314-155D3_forward Regulation 
SmoothMuscleCellsAorticDonor0CytoplasmicFraction_CNhs12401_ctss_fwd SmcAorticCytofracD0+ Smooth Muscle Cells - Aortic, donor0 (cytoplasmic fraction)_CNhs12401_14313-155D2_forward Regulation 
SmallAirwayEpithelialCellsDonor3NuclearFraction_CNhs12583_ctss_rev SmallAirwayEpithelialCellsD3- Small Airway Epithelial Cells, donor3 (nuclear fraction)_CNhs12583_14317-155D6_reverse Regulation 
SmallAirwayEpithelialCellsDonor3CytoplasmicFraction_CNhs14563_ctss_rev SmallAirwayEpithelialCellsD3- Small Airway Epithelial Cells donor3 (cytoplasmic fraction)_CNhs14563_14316-155D5_reverse Regulation 
SmallAirwayEpithelialCellsDonor3NuclearFraction_CNhs12583_ctss_fwd SmallAirwayEpithelialCellsD3+ Small Airway Epithelial Cells, donor3 (nuclear fraction)_CNhs12583_14317-155D6_forward Regulation 
SmallAirwayEpithelialCellsDonor3CytoplasmicFraction_CNhs14563_ctss_fwd SmallAirwayEpithelialCellsD3+ Small Airway Epithelial Cells donor3 (cytoplasmic fraction)_CNhs14563_14316-155D5_forward Regulation 
SmallAirwayEpithelialCellsDonor2NuclearFraction_CNhs14565_ctss_rev SmallAirwayEpithelialCellsD2- Small Airway Epithelial Cells donor2 (nuclear fraction)_CNhs14565_14335-155F6_reverse Regulation 
SmallAirwayEpithelialCellsDonor2CytoplasmicFraction_CNhs14564_ctss_rev SmallAirwayEpithelialCellsD2- Small Airway Epithelial Cells donor2 (cytoplasmic fraction)_CNhs14564_14334-155F5_reverse Regulation 
SmallAirwayEpithelialCellsDonor2NuclearFraction_CNhs14565_ctss_fwd SmallAirwayEpithelialCellsD2+ Small Airway Epithelial Cells donor2 (nuclear fraction)_CNhs14565_14335-155F6_forward Regulation 
SmallAirwayEpithelialCellsDonor2CytoplasmicFraction_CNhs14564_ctss_fwd SmallAirwayEpithelialCellsD2+ Small Airway Epithelial Cells donor2 (cytoplasmic fraction)_CNhs14564_14334-155F5_forward Regulation 
PreadipocyteBreastDonor2CytoplasmicFraction_CNhs14562_ctss_rev PreadipocyteBreastD2- Preadipocyte - breast donor2 (cytoplasmic fraction)_CNhs14562_14319-155D8_reverse Regulation 
PreadipocyteBreastDonor2NuclearFraction_CNhs12584_ctss_rev PreadipocyteBreastD2- Preadipocyte - breast, donor2 (nuclear fraction)_CNhs12584_14320-155D9_reverse Regulation 
PreadipocyteBreastDonor2CytoplasmicFraction_CNhs14562_ctss_fwd PreadipocyteBreastD2+ Preadipocyte - breast donor2 (cytoplasmic fraction)_CNhs14562_14319-155D8_forward Regulation 
PreadipocyteBreastDonor2NuclearFraction_CNhs12584_ctss_fwd PreadipocyteBreastD2+ Preadipocyte - breast, donor2 (nuclear fraction)_CNhs12584_14320-155D9_forward Regulation 
FibroblastSkinNormalDonor2CytoplasmicFraction_CNhs14561_ctss_rev FibrosSkinD2- Fibroblast - skin, normal donor2 (cytoplasmic fraction)_CNhs14561_14301-155B8_reverse Regulation 
FibroblastSkinNormalDonor2CytoplasmicFraction_CNhs14561_ctss_fwd FibrosSkinD2+ Fibroblast - skin, normal donor2 (cytoplasmic fraction)_CNhs14561_14301-155B8_forward Regulation 
FibroblastSkinNormalDonor1CytoplasmicFraction_CNhs14560_ctss_rev FibrosSkinD1- Fibroblast - skin, normal donor1 (cytoplasmic fraction)_CNhs14560_14322-155E2_reverse Regulation 
FibroblastSkinNormalDonor1CytoplasmicFraction_CNhs14560_ctss_fwd FibrosSkinD1+ Fibroblast - skin, normal donor1 (cytoplasmic fraction)_CNhs14560_14322-155E2_forward Regulation 
FibroblastSkinSpinalMuscularAtrophyDonor3NuclearFraction_CNhs12398_ctss_rev FibroSkinSpinalMuscularAtrophyNucfracD3- Fibroblast - skin spinal muscular atrophy, donor3 (nuclear fraction)_CNhs12398_14305-155C3_reverse Regulation 
FibroblastSkinSpinalMuscularAtrophyDonor3NuclearFraction_CNhs12398_ctss_fwd FibroSkinSpinalMuscularAtrophyNucfracD3+ Fibroblast - skin spinal muscular atrophy, donor3 (nuclear fraction)_CNhs12398_14305-155C3_forward Regulation 
FibroblastSkinSpinalMuscularAtrophyDonor1NuclearFraction_CNhs12404_ctss_rev FibroSkinSpinalMuscularAtrophyNucfracD1- Fibroblast - skin spinal muscular atrophy, donor1 (nuclear fraction)_CNhs12404_14326-155E6_reverse Regulation 
FibroblastSkinSpinalMuscularAtrophyDonor1NuclearFraction_CNhs12404_ctss_fwd FibroSkinSpinalMuscularAtrophyNucfracD1+ Fibroblast - skin spinal muscular atrophy, donor1 (nuclear fraction)_CNhs12404_14326-155E6_forward Regulation 
FibroblastSkinNormalDonor2NuclearFraction_CNhs12582_ctss_rev FibroSkinNormalNucfracD2- Fibroblast - skin normal, donor2 (nuclear fraction)_CNhs12582_14302-155B9_reverse Regulation 
FibroblastSkinNormalDonor2NuclearFraction_CNhs12582_ctss_fwd FibroSkinNormalNucfracD2+ Fibroblast - skin normal, donor2 (nuclear fraction)_CNhs12582_14302-155B9_forward Regulation 
FibroblastSkinNormalDonor1NuclearFraction_CNhs12403_ctss_rev FibroSkinNormalNucfracD1- Fibroblast - skin normal, donor1 (nuclear fraction)_CNhs12403_14323-155E3_reverse Regulation 
FibroblastSkinNormalDonor1NuclearFraction_CNhs12403_ctss_fwd FibroSkinNormalNucfracD1+ Fibroblast - skin normal, donor1 (nuclear fraction)_CNhs12403_14323-155E3_forward Regulation 
FibroblastSkinDystrophiaMyotonicaDonor3NuclearFraction_CNhs12399_ctss_rev FibroSkinDystrophiaMyotonicaNucfracD3- Fibroblast - skin dystrophia myotonica, donor3 (nuclear fraction)_CNhs12399_14308-155C6_reverse Regulation 
FibroblastSkinDystrophiaMyotonicaDonor3NuclearFraction_CNhs12399_ctss_fwd FibroSkinDystrophiaMyotonicaNucfracD3+ Fibroblast - skin dystrophia myotonica, donor3 (nuclear fraction)_CNhs12399_14308-155C6_forward Regulation 
FibroblastSkinDystrophiaMyotonicaDonor1NuclearFraction_CNhs12405_ctss_rev FibroSkinDystrophiaMyotonicaNucfracD1- Fibroblast - skin dystrophia myotonica, donor1 (nuclear fraction)_CNhs12405_14329-155E9_reverse Regulation 
FibroblastSkinDystrophiaMyotonicaDonor1NuclearFraction_CNhs12405_ctss_fwd FibroSkinDystrophiaMyotonicaNucfracD1+ Fibroblast - skin dystrophia myotonica, donor1 (nuclear fraction)_CNhs12405_14329-155E9_forward Regulation 
FibroblastAorticAdventitialDonor3CytoplasmicFraction_CNhs14559_ctss_rev FibroAorticAdventitialD3- Fibroblast - Aortic Adventitial donor3 (cytoplasmic fraction)_CNhs14559_14310-155C8_reverse Regulation 
FibroblastAorticAdventitialDonor3NuclearFraction_CNhs12400_ctss_rev FibroAorticAdventitialD3- Fibroblast - Aortic Adventitial, donor3 (nuclear fraction)_CNhs12400_14311-155C9_reverse Regulation 
FibroblastAorticAdventitialDonor3CytoplasmicFraction_CNhs14559_ctss_fwd FibroAorticAdventitialD3+ Fibroblast - Aortic Adventitial donor3 (cytoplasmic fraction)_CNhs14559_14310-155C8_forward Regulation 
FibroblastAorticAdventitialDonor3NuclearFraction_CNhs12400_ctss_fwd FibroAorticAdventitialD3+ Fibroblast - Aortic Adventitial, donor3 (nuclear fraction)_CNhs12400_14311-155C9_forward Regulation 
FibroblastAorticAdventitialDonor2NuclearFraction_CNhs12581_ctss_rev FibroAorticAdventitialD2- Fibroblast - Aortic Adventitial, donor2 (nuclear fraction)_CNhs12581_14332-155F3_reverse Regulation 
FibroblastAorticAdventitialDonor2CytoplasmicFraction_CNhs14558_ctss_rev FibroAorticAdventitialD2- Fibroblast - Aortic Adventitial donor2 (cytoplasmic fraction)_CNhs14558_14331-155F2_reverse Regulation 
FibroblastAorticAdventitialDonor2NuclearFraction_CNhs12581_ctss_fwd FibroAorticAdventitialD2+ Fibroblast - Aortic Adventitial, donor2 (nuclear fraction)_CNhs12581_14332-155F3_forward Regulation 
FibroblastAorticAdventitialDonor2CytoplasmicFraction_CNhs14558_ctss_fwd FibroAorticAdventitialD2+ Fibroblast - Aortic Adventitial donor2 (cytoplasmic fraction)_CNhs14558_14331-155F2_forward Regulation 
AcuteMyeloidLeukemiaFABM5CellLineTHP1CytoplasmicFraction_CNhs14556_ctss_rev Cl:THP-1cyto- acute myeloid leukemia (FAB M5) cell line:THP-1 (cytoplasmic fraction)_CNhs14556_14298-155B5_reverse Regulation 
AcuteMyeloidLeukemiaFABM5CellLineTHP1CytoplasmicFraction_CNhs14556_ctss_fwd Cl:THP-1cyto+ acute myeloid leukemia (FAB M5) cell line:THP-1 (cytoplasmic fraction)_CNhs14556_14298-155B5_forward Regulation 
Hep2CellsTreatedWithStreptococciStrainJRS4BiolRep3_CNhs13499_ctss_rev Hep2W/StreptococciJrs4Br3- Hep-2 cells treated with Streptococci strain JRS4, biol_rep3_CNhs13499_11896-125E6_reverse Regulation 
Hep2CellsTreatedWithStreptococciStrainJRS4BiolRep3_CNhs13499_ctss_fwd Hep2W/StreptococciJrs4Br3+ Hep-2 cells treated with Streptococci strain JRS4, biol_rep3_CNhs13499_11896-125E6_forward Regulation 
Hep2CellsTreatedWithStreptococciStrainJRS4BiolRep2_CNhs13498_ctss_rev Hep2W/StreptococciJrs4Br2- Hep-2 cells treated with Streptococci strain JRS4, biol_rep2_CNhs13498_11895-125E5_reverse Regulation 
Hep2CellsTreatedWithStreptococciStrainJRS4BiolRep2_CNhs13498_ctss_fwd Hep2W/StreptococciJrs4Br2+ Hep-2 cells treated with Streptococci strain JRS4, biol_rep2_CNhs13498_11895-125E5_forward Regulation 
Hep2CellsTreatedWithStreptococciStrainJRS4BiolRep1_CNhs13478_ctss_rev Hep2W/StreptococciJrs4Br1- Hep-2 cells treated with Streptococci strain JRS4, biol_rep1_CNhs13478_11894-125E4_reverse Regulation 
Hep2CellsTreatedWithStreptococciStrainJRS4BiolRep1_CNhs13478_ctss_fwd Hep2W/StreptococciJrs4Br1+ Hep-2 cells treated with Streptococci strain JRS4, biol_rep1_CNhs13478_11894-125E4_forward Regulation 
Hep2CellsTreatedWithStreptococciStrain5448BiolRep3_CNhs13497_ctss_rev Hep2W/Streptococci5448Br3- Hep-2 cells treated with Streptococci strain 5448, biol_rep3_CNhs13497_11892-125E2_reverse Regulation 
Hep2CellsTreatedWithStreptococciStrain5448BiolRep3_CNhs13497_ctss_fwd Hep2W/Streptococci5448Br3+ Hep-2 cells treated with Streptococci strain 5448, biol_rep3_CNhs13497_11892-125E2_forward Regulation 
Hep2CellsTreatedWithStreptococciStrain5448BiolRep2_CNhs13496_ctss_rev Hep2W/Streptococci5448Br2- Hep-2 cells treated with Streptococci strain 5448, biol_rep2_CNhs13496_11891-125E1_reverse Regulation 
Hep2CellsTreatedWithStreptococciStrain5448BiolRep2_CNhs13496_ctss_fwd Hep2W/Streptococci5448Br2+ Hep-2 cells treated with Streptococci strain 5448, biol_rep2_CNhs13496_11891-125E1_forward Regulation 
Hep2CellsTreatedWithStreptococciStrain5448BiolRep1_CNhs13477_ctss_rev Hep2W/Streptococci5448Br1- Hep-2 cells treated with Streptococci strain 5448, biol_rep1_CNhs13477_11890-125D9_reverse Regulation 
Hep2CellsTreatedWithStreptococciStrain5448BiolRep1_CNhs13477_ctss_fwd Hep2W/Streptococci5448Br1+ Hep-2 cells treated with Streptococci strain 5448, biol_rep1_CNhs13477_11890-125D9_forward Regulation 
Hep2CellsMockTreatedBiolRep3_CNhs13501_ctss_rev Hep2MockTreatedBr3- Hep-2 cells mock treated, biol_rep3_CNhs13501_11900-125F1_reverse Regulation 
Hep2CellsMockTreatedBiolRep3_CNhs13501_ctss_fwd Hep2MockTreatedBr3+ Hep-2 cells mock treated, biol_rep3_CNhs13501_11900-125F1_forward Regulation 
Hep2CellsMockTreatedBiolRep2_CNhs13500_ctss_rev Hep2MockTreatedBr2- Hep-2 cells mock treated, biol_rep2_CNhs13500_11899-125E9_reverse Regulation 
Hep2CellsMockTreatedBiolRep2_CNhs13500_ctss_fwd Hep2MockTreatedBr2+ Hep-2 cells mock treated, biol_rep2_CNhs13500_11899-125E9_forward Regulation 
Hep2CellsMockTreatedBiolRep1_CNhs13479_ctss_rev Hep2MockTreatedBr1- Hep-2 cells mock treated, biol_rep1_CNhs13479_11898-125E8_reverse Regulation 
Hep2CellsMockTreatedBiolRep1_CNhs13479_ctss_fwd Hep2MockTreatedBr1+ Hep-2 cells mock treated, biol_rep1_CNhs13479_11898-125E8_forward Regulation 
RetinoblastomaCellLineY79_CNhs11267_ctss_rev Cl:Y79- retinoblastoma cell line:Y79_CNhs11267_10475-106I7_reverse Regulation 
RetinoblastomaCellLineY79_CNhs11267_ctss_fwd Cl:Y79+ retinoblastoma cell line:Y79_CNhs11267_10475-106I7_forward Regulation 
XerodermaPigentosumBCellLineXPL17_CNhs11813_ctss_rev Cl:XPL17- xeroderma pigentosum b cell line:XPL 17_CNhs11813_10563-108A5_reverse Regulation 
XerodermaPigentosumBCellLineXPL17_CNhs11813_ctss_fwd Cl:XPL17+ xeroderma pigentosum b cell line:XPL 17_CNhs11813_10563-108A5_forward Regulation 
HereditarySpherocyticAnemiaCellLineWIL2NS_CNhs11891_ctss_rev Cl:WIL2-NS- hereditary spherocytic anemia cell line:WIL2-NS_CNhs11891_10808-111A7_reverse Regulation 
HereditarySpherocyticAnemiaCellLineWIL2NS_CNhs11891_ctss_fwd Cl:WIL2-NS+ hereditary spherocytic anemia cell line:WIL2-NS_CNhs11891_10808-111A7_forward Regulation 
SmallCellLungCarcinomaCellLineWAhT_CNhs11812_ctss_rev Cl:WA-hT- small cell lung carcinoma cell line:WA-hT_CNhs11812_10562-108A4_reverse Regulation 
SmallCellLungCarcinomaCellLineWAhT_CNhs11812_ctss_fwd Cl:WA-hT+ small cell lung carcinoma cell line:WA-hT_CNhs11812_10562-108A4_forward Regulation 
AcuteMyeloidLeukemiaFABM5CellLineU937DE4_CNhs13058_ctss_rev Cl:U-937DE-4- acute myeloid leukemia (FAB M5) cell line:U-937 DE-4_CNhs13058_10834-111D6_reverse Regulation 
AcuteMyeloidLeukemiaFABM5CellLineU937DE4_CNhs13058_ctss_fwd Cl:U-937DE-4+ acute myeloid leukemia (FAB M5) cell line:U-937 DE-4_CNhs13058_10834-111D6_forward Regulation 
ThymicCarcinomaCellLineTy82_CNhs14139_ctss_rev Cl:Ty-82- thymic carcinoma cell line:Ty-82_CNhs14139_10803-111A2_reverse Regulation 
ThymicCarcinomaCellLineTy82_CNhs14139_ctss_fwd Cl:Ty-82+ thymic carcinoma cell line:Ty-82_CNhs14139_10803-111A2_forward Regulation 
RenalCellCarcinomaCellLineTUHR10TKB_CNhs11257_ctss_rev Cl:TUHR10TKB- renal cell carcinoma cell line:TUHR10TKB_CNhs11257_10471-106I3_reverse Regulation 
RenalCellCarcinomaCellLineTUHR10TKB_CNhs11257_ctss_fwd Cl:TUHR10TKB+ renal cell carcinoma cell line:TUHR10TKB_CNhs11257_10471-106I3_forward Regulation 
RectalCancerCellLineTT1TKB_CNhs11255_ctss_rev Cl:TT1TKB- rectal cancer cell line:TT1TKB_CNhs11255_10469-106I1_reverse Regulation 
RectalCancerCellLineTT1TKB_CNhs11255_ctss_fwd Cl:TT1TKB+ rectal cancer cell line:TT1TKB_CNhs11255_10469-106I1_forward Regulation 
AstrocytomaCellLineTM31_CNhs10742_ctss_rev Cl:TM-31- astrocytoma cell line:TM-31_CNhs10742_10425-106D2_reverse Regulation 
AstrocytomaCellLineTM31_CNhs10742_ctss_fwd Cl:TM-31+ astrocytoma cell line:TM-31_CNhs10742_10425-106D2_forward Regulation 
AcuteMyeloidLeukemiaFABM5CellLineTHP1Thawed_CNhs10724_ctss_rev Cl:THP-1thawed- acute myeloid leukemia (FAB M5) cell line:THP-1 (thawed)_CNhs10724_10405-106A9_reverse Regulation 
AcuteMyeloidLeukemiaFABM5CellLineTHP1Thawed_CNhs10724_ctss_fwd Cl:THP-1thawed+ acute myeloid leukemia (FAB M5) cell line:THP-1 (thawed)_CNhs10724_10405-106A9_forward Regulation 
AcuteMyeloidLeukemiaFABM5CellLineTHP1Revived_CNhs10723_ctss_rev Cl:THP-1revived- acute myeloid leukemia (FAB M5) cell line:THP-1 (revived)_CNhs10723_10400-106A4_reverse Regulation 
AcuteMyeloidLeukemiaFABM5CellLineTHP1Revived_CNhs10723_ctss_fwd Cl:THP-1revived+ acute myeloid leukemia (FAB M5) cell line:THP-1 (revived)_CNhs10723_10400-106A4_forward Regulation 
AcuteMyeloidLeukemiaFABM5CellLineTHP1Fresh_CNhs10722_ctss_rev Cl:THP-1fresh- acute myeloid leukemia (FAB M5) cell line:THP-1 (fresh)_CNhs10722_10399-106A3_reverse Regulation 
AcuteMyeloidLeukemiaFABM5CellLineTHP1Fresh_CNhs10722_ctss_fwd Cl:THP-1fresh+ acute myeloid leukemia (FAB M5) cell line:THP-1 (fresh)_CNhs10722_10399-106A3_forward Regulation 
GallBladderCarcinomaCellLineTGBC2TKB_CNhs10733_ctss_rev Cl:TGBC2TKB- gall bladder carcinoma cell line:TGBC2TKB_CNhs10733_10415-106C1_reverse Regulation 
GallBladderCarcinomaCellLineTGBC2TKB_CNhs10733_ctss_fwd Cl:TGBC2TKB+ gall bladder carcinoma cell line:TGBC2TKB_CNhs10733_10415-106C1_forward Regulation 
PapillotubularAdenocarcinomaCellLineTGBC18TKB_CNhs10734_ctss_rev Cl:TGBC18TKB- papillotubular adenocarcinoma cell line:TGBC18TKB_CNhs10734_10417-106C3_reverse Regulation 
PapillotubularAdenocarcinomaCellLineTGBC18TKB_CNhs10734_ctss_fwd Cl:TGBC18TKB+ papillotubular adenocarcinoma cell line:TGBC18TKB_CNhs10734_10417-106C3_forward Regulation 
GallBladderCarcinomaCellLineTGBC14TKB_CNhs11256_ctss_rev Cl:TGBC14TKB- gall bladder carcinoma cell line:TGBC14TKB_CNhs11256_10470-106I2_reverse Regulation 
GallBladderCarcinomaCellLineTGBC14TKB_CNhs11256_ctss_fwd Cl:TGBC14TKB+ gall bladder carcinoma cell line:TGBC14TKB_CNhs11256_10470-106I2_forward Regulation 
BileDuctCarcinomaCellLineTFK1_CNhs11265_ctss_rev Cl:TFK-1- bile duct carcinoma cell line:TFK-1_CNhs11265_10496-107C1_reverse Regulation 
BileDuctCarcinomaCellLineTFK1_CNhs11265_ctss_fwd Cl:TFK-1+ bile duct carcinoma cell line:TFK-1_CNhs11265_10496-107C1_forward Regulation 
ClearCellCarcinomaCellLineTEN_CNhs11930_ctss_rev Cl:TEN- clear cell carcinoma cell line:TEN_CNhs11930_10636-108I6_reverse Regulation 
ClearCellCarcinomaCellLineTEN_CNhs11930_ctss_fwd Cl:TEN+ clear cell carcinoma cell line:TEN_CNhs11930_10636-108I6_forward Regulation 
BasalCellCarcinomaCellLineTE354_T_CNhs11932_ctss_rev Cl:TE354_T- basal cell carcinoma cell line:TE 354_T_CNhs11932_10702-109G9_reverse Regulation 
BasalCellCarcinomaCellLineTE354_T_CNhs11932_ctss_fwd Cl:TE354_T+ basal cell carcinoma cell line:TE 354_T_CNhs11932_10702-109G9_forward Regulation 
ThyroidCarcinomaCellLineTCO1_CNhs11872_ctss_rev Cl:TCO-1- thyroid carcinoma cell line:TCO-1_CNhs11872_10783-110G9_reverse Regulation 
ThyroidCarcinomaCellLineTCO1_CNhs11872_ctss_fwd Cl:TCO-1+ thyroid carcinoma cell line:TCO-1_CNhs11872_10783-110G9_forward Regulation 
ArgyrophilSmallCellCarcinomaCellLineTCYIK_CNhs11725_ctss_rev Cl:TC-YIK- argyrophil small cell carcinoma cell line:TC-YIK_CNhs11725_10589-108D4_reverse Regulation 
ArgyrophilSmallCellCarcinomaCellLineTCYIK_CNhs11725_ctss_fwd Cl:TC-YIK+ argyrophil small cell carcinoma cell line:TC-YIK_CNhs11725_10589-108D4_forward Regulation 
NeuroectodermalTumorCellLineTASK1_CNhs11866_ctss_rev Cl:TASK1- neuroectodermal tumor cell line:TASK1_CNhs11866_10774-110F9_reverse Regulation 
NeuroectodermalTumorCellLineTASK1_CNhs11866_ctss_fwd Cl:TASK1+ neuroectodermal tumor cell line:TASK1_CNhs11866_10774-110F9_forward Regulation 
GlioblastomaCellLineT98G_CNhs11272_ctss_rev Cl:T98G- glioblastoma cell line:T98G_CNhs11272_10485-107A8_reverse Regulation 
GlioblastomaCellLineT98G_CNhs11272_ctss_fwd Cl:T98G+ glioblastoma cell line:T98G_CNhs11272_10485-107A8_forward Regulation 
SquamousCellCarcinomaCellLineT3M5_CNhs11739_ctss_rev Cl:T3M-5- squamous cell carcinoma cell line:T3M-5_CNhs11739_10616-108G4_reverse Regulation 
SquamousCellCarcinomaCellLineT3M5_CNhs11739_ctss_fwd Cl:T3M-5+ squamous cell carcinoma cell line:T3M-5_CNhs11739_10616-108G4_forward Regulation 
ChoriocarcinomaCellLineT3M3_CNhs11820_ctss_rev Cl:T3M-3- choriocarcinoma cell line:T3M-3_CNhs11820_10618-108G6_reverse Regulation 
ChoriocarcinomaCellLineT3M3_CNhs11820_ctss_fwd Cl:T3M-3+ choriocarcinoma cell line:T3M-3_CNhs11820_10618-108G6_forward Regulation 
LiposarcomaCellLineSW872_CNhs11851_ctss_rev Cl:SW872- liposarcoma cell line:SW 872_CNhs11851_10726-110A6_reverse Regulation 
LiposarcomaCellLineSW872_CNhs11851_ctss_fwd Cl:SW872+ liposarcoma cell line:SW 872_CNhs11851_10726-110A6_forward Regulation 
AlveolarCellCarcinomaCellLineSW1573_CNhs11838_ctss_rev Cl:SW1573- alveolar cell carcinoma cell line:SW 1573_CNhs11838_10708-109H6_reverse Regulation 
AlveolarCellCarcinomaCellLineSW1573_CNhs11838_ctss_fwd Cl:SW1573+ alveolar cell carcinoma cell line:SW 1573_CNhs11838_10708-109H6_forward Regulation 
ChondrosarcomaCellLineSW1353_CNhs11833_ctss_rev Cl:SW1353- chondrosarcoma cell line:SW 1353_CNhs11833_10700-109G7_reverse Regulation 
ChondrosarcomaCellLineSW1353_CNhs11833_ctss_fwd Cl:SW1353+ chondrosarcoma cell line:SW 1353_CNhs11833_10700-109G7_forward Regulation 
AdrenalCortexAdenocarcinomaCellLineSW13_CNhs11893_ctss_rev Cl:SW-13- adrenal cortex adenocarcinoma cell line:SW-13_CNhs11893_10810-111A9_reverse Regulation 
AdrenalCortexAdenocarcinomaCellLineSW13_CNhs11893_ctss_fwd Cl:SW-13+ adrenal cortex adenocarcinoma cell line:SW-13_CNhs11893_10810-111A9_forward Regulation 
TubularAdenocarcinomaCellLineSUIT2_CNhs11883_ctss_rev Cl:SUIT-2- tubular adenocarcinoma cell line:SUIT-2_CNhs11883_10797-110I5_reverse Regulation 
TubularAdenocarcinomaCellLineSUIT2_CNhs11883_ctss_fwd Cl:SUIT-2+ tubular adenocarcinoma cell line:SUIT-2_CNhs11883_10797-110I5_forward Regulation 
BoneMarrowStromalCellLineStromaNKtert_CNhs11931_ctss_rev Cl:StromaNKtert- bone marrow stromal cell line:StromaNKtert_CNhs11931_10686-109F2_reverse Regulation 
BoneMarrowStromalCellLineStromaNKtert_CNhs11931_ctss_fwd Cl:StromaNKtert+ bone marrow stromal cell line:StromaNKtert_CNhs11931_10686-109F2_forward Regulation 
LensEpithelialCellLineSRA0104_CNhs11750_ctss_rev Cl:SRA01/04- lens epithelial cell line:SRA 01/04_CNhs11750_10647-109A8_reverse Regulation 
LensEpithelialCellLineSRA0104_CNhs11750_ctss_fwd Cl:SRA01/04+ lens epithelial cell line:SRA 01/04_CNhs11750_10647-109A8_forward Regulation 
PleomorphicHepatocellularCarcinomaCellLineSNU387_CNhs11933_ctss_rev Cl:SNU-387- pleomorphic hepatocellular carcinoma cell line:SNU-387_CNhs11933_10706-109H4_reverse Regulation 
PleomorphicHepatocellularCarcinomaCellLineSNU387_CNhs11933_ctss_fwd Cl:SNU-387+ pleomorphic hepatocellular carcinoma cell line:SNU-387_CNhs11933_10706-109H4_forward Regulation 
SplenicLymphomaWithVillousLymphocytesCellLineSLVL_CNhs10741_ctss_rev Cl:SLVL- splenic lymphoma with villous lymphocytes cell line:SLVL_CNhs10741_10424-106D1_reverse Regulation 
SplenicLymphomaWithVillousLymphocytesCellLineSLVL_CNhs10741_ctss_fwd Cl:SLVL+ splenic lymphoma with villous lymphocytes cell line:SLVL_CNhs10741_10424-106D1_forward Regulation 
ChronicLymphocyticLeukemiaTCLLCellLineSKW3_CNhs11714_ctss_rev Cl:SKW-3- chronic lymphocytic leukemia (T-CLL) cell line:SKW-3_CNhs11714_10416-106C2_reverse Regulation 
ChronicLymphocyticLeukemiaTCLLCellLineSKW3_CNhs11714_ctss_fwd Cl:SKW-3+ chronic lymphocytic leukemia (T-CLL) cell line:SKW-3_CNhs11714_10416-106C2_forward Regulation 
MyelodysplasticSyndromeCellLineSKM1_CNhs11934_ctss_rev Cl:SKM-1- myelodysplastic syndrome cell line:SKM-1_CNhs11934_10772-110F7_reverse Regulation 
MyelodysplasticSyndromeCellLineSKM1_CNhs11934_ctss_fwd Cl:SKM-1+ myelodysplastic syndrome cell line:SKM-1_CNhs11934_10772-110F7_forward Regulation 
LargeCellNonkeratinizingSquamousCarcinomaCellLineSKGIISF_CNhs11825_ctss_rev Cl:SKG-II-SF- large cell non-keratinizing squamous carcinoma cell line:SKG-II-SF_CNhs11825_10692-109F8_reverse Regulation 
LargeCellNonkeratinizingSquamousCarcinomaCellLineSKGIISF_CNhs11825_ctss_fwd Cl:SKG-II-SF+ large cell non-keratinizing squamous carcinoma cell line:SKG-II-SF_CNhs11825_10692-109F8_forward Regulation 
CarcinoidCellLineSKPNDW_CNhs11846_ctss_rev Cl:SK-PN-DW- carcinoid cell line:SK-PN-DW_CNhs11846_10719-109I8_reverse Regulation 
CarcinoidCellLineSKPNDW_CNhs11846_ctss_fwd Cl:SK-PN-DW+ carcinoid cell line:SK-PN-DW_CNhs11846_10719-109I8_forward Regulation 
SerousAdenocarcinomaCellLineSKOV3RAfterCocultureWithSOC5702GBiolRep1_CNhs13508_ctss_rev Cl:SK-OV-3-RwithSOC-57-02-GBr1- serous adenocarcinoma cell line:SK-OV-3-R after co-culture with SOC-57-02-G, biol_rep1_CNhs13508_11843-124H7_reverse Regulation 
SerousAdenocarcinomaCellLineSKOV3RAfterCocultureWithSOC5702GBiolRep1_CNhs13508_ctss_fwd Cl:SK-OV-3-RwithSOC-57-02-GBr1+ serous adenocarcinoma cell line:SK-OV-3-R after co-culture with SOC-57-02-G, biol_rep1_CNhs13508_11843-124H7_forward Regulation 
SerousAdenocarcinomaCellLineSKOV3RBiolRep1_CNhs13099_ctss_rev Cl:SK-OV-3-RBr1- serous adenocarcinoma cell line:SK-OV-3-R, biol_rep1_CNhs13099_11841-124H5_reverse Regulation 
SerousAdenocarcinomaCellLineSKOV3RBiolRep1_CNhs13099_ctss_fwd Cl:SK-OV-3-RBr1+ serous adenocarcinoma cell line:SK-OV-3-R, biol_rep1_CNhs13099_11841-124H5_forward Regulation 
NeuroepitheliomaCellLineSKNMC_CNhs11853_ctss_rev Cl:SK-N-MC- neuroepithelioma cell line:SK-N-MC_CNhs11853_10728-110A8_reverse Regulation 
NeuroepitheliomaCellLineSKNMC_CNhs11853_ctss_fwd Cl:SK-N-MC+ neuroepithelioma cell line:SK-N-MC_CNhs11853_10728-110A8_forward Regulation 
ChoriocarcinomaCellLineSCH_CNhs11875_ctss_rev Cl:SCH- choriocarcinoma cell line:SCH_CNhs11875_10785-110H2_reverse Regulation 
ChoriocarcinomaCellLineSCH_CNhs11875_ctss_fwd Cl:SCH+ choriocarcinoma cell line:SCH_CNhs11875_10785-110H2_forward Regulation 
OralSquamousCellCarcinomaCellLineSAS_CNhs11810_ctss_rev Cl:SAS- oral squamous cell carcinoma cell line:SAS_CNhs11810_10544-107H4_reverse Regulation 
OralSquamousCellCarcinomaCellLineSAS_CNhs11810_ctss_fwd Cl:SAS+ oral squamous cell carcinoma cell line:SAS_CNhs11810_10544-107H4_forward Regulation 
AnaplasticSquamousCellCarcinomaCellLineRPMI2650_CNhs11889_ctss_rev Cl:RPMI2650- anaplastic squamous cell carcinoma cell line:RPMI 2650_CNhs11889_10805-111A4_reverse Regulation 
AnaplasticSquamousCellCarcinomaCellLineRPMI2650_CNhs11889_ctss_fwd Cl:RPMI2650+ anaplastic squamous cell carcinoma cell line:RPMI 2650_CNhs11889_10805-111A4_forward Regulation 
BCellLineRPMI1788_CNhs10744_ctss_rev Cl:RPMI1788- b cell line:RPMI1788_CNhs10744_10427-106D4_reverse Regulation 
BCellLineRPMI1788_CNhs10744_ctss_fwd Cl:RPMI1788+ b cell line:RPMI1788_CNhs10744_10427-106D4_forward Regulation 
RhabdomyosarcomaCellLineRMSYM_CNhs11269_ctss_rev Cl:RMS-YM- rhabdomyosarcoma cell line:RMS-YM_CNhs11269_10477-106I9_reverse Regulation 
RhabdomyosarcomaCellLineRMSYM_CNhs11269_ctss_fwd Cl:RMS-YM+ rhabdomyosarcoma cell line:RMS-YM_CNhs11269_10477-106I9_forward Regulation 
SquamousCellLungCarcinomaCellLineRERFLCAI_CNhs14240_ctss_rev Cl:RERF-LC-AI- squamous cell lung carcinoma cell line:RERF-LC-AI_CNhs14240_10501-107C6_reverse Regulation 
SquamousCellLungCarcinomaCellLineRERFLCAI_CNhs14240_ctss_fwd Cl:RERF-LC-AI+ squamous cell lung carcinoma cell line:RERF-LC-AI_CNhs14240_10501-107C6_forward Regulation 
BurkittsLymphomaCellLineRAJI_CNhs11268_ctss_rev Cl:RAJI- Burkitt's lymphoma cell line:RAJI_CNhs11268_10476-106I8_reverse Regulation 
BurkittsLymphomaCellLineRAJI_CNhs11268_ctss_fwd Cl:RAJI+ Burkitt's lymphoma cell line:RAJI_CNhs11268_10476-106I8_forward Regulation 
SomatostatinomaCellLineQGP1_CNhs11869_ctss_rev Cl:QGP-1- somatostatinoma cell line:QGP-1_CNhs11869_10781-110G7_reverse Regulation 
SomatostatinomaCellLineQGP1_CNhs11869_ctss_fwd Cl:QGP-1+ somatostatinoma cell line:QGP-1_CNhs11869_10781-110G7_forward Regulation 
MyelomaCellLinePCM6_CNhs11258_ctss_rev Cl:PCM6- myeloma cell line:PCM6_CNhs11258_10474-106I6_reverse Regulation 
MyelomaCellLinePCM6_CNhs11258_ctss_fwd Cl:PCM6+ myeloma cell line:PCM6_CNhs11258_10474-106I6_forward Regulation 
ProstateCancerCellLinePC3_CNhs11243_ctss_rev Cl:PC-3- prostate cancer cell line:PC-3_CNhs11243_10439-106E7_reverse Regulation 
ProstateCancerCellLinePC3_CNhs11243_ctss_fwd Cl:PC-3+ prostate cancer cell line:PC-3_CNhs11243_10439-106E7_forward Regulation 
LungAdenocarcinomaCellLinePC14_CNhs10726_ctss_rev Cl:PC-14- lung adenocarcinoma cell line:PC-14_CNhs10726_10408-106B3_reverse Regulation 
LungAdenocarcinomaCellLinePC14_CNhs10726_ctss_fwd Cl:PC-14+ lung adenocarcinoma cell line:PC-14_CNhs10726_10408-106B3_forward Regulation 
TeratocarcinomaCellLinePA1_CNhs11890_ctss_rev Cl:PA-1- teratocarcinoma cell line:PA-1_CNhs11890_10807-111A6_reverse Regulation 
TeratocarcinomaCellLinePA1_CNhs11890_ctss_fwd Cl:PA-1+ teratocarcinoma cell line:PA-1_CNhs11890_10807-111A6_forward Regulation 
AcuteMyeloidLeukemiaFABM5CellLineP31FUJ_CNhs13051_ctss_rev Cl:P31/FUJ- acute myeloid leukemia (FAB M5) cell line:P31/FUJ_CNhs13051_10770-110F5_reverse Regulation 
AcuteMyeloidLeukemiaFABM5CellLineP31FUJ_CNhs13051_ctss_fwd Cl:P31/FUJ+ acute myeloid leukemia (FAB M5) cell line:P31/FUJ_CNhs13051_10770-110F5_forward Regulation 
NonTNonBAcuteLymphoblasticLeukemiaALLCellLineP30OHK_CNhs10747_ctss_rev Cl:P30/OHK- non T non B acute lymphoblastic leukemia (ALL) cell line:P30/OHK_CNhs10747_10430-106D7_reverse Regulation 
NonTNonBAcuteLymphoblasticLeukemiaALLCellLineP30OHK_CNhs10747_ctss_fwd Cl:P30/OHK+ non T non B acute lymphoblastic leukemia (ALL) cell line:P30/OHK_CNhs10747_10430-106D7_forward Regulation 
RenalCellCarcinomaCellLineOSRC2_CNhs10729_ctss_rev Cl:OS-RC-2- renal cell carcinoma cell line:OS-RC-2_CNhs10729_10411-106B6_reverse Regulation 
RenalCellCarcinomaCellLineOSRC2_CNhs10729_ctss_fwd Cl:OS-RC-2+ renal cell carcinoma cell line:OS-RC-2_CNhs10729_10411-106B6_forward Regulation 
MedulloblastomaCellLineONS76_CNhs11861_ctss_rev Cl:ONS-76- medulloblastoma cell line:ONS-76_CNhs11861_10759-110E3_reverse Regulation 
MedulloblastomaCellLineONS76_CNhs11861_ctss_fwd Cl:ONS-76+ medulloblastoma cell line:ONS-76_CNhs11861_10759-110E3_forward Regulation 
MesotheliomaCellLineONE58_CNhs13075_ctss_rev Cl:ONE58- mesothelioma cell line:ONE58_CNhs13075_10858-111G3_reverse Regulation 
MesotheliomaCellLineONE58_CNhs13075_ctss_fwd Cl:ONE58+ mesothelioma cell line:ONE58_CNhs13075_10858-111G3_forward Regulation 
EndometrialStromalSarcomaCellLineOMC9_CNhs11249_ctss_rev Cl:OMC-9- endometrial stromal sarcoma cell line:OMC-9_CNhs11249_10448-106F7_reverse Regulation 
EndometrialStromalSarcomaCellLineOMC9_CNhs11249_ctss_fwd Cl:OMC-9+ endometrial stromal sarcoma cell line:OMC-9_CNhs11249_10448-106F7_forward Regulation 
EndometrialCarcinomaCellLineOMC2_CNhs11266_ctss_rev Cl:OMC-2- endometrial carcinoma cell line:OMC-2_CNhs11266_10497-107C2_reverse Regulation 
EndometrialCarcinomaCellLineOMC2_CNhs11266_ctss_fwd Cl:OMC-2+ endometrial carcinoma cell line:OMC-2_CNhs11266_10497-107C2_forward Regulation 
SignetRingCarcinomaCellLineNUGC4_CNhs11270_ctss_rev Cl:NUGC-4- signet ring carcinoma cell line:NUGC-4_CNhs11270_10483-107A6_reverse Regulation 
SignetRingCarcinomaCellLineNUGC4_CNhs11270_ctss_fwd Cl:NUGC-4+ signet ring carcinoma cell line:NUGC-4_CNhs11270_10483-107A6_forward Regulation 
PancreaticCarcinomaCellLineNORP1_CNhs11832_ctss_rev Cl:NOR-P1- pancreatic carcinoma cell line:NOR-P1_CNhs11832_10698-109G5_reverse Regulation 
PancreaticCarcinomaCellLineNORP1_CNhs11832_ctss_fwd Cl:NOR-P1+ pancreatic carcinoma cell line:NOR-P1_CNhs11832_10698-109G5_forward Regulation 
AcuteMyeloidLeukemiaFABM5CellLineNOMO1_CNhs13050_ctss_rev Cl:NOMO-1- acute myeloid leukemia (FAB M5) cell line:NOMO-1_CNhs13050_10764-110E8_reverse Regulation 
AcuteMyeloidLeukemiaFABM5CellLineNOMO1_CNhs13050_ctss_fwd Cl:NOMO-1+ acute myeloid leukemia (FAB M5) cell line:NOMO-1_CNhs13050_10764-110E8_forward Regulation 
MesotheliomaCellLineNo36_CNhs13074_ctss_rev Cl:No36- mesothelioma cell line:No36_CNhs13074_10857-111G2_reverse Regulation 
MesotheliomaCellLineNo36_CNhs13074_ctss_fwd Cl:No36+ mesothelioma cell line:No36_CNhs13074_10857-111G2_forward Regulation 
MyxofibrosarcomaCellLineNMFH1_CNhs11821_ctss_rev Cl:NMFH-1- myxofibrosarcoma cell line:NMFH-1_CNhs11821_10684-109E9_reverse Regulation 
MyxofibrosarcomaCellLineNMFH1_CNhs11821_ctss_fwd Cl:NMFH-1+ myxofibrosarcoma cell line:NMFH-1_CNhs11821_10684-109E9_forward Regulation 
AcuteMyeloidLeukemiaFABM2CellLineNKM1_CNhs11864_ctss_rev Cl:NKM-1- acute myeloid leukemia (FAB M2) cell line:NKM-1_CNhs11864_10765-110E9_reverse Regulation 
AcuteMyeloidLeukemiaFABM2CellLineNKM1_CNhs11864_ctss_fwd Cl:NKM-1+ acute myeloid leukemia (FAB M2) cell line:NKM-1_CNhs11864_10765-110E9_forward Regulation 
NeuroblastomaCellLineNH12_CNhs11811_ctss_rev Cl:NH-12- neuroblastoma cell line:NH-12_CNhs11811_10555-107I6_reverse Regulation 
NeuroblastomaCellLineNH12_CNhs11811_ctss_fwd Cl:NH-12+ neuroblastoma cell line:NH-12_CNhs11811_10555-107I6_forward Regulation 
TesticularGermCellEmbryonalCarcinomaCellLineNEC8_CNhs11726_ctss_rev Cl:NEC8- testicular germ cell embryonal carcinoma cell line:NEC8_CNhs11726_10590-108D5_reverse Regulation 
TesticularGermCellEmbryonalCarcinomaCellLineNEC8_CNhs11726_ctss_fwd Cl:NEC8+ testicular germ cell embryonal carcinoma cell line:NEC8_CNhs11726_10590-108D5_forward Regulation 
TesticularGermCellEmbryonalCarcinomaCellLineNEC15_CNhs12362_ctss_rev Cl:NEC15- testicular germ cell embryonal carcinoma cell line:NEC15_CNhs12362_10593-108D8_reverse Regulation 
TesticularGermCellEmbryonalCarcinomaCellLineNEC15_CNhs12362_ctss_fwd Cl:NEC15+ testicular germ cell embryonal carcinoma cell line:NEC15_CNhs12362_10593-108D8_forward Regulation 
TesticularGermCellEmbryonalCarcinomaCellLineNEC14_CNhs12351_ctss_rev Cl:NEC14- testicular germ cell embryonal carcinoma cell line:NEC14_CNhs12351_10591-108D6_reverse Regulation 
TesticularGermCellEmbryonalCarcinomaCellLineNEC14_CNhs12351_ctss_fwd Cl:NEC14+ testicular germ cell embryonal carcinoma cell line:NEC14_CNhs12351_10591-108D6_forward Regulation 
TeratocarcinomaCellLineNCRG1_CNhs11884_ctss_rev Cl:NCR-G1- teratocarcinoma cell line:NCR-G1_CNhs11884_10798-110I6_reverse Regulation 
TeratocarcinomaCellLineNCRG1_CNhs11884_ctss_fwd Cl:NCR-G1+ teratocarcinoma cell line:NCR-G1_CNhs11884_10798-110I6_forward Regulation 
SmallCellLungCarcinomaCellLineNCIH82_CNhs12809_ctss_rev Cl:NCI-H82- small cell lung carcinoma cell line:NCI-H82_CNhs12809_10842-111E5_reverse Regulation 
SmallCellLungCarcinomaCellLineNCIH82_CNhs12809_ctss_fwd Cl:NCI-H82+ small cell lung carcinoma cell line:NCI-H82_CNhs12809_10842-111E5_forward Regulation 
CarcinoidCellLineNCIH727_CNhs14244_ctss_rev Cl:NCI-H727- carcinoid cell line:NCI-H727_CNhs14244_10735-110B6_reverse Regulation 
CarcinoidCellLineNCIH727_CNhs14244_ctss_fwd Cl:NCI-H727+ carcinoid cell line:NCI-H727_CNhs14244_10735-110B6_forward Regulation 
BronchioalveolarCarcinomaCellLineNCIH650_CNhs14138_ctss_rev Cl:NCI-H650- bronchioalveolar carcinoma cell line:NCI-H650_CNhs14138_10715-109I4_reverse Regulation 
BronchioalveolarCarcinomaCellLineNCIH650_CNhs14138_ctss_fwd Cl:NCI-H650+ bronchioalveolar carcinoma cell line:NCI-H650_CNhs14138_10715-109I4_forward Regulation 
LargeCellLungCarcinomaCellLineNCIH460_CNhs12806_ctss_rev Cl:NCI-H460- large cell lung carcinoma cell line:NCI-H460_CNhs12806_10839-111E2_reverse Regulation 
LargeCellLungCarcinomaCellLineNCIH460_CNhs12806_ctss_fwd Cl:NCI-H460+ large cell lung carcinoma cell line:NCI-H460_CNhs12806_10839-111E2_forward Regulation 
LungAdenocarcinomaPapillaryCellLineNCIH441_CNhs14245_ctss_rev Cl:NCI-H441- lung adenocarcinoma, papillary cell line:NCI-H441_CNhs14245_10742-110C4_reverse Regulation 
LungAdenocarcinomaPapillaryCellLineNCIH441_CNhs14245_ctss_fwd Cl:NCI-H441+ lung adenocarcinoma, papillary cell line:NCI-H441_CNhs14245_10742-110C4_forward Regulation 
BronchioalveolarCarcinomaCellLineNCIH358_CNhs11840_ctss_rev Cl:NCI-H358- bronchioalveolar carcinoma cell line:NCI-H358_CNhs11840_10709-109H7_reverse Regulation 
BronchioalveolarCarcinomaCellLineNCIH358_CNhs11840_ctss_fwd Cl:NCI-H358+ bronchioalveolar carcinoma cell line:NCI-H358_CNhs11840_10709-109H7_forward Regulation 
MesotheliomaCellLineNCIH28_CNhs13061_ctss_rev Cl:NCI-H28- mesothelioma cell line:NCI-H28_CNhs13061_10845-111E8_reverse Regulation 
MesotheliomaCellLineNCIH28_CNhs13061_ctss_fwd Cl:NCI-H28+ mesothelioma cell line:NCI-H28_CNhs13061_10845-111E8_forward Regulation 
MesotheliomaCellLineNCIH2452_CNhs13064_ctss_rev Cl:NCI-H2452- mesothelioma cell line:NCI-H2452_CNhs13064_10848-111F2_reverse Regulation 
MesotheliomaCellLineNCIH2452_CNhs13064_ctss_fwd Cl:NCI-H2452+ mesothelioma cell line:NCI-H2452_CNhs13064_10848-111F2_forward Regulation 
MesotheliomaCellLineNCIH226_CNhs13062_ctss_rev Cl:NCI-H226- mesothelioma cell line:NCI-H226_CNhs13062_10846-111E9_reverse Regulation 
MesotheliomaCellLineNCIH226_CNhs13062_ctss_fwd Cl:NCI-H226+ mesothelioma cell line:NCI-H226_CNhs13062_10846-111E9_forward Regulation 
MesotheliomaCellLineNCIH2052_CNhs13063_ctss_rev Cl:NCI-H2052- mesothelioma cell line:NCI-H2052_CNhs13063_10847-111F1_reverse Regulation 
MesotheliomaCellLineNCIH2052_CNhs13063_ctss_fwd Cl:NCI-H2052+ mesothelioma cell line:NCI-H2052_CNhs13063_10847-111F1_forward Regulation 
CarcinoidCellLineNCIH1770_CNhs11834_ctss_rev Cl:NCI-H1770- carcinoid cell line:NCI-H1770_CNhs11834_10703-109H1_reverse Regulation 
CarcinoidCellLineNCIH1770_CNhs11834_ctss_fwd Cl:NCI-H1770+ carcinoid cell line:NCI-H1770_CNhs11834_10703-109H1_forward Regulation 
TeratocarcinomaCellLineNCCITA3_CNhs11878_ctss_rev Cl:NCC-IT-A3- teratocarcinoma cell line:NCC-IT-A3_CNhs11878_10790-110H7_reverse Regulation 
TeratocarcinomaCellLineNCCITA3_CNhs11878_ctss_fwd Cl:NCC-IT-A3+ teratocarcinoma cell line:NCC-IT-A3_CNhs11878_10790-110H7_forward Regulation 
NeuroblastomaCellLineNBsusSR_CNhs11818_ctss_rev Cl:NBsusSR- neuroblastoma cell line:NBsusSR_CNhs11818_10607-108F4_reverse Regulation 
NeuroblastomaCellLineNBsusSR_CNhs11818_ctss_fwd Cl:NBsusSR+ neuroblastoma cell line:NBsusSR_CNhs11818_10607-108F4_forward Regulation 
NeuroblastomaCellLineNB1_CNhs11284_ctss_rev Cl:NB-1- neuroblastoma cell line:NB-1_CNhs11284_10539-107G8_reverse Regulation 
NeuroblastomaCellLineNB1_CNhs11284_ctss_fwd Cl:NB-1+ neuroblastoma cell line:NB-1_CNhs11284_10539-107G8_forward Regulation 
AcuteLymphoblasticLeukemiaBALLCellLineNALM6_CNhs11282_ctss_rev Cl:NALM-6- acute lymphoblastic leukemia (B-ALL) cell line:NALM-6_CNhs11282_10534-107G3_reverse Regulation 
AcuteLymphoblasticLeukemiaBALLCellLineNALM6_CNhs11282_ctss_fwd Cl:NALM-6+ acute lymphoblastic leukemia (B-ALL) cell line:NALM-6_CNhs11282_10534-107G3_forward Regulation 
BiphenotypicBMyelomonocyticLeukemiaCellLineMV411_CNhs11845_ctss_rev Cl:MV-4-11- biphenotypic B myelomonocytic leukemia cell line:MV-4-11_CNhs11845_10718-109I7_reverse Regulation 
BiphenotypicBMyelomonocyticLeukemiaCellLineMV411_CNhs11845_ctss_fwd Cl:MV-4-11+ biphenotypic B myelomonocytic leukemia cell line:MV-4-11_CNhs11845_10718-109I7_forward Regulation 
MerkelCellCarcinomaCellLineMS1_CNhs12839_ctss_rev Cl:MS-1- merkel cell carcinoma cell line:MS-1_CNhs12839_10844-111E7_reverse Regulation 
MerkelCellCarcinomaCellLineMS1_CNhs12839_ctss_fwd Cl:MS-1+ merkel cell carcinoma cell line:MS-1_CNhs12839_10844-111E7_forward Regulation 
HairyCellLeukemiaCellLineMo_CNhs11843_ctss_rev Cl:Mo- hairy cell leukemia cell line:Mo_CNhs11843_10712-109I1_reverse Regulation 
HairyCellLeukemiaCellLineMo_CNhs11843_ctss_fwd Cl:Mo+ hairy cell leukemia cell line:Mo_CNhs11843_10712-109I1_forward Regulation 
LymphomaMalignantHairyBcellCellLineMLMA_CNhs11935_ctss_rev Cl:MLMA- lymphoma, malignant, hairy B-cell cell line:MLMA_CNhs11935_10775-110G1_reverse Regulation 
LymphomaMalignantHairyBcellCellLineMLMA_CNhs11935_ctss_fwd Cl:MLMA+ lymphoma, malignant, hairy B-cell cell line:MLMA_CNhs11935_10775-110G1_forward Regulation 
AcuteMyeloidLeukemiaFABM7CellLineMKPL1_CNhs11888_ctss_rev Cl:MKPL-1- acute myeloid leukemia (FAB M7) cell line:MKPL-1_CNhs11888_10802-111A1_reverse Regulation 
AcuteMyeloidLeukemiaFABM7CellLineMKPL1_CNhs11888_ctss_fwd Cl:MKPL-1+ acute myeloid leukemia (FAB M7) cell line:MKPL-1_CNhs11888_10802-111A1_forward Regulation 
GastricAdenocarcinomaCellLineMKN45_CNhs11819_ctss_rev Cl:MKN45- gastric adenocarcinoma cell line:MKN45_CNhs11819_10612-108F9_reverse Regulation 
GastricAdenocarcinomaCellLineMKN45_CNhs11819_ctss_fwd Cl:MKN45+ gastric adenocarcinoma cell line:MKN45_CNhs11819_10612-108F9_forward Regulation 
GastricAdenocarcinomaCellLineMKN1_CNhs11737_ctss_rev Cl:MKN1- gastric adenocarcinoma cell line:MKN1_CNhs11737_10614-108G2_reverse Regulation 
GastricAdenocarcinomaCellLineMKN1_CNhs11737_ctss_fwd Cl:MKN1+ gastric adenocarcinoma cell line:MKN1_CNhs11737_10614-108G2_forward Regulation 
MerkelCellCarcinomaCellLineMKL1_CNhs12838_ctss_rev Cl:MKL-1- merkel cell carcinoma cell line:MKL-1_CNhs12838_10843-111E6_reverse Regulation 
MerkelCellCarcinomaCellLineMKL1_CNhs12838_ctss_fwd Cl:MKL-1+ merkel cell carcinoma cell line:MKL-1_CNhs12838_10843-111E6_forward Regulation 
DuctalCellCarcinomaCellLineMIAPaca2_CNhs11259_ctss_rev Cl:MIAPaca2- ductal cell carcinoma cell line:MIA Paca2_CNhs11259_10488-107B2_reverse Regulation 
DuctalCellCarcinomaCellLineMIAPaca2_CNhs11259_ctss_fwd Cl:MIAPaca2+ ductal cell carcinoma cell line:MIA Paca2_CNhs11259_10488-107B2_forward Regulation 
MyxofibrosarcomaCellLineMFHino_CNhs11729_ctss_rev Cl:MFH-ino- myxofibrosarcoma cell line:MFH-ino_CNhs11729_10600-108E6_reverse Regulation 
MyxofibrosarcomaCellLineMFHino_CNhs11729_ctss_fwd Cl:MFH-ino+ myxofibrosarcoma cell line:MFH-ino_CNhs11729_10600-108E6_forward Regulation 
MesotheliomaCellLineMero95_CNhs13073_ctss_rev Cl:Mero-95- mesothelioma cell line:Mero-95_CNhs13073_10856-111G1_reverse Regulation 
MesotheliomaCellLineMero95_CNhs13073_ctss_fwd Cl:Mero-95+ mesothelioma cell line:Mero-95_CNhs13073_10856-111G1_forward Regulation 
MesotheliomaCellLineMero84_CNhs13072_ctss_rev Cl:Mero-84- mesothelioma cell line:Mero-84_CNhs13072_10855-111F9_reverse Regulation 
MesotheliomaCellLineMero84_CNhs13072_ctss_fwd Cl:Mero-84+ mesothelioma cell line:Mero-84_CNhs13072_10855-111F9_forward Regulation 
MesotheliomaCellLineMero83_CNhs13070_ctss_rev Cl:Mero-83- mesothelioma cell line:Mero-83_CNhs13070_10854-111F8_reverse Regulation 
MesotheliomaCellLineMero83_CNhs13070_ctss_fwd Cl:Mero-83+ mesothelioma cell line:Mero-83_CNhs13070_10854-111F8_forward Regulation 
MesotheliomaCellLineMero82_CNhs13069_ctss_rev Cl:Mero-82- mesothelioma cell line:Mero-82_CNhs13069_10853-111F7_reverse Regulation 
MesotheliomaCellLineMero82_CNhs13069_ctss_fwd Cl:Mero-82+ mesothelioma cell line:Mero-82_CNhs13069_10853-111F7_forward Regulation 
MesotheliomaCellLineMero48a_CNhs13068_ctss_rev Cl:Mero-48a- mesothelioma cell line:Mero-48a_CNhs13068_10852-111F6_reverse Regulation 
MesotheliomaCellLineMero48a_CNhs13068_ctss_fwd Cl:Mero-48a+ mesothelioma cell line:Mero-48a_CNhs13068_10852-111F6_forward Regulation 
MesotheliomaCellLineMero41_CNhs13067_ctss_rev Cl:Mero-41- mesothelioma cell line:Mero-41_CNhs13067_10851-111F5_reverse Regulation 
MesotheliomaCellLineMero41_CNhs13067_ctss_fwd Cl:Mero-41+ mesothelioma cell line:Mero-41_CNhs13067_10851-111F5_forward Regulation 
MesotheliomaCellLineMero25_CNhs13066_ctss_rev Cl:Mero-25- mesothelioma cell line:Mero-25_CNhs13066_10850-111F4_reverse Regulation 
MesotheliomaCellLineMero25_CNhs13066_ctss_fwd Cl:Mero-25+ mesothelioma cell line:Mero-25_CNhs13066_10850-111F4_forward Regulation 
MesotheliomaCellLineMero14TechRep1_CNhs13065_ctss_rev Cl:Mero-14Tr1- mesothelioma cell line:Mero-14, tech_rep1_CNhs13065_10849-111F3_reverse Regulation 
MesotheliomaCellLineMero14TechRep1_CNhs13065_ctss_fwd Cl:Mero-14Tr1+ mesothelioma cell line:Mero-14, tech_rep1_CNhs13065_10849-111F3_forward Regulation 
ChronicMyelogenousLeukemiaCMLCellLineMEGA2_CNhs11865_ctss_rev Cl:MEG-A2- chronic myelogenous leukemia (CML) cell line:MEG-A2_CNhs11865_10766-110F1_reverse Regulation 
ChronicMyelogenousLeukemiaCMLCellLineMEGA2_CNhs11865_ctss_fwd Cl:MEG-A2+ chronic myelogenous leukemia (CML) cell line:MEG-A2_CNhs11865_10766-110F1_forward Regulation 
LeukemiaChronicMegakaryoblasticCellLineMEG01_CNhs11859_ctss_rev Cl:MEG-01- leukemia, chronic megakaryoblastic cell line:MEG-01_CNhs11859_10752-110D5_reverse Regulation 
LeukemiaChronicMegakaryoblasticCellLineMEG01_CNhs11859_ctss_fwd Cl:MEG-01+ leukemia, chronic megakaryoblastic cell line:MEG-01_CNhs11859_10752-110D5_forward Regulation 
CervicalCancerCellLineME180_CNhs11289_ctss_rev Cl:ME-180- cervical cancer cell line:ME-180_CNhs11289_10553-107I4_reverse Regulation 
CervicalCancerCellLineME180_CNhs11289_ctss_fwd Cl:ME-180+ cervical cancer cell line:ME-180_CNhs11289_10553-107I4_forward Regulation 
BreastCarcinomaCellLineMDAMB453_CNhs10736_ctss_rev Cl:MDA-MB-453- breast carcinoma cell line:MDA-MB-453_CNhs10736_10419-106C5_reverse Regulation 
BreastCarcinomaCellLineMDAMB453_CNhs10736_ctss_fwd Cl:MDA-MB-453+ breast carcinoma cell line:MDA-MB-453_CNhs10736_10419-106C5_forward Regulation 
BreastCarcinomaCellLineMCF7_CNhs11943_ctss_rev Cl:MCF7- breast carcinoma cell line:MCF7_CNhs11943_10482-107A5_reverse Regulation 
BreastCarcinomaCellLineMCF7_CNhs11943_ctss_fwd Cl:MCF7+ breast carcinoma cell line:MCF7_CNhs11943_10482-107A5_forward Regulation 
MucinousCystadenocarcinomaCellLineMCAS_CNhs11873_ctss_rev Cl:MCAS- mucinous cystadenocarcinoma cell line:MCAS_CNhs11873_10784-110H1_reverse Regulation 
MucinousCystadenocarcinomaCellLineMCAS_CNhs11873_ctss_fwd Cl:MCAS+ mucinous cystadenocarcinoma cell line:MCAS_CNhs11873_10784-110H1_forward Regulation 
AcuteMyeloidLeukemiaFABM7CellLineMMOK_CNhs13049_ctss_rev Cl:M-MOK- acute myeloid leukemia (FAB M7) cell line:M-MOK_CNhs13049_10699-109G6_reverse Regulation 
AcuteMyeloidLeukemiaFABM7CellLineMMOK_CNhs13049_ctss_fwd Cl:M-MOK+ acute myeloid leukemia (FAB M7) cell line:M-MOK_CNhs13049_10699-109G6_forward Regulation 
GiantCellCarcinomaCellLineLu99B_CNhs10751_ctss_rev Cl:Lu99B- giant cell carcinoma cell line:Lu99B_CNhs10751_10433-106E1_reverse Regulation 
GiantCellCarcinomaCellLineLu99B_CNhs10751_ctss_fwd Cl:Lu99B+ giant cell carcinoma cell line:Lu99B_CNhs10751_10433-106E1_forward Regulation 
GiantCellCarcinomaCellLineLU65_CNhs11274_ctss_rev Cl:LU65- giant cell carcinoma cell line:LU65_CNhs11274_10487-107B1_reverse Regulation 
GiantCellCarcinomaCellLineLU65_CNhs11274_ctss_fwd Cl:LU65+ giant cell carcinoma cell line:LU65_CNhs11274_10487-107B1_forward Regulation 
SmallCellLungCarcinomaCellLineLK2_CNhs11285_ctss_rev Cl:LK-2- small cell lung carcinoma cell line:LK-2_CNhs11285_10541-107H1_reverse Regulation 
SmallCellLungCarcinomaCellLineLK2_CNhs11285_ctss_fwd Cl:LK-2+ small cell lung carcinoma cell line:LK-2_CNhs11285_10541-107H1_forward Regulation 
HepaticMesenchymalTumorCellLineLI90_CNhs11868_ctss_rev Cl:LI90- hepatic mesenchymal tumor cell line:LI90_CNhs11868_10778-110G4_reverse Regulation 
HepaticMesenchymalTumorCellLineLI90_CNhs11868_ctss_fwd Cl:LI90+ hepatic mesenchymal tumor cell line:LI90_CNhs11868_10778-110G4_forward Regulation 
HepatomaCellLineLi7_CNhs11271_ctss_rev Cl:Li-7- hepatoma cell line:Li-7_CNhs11271_10484-107A7_reverse Regulation 
HepatomaCellLineLi7_CNhs11271_ctss_fwd Cl:Li-7+ hepatoma cell line:Li-7_CNhs11271_10484-107A7_forward Regulation 
SquamousCellLungCarcinomaCellLineLC1F_CNhs14238_ctss_rev Cl:LC-1F- squamous cell lung carcinoma cell line:LC-1F_CNhs14238_10457-106G7_reverse Regulation 
SquamousCellLungCarcinomaCellLineLC1F_CNhs14238_ctss_fwd Cl:LC-1F+ squamous cell lung carcinoma cell line:LC-1F_CNhs14238_10457-106G7_forward Regulation 
RhabdomyosarcomaCellLineKYM1_CNhs11877_ctss_rev Cl:KYM-1- rhabdomyosarcoma cell line:KYM-1_CNhs11877_10787-110H4_reverse Regulation 
RhabdomyosarcomaCellLineKYM1_CNhs11877_ctss_fwd Cl:KYM-1+ rhabdomyosarcoma cell line:KYM-1_CNhs11877_10787-110H4_forward Regulation 
ChronicMyelogenousLeukemiaCellLineKU812_CNhs10727_ctss_rev Cl:KU812- chronic myelogenous leukemia cell line:KU812_CNhs10727_10409-106B4_reverse Regulation 
ChronicMyelogenousLeukemiaCellLineKU812_CNhs10727_ctss_fwd Cl:KU812+ chronic myelogenous leukemia cell line:KU812_CNhs10727_10409-106B4_forward Regulation 
PeripheralNeuroectodermalTumorCellLineKUSN_CNhs11830_ctss_rev Cl:KU-SN- peripheral neuroectodermal tumor cell line:KU-SN_CNhs11830_10697-109G4_reverse Regulation 
PeripheralNeuroectodermalTumorCellLineKUSN_CNhs11830_ctss_fwd Cl:KU-SN+ peripheral neuroectodermal tumor cell line:KU-SN_CNhs11830_10697-109G4_forward Regulation 
BronchialSquamousCellCarcinomaCellLineKNS62_CNhs11862_ctss_rev Cl:KNS-62- bronchial squamous cell carcinoma cell line:KNS-62_CNhs11862_10760-110E4_reverse Regulation 
BronchialSquamousCellCarcinomaCellLineKNS62_CNhs11862_ctss_fwd Cl:KNS-62+ bronchial squamous cell carcinoma cell line:KNS-62_CNhs11862_10760-110E4_forward Regulation 
LiposarcomaCellLineKMLS1_CNhs11870_ctss_rev Cl:KMLS-1- liposarcoma cell line:KMLS-1_CNhs11870_10782-110G8_reverse Regulation 
LiposarcomaCellLineKMLS1_CNhs11870_ctss_fwd Cl:KMLS-1+ liposarcoma cell line:KMLS-1_CNhs11870_10782-110G8_forward Regulation 
DuctalCellCarcinomaCellLineKLM1_CNhs11100_ctss_rev Cl:KLM-1- ductal cell carcinoma cell line:KLM-1_CNhs11100_10438-106E6_reverse Regulation 
DuctalCellCarcinomaCellLineKLM1_CNhs11100_ctss_fwd Cl:KLM-1+ ductal cell carcinoma cell line:KLM-1_CNhs11100_10438-106E6_forward Regulation 
AnaplasticLargeCellLymphomaCellLineKiJK_CNhs11881_ctss_rev Cl:Ki-JK- anaplastic large cell lymphoma cell line:Ki-JK_CNhs11881_10795-110I3_reverse Regulation 
AnaplasticLargeCellLymphomaCellLineKiJK_CNhs11881_ctss_fwd Cl:Ki-JK+ anaplastic large cell lymphoma cell line:Ki-JK_CNhs11881_10795-110I3_forward Regulation 
NKTCellLeukemiaCellLineKHYG1_CNhs11867_ctss_rev Cl:KHYG-1- NK T cell leukemia cell line:KHYG-1_CNhs11867_10777-110G3_reverse Regulation 
NKTCellLeukemiaCellLineKHYG1_CNhs11867_ctss_fwd Cl:KHYG-1+ NK T cell leukemia cell line:KHYG-1_CNhs11867_10777-110G3_forward Regulation 
ThyroidCarcinomaCellLineKHM5M_CNhs14140_ctss_rev Cl:KHM-5M- thyroid carcinoma cell line:KHM-5M_CNhs14140_10776-110G2_reverse Regulation 
ThyroidCarcinomaCellLineKHM5M_CNhs14140_ctss_fwd Cl:KHM-5M+ thyroid carcinoma cell line:KHM-5M_CNhs14140_10776-110G2_forward Regulation 
GranulosaCellTumorCellLineKGN_CNhs11740_ctss_rev Cl:KGN- granulosa cell tumor cell line:KGN_CNhs11740_10624-108H3_reverse Regulation 
GranulosaCellTumorCellLineKGN_CNhs11740_ctss_fwd Cl:KGN+ granulosa cell tumor cell line:KGN_CNhs11740_10624-108H3_forward Regulation 
AcuteMyeloidLeukemiaFABM0CellLineKG1_CNhs13053_ctss_rev Cl:KG-1- acute myeloid leukemia (FAB M0) cell line:KG-1_CNhs13053_10827-111C8_reverse Regulation 
AcuteMyeloidLeukemiaFABM0CellLineKG1_CNhs13053_ctss_fwd Cl:KG-1+ acute myeloid leukemia (FAB M0) cell line:KG-1_CNhs13053_10827-111C8_forward Regulation 
ChronicMyeloblasticLeukemiaCMLCellLineKCL22_CNhs11886_ctss_rev Cl:KCL-22- chronic myeloblastic leukemia (CML) cell line:KCL-22_CNhs11886_10801-110I9_reverse Regulation 
ChronicMyeloblasticLeukemiaCMLCellLineKCL22_CNhs11886_ctss_fwd Cl:KCL-22+ chronic myeloblastic leukemia (CML) cell line:KCL-22_CNhs11886_10801-110I9_forward Regulation 
SignetRingCarcinomaCellLineKatoIII_CNhs10753_ctss_rev Cl:KatoIII- signet ring carcinoma cell line:Kato III_CNhs10753_10436-106E4_reverse Regulation 
SignetRingCarcinomaCellLineKatoIII_CNhs10753_ctss_fwd Cl:KatoIII+ signet ring carcinoma cell line:Kato III_CNhs10753_10436-106E4_forward Regulation 
AcuteMyeloidLeukemiaFABM2CellLineKasumi6_CNhs13052_ctss_rev Cl:Kasumi-6- acute myeloid leukemia (FAB M2) cell line:Kasumi-6_CNhs13052_10792-110H9_reverse Regulation 
AcuteMyeloidLeukemiaFABM2CellLineKasumi6_CNhs13052_ctss_fwd Cl:Kasumi-6+ acute myeloid leukemia (FAB M2) cell line:Kasumi-6_CNhs13052_10792-110H9_forward Regulation 
AcuteMyeloidLeukemiaFABM2CellLineKasumi1_CNhs13502_ctss_rev Cl:Kasumi-1- acute myeloid leukemia (FAB M2) cell line:Kasumi-1_CNhs13502_10788-110H5_reverse Regulation 
AcuteMyeloidLeukemiaFABM2CellLineKasumi1_CNhs13502_ctss_fwd Cl:Kasumi-1+ acute myeloid leukemia (FAB M2) cell line:Kasumi-1_CNhs13502_10788-110H5_forward Regulation 
ChronicMyelogenousLeukemiaCellLineK562ENCODEBiolRep3_CNhs12336_ctss_rev Cl:K562Br3- chronic myelogenous leukemia cell line:K562 ENCODE, biol_rep3_CNhs12336_10826-111C7_reverse Regulation 
ChronicMyelogenousLeukemiaCellLineK562ENCODEBiolRep3_CNhs12336_ctss_fwd Cl:K562Br3+ chronic myelogenous leukemia cell line:K562 ENCODE, biol_rep3_CNhs12336_10826-111C7_forward Regulation 
ChronicMyelogenousLeukemiaCellLineK562ENCODEBiolRep2_CNhs12335_ctss_rev Cl:K562Br2- chronic myelogenous leukemia cell line:K562 ENCODE, biol_rep2_CNhs12335_10825-111C6_reverse Regulation 
ChronicMyelogenousLeukemiaCellLineK562ENCODEBiolRep2_CNhs12335_ctss_fwd Cl:K562Br2+ chronic myelogenous leukemia cell line:K562 ENCODE, biol_rep2_CNhs12335_10825-111C6_forward Regulation 
ChronicMyelogenousLeukemiaCellLineK562ENCODEBiolRep1_CNhs12334_ctss_rev Cl:K562Br1- chronic myelogenous leukemia cell line:K562 ENCODE, biol_rep1_CNhs12334_10824-111C5_reverse Regulation 
ChronicMyelogenousLeukemiaCellLineK562ENCODEBiolRep1_CNhs12334_ctss_fwd Cl:K562Br1+ chronic myelogenous leukemia cell line:K562 ENCODE, biol_rep1_CNhs12334_10824-111C5_forward Regulation 
ChronicMyelogenousLeukemiaCellLineK562_CNhs11250_ctss_rev Cl:K562- chronic myelogenous leukemia cell line:K562_CNhs11250_10454-106G4_reverse Regulation 
ChronicMyelogenousLeukemiaCellLineK562_CNhs11250_ctss_fwd Cl:K562+ chronic myelogenous leukemia cell line:K562_CNhs11250_10454-106G4_forward Regulation 
AcuteLymphoblasticLeukemiaTALLCellLineJurkat_CNhs11253_ctss_rev Cl:Jurkat- acute lymphoblastic leukemia (T-ALL) cell line:Jurkat_CNhs11253_10464-106H5_reverse Regulation 
AcuteLymphoblasticLeukemiaTALLCellLineJurkat_CNhs11253_ctss_fwd Cl:Jurkat+ acute lymphoblastic leukemia (T-ALL) cell line:Jurkat_CNhs11253_10464-106H5_forward Regulation 
TransitionalcellCarcinomaCellLineJMSU1_CNhs11261_ctss_rev Cl:JMSU1- transitional-cell carcinoma cell line:JMSU1_CNhs11261_10492-107B6_reverse Regulation 
TransitionalcellCarcinomaCellLineJMSU1_CNhs11261_ctss_fwd Cl:JMSU1+ transitional-cell carcinoma cell line:JMSU1_CNhs11261_10492-107B6_forward Regulation 
SquamousCellCarcinomaCellLineJHUSnk1_CNhs11749_ctss_rev Cl:JHUS-nk1- squamous cell carcinoma cell line:JHUS-nk1_CNhs11749_10646-109A7_reverse Regulation 
SquamousCellCarcinomaCellLineJHUSnk1_CNhs11749_ctss_fwd Cl:JHUS-nk1+ squamous cell carcinoma cell line:JHUS-nk1_CNhs11749_10646-109A7_forward Regulation 
EndometrioidAdenocarcinomaCellLineJHUEM1_CNhs11748_ctss_rev Cl:JHUEM-1- endometrioid adenocarcinoma cell line:JHUEM-1_CNhs11748_10643-109A4_reverse Regulation 
EndometrioidAdenocarcinomaCellLineJHUEM1_CNhs11748_ctss_fwd Cl:JHUEM-1+ endometrioid adenocarcinoma cell line:JHUEM-1_CNhs11748_10643-109A4_forward Regulation 
CarcinosarcomaCellLineJHUCS1_CNhs11747_ctss_rev Cl:JHUCS-1- carcinosarcoma cell line:JHUCS-1_CNhs11747_10642-109A3_reverse Regulation 
CarcinosarcomaCellLineJHUCS1_CNhs11747_ctss_fwd Cl:JHUCS-1+ carcinosarcoma cell line:JHUCS-1_CNhs11747_10642-109A3_forward Regulation 
SerousAdenocarcinomaCellLineJHOS2_CNhs11746_ctss_rev Cl:JHOS-2- serous adenocarcinoma cell line:JHOS-2_CNhs11746_10639-108I9_reverse Regulation 
SerousAdenocarcinomaCellLineJHOS2_CNhs11746_ctss_fwd Cl:JHOS-2+ serous adenocarcinoma cell line:JHOS-2_CNhs11746_10639-108I9_forward Regulation 
MucinousAdenocarcinomaCellLineJHOM1_CNhs11752_ctss_rev Cl:JHOM-1- mucinous adenocarcinoma cell line:JHOM-1_CNhs11752_10648-109A9_reverse Regulation 
MucinousAdenocarcinomaCellLineJHOM1_CNhs11752_ctss_fwd Cl:JHOM-1+ mucinous adenocarcinoma cell line:JHOM-1_CNhs11752_10648-109A9_forward Regulation 
ClearCellCarcinomaCellLineJHOC5_CNhs11745_ctss_rev Cl:JHOC-5- clear cell carcinoma cell line:JHOC-5_CNhs11745_10638-108I8_reverse Regulation 
ClearCellCarcinomaCellLineJHOC5_CNhs11745_ctss_fwd Cl:JHOC-5+ clear cell carcinoma cell line:JHOC-5_CNhs11745_10638-108I8_forward Regulation 
TesticularGermCellEmbryonalCarcinomaCellLineITOII_CNhs11876_ctss_rev Cl:ITO-II- testicular germ cell embryonal carcinoma cell line:ITO-II_CNhs11876_10786-110H3_reverse Regulation 
TesticularGermCellEmbryonalCarcinomaCellLineITOII_CNhs11876_ctss_fwd Cl:ITO-II+ testicular germ cell embryonal carcinoma cell line:ITO-II_CNhs11876_10786-110H3_forward Regulation 
AdenocarcinomaCellLineIM95m_CNhs11882_ctss_rev Cl:IM95m- adenocarcinoma cell line:IM95m_CNhs11882_10796-110I4_reverse Regulation 
AdenocarcinomaCellLineIM95m_CNhs11882_ctss_fwd Cl:IM95m+ adenocarcinoma cell line:IM95m_CNhs11882_10796-110I4_forward Regulation 
LargeCellLungCarcinomaCellLineIALM_CNhs11277_ctss_rev Cl:IA-LM- large cell lung carcinoma cell line:IA-LM_CNhs11277_10509-107D5_reverse Regulation 
LargeCellLungCarcinomaCellLineIALM_CNhs11277_ctss_fwd Cl:IA-LM+ large cell lung carcinoma cell line:IA-LM_CNhs11277_10509-107D5_forward Regulation 
AcuteMyeloidLeukemiaFABM1CellLineHYT1_CNhs13054_ctss_rev Cl:HYT-1- acute myeloid leukemia (FAB M1) cell line:HYT-1_CNhs13054_10828-111C9_reverse Regulation 
AcuteMyeloidLeukemiaFABM1CellLineHYT1_CNhs13054_ctss_fwd Cl:HYT-1+ acute myeloid leukemia (FAB M1) cell line:HYT-1_CNhs13054_10828-111C9_forward Regulation 
MycosisFungoidesTCellLymphomaCellLineHuT102TIB162_CNhs11858_ctss_rev Cl:HuT102TIB-162- mycosis fungoides, T cell lymphoma cell line:HuT 102 TIB-162_CNhs11858_10744-110C6_reverse Regulation 
MycosisFungoidesTCellLymphomaCellLineHuT102TIB162_CNhs11858_ctss_fwd Cl:HuT102TIB-162+ mycosis fungoides, T cell lymphoma cell line:HuT 102 TIB-162_CNhs11858_10744-110C6_forward Regulation 
HepatoblastomaCellLineHuH6_CNhs11742_ctss_rev Cl:HuH-6- hepatoblastoma cell line:HuH-6_CNhs11742_10633-108I3_reverse Regulation 
HepatoblastomaCellLineHuH6_CNhs11742_ctss_fwd Cl:HuH-6+ hepatoblastoma cell line:HuH-6_CNhs11742_10633-108I3_forward Regulation 
CholangiocellularCarcinomaCellLineHuH28_CNhs11283_ctss_rev Cl:HuH-28- cholangiocellular carcinoma cell line:HuH-28_CNhs11283_10536-107G5_reverse Regulation 
CholangiocellularCarcinomaCellLineHuH28_CNhs11283_ctss_fwd Cl:HuH-28+ cholangiocellular carcinoma cell line:HuH-28_CNhs11283_10536-107G5_forward Regulation 
BileDuctCarcinomaCellLineHuCCT1_CNhs10750_ctss_rev Cl:HuCCT1- bile duct carcinoma cell line:HuCCT1_CNhs10750_10432-106D9_reverse Regulation 
BileDuctCarcinomaCellLineHuCCT1_CNhs10750_ctss_fwd Cl:HuCCT1+ bile duct carcinoma cell line:HuCCT1_CNhs10750_10432-106D9_forward Regulation 
MesenchymalStemCellLineHu5E18_CNhs11718_ctss_rev Cl:Hu5/E18- mesenchymal stem cell line:Hu5/E18_CNhs11718_10568-108B1_reverse Regulation 
MesenchymalStemCellLineHu5E18_CNhs11718_ctss_fwd Cl:Hu5/E18+ mesenchymal stem cell line:Hu5/E18_CNhs11718_10568-108B1_forward Regulation 
SacrococcigealTeratomaCellLineHTST_CNhs11829_ctss_rev Cl:HTST- sacrococcigeal teratoma cell line:HTST_CNhs11829_10695-109G2_reverse Regulation 
SacrococcigealTeratomaCellLineHTST_CNhs11829_ctss_fwd Cl:HTST+ sacrococcigeal teratoma cell line:HTST_CNhs11829_10695-109G2_forward Regulation 
SerousCystadenocarcinomaCellLineHTOA_CNhs11827_ctss_rev Cl:HTOA- serous cystadenocarcinoma cell line:HTOA_CNhs11827_10693-109F9_reverse Regulation 
SerousCystadenocarcinomaCellLineHTOA_CNhs11827_ctss_fwd Cl:HTOA+ serous cystadenocarcinoma cell line:HTOA_CNhs11827_10693-109F9_forward Regulation 
MixedMullerianTumorCellLineHTMMT_CNhs11944_ctss_rev Cl:HTMMT- mixed mullerian tumor cell line:HTMMT_CNhs11944_10689-109F5_reverse Regulation 
MixedMullerianTumorCellLineHTMMT_CNhs11944_ctss_fwd Cl:HTMMT+ mixed mullerian tumor cell line:HTMMT_CNhs11944_10689-109F5_forward Regulation 
FibrosarcomaCellLineHT1080_CNhs11860_ctss_rev Cl:HT-1080- fibrosarcoma cell line:HT-1080_CNhs11860_10758-110E2_reverse Regulation 
FibrosarcomaCellLineHT1080_CNhs11860_ctss_fwd Cl:HT-1080+ fibrosarcoma cell line:HT-1080_CNhs11860_10758-110E2_forward Regulation 
MaxillarySinusTumorCellLineHSQ89_CNhs10732_ctss_rev Cl:HSQ-89- maxillary sinus tumor cell line:HSQ-89_CNhs10732_10414-106B9_reverse Regulation 
MaxillarySinusTumorCellLineHSQ89_CNhs10732_ctss_fwd Cl:HSQ-89+ maxillary sinus tumor cell line:HSQ-89_CNhs10732_10414-106B9_forward Regulation 
KrukenbergTumorCellLineHSKTC_CNhs11822_ctss_rev Cl:HSKTC- Krukenberg tumor cell line:HSKTC_CNhs11822_10687-109F3_reverse Regulation 
KrukenbergTumorCellLineHSKTC_CNhs11822_ctss_fwd Cl:HSKTC+ Krukenberg tumor cell line:HSKTC_CNhs11822_10687-109F3_forward Regulation 
OralSquamousCellCarcinomaCellLineHSC3_CNhs11717_ctss_rev Cl:HSC-3- oral squamous cell carcinoma cell line:HSC-3_CNhs11717_10545-107H5_reverse Regulation 
OralSquamousCellCarcinomaCellLineHSC3_CNhs11717_ctss_fwd Cl:HSC-3+ oral squamous cell carcinoma cell line:HSC-3_CNhs11717_10545-107H5_forward Regulation 
PagetoidSarcomaCellLineHs925_T_CNhs11856_ctss_rev Cl:Hs925_T- pagetoid sarcoma cell line:Hs 925_T_CNhs11856_10732-110B3_reverse Regulation 
PagetoidSarcomaCellLineHs925_T_CNhs11856_ctss_fwd Cl:Hs925_T+ pagetoid sarcoma cell line:Hs 925_T_CNhs11856_10732-110B3_forward Regulation 
EwingsSarcomaCellLineHs863_T_CNhs11836_ctss_rev Cl:Hs863_T- Ewing's sarcoma cell line:Hs 863_T_CNhs11836_10705-109H3_reverse Regulation 
EwingsSarcomaCellLineHs863_T_CNhs11836_ctss_fwd Cl:Hs863_T+ Ewing's sarcoma cell line:Hs 863_T_CNhs11836_10705-109H3_forward Regulation 
TransitionalCellCarcinomaCellLineHs769_T_CNhs11837_ctss_rev Cl:Hs769_T- transitional cell carcinoma cell line:Hs 769_T_CNhs11837_10707-109H5_reverse Regulation 
TransitionalCellCarcinomaCellLineHs769_T_CNhs11837_ctss_fwd Cl:Hs769_T+ transitional cell carcinoma cell line:Hs 769_T_CNhs11837_10707-109H5_forward Regulation 
OsteoclastomaCellLineHs706_T_CNhs11835_ctss_rev Cl:Hs706_T- osteoclastoma cell line:Hs 706_T_CNhs11835_10704-109H2_reverse Regulation 
OsteoclastomaCellLineHs706_T_CNhs11835_ctss_fwd Cl:Hs706_T+ osteoclastoma cell line:Hs 706_T_CNhs11835_10704-109H2_forward Regulation 
NeurofibromaCellLineHs53_T_CNhs11854_ctss_rev Cl:Hs53_T- neurofibroma cell line:Hs 53_T_CNhs11854_10729-110A9_reverse Regulation 
NeurofibromaCellLineHs53_T_CNhs11854_ctss_fwd Cl:Hs53_T+ neurofibroma cell line:Hs 53_T_CNhs11854_10729-110A9_forward Regulation 
SpindleCellSarcomaCellLineHs132_T_CNhs11857_ctss_rev Cl:Hs132_T- spindle cell sarcoma cell line:Hs 132_T_CNhs11857_10737-110B8_reverse Regulation 
SpindleCellSarcomaCellLineHs132_T_CNhs11857_ctss_fwd Cl:Hs132_T+ spindle cell sarcoma cell line:Hs 132_T_CNhs11857_10737-110B8_forward Regulation 
SynovialSarcomaCellLineHSSYII_CNhs11244_ctss_rev Cl:HS-SY-II- synovial sarcoma cell line:HS-SY-II_CNhs11244_10441-106E9_reverse Regulation 
SynovialSarcomaCellLineHSSYII_CNhs11244_ctss_fwd Cl:HS-SY-II+ synovial sarcoma cell line:HS-SY-II_CNhs11244_10441-106E9_forward Regulation 
SchwannomaCellLineHSPSSTechRep2_CNhs11245_ctss_rev Cl:HS-PSSTr2- schwannoma cell line:HS-PSS, tech_rep2_CNhs11245_10442-106F1_reverse Regulation 
SchwannomaCellLineHSPSSTechRep2_CNhs11245_ctss_fwd Cl:HS-PSSTr2+ schwannoma cell line:HS-PSS, tech_rep2_CNhs11245_10442-106F1_forward Regulation 
OsteosarcomaCellLineHSOs1_CNhs11290_ctss_rev Cl:HS-Os-1- osteosarcoma cell line:HS-Os-1_CNhs11290_10558-107I9_reverse Regulation 
OsteosarcomaCellLineHSOs1_CNhs11290_ctss_fwd Cl:HS-Os-1+ osteosarcoma cell line:HS-Os-1_CNhs11290_10558-107I9_forward Regulation 
EpithelioidSarcomaCellLineHSES2R_CNhs14239_ctss_rev Cl:HS-ES-2R- epithelioid sarcoma cell line:HS-ES-2R_CNhs14239_10495-107B9_reverse Regulation 
EpithelioidSarcomaCellLineHSES2R_CNhs14239_ctss_fwd Cl:HS-ES-2R+ epithelioid sarcoma cell line:HS-ES-2R_CNhs14239_10495-107B9_forward Regulation 
EpithelioidSarcomaCellLineHSES1_CNhs11247_ctss_rev Cl:HS-ES-1- epithelioid sarcoma cell line:HS-ES-1_CNhs11247_10443-106F2_reverse Regulation 
EpithelioidSarcomaCellLineHSES1_CNhs11247_ctss_fwd Cl:HS-ES-1+ epithelioid sarcoma cell line:HS-ES-1_CNhs11247_10443-106F2_forward Regulation 
AcuteLymphoblasticLeukemiaTALLCellLineHPBALL_CNhs10746_ctss_rev Cl:HPB-ALL- acute lymphoblastic leukemia (T-ALL) cell line:HPB-ALL_CNhs10746_10429-106D6_reverse Regulation 
AcuteLymphoblasticLeukemiaTALLCellLineHPBALL_CNhs10746_ctss_fwd Cl:HPB-ALL+ acute lymphoblastic leukemia (T-ALL) cell line:HPB-ALL_CNhs10746_10429-106D6_forward Regulation 
GlassyCellCarcinomaCellLineHOKUG_CNhs11824_ctss_rev Cl:HOKUG- glassy cell carcinoma cell line:HOKUG_CNhs11824_10688-109F4_reverse Regulation 
GlassyCellCarcinomaCellLineHOKUG_CNhs11824_ctss_fwd Cl:HOKUG+ glassy cell carcinoma cell line:HOKUG_CNhs11824_10688-109F4_forward Regulation 
OralSquamousCellCarcinomaCellLineHO1u1_CNhs11287_ctss_rev Cl:HO-1-u-1- oral squamous cell carcinoma cell line:HO-1-u-1_CNhs11287_10550-107I1_reverse Regulation 
OralSquamousCellCarcinomaCellLineHO1u1_CNhs11287_ctss_fwd Cl:HO-1-u-1+ oral squamous cell carcinoma cell line:HO-1-u-1_CNhs11287_10550-107I1_forward Regulation 
AcuteMyeloidLeukemiaFABM4CellLineHNT34_CNhs13504_ctss_rev Cl:HNT-34- acute myeloid leukemia (FAB M4) cell line:HNT-34_CNhs13504_10831-111D3_reverse Regulation 
AcuteMyeloidLeukemiaFABM4CellLineHNT34_CNhs13504_ctss_fwd Cl:HNT-34+ acute myeloid leukemia (FAB M4) cell line:HNT-34_CNhs13504_10831-111D3_forward Regulation 
AcuteMyeloidLeukemiaFABM3CellLineHL60_CNhs13055_ctss_rev Cl:HL60- acute myeloid leukemia (FAB M3) cell line:HL60_CNhs13055_10829-111D1_reverse Regulation 
AcuteMyeloidLeukemiaFABM3CellLineHL60_CNhs13055_ctss_fwd Cl:HL60+ acute myeloid leukemia (FAB M3) cell line:HL60_CNhs13055_10829-111D1_forward Regulation 
MeningiomaCellLineHKBMM_CNhs11945_ctss_rev Cl:HKBMM- meningioma cell line:HKBMM_CNhs11945_10691-109F7_reverse Regulation 
MeningiomaCellLineHKBMM_CNhs11945_ctss_fwd Cl:HKBMM+ meningioma cell line:HKBMM_CNhs11945_10691-109F7_forward Regulation 
KeratoacanthomaCellLineHKA1_CNhs11880_ctss_rev Cl:HKA-1- keratoacanthoma cell line:HKA-1_CNhs11880_10791-110H8_reverse Regulation 
KeratoacanthomaCellLineHKA1_CNhs11880_ctss_fwd Cl:HKA-1+ keratoacanthoma cell line:HKA-1_CNhs11880_10791-110H8_forward Regulation 
TridermalTeratomaCellLineHGRT_CNhs11828_ctss_rev Cl:HGRT- tridermal teratoma cell line:HGRT_CNhs11828_10694-109G1_reverse Regulation 
TridermalTeratomaCellLineHGRT_CNhs11828_ctss_fwd Cl:HGRT+ tridermal teratoma cell line:HGRT_CNhs11828_10694-109G1_forward Regulation 
WilmsTumorCellLineHFWT_CNhs11728_ctss_rev Cl:HFWT- Wilms' tumor cell line:HFWT_CNhs11728_10597-108E3_reverse Regulation 
WilmsTumorCellLineHFWT_CNhs11728_ctss_fwd Cl:HFWT+ Wilms' tumor cell line:HFWT_CNhs11728_10597-108E3_forward Regulation 
NormalEmbryonicPalatalMesenchymalCellLineHEPM_CNhs11894_ctss_rev Cl:HEPM- normal embryonic palatal mesenchymal cell line:HEPM_CNhs11894_10813-111B3_reverse Regulation 
NormalEmbryonicPalatalMesenchymalCellLineHEPM_CNhs11894_ctss_fwd Cl:HEPM+ normal embryonic palatal mesenchymal cell line:HEPM_CNhs11894_10813-111B3_forward Regulation 
HepatocellularCarcinomaCellLineHepG2ENCODEBiolRep3_CNhs12330_ctss_rev Cl:HepG2Br3- hepatocellular carcinoma cell line: HepG2 ENCODE, biol_rep3_CNhs12330_10820-111C1_reverse Regulation 
HepatocellularCarcinomaCellLineHepG2ENCODEBiolRep3_CNhs12330_ctss_fwd Cl:HepG2Br3+ hepatocellular carcinoma cell line: HepG2 ENCODE, biol_rep3_CNhs12330_10820-111C1_forward Regulation 
HepatocellularCarcinomaCellLineHepG2ENCODEBiolRep2_CNhs12329_ctss_rev Cl:HepG2Br2- hepatocellular carcinoma cell line: HepG2 ENCODE, biol_rep2_CNhs12329_10819-111B9_reverse Regulation 
HepatocellularCarcinomaCellLineHepG2ENCODEBiolRep2_CNhs12329_ctss_fwd Cl:HepG2Br2+ hepatocellular carcinoma cell line: HepG2 ENCODE, biol_rep2_CNhs12329_10819-111B9_forward Regulation 
HepatocellularCarcinomaCellLineHepG2ENCODEBiolRep1_CNhs12328_ctss_rev Cl:HepG2Br1- hepatocellular carcinoma cell line: HepG2 ENCODE, biol_rep1_CNhs12328_10818-111B8_reverse Regulation 
HepatocellularCarcinomaCellLineHepG2ENCODEBiolRep1_CNhs12328_ctss_fwd Cl:HepG2Br1+ hepatocellular carcinoma cell line: HepG2 ENCODE, biol_rep1_CNhs12328_10818-111B8_forward Regulation 
EpitheloidCarcinomaCellLineHelaS3ENCODEBiolRep3_CNhs12327_ctss_rev Cl:HelaS3Br3- epitheloid carcinoma cell line: HelaS3 ENCODE, biol_rep3_CNhs12327_10817-111B7_reverse Regulation 
EpitheloidCarcinomaCellLineHelaS3ENCODEBiolRep3_CNhs12327_ctss_fwd Cl:HelaS3Br3+ epitheloid carcinoma cell line: HelaS3 ENCODE, biol_rep3_CNhs12327_10817-111B7_forward Regulation 
EpitheloidCarcinomaCellLineHelaS3ENCODEBiolRep2_CNhs12326_ctss_rev Cl:HelaS3Br2- epitheloid carcinoma cell line: HelaS3 ENCODE, biol_rep2_CNhs12326_10816-111B6_reverse Regulation 
EpitheloidCarcinomaCellLineHelaS3ENCODEBiolRep2_CNhs12326_ctss_fwd Cl:HelaS3Br2+ epitheloid carcinoma cell line: HelaS3 ENCODE, biol_rep2_CNhs12326_10816-111B6_forward Regulation 
EpitheloidCarcinomaCellLineHelaS3ENCODEBiolRep1_CNhs12325_ctss_rev Cl:HelaS3Br1- epitheloid carcinoma cell line: HelaS3 ENCODE, biol_rep1_CNhs12325_10815-111B5_reverse Regulation 
EpitheloidCarcinomaCellLineHelaS3ENCODEBiolRep1_CNhs12325_ctss_fwd Cl:HelaS3Br1+ epitheloid carcinoma cell line: HelaS3 ENCODE, biol_rep1_CNhs12325_10815-111B5_forward Regulation 
EmbryonicKidneyCellLineHEK293SLAMUntreated_CNhs11046_ctss_rev Cl:HEK293/SLAMuntreated- embryonic kidney cell line: HEK293/SLAM untreated_CNhs11046_10450-106F9_reverse Regulation 
EmbryonicKidneyCellLineHEK293SLAMUntreated_CNhs11046_ctss_fwd Cl:HEK293/SLAMuntreated+ embryonic kidney cell line: HEK293/SLAM untreated_CNhs11046_10450-106F9_forward Regulation 
EmbryonicKidneyCellLineHEK293SLAMInfection24hr_CNhs11047_ctss_rev Cl:HEK293/SLAMinfection,24hr- embryonic kidney cell line: HEK293/SLAM infection, 24hr_CNhs11047_10451-106G1_reverse Regulation 
EmbryonicKidneyCellLineHEK293SLAMInfection24hr_CNhs11047_ctss_fwd Cl:HEK293/SLAMinfection,24hr+ embryonic kidney cell line: HEK293/SLAM infection, 24hr_CNhs11047_10451-106G1_forward Regulation 
HodgkinsLymphomaCellLineHDMar2_CNhs11715_ctss_rev Cl:HD-Mar2- Hodgkin's lymphoma cell line:HD-Mar2_CNhs11715_10435-106E3_reverse Regulation 
HodgkinsLymphomaCellLineHDMar2_CNhs11715_ctss_fwd Cl:HD-Mar2+ Hodgkin's lymphoma cell line:HD-Mar2_CNhs11715_10435-106E3_forward Regulation 
SmallCellCervicalCancerCellLineHCSC1_CNhs11885_ctss_rev Cl:HCSC-1- small cell cervical cancer cell line:HCSC-1_CNhs11885_10800-110I8_reverse Regulation 
SmallCellCervicalCancerCellLineHCSC1_CNhs11885_ctss_fwd Cl:HCSC-1+ small cell cervical cancer cell line:HCSC-1_CNhs11885_10800-110I8_forward Regulation 
AcantholyticSquamousCarcinomaCellLineHCC1806_CNhs11844_ctss_rev Cl:HCC1806- acantholytic squamous carcinoma cell line:HCC1806_CNhs11844_10717-109I6_reverse Regulation 
AcantholyticSquamousCarcinomaCellLineHCC1806_CNhs11844_ctss_fwd Cl:HCC1806+ acantholytic squamous carcinoma cell line:HCC1806_CNhs11844_10717-109I6_forward Regulation 
ExtraskeletalMyxoidChondrosarcomaCellLineHEMCSS_CNhs10728_ctss_rev Cl:H-EMC-SS- extraskeletal myxoid chondrosarcoma cell line:H-EMC-SS_CNhs10728_10410-106B5_reverse Regulation 
ExtraskeletalMyxoidChondrosarcomaCellLineHEMCSS_CNhs10728_ctss_fwd Cl:H-EMC-SS+ extraskeletal myxoid chondrosarcoma cell line:H-EMC-SS_CNhs10728_10410-106B5_forward Regulation 
GastricCancerCellLineGSS_CNhs14241_ctss_rev Cl:GSS- gastric cancer cell line:GSS_CNhs14241_10560-108A2_reverse Regulation 
GastricCancerCellLineGSS_CNhs14241_ctss_fwd Cl:GSS+ gastric cancer cell line:GSS_CNhs14241_10560-108A2_forward Regulation 
BLymphoblastoidCellLineGM12878ENCODEBiolRep3_CNhs12333_ctss_rev Cl:GM12878Br3- B lymphoblastoid cell line: GM12878 ENCODE, biol_rep3_CNhs12333_10823-111C4_reverse Regulation 
BLymphoblastoidCellLineGM12878ENCODEBiolRep3_CNhs12333_ctss_fwd Cl:GM12878Br3+ B lymphoblastoid cell line: GM12878 ENCODE, biol_rep3_CNhs12333_10823-111C4_forward Regulation 
BLymphoblastoidCellLineGM12878ENCODEBiolRep2_CNhs12332_ctss_rev Cl:GM12878Br2- B lymphoblastoid cell line: GM12878 ENCODE, biol_rep2_CNhs12332_10822-111C3_reverse Regulation 
BLymphoblastoidCellLineGM12878ENCODEBiolRep2_CNhs12332_ctss_fwd Cl:GM12878Br2+ B lymphoblastoid cell line: GM12878 ENCODE, biol_rep2_CNhs12332_10822-111C3_forward Regulation 
BLymphoblastoidCellLineGM12878ENCODEBiolRep1_CNhs12331_ctss_rev Cl:GM12878Br1- B lymphoblastoid cell line: GM12878 ENCODE, biol_rep1_CNhs12331_10821-111C2_reverse Regulation 
BLymphoblastoidCellLineGM12878ENCODEBiolRep1_CNhs12331_ctss_fwd Cl:GM12878Br1+ B lymphoblastoid cell line: GM12878 ENCODE, biol_rep1_CNhs12331_10821-111C2_forward Regulation 
GliomaCellLineGI1_CNhs10731_ctss_rev Cl:GI-1- glioma cell line:GI-1_CNhs10731_10413-106B8_reverse Regulation 
GliomaCellLineGI1_CNhs10731_ctss_fwd Cl:GI-1+ glioma cell line:GI-1_CNhs10731_10413-106B8_forward Regulation 
FibrousHistiocytomaCellLineGCTTIB223_CNhs11842_ctss_rev Cl:GCTTIB-223- fibrous histiocytoma cell line:GCT TIB-223_CNhs11842_10711-109H9_reverse Regulation 
FibrousHistiocytomaCellLineGCTTIB223_CNhs11842_ctss_fwd Cl:GCTTIB-223+ fibrous histiocytoma cell line:GCT TIB-223_CNhs11842_10711-109H9_forward Regulation 
LeiomyoblastomaCellLineG402_CNhs11848_ctss_rev Cl:G-402- leiomyoblastoma cell line:G-402_CNhs11848_10721-110A1_reverse Regulation 
LeiomyoblastomaCellLineG402_CNhs11848_ctss_fwd Cl:G-402+ leiomyoblastoma cell line:G-402_CNhs11848_10721-110A1_forward Regulation 
WilmsTumorCellLineG401_CNhs11892_ctss_rev Cl:G-401- Wilms' tumor cell line:G-401_CNhs11892_10809-111A8_reverse Regulation 
WilmsTumorCellLineG401_CNhs11892_ctss_fwd Cl:G-401+ Wilms' tumor cell line:G-401_CNhs11892_10809-111A8_forward Regulation 
MelanomaCellLineG361_CNhs11254_ctss_rev Cl:G-361- melanoma cell line:G-361_CNhs11254_10465-106H6_reverse Regulation 
MelanomaCellLineG361_CNhs11254_ctss_fwd Cl:G-361+ melanoma cell line:G-361_CNhs11254_10465-106H6_forward Regulation 
NeuroectodermalTumorCellLineFURPNT2_CNhs11753_ctss_rev Cl:FU-RPNT-2- neuroectodermal tumor cell line:FU-RPNT-2_CNhs11753_10663-109C6_reverse Regulation 
NeuroectodermalTumorCellLineFURPNT2_CNhs11753_ctss_fwd Cl:FU-RPNT-2+ neuroectodermal tumor cell line:FU-RPNT-2_CNhs11753_10663-109C6_forward Regulation 
NeuroectodermalTumorCellLineFURPNT1_CNhs11744_ctss_rev Cl:FU-RPNT-1- neuroectodermal tumor cell line:FU-RPNT-1_CNhs11744_10637-108I7_reverse Regulation 
NeuroectodermalTumorCellLineFURPNT1_CNhs11744_ctss_fwd Cl:FU-RPNT-1+ neuroectodermal tumor cell line:FU-RPNT-1_CNhs11744_10637-108I7_forward Regulation 
AcuteMyeloidLeukemiaFABM4CellLineFKH1_CNhs13503_ctss_rev Cl:FKH-1- acute myeloid leukemia (FAB M4) cell line:FKH-1_CNhs13503_10830-111D2_reverse Regulation 
AcuteMyeloidLeukemiaFABM4CellLineFKH1_CNhs13503_ctss_fwd Cl:FKH-1+ acute myeloid leukemia (FAB M4) cell line:FKH-1_CNhs13503_10830-111D2_forward Regulation 
NormalIntestinalEpithelialCellLineFHs74Int_CNhs11950_ctss_rev Cl:FHs74Int- normal intestinal epithelial cell line:FHs 74 Int_CNhs11950_10812-111B2_reverse Regulation 
NormalIntestinalEpithelialCellLineFHs74Int_CNhs11950_ctss_fwd Cl:FHs74Int+ normal intestinal epithelial cell line:FHs 74 Int_CNhs11950_10812-111B2_forward Regulation 
AcuteMyeloidLeukemiaFABM6CellLineF36P_CNhs13505_ctss_rev Cl:F-36P- acute myeloid leukemia (FAB M6) cell line:F-36P_CNhs13505_10837-111D9_reverse Regulation 
AcuteMyeloidLeukemiaFABM6CellLineF36P_CNhs13505_ctss_fwd Cl:F-36P+ acute myeloid leukemia (FAB M6) cell line:F-36P_CNhs13505_10837-111D9_forward Regulation 
AcuteMyeloidLeukemiaFABM6CellLineF36E_CNhs13060_ctss_rev Cl:F-36E- acute myeloid leukemia (FAB M6) cell line:F-36E_CNhs13060_10836-111D8_reverse Regulation 
AcuteMyeloidLeukemiaFABM6CellLineF36E_CNhs13060_ctss_fwd Cl:F-36E+ acute myeloid leukemia (FAB M6) cell line:F-36E_CNhs13060_10836-111D8_forward Regulation 
AcuteMyeloidLeukemiaFABM4eoCellLineEoL3_CNhs13057_ctss_rev Cl:EoL-3- acute myeloid leukemia (FAB M4eo) cell line:EoL-3_CNhs13057_10833-111D5_reverse Regulation 
AcuteMyeloidLeukemiaFABM4eoCellLineEoL3_CNhs13057_ctss_fwd Cl:EoL-3+ acute myeloid leukemia (FAB M4eo) cell line:EoL-3_CNhs13057_10833-111D5_forward Regulation 
AcuteMyeloidLeukemiaFABM4eoCellLineEoL1_CNhs13056_ctss_rev Cl:EoL-1- acute myeloid leukemia (FAB M4eo) cell line:EoL-1_CNhs13056_10832-111D4_reverse Regulation 
AcuteMyeloidLeukemiaFABM4eoCellLineEoL1_CNhs13056_ctss_fwd Cl:EoL-1+ acute myeloid leukemia (FAB M4eo) cell line:EoL-1_CNhs13056_10832-111D4_forward Regulation 
AcuteMyeloidLeukemiaFABM6CellLineEEB_CNhs13059_ctss_rev Cl:EEB- acute myeloid leukemia (FAB M6) cell line:EEB_CNhs13059_10835-111D7_reverse Regulation 
AcuteMyeloidLeukemiaFABM6CellLineEEB_CNhs13059_ctss_fwd Cl:EEB+ acute myeloid leukemia (FAB M6) cell line:EEB_CNhs13059_10835-111D7_forward Regulation 
SmallcellGastrointestinalCarcinomaCellLineECC4_CNhs11734_ctss_rev Cl:ECC4- small-cell gastrointestinal carcinoma cell line:ECC4_CNhs11734_10609-108F6_reverse Regulation 
SmallcellGastrointestinalCarcinomaCellLineECC4_CNhs11734_ctss_fwd Cl:ECC4+ small-cell gastrointestinal carcinoma cell line:ECC4_CNhs11734_10609-108F6_forward Regulation 
GastrointestinalCarcinomaCellLineECC12_CNhs11738_ctss_rev Cl:ECC12- gastrointestinal carcinoma cell line:ECC12_CNhs11738_10615-108G3_reverse Regulation 
GastrointestinalCarcinomaCellLineECC12_CNhs11738_ctss_fwd Cl:ECC12+ gastrointestinal carcinoma cell line:ECC12_CNhs11738_10615-108G3_forward Regulation 
SmallCellGastrointestinalCarcinomaCellLineECC10_CNhs11736_ctss_rev Cl:ECC10- small cell gastrointestinal carcinoma cell line:ECC10_CNhs11736_10610-108F7_reverse Regulation 
SmallCellGastrointestinalCarcinomaCellLineECC10_CNhs11736_ctss_fwd Cl:ECC10+ small cell gastrointestinal carcinoma cell line:ECC10_CNhs11736_10610-108F7_forward Regulation 
SquamousCellCarcinomaCellLineECGI10_CNhs11252_ctss_rev Cl:EC-GI-10- squamous cell carcinoma cell line:EC-GI-10_CNhs11252_10463-106H4_reverse Regulation 
SquamousCellCarcinomaCellLineECGI10_CNhs11252_ctss_fwd Cl:EC-GI-10+ squamous cell carcinoma cell line:EC-GI-10_CNhs11252_10463-106H4_forward Regulation 
SquamousCellLungCarcinomaCellLineEBC1_CNhs11273_ctss_rev Cl:EBC-1- squamous cell lung carcinoma cell line:EBC-1_CNhs11273_10486-107A9_reverse Regulation 
SquamousCellLungCarcinomaCellLineEBC1_CNhs11273_ctss_fwd Cl:EBC-1+ squamous cell lung carcinoma cell line:EBC-1_CNhs11273_10486-107A9_forward Regulation 
ProstateCancerCellLineDU145_CNhs11260_ctss_rev Cl:DU145- prostate cancer cell line:DU145_CNhs11260_10490-107B4_reverse Regulation 
ProstateCancerCellLineDU145_CNhs11260_ctss_fwd Cl:DU145+ prostate cancer cell line:DU145_CNhs11260_10490-107B4_forward Regulation 
LymphangiectasiaCellLineDS1_CNhs11852_ctss_rev Cl:DS-1- lymphangiectasia cell line:DS-1_CNhs11852_10727-110A7_reverse Regulation 
LymphangiectasiaCellLineDS1_CNhs11852_ctss_fwd Cl:DS-1+ lymphangiectasia cell line:DS-1_CNhs11852_10727-110A7_forward Regulation 
SmallCellLungCarcinomaCellLineDMS144_CNhs12808_ctss_rev Cl:DMS144- small cell lung carcinoma cell line:DMS 144_CNhs12808_10841-111E4_reverse Regulation 
SmallCellLungCarcinomaCellLineDMS144_CNhs12808_ctss_fwd Cl:DMS144+ small cell lung carcinoma cell line:DMS 144_CNhs12808_10841-111E4_forward Regulation 
MalignantTrichilemmalCystCellLineDJM1_CNhs10730_ctss_rev Cl:DJM-1- malignant trichilemmal cyst cell line:DJM-1_CNhs10730_10412-106B7_reverse Regulation 
MalignantTrichilemmalCystCellLineDJM1_CNhs10730_ctss_fwd Cl:DJM-1+ malignant trichilemmal cyst cell line:DJM-1_CNhs10730_10412-106B7_forward Regulation 
PharyngealCarcinomaCellLineDetroit562_CNhs11849_ctss_rev Cl:Detroit562- pharyngeal carcinoma cell line:Detroit 562_CNhs11849_10723-110A3_reverse Regulation 
PharyngealCarcinomaCellLineDetroit562_CNhs11849_ctss_fwd Cl:Detroit562+ pharyngeal carcinoma cell line:Detroit 562_CNhs11849_10723-110A3_forward Regulation 
BurkittsLymphomaCellLineDAUDI_CNhs10739_ctss_rev Cl:DAUDI- Burkitt's lymphoma cell line:DAUDI_CNhs10739_10422-106C8_reverse Regulation 
BurkittsLymphomaCellLineDAUDI_CNhs10739_ctss_fwd Cl:DAUDI+ Burkitt's lymphoma cell line:DAUDI_CNhs10739_10422-106C8_forward Regulation 
CervicalCancerCellLineD98AH2_CNhs11288_ctss_rev Cl:D98-AH2- cervical cancer cell line:D98-AH2_CNhs11288_10552-107I3_reverse Regulation 
CervicalCancerCellLineD98AH2_CNhs11288_ctss_fwd Cl:D98-AH2+ cervical cancer cell line:D98-AH2_CNhs11288_10552-107I3_forward Regulation 
MedulloblastomaCellLineD283Med_CNhs12805_ctss_rev Cl:D283Med- medulloblastoma cell line:D283 Med_CNhs12805_10838-111E1_reverse Regulation 
MedulloblastomaCellLineD283Med_CNhs12805_ctss_fwd Cl:D283Med+ medulloblastoma cell line:D283 Med_CNhs12805_10838-111E1_forward Regulation 
DiffuseLargeBcellLymphomaCellLineCTB1_CNhs11741_ctss_rev Cl:CTB-1- diffuse large B-cell lymphoma cell line:CTB-1_CNhs11741_10631-108I1_reverse Regulation 
DiffuseLargeBcellLymphomaCellLineCTB1_CNhs11741_ctss_fwd Cl:CTB-1+ diffuse large B-cell lymphoma cell line:CTB-1_CNhs11741_10631-108I1_forward Regulation 
MelanomaCellLineCOLO679_CNhs11281_ctss_rev Cl:COLO679- melanoma cell line:COLO 679_CNhs11281_10514-107E1_reverse Regulation 
MelanomaCellLineCOLO679_CNhs11281_ctss_fwd Cl:COLO679+ melanoma cell line:COLO 679_CNhs11281_10514-107E1_forward Regulation 
ColonCarcinomaCellLineCOLO320_CNhs10737_ctss_rev Cl:COLO-320- colon carcinoma cell line:COLO-320_CNhs10737_10420-106C6_reverse Regulation 
ColonCarcinomaCellLineCOLO320_CNhs10737_ctss_fwd Cl:COLO-320+ colon carcinoma cell line:COLO-320_CNhs10737_10420-106C6_forward Regulation 
CordBloodDerivedCellLineCOBLaUntreated_CNhs11045_ctss_rev Cl:COBL-auntreated- cord blood derived cell line:COBL-a untreated_CNhs11045_10449-106F8_reverse Regulation 
CordBloodDerivedCellLineCOBLaUntreated_CNhs11045_ctss_fwd Cl:COBL-auntreated+ cord blood derived cell line:COBL-a untreated_CNhs11045_10449-106F8_forward Regulation 
CordBloodDerivedCellLineCOBLa24hInfection_CNhs11050_ctss_rev Cl:COBL-a24hinfection- cord blood derived cell line:COBL-a 24h infection_CNhs11050_10453-106G3_reverse Regulation 
CordBloodDerivedCellLineCOBLa24hInfection_CNhs11050_ctss_fwd Cl:COBL-a24hinfection+ cord blood derived cell line:COBL-a 24h infection_CNhs11050_10453-106G3_forward Regulation 
CordBloodDerivedCellLineCOBLa24hInfectionC_CNhs11049_ctss_rev Cl:COBL-a24hinfection(-C)- cord blood derived cell line:COBL-a 24h infection(-C)_CNhs11049_10452-106G2_reverse Regulation 
CordBloodDerivedCellLineCOBLa24hInfectionC_CNhs11049_ctss_fwd Cl:COBL-a24hinfection(-C)+ cord blood derived cell line:COBL-a 24h infection(-C)_CNhs11049_10452-106G2_forward Regulation 
NeuroblastomaCellLineCHP134_CNhs11276_ctss_rev Cl:CHP-134- neuroblastoma cell line:CHP-134_CNhs11276_10508-107D4_reverse Regulation 
NeuroblastomaCellLineCHP134_CNhs11276_ctss_fwd Cl:CHP-134+ neuroblastoma cell line:CHP-134_CNhs11276_10508-107D4_forward Regulation 
BronchogenicCarcinomaCellLineChaGoK1_CNhs11841_ctss_rev Cl:ChaGo-K-1- bronchogenic carcinoma cell line:ChaGo-K-1_CNhs11841_10710-109H8_reverse Regulation 
BronchogenicCarcinomaCellLineChaGoK1_CNhs11841_ctss_fwd Cl:ChaGo-K-1+ bronchogenic carcinoma cell line:ChaGo-K-1_CNhs11841_10710-109H8_forward Regulation 
EpidermoidCarcinomaCellLineCaSki_CNhs10748_ctss_rev Cl:CaSki- epidermoid carcinoma cell line:Ca Ski_CNhs10748_10431-106D8_reverse Regulation 
EpidermoidCarcinomaCellLineCaSki_CNhs10748_ctss_fwd Cl:CaSki+ epidermoid carcinoma cell line:Ca Ski_CNhs10748_10431-106D8_forward Regulation 
ColonCarcinomaCellLineCACO2_CNhs11280_ctss_rev Cl:CACO-2- colon carcinoma cell line:CACO-2_CNhs11280_10513-107D9_reverse Regulation 
ColonCarcinomaCellLineCACO2_CNhs11280_ctss_fwd Cl:CACO-2+ colon carcinoma cell line:CACO-2_CNhs11280_10513-107D9_forward Regulation 
OralSquamousCellCarcinomaCellLineCa922_CNhs10752_ctss_rev Cl:Ca9-22- oral squamous cell carcinoma cell line:Ca9-22_CNhs10752_10434-106E2_reverse Regulation 
OralSquamousCellCarcinomaCellLineCa922_CNhs10752_ctss_fwd Cl:Ca9-22+ oral squamous cell carcinoma cell line:Ca9-22_CNhs10752_10434-106E2_forward Regulation 
ChoriocarcinomaCellLineBeWo_CNhs10740_ctss_rev Cl:BeWo- choriocarcinoma cell line:BeWo_CNhs10740_10423-106C9_reverse Regulation 
ChoriocarcinomaCellLineBeWo_CNhs10740_ctss_fwd Cl:BeWo+ choriocarcinoma cell line:BeWo_CNhs10740_10423-106C9_forward Regulation 
AcuteLymphoblasticLeukemiaBALLCellLineBALL1_CNhs11251_ctss_rev Cl:BALL-1- acute lymphoblastic leukemia (B-ALL) cell line:BALL-1_CNhs11251_10455-106G5_reverse Regulation 
AcuteLymphoblasticLeukemiaBALLCellLineBALL1_CNhs11251_ctss_fwd Cl:BALL-1+ acute lymphoblastic leukemia (B-ALL) cell line:BALL-1_CNhs11251_10455-106G5_forward Regulation 
GastricCancerCellLineAZ521_CNhs11286_ctss_rev Cl:AZ521- gastric cancer cell line:AZ521_CNhs11286_10549-107H9_reverse Regulation 
GastricCancerCellLineAZ521_CNhs11286_ctss_fwd Cl:AZ521+ gastric cancer cell line:AZ521_CNhs11286_10549-107H9_forward Regulation 
AdultTcellLeukemiaCellLineATN1_CNhs10738_ctss_rev Cl:ATN-1- adult T-cell leukemia cell line:ATN-1_CNhs10738_10421-106C7_reverse Regulation 
AdultTcellLeukemiaCellLineATN1_CNhs10738_ctss_fwd Cl:ATN-1+ adult T-cell leukemia cell line:ATN-1_CNhs10738_10421-106C7_forward Regulation 
PlasmaCellLeukemiaCellLineARH77_CNhs12807_ctss_rev Cl:ARH-77- plasma cell leukemia cell line:ARH-77_CNhs12807_10840-111E3_reverse Regulation 
PlasmaCellLeukemiaCellLineARH77_CNhs12807_ctss_fwd Cl:ARH-77+ plasma cell leukemia cell line:ARH-77_CNhs12807_10840-111E3_forward Regulation 
MesotheliomaCellLineACCMESO4_CNhs11264_ctss_rev Cl:ACC-MESO-4- mesothelioma cell line:ACC-MESO-4_CNhs11264_10494-107B8_reverse Regulation 
MesotheliomaCellLineACCMESO4_CNhs11264_ctss_fwd Cl:ACC-MESO-4+ mesothelioma cell line:ACC-MESO-4_CNhs11264_10494-107B8_forward Regulation 
MesotheliomaCellLineACCMESO1_CNhs11263_ctss_rev Cl:ACC-MESO-1- mesothelioma cell line:ACC-MESO-1_CNhs11263_10493-107B7_reverse Regulation 
MesotheliomaCellLineACCMESO1_CNhs11263_ctss_fwd Cl:ACC-MESO-1+ mesothelioma cell line:ACC-MESO-1_CNhs11263_10493-107B7_forward Regulation 
LungAdenocarcinomaCellLineA549_CNhs11275_ctss_rev Cl:A549- lung adenocarcinoma cell line:A549_CNhs11275_10499-107C4_reverse Regulation 
LungAdenocarcinomaCellLineA549_CNhs11275_ctss_fwd Cl:A549+ lung adenocarcinoma cell line:A549_CNhs11275_10499-107C4_forward Regulation 
EpidermoidCarcinomaCellLineA431_CNhs10743_ctss_rev Cl:A431- epidermoid carcinoma cell line:A431_CNhs10743_10426-106D3_reverse Regulation 
EpidermoidCarcinomaCellLineA431_CNhs10743_ctss_fwd Cl:A431+ epidermoid carcinoma cell line:A431_CNhs10743_10426-106D3_forward Regulation 
GlioblastomaCellLineA172TechRep2_CNhs11248_ctss_rev Cl:A172Tr2- glioblastoma cell line:A172, tech_rep2_CNhs11248_10444-106F3_reverse Regulation 
GlioblastomaCellLineA172TechRep2_CNhs11248_ctss_fwd Cl:A172Tr2+ glioblastoma cell line:A172, tech_rep2_CNhs11248_10444-106F3_forward Regulation 
PapillaryAdenocarcinomaCellLine8505C_CNhs11716_ctss_rev Cl:8505C- papillary adenocarcinoma cell line:8505C_CNhs11716_10437-106E5_reverse Regulation 
PapillaryAdenocarcinomaCellLine8505C_CNhs11716_ctss_fwd Cl:8505C+ papillary adenocarcinoma cell line:8505C_CNhs11716_10437-106E5_forward Regulation 
AnaplasticCarcinomaCellLine8305C_CNhs10745_ctss_rev Cl:8305C- anaplastic carcinoma cell line:8305C_CNhs10745_10428-106D5_reverse Regulation 
AnaplasticCarcinomaCellLine8305C_CNhs10745_ctss_fwd Cl:8305C+ anaplastic carcinoma cell line:8305C_CNhs10745_10428-106D5_forward Regulation 
TransitionalcellCarcinomaCellLine5637_CNhs10735_ctss_rev Cl:5637- transitional-cell carcinoma cell line:5637_CNhs10735_10418-106C4_reverse Regulation 
TransitionalcellCarcinomaCellLine5637_CNhs10735_ctss_fwd Cl:5637+ transitional-cell carcinoma cell line:5637_CNhs10735_10418-106C4_forward Regulation 
EmbryonicPancreasCellLine2C6_CNhs11814_ctss_rev Cl:2C6- embryonic pancreas cell line:2C6_CNhs11814_10603-108E9_reverse Regulation 
EmbryonicPancreasCellLine2C6_CNhs11814_ctss_fwd Cl:2C6+ embryonic pancreas cell line:2C6_CNhs11814_10603-108E9_forward Regulation 
EmbryonicPancreasCellLine1C3IKEI_CNhs11733_ctss_rev Cl:1C3IKEI- embryonic pancreas cell line:1C3IKEI_CNhs11733_10606-108F3_reverse Regulation 
EmbryonicPancreasCellLine1C3IKEI_CNhs11733_ctss_fwd Cl:1C3IKEI+ embryonic pancreas cell line:1C3IKEI_CNhs11733_10606-108F3_forward Regulation 
EmbryonicPancreasCellLine1C3D3_CNhs11732_ctss_rev Cl:1C3D3- embryonic pancreas cell line:1C3D3_CNhs11732_10605-108F2_reverse Regulation 
EmbryonicPancreasCellLine1C3D3_CNhs11732_ctss_fwd Cl:1C3D3+ embryonic pancreas cell line:1C3D3_CNhs11732_10605-108F2_forward Regulation 
EmbryonicPancreasCellLine1B2C6_CNhs11731_ctss_rev Cl:1B2C6- embryonic pancreas cell line:1B2C6_CNhs11731_10604-108F1_reverse Regulation 
EmbryonicPancreasCellLine1B2C6_CNhs11731_ctss_fwd Cl:1B2C6+ embryonic pancreas cell line:1B2C6_CNhs11731_10604-108F1_forward Regulation 
LeiomyomaCellLine15425_CNhs11724_ctss_rev Cl:15425- leiomyoma cell line:15425_CNhs11724_10571-108B4_reverse Regulation 
LeiomyomaCellLine15425_CNhs11724_ctss_fwd Cl:15425+ leiomyoma cell line:15425_CNhs11724_10571-108B4_forward Regulation 
LeiomyomaCellLine15242A_CNhs11723_ctss_rev Cl:15242A- leiomyoma cell line:15242A_CNhs11723_10570-108B3_reverse Regulation 
LeiomyomaCellLine15242A_CNhs11723_ctss_fwd Cl:15242A+ leiomyoma cell line:15242A_CNhs11723_10570-108B3_forward Regulation 
OsteosarcomaCellLine143BTKneoR_CNhs11279_ctss_rev Cl:143B/TK^(-)neo^(R)- osteosarcoma cell line:143B/TK^(-)neo^(R)_CNhs11279_10510-107D6_reverse Regulation 
OsteosarcomaCellLine143BTKneoR_CNhs11279_ctss_fwd Cl:143B/TK^(-)neo^(R)+ osteosarcoma cell line:143B/TK^(-)neo^(R)_CNhs11279_10510-107D6_forward Regulation 
LeiomyomaCellLine10964C_CNhs11722_ctss_rev Cl:10964C- leiomyoma cell line:10964C_CNhs11722_10569-108B2_reverse Regulation 
LeiomyomaCellLine10964C_CNhs11722_ctss_fwd Cl:10964C+ leiomyoma cell line:10964C_CNhs11722_10569-108B2_forward Regulation 
NonsmallCellLungCancerCellLineNCIH1385_CNhs12193_ctss_rev Cl:NCI-H1385- non-small cell lung cancer cell line:NCI-H1385_CNhs12193_10730-110B1_reverse Regulation 
NonsmallCellLungCancerCellLineNCIH1385_CNhs12193_ctss_fwd Cl:NCI-H1385+ non-small cell lung cancer cell line:NCI-H1385_CNhs12193_10730-110B1_forward Regulation 
MesotheliomaCellLineMero14TechRep2_CNhs14376_ctss_rev Cl:Mero-14Tr2- mesothelioma cell line:Mero-14, tech_rep2_CNhs14376_10849-111F3_reverse Regulation 
MesotheliomaCellLineMero14TechRep2_CNhs14376_ctss_fwd Cl:Mero-14Tr2+ mesothelioma cell line:Mero-14, tech_rep2_CNhs14376_10849-111F3_forward Regulation 
AcuteMyeloidLeukemiaFABM0CellLineKasumi3_CNhs13241_ctss_rev Cl:Kasumi-3- acute myeloid leukemia (FAB M0) cell line:Kasumi-3_CNhs13241_10789-110H6_reverse Regulation 
AcuteMyeloidLeukemiaFABM0CellLineKasumi3_CNhs13241_ctss_fwd Cl:Kasumi-3+ acute myeloid leukemia (FAB M0) cell line:Kasumi-3_CNhs13241_10789-110H6_forward Regulation 
LeiomyosarcomaCellLineHs5_T_CNhs12192_ctss_rev Cl:Hs5_T- leiomyosarcoma cell line:Hs 5_T_CNhs12192_10722-110A2_reverse Regulation 
LeiomyosarcomaCellLineHs5_T_CNhs12192_ctss_fwd Cl:Hs5_T+ leiomyosarcoma cell line:Hs 5_T_CNhs12192_10722-110A2_forward Regulation 
MesodermalTumorCellLineHIRSBM_CNhs12191_ctss_rev Cl:HIRS-BM- mesodermal tumor cell line:HIRS-BM_CNhs12191_10696-109G3_reverse Regulation 
MesodermalTumorCellLineHIRSBM_CNhs12191_ctss_fwd Cl:HIRS-BM+ mesodermal tumor cell line:HIRS-BM_CNhs12191_10696-109G3_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay28BiolRep3A3T17_CNhs12892_ctss_rev Saos-2W/AscorbicAcidBgp_Day28Br3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day28, biol_rep3 (A3 T17)_CNhs12892_12875-137F4_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay28BiolRep3A3T17_CNhs12892_ctss_fwd Saos-2W/AscorbicAcidBgp_Day28Br3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day28, biol_rep3 (A3 T17)_CNhs12892_12875-137F4_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay28BiolRep2A2T17_CNhs12876_ctss_rev Saos-2W/AscorbicAcidBgp_Day28Br2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day28, biol_rep2 (A2 T17)_CNhs12876_12777-136D5_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay28BiolRep2A2T17_CNhs12876_ctss_fwd Saos-2W/AscorbicAcidBgp_Day28Br2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day28, biol_rep2 (A2 T17)_CNhs12876_12777-136D5_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay28BiolRep1A1T17_CNhs11919_ctss_rev Saos-2W/AscorbicAcidBgp_Day28Br1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day28, biol_rep1 (A1 T17)_CNhs11919_12679-135B6_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay28BiolRep1A1T17_CNhs11919_ctss_fwd Saos-2W/AscorbicAcidBgp_Day28Br1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day28, biol_rep1 (A1 T17)_CNhs11919_12679-135B6_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay21BiolRep3A3T16_CNhs12891_ctss_rev Saos-2W/AscorbicAcidBgp_Day21Br3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day21, biol_rep3 (A3 T16)_CNhs12891_12874-137F3_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay21BiolRep3A3T16_CNhs12891_ctss_fwd Saos-2W/AscorbicAcidBgp_Day21Br3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day21, biol_rep3 (A3 T16)_CNhs12891_12874-137F3_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay21BiolRep2A2T16_CNhs12875_ctss_rev Saos-2W/AscorbicAcidBgp_Day21Br2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day21, biol_rep2 (A2 T16)_CNhs12875_12776-136D4_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay21BiolRep2A2T16_CNhs12875_ctss_fwd Saos-2W/AscorbicAcidBgp_Day21Br2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day21, biol_rep2 (A2 T16)_CNhs12875_12776-136D4_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay21BiolRep1A1T16_CNhs12397_ctss_rev Saos-2W/AscorbicAcidBgp_Day21Br1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day21, biol_rep1 (A1 T16)_CNhs12397_12678-135B5_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay21BiolRep1A1T16_CNhs12397_ctss_fwd Saos-2W/AscorbicAcidBgp_Day21Br1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day21, biol_rep1 (A1 T16)_CNhs12397_12678-135B5_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay14BiolRep3A3T15_CNhs12890_ctss_rev Saos-2W/AscorbicAcidBgp_Day14Br3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day14, biol_rep3 (A3 T15)_CNhs12890_12873-137F2_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay14BiolRep3A3T15_CNhs12890_ctss_fwd Saos-2W/AscorbicAcidBgp_Day14Br3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day14, biol_rep3 (A3 T15)_CNhs12890_12873-137F2_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay14BiolRep2A2T15_CNhs12953_ctss_rev Saos-2W/AscorbicAcidBgp_Day14Br2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day14, biol_rep2 (A2 T15)_CNhs12953_12775-136D3_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay14BiolRep2A2T15_CNhs12953_ctss_fwd Saos-2W/AscorbicAcidBgp_Day14Br2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day14, biol_rep2 (A2 T15)_CNhs12953_12775-136D3_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay14BiolRep1A1T15_CNhs12396_ctss_rev Saos-2W/AscorbicAcidBgp_Day14Br1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day14, biol_rep1 (A1 T15)_CNhs12396_12677-135B4_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay14BiolRep1A1T15_CNhs12396_ctss_fwd Saos-2W/AscorbicAcidBgp_Day14Br1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day14, biol_rep1 (A1 T15)_CNhs12396_12677-135B4_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay07BiolRep3A3T14_CNhs12888_ctss_rev Saos-2W/AscorbicAcidBgp_Day07Br3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day07, biol_rep3 (A3 T14)_CNhs12888_12872-137F1_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay07BiolRep3A3T14_CNhs12888_ctss_fwd Saos-2W/AscorbicAcidBgp_Day07Br3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day07, biol_rep3 (A3 T14)_CNhs12888_12872-137F1_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay07BiolRep2A2T14_CNhs12874_ctss_rev Saos-2W/AscorbicAcidBgp_Day07Br2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day07, biol_rep2 (A2 T14)_CNhs12874_12774-136D2_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay07BiolRep2A2T14_CNhs12874_ctss_fwd Saos-2W/AscorbicAcidBgp_Day07Br2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day07, biol_rep2 (A2 T14)_CNhs12874_12774-136D2_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay07BiolRep1A1T14_CNhs12395_ctss_rev Saos-2W/AscorbicAcidBgp_Day07Br1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day07, biol_rep1 (A1 T14)_CNhs12395_12676-135B3_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay07BiolRep1A1T14_CNhs12395_ctss_fwd Saos-2W/AscorbicAcidBgp_Day07Br1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day07, biol_rep1 (A1 T14)_CNhs12395_12676-135B3_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay04BiolRep3A3T13_CNhs12887_ctss_rev Saos-2W/AscorbicAcidBgp_Day04Br3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day04, biol_rep3 (A3 T13)_CNhs12887_12871-137E9_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay04BiolRep3A3T13_CNhs12887_ctss_fwd Saos-2W/AscorbicAcidBgp_Day04Br3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day04, biol_rep3 (A3 T13)_CNhs12887_12871-137E9_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay04BiolRep2A2T13_CNhs12873_ctss_rev Saos-2W/AscorbicAcidBgp_Day04Br2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day04, biol_rep2 (A2 T13)_CNhs12873_12773-136D1_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay04BiolRep2A2T13_CNhs12873_ctss_fwd Saos-2W/AscorbicAcidBgp_Day04Br2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day04, biol_rep2 (A2 T13)_CNhs12873_12773-136D1_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay04BiolRep1A1T13_CNhs12394_ctss_rev Saos-2W/AscorbicAcidBgp_Day04Br1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day04, biol_rep1 (A1 T13)_CNhs12394_12675-135B2_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay04BiolRep1A1T13_CNhs12394_ctss_fwd Saos-2W/AscorbicAcidBgp_Day04Br1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day04, biol_rep1 (A1 T13)_CNhs12394_12675-135B2_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification24hrBiolRep3A3T12_CNhs12886_ctss_rev Saos-2W/AscorbicAcidBgp_24hrBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 24hr, biol_rep3 (A3 T12)_CNhs12886_12870-137E8_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification24hrBiolRep3A3T12_CNhs12886_ctss_fwd Saos-2W/AscorbicAcidBgp_24hrBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 24hr, biol_rep3 (A3 T12)_CNhs12886_12870-137E8_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification24hrBiolRep2A2T12_CNhs12872_ctss_rev Saos-2W/AscorbicAcidBgp_24hrBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 24hr, biol_rep2 (A2 T12)_CNhs12872_12772-136C9_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification24hrBiolRep2A2T12_CNhs12872_ctss_fwd Saos-2W/AscorbicAcidBgp_24hrBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 24hr, biol_rep2 (A2 T12)_CNhs12872_12772-136C9_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification24hrBiolRep1A1T12_CNhs12393_ctss_rev Saos-2W/AscorbicAcidBgp_24hrBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 24hr, biol_rep1 (A1 T12)_CNhs12393_12674-135B1_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification24hrBiolRep1A1T12_CNhs12393_ctss_fwd Saos-2W/AscorbicAcidBgp_24hrBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 24hr, biol_rep1 (A1 T12)_CNhs12393_12674-135B1_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification08hrBiolRep3A3T11_CNhs12885_ctss_rev Saos-2W/AscorbicAcidBgp_08hrBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 08hr, biol_rep3 (A3 T11)_CNhs12885_12869-137E7_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification08hrBiolRep3A3T11_CNhs12885_ctss_fwd Saos-2W/AscorbicAcidBgp_08hrBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 08hr, biol_rep3 (A3 T11)_CNhs12885_12869-137E7_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification08hrBiolRep2A2T11_CNhs12871_ctss_rev Saos-2W/AscorbicAcidBgp_08hrBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 08hr, biol_rep2 (A2 T11)_CNhs12871_12771-136C8_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification08hrBiolRep2A2T11_CNhs12871_ctss_fwd Saos-2W/AscorbicAcidBgp_08hrBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 08hr, biol_rep2 (A2 T11)_CNhs12871_12771-136C8_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification08hrBiolRep1A1T11_CNhs12392_ctss_rev Saos-2W/AscorbicAcidBgp_08hrBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 08hr, biol_rep1 (A1 T11)_CNhs12392_12673-135A9_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification08hrBiolRep1A1T11_CNhs12392_ctss_fwd Saos-2W/AscorbicAcidBgp_08hrBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 08hr, biol_rep1 (A1 T11)_CNhs12392_12673-135A9_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification04hrBiolRep3A3T10_CNhs12884_ctss_rev Saos-2W/AscorbicAcidBgp_04hrBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 04hr, biol_rep3 (A3 T10)_CNhs12884_12868-137E6_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification04hrBiolRep3A3T10_CNhs12884_ctss_fwd Saos-2W/AscorbicAcidBgp_04hrBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 04hr, biol_rep3 (A3 T10)_CNhs12884_12868-137E6_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification04hrBiolRep2A2T10_CNhs12870_ctss_rev Saos-2W/AscorbicAcidBgp_04hrBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 04hr, biol_rep2 (A2 T10)_CNhs12870_12770-136C7_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification04hrBiolRep2A2T10_CNhs12870_ctss_fwd Saos-2W/AscorbicAcidBgp_04hrBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 04hr, biol_rep2 (A2 T10)_CNhs12870_12770-136C7_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification04hrBiolRep1A1T10_CNhs12391_ctss_rev Saos-2W/AscorbicAcidBgp_04hrBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 04hr, biol_rep1 (A1 T10)_CNhs12391_12672-135A8_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification04hrBiolRep1A1T10_CNhs12391_ctss_fwd Saos-2W/AscorbicAcidBgp_04hrBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 04hr, biol_rep1 (A1 T10)_CNhs12391_12672-135A8_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification03hrBiolRep3A3T9_CNhs12883_ctss_rev Saos-2W/AscorbicAcidBgp_03hrBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 03hr, biol_rep3 (A3 T9)_CNhs12883_12867-137E5_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification03hrBiolRep3A3T9_CNhs12883_ctss_fwd Saos-2W/AscorbicAcidBgp_03hrBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 03hr, biol_rep3 (A3 T9)_CNhs12883_12867-137E5_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification03hrBiolRep2A2T9_CNhs12869_ctss_rev Saos-2W/AscorbicAcidBgp_03hrBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 03hr, biol_rep2 (A2 T9)_CNhs12869_12769-136C6_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification03hrBiolRep2A2T9_CNhs12869_ctss_fwd Saos-2W/AscorbicAcidBgp_03hrBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 03hr, biol_rep2 (A2 T9)_CNhs12869_12769-136C6_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification03hrBiolRep1A1T9_CNhs12390_ctss_rev Saos-2W/AscorbicAcidBgp_03hrBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 03hr, biol_rep1 (A1 T9)_CNhs12390_12671-135A7_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification03hrBiolRep1A1T9_CNhs12390_ctss_fwd Saos-2W/AscorbicAcidBgp_03hrBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 03hr, biol_rep1 (A1 T9)_CNhs12390_12671-135A7_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr30minBiolRep3A3T8_CNhs12882_ctss_rev Saos-2W/AscorbicAcidBgp_02hr30minBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr30min, biol_rep3 (A3 T8)_CNhs12882_12866-137E4_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr30minBiolRep3A3T8_CNhs12882_ctss_fwd Saos-2W/AscorbicAcidBgp_02hr30minBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr30min, biol_rep3 (A3 T8)_CNhs12882_12866-137E4_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr30minBiolRep2A2T8_CNhs12868_ctss_rev Saos-2W/AscorbicAcidBgp_02hr30minBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr30min, biol_rep2 (A2 T8)_CNhs12868_12768-136C5_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr30minBiolRep2A2T8_CNhs12868_ctss_fwd Saos-2W/AscorbicAcidBgp_02hr30minBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr30min, biol_rep2 (A2 T8)_CNhs12868_12768-136C5_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr30minBiolRep1A1T8_CNhs12389_ctss_rev Saos-2W/AscorbicAcidBgp_02hr30minBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr30min, biol_rep1 (A1 T8)_CNhs12389_12670-135A6_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr30minBiolRep1A1T8_CNhs12389_ctss_fwd Saos-2W/AscorbicAcidBgp_02hr30minBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr30min, biol_rep1 (A1 T8)_CNhs12389_12670-135A6_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr00minBiolRep3A3T7_CNhs12881_ctss_rev Saos-2W/AscorbicAcidBgp_02hr00minBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr00min, biol_rep3 (A3 T7)_CNhs12881_12865-137E3_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr00minBiolRep3A3T7_CNhs12881_ctss_fwd Saos-2W/AscorbicAcidBgp_02hr00minBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr00min, biol_rep3 (A3 T7)_CNhs12881_12865-137E3_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr00minBiolRep2A2T7_CNhs12867_ctss_rev Saos-2W/AscorbicAcidBgp_02hr00minBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr00min, biol_rep2 (A2 T7)_CNhs12867_12767-136C4_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr00minBiolRep2A2T7_CNhs12867_ctss_fwd Saos-2W/AscorbicAcidBgp_02hr00minBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr00min, biol_rep2 (A2 T7)_CNhs12867_12767-136C4_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr00minBiolRep1A1T7_CNhs12388_ctss_rev Saos-2W/AscorbicAcidBgp_02hr00minBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr00min, biol_rep1 (A1 T7)_CNhs12388_12669-135A5_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr00minBiolRep1A1T7_CNhs12388_ctss_fwd Saos-2W/AscorbicAcidBgp_02hr00minBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr00min, biol_rep1 (A1 T7)_CNhs12388_12669-135A5_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr40minBiolRep3A3T6_CNhs12880_ctss_rev Saos-2W/AscorbicAcidBgp_01hr40minBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr40min, biol_rep3 (A3 T6)_CNhs12880_12864-137E2_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr40minBiolRep3A3T6_CNhs12880_ctss_fwd Saos-2W/AscorbicAcidBgp_01hr40minBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr40min, biol_rep3 (A3 T6)_CNhs12880_12864-137E2_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr40minBiolRep2A2T6_CNhs12866_ctss_rev Saos-2W/AscorbicAcidBgp_01hr40minBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr40min, biol_rep2 (A2 T6)_CNhs12866_12766-136C3_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr40minBiolRep2A2T6_CNhs12866_ctss_fwd Saos-2W/AscorbicAcidBgp_01hr40minBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr40min, biol_rep2 (A2 T6)_CNhs12866_12766-136C3_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr40minBiolRep1A1T6_CNhs12387_ctss_rev Saos-2W/AscorbicAcidBgp_01hr40minBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr40min, biol_rep1 (A1 T6)_CNhs12387_12668-135A4_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr40minBiolRep1A1T6_CNhs12387_ctss_fwd Saos-2W/AscorbicAcidBgp_01hr40minBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr40min, biol_rep1 (A1 T6)_CNhs12387_12668-135A4_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr20minBiolRep3A3T5_CNhs12879_ctss_rev Saos-2W/AscorbicAcidBgp_01hr20minBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr20min, biol_rep3 (A3 T5)_CNhs12879_12863-137E1_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr20minBiolRep3A3T5_CNhs12879_ctss_fwd Saos-2W/AscorbicAcidBgp_01hr20minBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr20min, biol_rep3 (A3 T5)_CNhs12879_12863-137E1_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr20minBiolRep2A2T5_CNhs12864_ctss_rev Saos-2W/AscorbicAcidBgp_01hr20minBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr20min, biol_rep2 (A2 T5)_CNhs12864_12765-136C2_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr20minBiolRep2A2T5_CNhs12864_ctss_fwd Saos-2W/AscorbicAcidBgp_01hr20minBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr20min, biol_rep2 (A2 T5)_CNhs12864_12765-136C2_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr20minBiolRep1A1T5_CNhs12386_ctss_rev Saos-2W/AscorbicAcidBgp_01hr20minBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr20min, biol_rep1 (A1 T5)_CNhs12386_12667-135A3_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr20minBiolRep1A1T5_CNhs12386_ctss_fwd Saos-2W/AscorbicAcidBgp_01hr20minBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr20min, biol_rep1 (A1 T5)_CNhs12386_12667-135A3_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr00minBiolRep3A3T4_CNhs12955_ctss_rev Saos-2W/AscorbicAcidBgp_01hr00minBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr00min, biol_rep3 (A3 T4)_CNhs12955_12862-137D9_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr00minBiolRep3A3T4_CNhs12955_ctss_fwd Saos-2W/AscorbicAcidBgp_01hr00minBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr00min, biol_rep3 (A3 T4)_CNhs12955_12862-137D9_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr00minBiolRep2A2T4_CNhs12863_ctss_rev Saos-2W/AscorbicAcidBgp_01hr00minBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr00min, biol_rep2 (A2 T4)_CNhs12863_12764-136C1_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr00minBiolRep2A2T4_CNhs12863_ctss_fwd Saos-2W/AscorbicAcidBgp_01hr00minBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr00min, biol_rep2 (A2 T4)_CNhs12863_12764-136C1_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr00minBiolRep1A1T4_CNhs12384_ctss_rev Saos-2W/AscorbicAcidBgp_01hr00minBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr00min, biol_rep1 (A1 T4)_CNhs12384_12666-135A2_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr00minBiolRep1A1T4_CNhs12384_ctss_fwd Saos-2W/AscorbicAcidBgp_01hr00minBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr00min, biol_rep1 (A1 T4)_CNhs12384_12666-135A2_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr45minBiolRep3A3T3_CNhs12878_ctss_rev Saos-2W/AscorbicAcidBgp_00hr45minBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr45min, biol_rep3 (A3 T3)_CNhs12878_12861-137D8_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr45minBiolRep3A3T3_CNhs12878_ctss_fwd Saos-2W/AscorbicAcidBgp_00hr45minBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr45min, biol_rep3 (A3 T3)_CNhs12878_12861-137D8_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr45minBiolRep2A2T3_CNhs12862_ctss_rev Saos-2W/AscorbicAcidBgp_00hr45minBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr45min, biol_rep2 (A2 T3)_CNhs12862_12763-136B9_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr45minBiolRep2A2T3_CNhs12862_ctss_fwd Saos-2W/AscorbicAcidBgp_00hr45minBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr45min, biol_rep2 (A2 T3)_CNhs12862_12763-136B9_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr45minBiolRep1A1T3_CNhs12383_ctss_rev Saos-2W/AscorbicAcidBgp_00hr45minBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr45min, biol_rep1 (A1 T3)_CNhs12383_12665-135A1_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr45minBiolRep1A1T3_CNhs12383_ctss_fwd Saos-2W/AscorbicAcidBgp_00hr45minBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr45min, biol_rep1 (A1 T3)_CNhs12383_12665-135A1_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr30minBiolRep3A3T2_CNhs12954_ctss_rev Saos-2W/AscorbicAcidBgp_00hr30minBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr30min, biol_rep3 (A3 T2)_CNhs12954_12860-137D7_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr30minBiolRep3A3T2_CNhs12954_ctss_fwd Saos-2W/AscorbicAcidBgp_00hr30minBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr30min, biol_rep3 (A3 T2)_CNhs12954_12860-137D7_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr30minBiolRep2A2T2_CNhs12861_ctss_rev Saos-2W/AscorbicAcidBgp_00hr30minBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr30min, biol_rep2 (A2 T2)_CNhs12861_12762-136B8_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr30minBiolRep2A2T2_CNhs12861_ctss_fwd Saos-2W/AscorbicAcidBgp_00hr30minBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr30min, biol_rep2 (A2 T2)_CNhs12861_12762-136B8_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr30minBiolRep1A1T2_CNhs12382_ctss_rev Saos-2W/AscorbicAcidBgp_00hr30minBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr30min, biol_rep1 (A1 T2)_CNhs12382_12664-134I9_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr30minBiolRep1A1T2_CNhs12382_ctss_fwd Saos-2W/AscorbicAcidBgp_00hr30minBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr30min, biol_rep1 (A1 T2)_CNhs12382_12664-134I9_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr15minBiolRep3A3T1_CNhs12877_ctss_rev Saos-2W/AscorbicAcidBgp_00hr15minBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr15min, biol_rep3 (A3 T1)_CNhs12877_12859-137D6_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr15minBiolRep3A3T1_CNhs12877_ctss_fwd Saos-2W/AscorbicAcidBgp_00hr15minBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr15min, biol_rep3 (A3 T1)_CNhs12877_12859-137D6_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr15minBiolRep2A2T1_CNhs12860_ctss_rev Saos-2W/AscorbicAcidBgp_00hr15minBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr15min, biol_rep2 (A2 T1)_CNhs12860_12761-136B7_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr15minBiolRep2A2T1_CNhs12860_ctss_fwd Saos-2W/AscorbicAcidBgp_00hr15minBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr15min, biol_rep2 (A2 T1)_CNhs12860_12761-136B7_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr15minBiolRep1A1T1_CNhs12381_ctss_rev Saos-2W/AscorbicAcidBgp_00hr15minBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr15min, biol_rep1 (A1 T1)_CNhs12381_12663-134I8_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr15minBiolRep1A1T1_CNhs12381_ctss_fwd Saos-2W/AscorbicAcidBgp_00hr15minBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr15min, biol_rep1 (A1 T1)_CNhs12381_12663-134I8_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr00minBiolRep3A3T0_CNhs12952_ctss_rev Saos-2W/AscorbicAcidBgp_00hr00minBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr00min, biol_rep3 (A3 T0)_CNhs12952_12858-137D5_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr00minBiolRep3A3T0_CNhs12952_ctss_fwd Saos-2W/AscorbicAcidBgp_00hr00minBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr00min, biol_rep3 (A3 T0)_CNhs12952_12858-137D5_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr00minBiolRep2A2T0_CNhs12859_ctss_rev Saos-2W/AscorbicAcidBgp_00hr00minBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr00min, biol_rep2 (A2 T0)_CNhs12859_12760-136B6_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr00minBiolRep2A2T0_CNhs12859_ctss_fwd Saos-2W/AscorbicAcidBgp_00hr00minBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr00min, biol_rep2 (A2 T0)_CNhs12859_12760-136B6_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr00minBiolRep1A1T0_CNhs11918_ctss_rev Saos-2W/AscorbicAcidBgp_00hr00minBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr00min, biol_rep1 (A1 T0)_CNhs11918_12662-134I7_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr00minBiolRep1A1T0_CNhs11918_ctss_fwd Saos-2W/AscorbicAcidBgp_00hr00minBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr00min, biol_rep1 (A1 T0)_CNhs11918_12662-134I7_forward Regulation 
COBLaRinderpestInfection48hrBiolRep3_CNhs14434_ctss_rev Tc:COBL-aRinderpest_48hrBr3- COBL-a rinderpest infection, 48hr, biol_rep3_CNhs14434_13567-146B3_reverse Regulation 
COBLaRinderpestInfection48hrBiolRep3_CNhs14434_ctss_fwd Tc:COBL-aRinderpest_48hrBr3+ COBL-a rinderpest infection, 48hr, biol_rep3_CNhs14434_13567-146B3_forward Regulation 
COBLaRinderpestInfection48hrBiolRep2_CNhs14432_ctss_rev Tc:COBL-aRinderpest_48hrBr2- COBL-a rinderpest infection, 48hr, biol_rep2_CNhs14432_13566-146B2_reverse Regulation 
COBLaRinderpestInfection48hrBiolRep2_CNhs14432_ctss_fwd Tc:COBL-aRinderpest_48hrBr2+ COBL-a rinderpest infection, 48hr, biol_rep2_CNhs14432_13566-146B2_forward Regulation 
COBLaRinderpestInfection48hrBiolRep1_CNhs14431_ctss_rev Tc:COBL-aRinderpest_48hrBr1- COBL-a rinderpest infection, 48hr, biol_rep1_CNhs14431_13565-146B1_reverse Regulation 
COBLaRinderpestInfection48hrBiolRep1_CNhs14431_ctss_fwd Tc:COBL-aRinderpest_48hrBr1+ COBL-a rinderpest infection, 48hr, biol_rep1_CNhs14431_13565-146B1_forward Regulation 
COBLaRinderpestInfection24hrBiolRep3_CNhs14430_ctss_rev Tc:COBL-aRinderpest_24hrBr3- COBL-a rinderpest infection, 24hr, biol_rep3_CNhs14430_13564-146A9_reverse Regulation 
COBLaRinderpestInfection24hrBiolRep3_CNhs14430_ctss_fwd Tc:COBL-aRinderpest_24hrBr3+ COBL-a rinderpest infection, 24hr, biol_rep3_CNhs14430_13564-146A9_forward Regulation 
COBLaRinderpestInfection24hrBiolRep2_CNhs14429_ctss_rev Tc:COBL-aRinderpest_24hrBr2- COBL-a rinderpest infection, 24hr, biol_rep2_CNhs14429_13563-146A8_reverse Regulation 
COBLaRinderpestInfection24hrBiolRep2_CNhs14429_ctss_fwd Tc:COBL-aRinderpest_24hrBr2+ COBL-a rinderpest infection, 24hr, biol_rep2_CNhs14429_13563-146A8_forward Regulation 
COBLaRinderpestInfection24hrBiolRep1_CNhs14428_ctss_rev Tc:COBL-aRinderpest_24hrBr1- COBL-a rinderpest infection, 24hr, biol_rep1_CNhs14428_13562-146A7_reverse Regulation 
COBLaRinderpestInfection24hrBiolRep1_CNhs14428_ctss_fwd Tc:COBL-aRinderpest_24hrBr1+ COBL-a rinderpest infection, 24hr, biol_rep1_CNhs14428_13562-146A7_forward Regulation 
COBLaRinderpestInfection12hrBiolRep3_CNhs14427_ctss_rev Tc:COBL-aRinderpest_12hrBr3- COBL-a rinderpest infection, 12hr, biol_rep3_CNhs14427_13561-146A6_reverse Regulation 
COBLaRinderpestInfection12hrBiolRep3_CNhs14427_ctss_fwd Tc:COBL-aRinderpest_12hrBr3+ COBL-a rinderpest infection, 12hr, biol_rep3_CNhs14427_13561-146A6_forward Regulation 
COBLaRinderpestInfection12hrBiolRep2_CNhs14426_ctss_rev Tc:COBL-aRinderpest_12hrBr2- COBL-a rinderpest infection, 12hr, biol_rep2_CNhs14426_13560-146A5_reverse Regulation 
COBLaRinderpestInfection12hrBiolRep2_CNhs14426_ctss_fwd Tc:COBL-aRinderpest_12hrBr2+ COBL-a rinderpest infection, 12hr, biol_rep2_CNhs14426_13560-146A5_forward Regulation 
COBLaRinderpestInfection12hrBiolRep1_CNhs14425_ctss_rev Tc:COBL-aRinderpest_12hrBr1- COBL-a rinderpest infection, 12hr, biol_rep1_CNhs14425_13559-146A4_reverse Regulation 
COBLaRinderpestInfection12hrBiolRep1_CNhs14425_ctss_fwd Tc:COBL-aRinderpest_12hrBr1+ COBL-a rinderpest infection, 12hr, biol_rep1_CNhs14425_13559-146A4_forward Regulation 
COBLaRinderpestInfection06hrBiolRep3_CNhs14424_ctss_rev Tc:COBL-aRinderpest_06hrBr3- COBL-a rinderpest infection, 06hr, biol_rep3_CNhs14424_13558-146A3_reverse Regulation 
COBLaRinderpestInfection06hrBiolRep3_CNhs14424_ctss_fwd Tc:COBL-aRinderpest_06hrBr3+ COBL-a rinderpest infection, 06hr, biol_rep3_CNhs14424_13558-146A3_forward Regulation 
COBLaRinderpestInfection06hrBiolRep2_CNhs14423_ctss_rev Tc:COBL-aRinderpest_06hrBr2- COBL-a rinderpest infection, 06hr, biol_rep2_CNhs14423_13557-146A2_reverse Regulation 
COBLaRinderpestInfection06hrBiolRep2_CNhs14423_ctss_fwd Tc:COBL-aRinderpest_06hrBr2+ COBL-a rinderpest infection, 06hr, biol_rep2_CNhs14423_13557-146A2_forward Regulation 
COBLaRinderpestInfection06hrBiolRep1_CNhs14422_ctss_rev Tc:COBL-aRinderpest_06hrBr1- COBL-a rinderpest infection, 06hr, biol_rep1_CNhs14422_13556-146A1_reverse Regulation 
COBLaRinderpestInfection06hrBiolRep1_CNhs14422_ctss_fwd Tc:COBL-aRinderpest_06hrBr1+ COBL-a rinderpest infection, 06hr, biol_rep1_CNhs14422_13556-146A1_forward Regulation 
COBLaRinderpestInfection00hrBiolRep3_CNhs14421_ctss_rev Tc:COBL-aRinderpest_00hrBr3- COBL-a rinderpest infection, 00hr, biol_rep3_CNhs14421_13555-145I9_reverse Regulation 
COBLaRinderpestInfection00hrBiolRep3_CNhs14421_ctss_fwd Tc:COBL-aRinderpest_00hrBr3+ COBL-a rinderpest infection, 00hr, biol_rep3_CNhs14421_13555-145I9_forward Regulation 
COBLaRinderpestInfection00hrBiolRep2_CNhs14420_ctss_rev Tc:COBL-aRinderpest_00hrBr2- COBL-a rinderpest infection, 00hr, biol_rep2_CNhs14420_13554-145I8_reverse Regulation 
COBLaRinderpestInfection00hrBiolRep2_CNhs14420_ctss_fwd Tc:COBL-aRinderpest_00hrBr2+ COBL-a rinderpest infection, 00hr, biol_rep2_CNhs14420_13554-145I8_forward Regulation 
COBLaRinderpestInfection00hrBiolRep1_CNhs14419_ctss_rev Tc:COBL-aRinderpest_00hrBr1- COBL-a rinderpest infection, 00hr, biol_rep1_CNhs14419_13553-145I7_reverse Regulation 
COBLaRinderpestInfection00hrBiolRep1_CNhs14419_ctss_fwd Tc:COBL-aRinderpest_00hrBr1+ COBL-a rinderpest infection, 00hr, biol_rep1_CNhs14419_13553-145I7_forward Regulation 
COBLaRinderpestCInfection48hrBiolRep3_CNhs14446_ctss_rev Tc:COBL-aRinderpest(-C)_48hrBr3- COBL-a rinderpest(-C) infection, 48hr, biol_rep3_CNhs14446_13579-146C6_reverse Regulation 
COBLaRinderpestCInfection48hrBiolRep3_CNhs14446_ctss_fwd Tc:COBL-aRinderpest(-C)_48hrBr3+ COBL-a rinderpest(-C) infection, 48hr, biol_rep3_CNhs14446_13579-146C6_forward Regulation 
COBLaRinderpestCInfection48hrBiolRep2_CNhs14445_ctss_rev Tc:COBL-aRinderpest(-C)_48hrBr2- COBL-a rinderpest(-C) infection, 48hr, biol_rep2_CNhs14445_13578-146C5_reverse Regulation 
COBLaRinderpestCInfection48hrBiolRep2_CNhs14445_ctss_fwd Tc:COBL-aRinderpest(-C)_48hrBr2+ COBL-a rinderpest(-C) infection, 48hr, biol_rep2_CNhs14445_13578-146C5_forward Regulation 
COBLaRinderpestCInfection48hrBiolRep1_CNhs14444_ctss_rev Tc:COBL-aRinderpest(-C)_48hrBr1- COBL-a rinderpest(-C) infection, 48hr, biol_rep1_CNhs14444_13577-146C4_reverse Regulation 
COBLaRinderpestCInfection48hrBiolRep1_CNhs14444_ctss_fwd Tc:COBL-aRinderpest(-C)_48hrBr1+ COBL-a rinderpest(-C) infection, 48hr, biol_rep1_CNhs14444_13577-146C4_forward Regulation 
COBLaRinderpestCInfection24hrBiolRep3_CNhs14443_ctss_rev Tc:COBL-aRinderpest(-C)_24hrBr3- COBL-a rinderpest(-C) infection, 24hr, biol_rep3_CNhs14443_13576-146C3_reverse Regulation 
COBLaRinderpestCInfection24hrBiolRep3_CNhs14443_ctss_fwd Tc:COBL-aRinderpest(-C)_24hrBr3+ COBL-a rinderpest(-C) infection, 24hr, biol_rep3_CNhs14443_13576-146C3_forward Regulation 
COBLaRinderpestCInfection24hrBiolRep2_CNhs14442_ctss_rev Tc:COBL-aRinderpest(-C)_24hrBr2- COBL-a rinderpest(-C) infection, 24hr, biol_rep2_CNhs14442_13575-146C2_reverse Regulation 
COBLaRinderpestCInfection24hrBiolRep2_CNhs14442_ctss_fwd Tc:COBL-aRinderpest(-C)_24hrBr2+ COBL-a rinderpest(-C) infection, 24hr, biol_rep2_CNhs14442_13575-146C2_forward Regulation 
COBLaRinderpestCInfection24hrBiolRep1_CNhs14441_ctss_rev Tc:COBL-aRinderpest(-C)_24hrBr1- COBL-a rinderpest(-C) infection, 24hr, biol_rep1_CNhs14441_13574-146C1_reverse Regulation 
COBLaRinderpestCInfection24hrBiolRep1_CNhs14441_ctss_fwd Tc:COBL-aRinderpest(-C)_24hrBr1+ COBL-a rinderpest(-C) infection, 24hr, biol_rep1_CNhs14441_13574-146C1_forward Regulation 
COBLaRinderpestCInfection12hrBiolRep3_CNhs14440_ctss_rev Tc:COBL-aRinderpest(-C)_12hrBr3- COBL-a rinderpest(-C) infection, 12hr, biol_rep3_CNhs14440_13573-146B9_reverse Regulation 
COBLaRinderpestCInfection12hrBiolRep3_CNhs14440_ctss_fwd Tc:COBL-aRinderpest(-C)_12hrBr3+ COBL-a rinderpest(-C) infection, 12hr, biol_rep3_CNhs14440_13573-146B9_forward Regulation 
COBLaRinderpestCInfection12hrBiolRep2_CNhs14439_ctss_rev Tc:COBL-aRinderpest(-C)_12hrBr2- COBL-a rinderpest(-C) infection, 12hr, biol_rep2_CNhs14439_13572-146B8_reverse Regulation 
COBLaRinderpestCInfection12hrBiolRep2_CNhs14439_ctss_fwd Tc:COBL-aRinderpest(-C)_12hrBr2+ COBL-a rinderpest(-C) infection, 12hr, biol_rep2_CNhs14439_13572-146B8_forward Regulation 
COBLaRinderpestCInfection12hrBiolRep1_CNhs14438_ctss_rev Tc:COBL-aRinderpest(-C)_12hrBr1- COBL-a rinderpest(-C) infection, 12hr, biol_rep1_CNhs14438_13571-146B7_reverse Regulation 
COBLaRinderpestCInfection12hrBiolRep1_CNhs14438_ctss_fwd Tc:COBL-aRinderpest(-C)_12hrBr1+ COBL-a rinderpest(-C) infection, 12hr, biol_rep1_CNhs14438_13571-146B7_forward Regulation 
COBLaRinderpestCInfection06hrBiolRep3_CNhs14437_ctss_rev Tc:COBL-aRinderpest(-C)_06hrBr3- COBL-a rinderpest(-C) infection, 06hr, biol_rep3_CNhs14437_13570-146B6_reverse Regulation 
COBLaRinderpestCInfection06hrBiolRep3_CNhs14437_ctss_fwd Tc:COBL-aRinderpest(-C)_06hrBr3+ COBL-a rinderpest(-C) infection, 06hr, biol_rep3_CNhs14437_13570-146B6_forward Regulation 
COBLaRinderpestCInfection06hrBiolRep2_CNhs14436_ctss_rev Tc:COBL-aRinderpest(-C)_06hrBr2- COBL-a rinderpest(-C) infection, 06hr, biol_rep2_CNhs14436_13569-146B5_reverse Regulation 
COBLaRinderpestCInfection06hrBiolRep2_CNhs14436_ctss_fwd Tc:COBL-aRinderpest(-C)_06hrBr2+ COBL-a rinderpest(-C) infection, 06hr, biol_rep2_CNhs14436_13569-146B5_forward Regulation 
COBLaRinderpestCInfection06hrBiolRep1_CNhs14435_ctss_rev Tc:COBL-aRinderpest(-C)_06hrBr1- COBL-a rinderpest(-C) infection, 06hr, biol_rep1_CNhs14435_13568-146B4_reverse Regulation 
COBLaRinderpestCInfection06hrBiolRep1_CNhs14435_ctss_fwd Tc:COBL-aRinderpest(-C)_06hrBr1+ COBL-a rinderpest(-C) infection, 06hr, biol_rep1_CNhs14435_13568-146B4_forward Regulation 
293SLAMRinderpestInfection24hrBiolRep3_CNhs14418_ctss_rev Tc:293SlamRinderpest_24hrBr3- 293SLAM rinderpest infection, 24hr, biol_rep3_CNhs14418_13552-145I6_reverse Regulation 
293SLAMRinderpestInfection24hrBiolRep3_CNhs14418_ctss_fwd Tc:293SlamRinderpest_24hrBr3+ 293SLAM rinderpest infection, 24hr, biol_rep3_CNhs14418_13552-145I6_forward Regulation 
293SLAMRinderpestInfection24hrBiolRep2_CNhs14417_ctss_rev Tc:293SlamRinderpest_24hrBr2- 293SLAM rinderpest infection, 24hr, biol_rep2_CNhs14417_13551-145I5_reverse Regulation 
293SLAMRinderpestInfection24hrBiolRep2_CNhs14417_ctss_fwd Tc:293SlamRinderpest_24hrBr2+ 293SLAM rinderpest infection, 24hr, biol_rep2_CNhs14417_13551-145I5_forward Regulation 
293SLAMRinderpestInfection24hrBiolRep1_CNhs14416_ctss_rev Tc:293SlamRinderpest_24hrBr1- 293SLAM rinderpest infection, 24hr, biol_rep1_CNhs14416_13550-145I4_reverse Regulation 
293SLAMRinderpestInfection24hrBiolRep1_CNhs14416_ctss_fwd Tc:293SlamRinderpest_24hrBr1+ 293SLAM rinderpest infection, 24hr, biol_rep1_CNhs14416_13550-145I4_forward Regulation 
293SLAMRinderpestInfection12hrBiolRep3_CNhs14415_ctss_rev Tc:293SlamRinderpest_12hrBr3- 293SLAM rinderpest infection, 12hr, biol_rep3_CNhs14415_13549-145I3_reverse Regulation 
293SLAMRinderpestInfection12hrBiolRep3_CNhs14415_ctss_fwd Tc:293SlamRinderpest_12hrBr3+ 293SLAM rinderpest infection, 12hr, biol_rep3_CNhs14415_13549-145I3_forward Regulation 
293SLAMRinderpestInfection12hrBiolRep2_CNhs14414_ctss_rev Tc:293SlamRinderpest_12hrBr2- 293SLAM rinderpest infection, 12hr, biol_rep2_CNhs14414_13548-145I2_reverse Regulation 
293SLAMRinderpestInfection12hrBiolRep2_CNhs14414_ctss_fwd Tc:293SlamRinderpest_12hrBr2+ 293SLAM rinderpest infection, 12hr, biol_rep2_CNhs14414_13548-145I2_forward Regulation 
293SLAMRinderpestInfection12hrBiolRep1_CNhs14413_ctss_rev Tc:293SlamRinderpest_12hrBr1- 293SLAM rinderpest infection, 12hr, biol_rep1_CNhs14413_13547-145I1_reverse Regulation 
293SLAMRinderpestInfection12hrBiolRep1_CNhs14413_ctss_fwd Tc:293SlamRinderpest_12hrBr1+ 293SLAM rinderpest infection, 12hr, biol_rep1_CNhs14413_13547-145I1_forward Regulation 
293SLAMRinderpestInfection06hrBiolRep3_CNhs14412_ctss_rev Tc:293SlamRinderpest_06hrBr3- 293SLAM rinderpest infection, 06hr, biol_rep3_CNhs14412_13546-145H9_reverse Regulation 
293SLAMRinderpestInfection06hrBiolRep3_CNhs14412_ctss_fwd Tc:293SlamRinderpest_06hrBr3+ 293SLAM rinderpest infection, 06hr, biol_rep3_CNhs14412_13546-145H9_forward Regulation 
293SLAMRinderpestInfection06hrBiolRep2_CNhs14411_ctss_rev Tc:293SlamRinderpest_06hrBr2- 293SLAM rinderpest infection, 06hr, biol_rep2_CNhs14411_13545-145H8_reverse Regulation 
293SLAMRinderpestInfection06hrBiolRep2_CNhs14411_ctss_fwd Tc:293SlamRinderpest_06hrBr2+ 293SLAM rinderpest infection, 06hr, biol_rep2_CNhs14411_13545-145H8_forward Regulation 
293SLAMRinderpestInfection06hrBiolRep1_CNhs14410_ctss_rev Tc:293SlamRinderpest_06hrBr1- 293SLAM rinderpest infection, 06hr, biol_rep1_CNhs14410_13544-145H7_reverse Regulation 
293SLAMRinderpestInfection06hrBiolRep1_CNhs14410_ctss_fwd Tc:293SlamRinderpest_06hrBr1+ 293SLAM rinderpest infection, 06hr, biol_rep1_CNhs14410_13544-145H7_forward Regulation 
293SLAMRinderpestInfection00hrBiolRep3_CNhs14408_ctss_rev Tc:293SlamRinderpest_00hrBr3- 293SLAM rinderpest infection, 00hr, biol_rep3_CNhs14408_13543-145H6_reverse Regulation 
293SLAMRinderpestInfection00hrBiolRep3_CNhs14408_ctss_fwd Tc:293SlamRinderpest_00hrBr3+ 293SLAM rinderpest infection, 00hr, biol_rep3_CNhs14408_13543-145H6_forward Regulation 
293SLAMRinderpestInfection00hrBiolRep2_CNhs14407_ctss_rev Tc:293SlamRinderpest_00hrBr2- 293SLAM rinderpest infection, 00hr, biol_rep2_CNhs14407_13542-145H5_reverse Regulation 
293SLAMRinderpestInfection00hrBiolRep2_CNhs14407_ctss_fwd Tc:293SlamRinderpest_00hrBr2+ 293SLAM rinderpest infection, 00hr, biol_rep2_CNhs14407_13542-145H5_forward Regulation 
293SLAMRinderpestInfection00hrBiolRep1_CNhs14406_ctss_rev Tc:293SlamRinderpest_00hrBr1- 293SLAM rinderpest infection, 00hr, biol_rep1_CNhs14406_13541-145H4_reverse Regulation 
293SLAMRinderpestInfection00hrBiolRep1_CNhs14406_ctss_fwd Tc:293SlamRinderpest_00hrBr1+ 293SLAM rinderpest infection, 00hr, biol_rep1_CNhs14406_13541-145H4_forward Regulation 
AdipocyteDifferentiationDay12Donor4_CNhs13419_ctss_rev Tc:AdipoDiff_Day12D4- Adipocyte differentiation, day12, donor4_CNhs13419_13030-139E6_reverse Regulation 
AdipocyteDifferentiationDay12Donor4_CNhs13419_ctss_fwd Tc:AdipoDiff_Day12D4+ Adipocyte differentiation, day12, donor4_CNhs13419_13030-139E6_forward Regulation 
AdipocyteDifferentiationDay12Donor3_CNhs13416_ctss_rev Tc:AdipoDiff_Day12D3- Adipocyte differentiation, day12, donor3_CNhs13416_13027-139E3_reverse Regulation 
AdipocyteDifferentiationDay12Donor3_CNhs13416_ctss_fwd Tc:AdipoDiff_Day12D3+ Adipocyte differentiation, day12, donor3_CNhs13416_13027-139E3_forward Regulation 
AdipocyteDifferentiationDay12Donor2_CNhs13412_ctss_rev Tc:AdipoDiff_Day12D2- Adipocyte differentiation, day12, donor2_CNhs13412_13024-139D9_reverse Regulation 
AdipocyteDifferentiationDay12Donor2_CNhs13412_ctss_fwd Tc:AdipoDiff_Day12D2+ Adipocyte differentiation, day12, donor2_CNhs13412_13024-139D9_forward Regulation 
AdipocyteDifferentiationDay12Donor1_CNhs13336_ctss_rev Tc:AdipoDiff_Day12D1- Adipocyte differentiation, day12, donor1_CNhs13336_13021-139D6_reverse Regulation 
AdipocyteDifferentiationDay12Donor1_CNhs13336_ctss_fwd Tc:AdipoDiff_Day12D1+ Adipocyte differentiation, day12, donor1_CNhs13336_13021-139D6_forward Regulation 
AdipocyteDifferentiationDay08Donor4_CNhs13418_ctss_rev Tc:AdipoDiff_Day08D4- Adipocyte differentiation, day08, donor4_CNhs13418_13029-139E5_reverse Regulation 
AdipocyteDifferentiationDay08Donor4_CNhs13418_ctss_fwd Tc:AdipoDiff_Day08D4+ Adipocyte differentiation, day08, donor4_CNhs13418_13029-139E5_forward Regulation 
AdipocyteDifferentiationDay08Donor3_CNhs13415_ctss_rev Tc:AdipoDiff_Day08D3- Adipocyte differentiation, day08, donor3_CNhs13415_13026-139E2_reverse Regulation 
AdipocyteDifferentiationDay08Donor3_CNhs13415_ctss_fwd Tc:AdipoDiff_Day08D3+ Adipocyte differentiation, day08, donor3_CNhs13415_13026-139E2_forward Regulation 
AdipocyteDifferentiationDay08Donor2_CNhs13411_ctss_rev Tc:AdipoDiff_Day08D2- Adipocyte differentiation, day08, donor2_CNhs13411_13023-139D8_reverse Regulation 
AdipocyteDifferentiationDay08Donor2_CNhs13411_ctss_fwd Tc:AdipoDiff_Day08D2+ Adipocyte differentiation, day08, donor2_CNhs13411_13023-139D8_forward Regulation 
AdipocyteDifferentiationDay08Donor1_CNhs12517_ctss_rev Tc:AdipoDiff_Day08D1- Adipocyte differentiation, day08, donor1_CNhs12517_13020-139D5_reverse Regulation 
AdipocyteDifferentiationDay08Donor1_CNhs12517_ctss_fwd Tc:AdipoDiff_Day08D1+ Adipocyte differentiation, day08, donor1_CNhs12517_13020-139D5_forward Regulation 
AdipocyteDifferentiationDay04Donor4_CNhs13417_ctss_rev Tc:AdipoDiff_Day04D4- Adipocyte differentiation, day04, donor4_CNhs13417_13028-139E4_reverse Regulation 
AdipocyteDifferentiationDay04Donor4_CNhs13417_ctss_fwd Tc:AdipoDiff_Day04D4+ Adipocyte differentiation, day04, donor4_CNhs13417_13028-139E4_forward Regulation 
AdipocyteDifferentiationDay04Donor3_CNhs13413_ctss_rev Tc:AdipoDiff_Day04D3- Adipocyte differentiation, day04, donor3_CNhs13413_13025-139E1_reverse Regulation 
AdipocyteDifferentiationDay04Donor3_CNhs13413_ctss_fwd Tc:AdipoDiff_Day04D3+ Adipocyte differentiation, day04, donor3_CNhs13413_13025-139E1_forward Regulation 
AdipocyteDifferentiationDay04Donor2_CNhs13410_ctss_rev Tc:AdipoDiff_Day04D2- Adipocyte differentiation, day04, donor2_CNhs13410_13022-139D7_reverse Regulation 
AdipocyteDifferentiationDay04Donor2_CNhs13410_ctss_fwd Tc:AdipoDiff_Day04D2+ Adipocyte differentiation, day04, donor2_CNhs13410_13022-139D7_forward Regulation 
AdipocyteDifferentiationDay04Donor1_CNhs12516_ctss_rev Tc:AdipoDiff_Day04D1- Adipocyte differentiation, day04, donor1_CNhs12516_13019-139D4_reverse Regulation 
AdipocyteDifferentiationDay04Donor1_CNhs12516_ctss_fwd Tc:AdipoDiff_Day04D1+ Adipocyte differentiation, day04, donor1_CNhs12516_13019-139D4_forward Regulation 
MyoblastDifferentiationToMyotubesDay12DuchenneMuscularDystrophyDonor3_CNhs14613_ctss_rev MyoblastToMyotubes_Day12D3- Myoblast differentiation to myotubes, day12, Duchenne Muscular Dystrophy donor3_CNhs14613_13522-145F3_reverse Regulation 
MyoblastDifferentiationToMyotubesDay12ControlDonor3_CNhs14585_ctss_rev MyoblastToMyotubes_Day12D3- Myoblast differentiation to myotubes, day12, control donor3_CNhs14585_13495-145C3_reverse Regulation 
MyoblastDifferentiationToMyotubesDay12DuchenneMuscularDystrophyDonor3_CNhs14613_ctss_fwd MyoblastToMyotubes_Day12D3+ Myoblast differentiation to myotubes, day12, Duchenne Muscular Dystrophy donor3_CNhs14613_13522-145F3_forward Regulation 
MyoblastDifferentiationToMyotubesDay12ControlDonor3_CNhs14585_ctss_fwd MyoblastToMyotubes_Day12D3+ Myoblast differentiation to myotubes, day12, control donor3_CNhs14585_13495-145C3_forward Regulation 
MyoblastDifferentiationToMyotubesDay12DuchenneMuscularDystrophyDonor2_CNhs14604_ctss_rev MyoblastToMyotubes_Day12D2- Myoblast differentiation to myotubes, day12, Duchenne Muscular Dystrophy donor2_CNhs14604_13513-145E3_reverse Regulation 
MyoblastDifferentiationToMyotubesDay12ControlDonor2_CNhs14576_ctss_rev MyoblastToMyotubes_Day12D2- Myoblast differentiation to myotubes, day12, control donor2_CNhs14576_13486-145B3_reverse Regulation 
MyoblastDifferentiationToMyotubesDay12DuchenneMuscularDystrophyDonor2_CNhs14604_ctss_fwd MyoblastToMyotubes_Day12D2+ Myoblast differentiation to myotubes, day12, Duchenne Muscular Dystrophy donor2_CNhs14604_13513-145E3_forward Regulation 
MyoblastDifferentiationToMyotubesDay12ControlDonor2_CNhs14576_ctss_fwd MyoblastToMyotubes_Day12D2+ Myoblast differentiation to myotubes, day12, control donor2_CNhs14576_13486-145B3_forward Regulation 
MyoblastDifferentiationToMyotubesDay12ControlDonor1_CNhs14566_ctss_rev MyoblastToMyotubes_Day12D1- Myoblast differentiation to myotubes, day12, control donor1_CNhs14566_13477-145A3_reverse Regulation 
MyoblastDifferentiationToMyotubesDay12DuchenneMuscularDystrophyDonor1_CNhs14595_ctss_rev MyoblastToMyotubes_Day12D1- Myoblast differentiation to myotubes, day12, Duchenne Muscular Dystrophy donor1_CNhs14595_13504-145D3_reverse Regulation 
MyoblastDifferentiationToMyotubesDay12ControlDonor1_CNhs14566_ctss_fwd MyoblastToMyotubes_Day12D1+ Myoblast differentiation to myotubes, day12, control donor1_CNhs14566_13477-145A3_forward Regulation 
MyoblastDifferentiationToMyotubesDay12DuchenneMuscularDystrophyDonor1_CNhs14595_ctss_fwd MyoblastToMyotubes_Day12D1+ Myoblast differentiation to myotubes, day12, Duchenne Muscular Dystrophy donor1_CNhs14595_13504-145D3_forward Regulation 
MyoblastDifferentiationToMyotubesDay10DuchenneMuscularDystrophyDonor3_CNhs14612_ctss_rev MyoblastToMyotubes_Day10D3- Myoblast differentiation to myotubes, day10, Duchenne Muscular Dystrophy donor3_CNhs14612_13521-145F2_reverse Regulation 
MyoblastDifferentiationToMyotubesDay10DuchenneMuscularDystrophyDonor3_CNhs14612_ctss_fwd MyoblastToMyotubes_Day10D3+ Myoblast differentiation to myotubes, day10, Duchenne Muscular Dystrophy donor3_CNhs14612_13521-145F2_forward Regulation 
MyoblastDifferentiationToMyotubesDay10ControlDonor2_CNhs14575_ctss_rev MyoblastToMyotubes_Day10D2- Myoblast differentiation to myotubes, day10, control donor2_CNhs14575_13485-145B2_reverse Regulation 
MyoblastDifferentiationToMyotubesDay10DuchenneMuscularDystrophyDonor2_CNhs14603_ctss_rev MyoblastToMyotubes_Day10D2- Myoblast differentiation to myotubes, day10, Duchenne Muscular Dystrophy donor2_CNhs14603_13512-145E2_reverse Regulation 
MyoblastDifferentiationToMyotubesDay10ControlDonor2_CNhs14575_ctss_fwd MyoblastToMyotubes_Day10D2+ Myoblast differentiation to myotubes, day10, control donor2_CNhs14575_13485-145B2_forward Regulation 
MyoblastDifferentiationToMyotubesDay10DuchenneMuscularDystrophyDonor2_CNhs14603_ctss_fwd MyoblastToMyotubes_Day10D2+ Myoblast differentiation to myotubes, day10, Duchenne Muscular Dystrophy donor2_CNhs14603_13512-145E2_forward Regulation 
MyoblastDifferentiationToMyotubesDay10DuchenneMuscularDystrophyDonor1_CNhs14594_ctss_rev MyoblastToMyotubes_Day10D1- Myoblast differentiation to myotubes, day10, Duchenne Muscular Dystrophy donor1_CNhs14594_13503-145D2_reverse Regulation 
MyoblastDifferentiationToMyotubesDay10ControlDonor1_CNhs13854_ctss_rev MyoblastToMyotubes_Day10D1- Myoblast differentiation to myotubes, day10, control donor1_CNhs13854_13476-145A2_reverse Regulation 
MyoblastDifferentiationToMyotubesDay10ControlDonor1_CNhs13854_ctss_fwd MyoblastToMyotubes_Day10D1+ Myoblast differentiation to myotubes, day10, control donor1_CNhs13854_13476-145A2_forward Regulation 
MyoblastDifferentiationToMyotubesDay10DuchenneMuscularDystrophyDonor1_CNhs14594_ctss_fwd MyoblastToMyotubes_Day10D1+ Myoblast differentiation to myotubes, day10, Duchenne Muscular Dystrophy donor1_CNhs14594_13503-145D2_forward Regulation 
MyoblastDifferentiationToMyotubesDay08DuchenneMuscularDystrophyDonor3_CNhs14611_ctss_rev MyoblastToMyotubes_Day08D3- Myoblast differentiation to myotubes, day08, Duchenne Muscular Dystrophy donor3_CNhs14611_13520-145F1_reverse Regulation 
MyoblastDifferentiationToMyotubesDay08ControlDonor3_CNhs14583_ctss_rev MyoblastToMyotubes_Day08D3- Myoblast differentiation to myotubes, day08, control donor3_CNhs14583_13493-145C1_reverse Regulation 
MyoblastDifferentiationToMyotubesDay08DuchenneMuscularDystrophyDonor3_CNhs14611_ctss_fwd MyoblastToMyotubes_Day08D3+ Myoblast differentiation to myotubes, day08, Duchenne Muscular Dystrophy donor3_CNhs14611_13520-145F1_forward Regulation 
MyoblastDifferentiationToMyotubesDay08ControlDonor3_CNhs14583_ctss_fwd MyoblastToMyotubes_Day08D3+ Myoblast differentiation to myotubes, day08, control donor3_CNhs14583_13493-145C1_forward Regulation 
MyoblastDifferentiationToMyotubesDay08DuchenneMuscularDystrophyDonor2_CNhs14602_ctss_rev MyoblastToMyotubes_Day08D2- Myoblast differentiation to myotubes, day08, Duchenne Muscular Dystrophy donor2_CNhs14602_13511-145E1_reverse Regulation 
MyoblastDifferentiationToMyotubesDay08ControlDonor2_CNhs14574_ctss_rev MyoblastToMyotubes_Day08D2- Myoblast differentiation to myotubes, day08, control donor2_CNhs14574_13484-145B1_reverse Regulation 
MyoblastDifferentiationToMyotubesDay08DuchenneMuscularDystrophyDonor2_CNhs14602_ctss_fwd MyoblastToMyotubes_Day08D2+ Myoblast differentiation to myotubes, day08, Duchenne Muscular Dystrophy donor2_CNhs14602_13511-145E1_forward Regulation 
MyoblastDifferentiationToMyotubesDay08ControlDonor2_CNhs14574_ctss_fwd MyoblastToMyotubes_Day08D2+ Myoblast differentiation to myotubes, day08, control donor2_CNhs14574_13484-145B1_forward Regulation 
MyoblastDifferentiationToMyotubesDay08DuchenneMuscularDystrophyDonor1_CNhs14592_ctss_rev MyoblastToMyotubes_Day08D1- Myoblast differentiation to myotubes, day08, Duchenne Muscular Dystrophy donor1_CNhs14592_13502-145D1_reverse Regulation 
MyoblastDifferentiationToMyotubesDay08ControlDonor1_CNhs13853_ctss_rev MyoblastToMyotubes_Day08D1- Myoblast differentiation to myotubes, day08, control donor1_CNhs13853_13475-145A1_reverse Regulation 
MyoblastDifferentiationToMyotubesDay08DuchenneMuscularDystrophyDonor1_CNhs14592_ctss_fwd MyoblastToMyotubes_Day08D1+ Myoblast differentiation to myotubes, day08, Duchenne Muscular Dystrophy donor1_CNhs14592_13502-145D1_forward Regulation 
MyoblastDifferentiationToMyotubesDay08ControlDonor1_CNhs13853_ctss_fwd MyoblastToMyotubes_Day08D1+ Myoblast differentiation to myotubes, day08, control donor1_CNhs13853_13475-145A1_forward Regulation 
MyoblastDifferentiationToMyotubesDay06ControlDonor3_CNhs14582_ctss_rev MyoblastToMyotubes_Day06D3- Myoblast differentiation to myotubes, day06, control donor3_CNhs14582_13492-145B9_reverse Regulation 
MyoblastDifferentiationToMyotubesDay06DuchenneMuscularDystrophyDonor3_CNhs14610_ctss_rev MyoblastToMyotubes_Day06D3- Myoblast differentiation to myotubes, day06, Duchenne Muscular Dystrophy donor3_CNhs14610_13519-145E9_reverse Regulation 
MyoblastDifferentiationToMyotubesDay06ControlDonor3_CNhs14582_ctss_fwd MyoblastToMyotubes_Day06D3+ Myoblast differentiation to myotubes, day06, control donor3_CNhs14582_13492-145B9_forward Regulation 
MyoblastDifferentiationToMyotubesDay06DuchenneMuscularDystrophyDonor3_CNhs14610_ctss_fwd MyoblastToMyotubes_Day06D3+ Myoblast differentiation to myotubes, day06, Duchenne Muscular Dystrophy donor3_CNhs14610_13519-145E9_forward Regulation 
MyoblastDifferentiationToMyotubesDay06ControlDonor2_CNhs14573_ctss_rev MyoblastToMyotubes_Day06D2- Myoblast differentiation to myotubes, day06, control donor2_CNhs14573_13483-145A9_reverse Regulation 
MyoblastDifferentiationToMyotubesDay06ControlDonor2_CNhs14573_ctss_fwd MyoblastToMyotubes_Day06D2+ Myoblast differentiation to myotubes, day06, control donor2_CNhs14573_13483-145A9_forward Regulation 
MyoblastDifferentiationToMyotubesDay06DuchenneMuscularDystrophyDonor1_CNhs14591_ctss_rev MyoblastToMyotubes_Day06D1- Myoblast differentiation to myotubes, day06, Duchenne Muscular Dystrophy donor1_CNhs14591_13501-145C9_reverse Regulation 
MyoblastDifferentiationToMyotubesDay06ControlDonor1_CNhs13852_ctss_rev MyoblastToMyotubes_Day06D1- Myoblast differentiation to myotubes, day06, control donor1_CNhs13852_13474-144I9_reverse Regulation 
MyoblastDifferentiationToMyotubesDay06DuchenneMuscularDystrophyDonor1_CNhs14591_ctss_fwd MyoblastToMyotubes_Day06D1+ Myoblast differentiation to myotubes, day06, Duchenne Muscular Dystrophy donor1_CNhs14591_13501-145C9_forward Regulation 
MyoblastDifferentiationToMyotubesDay06ControlDonor1_CNhs13852_ctss_fwd MyoblastToMyotubes_Day06D1+ Myoblast differentiation to myotubes, day06, control donor1_CNhs13852_13474-144I9_forward Regulation 
MyoblastDifferentiationToMyotubesDay04ControlDonor3_CNhs14581_ctss_rev MyoblastToMyotubes_Day04D3- Myoblast differentiation to myotubes, day04, control donor3_CNhs14581_13491-145B8_reverse Regulation 
MyoblastDifferentiationToMyotubesDay04DuchenneMuscularDystrophyDonor3_CNhs14609_ctss_rev MyoblastToMyotubes_Day04D3- Myoblast differentiation to myotubes, day04, Duchenne Muscular Dystrophy donor3_CNhs14609_13518-145E8_reverse Regulation 
MyoblastDifferentiationToMyotubesDay04ControlDonor3_CNhs14581_ctss_fwd MyoblastToMyotubes_Day04D3+ Myoblast differentiation to myotubes, day04, control donor3_CNhs14581_13491-145B8_forward Regulation 
MyoblastDifferentiationToMyotubesDay04DuchenneMuscularDystrophyDonor3_CNhs14609_ctss_fwd MyoblastToMyotubes_Day04D3+ Myoblast differentiation to myotubes, day04, Duchenne Muscular Dystrophy donor3_CNhs14609_13518-145E8_forward Regulation 
MyoblastDifferentiationToMyotubesDay04DuchenneMuscularDystrophyDonor2_CNhs14600_ctss_rev MyoblastToMyotubes_Day04D2- Myoblast differentiation to myotubes, day04, Duchenne Muscular Dystrophy donor2_CNhs14600_13509-145D8_reverse Regulation 
MyoblastDifferentiationToMyotubesDay04ControlDonor2_CNhs14572_ctss_rev MyoblastToMyotubes_Day04D2- Myoblast differentiation to myotubes, day04, control donor2_CNhs14572_13482-145A8_reverse Regulation 
MyoblastDifferentiationToMyotubesDay04DuchenneMuscularDystrophyDonor2_CNhs14600_ctss_fwd MyoblastToMyotubes_Day04D2+ Myoblast differentiation to myotubes, day04, Duchenne Muscular Dystrophy donor2_CNhs14600_13509-145D8_forward Regulation 
MyoblastDifferentiationToMyotubesDay04ControlDonor2_CNhs14572_ctss_fwd MyoblastToMyotubes_Day04D2+ Myoblast differentiation to myotubes, day04, control donor2_CNhs14572_13482-145A8_forward Regulation 
MyoblastDifferentiationToMyotubesDay04DuchenneMuscularDystrophyDonor1_CNhs14590_ctss_rev MyoblastToMyotubes_Day04D1- Myoblast differentiation to myotubes, day04, Duchenne Muscular Dystrophy donor1_CNhs14590_13500-145C8_reverse Regulation 
MyoblastDifferentiationToMyotubesDay04ControlDonor1_CNhs13851_ctss_rev MyoblastToMyotubes_Day04D1- Myoblast differentiation to myotubes, day04, control donor1_CNhs13851_13473-144I8_reverse Regulation 
MyoblastDifferentiationToMyotubesDay04DuchenneMuscularDystrophyDonor1_CNhs14590_ctss_fwd MyoblastToMyotubes_Day04D1+ Myoblast differentiation to myotubes, day04, Duchenne Muscular Dystrophy donor1_CNhs14590_13500-145C8_forward Regulation 
MyoblastDifferentiationToMyotubesDay04ControlDonor1_CNhs13851_ctss_fwd MyoblastToMyotubes_Day04D1+ Myoblast differentiation to myotubes, day04, control donor1_CNhs13851_13473-144I8_forward Regulation 
MyoblastDifferentiationToMyotubesDay03ControlDonor3_CNhs14580_ctss_rev MyoblastToMyotubes_Day03D3- Myoblast differentiation to myotubes, day03, control donor3_CNhs14580_13490-145B7_reverse Regulation 
MyoblastDifferentiationToMyotubesDay03ControlDonor3_CNhs14580_ctss_fwd MyoblastToMyotubes_Day03D3+ Myoblast differentiation to myotubes, day03, control donor3_CNhs14580_13490-145B7_forward Regulation 
MyoblastDifferentiationToMyotubesDay03DuchenneMuscularDystrophyDonor2_CNhs14599_ctss_rev MyoblastToMyotubes_Day03D2- Myoblast differentiation to myotubes, day03, Duchenne Muscular Dystrophy donor2_CNhs14599_13508-145D7_reverse Regulation 
MyoblastDifferentiationToMyotubesDay03ControlDonor2_CNhs14571_ctss_rev MyoblastToMyotubes_Day03D2- Myoblast differentiation to myotubes, day03, control donor2_CNhs14571_13481-145A7_reverse Regulation 
MyoblastDifferentiationToMyotubesDay03DuchenneMuscularDystrophyDonor2_CNhs14599_ctss_fwd MyoblastToMyotubes_Day03D2+ Myoblast differentiation to myotubes, day03, Duchenne Muscular Dystrophy donor2_CNhs14599_13508-145D7_forward Regulation 
MyoblastDifferentiationToMyotubesDay03ControlDonor2_CNhs14571_ctss_fwd MyoblastToMyotubes_Day03D2+ Myoblast differentiation to myotubes, day03, control donor2_CNhs14571_13481-145A7_forward Regulation 
MyoblastDifferentiationToMyotubesDay03DuchenneMuscularDystrophyDonor1_CNhs14589_ctss_rev MyoblastToMyotubes_Day03D1- Myoblast differentiation to myotubes, day03, Duchenne Muscular Dystrophy donor1_CNhs14589_13499-145C7_reverse Regulation 
MyoblastDifferentiationToMyotubesDay03ControlDonor1_CNhs13850_ctss_rev MyoblastToMyotubes_Day03D1- Myoblast differentiation to myotubes, day03, control donor1_CNhs13850_13472-144I7_reverse Regulation 
MyoblastDifferentiationToMyotubesDay03DuchenneMuscularDystrophyDonor1_CNhs14589_ctss_fwd MyoblastToMyotubes_Day03D1+ Myoblast differentiation to myotubes, day03, Duchenne Muscular Dystrophy donor1_CNhs14589_13499-145C7_forward Regulation 
MyoblastDifferentiationToMyotubesDay03ControlDonor1_CNhs13850_ctss_fwd MyoblastToMyotubes_Day03D1+ Myoblast differentiation to myotubes, day03, control donor1_CNhs13850_13472-144I7_forward Regulation 
MyoblastDifferentiationToMyotubesDay02DuchenneMuscularDystrophyDonor3_CNhs14607_ctss_rev MyoblastToMyotubes_Day02D3- Myoblast differentiation to myotubes, day02, Duchenne Muscular Dystrophy donor3_CNhs14607_13516-145E6_reverse Regulation 
MyoblastDifferentiationToMyotubesDay02ControlDonor3_CNhs14579_ctss_rev MyoblastToMyotubes_Day02D3- Myoblast differentiation to myotubes, day02, control donor3_CNhs14579_13489-145B6_reverse Regulation 
MyoblastDifferentiationToMyotubesDay02DuchenneMuscularDystrophyDonor3_CNhs14607_ctss_fwd MyoblastToMyotubes_Day02D3+ Myoblast differentiation to myotubes, day02, Duchenne Muscular Dystrophy donor3_CNhs14607_13516-145E6_forward Regulation 
MyoblastDifferentiationToMyotubesDay02ControlDonor3_CNhs14579_ctss_fwd MyoblastToMyotubes_Day02D3+ Myoblast differentiation to myotubes, day02, control donor3_CNhs14579_13489-145B6_forward Regulation 
MyoblastDifferentiationToMyotubesDay02ControlDonor2_CNhs14570_ctss_rev MyoblastToMyotubes_Day02D2- Myoblast differentiation to myotubes, day02, control donor2_CNhs14570_13480-145A6_reverse Regulation 
MyoblastDifferentiationToMyotubesDay02DuchenneMuscularDystrophyDonor2_CNhs14598_ctss_rev MyoblastToMyotubes_Day02D2- Myoblast differentiation to myotubes, day02, Duchenne Muscular Dystrophy donor2_CNhs14598_13507-145D6_reverse Regulation 
MyoblastDifferentiationToMyotubesDay02ControlDonor2_CNhs14570_ctss_fwd MyoblastToMyotubes_Day02D2+ Myoblast differentiation to myotubes, day02, control donor2_CNhs14570_13480-145A6_forward Regulation 
MyoblastDifferentiationToMyotubesDay02DuchenneMuscularDystrophyDonor2_CNhs14598_ctss_fwd MyoblastToMyotubes_Day02D2+ Myoblast differentiation to myotubes, day02, Duchenne Muscular Dystrophy donor2_CNhs14598_13507-145D6_forward Regulation 
MyoblastDifferentiationToMyotubesDay02ControlDonor1_CNhs13849_ctss_rev MyoblastToMyotubes_Day02D1- Myoblast differentiation to myotubes, day02, control donor1_CNhs13849_13471-144I6_reverse Regulation 
MyoblastDifferentiationToMyotubesDay02DuchenneMuscularDystrophyDonor1_CNhs14588_ctss_rev MyoblastToMyotubes_Day02D1- Myoblast differentiation to myotubes, day02, Duchenne Muscular Dystrophy donor1_CNhs14588_13498-145C6_reverse Regulation 
MyoblastDifferentiationToMyotubesDay02ControlDonor1_CNhs13849_ctss_fwd MyoblastToMyotubes_Day02D1+ Myoblast differentiation to myotubes, day02, control donor1_CNhs13849_13471-144I6_forward Regulation 
MyoblastDifferentiationToMyotubesDay02DuchenneMuscularDystrophyDonor1_CNhs14588_ctss_fwd MyoblastToMyotubes_Day02D1+ Myoblast differentiation to myotubes, day02, Duchenne Muscular Dystrophy donor1_CNhs14588_13498-145C6_forward Regulation 
MyoblastDifferentiationToMyotubesDay01DuchenneMuscularDystrophyDonor3_CNhs14606_ctss_rev MyoblastToMyotubes_Day01D3- Myoblast differentiation to myotubes, day01, Duchenne Muscular Dystrophy donor3_CNhs14606_13515-145E5_reverse Regulation 
MyoblastDifferentiationToMyotubesDay01ControlDonor3_CNhs14578_ctss_rev MyoblastToMyotubes_Day01D3- Myoblast differentiation to myotubes, day01, control donor3_CNhs14578_13488-145B5_reverse Regulation 
MyoblastDifferentiationToMyotubesDay01ControlDonor3_CNhs14578_ctss_fwd MyoblastToMyotubes_Day01D3+ Myoblast differentiation to myotubes, day01, control donor3_CNhs14578_13488-145B5_forward Regulation 
MyoblastDifferentiationToMyotubesDay01DuchenneMuscularDystrophyDonor3_CNhs14606_ctss_fwd MyoblastToMyotubes_Day01D3+ Myoblast differentiation to myotubes, day01, Duchenne Muscular Dystrophy donor3_CNhs14606_13515-145E5_forward Regulation 
MyoblastDifferentiationToMyotubesDay01DuchenneMuscularDystrophyDonor2_CNhs14597_ctss_rev MyoblastToMyotubes_Day01D2- Myoblast differentiation to myotubes, day01, Duchenne Muscular Dystrophy donor2_CNhs14597_13506-145D5_reverse Regulation 
MyoblastDifferentiationToMyotubesDay01DuchenneMuscularDystrophyDonor2_CNhs14597_ctss_fwd MyoblastToMyotubes_Day01D2+ Myoblast differentiation to myotubes, day01, Duchenne Muscular Dystrophy donor2_CNhs14597_13506-145D5_forward Regulation 
MyoblastDifferentiationToMyotubesDay01ControlDonor1_CNhs13848_ctss_rev MyoblastToMyotubes_Day01D1- Myoblast differentiation to myotubes, day01, control donor1_CNhs13848_13470-144I5_reverse Regulation 
MyoblastDifferentiationToMyotubesDay01DuchenneMuscularDystrophyDonor1_CNhs14587_ctss_rev MyoblastToMyotubes_Day01D1- Myoblast differentiation to myotubes, day01, Duchenne Muscular Dystrophy donor1_CNhs14587_13497-145C5_reverse Regulation 
MyoblastDifferentiationToMyotubesDay01ControlDonor1_CNhs13848_ctss_fwd MyoblastToMyotubes_Day01D1+ Myoblast differentiation to myotubes, day01, control donor1_CNhs13848_13470-144I5_forward Regulation 
MyoblastDifferentiationToMyotubesDay01DuchenneMuscularDystrophyDonor1_CNhs14587_ctss_fwd MyoblastToMyotubes_Day01D1+ Myoblast differentiation to myotubes, day01, Duchenne Muscular Dystrophy donor1_CNhs14587_13497-145C5_forward Regulation 
MyoblastDifferentiationToMyotubesDay00ControlDonor3_CNhs14577_ctss_rev MyoblastToMyotubes_Day00D3- Myoblast differentiation to myotubes, day00, control donor3_CNhs14577_13487-145B4_reverse Regulation 
MyoblastDifferentiationToMyotubesDay00DuchenneMuscularDystrophyDonor3_CNhs14605_ctss_rev MyoblastToMyotubes_Day00D3- Myoblast differentiation to myotubes, day00, Duchenne Muscular Dystrophy donor3_CNhs14605_13514-145E4_reverse Regulation 
MyoblastDifferentiationToMyotubesDay00ControlDonor3_CNhs14577_ctss_fwd MyoblastToMyotubes_Day00D3+ Myoblast differentiation to myotubes, day00, control donor3_CNhs14577_13487-145B4_forward Regulation 
MyoblastDifferentiationToMyotubesDay00DuchenneMuscularDystrophyDonor3_CNhs14605_ctss_fwd MyoblastToMyotubes_Day00D3+ Myoblast differentiation to myotubes, day00, Duchenne Muscular Dystrophy donor3_CNhs14605_13514-145E4_forward Regulation 
MyoblastDifferentiationToMyotubesDay00ControlDonor2_CNhs14567_ctss_rev MyoblastToMyotubes_Day00D2- Myoblast differentiation to myotubes, day00, control donor2_CNhs14567_13478-145A4_reverse Regulation 
MyoblastDifferentiationToMyotubesDay00DuchenneMuscularDystrophyDonor2_CNhs14596_ctss_rev MyoblastToMyotubes_Day00D2- Myoblast differentiation to myotubes, day00, Duchenne Muscular Dystrophy donor2_CNhs14596_13505-145D4_reverse Regulation 
MyoblastDifferentiationToMyotubesDay00DuchenneMuscularDystrophyDonor2_CNhs14596_ctss_fwd MyoblastToMyotubes_Day00D2+ Myoblast differentiation to myotubes, day00, Duchenne Muscular Dystrophy donor2_CNhs14596_13505-145D4_forward Regulation 
MyoblastDifferentiationToMyotubesDay00ControlDonor2_CNhs14567_ctss_fwd MyoblastToMyotubes_Day00D2+ Myoblast differentiation to myotubes, day00, control donor2_CNhs14567_13478-145A4_forward Regulation 
MyoblastDifferentiationToMyotubesDay00DuchenneMuscularDystrophyDonor1_CNhs14586_ctss_rev MyoblastToMyotubes_Day00D1- Myoblast differentiation to myotubes, day00, Duchenne Muscular Dystrophy donor1_CNhs14586_13496-145C4_reverse Regulation 
MyoblastDifferentiationToMyotubesDay00ControlDonor1_CNhs13847_ctss_rev MyoblastToMyotubes_Day00D1- Myoblast differentiation to myotubes, day00, control donor1_CNhs13847_13469-144I4_reverse Regulation 
MyoblastDifferentiationToMyotubesDay00DuchenneMuscularDystrophyDonor1_CNhs14586_ctss_fwd MyoblastToMyotubes_Day00D1+ Myoblast differentiation to myotubes, day00, Duchenne Muscular Dystrophy donor1_CNhs14586_13496-145C4_forward Regulation 
MyoblastDifferentiationToMyotubesDay00ControlDonor1_CNhs13847_ctss_fwd MyoblastToMyotubes_Day00D1+ Myoblast differentiation to myotubes, day00, control donor1_CNhs13847_13469-144I4_forward Regulation 
MonocytederivedMacrophagesResponseToLPS48hrDonor2T26Subject2_CNhs13405_ctss_rev Tc:MdmToLps_48hrD2- Monocyte-derived macrophages response to LPS, 48hr, donor2 (t26 Subject2)_CNhs13405_12821-136I4_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS48hrDonor2T26Subject2_CNhs13405_ctss_fwd Tc:MdmToLps_48hrD2+ Monocyte-derived macrophages response to LPS, 48hr, donor2 (t26 Subject2)_CNhs13405_12821-136I4_forward Regulation 
MonocytederivedMacrophagesResponseToLPS48hrDonor1T26Subject1_CNhs11942_ctss_rev Tc:MdmToLps_48hrD1- Monocyte-derived macrophages response to LPS, 48hr, donor1 (t26 Subject1)_CNhs11942_12723-135G5_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS48hrDonor1T26Subject1_CNhs11942_ctss_fwd Tc:MdmToLps_48hrD1+ Monocyte-derived macrophages response to LPS, 48hr, donor1 (t26 Subject1)_CNhs11942_12723-135G5_forward Regulation 
MonocytederivedMacrophagesResponseToLPS36hrDonor3T25Subject3_CNhs13335_ctss_rev Tc:MdmToLps_36hrD3- Monocyte-derived macrophages response to LPS, 36hr, donor3 (t25 Subject3)_CNhs13335_12918-138B2_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS36hrDonor3T25Subject3_CNhs13335_ctss_fwd Tc:MdmToLps_36hrD3+ Monocyte-derived macrophages response to LPS, 36hr, donor3 (t25 Subject3)_CNhs13335_12918-138B2_forward Regulation 
MonocytederivedMacrophagesResponseToLPS36hrDonor2T25Subject2_CNhs13404_ctss_rev Tc:MdmToLps_36hrD2- Monocyte-derived macrophages response to LPS, 36hr, donor2 (t25 Subject2)_CNhs13404_12820-136I3_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS36hrDonor2T25Subject2_CNhs13404_ctss_fwd Tc:MdmToLps_36hrD2+ Monocyte-derived macrophages response to LPS, 36hr, donor2 (t25 Subject2)_CNhs13404_12820-136I3_forward Regulation 
MonocytederivedMacrophagesResponseToLPS36hrDonor1T25Subject1_CNhs12933_ctss_rev Tc:MdmToLps_36hrD1- Monocyte-derived macrophages response to LPS, 36hr, donor1 (t25 Subject1)_CNhs12933_12722-135G4_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS36hrDonor1T25Subject1_CNhs12933_ctss_fwd Tc:MdmToLps_36hrD1+ Monocyte-derived macrophages response to LPS, 36hr, donor1 (t25 Subject1)_CNhs12933_12722-135G4_forward Regulation 
MonocytederivedMacrophagesResponseToLPS24hrDonor3T24Subject3_CNhs13334_ctss_rev Tc:MdmToLps_24hrD3- Monocyte-derived macrophages response to LPS, 24hr, donor3 (t24 Subject3)_CNhs13334_12917-138B1_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS24hrDonor3T24Subject3_CNhs13334_ctss_fwd Tc:MdmToLps_24hrD3+ Monocyte-derived macrophages response to LPS, 24hr, donor3 (t24 Subject3)_CNhs13334_12917-138B1_forward Regulation 
MonocytederivedMacrophagesResponseToLPS24hrDonor2T24Subject2_CNhs13403_ctss_rev Tc:MdmToLps_24hrD2- Monocyte-derived macrophages response to LPS, 24hr, donor2 (t24 Subject2)_CNhs13403_12819-136I2_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS24hrDonor2T24Subject2_CNhs13403_ctss_fwd Tc:MdmToLps_24hrD2+ Monocyte-derived macrophages response to LPS, 24hr, donor2 (t24 Subject2)_CNhs13403_12819-136I2_forward Regulation 
MonocytederivedMacrophagesResponseToLPS24hrDonor1T24Subject1_CNhs12932_ctss_rev Tc:MdmToLps_24hrD1- Monocyte-derived macrophages response to LPS, 24hr, donor1 (t24 Subject1)_CNhs12932_12721-135G3_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS24hrDonor1T24Subject1_CNhs12932_ctss_fwd Tc:MdmToLps_24hrD1+ Monocyte-derived macrophages response to LPS, 24hr, donor1 (t24 Subject1)_CNhs12932_12721-135G3_forward Regulation 
MonocytederivedMacrophagesResponseToLPS22hrDonor3T23Subject3_CNhs13333_ctss_rev Tc:MdmToLps_22hrD3- Monocyte-derived macrophages response to LPS, 22hr, donor3 (t23 Subject3)_CNhs13333_12916-138A9_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS22hrDonor3T23Subject3_CNhs13333_ctss_fwd Tc:MdmToLps_22hrD3+ Monocyte-derived macrophages response to LPS, 22hr, donor3 (t23 Subject3)_CNhs13333_12916-138A9_forward Regulation 
MonocytederivedMacrophagesResponseToLPS22hrDonor2T23Subject2_CNhs13402_ctss_rev Tc:MdmToLps_22hrD2- Monocyte-derived macrophages response to LPS, 22hr, donor2 (t23 Subject2)_CNhs13402_12818-136I1_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS22hrDonor2T23Subject2_CNhs13402_ctss_fwd Tc:MdmToLps_22hrD2+ Monocyte-derived macrophages response to LPS, 22hr, donor2 (t23 Subject2)_CNhs13402_12818-136I1_forward Regulation 
MonocytederivedMacrophagesResponseToLPS22hrDonor1T23Subject1_CNhs12815_ctss_rev Tc:MdmToLps_22hrD1- Monocyte-derived macrophages response to LPS, 22hr, donor1 (t23 Subject1)_CNhs12815_12720-135G2_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS22hrDonor1T23Subject1_CNhs12815_ctss_fwd Tc:MdmToLps_22hrD1+ Monocyte-derived macrophages response to LPS, 22hr, donor1 (t23 Subject1)_CNhs12815_12720-135G2_forward Regulation 
MonocytederivedMacrophagesResponseToLPS20hrDonor3T22Subject3_CNhs13332_ctss_rev Tc:MdmToLps_20hrD3- Monocyte-derived macrophages response to LPS, 20hr, donor3 (t22 Subject3)_CNhs13332_12915-138A8_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS20hrDonor3T22Subject3_CNhs13332_ctss_fwd Tc:MdmToLps_20hrD3+ Monocyte-derived macrophages response to LPS, 20hr, donor3 (t22 Subject3)_CNhs13332_12915-138A8_forward Regulation 
MonocytederivedMacrophagesResponseToLPS20hrDonor2T22Subject2_CNhs13401_ctss_rev Tc:MdmToLps_20hrD2- Monocyte-derived macrophages response to LPS, 20hr, donor2 (t22 Subject2)_CNhs13401_12817-136H9_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS20hrDonor2T22Subject2_CNhs13401_ctss_fwd Tc:MdmToLps_20hrD2+ Monocyte-derived macrophages response to LPS, 20hr, donor2 (t22 Subject2)_CNhs13401_12817-136H9_forward Regulation 
MonocytederivedMacrophagesResponseToLPS20hrDonor1T22Subject1_CNhs12931_ctss_rev Tc:MdmToLps_20hrD1- Monocyte-derived macrophages response to LPS, 20hr, donor1 (t22 Subject1)_CNhs12931_12719-135G1_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS20hrDonor1T22Subject1_CNhs12931_ctss_fwd Tc:MdmToLps_20hrD1+ Monocyte-derived macrophages response to LPS, 20hr, donor1 (t22 Subject1)_CNhs12931_12719-135G1_forward Regulation 
MonocytederivedMacrophagesResponseToLPS18hrDonor3T21Subject3_CNhs13331_ctss_rev Tc:MdmToLps_18hrD3- Monocyte-derived macrophages response to LPS, 18hr, donor3 (t21 Subject3)_CNhs13331_12914-138A7_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS18hrDonor3T21Subject3_CNhs13331_ctss_fwd Tc:MdmToLps_18hrD3+ Monocyte-derived macrophages response to LPS, 18hr, donor3 (t21 Subject3)_CNhs13331_12914-138A7_forward Regulation 
MonocytederivedMacrophagesResponseToLPS18hrDonor2T21Subject2_CNhs13400_ctss_rev Tc:MdmToLps_18hrD2- Monocyte-derived macrophages response to LPS, 18hr, donor2 (t21 Subject2)_CNhs13400_12816-136H8_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS18hrDonor2T21Subject2_CNhs13400_ctss_fwd Tc:MdmToLps_18hrD2+ Monocyte-derived macrophages response to LPS, 18hr, donor2 (t21 Subject2)_CNhs13400_12816-136H8_forward Regulation 
MonocytederivedMacrophagesResponseToLPS18hrDonor1T21Subject1_CNhs12814_ctss_rev Tc:MdmToLps_18hrD1- Monocyte-derived macrophages response to LPS, 18hr, donor1 (t21 Subject1)_CNhs12814_12718-135F9_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS18hrDonor1T21Subject1_CNhs12814_ctss_fwd Tc:MdmToLps_18hrD1+ Monocyte-derived macrophages response to LPS, 18hr, donor1 (t21 Subject1)_CNhs12814_12718-135F9_forward Regulation 
MonocytederivedMacrophagesResponseToLPS16hrDonor3T20Subject3_CNhs13330_ctss_rev Tc:MdmToLps_16hrD3- Monocyte-derived macrophages response to LPS, 16hr, donor3 (t20 Subject3)_CNhs13330_12913-138A6_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS16hrDonor3T20Subject3_CNhs13330_ctss_fwd Tc:MdmToLps_16hrD3+ Monocyte-derived macrophages response to LPS, 16hr, donor3 (t20 Subject3)_CNhs13330_12913-138A6_forward Regulation 
MonocytederivedMacrophagesResponseToLPS16hrDonor2T20Subject2_CNhs13399_ctss_rev Tc:MdmToLps_16hrD2- Monocyte-derived macrophages response to LPS, 16hr, donor2 (t20 Subject2)_CNhs13399_12815-136H7_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS16hrDonor2T20Subject2_CNhs13399_ctss_fwd Tc:MdmToLps_16hrD2+ Monocyte-derived macrophages response to LPS, 16hr, donor2 (t20 Subject2)_CNhs13399_12815-136H7_forward Regulation 
MonocytederivedMacrophagesResponseToLPS14hrDonor3T19Subject3_CNhs13329_ctss_rev Tc:MdmToLps_14hrD3- Monocyte-derived macrophages response to LPS, 14hr, donor3 (t19 Subject3)_CNhs13329_12912-138A5_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS14hrDonor3T19Subject3_CNhs13329_ctss_fwd Tc:MdmToLps_14hrD3+ Monocyte-derived macrophages response to LPS, 14hr, donor3 (t19 Subject3)_CNhs13329_12912-138A5_forward Regulation 
MonocytederivedMacrophagesResponseToLPS14hrDonor2T19Subject2_CNhs13398_ctss_rev Tc:MdmToLps_14hrD2- Monocyte-derived macrophages response to LPS, 14hr, donor2 (t19 Subject2)_CNhs13398_12814-136H6_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS14hrDonor2T19Subject2_CNhs13398_ctss_fwd Tc:MdmToLps_14hrD2+ Monocyte-derived macrophages response to LPS, 14hr, donor2 (t19 Subject2)_CNhs13398_12814-136H6_forward Regulation 
MonocytederivedMacrophagesResponseToLPS14hrDonor1T19Subject1_CNhs12929_ctss_rev Tc:MdmToLps_14hrD1- Monocyte-derived macrophages response to LPS, 14hr, donor1 (t19 Subject1)_CNhs12929_12716-135F7_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS14hrDonor1T19Subject1_CNhs12929_ctss_fwd Tc:MdmToLps_14hrD1+ Monocyte-derived macrophages response to LPS, 14hr, donor1 (t19 Subject1)_CNhs12929_12716-135F7_forward Regulation 
MonocytederivedMacrophagesResponseToLPS12hrDonor3T18Subject3_CNhs13328_ctss_rev Tc:MdmToLps_12hrD3- Monocyte-derived macrophages response to LPS, 12hr, donor3 (t18 Subject3)_CNhs13328_12911-138A4_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS12hrDonor3T18Subject3_CNhs13328_ctss_fwd Tc:MdmToLps_12hrD3+ Monocyte-derived macrophages response to LPS, 12hr, donor3 (t18 Subject3)_CNhs13328_12911-138A4_forward Regulation 
MonocytederivedMacrophagesResponseToLPS12hrDonor2T18Subject2_CNhs13397_ctss_rev Tc:MdmToLps_12hrD2- Monocyte-derived macrophages response to LPS, 12hr, donor2 (t18 Subject2)_CNhs13397_12813-136H5_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS12hrDonor2T18Subject2_CNhs13397_ctss_fwd Tc:MdmToLps_12hrD2+ Monocyte-derived macrophages response to LPS, 12hr, donor2 (t18 Subject2)_CNhs13397_12813-136H5_forward Regulation 
MonocytederivedMacrophagesResponseToLPS12hrDonor1T18Subject1_CNhs12813_ctss_rev Tc:MdmToLps_12hrD1- Monocyte-derived macrophages response to LPS, 12hr, donor1 (t18 Subject1)_CNhs12813_12715-135F6_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS12hrDonor1T18Subject1_CNhs12813_ctss_fwd Tc:MdmToLps_12hrD1+ Monocyte-derived macrophages response to LPS, 12hr, donor1 (t18 Subject1)_CNhs12813_12715-135F6_forward Regulation 
MonocytederivedMacrophagesResponseToLPS10hrDonor3T17Subject3_CNhs13327_ctss_rev Tc:MdmToLps_10hrD3- Monocyte-derived macrophages response to LPS, 10hr, donor3 (t17 Subject3)_CNhs13327_12910-138A3_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS10hrDonor3T17Subject3_CNhs13327_ctss_fwd Tc:MdmToLps_10hrD3+ Monocyte-derived macrophages response to LPS, 10hr, donor3 (t17 Subject3)_CNhs13327_12910-138A3_forward Regulation 
MonocytederivedMacrophagesResponseToLPS10hrDonor2T17Subject2_CNhs13396_ctss_rev Tc:MdmToLps_10hrD2- Monocyte-derived macrophages response to LPS, 10hr, donor2 (t17 Subject2)_CNhs13396_12812-136H4_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS10hrDonor2T17Subject2_CNhs13396_ctss_fwd Tc:MdmToLps_10hrD2+ Monocyte-derived macrophages response to LPS, 10hr, donor2 (t17 Subject2)_CNhs13396_12812-136H4_forward Regulation 
MonocytederivedMacrophagesResponseToLPS08hrDonor3T16Subject3_CNhs13326_ctss_rev Tc:MdmToLps_08hrD3- Monocyte-derived macrophages response to LPS, 08hr, donor3 (t16 Subject3)_CNhs13326_12909-138A2_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS08hrDonor3T16Subject3_CNhs13326_ctss_fwd Tc:MdmToLps_08hrD3+ Monocyte-derived macrophages response to LPS, 08hr, donor3 (t16 Subject3)_CNhs13326_12909-138A2_forward Regulation 
MonocytederivedMacrophagesResponseToLPS08hrDonor2T16Subject2_CNhs13395_ctss_rev Tc:MdmToLps_08hrD2- Monocyte-derived macrophages response to LPS, 08hr, donor2 (t16 Subject2)_CNhs13395_12811-136H3_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS08hrDonor2T16Subject2_CNhs13395_ctss_fwd Tc:MdmToLps_08hrD2+ Monocyte-derived macrophages response to LPS, 08hr, donor2 (t16 Subject2)_CNhs13395_12811-136H3_forward Regulation 
MonocytederivedMacrophagesResponseToLPS08hrDonor1T16Subject1_CNhs12927_ctss_rev Tc:MdmToLps_08hrD1- Monocyte-derived macrophages response to LPS, 08hr, donor1 (t16 Subject1)_CNhs12927_12713-135F4_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS08hrDonor1T16Subject1_CNhs12927_ctss_fwd Tc:MdmToLps_08hrD1+ Monocyte-derived macrophages response to LPS, 08hr, donor1 (t16 Subject1)_CNhs12927_12713-135F4_forward Regulation 
MonocytederivedMacrophagesResponseToLPS05hrDonor3T13Subject3_CNhs13186_ctss_rev Tc:MdmToLps_05hrD3- Monocyte-derived macrophages response to LPS, 05hr, donor3 (t13 Subject3)_CNhs13186_12906-137I8_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS05hrDonor3T13Subject3_CNhs13186_ctss_fwd Tc:MdmToLps_05hrD3+ Monocyte-derived macrophages response to LPS, 05hr, donor3 (t13 Subject3)_CNhs13186_12906-137I8_forward Regulation 
MonocytederivedMacrophagesResponseToLPS05hrDonor2T13Subject2_CNhs13392_ctss_rev Tc:MdmToLps_05hrD2- Monocyte-derived macrophages response to LPS, 05hr, donor2 (t13 Subject2)_CNhs13392_12808-136G9_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS05hrDonor2T13Subject2_CNhs13392_ctss_fwd Tc:MdmToLps_05hrD2+ Monocyte-derived macrophages response to LPS, 05hr, donor2 (t13 Subject2)_CNhs13392_12808-136G9_forward Regulation 
MonocytederivedMacrophagesResponseToLPS05hrDonor1T13Subject1_CNhs13155_ctss_rev Tc:MdmToLps_05hrD1- Monocyte-derived macrophages response to LPS, 05hr, donor1 (t13 Subject1)_CNhs13155_12710-135F1_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS05hrDonor1T13Subject1_CNhs13155_ctss_fwd Tc:MdmToLps_05hrD1+ Monocyte-derived macrophages response to LPS, 05hr, donor1 (t13 Subject1)_CNhs13155_12710-135F1_forward Regulation 
MonocytederivedMacrophagesResponseToLPS04hrDonor3T12Subject3_CNhs13185_ctss_rev Tc:MdmToLps_04hrD3- Monocyte-derived macrophages response to LPS, 04hr, donor3 (t12 Subject3)_CNhs13185_12905-137I7_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS04hrDonor3T12Subject3_CNhs13185_ctss_fwd Tc:MdmToLps_04hrD3+ Monocyte-derived macrophages response to LPS, 04hr, donor3 (t12 Subject3)_CNhs13185_12905-137I7_forward Regulation 
MonocytederivedMacrophagesResponseToLPS04hrDonor2T12Subject2_CNhs13391_ctss_rev Tc:MdmToLps_04hrD2- Monocyte-derived macrophages response to LPS, 04hr, donor2 (t12 Subject2)_CNhs13391_12807-136G8_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS04hrDonor2T12Subject2_CNhs13391_ctss_fwd Tc:MdmToLps_04hrD2+ Monocyte-derived macrophages response to LPS, 04hr, donor2 (t12 Subject2)_CNhs13391_12807-136G8_forward Regulation 
MonocytederivedMacrophagesResponseToLPS03hr30minDonor3T11Subject3_CNhs13184_ctss_rev Tc:MdmToLps_03hr30minD3- Monocyte-derived macrophages response to LPS, 03hr30min, donor3 (t11 Subject3)_CNhs13184_12904-137I6_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS03hr30minDonor3T11Subject3_CNhs13184_ctss_fwd Tc:MdmToLps_03hr30minD3+ Monocyte-derived macrophages response to LPS, 03hr30min, donor3 (t11 Subject3)_CNhs13184_12904-137I6_forward Regulation 
MonocytederivedMacrophagesResponseToLPS03hr30minDonor2T11Subject2_CNhs13389_ctss_rev Tc:MdmToLps_03hr30minD2- Monocyte-derived macrophages response to LPS, 03hr30min, donor2 (t11 Subject2)_CNhs13389_12806-136G7_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS03hr30minDonor2T11Subject2_CNhs13389_ctss_fwd Tc:MdmToLps_03hr30minD2+ Monocyte-derived macrophages response to LPS, 03hr30min, donor2 (t11 Subject2)_CNhs13389_12806-136G7_forward Regulation 
MonocytederivedMacrophagesResponseToLPS03hr00minDonor3T10Subject3_CNhs13183_ctss_rev Tc:MdmToLps_03hr00minD3- Monocyte-derived macrophages response to LPS, 03hr00min, donor3 (t10 Subject3)_CNhs13183_12903-137I5_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS03hr00minDonor3T10Subject3_CNhs13183_ctss_fwd Tc:MdmToLps_03hr00minD3+ Monocyte-derived macrophages response to LPS, 03hr00min, donor3 (t10 Subject3)_CNhs13183_12903-137I5_forward Regulation 
MonocytederivedMacrophagesResponseToLPS03hr00minDonor2T10Subject2_CNhs13388_ctss_rev Tc:MdmToLps_03hr00minD2- Monocyte-derived macrophages response to LPS, 03hr00min, donor2 (t10 Subject2)_CNhs13388_12805-136G6_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS03hr00minDonor2T10Subject2_CNhs13388_ctss_fwd Tc:MdmToLps_03hr00minD2+ Monocyte-derived macrophages response to LPS, 03hr00min, donor2 (t10 Subject2)_CNhs13388_12805-136G6_forward Regulation 
MonocytederivedMacrophagesResponseToLPS03hr00minDonor1T10Subject1_CNhs12924_ctss_rev Tc:MdmToLps_03hr00minD1- Monocyte-derived macrophages response to LPS, 03hr00min, donor1 (t10 Subject1)_CNhs12924_12707-135E7_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS03hr00minDonor1T10Subject1_CNhs12924_ctss_fwd Tc:MdmToLps_03hr00minD1+ Monocyte-derived macrophages response to LPS, 03hr00min, donor1 (t10 Subject1)_CNhs12924_12707-135E7_forward Regulation 
MonocytederivedMacrophagesResponseToLPS02hr30minDonor3T9Subject3_CNhs13182_ctss_rev Tc:MdmToLps_02hr30minD3- Monocyte-derived macrophages response to LPS, 02hr30min, donor3 (t9 Subject3)_CNhs13182_12902-137I4_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS02hr30minDonor3T9Subject3_CNhs13182_ctss_fwd Tc:MdmToLps_02hr30minD3+ Monocyte-derived macrophages response to LPS, 02hr30min, donor3 (t9 Subject3)_CNhs13182_12902-137I4_forward Regulation 
MonocytederivedMacrophagesResponseToLPS02hr30minDonor2T9Subject2_CNhs13387_ctss_rev Tc:MdmToLps_02hr30minD2- Monocyte-derived macrophages response to LPS, 02hr30min, donor2 (t9 Subject2)_CNhs13387_12804-136G5_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS02hr30minDonor2T9Subject2_CNhs13387_ctss_fwd Tc:MdmToLps_02hr30minD2+ Monocyte-derived macrophages response to LPS, 02hr30min, donor2 (t9 Subject2)_CNhs13387_12804-136G5_forward Regulation 
MonocytederivedMacrophagesResponseToLPS02hr30minDonor1T9Subject1_CNhs13152_ctss_rev Tc:MdmToLps_02hr30minD1- Monocyte-derived macrophages response to LPS, 02hr30min, donor1 (t9 Subject1)_CNhs13152_12706-135E6_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS02hr30minDonor1T9Subject1_CNhs13152_ctss_fwd Tc:MdmToLps_02hr30minD1+ Monocyte-derived macrophages response to LPS, 02hr30min, donor1 (t9 Subject1)_CNhs13152_12706-135E6_forward Regulation 
MonocytederivedMacrophagesResponseToLPS02hr00minDonor3T8Subject3_CNhs13181_ctss_rev Tc:MdmToLps_02hr00minD3- Monocyte-derived macrophages response to LPS, 02hr00min, donor3 (t8 Subject3)_CNhs13181_12901-137I3_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS02hr00minDonor3T8Subject3_CNhs13181_ctss_fwd Tc:MdmToLps_02hr00minD3+ Monocyte-derived macrophages response to LPS, 02hr00min, donor3 (t8 Subject3)_CNhs13181_12901-137I3_forward Regulation 
MonocytederivedMacrophagesResponseToLPS02hr00minDonor2T8Subject2_CNhs13386_ctss_rev Tc:MdmToLps_02hr00minD2- Monocyte-derived macrophages response to LPS, 02hr00min, donor2 (t8 Subject2)_CNhs13386_12803-136G4_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS02hr00minDonor2T8Subject2_CNhs13386_ctss_fwd Tc:MdmToLps_02hr00minD2+ Monocyte-derived macrophages response to LPS, 02hr00min, donor2 (t8 Subject2)_CNhs13386_12803-136G4_forward Regulation 
MonocytederivedMacrophagesResponseToLPS01hr20minDonor3T6Subject3_CNhs13179_ctss_rev Tc:MdmToLps_01hr20minD3- Monocyte-derived macrophages response to LPS, 01hr20min, donor3 (t6 Subject3)_CNhs13179_12899-137I1_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS01hr20minDonor3T6Subject3_CNhs13179_ctss_fwd Tc:MdmToLps_01hr20minD3+ Monocyte-derived macrophages response to LPS, 01hr20min, donor3 (t6 Subject3)_CNhs13179_12899-137I1_forward Regulation 
MonocytederivedMacrophagesResponseToLPS01hr20minDonor2T6Subject2_CNhs13384_ctss_rev Tc:MdmToLps_01hr20minD2- Monocyte-derived macrophages response to LPS, 01hr20min, donor2 (t6 Subject2)_CNhs13384_12801-136G2_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS01hr20minDonor2T6Subject2_CNhs13384_ctss_fwd Tc:MdmToLps_01hr20minD2+ Monocyte-derived macrophages response to LPS, 01hr20min, donor2 (t6 Subject2)_CNhs13384_12801-136G2_forward Regulation 
MonocytederivedMacrophagesResponseToLPS01hr00minDonor3T5Subject3_CNhs13178_ctss_rev Tc:MdmToLps_01hr00minD3- Monocyte-derived macrophages response to LPS, 01hr00min, donor3 (t5 Subject3)_CNhs13178_12898-137H9_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS01hr00minDonor3T5Subject3_CNhs13178_ctss_fwd Tc:MdmToLps_01hr00minD3+ Monocyte-derived macrophages response to LPS, 01hr00min, donor3 (t5 Subject3)_CNhs13178_12898-137H9_forward Regulation 
MonocytederivedMacrophagesResponseToLPS01hr00minDonor2T5Subject2_CNhs13383_ctss_rev Tc:MdmToLps_01hr00minD2- Monocyte-derived macrophages response to LPS, 01hr00min, donor2 (t5 Subject2)_CNhs13383_12800-136G1_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS01hr00minDonor2T5Subject2_CNhs13383_ctss_fwd Tc:MdmToLps_01hr00minD2+ Monocyte-derived macrophages response to LPS, 01hr00min, donor2 (t5 Subject2)_CNhs13383_12800-136G1_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr45minDonor3T4Subject3_CNhs13177_ctss_rev Tc:MdmToLps_00hr45minD3- Monocyte-derived macrophages response to LPS, 00hr45min, donor3 (t4 Subject3)_CNhs13177_12897-137H8_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr45minDonor3T4Subject3_CNhs13177_ctss_fwd Tc:MdmToLps_00hr45minD3+ Monocyte-derived macrophages response to LPS, 00hr45min, donor3 (t4 Subject3)_CNhs13177_12897-137H8_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr45minDonor2T4Subject2_CNhs13382_ctss_rev Tc:MdmToLps_00hr45minD2- Monocyte-derived macrophages response to LPS, 00hr45min, donor2 (t4 Subject2)_CNhs13382_12799-136F9_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr45minDonor2T4Subject2_CNhs13382_ctss_fwd Tc:MdmToLps_00hr45minD2+ Monocyte-derived macrophages response to LPS, 00hr45min, donor2 (t4 Subject2)_CNhs13382_12799-136F9_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr30minDonor3T3Subject3_CNhs13176_ctss_rev Tc:MdmToLps_00hr30minD3- Monocyte-derived macrophages response to LPS, 00hr30min, donor3 (t3 Subject3)_CNhs13176_12896-137H7_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr30minDonor3T3Subject3_CNhs13176_ctss_fwd Tc:MdmToLps_00hr30minD3+ Monocyte-derived macrophages response to LPS, 00hr30min, donor3 (t3 Subject3)_CNhs13176_12896-137H7_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr30minDonor2T3Subject2_CNhs13381_ctss_rev Tc:MdmToLps_00hr30minD2- Monocyte-derived macrophages response to LPS, 00hr30min, donor2 (t3 Subject2)_CNhs13381_12798-136F8_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr30minDonor2T3Subject2_CNhs13381_ctss_fwd Tc:MdmToLps_00hr30minD2+ Monocyte-derived macrophages response to LPS, 00hr30min, donor2 (t3 Subject2)_CNhs13381_12798-136F8_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr15minDonor3T2Subject3_CNhs13175_ctss_rev Tc:MdmToLps_00hr15minD3- Monocyte-derived macrophages response to LPS, 00hr15min, donor3 (t2 Subject3)_CNhs13175_12895-137H6_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr15minDonor3T2Subject3_CNhs13175_ctss_fwd Tc:MdmToLps_00hr15minD3+ Monocyte-derived macrophages response to LPS, 00hr15min, donor3 (t2 Subject3)_CNhs13175_12895-137H6_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr15minDonor2T2Subject2_CNhs13380_ctss_rev Tc:MdmToLps_00hr15minD2- Monocyte-derived macrophages response to LPS, 00hr15min, donor2 (t2 Subject2)_CNhs13380_12797-136F7_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr15minDonor2T2Subject2_CNhs13380_ctss_fwd Tc:MdmToLps_00hr15minD2+ Monocyte-derived macrophages response to LPS, 00hr15min, donor2 (t2 Subject2)_CNhs13380_12797-136F7_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr00minDonor3T1Subject3_CNhs13174_ctss_rev Tc:MdmToLps_00hr00minD3- Monocyte-derived macrophages response to LPS, 00hr00min, donor3 (t1 Subject3)_CNhs13174_12894-137H5_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr00minDonor3T1Subject3_CNhs13174_ctss_fwd Tc:MdmToLps_00hr00minD3+ Monocyte-derived macrophages response to LPS, 00hr00min, donor3 (t1 Subject3)_CNhs13174_12894-137H5_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr00minDonor2T1Subject2_CNhs13379_ctss_rev Tc:MdmToLps_00hr00minD2- Monocyte-derived macrophages response to LPS, 00hr00min, donor2 (t1 Subject2)_CNhs13379_12796-136F6_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr00minDonor2T1Subject2_CNhs13379_ctss_fwd Tc:MdmToLps_00hr00minD2+ Monocyte-derived macrophages response to LPS, 00hr00min, donor2 (t1 Subject2)_CNhs13379_12796-136F6_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr00minDonor1T1Subject1_CNhs11941_ctss_rev Tc:MdmToLps_00hr00minD1- Monocyte-derived macrophages response to LPS, 00hr00min, donor1 (t1 Subject1)_CNhs11941_12698-135D7_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr00minDonor1T1Subject1_CNhs11941_ctss_fwd Tc:MdmToLps_00hr00minD1+ Monocyte-derived macrophages response to LPS, 00hr00min, donor1 (t1 Subject1)_CNhs11941_12698-135D7_forward Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection24hr00minDonor4227_121MI_24h_CNhs13644_ctss_rev Tc:MdmToMock_24hr00minD4- Monocyte-derived macrophages response to mock influenza infection, 24hr00min, donor4 (227_121:MI_24h)_CNhs13644_13315-143A3_reverse Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection24hr00minDonor4227_121MI_24h_CNhs13644_ctss_fwd Tc:MdmToMock_24hr00minD4+ Monocyte-derived macrophages response to mock influenza infection, 24hr00min, donor4 (227_121:MI_24h)_CNhs13644_13315-143A3_forward Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection24hr00minDonor3536_119MI_24h_CNhs13652_ctss_rev Tc:MdmToMock_24hr00minD3- Monocyte-derived macrophages response to mock influenza infection, 24hr00min, donor3 (536_119:MI_24h)_CNhs13652_13327-143B6_reverse Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection24hr00minDonor3536_119MI_24h_CNhs13652_ctss_fwd Tc:MdmToMock_24hr00minD3+ Monocyte-derived macrophages response to mock influenza infection, 24hr00min, donor3 (536_119:MI_24h)_CNhs13652_13327-143B6_forward Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection24hr00minDonor2150_120MI_24h_CNhs13648_ctss_rev Tc:MdmToMock_24hr00minD2- Monocyte-derived macrophages response to mock influenza infection, 24hr00min, donor2 (150_120:MI_24h)_CNhs13648_13321-143A9_reverse Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection24hr00minDonor2150_120MI_24h_CNhs13648_ctss_fwd Tc:MdmToMock_24hr00minD2+ Monocyte-derived macrophages response to mock influenza infection, 24hr00min, donor2 (150_120:MI_24h)_CNhs13648_13321-143A9_forward Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection24hr00minDonor1868_121MI_24h_CNhs13693_ctss_rev Tc:MdmToMock_24hr00minD1- Monocyte-derived macrophages response to mock influenza infection, 24hr00min, donor1 (868_121:MI_24h)_CNhs13693_13309-142I6_reverse Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection24hr00minDonor1868_121MI_24h_CNhs13693_ctss_fwd Tc:MdmToMock_24hr00minD1+ Monocyte-derived macrophages response to mock influenza infection, 24hr00min, donor1 (868_121:MI_24h)_CNhs13693_13309-142I6_forward Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection00hr00minDonor4227_121MI_0h_CNhs13638_ctss_rev Tc:MdmToMock_00hr00minD4- Monocyte-derived macrophages response to mock influenza infection, 00hr00min, donor4 (227_121:MI_0h)_CNhs13638_13310-142I7_reverse Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection00hr00minDonor4227_121MI_0h_CNhs13638_ctss_fwd Tc:MdmToMock_00hr00minD4+ Monocyte-derived macrophages response to mock influenza infection, 00hr00min, donor4 (227_121:MI_0h)_CNhs13638_13310-142I7_forward Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection00hr00minDonor3536_119MI_0h_CNhs13649_ctss_rev Tc:MdmToMock_00hr00minD3- Monocyte-derived macrophages response to mock influenza infection, 00hr00min, donor3 (536_119:MI_0h)_CNhs13649_13322-143B1_reverse Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection00hr00minDonor3536_119MI_0h_CNhs13649_ctss_fwd Tc:MdmToMock_00hr00minD3+ Monocyte-derived macrophages response to mock influenza infection, 00hr00min, donor3 (536_119:MI_0h)_CNhs13649_13322-143B1_forward Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection00hr00minDonor2150_120MI_0h_CNhs13645_ctss_rev Tc:MdmToMock_00hr00minD2- Monocyte-derived macrophages response to mock influenza infection, 00hr00min, donor2 (150_120:MI_0h)_CNhs13645_13316-143A4_reverse Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection00hr00minDonor2150_120MI_0h_CNhs13645_ctss_fwd Tc:MdmToMock_00hr00minD2+ Monocyte-derived macrophages response to mock influenza infection, 00hr00min, donor2 (150_120:MI_0h)_CNhs13645_13316-143A4_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection24hr00minDonor3536_119Ud_24h_CNhs13562_ctss_rev MonocyteMacrophageUdornInfluenza_24hr00minD3- Monocyte-derived macrophages response to udorn influenza infection, 24hr00min, donor3 (536_119:Ud_24h)_CNhs13562_13326-143B5_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection24hr00minDonor3536_119Ud_24h_CNhs13562_ctss_fwd MonocyteMacrophageUdornInfluenza_24hr00minD3+ Monocyte-derived macrophages response to udorn influenza infection, 24hr00min, donor3 (536_119:Ud_24h)_CNhs13562_13326-143B5_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection24hr00minDonor2150_120Ud_24h_CNhs13560_ctss_rev MonocyteMacrophageUdornInfluenza_24hr00minD2- Monocyte-derived macrophages response to udorn influenza infection, 24hr00min, donor2 (150_120:Ud_24h)_CNhs13560_13320-143A8_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection24hr00minDonor2150_120Ud_24h_CNhs13560_ctss_fwd MonocyteMacrophageUdornInfluenza_24hr00minD2+ Monocyte-derived macrophages response to udorn influenza infection, 24hr00min, donor2 (150_120:Ud_24h)_CNhs13560_13320-143A8_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection24hr00minDonor1868_121Ud_24h_CNhs13557_ctss_rev MonocyteMacrophageUdornInfluenza_24hr00minD1- Monocyte-derived macrophages response to udorn influenza infection, 24hr00min, donor1 (868_121:Ud_24h)_CNhs13557_13308-142I5_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection24hr00minDonor1868_121Ud_24h_CNhs13557_ctss_fwd MonocyteMacrophageUdornInfluenza_24hr00minD1+ Monocyte-derived macrophages response to udorn influenza infection, 24hr00min, donor1 (868_121:Ud_24h)_CNhs13557_13308-142I5_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection07hr00minDonor4227_121Ud_7h_CNhs13641_ctss_rev MonocyteMacrophageUdornInfluenza_07hr00minD4- Monocyte-derived macrophages response to udorn influenza infection, 07hr00min, donor4 (227_121:Ud_7h)_CNhs13641_13313-143A1_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection07hr00minDonor4227_121Ud_7h_CNhs13641_ctss_fwd MonocyteMacrophageUdornInfluenza_07hr00minD4+ Monocyte-derived macrophages response to udorn influenza infection, 07hr00min, donor4 (227_121:Ud_7h)_CNhs13641_13313-143A1_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection07hr00minDonor3536_119Ud_7h_CNhs13561_ctss_rev MonocyteMacrophageUdornInfluenza_07hr00minD3- Monocyte-derived macrophages response to udorn influenza infection, 07hr00min, donor3 (536_119:Ud_7h)_CNhs13561_13325-143B4_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection07hr00minDonor3536_119Ud_7h_CNhs13561_ctss_fwd MonocyteMacrophageUdornInfluenza_07hr00minD3+ Monocyte-derived macrophages response to udorn influenza infection, 07hr00min, donor3 (536_119:Ud_7h)_CNhs13561_13325-143B4_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection07hr00minDonor2150_120Ud_7h_CNhs13559_ctss_rev MonocyteMacrophageUdornInfluenza_07hr00minD2- Monocyte-derived macrophages response to udorn influenza infection, 07hr00min, donor2 (150_120:Ud_7h)_CNhs13559_13319-143A7_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection07hr00minDonor2150_120Ud_7h_CNhs13559_ctss_fwd MonocyteMacrophageUdornInfluenza_07hr00minD2+ Monocyte-derived macrophages response to udorn influenza infection, 07hr00min, donor2 (150_120:Ud_7h)_CNhs13559_13319-143A7_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection07hr00minDonor1868_121Ud_7h_CNhs13556_ctss_rev MonocyteMacrophageUdornInfluenza_07hr00minD1- Monocyte-derived macrophages response to udorn influenza infection, 07hr00min, donor1 (868_121:Ud_7h)_CNhs13556_13307-142I4_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection07hr00minDonor1868_121Ud_7h_CNhs13556_ctss_fwd MonocyteMacrophageUdornInfluenza_07hr00minD1+ Monocyte-derived macrophages response to udorn influenza infection, 07hr00min, donor1 (868_121:Ud_7h)_CNhs13556_13307-142I4_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection02hr00minDonor4227_121Ud_2h_CNhs13640_ctss_rev MonocyteMacrophageUdornInfluenza_02hr00minD4- Monocyte-derived macrophages response to udorn influenza infection, 02hr00min, donor4 (227_121:Ud_2h)_CNhs13640_13312-142I9_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection02hr00minDonor4227_121Ud_2h_CNhs13640_ctss_fwd MonocyteMacrophageUdornInfluenza_02hr00minD4+ Monocyte-derived macrophages response to udorn influenza infection, 02hr00min, donor4 (227_121:Ud_2h)_CNhs13640_13312-142I9_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection02hr00minDonor3536_119Ud_2h_CNhs13651_ctss_rev MonocyteMacrophageUdornInfluenza_02hr00minD3- Monocyte-derived macrophages response to udorn influenza infection, 02hr00min, donor3 (536_119:Ud_2h)_CNhs13651_13324-143B3_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection02hr00minDonor3536_119Ud_2h_CNhs13651_ctss_fwd MonocyteMacrophageUdornInfluenza_02hr00minD3+ Monocyte-derived macrophages response to udorn influenza infection, 02hr00min, donor3 (536_119:Ud_2h)_CNhs13651_13324-143B3_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection02hr00minDonor1868_121Ud_2h_CNhs13555_ctss_rev MonocyteMacrophageUdornInfluenza_02hr00minD1- Monocyte-derived macrophages response to udorn influenza infection, 02hr00min, donor1 (868_121:Ud_2h)_CNhs13555_13306-142I3_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection02hr00minDonor1868_121Ud_2h_CNhs13555_ctss_fwd MonocyteMacrophageUdornInfluenza_02hr00minD1+ Monocyte-derived macrophages response to udorn influenza infection, 02hr00min, donor1 (868_121:Ud_2h)_CNhs13555_13306-142I3_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection00hr00minDonor4227_121Ud_0h_CNhs13639_ctss_rev MonocyteMacrophageUdornInfluenza_00hr00minD4- Monocyte-derived macrophages response to udorn influenza infection, 00hr00min, donor4 (227_121:Ud_0h)_CNhs13639_13311-142I8_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection00hr00minDonor4227_121Ud_0h_CNhs13639_ctss_fwd MonocyteMacrophageUdornInfluenza_00hr00minD4+ Monocyte-derived macrophages response to udorn influenza infection, 00hr00min, donor4 (227_121:Ud_0h)_CNhs13639_13311-142I8_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection00hr00minDonor3536_119Ud_0h_CNhs13650_ctss_rev MonocyteMacrophageUdornInfluenza_00hr00minD3- Monocyte-derived macrophages response to udorn influenza infection, 00hr00min, donor3 (536_119:Ud_0h)_CNhs13650_13323-143B2_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection00hr00minDonor3536_119Ud_0h_CNhs13650_ctss_fwd MonocyteMacrophageUdornInfluenza_00hr00minD3+ Monocyte-derived macrophages response to udorn influenza infection, 00hr00min, donor3 (536_119:Ud_0h)_CNhs13650_13323-143B2_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection00hr00minDonor2150_120Ud_0h_CNhs13646_ctss_rev MonocyteMacrophageUdornInfluenza_00hr00minD2- Monocyte-derived macrophages response to udorn influenza infection, 00hr00min, donor2 (150_120:Ud_0h)_CNhs13646_13317-143A5_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection00hr00minDonor2150_120Ud_0h_CNhs13646_ctss_fwd MonocyteMacrophageUdornInfluenza_00hr00minD2+ Monocyte-derived macrophages response to udorn influenza infection, 00hr00min, donor2 (150_120:Ud_0h)_CNhs13646_13317-143A5_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection00hr00minDonor1868_121Ud_0h_CNhs13554_ctss_rev MonocyteMacrophageUdornInfluenza_00hr00minD1- Monocyte-derived macrophages response to udorn influenza infection, 00hr00min, donor1 (868_121:Ud_0h)_CNhs13554_13305-142I2_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection00hr00minDonor1868_121Ud_0h_CNhs13554_ctss_fwd MonocyteMacrophageUdornInfluenza_00hr00minD1+ Monocyte-derived macrophages response to udorn influenza infection, 00hr00min, donor1 (868_121:Ud_0h)_CNhs13554_13305-142I2_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay14BiolRep3_CNhs13632_ctss_rev MscAdipogenicInduction_Day14Br3- mesenchymal stem cells (adipose derived), adipogenic induction, day14, biol_rep3_CNhs13632_13279-142F3_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay14BiolRep3_CNhs13632_ctss_fwd MscAdipogenicInduction_Day14Br3+ mesenchymal stem cells (adipose derived), adipogenic induction, day14, biol_rep3_CNhs13632_13279-142F3_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay14BiolRep1_CNhs13338_ctss_rev MscAdipogenicInduction_Day14Br1- mesenchymal stem cells (adipose derived), adipogenic induction, day14, biol_rep1_CNhs13338_13277-142F1_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay14BiolRep1_CNhs13338_ctss_fwd MscAdipogenicInduction_Day14Br1+ mesenchymal stem cells (adipose derived), adipogenic induction, day14, biol_rep1_CNhs13338_13277-142F1_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay12BiolRep3_CNhs13630_ctss_rev MscAdipogenicInduction_Day12Br3- mesenchymal stem cells (adipose derived), adipogenic induction, day12, biol_rep3_CNhs13630_13276-142E9_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay12BiolRep3_CNhs13630_ctss_fwd MscAdipogenicInduction_Day12Br3+ mesenchymal stem cells (adipose derived), adipogenic induction, day12, biol_rep3_CNhs13630_13276-142E9_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay12BiolRep2_CNhs13629_ctss_rev MscAdipogenicInduction_Day12Br2- mesenchymal stem cells (adipose derived), adipogenic induction, day12, biol_rep2_CNhs13629_13275-142E8_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay12BiolRep2_CNhs13629_ctss_fwd MscAdipogenicInduction_Day12Br2+ mesenchymal stem cells (adipose derived), adipogenic induction, day12, biol_rep2_CNhs13629_13275-142E8_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay12BiolRep1_CNhs13628_ctss_rev MscAdipogenicInduction_Day12Br1- mesenchymal stem cells (adipose derived), adipogenic induction, day12, biol_rep1_CNhs13628_13274-142E7_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay12BiolRep1_CNhs13628_ctss_fwd MscAdipogenicInduction_Day12Br1+ mesenchymal stem cells (adipose derived), adipogenic induction, day12, biol_rep1_CNhs13628_13274-142E7_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay08BiolRep3_CNhs13627_ctss_rev MscAdipogenicInduction_Day08Br3- mesenchymal stem cells (adipose derived), adipogenic induction, day08, biol_rep3_CNhs13627_13273-142E6_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay08BiolRep3_CNhs13627_ctss_fwd MscAdipogenicInduction_Day08Br3+ mesenchymal stem cells (adipose derived), adipogenic induction, day08, biol_rep3_CNhs13627_13273-142E6_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay08BiolRep2_CNhs13626_ctss_rev MscAdipogenicInduction_Day08Br2- mesenchymal stem cells (adipose derived), adipogenic induction, day08, biol_rep2_CNhs13626_13272-142E5_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay08BiolRep2_CNhs13626_ctss_fwd MscAdipogenicInduction_Day08Br2+ mesenchymal stem cells (adipose derived), adipogenic induction, day08, biol_rep2_CNhs13626_13272-142E5_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay08BiolRep1_CNhs13625_ctss_rev MscAdipogenicInduction_Day08Br1- mesenchymal stem cells (adipose derived), adipogenic induction, day08, biol_rep1_CNhs13625_13271-142E4_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay08BiolRep1_CNhs13625_ctss_fwd MscAdipogenicInduction_Day08Br1+ mesenchymal stem cells (adipose derived), adipogenic induction, day08, biol_rep1_CNhs13625_13271-142E4_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay04BiolRep3_CNhs13624_ctss_rev MscAdipogenicInduction_Day04Br3- mesenchymal stem cells (adipose derived), adipogenic induction, day04, biol_rep3_CNhs13624_13270-142E3_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay04BiolRep3_CNhs13624_ctss_fwd MscAdipogenicInduction_Day04Br3+ mesenchymal stem cells (adipose derived), adipogenic induction, day04, biol_rep3_CNhs13624_13270-142E3_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay04BiolRep1_CNhs13622_ctss_rev MscAdipogenicInduction_Day04Br1- mesenchymal stem cells (adipose derived), adipogenic induction, day04, biol_rep1_CNhs13622_13268-142E1_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay04BiolRep1_CNhs13622_ctss_fwd MscAdipogenicInduction_Day04Br1+ mesenchymal stem cells (adipose derived), adipogenic induction, day04, biol_rep1_CNhs13622_13268-142E1_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay02BiolRep3_CNhs13621_ctss_rev MscAdipogenicInduction_Day02Br3- mesenchymal stem cells (adipose derived), adipogenic induction, day02, biol_rep3_CNhs13621_13267-142D9_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay02BiolRep3_CNhs13621_ctss_fwd MscAdipogenicInduction_Day02Br3+ mesenchymal stem cells (adipose derived), adipogenic induction, day02, biol_rep3_CNhs13621_13267-142D9_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay02BiolRep2_CNhs13620_ctss_rev MscAdipogenicInduction_Day02Br2- mesenchymal stem cells (adipose derived), adipogenic induction, day02, biol_rep2_CNhs13620_13266-142D8_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay02BiolRep2_CNhs13620_ctss_fwd MscAdipogenicInduction_Day02Br2+ mesenchymal stem cells (adipose derived), adipogenic induction, day02, biol_rep2_CNhs13620_13266-142D8_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay02BiolRep1_CNhs13619_ctss_rev MscAdipogenicInduction_Day02Br1- mesenchymal stem cells (adipose derived), adipogenic induction, day02, biol_rep1_CNhs13619_13265-142D7_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay02BiolRep1_CNhs13619_ctss_fwd MscAdipogenicInduction_Day02Br1+ mesenchymal stem cells (adipose derived), adipogenic induction, day02, biol_rep1_CNhs13619_13265-142D7_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay01BiolRep3_CNhs13617_ctss_rev MscAdipogenicInduction_Day01Br3- mesenchymal stem cells (adipose derived), adipogenic induction, day01, biol_rep3_CNhs13617_13264-142D6_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay01BiolRep3_CNhs13617_ctss_fwd MscAdipogenicInduction_Day01Br3+ mesenchymal stem cells (adipose derived), adipogenic induction, day01, biol_rep3_CNhs13617_13264-142D6_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay01BiolRep2_CNhs13616_ctss_rev MscAdipogenicInduction_Day01Br2- mesenchymal stem cells (adipose derived), adipogenic induction, day01, biol_rep2_CNhs13616_13263-142D5_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay01BiolRep2_CNhs13616_ctss_fwd MscAdipogenicInduction_Day01Br2+ mesenchymal stem cells (adipose derived), adipogenic induction, day01, biol_rep2_CNhs13616_13263-142D5_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction03hr00minBiolRep3_CNhs13611_ctss_rev MscAdipogenicInduction_03hr00minBr3- mesenchymal stem cells (adipose derived), adipogenic induction, 03hr00min, biol_rep3_CNhs13611_13258-142C9_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction03hr00minBiolRep3_CNhs13611_ctss_fwd MscAdipogenicInduction_03hr00minBr3+ mesenchymal stem cells (adipose derived), adipogenic induction, 03hr00min, biol_rep3_CNhs13611_13258-142C9_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction03hr00minBiolRep1_CNhs13609_ctss_rev MscAdipogenicInduction_03hr00minBr1- mesenchymal stem cells (adipose derived), adipogenic induction, 03hr00min, biol_rep1_CNhs13609_13256-142C7_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction03hr00minBiolRep1_CNhs13609_ctss_fwd MscAdipogenicInduction_03hr00minBr1+ mesenchymal stem cells (adipose derived), adipogenic induction, 03hr00min, biol_rep1_CNhs13609_13256-142C7_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr30minBiolRep3_CNhs13608_ctss_rev MscAdipogenicInduction_02hr30minBr3- mesenchymal stem cells (adipose derived), adipogenic induction, 02hr30min, biol_rep3_CNhs13608_13255-142C6_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr30minBiolRep3_CNhs13608_ctss_fwd MscAdipogenicInduction_02hr30minBr3+ mesenchymal stem cells (adipose derived), adipogenic induction, 02hr30min, biol_rep3_CNhs13608_13255-142C6_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr30minBiolRep1_CNhs13606_ctss_rev MscAdipogenicInduction_02hr30minBr1- mesenchymal stem cells (adipose derived), adipogenic induction, 02hr30min, biol_rep1_CNhs13606_13253-142C4_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr30minBiolRep1_CNhs13606_ctss_fwd MscAdipogenicInduction_02hr30minBr1+ mesenchymal stem cells (adipose derived), adipogenic induction, 02hr30min, biol_rep1_CNhs13606_13253-142C4_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr00minBiolRep2_CNhs13604_ctss_rev MscAdipogenicInduction_02hr00minBr2- mesenchymal stem cells (adipose derived), adipogenic induction, 02hr00min, biol_rep2_CNhs13604_13251-142C2_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr00minBiolRep2_CNhs13604_ctss_fwd MscAdipogenicInduction_02hr00minBr2+ mesenchymal stem cells (adipose derived), adipogenic induction, 02hr00min, biol_rep2_CNhs13604_13251-142C2_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr00minBiolRep1_CNhs13603_ctss_rev MscAdipogenicInduction_02hr00minBr1- mesenchymal stem cells (adipose derived), adipogenic induction, 02hr00min, biol_rep1_CNhs13603_13250-142C1_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr00minBiolRep1_CNhs13603_ctss_fwd MscAdipogenicInduction_02hr00minBr1+ mesenchymal stem cells (adipose derived), adipogenic induction, 02hr00min, biol_rep1_CNhs13603_13250-142C1_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr40minBiolRep3_CNhs13602_ctss_rev MscAdipogenicInduction_01hr40minBr3- mesenchymal stem cells (adipose derived), adipogenic induction, 01hr40min, biol_rep3_CNhs13602_13249-142B9_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr40minBiolRep3_CNhs13602_ctss_fwd MscAdipogenicInduction_01hr40minBr3+ mesenchymal stem cells (adipose derived), adipogenic induction, 01hr40min, biol_rep3_CNhs13602_13249-142B9_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr40minBiolRep2_CNhs13601_ctss_rev MscAdipogenicInduction_01hr40minBr2- mesenchymal stem cells (adipose derived), adipogenic induction, 01hr40min, biol_rep2_CNhs13601_13248-142B8_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr40minBiolRep2_CNhs13601_ctss_fwd MscAdipogenicInduction_01hr40minBr2+ mesenchymal stem cells (adipose derived), adipogenic induction, 01hr40min, biol_rep2_CNhs13601_13248-142B8_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr40minBiolRep1_CNhs13600_ctss_rev MscAdipogenicInduction_01hr40minBr1- mesenchymal stem cells (adipose derived), adipogenic induction, 01hr40min, biol_rep1_CNhs13600_13247-142B7_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr40minBiolRep1_CNhs13600_ctss_fwd MscAdipogenicInduction_01hr40minBr1+ mesenchymal stem cells (adipose derived), adipogenic induction, 01hr40min, biol_rep1_CNhs13600_13247-142B7_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr00minBiolRep3_CNhs13433_ctss_rev MscAdipogenicInduction_01hr00minBr3- mesenchymal stem cells (adipose derived), adipogenic induction, 01hr00min, biol_rep3_CNhs13433_13243-142B3_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr00minBiolRep3_CNhs13433_ctss_fwd MscAdipogenicInduction_01hr00minBr3+ mesenchymal stem cells (adipose derived), adipogenic induction, 01hr00min, biol_rep3_CNhs13433_13243-142B3_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr00minBiolRep2_CNhs13432_ctss_rev MscAdipogenicInduction_01hr00minBr2- mesenchymal stem cells (adipose derived), adipogenic induction, 01hr00min, biol_rep2_CNhs13432_13242-142B2_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr00minBiolRep2_CNhs13432_ctss_fwd MscAdipogenicInduction_01hr00minBr2+ mesenchymal stem cells (adipose derived), adipogenic induction, 01hr00min, biol_rep2_CNhs13432_13242-142B2_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr00minBiolRep1_CNhs13431_ctss_rev MscAdipogenicInduction_01hr00minBr1- mesenchymal stem cells (adipose derived), adipogenic induction, 01hr00min, biol_rep1_CNhs13431_13241-142B1_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr00minBiolRep1_CNhs13431_ctss_fwd MscAdipogenicInduction_01hr00minBr1+ mesenchymal stem cells (adipose derived), adipogenic induction, 01hr00min, biol_rep1_CNhs13431_13241-142B1_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr45minBiolRep3_CNhs13430_ctss_rev MscAdipogenicInduction_00hr45minBr3- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr45min, biol_rep3_CNhs13430_13240-142A9_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr45minBiolRep3_CNhs13430_ctss_fwd MscAdipogenicInduction_00hr45minBr3+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr45min, biol_rep3_CNhs13430_13240-142A9_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr45minBiolRep2_CNhs13429_ctss_rev MscAdipogenicInduction_00hr45minBr2- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr45min, biol_rep2_CNhs13429_13239-142A8_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr45minBiolRep2_CNhs13429_ctss_fwd MscAdipogenicInduction_00hr45minBr2+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr45min, biol_rep2_CNhs13429_13239-142A8_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr45minBiolRep1_CNhs13428_ctss_rev MscAdipogenicInduction_00hr45minBr1- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr45min, biol_rep1_CNhs13428_13238-142A7_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr45minBiolRep1_CNhs13428_ctss_fwd MscAdipogenicInduction_00hr45minBr1+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr45min, biol_rep1_CNhs13428_13238-142A7_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr30minBiolRep2_CNhs13426_ctss_rev MscAdipogenicInduction_00hr30minBr2- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr30min, biol_rep2_CNhs13426_13236-142A5_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr30minBiolRep2_CNhs13426_ctss_fwd MscAdipogenicInduction_00hr30minBr2+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr30min, biol_rep2_CNhs13426_13236-142A5_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr30minBiolRep1_CNhs13425_ctss_rev MscAdipogenicInduction_00hr30minBr1- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr30min, biol_rep1_CNhs13425_13235-142A4_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr30minBiolRep1_CNhs13425_ctss_fwd MscAdipogenicInduction_00hr30minBr1+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr30min, biol_rep1_CNhs13425_13235-142A4_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr15minBiolRep3_CNhs13424_ctss_rev MscAdipogenicInduction_00hr15minBr3- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr15min, biol_rep3_CNhs13424_13234-142A3_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr15minBiolRep3_CNhs13424_ctss_fwd MscAdipogenicInduction_00hr15minBr3+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr15min, biol_rep3_CNhs13424_13234-142A3_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr15minBiolRep2_CNhs13423_ctss_rev MscAdipogenicInduction_00hr15minBr2- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr15min, biol_rep2_CNhs13423_13233-142A2_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr15minBiolRep2_CNhs13423_ctss_fwd MscAdipogenicInduction_00hr15minBr2+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr15min, biol_rep2_CNhs13423_13233-142A2_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr15minBiolRep1_CNhs13422_ctss_rev MscAdipogenicInduction_00hr15minBr1- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr15min, biol_rep1_CNhs13422_13232-142A1_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr15minBiolRep1_CNhs13422_ctss_fwd MscAdipogenicInduction_00hr15minBr1+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr15min, biol_rep1_CNhs13422_13232-142A1_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr00minBiolRep3_CNhs13421_ctss_rev MscAdipogenicInduction_00hr00minBr3- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr00min, biol_rep3_CNhs13421_13231-141I9_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr00minBiolRep3_CNhs13421_ctss_fwd MscAdipogenicInduction_00hr00minBr3+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr00min, biol_rep3_CNhs13421_13231-141I9_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr00minBiolRep2_CNhs13420_ctss_rev MscAdipogenicInduction_00hr00minBr2- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr00min, biol_rep2_CNhs13420_13230-141I8_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr00minBiolRep2_CNhs13420_ctss_fwd MscAdipogenicInduction_00hr00minBr2+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr00min, biol_rep2_CNhs13420_13230-141I8_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr00minBiolRep1_CNhs13337_ctss_rev MscAdipogenicInduction_00hr00minBr1- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr00min, biol_rep1_CNhs13337_13229-141I7_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr00minBiolRep1_CNhs13337_ctss_fwd MscAdipogenicInduction_00hr00minBr1+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr00min, biol_rep1_CNhs13337_13229-141I7_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG08hrBiolRep3_CNhs12768_ctss_rev Tc:Mcf7ToHrg_08hrBr3- MCF7 breast cancer cell line response to HRG, 08hr, biol_rep3_CNhs12768_13194-141E8_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG08hrBiolRep3_CNhs12768_ctss_fwd Tc:Mcf7ToHrg_08hrBr3+ MCF7 breast cancer cell line response to HRG, 08hr, biol_rep3_CNhs12768_13194-141E8_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG08hrBiolRep2_CNhs12667_ctss_rev Tc:Mcf7ToHrg_08hrBr2- MCF7 breast cancer cell line response to HRG, 08hr, biol_rep2_CNhs12667_13128-140G5_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG08hrBiolRep2_CNhs12667_ctss_fwd Tc:Mcf7ToHrg_08hrBr2+ MCF7 breast cancer cell line response to HRG, 08hr, biol_rep2_CNhs12667_13128-140G5_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG08hrBiolRep1_CNhs12740_ctss_rev Tc:Mcf7ToHrg_08hrBr1- MCF7 breast cancer cell line response to HRG, 08hr, biol_rep1_CNhs12740_13062-139I2_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG08hrBiolRep1_CNhs12740_ctss_fwd Tc:Mcf7ToHrg_08hrBr1+ MCF7 breast cancer cell line response to HRG, 08hr, biol_rep1_CNhs12740_13062-139I2_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG07hrBiolRep3_CNhs12767_ctss_rev Tc:Mcf7ToHrg_07hrBr3- MCF7 breast cancer cell line response to HRG, 07hr, biol_rep3_CNhs12767_13193-141E7_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG07hrBiolRep3_CNhs12767_ctss_fwd Tc:Mcf7ToHrg_07hrBr3+ MCF7 breast cancer cell line response to HRG, 07hr, biol_rep3_CNhs12767_13193-141E7_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG07hrBiolRep2_CNhs12666_ctss_rev Tc:Mcf7ToHrg_07hrBr2- MCF7 breast cancer cell line response to HRG, 07hr, biol_rep2_CNhs12666_13127-140G4_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG07hrBiolRep2_CNhs12666_ctss_fwd Tc:Mcf7ToHrg_07hrBr2+ MCF7 breast cancer cell line response to HRG, 07hr, biol_rep2_CNhs12666_13127-140G4_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG07hrBiolRep1_CNhs12448_ctss_rev Tc:Mcf7ToHrg_07hrBr1- MCF7 breast cancer cell line response to HRG, 07hr, biol_rep1_CNhs12448_13061-139I1_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG07hrBiolRep1_CNhs12448_ctss_fwd Tc:Mcf7ToHrg_07hrBr1+ MCF7 breast cancer cell line response to HRG, 07hr, biol_rep1_CNhs12448_13061-139I1_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG06hrBiolRep3_CNhs12766_ctss_rev Tc:Mcf7ToHrg_06hrBr3- MCF7 breast cancer cell line response to HRG, 06hr, biol_rep3_CNhs12766_13192-141E6_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG06hrBiolRep3_CNhs12766_ctss_fwd Tc:Mcf7ToHrg_06hrBr3+ MCF7 breast cancer cell line response to HRG, 06hr, biol_rep3_CNhs12766_13192-141E6_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG06hrBiolRep2_CNhs12665_ctss_rev Tc:Mcf7ToHrg_06hrBr2- MCF7 breast cancer cell line response to HRG, 06hr, biol_rep2_CNhs12665_13126-140G3_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG06hrBiolRep2_CNhs12665_ctss_fwd Tc:Mcf7ToHrg_06hrBr2+ MCF7 breast cancer cell line response to HRG, 06hr, biol_rep2_CNhs12665_13126-140G3_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG06hrBiolRep1_CNhs12447_ctss_rev Tc:Mcf7ToHrg_06hrBr1- MCF7 breast cancer cell line response to HRG, 06hr, biol_rep1_CNhs12447_13060-139H9_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG06hrBiolRep1_CNhs12447_ctss_fwd Tc:Mcf7ToHrg_06hrBr1+ MCF7 breast cancer cell line response to HRG, 06hr, biol_rep1_CNhs12447_13060-139H9_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG05hrBiolRep3_CNhs12765_ctss_rev Tc:Mcf7ToHrg_05hrBr3- MCF7 breast cancer cell line response to HRG, 05hr, biol_rep3_CNhs12765_13191-141E5_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG05hrBiolRep3_CNhs12765_ctss_fwd Tc:Mcf7ToHrg_05hrBr3+ MCF7 breast cancer cell line response to HRG, 05hr, biol_rep3_CNhs12765_13191-141E5_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG05hrBiolRep2_CNhs12664_ctss_rev Tc:Mcf7ToHrg_05hrBr2- MCF7 breast cancer cell line response to HRG, 05hr, biol_rep2_CNhs12664_13125-140G2_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG05hrBiolRep2_CNhs12664_ctss_fwd Tc:Mcf7ToHrg_05hrBr2+ MCF7 breast cancer cell line response to HRG, 05hr, biol_rep2_CNhs12664_13125-140G2_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG05hrBiolRep1_CNhs12446_ctss_rev Tc:Mcf7ToHrg_05hrBr1- MCF7 breast cancer cell line response to HRG, 05hr, biol_rep1_CNhs12446_13059-139H8_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG05hrBiolRep1_CNhs12446_ctss_fwd Tc:Mcf7ToHrg_05hrBr1+ MCF7 breast cancer cell line response to HRG, 05hr, biol_rep1_CNhs12446_13059-139H8_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG04hrBiolRep3_CNhs12764_ctss_rev Tc:Mcf7ToHrg_04hrBr3- MCF7 breast cancer cell line response to HRG, 04hr, biol_rep3_CNhs12764_13190-141E4_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG04hrBiolRep3_CNhs12764_ctss_fwd Tc:Mcf7ToHrg_04hrBr3+ MCF7 breast cancer cell line response to HRG, 04hr, biol_rep3_CNhs12764_13190-141E4_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG04hrBiolRep2_CNhs12663_ctss_rev Tc:Mcf7ToHrg_04hrBr2- MCF7 breast cancer cell line response to HRG, 04hr, biol_rep2_CNhs12663_13124-140G1_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG04hrBiolRep2_CNhs12663_ctss_fwd Tc:Mcf7ToHrg_04hrBr2+ MCF7 breast cancer cell line response to HRG, 04hr, biol_rep2_CNhs12663_13124-140G1_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG04hrBiolRep1_CNhs12445_ctss_rev Tc:Mcf7ToHrg_04hrBr1- MCF7 breast cancer cell line response to HRG, 04hr, biol_rep1_CNhs12445_13058-139H7_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG04hrBiolRep1_CNhs12445_ctss_fwd Tc:Mcf7ToHrg_04hrBr1+ MCF7 breast cancer cell line response to HRG, 04hr, biol_rep1_CNhs12445_13058-139H7_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr30minBiolRep3_CNhs12763_ctss_rev Tc:Mcf7ToHrg_03hr30minBr3- MCF7 breast cancer cell line response to HRG, 03hr30min, biol_rep3_CNhs12763_13189-141E3_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr30minBiolRep3_CNhs12763_ctss_fwd Tc:Mcf7ToHrg_03hr30minBr3+ MCF7 breast cancer cell line response to HRG, 03hr30min, biol_rep3_CNhs12763_13189-141E3_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr30minBiolRep2_CNhs12662_ctss_rev Tc:Mcf7ToHrg_03hr30minBr2- MCF7 breast cancer cell line response to HRG, 03hr30min, biol_rep2_CNhs12662_13123-140F9_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr30minBiolRep2_CNhs12662_ctss_fwd Tc:Mcf7ToHrg_03hr30minBr2+ MCF7 breast cancer cell line response to HRG, 03hr30min, biol_rep2_CNhs12662_13123-140F9_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr30minBiolRep1_CNhs12444_ctss_rev Tc:Mcf7ToHrg_03hr30minBr1- MCF7 breast cancer cell line response to HRG, 03hr30min, biol_rep1_CNhs12444_13057-139H6_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr30minBiolRep1_CNhs12444_ctss_fwd Tc:Mcf7ToHrg_03hr30minBr1+ MCF7 breast cancer cell line response to HRG, 03hr30min, biol_rep1_CNhs12444_13057-139H6_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr00minBiolRep3_CNhs12762_ctss_rev Tc:Mcf7ToHrg_03hr00minBr3- MCF7 breast cancer cell line response to HRG, 03hr00min, biol_rep3_CNhs12762_13188-141E2_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr00minBiolRep3_CNhs12762_ctss_fwd Tc:Mcf7ToHrg_03hr00minBr3+ MCF7 breast cancer cell line response to HRG, 03hr00min, biol_rep3_CNhs12762_13188-141E2_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr00minBiolRep2_CNhs12660_ctss_rev Tc:Mcf7ToHrg_03hr00minBr2- MCF7 breast cancer cell line response to HRG, 03hr00min, biol_rep2_CNhs12660_13122-140F8_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr00minBiolRep2_CNhs12660_ctss_fwd Tc:Mcf7ToHrg_03hr00minBr2+ MCF7 breast cancer cell line response to HRG, 03hr00min, biol_rep2_CNhs12660_13122-140F8_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr00minBiolRep1_CNhs12443_ctss_rev Tc:Mcf7ToHrg_03hr00minBr1- MCF7 breast cancer cell line response to HRG, 03hr00min, biol_rep1_CNhs12443_13056-139H5_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr00minBiolRep1_CNhs12443_ctss_fwd Tc:Mcf7ToHrg_03hr00minBr1+ MCF7 breast cancer cell line response to HRG, 03hr00min, biol_rep1_CNhs12443_13056-139H5_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr30minBiolRep3_CNhs12761_ctss_rev Tc:Mcf7ToHrg_02hr30minBr3- MCF7 breast cancer cell line response to HRG, 02hr30min, biol_rep3_CNhs12761_13187-141E1_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr30minBiolRep3_CNhs12761_ctss_fwd Tc:Mcf7ToHrg_02hr30minBr3+ MCF7 breast cancer cell line response to HRG, 02hr30min, biol_rep3_CNhs12761_13187-141E1_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr30minBiolRep2_CNhs12659_ctss_rev Tc:Mcf7ToHrg_02hr30minBr2- MCF7 breast cancer cell line response to HRG, 02hr30min, biol_rep2_CNhs12659_13121-140F7_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr30minBiolRep2_CNhs12659_ctss_fwd Tc:Mcf7ToHrg_02hr30minBr2+ MCF7 breast cancer cell line response to HRG, 02hr30min, biol_rep2_CNhs12659_13121-140F7_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr30minBiolRep1_CNhs12442_ctss_rev Tc:Mcf7ToHrg_02hr30minBr1- MCF7 breast cancer cell line response to HRG, 02hr30min, biol_rep1_CNhs12442_13055-139H4_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr30minBiolRep1_CNhs12442_ctss_fwd Tc:Mcf7ToHrg_02hr30minBr1+ MCF7 breast cancer cell line response to HRG, 02hr30min, biol_rep1_CNhs12442_13055-139H4_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr00minBiolRep3_CNhs12760_ctss_rev Tc:Mcf7ToHrg_02hr00minBr3- MCF7 breast cancer cell line response to HRG, 02hr00min, biol_rep3_CNhs12760_13186-141D9_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr00minBiolRep3_CNhs12760_ctss_fwd Tc:Mcf7ToHrg_02hr00minBr3+ MCF7 breast cancer cell line response to HRG, 02hr00min, biol_rep3_CNhs12760_13186-141D9_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr00minBiolRep2_CNhs12658_ctss_rev Tc:Mcf7ToHrg_02hr00minBr2- MCF7 breast cancer cell line response to HRG, 02hr00min, biol_rep2_CNhs12658_13120-140F6_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr00minBiolRep2_CNhs12658_ctss_fwd Tc:Mcf7ToHrg_02hr00minBr2+ MCF7 breast cancer cell line response to HRG, 02hr00min, biol_rep2_CNhs12658_13120-140F6_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr00minBiolRep1_CNhs12441_ctss_rev Tc:Mcf7ToHrg_02hr00minBr1- MCF7 breast cancer cell line response to HRG, 02hr00min, biol_rep1_CNhs12441_13054-139H3_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr00minBiolRep1_CNhs12441_ctss_fwd Tc:Mcf7ToHrg_02hr00minBr1+ MCF7 breast cancer cell line response to HRG, 02hr00min, biol_rep1_CNhs12441_13054-139H3_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr40minBiolRep3_CNhs12759_ctss_rev Tc:Mcf7ToHrg_01hr40minBr3- MCF7 breast cancer cell line response to HRG, 01hr40min, biol_rep3_CNhs12759_13185-141D8_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr40minBiolRep3_CNhs12759_ctss_fwd Tc:Mcf7ToHrg_01hr40minBr3+ MCF7 breast cancer cell line response to HRG, 01hr40min, biol_rep3_CNhs12759_13185-141D8_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr40minBiolRep2_CNhs12657_ctss_rev Tc:Mcf7ToHrg_01hr40minBr2- MCF7 breast cancer cell line response to HRG, 01hr40min, biol_rep2_CNhs12657_13119-140F5_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr40minBiolRep2_CNhs12657_ctss_fwd Tc:Mcf7ToHrg_01hr40minBr2+ MCF7 breast cancer cell line response to HRG, 01hr40min, biol_rep2_CNhs12657_13119-140F5_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr40minBiolRep1_CNhs12440_ctss_rev Tc:Mcf7ToHrg_01hr40minBr1- MCF7 breast cancer cell line response to HRG, 01hr40min, biol_rep1_CNhs12440_13053-139H2_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr40minBiolRep1_CNhs12440_ctss_fwd Tc:Mcf7ToHrg_01hr40minBr1+ MCF7 breast cancer cell line response to HRG, 01hr40min, biol_rep1_CNhs12440_13053-139H2_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr20minBiolRep3_CNhs12758_ctss_rev Tc:Mcf7ToHrg_01hr20minBr3- MCF7 breast cancer cell line response to HRG, 01hr20min, biol_rep3_CNhs12758_13184-141D7_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr20minBiolRep3_CNhs12758_ctss_fwd Tc:Mcf7ToHrg_01hr20minBr3+ MCF7 breast cancer cell line response to HRG, 01hr20min, biol_rep3_CNhs12758_13184-141D7_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr20minBiolRep2_CNhs12656_ctss_rev Tc:Mcf7ToHrg_01hr20minBr2- MCF7 breast cancer cell line response to HRG, 01hr20min, biol_rep2_CNhs12656_13118-140F4_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr20minBiolRep2_CNhs12656_ctss_fwd Tc:Mcf7ToHrg_01hr20minBr2+ MCF7 breast cancer cell line response to HRG, 01hr20min, biol_rep2_CNhs12656_13118-140F4_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr20minBiolRep1_CNhs12439_ctss_rev Tc:Mcf7ToHrg_01hr20minBr1- MCF7 breast cancer cell line response to HRG, 01hr20min, biol_rep1_CNhs12439_13052-139H1_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr20minBiolRep1_CNhs12439_ctss_fwd Tc:Mcf7ToHrg_01hr20minBr1+ MCF7 breast cancer cell line response to HRG, 01hr20min, biol_rep1_CNhs12439_13052-139H1_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr00minBiolRep3_CNhs12757_ctss_rev Tc:Mcf7ToHrg_01hr00minBr3- MCF7 breast cancer cell line response to HRG, 01hr00min, biol_rep3_CNhs12757_13183-141D6_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr00minBiolRep3_CNhs12757_ctss_fwd Tc:Mcf7ToHrg_01hr00minBr3+ MCF7 breast cancer cell line response to HRG, 01hr00min, biol_rep3_CNhs12757_13183-141D6_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr00minBiolRep2_CNhs12655_ctss_rev Tc:Mcf7ToHrg_01hr00minBr2- MCF7 breast cancer cell line response to HRG, 01hr00min, biol_rep2_CNhs12655_13117-140F3_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr00minBiolRep2_CNhs12655_ctss_fwd Tc:Mcf7ToHrg_01hr00minBr2+ MCF7 breast cancer cell line response to HRG, 01hr00min, biol_rep2_CNhs12655_13117-140F3_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr00minBiolRep1_CNhs12438_ctss_rev Tc:Mcf7ToHrg_01hr00minBr1- MCF7 breast cancer cell line response to HRG, 01hr00min, biol_rep1_CNhs12438_13051-139G9_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr00minBiolRep1_CNhs12438_ctss_fwd Tc:Mcf7ToHrg_01hr00minBr1+ MCF7 breast cancer cell line response to HRG, 01hr00min, biol_rep1_CNhs12438_13051-139G9_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr45minBiolRep3_CNhs12756_ctss_rev Tc:Mcf7ToHrg_00hr45minBr3- MCF7 breast cancer cell line response to HRG, 00hr45min, biol_rep3_CNhs12756_13182-141D5_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr45minBiolRep3_CNhs12756_ctss_fwd Tc:Mcf7ToHrg_00hr45minBr3+ MCF7 breast cancer cell line response to HRG, 00hr45min, biol_rep3_CNhs12756_13182-141D5_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr45minBiolRep2_CNhs12654_ctss_rev Tc:Mcf7ToHrg_00hr45minBr2- MCF7 breast cancer cell line response to HRG, 00hr45min, biol_rep2_CNhs12654_13116-140F2_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr45minBiolRep2_CNhs12654_ctss_fwd Tc:Mcf7ToHrg_00hr45minBr2+ MCF7 breast cancer cell line response to HRG, 00hr45min, biol_rep2_CNhs12654_13116-140F2_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr45minBiolRep1_CNhs12437_ctss_rev Tc:Mcf7ToHrg_00hr45minBr1- MCF7 breast cancer cell line response to HRG, 00hr45min, biol_rep1_CNhs12437_13050-139G8_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr45minBiolRep1_CNhs12437_ctss_fwd Tc:Mcf7ToHrg_00hr45minBr1+ MCF7 breast cancer cell line response to HRG, 00hr45min, biol_rep1_CNhs12437_13050-139G8_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr30minBiolRep3_CNhs12755_ctss_rev Tc:Mcf7ToHrg_00hr30minBr3- MCF7 breast cancer cell line response to HRG, 00hr30min, biol_rep3_CNhs12755_13181-141D4_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr30minBiolRep3_CNhs12755_ctss_fwd Tc:Mcf7ToHrg_00hr30minBr3+ MCF7 breast cancer cell line response to HRG, 00hr30min, biol_rep3_CNhs12755_13181-141D4_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr30minBiolRep2_CNhs12653_ctss_rev Tc:Mcf7ToHrg_00hr30minBr2- MCF7 breast cancer cell line response to HRG, 00hr30min, biol_rep2_CNhs12653_13115-140F1_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr30minBiolRep2_CNhs12653_ctss_fwd Tc:Mcf7ToHrg_00hr30minBr2+ MCF7 breast cancer cell line response to HRG, 00hr30min, biol_rep2_CNhs12653_13115-140F1_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr30minBiolRep1_CNhs12436_ctss_rev Tc:Mcf7ToHrg_00hr30minBr1- MCF7 breast cancer cell line response to HRG, 00hr30min, biol_rep1_CNhs12436_13049-139G7_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr30minBiolRep1_CNhs12436_ctss_fwd Tc:Mcf7ToHrg_00hr30minBr1+ MCF7 breast cancer cell line response to HRG, 00hr30min, biol_rep1_CNhs12436_13049-139G7_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr15minBiolRep3_CNhs12754_ctss_rev Tc:Mcf7ToHrg_00hr15minBr3- MCF7 breast cancer cell line response to HRG, 00hr15min, biol_rep3_CNhs12754_13180-141D3_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr15minBiolRep3_CNhs12754_ctss_fwd Tc:Mcf7ToHrg_00hr15minBr3+ MCF7 breast cancer cell line response to HRG, 00hr15min, biol_rep3_CNhs12754_13180-141D3_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr15minBiolRep2_CNhs12652_ctss_rev Tc:Mcf7ToHrg_00hr15minBr2- MCF7 breast cancer cell line response to HRG, 00hr15min, biol_rep2_CNhs12652_13114-140E9_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr15minBiolRep2_CNhs12652_ctss_fwd Tc:Mcf7ToHrg_00hr15minBr2+ MCF7 breast cancer cell line response to HRG, 00hr15min, biol_rep2_CNhs12652_13114-140E9_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr15minBiolRep1_CNhs12435_ctss_rev Tc:Mcf7ToHrg_00hr15minBr1- MCF7 breast cancer cell line response to HRG, 00hr15min, biol_rep1_CNhs12435_13048-139G6_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr15minBiolRep1_CNhs12435_ctss_fwd Tc:Mcf7ToHrg_00hr15minBr1+ MCF7 breast cancer cell line response to HRG, 00hr15min, biol_rep1_CNhs12435_13048-139G6_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF108hrBiolRep3_CNhs12753_ctss_rev Mcf7ToEgf1_08hrBr3- MCF7 breast cancer cell line response to EGF1, 08hr, biol_rep3_CNhs12753_13178-141D1_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF108hrBiolRep3_CNhs12753_ctss_fwd Mcf7ToEgf1_08hrBr3+ MCF7 breast cancer cell line response to EGF1, 08hr, biol_rep3_CNhs12753_13178-141D1_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF108hrBiolRep2_CNhs12491_ctss_rev Mcf7ToEgf1_08hrBr2- MCF7 breast cancer cell line response to EGF1, 08hr, biol_rep2_CNhs12491_13112-140E7_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF108hrBiolRep2_CNhs12491_ctss_fwd Mcf7ToEgf1_08hrBr2+ MCF7 breast cancer cell line response to EGF1, 08hr, biol_rep2_CNhs12491_13112-140E7_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF107hrBiolRep3_CNhs12752_ctss_rev Mcf7ToEgf1_07hrBr3- MCF7 breast cancer cell line response to EGF1, 07hr, biol_rep3_CNhs12752_13177-141C9_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF107hrBiolRep3_CNhs12752_ctss_fwd Mcf7ToEgf1_07hrBr3+ MCF7 breast cancer cell line response to EGF1, 07hr, biol_rep3_CNhs12752_13177-141C9_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF107hrBiolRep2_CNhs12490_ctss_rev Mcf7ToEgf1_07hrBr2- MCF7 breast cancer cell line response to EGF1, 07hr, biol_rep2_CNhs12490_13111-140E6_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF107hrBiolRep2_CNhs12490_ctss_fwd Mcf7ToEgf1_07hrBr2+ MCF7 breast cancer cell line response to EGF1, 07hr, biol_rep2_CNhs12490_13111-140E6_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF107hrBiolRep1_CNhs12434_ctss_rev Mcf7ToEgf1_07hrBr1- MCF7 breast cancer cell line response to EGF1, 07hr, biol_rep1_CNhs12434_13045-139G3_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF107hrBiolRep1_CNhs12434_ctss_fwd Mcf7ToEgf1_07hrBr1+ MCF7 breast cancer cell line response to EGF1, 07hr, biol_rep1_CNhs12434_13045-139G3_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF106hrBiolRep3_CNhs12751_ctss_rev Mcf7ToEgf1_06hrBr3- MCF7 breast cancer cell line response to EGF1, 06hr, biol_rep3_CNhs12751_13176-141C8_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF106hrBiolRep3_CNhs12751_ctss_fwd Mcf7ToEgf1_06hrBr3+ MCF7 breast cancer cell line response to EGF1, 06hr, biol_rep3_CNhs12751_13176-141C8_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF106hrBiolRep2_CNhs12489_ctss_rev Mcf7ToEgf1_06hrBr2- MCF7 breast cancer cell line response to EGF1, 06hr, biol_rep2_CNhs12489_13110-140E5_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF106hrBiolRep2_CNhs12489_ctss_fwd Mcf7ToEgf1_06hrBr2+ MCF7 breast cancer cell line response to EGF1, 06hr, biol_rep2_CNhs12489_13110-140E5_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF106hrBiolRep1_CNhs12432_ctss_rev Mcf7ToEgf1_06hrBr1- MCF7 breast cancer cell line response to EGF1, 06hr, biol_rep1_CNhs12432_13044-139G2_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF106hrBiolRep1_CNhs12432_ctss_fwd Mcf7ToEgf1_06hrBr1+ MCF7 breast cancer cell line response to EGF1, 06hr, biol_rep1_CNhs12432_13044-139G2_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF105hrBiolRep3_CNhs12750_ctss_rev Mcf7ToEgf1_05hrBr3- MCF7 breast cancer cell line response to EGF1, 05hr, biol_rep3_CNhs12750_13175-141C7_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF105hrBiolRep3_CNhs12750_ctss_fwd Mcf7ToEgf1_05hrBr3+ MCF7 breast cancer cell line response to EGF1, 05hr, biol_rep3_CNhs12750_13175-141C7_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF105hrBiolRep2_CNhs12488_ctss_rev Mcf7ToEgf1_05hrBr2- MCF7 breast cancer cell line response to EGF1, 05hr, biol_rep2_CNhs12488_13109-140E4_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF105hrBiolRep2_CNhs12488_ctss_fwd Mcf7ToEgf1_05hrBr2+ MCF7 breast cancer cell line response to EGF1, 05hr, biol_rep2_CNhs12488_13109-140E4_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF105hrBiolRep1_CNhs12431_ctss_rev Mcf7ToEgf1_05hrBr1- MCF7 breast cancer cell line response to EGF1, 05hr, biol_rep1_CNhs12431_13043-139G1_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF105hrBiolRep1_CNhs12431_ctss_fwd Mcf7ToEgf1_05hrBr1+ MCF7 breast cancer cell line response to EGF1, 05hr, biol_rep1_CNhs12431_13043-139G1_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF104hrBiolRep3_CNhs12749_ctss_rev Mcf7ToEgf1_04hrBr3- MCF7 breast cancer cell line response to EGF1, 04hr, biol_rep3_CNhs12749_13174-141C6_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF104hrBiolRep3_CNhs12749_ctss_fwd Mcf7ToEgf1_04hrBr3+ MCF7 breast cancer cell line response to EGF1, 04hr, biol_rep3_CNhs12749_13174-141C6_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF104hrBiolRep2_CNhs12487_ctss_rev Mcf7ToEgf1_04hrBr2- MCF7 breast cancer cell line response to EGF1, 04hr, biol_rep2_CNhs12487_13108-140E3_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF104hrBiolRep2_CNhs12487_ctss_fwd Mcf7ToEgf1_04hrBr2+ MCF7 breast cancer cell line response to EGF1, 04hr, biol_rep2_CNhs12487_13108-140E3_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF104hrBiolRep1_CNhs12430_ctss_rev Mcf7ToEgf1_04hrBr1- MCF7 breast cancer cell line response to EGF1, 04hr, biol_rep1_CNhs12430_13042-139F9_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF104hrBiolRep1_CNhs12430_ctss_fwd Mcf7ToEgf1_04hrBr1+ MCF7 breast cancer cell line response to EGF1, 04hr, biol_rep1_CNhs12430_13042-139F9_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr30minBiolRep3_CNhs12748_ctss_rev Mcf7ToEgf1_03hr30minBr3- MCF7 breast cancer cell line response to EGF1, 03hr30min, biol_rep3_CNhs12748_13173-141C5_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr30minBiolRep3_CNhs12748_ctss_fwd Mcf7ToEgf1_03hr30minBr3+ MCF7 breast cancer cell line response to EGF1, 03hr30min, biol_rep3_CNhs12748_13173-141C5_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr30minBiolRep2_CNhs12486_ctss_rev Mcf7ToEgf1_03hr30minBr2- MCF7 breast cancer cell line response to EGF1, 03hr30min, biol_rep2_CNhs12486_13107-140E2_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr30minBiolRep2_CNhs12486_ctss_fwd Mcf7ToEgf1_03hr30minBr2+ MCF7 breast cancer cell line response to EGF1, 03hr30min, biol_rep2_CNhs12486_13107-140E2_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr30minBiolRep1_CNhs12429_ctss_rev Mcf7ToEgf1_03hr30minBr1- MCF7 breast cancer cell line response to EGF1, 03hr30min, biol_rep1_CNhs12429_13041-139F8_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr30minBiolRep1_CNhs12429_ctss_fwd Mcf7ToEgf1_03hr30minBr1+ MCF7 breast cancer cell line response to EGF1, 03hr30min, biol_rep1_CNhs12429_13041-139F8_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr00minBiolRep3_CNhs12747_ctss_rev Mcf7ToEgf1_03hr00minBr3- MCF7 breast cancer cell line response to EGF1, 03hr00min, biol_rep3_CNhs12747_13172-141C4_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr00minBiolRep3_CNhs12747_ctss_fwd Mcf7ToEgf1_03hr00minBr3+ MCF7 breast cancer cell line response to EGF1, 03hr00min, biol_rep3_CNhs12747_13172-141C4_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr00minBiolRep2_CNhs12485_ctss_rev Mcf7ToEgf1_03hr00minBr2- MCF7 breast cancer cell line response to EGF1, 03hr00min, biol_rep2_CNhs12485_13106-140E1_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr00minBiolRep2_CNhs12485_ctss_fwd Mcf7ToEgf1_03hr00minBr2+ MCF7 breast cancer cell line response to EGF1, 03hr00min, biol_rep2_CNhs12485_13106-140E1_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr00minBiolRep1_CNhs12428_ctss_rev Mcf7ToEgf1_03hr00minBr1- MCF7 breast cancer cell line response to EGF1, 03hr00min, biol_rep1_CNhs12428_13040-139F7_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr00minBiolRep1_CNhs12428_ctss_fwd Mcf7ToEgf1_03hr00minBr1+ MCF7 breast cancer cell line response to EGF1, 03hr00min, biol_rep1_CNhs12428_13040-139F7_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr30minBiolRep3_CNhs12746_ctss_rev Mcf7ToEgf1_02hr30minBr3- MCF7 breast cancer cell line response to EGF1, 02hr30min, biol_rep3_CNhs12746_13171-141C3_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr30minBiolRep3_CNhs12746_ctss_fwd Mcf7ToEgf1_02hr30minBr3+ MCF7 breast cancer cell line response to EGF1, 02hr30min, biol_rep3_CNhs12746_13171-141C3_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr30minBiolRep2_CNhs12484_ctss_rev Mcf7ToEgf1_02hr30minBr2- MCF7 breast cancer cell line response to EGF1, 02hr30min, biol_rep2_CNhs12484_13105-140D9_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr30minBiolRep2_CNhs12484_ctss_fwd Mcf7ToEgf1_02hr30minBr2+ MCF7 breast cancer cell line response to EGF1, 02hr30min, biol_rep2_CNhs12484_13105-140D9_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr30minBiolRep1_CNhs12427_ctss_rev Mcf7ToEgf1_02hr30minBr1- MCF7 breast cancer cell line response to EGF1, 02hr30min, biol_rep1_CNhs12427_13039-139F6_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr30minBiolRep1_CNhs12427_ctss_fwd Mcf7ToEgf1_02hr30minBr1+ MCF7 breast cancer cell line response to EGF1, 02hr30min, biol_rep1_CNhs12427_13039-139F6_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr00minBiolRep3_CNhs12744_ctss_rev Mcf7ToEgf1_02hr00minBr3- MCF7 breast cancer cell line response to EGF1, 02hr00min, biol_rep3_CNhs12744_13170-141C2_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr00minBiolRep3_CNhs12744_ctss_fwd Mcf7ToEgf1_02hr00minBr3+ MCF7 breast cancer cell line response to EGF1, 02hr00min, biol_rep3_CNhs12744_13170-141C2_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr00minBiolRep2_CNhs12483_ctss_rev Mcf7ToEgf1_02hr00minBr2- MCF7 breast cancer cell line response to EGF1, 02hr00min, biol_rep2_CNhs12483_13104-140D8_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr00minBiolRep2_CNhs12483_ctss_fwd Mcf7ToEgf1_02hr00minBr2+ MCF7 breast cancer cell line response to EGF1, 02hr00min, biol_rep2_CNhs12483_13104-140D8_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr00minBiolRep1_CNhs12426_ctss_rev Mcf7ToEgf1_02hr00minBr1- MCF7 breast cancer cell line response to EGF1, 02hr00min, biol_rep1_CNhs12426_13038-139F5_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr00minBiolRep1_CNhs12426_ctss_fwd Mcf7ToEgf1_02hr00minBr1+ MCF7 breast cancer cell line response to EGF1, 02hr00min, biol_rep1_CNhs12426_13038-139F5_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr40minBiolRep3_CNhs12743_ctss_rev Mcf7ToEgf1_01hr40minBr3- MCF7 breast cancer cell line response to EGF1, 01hr40min, biol_rep3_CNhs12743_13169-141C1_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr40minBiolRep3_CNhs12743_ctss_fwd Mcf7ToEgf1_01hr40minBr3+ MCF7 breast cancer cell line response to EGF1, 01hr40min, biol_rep3_CNhs12743_13169-141C1_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr40minBiolRep2_CNhs12482_ctss_rev Mcf7ToEgf1_01hr40minBr2- MCF7 breast cancer cell line response to EGF1, 01hr40min, biol_rep2_CNhs12482_13103-140D7_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr40minBiolRep2_CNhs12482_ctss_fwd Mcf7ToEgf1_01hr40minBr2+ MCF7 breast cancer cell line response to EGF1, 01hr40min, biol_rep2_CNhs12482_13103-140D7_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr40minBiolRep1_CNhs12425_ctss_rev Mcf7ToEgf1_01hr40minBr1- MCF7 breast cancer cell line response to EGF1, 01hr40min, biol_rep1_CNhs12425_13037-139F4_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr40minBiolRep1_CNhs12425_ctss_fwd Mcf7ToEgf1_01hr40minBr1+ MCF7 breast cancer cell line response to EGF1, 01hr40min, biol_rep1_CNhs12425_13037-139F4_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr20minBiolRep3_CNhs12742_ctss_rev Mcf7ToEgf1_01hr20minBr3- MCF7 breast cancer cell line response to EGF1, 01hr20min, biol_rep3_CNhs12742_13168-141B9_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr20minBiolRep3_CNhs12742_ctss_fwd Mcf7ToEgf1_01hr20minBr3+ MCF7 breast cancer cell line response to EGF1, 01hr20min, biol_rep3_CNhs12742_13168-141B9_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr20minBiolRep2_CNhs12480_ctss_rev Mcf7ToEgf1_01hr20minBr2- MCF7 breast cancer cell line response to EGF1, 01hr20min, biol_rep2_CNhs12480_13102-140D6_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr20minBiolRep2_CNhs12480_ctss_fwd Mcf7ToEgf1_01hr20minBr2+ MCF7 breast cancer cell line response to EGF1, 01hr20min, biol_rep2_CNhs12480_13102-140D6_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr20minBiolRep1_CNhs12424_ctss_rev Mcf7ToEgf1_01hr20minBr1- MCF7 breast cancer cell line response to EGF1, 01hr20min, biol_rep1_CNhs12424_13036-139F3_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr20minBiolRep1_CNhs12424_ctss_fwd Mcf7ToEgf1_01hr20minBr1+ MCF7 breast cancer cell line response to EGF1, 01hr20min, biol_rep1_CNhs12424_13036-139F3_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr00minBiolRep3_CNhs12705_ctss_rev Mcf7ToEgf1_01hr00minBr3- MCF7 breast cancer cell line response to EGF1, 01hr00min, biol_rep3_CNhs12705_13167-141B8_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr00minBiolRep3_CNhs12705_ctss_fwd Mcf7ToEgf1_01hr00minBr3+ MCF7 breast cancer cell line response to EGF1, 01hr00min, biol_rep3_CNhs12705_13167-141B8_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr00minBiolRep2_CNhs12479_ctss_rev Mcf7ToEgf1_01hr00minBr2- MCF7 breast cancer cell line response to EGF1, 01hr00min, biol_rep2_CNhs12479_13101-140D5_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr00minBiolRep2_CNhs12479_ctss_fwd Mcf7ToEgf1_01hr00minBr2+ MCF7 breast cancer cell line response to EGF1, 01hr00min, biol_rep2_CNhs12479_13101-140D5_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr00minBiolRep1_CNhs12423_ctss_rev Mcf7ToEgf1_01hr00minBr1- MCF7 breast cancer cell line response to EGF1, 01hr00min, biol_rep1_CNhs12423_13035-139F2_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr00minBiolRep1_CNhs12423_ctss_fwd Mcf7ToEgf1_01hr00minBr1+ MCF7 breast cancer cell line response to EGF1, 01hr00min, biol_rep1_CNhs12423_13035-139F2_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr45minBiolRep3_CNhs12739_ctss_rev Mcf7ToEgf1_00hr45minBr3- MCF7 breast cancer cell line response to EGF1, 00hr45min, biol_rep3_CNhs12739_13166-141B7_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr45minBiolRep3_CNhs12739_ctss_fwd Mcf7ToEgf1_00hr45minBr3+ MCF7 breast cancer cell line response to EGF1, 00hr45min, biol_rep3_CNhs12739_13166-141B7_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr45minBiolRep2_CNhs12478_ctss_rev Mcf7ToEgf1_00hr45minBr2- MCF7 breast cancer cell line response to EGF1, 00hr45min, biol_rep2_CNhs12478_13100-140D4_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr45minBiolRep2_CNhs12478_ctss_fwd Mcf7ToEgf1_00hr45minBr2+ MCF7 breast cancer cell line response to EGF1, 00hr45min, biol_rep2_CNhs12478_13100-140D4_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr45minBiolRep1_CNhs12422_ctss_rev Mcf7ToEgf1_00hr45minBr1- MCF7 breast cancer cell line response to EGF1, 00hr45min, biol_rep1_CNhs12422_13034-139F1_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr45minBiolRep1_CNhs12422_ctss_fwd Mcf7ToEgf1_00hr45minBr1+ MCF7 breast cancer cell line response to EGF1, 00hr45min, biol_rep1_CNhs12422_13034-139F1_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr30minBiolRep3_CNhs12738_ctss_rev Mcf7ToEgf1_00hr30minBr3- MCF7 breast cancer cell line response to EGF1, 00hr30min, biol_rep3_CNhs12738_13165-141B6_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr30minBiolRep3_CNhs12738_ctss_fwd Mcf7ToEgf1_00hr30minBr3+ MCF7 breast cancer cell line response to EGF1, 00hr30min, biol_rep3_CNhs12738_13165-141B6_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr30minBiolRep2_CNhs12477_ctss_rev Mcf7ToEgf1_00hr30minBr2- MCF7 breast cancer cell line response to EGF1, 00hr30min, biol_rep2_CNhs12477_13099-140D3_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr30minBiolRep2_CNhs12477_ctss_fwd Mcf7ToEgf1_00hr30minBr2+ MCF7 breast cancer cell line response to EGF1, 00hr30min, biol_rep2_CNhs12477_13099-140D3_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr30minBiolRep1_CNhs12421_ctss_rev Mcf7ToEgf1_00hr30minBr1- MCF7 breast cancer cell line response to EGF1, 00hr30min, biol_rep1_CNhs12421_13033-139E9_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr30minBiolRep1_CNhs12421_ctss_fwd Mcf7ToEgf1_00hr30minBr1+ MCF7 breast cancer cell line response to EGF1, 00hr30min, biol_rep1_CNhs12421_13033-139E9_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr15minBiolRep3_CNhs12704_ctss_rev Mcf7ToEgf1_00hr15minBr3- MCF7 breast cancer cell line response to EGF1, 00hr15min, biol_rep3_CNhs12704_13164-141B5_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr15minBiolRep3_CNhs12704_ctss_fwd Mcf7ToEgf1_00hr15minBr3+ MCF7 breast cancer cell line response to EGF1, 00hr15min, biol_rep3_CNhs12704_13164-141B5_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr15minBiolRep2_CNhs12476_ctss_rev Mcf7ToEgf1_00hr15minBr2- MCF7 breast cancer cell line response to EGF1, 00hr15min, biol_rep2_CNhs12476_13098-140D2_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr15minBiolRep2_CNhs12476_ctss_fwd Mcf7ToEgf1_00hr15minBr2+ MCF7 breast cancer cell line response to EGF1, 00hr15min, biol_rep2_CNhs12476_13098-140D2_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr15minBiolRep1_CNhs12420_ctss_rev Mcf7ToEgf1_00hr15minBr1- MCF7 breast cancer cell line response to EGF1, 00hr15min, biol_rep1_CNhs12420_13032-139E8_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr15minBiolRep1_CNhs12420_ctss_fwd Mcf7ToEgf1_00hr15minBr1+ MCF7 breast cancer cell line response to EGF1, 00hr15min, biol_rep1_CNhs12420_13032-139E8_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr00minBiolRep3_CNhs12703_ctss_rev Mcf7ToEgf1_00hr00minBr3- MCF7 breast cancer cell line response to EGF1, 00hr00min, biol_rep3_CNhs12703_13163-141B4_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr00minBiolRep3_CNhs12703_ctss_fwd Mcf7ToEgf1_00hr00minBr3+ MCF7 breast cancer cell line response to EGF1, 00hr00min, biol_rep3_CNhs12703_13163-141B4_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr00minBiolRep2_CNhs12475_ctss_rev Mcf7ToEgf1_00hr00minBr2- MCF7 breast cancer cell line response to EGF1, 00hr00min, biol_rep2_CNhs12475_13097-140D1_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr00minBiolRep2_CNhs12475_ctss_fwd Mcf7ToEgf1_00hr00minBr2+ MCF7 breast cancer cell line response to EGF1, 00hr00min, biol_rep2_CNhs12475_13097-140D1_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF108hrBiolRep1_CNhs12565_ctss_rev Mcf7ToEgf1_08hrBr1- MCF7 breast cancer cell line response to EGF1, 08hr, biol_rep1_CNhs12565_13046-139G4_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF108hrBiolRep1_CNhs12565_ctss_fwd Mcf7ToEgf1_08hrBr1+ MCF7 breast cancer cell line response to EGF1, 08hr, biol_rep1_CNhs12565_13046-139G4_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC08hrBiolRep3MMXXII16_CNhs13291_ctss_rev LymphaticEndothelialCellsToVegfc_08hrBr3- Lymphatic Endothelial cells response to VEGFC, 08hr, biol_rep3 (MM XXII - 16)_CNhs13291_12519-133B8_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC08hrBiolRep3MMXXII16_CNhs13291_ctss_fwd LymphaticEndothelialCellsToVegfc_08hrBr3+ Lymphatic Endothelial cells response to VEGFC, 08hr, biol_rep3 (MM XXII - 16)_CNhs13291_12519-133B8_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC08hrBiolRep2MMXIV16_CNhs13173_ctss_rev LymphaticEndothelialCellsToVegfc_08hrBr2- Lymphatic Endothelial cells response to VEGFC, 08hr, biol_rep2 (MM XIV - 16)_CNhs13173_12397-131G3_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC08hrBiolRep2MMXIV16_CNhs13173_ctss_fwd LymphaticEndothelialCellsToVegfc_08hrBr2+ Lymphatic Endothelial cells response to VEGFC, 08hr, biol_rep2 (MM XIV - 16)_CNhs13173_12397-131G3_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC08hrBiolRep1MMXIX16_CNhs11937_ctss_rev LymphaticEndothelialCellsToVegfc_08hrBr1- Lymphatic Endothelial cells response to VEGFC, 08hr, biol_rep1 (MM XIX - 16)_CNhs11937_12275-130B7_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC08hrBiolRep1MMXIX16_CNhs11937_ctss_fwd LymphaticEndothelialCellsToVegfc_08hrBr1+ Lymphatic Endothelial cells response to VEGFC, 08hr, biol_rep1 (MM XIX - 16)_CNhs11937_12275-130B7_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC07hrBiolRep3MMXXII15_CNhs13290_ctss_rev LymphaticEndothelialCellsToVegfc_07hrBr3- Lymphatic Endothelial cells response to VEGFC, 07hr, biol_rep3 (MM XXII - 15)_CNhs13290_12518-133B7_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC07hrBiolRep3MMXXII15_CNhs13290_ctss_fwd LymphaticEndothelialCellsToVegfc_07hrBr3+ Lymphatic Endothelial cells response to VEGFC, 07hr, biol_rep3 (MM XXII - 15)_CNhs13290_12518-133B7_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC07hrBiolRep2MMXIV15_CNhs13172_ctss_rev LymphaticEndothelialCellsToVegfc_07hrBr2- Lymphatic Endothelial cells response to VEGFC, 07hr, biol_rep2 (MM XIV - 15)_CNhs13172_12396-131G2_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC07hrBiolRep2MMXIV15_CNhs13172_ctss_fwd LymphaticEndothelialCellsToVegfc_07hrBr2+ Lymphatic Endothelial cells response to VEGFC, 07hr, biol_rep2 (MM XIV - 15)_CNhs13172_12396-131G2_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC07hrBiolRep1MMXIX15_CNhs13113_ctss_rev LymphaticEndothelialCellsToVegfc_07hrBr1- Lymphatic Endothelial cells response to VEGFC, 07hr, biol_rep1 (MM XIX - 15)_CNhs13113_12274-130B6_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC07hrBiolRep1MMXIX15_CNhs13113_ctss_fwd LymphaticEndothelialCellsToVegfc_07hrBr1+ Lymphatic Endothelial cells response to VEGFC, 07hr, biol_rep1 (MM XIX - 15)_CNhs13113_12274-130B6_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC06hrBiolRep3MMXXII14_CNhs13289_ctss_rev LymphaticEndothelialCellsToVegfc_06hrBr3- Lymphatic Endothelial cells response to VEGFC, 06hr, biol_rep3 (MM XXII - 14)_CNhs13289_12517-133B6_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC06hrBiolRep3MMXXII14_CNhs13289_ctss_fwd LymphaticEndothelialCellsToVegfc_06hrBr3+ Lymphatic Endothelial cells response to VEGFC, 06hr, biol_rep3 (MM XXII - 14)_CNhs13289_12517-133B6_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC06hrBiolRep2MMXIV14_CNhs13171_ctss_rev LymphaticEndothelialCellsToVegfc_06hrBr2- Lymphatic Endothelial cells response to VEGFC, 06hr, biol_rep2 (MM XIV - 14)_CNhs13171_12395-131G1_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC06hrBiolRep2MMXIV14_CNhs13171_ctss_fwd LymphaticEndothelialCellsToVegfc_06hrBr2+ Lymphatic Endothelial cells response to VEGFC, 06hr, biol_rep2 (MM XIV - 14)_CNhs13171_12395-131G1_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC06hrBiolRep1MMXIX14_CNhs13112_ctss_rev LymphaticEndothelialCellsToVegfc_06hrBr1- Lymphatic Endothelial cells response to VEGFC, 06hr, biol_rep1 (MM XIX - 14)_CNhs13112_12273-130B5_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC06hrBiolRep1MMXIX14_CNhs13112_ctss_fwd LymphaticEndothelialCellsToVegfc_06hrBr1+ Lymphatic Endothelial cells response to VEGFC, 06hr, biol_rep1 (MM XIX - 14)_CNhs13112_12273-130B5_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC05hrBiolRep3MMXXII13_CNhs13288_ctss_rev LymphaticEndothelialCellsToVegfc_05hrBr3- Lymphatic Endothelial cells response to VEGFC, 05hr, biol_rep3 (MM XXII - 13)_CNhs13288_12516-133B5_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC05hrBiolRep3MMXXII13_CNhs13288_ctss_fwd LymphaticEndothelialCellsToVegfc_05hrBr3+ Lymphatic Endothelial cells response to VEGFC, 05hr, biol_rep3 (MM XXII - 13)_CNhs13288_12516-133B5_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC05hrBiolRep2MMXIV13_CNhs13170_ctss_rev LymphaticEndothelialCellsToVegfc_05hrBr2- Lymphatic Endothelial cells response to VEGFC, 05hr, biol_rep2 (MM XIV - 13)_CNhs13170_12394-131F9_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC05hrBiolRep2MMXIV13_CNhs13170_ctss_fwd LymphaticEndothelialCellsToVegfc_05hrBr2+ Lymphatic Endothelial cells response to VEGFC, 05hr, biol_rep2 (MM XIV - 13)_CNhs13170_12394-131F9_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC05hrBiolRep1MMXIX13_CNhs13111_ctss_rev LymphaticEndothelialCellsToVegfc_05hrBr1- Lymphatic Endothelial cells response to VEGFC, 05hr, biol_rep1 (MM XIX - 13)_CNhs13111_12272-130B4_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC05hrBiolRep1MMXIX13_CNhs13111_ctss_fwd LymphaticEndothelialCellsToVegfc_05hrBr1+ Lymphatic Endothelial cells response to VEGFC, 05hr, biol_rep1 (MM XIX - 13)_CNhs13111_12272-130B4_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC04hrBiolRep3MMXXII12_CNhs13287_ctss_rev LymphaticEndothelialCellsToVegfc_04hrBr3- Lymphatic Endothelial cells response to VEGFC, 04hr, biol_rep3 (MM XXII - 12)_CNhs13287_12515-133B4_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC04hrBiolRep3MMXXII12_CNhs13287_ctss_fwd LymphaticEndothelialCellsToVegfc_04hrBr3+ Lymphatic Endothelial cells response to VEGFC, 04hr, biol_rep3 (MM XXII - 12)_CNhs13287_12515-133B4_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC04hrBiolRep2MMXIV12_CNhs13169_ctss_rev LymphaticEndothelialCellsToVegfc_04hrBr2- Lymphatic Endothelial cells response to VEGFC, 04hr, biol_rep2 (MM XIV - 12)_CNhs13169_12393-131F8_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC04hrBiolRep2MMXIV12_CNhs13169_ctss_fwd LymphaticEndothelialCellsToVegfc_04hrBr2+ Lymphatic Endothelial cells response to VEGFC, 04hr, biol_rep2 (MM XIV - 12)_CNhs13169_12393-131F8_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC04hrBiolRep1MMXIX12_CNhs13110_ctss_rev LymphaticEndothelialCellsToVegfc_04hrBr1- Lymphatic Endothelial cells response to VEGFC, 04hr, biol_rep1 (MM XIX - 12)_CNhs13110_12271-130B3_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC04hrBiolRep1MMXIX12_CNhs13110_ctss_fwd LymphaticEndothelialCellsToVegfc_04hrBr1+ Lymphatic Endothelial cells response to VEGFC, 04hr, biol_rep1 (MM XIX - 12)_CNhs13110_12271-130B3_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr30minBiolRep3MMXXII11_CNhs13286_ctss_rev LymphaticEndothelialCellsToVegfc_03hr30minBr3- Lymphatic Endothelial cells response to VEGFC, 03hr30min, biol_rep3 (MM XXII - 11)_CNhs13286_12514-133B3_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr30minBiolRep3MMXXII11_CNhs13286_ctss_fwd LymphaticEndothelialCellsToVegfc_03hr30minBr3+ Lymphatic Endothelial cells response to VEGFC, 03hr30min, biol_rep3 (MM XXII - 11)_CNhs13286_12514-133B3_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr30minBiolRep2MMXIV11_CNhs13168_ctss_rev LymphaticEndothelialCellsToVegfc_03hr30minBr2- Lymphatic Endothelial cells response to VEGFC, 03hr30min, biol_rep2 (MM XIV - 11)_CNhs13168_12392-131F7_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr30minBiolRep2MMXIV11_CNhs13168_ctss_fwd LymphaticEndothelialCellsToVegfc_03hr30minBr2+ Lymphatic Endothelial cells response to VEGFC, 03hr30min, biol_rep2 (MM XIV - 11)_CNhs13168_12392-131F7_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr30minBiolRep1MMXIX11_CNhs13109_ctss_rev LymphaticEndothelialCellsToVegfc_03hr30minBr1- Lymphatic Endothelial cells response to VEGFC, 03hr30min, biol_rep1 (MM XIX - 11)_CNhs13109_12270-130B2_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr30minBiolRep1MMXIX11_CNhs13109_ctss_fwd LymphaticEndothelialCellsToVegfc_03hr30minBr1+ Lymphatic Endothelial cells response to VEGFC, 03hr30min, biol_rep1 (MM XIX - 11)_CNhs13109_12270-130B2_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr00minBiolRep3MMXXII10_CNhs13285_ctss_rev LymphaticEndothelialCellsToVegfc_03hr00minBr3- Lymphatic Endothelial cells response to VEGFC, 03hr00min, biol_rep3 (MM XXII - 10)_CNhs13285_12513-133B2_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr00minBiolRep3MMXXII10_CNhs13285_ctss_fwd LymphaticEndothelialCellsToVegfc_03hr00minBr3+ Lymphatic Endothelial cells response to VEGFC, 03hr00min, biol_rep3 (MM XXII - 10)_CNhs13285_12513-133B2_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr00minBiolRep2MMXIV10_CNhs13166_ctss_rev LymphaticEndothelialCellsToVegfc_03hr00minBr2- Lymphatic Endothelial cells response to VEGFC, 03hr00min, biol_rep2 (MM XIV - 10)_CNhs13166_12391-131F6_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr00minBiolRep2MMXIV10_CNhs13166_ctss_fwd LymphaticEndothelialCellsToVegfc_03hr00minBr2+ Lymphatic Endothelial cells response to VEGFC, 03hr00min, biol_rep2 (MM XIV - 10)_CNhs13166_12391-131F6_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr00minBiolRep1MMXIX10_CNhs13108_ctss_rev LymphaticEndothelialCellsToVegfc_03hr00minBr1- Lymphatic Endothelial cells response to VEGFC, 03hr00min, biol_rep1 (MM XIX - 10)_CNhs13108_12269-130B1_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr00minBiolRep1MMXIX10_CNhs13108_ctss_fwd LymphaticEndothelialCellsToVegfc_03hr00minBr1+ Lymphatic Endothelial cells response to VEGFC, 03hr00min, biol_rep1 (MM XIX - 10)_CNhs13108_12269-130B1_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr30minBiolRep3MMXXII9_CNhs13284_ctss_rev LymphaticEndothelialCellsToVegfc_02hr30minBr3- Lymphatic Endothelial cells response to VEGFC, 02hr30min, biol_rep3 (MM XXII - 9)_CNhs13284_12512-133B1_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr30minBiolRep3MMXXII9_CNhs13284_ctss_fwd LymphaticEndothelialCellsToVegfc_02hr30minBr3+ Lymphatic Endothelial cells response to VEGFC, 02hr30min, biol_rep3 (MM XXII - 9)_CNhs13284_12512-133B1_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr30minBiolRep2MMXIV9_CNhs13165_ctss_rev LymphaticEndothelialCellsToVegfc_02hr30minBr2- Lymphatic Endothelial cells response to VEGFC, 02hr30min, biol_rep2 (MM XIV - 9)_CNhs13165_12390-131F5_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr30minBiolRep2MMXIV9_CNhs13165_ctss_fwd LymphaticEndothelialCellsToVegfc_02hr30minBr2+ Lymphatic Endothelial cells response to VEGFC, 02hr30min, biol_rep2 (MM XIV - 9)_CNhs13165_12390-131F5_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr30minBiolRep1MMXIX9_CNhs13107_ctss_rev LymphaticEndothelialCellsToVegfc_02hr30minBr1- Lymphatic Endothelial cells response to VEGFC, 02hr30min, biol_rep1 (MM XIX - 9)_CNhs13107_12268-130A9_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr30minBiolRep1MMXIX9_CNhs13107_ctss_fwd LymphaticEndothelialCellsToVegfc_02hr30minBr1+ Lymphatic Endothelial cells response to VEGFC, 02hr30min, biol_rep1 (MM XIX - 9)_CNhs13107_12268-130A9_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr00minBiolRep3MMXXII8_CNhs13283_ctss_rev LymphaticEndothelialCellsToVegfc_02hr00minBr3- Lymphatic Endothelial cells response to VEGFC, 02hr00min, biol_rep3 (MM XXII - 8)_CNhs13283_12511-133A9_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr00minBiolRep3MMXXII8_CNhs13283_ctss_fwd LymphaticEndothelialCellsToVegfc_02hr00minBr3+ Lymphatic Endothelial cells response to VEGFC, 02hr00min, biol_rep3 (MM XXII - 8)_CNhs13283_12511-133A9_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr00minBiolRep2MMXIV8_CNhs13164_ctss_rev LymphaticEndothelialCellsToVegfc_02hr00minBr2- Lymphatic Endothelial cells response to VEGFC, 02hr00min, biol_rep2 (MM XIV - 8)_CNhs13164_12389-131F4_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr00minBiolRep2MMXIV8_CNhs13164_ctss_fwd LymphaticEndothelialCellsToVegfc_02hr00minBr2+ Lymphatic Endothelial cells response to VEGFC, 02hr00min, biol_rep2 (MM XIV - 8)_CNhs13164_12389-131F4_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr00minBiolRep1MMXIX8_CNhs13106_ctss_rev LymphaticEndothelialCellsToVegfc_02hr00minBr1- Lymphatic Endothelial cells response to VEGFC, 02hr00min, biol_rep1 (MM XIX - 8)_CNhs13106_12267-130A8_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr00minBiolRep1MMXIX8_CNhs13106_ctss_fwd LymphaticEndothelialCellsToVegfc_02hr00minBr1+ Lymphatic Endothelial cells response to VEGFC, 02hr00min, biol_rep1 (MM XIX - 8)_CNhs13106_12267-130A8_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr40minBiolRep3MMXXII7_CNhs13282_ctss_rev LymphaticEndothelialCellsToVegfc_01hr40minBr3- Lymphatic Endothelial cells response to VEGFC, 01hr40min, biol_rep3 (MM XXII - 7)_CNhs13282_12510-133A8_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr40minBiolRep3MMXXII7_CNhs13282_ctss_fwd LymphaticEndothelialCellsToVegfc_01hr40minBr3+ Lymphatic Endothelial cells response to VEGFC, 01hr40min, biol_rep3 (MM XXII - 7)_CNhs13282_12510-133A8_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr40minBiolRep2MMXIV7_CNhs13163_ctss_rev LymphaticEndothelialCellsToVegfc_01hr40minBr2- Lymphatic Endothelial cells response to VEGFC, 01hr40min, biol_rep2 (MM XIV - 7)_CNhs13163_12388-131F3_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr40minBiolRep2MMXIV7_CNhs13163_ctss_fwd LymphaticEndothelialCellsToVegfc_01hr40minBr2+ Lymphatic Endothelial cells response to VEGFC, 01hr40min, biol_rep2 (MM XIV - 7)_CNhs13163_12388-131F3_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr40minBiolRep1MMXIX7_CNhs13105_ctss_rev LymphaticEndothelialCellsToVegfc_01hr40minBr1- Lymphatic Endothelial cells response to VEGFC, 01hr40min, biol_rep1 (MM XIX - 7)_CNhs13105_12266-130A7_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr40minBiolRep1MMXIX7_CNhs13105_ctss_fwd LymphaticEndothelialCellsToVegfc_01hr40minBr1+ Lymphatic Endothelial cells response to VEGFC, 01hr40min, biol_rep1 (MM XIX - 7)_CNhs13105_12266-130A7_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr20minBiolRep3MMXXII6_CNhs13281_ctss_rev LymphaticEndothelialCellsToVegfc_01hr20minBr3- Lymphatic Endothelial cells response to VEGFC, 01hr20min, biol_rep3 (MM XXII - 6)_CNhs13281_12509-133A7_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr20minBiolRep3MMXXII6_CNhs13281_ctss_fwd LymphaticEndothelialCellsToVegfc_01hr20minBr3+ Lymphatic Endothelial cells response to VEGFC, 01hr20min, biol_rep3 (MM XXII - 6)_CNhs13281_12509-133A7_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr20minBiolRep2MMXIV6_CNhs13162_ctss_rev LymphaticEndothelialCellsToVegfc_01hr20minBr2- Lymphatic Endothelial cells response to VEGFC, 01hr20min, biol_rep2 (MM XIV - 6)_CNhs13162_12387-131F2_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr20minBiolRep2MMXIV6_CNhs13162_ctss_fwd LymphaticEndothelialCellsToVegfc_01hr20minBr2+ Lymphatic Endothelial cells response to VEGFC, 01hr20min, biol_rep2 (MM XIV - 6)_CNhs13162_12387-131F2_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr20minBiolRep1MMXIX6_CNhs13104_ctss_rev LymphaticEndothelialCellsToVegfc_01hr20minBr1- Lymphatic Endothelial cells response to VEGFC, 01hr20min, biol_rep1 (MM XIX - 6)_CNhs13104_12265-130A6_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr20minBiolRep1MMXIX6_CNhs13104_ctss_fwd LymphaticEndothelialCellsToVegfc_01hr20minBr1+ Lymphatic Endothelial cells response to VEGFC, 01hr20min, biol_rep1 (MM XIX - 6)_CNhs13104_12265-130A6_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr00minBiolRep3MMXXII5_CNhs13280_ctss_rev LymphaticEndothelialCellsToVegfc_01hr00minBr3- Lymphatic Endothelial cells response to VEGFC, 01hr00min, biol_rep3 (MM XXII - 5)_CNhs13280_12508-133A6_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr00minBiolRep3MMXXII5_CNhs13280_ctss_fwd LymphaticEndothelialCellsToVegfc_01hr00minBr3+ Lymphatic Endothelial cells response to VEGFC, 01hr00min, biol_rep3 (MM XXII - 5)_CNhs13280_12508-133A6_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr00minBiolRep2MMXIV5_CNhs13161_ctss_rev LymphaticEndothelialCellsToVegfc_01hr00minBr2- Lymphatic Endothelial cells response to VEGFC, 01hr00min, biol_rep2 (MM XIV - 5)_CNhs13161_12386-131F1_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr00minBiolRep2MMXIV5_CNhs13161_ctss_fwd LymphaticEndothelialCellsToVegfc_01hr00minBr2+ Lymphatic Endothelial cells response to VEGFC, 01hr00min, biol_rep2 (MM XIV - 5)_CNhs13161_12386-131F1_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr00minBiolRep1MMXIX5_CNhs13103_ctss_rev LymphaticEndothelialCellsToVegfc_01hr00minBr1- Lymphatic Endothelial cells response to VEGFC, 01hr00min, biol_rep1 (MM XIX - 5)_CNhs13103_12264-130A5_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr00minBiolRep1MMXIX5_CNhs13103_ctss_fwd LymphaticEndothelialCellsToVegfc_01hr00minBr1+ Lymphatic Endothelial cells response to VEGFC, 01hr00min, biol_rep1 (MM XIX - 5)_CNhs13103_12264-130A5_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr45minBiolRep3MMXXII4_CNhs13279_ctss_rev LymphaticEndothelialCellsToVegfc_00hr45minBr3- Lymphatic Endothelial cells response to VEGFC, 00hr45min, biol_rep3 (MM XXII - 4)_CNhs13279_12507-133A5_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr45minBiolRep3MMXXII4_CNhs13279_ctss_fwd LymphaticEndothelialCellsToVegfc_00hr45minBr3+ Lymphatic Endothelial cells response to VEGFC, 00hr45min, biol_rep3 (MM XXII - 4)_CNhs13279_12507-133A5_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr45minBiolRep2MMXIV4_CNhs13160_ctss_rev LymphaticEndothelialCellsToVegfc_00hr45minBr2- Lymphatic Endothelial cells response to VEGFC, 00hr45min, biol_rep2 (MM XIV - 4)_CNhs13160_12385-131E9_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr45minBiolRep2MMXIV4_CNhs13160_ctss_fwd LymphaticEndothelialCellsToVegfc_00hr45minBr2+ Lymphatic Endothelial cells response to VEGFC, 00hr45min, biol_rep2 (MM XIV - 4)_CNhs13160_12385-131E9_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr45minBiolRep1MMXIX4_CNhs13102_ctss_rev LymphaticEndothelialCellsToVegfc_00hr45minBr1- Lymphatic Endothelial cells response to VEGFC, 00hr45min, biol_rep1 (MM XIX - 4)_CNhs13102_12263-130A4_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr45minBiolRep1MMXIX4_CNhs13102_ctss_fwd LymphaticEndothelialCellsToVegfc_00hr45minBr1+ Lymphatic Endothelial cells response to VEGFC, 00hr45min, biol_rep1 (MM XIX - 4)_CNhs13102_12263-130A4_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr30minBiolRep3MMXXII3_CNhs13278_ctss_rev LymphaticEndothelialCellsToVegfc_00hr30minBr3- Lymphatic Endothelial cells response to VEGFC, 00hr30min, biol_rep3 (MM XXII - 3)_CNhs13278_12506-133A4_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr30minBiolRep3MMXXII3_CNhs13278_ctss_fwd LymphaticEndothelialCellsToVegfc_00hr30minBr3+ Lymphatic Endothelial cells response to VEGFC, 00hr30min, biol_rep3 (MM XXII - 3)_CNhs13278_12506-133A4_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr30minBiolRep2MMXIV3_CNhs13159_ctss_rev LymphaticEndothelialCellsToVegfc_00hr30minBr2- Lymphatic Endothelial cells response to VEGFC, 00hr30min, biol_rep2 (MM XIV - 3)_CNhs13159_12384-131E8_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr30minBiolRep2MMXIV3_CNhs13159_ctss_fwd LymphaticEndothelialCellsToVegfc_00hr30minBr2+ Lymphatic Endothelial cells response to VEGFC, 00hr30min, biol_rep2 (MM XIV - 3)_CNhs13159_12384-131E8_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr30minBiolRep1MMXIX3_CNhs13101_ctss_rev LymphaticEndothelialCellsToVegfc_00hr30minBr1- Lymphatic Endothelial cells response to VEGFC, 00hr30min, biol_rep1 (MM XIX - 3)_CNhs13101_12262-130A3_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr30minBiolRep1MMXIX3_CNhs13101_ctss_fwd LymphaticEndothelialCellsToVegfc_00hr30minBr1+ Lymphatic Endothelial cells response to VEGFC, 00hr30min, biol_rep1 (MM XIX - 3)_CNhs13101_12262-130A3_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr15minBiolRep3MMXXII2_CNhs13277_ctss_rev LymphaticEndothelialCellsToVegfc_00hr15minBr3- Lymphatic Endothelial cells response to VEGFC, 00hr15min, biol_rep3 (MM XXII - 2)_CNhs13277_12505-133A3_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr15minBiolRep3MMXXII2_CNhs13277_ctss_fwd LymphaticEndothelialCellsToVegfc_00hr15minBr3+ Lymphatic Endothelial cells response to VEGFC, 00hr15min, biol_rep3 (MM XXII - 2)_CNhs13277_12505-133A3_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr15minBiolRep2MMXIV2_CNhs13158_ctss_rev LymphaticEndothelialCellsToVegfc_00hr15minBr2- Lymphatic Endothelial cells response to VEGFC, 00hr15min, biol_rep2 (MM XIV - 2)_CNhs13158_12383-131E7_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr15minBiolRep2MMXIV2_CNhs13158_ctss_fwd LymphaticEndothelialCellsToVegfc_00hr15minBr2+ Lymphatic Endothelial cells response to VEGFC, 00hr15min, biol_rep2 (MM XIV - 2)_CNhs13158_12383-131E7_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr15minBiolRep1MMXIX2_CNhs13100_ctss_rev LymphaticEndothelialCellsToVegfc_00hr15minBr1- Lymphatic Endothelial cells response to VEGFC, 00hr15min, biol_rep1 (MM XIX - 2)_CNhs13100_12261-130A2_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr15minBiolRep1MMXIX2_CNhs13100_ctss_fwd LymphaticEndothelialCellsToVegfc_00hr15minBr1+ Lymphatic Endothelial cells response to VEGFC, 00hr15min, biol_rep1 (MM XIX - 2)_CNhs13100_12261-130A2_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr00minBiolRep3MMXXII1_CNhs13276_ctss_rev LymphaticEndothelialCellsToVegfc_00hr00minBr3- Lymphatic Endothelial cells response to VEGFC, 00hr00min, biol_rep3 (MM XXII - 1 )_CNhs13276_12504-133A2_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr00minBiolRep3MMXXII1_CNhs13276_ctss_fwd LymphaticEndothelialCellsToVegfc_00hr00minBr3+ Lymphatic Endothelial cells response to VEGFC, 00hr00min, biol_rep3 (MM XXII - 1 )_CNhs13276_12504-133A2_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr00minBiolRep2MMXIV1_CNhs13157_ctss_rev LymphaticEndothelialCellsToVegfc_00hr00minBr2- Lymphatic Endothelial cells response to VEGFC, 00hr00min, biol_rep2 (MM XIV - 1)_CNhs13157_12382-131E6_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr00minBiolRep2MMXIV1_CNhs13157_ctss_fwd LymphaticEndothelialCellsToVegfc_00hr00minBr2+ Lymphatic Endothelial cells response to VEGFC, 00hr00min, biol_rep2 (MM XIV - 1)_CNhs13157_12382-131E6_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr00minBiolRep1MMXIX1_CNhs11936_ctss_rev LymphaticEndothelialCellsToVegfc_00hr00minBr1- Lymphatic Endothelial cells response to VEGFC, 00hr00min, biol_rep1 (MM XIX - 1)_CNhs11936_12260-130A1_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr00minBiolRep1MMXIX1_CNhs11936_ctss_fwd LymphaticEndothelialCellsToVegfc_00hr00minBr1+ Lymphatic Endothelial cells response to VEGFC, 00hr00min, biol_rep1 (MM XIX - 1)_CNhs11936_12260-130A1_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day18Rep3_CNhs14055_ctss_rev IpsToNeuronControlDnC11-CRL2429Day18R3- iPS differentiation to neuron, control donor C32-CRL1502, day18, rep3_CNhs14055_13444-144F6_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day18Rep3_CNhs14055_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day18R3+ iPS differentiation to neuron, control donor C32-CRL1502, day18, rep3_CNhs14055_13444-144F6_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day18Rep2_CNhs13842_ctss_rev IpsToNeuronControlDnC11-CRL2429Day18R2- iPS differentiation to neuron, control donor C32-CRL1502, day18, rep2_CNhs13842_13440-144F2_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day18Rep2_CNhs13842_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day18R2+ iPS differentiation to neuron, control donor C32-CRL1502, day18, rep2_CNhs13842_13440-144F2_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day18Rep1_CNhs13829_ctss_rev IpsToNeuronControlDnC11-CRL2429Day18R1- iPS differentiation to neuron, control donor C32-CRL1502, day18, rep1_CNhs13829_13436-144E7_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day18Rep1_CNhs13829_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day18R1+ iPS differentiation to neuron, control donor C32-CRL1502, day18, rep1_CNhs13829_13436-144E7_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day12Rep3_CNhs14054_ctss_rev IpsToNeuronControlDnC11-CRL2429Day12R3- iPS differentiation to neuron, control donor C32-CRL1502, day12, rep3_CNhs14054_13443-144F5_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day12Rep3_CNhs14054_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day12R3+ iPS differentiation to neuron, control donor C32-CRL1502, day12, rep3_CNhs14054_13443-144F5_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day12Rep2_CNhs13841_ctss_rev IpsToNeuronControlDnC11-CRL2429Day12R2- iPS differentiation to neuron, control donor C32-CRL1502, day12, rep2_CNhs13841_13439-144F1_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day12Rep2_CNhs13841_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day12R2+ iPS differentiation to neuron, control donor C32-CRL1502, day12, rep2_CNhs13841_13439-144F1_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day12Rep1_CNhs13828_ctss_rev IpsToNeuronControlDnC11-CRL2429Day12R1- iPS differentiation to neuron, control donor C32-CRL1502, day12, rep1_CNhs13828_13435-144E6_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day12Rep1_CNhs13828_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day12R1+ iPS differentiation to neuron, control donor C32-CRL1502, day12, rep1_CNhs13828_13435-144E6_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day06Rep3_CNhs14053_ctss_rev IpsToNeuronControlDnC11-CRL2429Day06R3- iPS differentiation to neuron, control donor C32-CRL1502, day06, rep3_CNhs14053_13442-144F4_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day06Rep3_CNhs14053_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day06R3+ iPS differentiation to neuron, control donor C32-CRL1502, day06, rep3_CNhs14053_13442-144F4_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day06Rep2_CNhs13840_ctss_rev IpsToNeuronControlDnC11-CRL2429Day06R2- iPS differentiation to neuron, control donor C32-CRL1502, day06, rep2_CNhs13840_13438-144E9_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day06Rep2_CNhs13840_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day06R2+ iPS differentiation to neuron, control donor C32-CRL1502, day06, rep2_CNhs13840_13438-144E9_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day06Rep1_CNhs13827_ctss_rev IpsToNeuronControlDnC11-CRL2429Day06R1- iPS differentiation to neuron, control donor C32-CRL1502, day06, rep1_CNhs13827_13434-144E5_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day06Rep1_CNhs13827_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day06R1+ iPS differentiation to neuron, control donor C32-CRL1502, day06, rep1_CNhs13827_13434-144E5_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day00Rep3_CNhs14052_ctss_rev IpsToNeuronControlDnC11-CRL2429Day00R3- iPS differentiation to neuron, control donor C32-CRL1502, day00, rep3_CNhs14052_13441-144F3_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day00Rep3_CNhs14052_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day00R3+ iPS differentiation to neuron, control donor C32-CRL1502, day00, rep3_CNhs14052_13441-144F3_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day00Rep2_CNhs13839_ctss_rev IpsToNeuronControlDnC11-CRL2429Day00R2- iPS differentiation to neuron, control donor C32-CRL1502, day00, rep2_CNhs13839_13437-144E8_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day00Rep2_CNhs13839_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day00R2+ iPS differentiation to neuron, control donor C32-CRL1502, day00, rep2_CNhs13839_13437-144E8_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day00Rep1_CNhs13826_ctss_rev IpsToNeuronControlDnC11-CRL2429Day00R1- iPS differentiation to neuron, control donor C32-CRL1502, day00, rep1_CNhs13826_13433-144E4_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day00Rep1_CNhs13826_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day00R1+ iPS differentiation to neuron, control donor C32-CRL1502, day00, rep1_CNhs13826_13433-144E4_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day18Rep3_CNhs13917_ctss_rev IpsToNeuronControlDnC11-CRL2429Day18R3- iPS differentiation to neuron, control donor C11-CRL2429, day18, rep3_CNhs13917_13432-144E3_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day18Rep3_CNhs13917_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day18R3+ iPS differentiation to neuron, control donor C11-CRL2429, day18, rep3_CNhs13917_13432-144E3_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day18Rep2_CNhs13825_ctss_rev IpsToNeuronControlDnC11-CRL2429Day18R2- iPS differentiation to neuron, control donor C11-CRL2429, day18, rep2_CNhs13825_13428-144D8_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day18Rep2_CNhs13825_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day18R2+ iPS differentiation to neuron, control donor C11-CRL2429, day18, rep2_CNhs13825_13428-144D8_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day18Rep1_CNhs13916_ctss_rev IpsToNeuronControlDnC11-CRL2429Day18R1- iPS differentiation to neuron, control donor C11-CRL2429, day18, rep1_CNhs13916_13424-144D4_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day18Rep1_CNhs13916_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day18R1+ iPS differentiation to neuron, control donor C11-CRL2429, day18, rep1_CNhs13916_13424-144D4_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day12Rep3_CNhs14051_ctss_rev IpsToNeuronControlDnC11-CRL2429Day12R3- iPS differentiation to neuron, control donor C11-CRL2429, day12, rep3_CNhs14051_13431-144E2_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day12Rep3_CNhs14051_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day12R3+ iPS differentiation to neuron, control donor C11-CRL2429, day12, rep3_CNhs14051_13431-144E2_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day12Rep2_CNhs13824_ctss_rev IpsToNeuronControlDnC11-CRL2429Day12R2- iPS differentiation to neuron, control donor C11-CRL2429, day12, rep2_CNhs13824_13427-144D7_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day12Rep2_CNhs13824_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day12R2+ iPS differentiation to neuron, control donor C11-CRL2429, day12, rep2_CNhs13824_13427-144D7_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day12Rep1_CNhs14047_ctss_rev IpsToNeuronControlDnC11-CRL2429Day12R1- iPS differentiation to neuron, control donor C11-CRL2429, day12, rep1_CNhs14047_13423-144D3_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day12Rep1_CNhs14047_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day12R1+ iPS differentiation to neuron, control donor C11-CRL2429, day12, rep1_CNhs14047_13423-144D3_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day06Rep3_CNhs14050_ctss_rev IpsToNeuronControlDnC11-CRL2429Day06R3- iPS differentiation to neuron, control donor C11-CRL2429, day06, rep3_CNhs14050_13430-144E1_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day06Rep3_CNhs14050_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day06R3+ iPS differentiation to neuron, control donor C11-CRL2429, day06, rep3_CNhs14050_13430-144E1_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day06Rep2_CNhs13823_ctss_rev IpsToNeuronControlDnC11-CRL2429Day06R2- iPS differentiation to neuron, control donor C11-CRL2429, day06, rep2_CNhs13823_13426-144D6_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day06Rep2_CNhs13823_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day06R2+ iPS differentiation to neuron, control donor C11-CRL2429, day06, rep2_CNhs13823_13426-144D6_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day06Rep1_CNhs14046_ctss_rev IpsToNeuronControlDnC11-CRL2429Day06R1- iPS differentiation to neuron, control donor C11-CRL2429, day06, rep1_CNhs14046_13422-144D2_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day06Rep1_CNhs14046_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day06R1+ iPS differentiation to neuron, control donor C11-CRL2429, day06, rep1_CNhs14046_13422-144D2_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day00Rep3_CNhs14049_ctss_rev IpsToNeuronControlDnC11-CRL2429Day00R3- iPS differentiation to neuron, control donor C11-CRL2429, day00, rep3_CNhs14049_13429-144D9_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day00Rep3_CNhs14049_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day00R3+ iPS differentiation to neuron, control donor C11-CRL2429, day00, rep3_CNhs14049_13429-144D9_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day00Rep2_CNhs13822_ctss_rev IpsToNeuronControlDnC11-CRL2429Day00R2- iPS differentiation to neuron, control donor C11-CRL2429, day00, rep2_CNhs13822_13425-144D5_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day00Rep2_CNhs13822_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day00R2+ iPS differentiation to neuron, control donor C11-CRL2429, day00, rep2_CNhs13822_13425-144D5_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day00Rep1_CNhs14045_ctss_rev IpsToNeuronControlDnC11-CRL2429Day00R1- iPS differentiation to neuron, control donor C11-CRL2429, day00, rep1_CNhs14045_13421-144D1_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day00Rep1_CNhs14045_ctss_fwd IpsToNeuronControlDnC11-CRL2429Day00R1+ iPS differentiation to neuron, control donor C11-CRL2429, day00, rep1_CNhs14045_13421-144D1_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day18Rep3_CNhs14066_ctss_rev Tc:iPStoNeuronDs_Day18R3- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day18, rep3_CNhs14066_13468-144I3_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day18Rep3_CNhs14066_ctss_fwd Tc:iPStoNeuronDs_Day18R3+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day18, rep3_CNhs14066_13468-144I3_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day18Rep2_CNhs13922_ctss_rev Tc:iPStoNeuronDs_Day18R2- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day18, rep2_CNhs13922_13464-144H8_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day18Rep2_CNhs13922_ctss_fwd Tc:iPStoNeuronDs_Day18R2+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day18, rep2_CNhs13922_13464-144H8_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day18Rep1_CNhs13838_ctss_rev Tc:iPStoNeuronDs_Day18R1- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day18, rep1_CNhs13838_13460-144H4_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day18Rep1_CNhs13838_ctss_fwd Tc:iPStoNeuronDs_Day18R1+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day18, rep1_CNhs13838_13460-144H4_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day12Rep3_CNhs14065_ctss_rev Tc:iPStoNeuronDs_Day12R3- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day12, rep3_CNhs14065_13467-144I2_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day12Rep3_CNhs14065_ctss_fwd Tc:iPStoNeuronDs_Day12R3+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day12, rep3_CNhs14065_13467-144I2_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day12Rep2_CNhs14062_ctss_rev Tc:iPStoNeuronDs_Day12R2- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day12, rep2_CNhs14062_13463-144H7_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day12Rep2_CNhs14062_ctss_fwd Tc:iPStoNeuronDs_Day12R2+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day12, rep2_CNhs14062_13463-144H7_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day12Rep1_CNhs13837_ctss_rev Tc:iPStoNeuronDs_Day12R1- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day12, rep1_CNhs13837_13459-144H3_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day12Rep1_CNhs13837_ctss_fwd Tc:iPStoNeuronDs_Day12R1+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day12, rep1_CNhs13837_13459-144H3_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day06Rep3_CNhs14064_ctss_rev Tc:iPStoNeuronDs_Day06R3- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day06, rep3_CNhs14064_13466-144I1_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day06Rep3_CNhs14064_ctss_fwd Tc:iPStoNeuronDs_Day06R3+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day06, rep3_CNhs14064_13466-144I1_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day06Rep2_CNhs14061_ctss_rev Tc:iPStoNeuronDs_Day06R2- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day06, rep2_CNhs14061_13462-144H6_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day06Rep2_CNhs14061_ctss_fwd Tc:iPStoNeuronDs_Day06R2+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day06, rep2_CNhs14061_13462-144H6_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day06Rep1_CNhs13836_ctss_rev Tc:iPStoNeuronDs_Day06R1- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day06, rep1_CNhs13836_13458-144H2_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day06Rep1_CNhs13836_ctss_fwd Tc:iPStoNeuronDs_Day06R1+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day06, rep1_CNhs13836_13458-144H2_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day00Rep3_CNhs14063_ctss_rev Tc:iPStoNeuronDs_Day00R3- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day00, rep3_CNhs14063_13465-144H9_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day00Rep3_CNhs14063_ctss_fwd Tc:iPStoNeuronDs_Day00R3+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day00, rep3_CNhs14063_13465-144H9_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day00Rep2_CNhs14060_ctss_rev Tc:iPStoNeuronDs_Day00R2- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day00, rep2_CNhs14060_13461-144H5_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day00Rep2_CNhs14060_ctss_fwd Tc:iPStoNeuronDs_Day00R2+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day00, rep2_CNhs14060_13461-144H5_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day00Rep1_CNhs13835_ctss_rev Tc:iPStoNeuronDs_Day00R1- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day00, rep1_CNhs13835_13457-144H1_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day00Rep1_CNhs13835_ctss_fwd Tc:iPStoNeuronDs_Day00R1+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day00, rep1_CNhs13835_13457-144H1_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day18Rep3_CNhs14059_ctss_rev Tc:iPStoNeuronDs_Day18R3- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day18, rep3_CNhs14059_13456-144G9_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day18Rep3_CNhs14059_ctss_fwd Tc:iPStoNeuronDs_Day18R3+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day18, rep3_CNhs14059_13456-144G9_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day18Rep2_CNhs13846_ctss_rev Tc:iPStoNeuronDs_Day18R2- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day18, rep2_CNhs13846_13452-144G5_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day18Rep2_CNhs13846_ctss_fwd Tc:iPStoNeuronDs_Day18R2+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day18, rep2_CNhs13846_13452-144G5_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day18Rep1_CNhs13833_ctss_rev Tc:iPStoNeuronDs_Day18R1- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day18, rep1_CNhs13833_13448-144G1_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day18Rep1_CNhs13833_ctss_fwd Tc:iPStoNeuronDs_Day18R1+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day18, rep1_CNhs13833_13448-144G1_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day12Rep3_CNhs14058_ctss_rev Tc:iPStoNeuronDs_Day12R3- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day12, rep3_CNhs14058_13455-144G8_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day12Rep3_CNhs14058_ctss_fwd Tc:iPStoNeuronDs_Day12R3+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day12, rep3_CNhs14058_13455-144G8_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day12Rep2_CNhs13845_ctss_rev Tc:iPStoNeuronDs_Day12R2- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day12, rep2_CNhs13845_13451-144G4_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day12Rep2_CNhs13845_ctss_fwd Tc:iPStoNeuronDs_Day12R2+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day12, rep2_CNhs13845_13451-144G4_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day12Rep1_CNhs13832_ctss_rev Tc:iPStoNeuronDs_Day12R1- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day12, rep1_CNhs13832_13447-144F9_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day12Rep1_CNhs13832_ctss_fwd Tc:iPStoNeuronDs_Day12R1+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day12, rep1_CNhs13832_13447-144F9_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day06Rep3_CNhs14057_ctss_rev Tc:iPStoNeuronDs_Day06R3- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day06, rep3_CNhs14057_13454-144G7_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day06Rep3_CNhs14057_ctss_fwd Tc:iPStoNeuronDs_Day06R3+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day06, rep3_CNhs14057_13454-144G7_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day06Rep2_CNhs13844_ctss_rev Tc:iPStoNeuronDs_Day06R2- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day06, rep2_CNhs13844_13450-144G3_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day06Rep2_CNhs13844_ctss_fwd Tc:iPStoNeuronDs_Day06R2+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day06, rep2_CNhs13844_13450-144G3_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day06Rep1_CNhs13831_ctss_rev Tc:iPStoNeuronDs_Day06R1- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day06, rep1_CNhs13831_13446-144F8_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day06Rep1_CNhs13831_ctss_fwd Tc:iPStoNeuronDs_Day06R1+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day06, rep1_CNhs13831_13446-144F8_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day00Rep3_CNhs14056_ctss_rev Tc:iPStoNeuronDs_Day00R3- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day00, rep3_CNhs14056_13453-144G6_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day00Rep3_CNhs14056_ctss_fwd Tc:iPStoNeuronDs_Day00R3+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day00, rep3_CNhs14056_13453-144G6_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day00Rep2_CNhs13843_ctss_rev Tc:iPStoNeuronDs_Day00R2- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day00, rep2_CNhs13843_13449-144G2_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day00Rep2_CNhs13843_ctss_fwd Tc:iPStoNeuronDs_Day00R2+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day00, rep2_CNhs13843_13449-144G2_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day00Rep1_CNhs13830_ctss_rev Tc:iPStoNeuronDs_Day00R1- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day00, rep1_CNhs13830_13445-144F7_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day00Rep1_CNhs13830_ctss_fwd Tc:iPStoNeuronDs_Day00R1+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day00, rep1_CNhs13830_13445-144F7_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha60hr00minBiolRep3_CNhs14543_ctss_rev Tc:ARPE-19Emt_60hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 60hr00min, biol_rep3_CNhs14543_13687-147F6_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha60hr00minBiolRep3_CNhs14543_ctss_fwd Tc:ARPE-19Emt_60hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 60hr00min, biol_rep3_CNhs14543_13687-147F6_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha60hr00minBiolRep2_CNhs14542_ctss_rev Tc:ARPE-19Emt_60hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 60hr00min, biol_rep2_CNhs14542_13686-147F5_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha60hr00minBiolRep2_CNhs14542_ctss_fwd Tc:ARPE-19Emt_60hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 60hr00min, biol_rep2_CNhs14542_13686-147F5_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha60hr00minBiolRep1_CNhs14541_ctss_rev Tc:ARPE-19Emt_60hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 60hr00min, biol_rep1_CNhs14541_13685-147F4_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha60hr00minBiolRep1_CNhs14541_ctss_fwd Tc:ARPE-19Emt_60hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 60hr00min, biol_rep1_CNhs14541_13685-147F4_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha42hr00minBiolRep3_CNhs14540_ctss_rev Tc:ARPE-19Emt_42hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 42hr00min, biol_rep3_CNhs14540_13684-147F3_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha42hr00minBiolRep3_CNhs14540_ctss_fwd Tc:ARPE-19Emt_42hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 42hr00min, biol_rep3_CNhs14540_13684-147F3_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha42hr00minBiolRep2_CNhs14539_ctss_rev Tc:ARPE-19Emt_42hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 42hr00min, biol_rep2_CNhs14539_13683-147F2_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha42hr00minBiolRep2_CNhs14539_ctss_fwd Tc:ARPE-19Emt_42hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 42hr00min, biol_rep2_CNhs14539_13683-147F2_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha42hr00minBiolRep1_CNhs14538_ctss_rev Tc:ARPE-19Emt_42hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 42hr00min, biol_rep1_CNhs14538_13682-147F1_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha42hr00minBiolRep1_CNhs14538_ctss_fwd Tc:ARPE-19Emt_42hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 42hr00min, biol_rep1_CNhs14538_13682-147F1_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha24hr00minBiolRep3_CNhs14537_ctss_rev Tc:ARPE-19Emt_24hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 24hr00min, biol_rep3_CNhs14537_13681-147E9_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha24hr00minBiolRep3_CNhs14537_ctss_fwd Tc:ARPE-19Emt_24hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 24hr00min, biol_rep3_CNhs14537_13681-147E9_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha24hr00minBiolRep1_CNhs14535_ctss_rev Tc:ARPE-19Emt_24hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 24hr00min, biol_rep1_CNhs14535_13679-147E7_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha24hr00minBiolRep1_CNhs14535_ctss_fwd Tc:ARPE-19Emt_24hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 24hr00min, biol_rep1_CNhs14535_13679-147E7_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha16hr00minBiolRep3_CNhs14534_ctss_rev Tc:ARPE-19Emt_16hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 16hr00min, biol_rep3_CNhs14534_13678-147E6_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha16hr00minBiolRep3_CNhs14534_ctss_fwd Tc:ARPE-19Emt_16hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 16hr00min, biol_rep3_CNhs14534_13678-147E6_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha16hr00minBiolRep2_CNhs14533_ctss_rev Tc:ARPE-19Emt_16hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 16hr00min, biol_rep2_CNhs14533_13677-147E5_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha16hr00minBiolRep2_CNhs14533_ctss_fwd Tc:ARPE-19Emt_16hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 16hr00min, biol_rep2_CNhs14533_13677-147E5_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha16hr00minBiolRep1_CNhs14532_ctss_rev Tc:ARPE-19Emt_16hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 16hr00min, biol_rep1_CNhs14532_13676-147E4_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha16hr00minBiolRep1_CNhs14532_ctss_fwd Tc:ARPE-19Emt_16hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 16hr00min, biol_rep1_CNhs14532_13676-147E4_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha12hr00minBiolRep3_CNhs14531_ctss_rev Tc:ARPE-19Emt_12hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 12hr00min, biol_rep3_CNhs14531_13675-147E3_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha12hr00minBiolRep3_CNhs14531_ctss_fwd Tc:ARPE-19Emt_12hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 12hr00min, biol_rep3_CNhs14531_13675-147E3_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha12hr00minBiolRep2_CNhs14530_ctss_rev Tc:ARPE-19Emt_12hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 12hr00min, biol_rep2_CNhs14530_13674-147E2_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha12hr00minBiolRep2_CNhs14530_ctss_fwd Tc:ARPE-19Emt_12hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 12hr00min, biol_rep2_CNhs14530_13674-147E2_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha08hr00minBiolRep3_CNhs14528_ctss_rev Tc:ARPE-19Emt_08hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 08hr00min, biol_rep3_CNhs14528_13672-147D9_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha08hr00minBiolRep3_CNhs14528_ctss_fwd Tc:ARPE-19Emt_08hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 08hr00min, biol_rep3_CNhs14528_13672-147D9_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha08hr00minBiolRep2_CNhs14527_ctss_rev Tc:ARPE-19Emt_08hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 08hr00min, biol_rep2_CNhs14527_13671-147D8_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha08hr00minBiolRep2_CNhs14527_ctss_fwd Tc:ARPE-19Emt_08hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 08hr00min, biol_rep2_CNhs14527_13671-147D8_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha08hr00minBiolRep1_CNhs14526_ctss_rev Tc:ARPE-19Emt_08hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 08hr00min, biol_rep1_CNhs14526_13670-147D7_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha08hr00minBiolRep1_CNhs14526_ctss_fwd Tc:ARPE-19Emt_08hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 08hr00min, biol_rep1_CNhs14526_13670-147D7_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha07hr00minBiolRep3_CNhs14525_ctss_rev Tc:ARPE-19Emt_07hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 07hr00min, biol_rep3_CNhs14525_13669-147D6_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha07hr00minBiolRep3_CNhs14525_ctss_fwd Tc:ARPE-19Emt_07hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 07hr00min, biol_rep3_CNhs14525_13669-147D6_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha07hr00minBiolRep2_CNhs14524_ctss_rev Tc:ARPE-19Emt_07hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 07hr00min, biol_rep2_CNhs14524_13668-147D5_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha07hr00minBiolRep2_CNhs14524_ctss_fwd Tc:ARPE-19Emt_07hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 07hr00min, biol_rep2_CNhs14524_13668-147D5_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha07hr00minBiolRep1_CNhs14523_ctss_rev Tc:ARPE-19Emt_07hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 07hr00min, biol_rep1_CNhs14523_13667-147D4_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha07hr00minBiolRep1_CNhs14523_ctss_fwd Tc:ARPE-19Emt_07hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 07hr00min, biol_rep1_CNhs14523_13667-147D4_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha06hr00minBiolRep3_CNhs14522_ctss_rev Tc:ARPE-19Emt_06hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 06hr00min, biol_rep3_CNhs14522_13666-147D3_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha06hr00minBiolRep3_CNhs14522_ctss_fwd Tc:ARPE-19Emt_06hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 06hr00min, biol_rep3_CNhs14522_13666-147D3_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha06hr00minBiolRep1_CNhs14519_ctss_rev Tc:ARPE-19Emt_06hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 06hr00min, biol_rep1_CNhs14519_13664-147D1_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha06hr00minBiolRep1_CNhs14519_ctss_fwd Tc:ARPE-19Emt_06hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 06hr00min, biol_rep1_CNhs14519_13664-147D1_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha05hr00minBiolRep3_CNhs14518_ctss_rev Tc:ARPE-19Emt_05hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 05hr00min, biol_rep3_CNhs14518_13663-147C9_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha05hr00minBiolRep3_CNhs14518_ctss_fwd Tc:ARPE-19Emt_05hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 05hr00min, biol_rep3_CNhs14518_13663-147C9_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha05hr00minBiolRep2_CNhs14501_ctss_rev Tc:ARPE-19Emt_05hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 05hr00min, biol_rep2_CNhs14501_13662-147C8_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha05hr00minBiolRep2_CNhs14501_ctss_fwd Tc:ARPE-19Emt_05hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 05hr00min, biol_rep2_CNhs14501_13662-147C8_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha05hr00minBiolRep1_CNhs14500_ctss_rev Tc:ARPE-19Emt_05hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 05hr00min, biol_rep1_CNhs14500_13661-147C7_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha05hr00minBiolRep1_CNhs14500_ctss_fwd Tc:ARPE-19Emt_05hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 05hr00min, biol_rep1_CNhs14500_13661-147C7_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha04hr00minBiolRep3_CNhs14499_ctss_rev Tc:ARPE-19Emt_04hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 04hr00min, biol_rep3_CNhs14499_13660-147C6_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha04hr00minBiolRep3_CNhs14499_ctss_fwd Tc:ARPE-19Emt_04hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 04hr00min, biol_rep3_CNhs14499_13660-147C6_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha04hr00minBiolRep2_CNhs14498_ctss_rev Tc:ARPE-19Emt_04hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 04hr00min, biol_rep2_CNhs14498_13659-147C5_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha04hr00minBiolRep2_CNhs14498_ctss_fwd Tc:ARPE-19Emt_04hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 04hr00min, biol_rep2_CNhs14498_13659-147C5_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha04hr00minBiolRep1_CNhs14497_ctss_rev Tc:ARPE-19Emt_04hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 04hr00min, biol_rep1_CNhs14497_13658-147C4_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha04hr00minBiolRep1_CNhs14497_ctss_fwd Tc:ARPE-19Emt_04hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 04hr00min, biol_rep1_CNhs14497_13658-147C4_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr30minBiolRep3_CNhs14496_ctss_rev Tc:ARPE-19Emt_03hr30minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr30min, biol_rep3_CNhs14496_13657-147C3_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr30minBiolRep3_CNhs14496_ctss_fwd Tc:ARPE-19Emt_03hr30minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr30min, biol_rep3_CNhs14496_13657-147C3_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr30minBiolRep2_CNhs14495_ctss_rev Tc:ARPE-19Emt_03hr30minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr30min, biol_rep2_CNhs14495_13656-147C2_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr30minBiolRep2_CNhs14495_ctss_fwd Tc:ARPE-19Emt_03hr30minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr30min, biol_rep2_CNhs14495_13656-147C2_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr30minBiolRep1_CNhs14494_ctss_rev Tc:ARPE-19Emt_03hr30minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr30min, biol_rep1_CNhs14494_13655-147C1_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr30minBiolRep1_CNhs14494_ctss_fwd Tc:ARPE-19Emt_03hr30minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr30min, biol_rep1_CNhs14494_13655-147C1_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr00minBiolRep3_CNhs14493_ctss_rev Tc:ARPE-19Emt_03hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr00min, biol_rep3_CNhs14493_13654-147B9_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr00minBiolRep3_CNhs14493_ctss_fwd Tc:ARPE-19Emt_03hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr00min, biol_rep3_CNhs14493_13654-147B9_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr00minBiolRep2_CNhs14492_ctss_rev Tc:ARPE-19Emt_03hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr00min, biol_rep2_CNhs14492_13653-147B8_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr00minBiolRep2_CNhs14492_ctss_fwd Tc:ARPE-19Emt_03hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr00min, biol_rep2_CNhs14492_13653-147B8_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr00minBiolRep1_CNhs14491_ctss_rev Tc:ARPE-19Emt_03hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr00min, biol_rep1_CNhs14491_13652-147B7_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr00minBiolRep1_CNhs14491_ctss_fwd Tc:ARPE-19Emt_03hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr00min, biol_rep1_CNhs14491_13652-147B7_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr30minBiolRep3_CNhs14490_ctss_rev Tc:ARPE-19Emt_02hr30minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr30min, biol_rep3_CNhs14490_13651-147B6_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr30minBiolRep3_CNhs14490_ctss_fwd Tc:ARPE-19Emt_02hr30minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr30min, biol_rep3_CNhs14490_13651-147B6_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr30minBiolRep2_CNhs14489_ctss_rev Tc:ARPE-19Emt_02hr30minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr30min, biol_rep2_CNhs14489_13650-147B5_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr30minBiolRep2_CNhs14489_ctss_fwd Tc:ARPE-19Emt_02hr30minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr30min, biol_rep2_CNhs14489_13650-147B5_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr30minBiolRep1_CNhs14488_ctss_rev Tc:ARPE-19Emt_02hr30minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr30min, biol_rep1_CNhs14488_13649-147B4_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr30minBiolRep1_CNhs14488_ctss_fwd Tc:ARPE-19Emt_02hr30minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr30min, biol_rep1_CNhs14488_13649-147B4_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr00minBiolRep3_CNhs14487_ctss_rev Tc:ARPE-19Emt_02hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr00min, biol_rep3_CNhs14487_13648-147B3_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr00minBiolRep3_CNhs14487_ctss_fwd Tc:ARPE-19Emt_02hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr00min, biol_rep3_CNhs14487_13648-147B3_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr00minBiolRep2_CNhs14486_ctss_rev Tc:ARPE-19Emt_02hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr00min, biol_rep2_CNhs14486_13647-147B2_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr00minBiolRep2_CNhs14486_ctss_fwd Tc:ARPE-19Emt_02hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr00min, biol_rep2_CNhs14486_13647-147B2_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr00minBiolRep1_CNhs14485_ctss_rev Tc:ARPE-19Emt_02hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr00min, biol_rep1_CNhs14485_13646-147B1_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr00minBiolRep1_CNhs14485_ctss_fwd Tc:ARPE-19Emt_02hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr00min, biol_rep1_CNhs14485_13646-147B1_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr40minBiolRep3_CNhs14484_ctss_rev Tc:ARPE-19Emt_01hr40minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr40min, biol_rep3_CNhs14484_13645-147A9_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr40minBiolRep3_CNhs14484_ctss_fwd Tc:ARPE-19Emt_01hr40minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr40min, biol_rep3_CNhs14484_13645-147A9_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr40minBiolRep2_CNhs14483_ctss_rev Tc:ARPE-19Emt_01hr40minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr40min, biol_rep2_CNhs14483_13644-147A8_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr40minBiolRep2_CNhs14483_ctss_fwd Tc:ARPE-19Emt_01hr40minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr40min, biol_rep2_CNhs14483_13644-147A8_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr40minBiolRep1_CNhs14482_ctss_rev Tc:ARPE-19Emt_01hr40minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr40min, biol_rep1_CNhs14482_13643-147A7_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr40minBiolRep1_CNhs14482_ctss_fwd Tc:ARPE-19Emt_01hr40minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr40min, biol_rep1_CNhs14482_13643-147A7_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr20minBiolRep3_CNhs14480_ctss_rev Tc:ARPE-19Emt_01hr20minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr20min, biol_rep3_CNhs14480_13642-147A6_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr20minBiolRep3_CNhs14480_ctss_fwd Tc:ARPE-19Emt_01hr20minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr20min, biol_rep3_CNhs14480_13642-147A6_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr20minBiolRep2_CNhs14479_ctss_rev Tc:ARPE-19Emt_01hr20minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr20min, biol_rep2_CNhs14479_13641-147A5_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr20minBiolRep2_CNhs14479_ctss_fwd Tc:ARPE-19Emt_01hr20minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr20min, biol_rep2_CNhs14479_13641-147A5_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr20minBiolRep1_CNhs14478_ctss_rev Tc:ARPE-19Emt_01hr20minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr20min, biol_rep1_CNhs14478_13640-147A4_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr20minBiolRep1_CNhs14478_ctss_fwd Tc:ARPE-19Emt_01hr20minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr20min, biol_rep1_CNhs14478_13640-147A4_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr00minBiolRep3_CNhs14477_ctss_rev Tc:ARPE-19Emt_01hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr00min, biol_rep3_CNhs14477_13639-147A3_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr00minBiolRep3_CNhs14477_ctss_fwd Tc:ARPE-19Emt_01hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr00min, biol_rep3_CNhs14477_13639-147A3_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr00minBiolRep2_CNhs14476_ctss_rev Tc:ARPE-19Emt_01hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr00min, biol_rep2_CNhs14476_13638-147A2_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr00minBiolRep2_CNhs14476_ctss_fwd Tc:ARPE-19Emt_01hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr00min, biol_rep2_CNhs14476_13638-147A2_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr00minBiolRep1_CNhs14475_ctss_rev Tc:ARPE-19Emt_01hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr00min, biol_rep1_CNhs14475_13637-147A1_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr00minBiolRep1_CNhs14475_ctss_fwd Tc:ARPE-19Emt_01hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr00min, biol_rep1_CNhs14475_13637-147A1_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr45minBiolRep3_CNhs14474_ctss_rev Tc:ARPE-19Emt_00hr45minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr45min, biol_rep3_CNhs14474_13636-146I9_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr45minBiolRep3_CNhs14474_ctss_fwd Tc:ARPE-19Emt_00hr45minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr45min, biol_rep3_CNhs14474_13636-146I9_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr45minBiolRep2_CNhs14473_ctss_rev Tc:ARPE-19Emt_00hr45minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr45min, biol_rep2_CNhs14473_13635-146I8_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr45minBiolRep2_CNhs14473_ctss_fwd Tc:ARPE-19Emt_00hr45minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr45min, biol_rep2_CNhs14473_13635-146I8_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr45minBiolRep1_CNhs14472_ctss_rev Tc:ARPE-19Emt_00hr45minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr45min, biol_rep1_CNhs14472_13634-146I7_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr45minBiolRep1_CNhs14472_ctss_fwd Tc:ARPE-19Emt_00hr45minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr45min, biol_rep1_CNhs14472_13634-146I7_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr30minBiolRep3_CNhs14471_ctss_rev Tc:ARPE-19Emt_00hr30minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr30min, biol_rep3_CNhs14471_13633-146I6_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr30minBiolRep3_CNhs14471_ctss_fwd Tc:ARPE-19Emt_00hr30minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr30min, biol_rep3_CNhs14471_13633-146I6_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr30minBiolRep2_CNhs14470_ctss_rev Tc:ARPE-19Emt_00hr30minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr30min, biol_rep2_CNhs14470_13632-146I5_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr30minBiolRep2_CNhs14470_ctss_fwd Tc:ARPE-19Emt_00hr30minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr30min, biol_rep2_CNhs14470_13632-146I5_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr30minBiolRep1_CNhs14469_ctss_rev Tc:ARPE-19Emt_00hr30minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr30min, biol_rep1_CNhs14469_13631-146I4_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr30minBiolRep1_CNhs14469_ctss_fwd Tc:ARPE-19Emt_00hr30minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr30min, biol_rep1_CNhs14469_13631-146I4_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr15minBiolRep3_CNhs14468_ctss_rev Tc:ARPE-19Emt_00hr15minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr15min, biol_rep3_CNhs14468_13630-146I3_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr15minBiolRep3_CNhs14468_ctss_fwd Tc:ARPE-19Emt_00hr15minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr15min, biol_rep3_CNhs14468_13630-146I3_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr15minBiolRep2_CNhs14467_ctss_rev Tc:ARPE-19Emt_00hr15minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr15min, biol_rep2_CNhs14467_13629-146I2_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr15minBiolRep2_CNhs14467_ctss_fwd Tc:ARPE-19Emt_00hr15minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr15min, biol_rep2_CNhs14467_13629-146I2_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr15minBiolRep1_CNhs14466_ctss_rev Tc:ARPE-19Emt_00hr15minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr15min, biol_rep1_CNhs14466_13628-146I1_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr15minBiolRep1_CNhs14466_ctss_fwd Tc:ARPE-19Emt_00hr15minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr15min, biol_rep1_CNhs14466_13628-146I1_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr00minBiolRep3_CNhs14465_ctss_rev Tc:ARPE-19Emt_00hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr00min, biol_rep3_CNhs14465_13627-146H9_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr00minBiolRep3_CNhs14465_ctss_fwd Tc:ARPE-19Emt_00hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr00min, biol_rep3_CNhs14465_13627-146H9_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr00minBiolRep2_CNhs14464_ctss_rev Tc:ARPE-19Emt_00hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr00min, biol_rep2_CNhs14464_13626-146H8_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr00minBiolRep2_CNhs14464_ctss_fwd Tc:ARPE-19Emt_00hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr00min, biol_rep2_CNhs14464_13626-146H8_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr00minBiolRep1_CNhs14463_ctss_rev Tc:ARPE-19Emt_00hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr00min, biol_rep1_CNhs14463_13625-146H7_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr00minBiolRep1_CNhs14463_ctss_fwd Tc:ARPE-19Emt_00hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr00min, biol_rep1_CNhs14463_13625-146H7_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay41BiolRep3H9EB3D41_CNhs12950_ctss_rev H9MelanocyticInduction_Day41Br3- H9 Embryoid body cells, melanocytic induction, day41, biol_rep3 (H9EB-3 d41)_CNhs12950_12836-137B1_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay41BiolRep3H9EB3D41_CNhs12950_ctss_fwd H9MelanocyticInduction_Day41Br3+ H9 Embryoid body cells, melanocytic induction, day41, biol_rep3 (H9EB-3 d41)_CNhs12950_12836-137B1_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay41BiolRep2H9EB2D41_CNhs12907_ctss_rev H9MelanocyticInduction_Day41Br2- H9 Embryoid body cells, melanocytic induction, day41, biol_rep2 (H9EB-2 d41)_CNhs12907_12738-135I2_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay41BiolRep2H9EB2D41_CNhs12907_ctss_fwd H9MelanocyticInduction_Day41Br2+ H9 Embryoid body cells, melanocytic induction, day41, biol_rep2 (H9EB-2 d41)_CNhs12907_12738-135I2_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay41BiolRep1H9EB1D41_CNhs12905_ctss_rev H9MelanocyticInduction_Day41Br1- H9 Embryoid body cells, melanocytic induction, day41, biol_rep1 (H9EB-1 d41)_CNhs12905_12640-134G3_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay41BiolRep1H9EB1D41_CNhs12905_ctss_fwd H9MelanocyticInduction_Day41Br1+ H9 Embryoid body cells, melanocytic induction, day41, biol_rep1 (H9EB-1 d41)_CNhs12905_12640-134G3_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay34BiolRep3H9EB3D34_CNhs12919_ctss_rev H9MelanocyticInduction_Day34Br3- H9 Embryoid body cells, melanocytic induction, day34, biol_rep3 (H9EB-3 d34)_CNhs12919_12835-137A9_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay34BiolRep3H9EB3D34_CNhs12919_ctss_fwd H9MelanocyticInduction_Day34Br3+ H9 Embryoid body cells, melanocytic induction, day34, biol_rep3 (H9EB-3 d34)_CNhs12919_12835-137A9_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay34BiolRep2H9EB2D34_CNhs12906_ctss_rev H9MelanocyticInduction_Day34Br2- H9 Embryoid body cells, melanocytic induction, day34, biol_rep2 (H9EB-2 d34)_CNhs12906_12737-135I1_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay34BiolRep2H9EB2D34_CNhs12906_ctss_fwd H9MelanocyticInduction_Day34Br2+ H9 Embryoid body cells, melanocytic induction, day34, biol_rep2 (H9EB-2 d34)_CNhs12906_12737-135I1_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay34BiolRep1H9EB1D34_CNhs12904_ctss_rev H9MelanocyticInduction_Day34Br1- H9 Embryoid body cells, melanocytic induction, day34, biol_rep1 (H9EB-1 d34)_CNhs12904_12639-134G2_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay34BiolRep1H9EB1D34_CNhs12904_ctss_fwd H9MelanocyticInduction_Day34Br1+ H9 Embryoid body cells, melanocytic induction, day34, biol_rep1 (H9EB-1 d34)_CNhs12904_12639-134G2_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay30BiolRep3H9EB3D30_CNhs12918_ctss_rev H9MelanocyticInduction_Day30Br3- H9 Embryoid body cells, melanocytic induction, day30, biol_rep3 (H9EB-3 d30)_CNhs12918_12834-137A8_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay30BiolRep3H9EB3D30_CNhs12918_ctss_fwd H9MelanocyticInduction_Day30Br3+ H9 Embryoid body cells, melanocytic induction, day30, biol_rep3 (H9EB-3 d30)_CNhs12918_12834-137A8_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay30BiolRep2H9EB2D30_CNhs12836_ctss_rev H9MelanocyticInduction_Day30Br2- H9 Embryoid body cells, melanocytic induction, day30, biol_rep2 (H9EB-2 d30)_CNhs12836_12736-135H9_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay30BiolRep2H9EB2D30_CNhs12836_ctss_fwd H9MelanocyticInduction_Day30Br2+ H9 Embryoid body cells, melanocytic induction, day30, biol_rep2 (H9EB-2 d30)_CNhs12836_12736-135H9_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay30BiolRep1H9EB1D30_CNhs12903_ctss_rev H9MelanocyticInduction_Day30Br1- H9 Embryoid body cells, melanocytic induction, day30, biol_rep1 (H9EB-1 d30)_CNhs12903_12638-134G1_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay30BiolRep1H9EB1D30_CNhs12903_ctss_fwd H9MelanocyticInduction_Day30Br1+ H9 Embryoid body cells, melanocytic induction, day30, biol_rep1 (H9EB-1 d30)_CNhs12903_12638-134G1_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay27BiolRep3H9EB3D27_CNhs12917_ctss_rev H9MelanocyticInduction_Day27Br3- H9 Embryoid body cells, melanocytic induction, day27, biol_rep3 (H9EB-3 d27)_CNhs12917_12833-137A7_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay27BiolRep3H9EB3D27_CNhs12917_ctss_fwd H9MelanocyticInduction_Day27Br3+ H9 Embryoid body cells, melanocytic induction, day27, biol_rep3 (H9EB-3 d27)_CNhs12917_12833-137A7_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay27BiolRep2H9EB2D27_CNhs12835_ctss_rev H9MelanocyticInduction_Day27Br2- H9 Embryoid body cells, melanocytic induction, day27, biol_rep2 (H9EB-2 d27)_CNhs12835_12735-135H8_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay27BiolRep2H9EB2D27_CNhs12835_ctss_fwd H9MelanocyticInduction_Day27Br2+ H9 Embryoid body cells, melanocytic induction, day27, biol_rep2 (H9EB-2 d27)_CNhs12835_12735-135H8_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay27BiolRep1H9EB1D27_CNhs12902_ctss_rev H9MelanocyticInduction_Day27Br1- H9 Embryoid body cells, melanocytic induction, day27, biol_rep1 (H9EB-1 d27)_CNhs12902_12637-134F9_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay27BiolRep1H9EB1D27_CNhs12902_ctss_fwd H9MelanocyticInduction_Day27Br1+ H9 Embryoid body cells, melanocytic induction, day27, biol_rep1 (H9EB-1 d27)_CNhs12902_12637-134F9_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay24BiolRep3H9EB3D24_CNhs12916_ctss_rev H9MelanocyticInduction_Day24Br3- H9 Embryoid body cells, melanocytic induction, day24, biol_rep3 (H9EB-3 d24)_CNhs12916_12832-137A6_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay24BiolRep3H9EB3D24_CNhs12916_ctss_fwd H9MelanocyticInduction_Day24Br3+ H9 Embryoid body cells, melanocytic induction, day24, biol_rep3 (H9EB-3 d24)_CNhs12916_12832-137A6_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay24BiolRep2H9EB2D24_CNhs12834_ctss_rev H9MelanocyticInduction_Day24Br2- H9 Embryoid body cells, melanocytic induction, day24, biol_rep2 (H9EB-2 d24)_CNhs12834_12734-135H7_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay24BiolRep2H9EB2D24_CNhs12834_ctss_fwd H9MelanocyticInduction_Day24Br2+ H9 Embryoid body cells, melanocytic induction, day24, biol_rep2 (H9EB-2 d24)_CNhs12834_12734-135H7_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay24BiolRep1H9EB1D24_CNhs12901_ctss_rev H9MelanocyticInduction_Day24Br1- H9 Embryoid body cells, melanocytic induction, day24, biol_rep1 (H9EB-1 d24)_CNhs12901_12636-134F8_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay24BiolRep1H9EB1D24_CNhs12901_ctss_fwd H9MelanocyticInduction_Day24Br1+ H9 Embryoid body cells, melanocytic induction, day24, biol_rep1 (H9EB-1 d24)_CNhs12901_12636-134F8_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay21BiolRep3H9EB3D21_CNhs12915_ctss_rev H9MelanocyticInduction_Day21Br3- H9 Embryoid body cells, melanocytic induction, day21, biol_rep3 (H9EB-3 d21)_CNhs12915_12831-137A5_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay21BiolRep3H9EB3D21_CNhs12915_ctss_fwd H9MelanocyticInduction_Day21Br3+ H9 Embryoid body cells, melanocytic induction, day21, biol_rep3 (H9EB-3 d21)_CNhs12915_12831-137A5_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay21BiolRep2H9EB2D21_CNhs12833_ctss_rev H9MelanocyticInduction_Day21Br2- H9 Embryoid body cells, melanocytic induction, day21, biol_rep2 (H9EB-2 d21)_CNhs12833_12733-135H6_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay21BiolRep2H9EB2D21_CNhs12833_ctss_fwd H9MelanocyticInduction_Day21Br2+ H9 Embryoid body cells, melanocytic induction, day21, biol_rep2 (H9EB-2 d21)_CNhs12833_12733-135H6_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay21BiolRep1H9EB1D21_CNhs12900_ctss_rev H9MelanocyticInduction_Day21Br1- H9 Embryoid body cells, melanocytic induction, day21, biol_rep1 (H9EB-1 d21)_CNhs12900_12635-134F7_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay21BiolRep1H9EB1D21_CNhs12900_ctss_fwd H9MelanocyticInduction_Day21Br1+ H9 Embryoid body cells, melanocytic induction, day21, biol_rep1 (H9EB-1 d21)_CNhs12900_12635-134F7_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay18BiolRep3H9EB3D18_CNhs12914_ctss_rev H9MelanocyticInduction_Day18Br3- H9 Embryoid body cells, melanocytic induction, day18, biol_rep3 (H9EB-3 d18)_CNhs12914_12830-137A4_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay18BiolRep3H9EB3D18_CNhs12914_ctss_fwd H9MelanocyticInduction_Day18Br3+ H9 Embryoid body cells, melanocytic induction, day18, biol_rep3 (H9EB-3 d18)_CNhs12914_12830-137A4_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay18BiolRep2H9EB2D18_CNhs12832_ctss_rev H9MelanocyticInduction_Day18Br2- H9 Embryoid body cells, melanocytic induction, day18, biol_rep2 (H9EB-2 d18)_CNhs12832_12732-135H5_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay18BiolRep2H9EB2D18_CNhs12832_ctss_fwd H9MelanocyticInduction_Day18Br2+ H9 Embryoid body cells, melanocytic induction, day18, biol_rep2 (H9EB-2 d18)_CNhs12832_12732-135H5_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay18BiolRep1H9EB1D18_CNhs12899_ctss_rev H9MelanocyticInduction_Day18Br1- H9 Embryoid body cells, melanocytic induction, day18, biol_rep1 (H9EB-1 d18)_CNhs12899_12634-134F6_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay18BiolRep1H9EB1D18_CNhs12899_ctss_fwd H9MelanocyticInduction_Day18Br1+ H9 Embryoid body cells, melanocytic induction, day18, biol_rep1 (H9EB-1 d18)_CNhs12899_12634-134F6_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay15BiolRep3H9EB3D15_CNhs12912_ctss_rev H9MelanocyticInduction_Day15Br3- H9 Embryoid body cells, melanocytic induction, day15, biol_rep3 (H9EB-3 d15)_CNhs12912_12829-137A3_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay15BiolRep3H9EB3D15_CNhs12912_ctss_fwd H9MelanocyticInduction_Day15Br3+ H9 Embryoid body cells, melanocytic induction, day15, biol_rep3 (H9EB-3 d15)_CNhs12912_12829-137A3_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay15BiolRep2H9EB2D15_CNhs12831_ctss_rev H9MelanocyticInduction_Day15Br2- H9 Embryoid body cells, melanocytic induction, day15, biol_rep2 (H9EB-2 d15)_CNhs12831_12731-135H4_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay15BiolRep2H9EB2D15_CNhs12831_ctss_fwd H9MelanocyticInduction_Day15Br2+ H9 Embryoid body cells, melanocytic induction, day15, biol_rep2 (H9EB-2 d15)_CNhs12831_12731-135H4_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay15BiolRep1H9EB1D15_CNhs12898_ctss_rev H9MelanocyticInduction_Day15Br1- H9 Embryoid body cells, melanocytic induction, day15, biol_rep1 (H9EB-1 d15)_CNhs12898_12633-134F5_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay15BiolRep1H9EB1D15_CNhs12898_ctss_fwd H9MelanocyticInduction_Day15Br1+ H9 Embryoid body cells, melanocytic induction, day15, biol_rep1 (H9EB-1 d15)_CNhs12898_12633-134F5_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay12BiolRep3H9EB3D12_CNhs12949_ctss_rev H9MelanocyticInduction_Day12Br3- H9 Embryoid body cells, melanocytic induction, day12, biol_rep3 (H9EB-3 d12)_CNhs12949_12828-137A2_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay12BiolRep3H9EB3D12_CNhs12995_ctss_rev H9MelanocyticInduction_Day12Br3- H9 Embryoid body cells, melanocytic induction, day12, biol_rep3 (H9EB-3 d12)_CNhs12995_12828-137A2_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay12BiolRep3H9EB3D12_CNhs12949_ctss_fwd H9MelanocyticInduction_Day12Br3+ H9 Embryoid body cells, melanocytic induction, day12, biol_rep3 (H9EB-3 d12)_CNhs12949_12828-137A2_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay12BiolRep3H9EB3D12_CNhs12995_ctss_fwd H9MelanocyticInduction_Day12Br3+ H9 Embryoid body cells, melanocytic induction, day12, biol_rep3 (H9EB-3 d12)_CNhs12995_12828-137A2_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay12BiolRep2H9EB2D12_CNhs12830_ctss_rev H9MelanocyticInduction_Day12Br2- H9 Embryoid body cells, melanocytic induction, day12, biol_rep2 (H9EB-2 d12)_CNhs12830_12730-135H3_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay12BiolRep2H9EB2D12_CNhs12830_ctss_fwd H9MelanocyticInduction_Day12Br2+ H9 Embryoid body cells, melanocytic induction, day12, biol_rep2 (H9EB-2 d12)_CNhs12830_12730-135H3_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay12BiolRep1H9EB1D12_CNhs12948_ctss_rev H9MelanocyticInduction_Day12Br1- H9 Embryoid body cells, melanocytic induction, day12, biol_rep1 (H9EB-1 d12)_CNhs12948_12632-134F4_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay12BiolRep1H9EB1D12_CNhs12994_ctss_rev H9MelanocyticInduction_Day12Br1- H9 Embryoid body cells, melanocytic induction, day12, biol_rep1 (H9EB-1 d12)_CNhs12994_12632-134F4_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay12BiolRep1H9EB1D12_CNhs12948_ctss_fwd H9MelanocyticInduction_Day12Br1+ H9 Embryoid body cells, melanocytic induction, day12, biol_rep1 (H9EB-1 d12)_CNhs12948_12632-134F4_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay12BiolRep1H9EB1D12_CNhs12994_ctss_fwd H9MelanocyticInduction_Day12Br1+ H9 Embryoid body cells, melanocytic induction, day12, biol_rep1 (H9EB-1 d12)_CNhs12994_12632-134F4_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay09BiolRep3H9EB3D9_CNhs12951_ctss_rev H9MelanocyticInduction_Day09Br3- H9 Embryoid body cells, melanocytic induction, day09, biol_rep3 (H9EB-3 d9)_CNhs12951_12827-137A1_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay09BiolRep3H9EB3D9_CNhs12951_ctss_fwd H9MelanocyticInduction_Day09Br3+ H9 Embryoid body cells, melanocytic induction, day09, biol_rep3 (H9EB-3 d9)_CNhs12951_12827-137A1_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay09BiolRep2H9EB2D9_CNhs12829_ctss_rev H9MelanocyticInduction_Day09Br2- H9 Embryoid body cells, melanocytic induction, day09, biol_rep2 (H9EB-2 d9)_CNhs12829_12729-135H2_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay09BiolRep2H9EB2D9_CNhs12829_ctss_fwd H9MelanocyticInduction_Day09Br2+ H9 Embryoid body cells, melanocytic induction, day09, biol_rep2 (H9EB-2 d9)_CNhs12829_12729-135H2_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay09BiolRep1H9EB1D9_CNhs12897_ctss_rev H9MelanocyticInduction_Day09Br1- H9 Embryoid body cells, melanocytic induction, day09, biol_rep1 (H9EB-1 d9)_CNhs12897_12631-134F3_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay09BiolRep1H9EB1D9_CNhs12897_ctss_fwd H9MelanocyticInduction_Day09Br1+ H9 Embryoid body cells, melanocytic induction, day09, biol_rep1 (H9EB-1 d9)_CNhs12897_12631-134F3_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay06BiolRep3H9EB3D6_CNhs12911_ctss_rev H9MelanocyticInduction_Day06Br3- H9 Embryoid body cells, melanocytic induction, day06, biol_rep3 (H9EB-3 d6)_CNhs12911_12826-136I9_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay06BiolRep3H9EB3D6_CNhs12911_ctss_fwd H9MelanocyticInduction_Day06Br3+ H9 Embryoid body cells, melanocytic induction, day06, biol_rep3 (H9EB-3 d6)_CNhs12911_12826-136I9_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay06BiolRep2H9EB2D6_CNhs12828_ctss_rev H9MelanocyticInduction_Day06Br2- H9 Embryoid body cells, melanocytic induction, day06, biol_rep2 (H9EB-2 d6)_CNhs12828_12728-135H1_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay06BiolRep2H9EB2D6_CNhs12828_ctss_fwd H9MelanocyticInduction_Day06Br2+ H9 Embryoid body cells, melanocytic induction, day06, biol_rep2 (H9EB-2 d6)_CNhs12828_12728-135H1_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay06BiolRep1H9EB1D6_CNhs12896_ctss_rev H9MelanocyticInduction_Day06Br1- H9 Embryoid body cells, melanocytic induction, day06, biol_rep1 (H9EB-1 d6)_CNhs12896_12630-134F2_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay06BiolRep1H9EB1D6_CNhs12896_ctss_fwd H9MelanocyticInduction_Day06Br1+ H9 Embryoid body cells, melanocytic induction, day06, biol_rep1 (H9EB-1 d6)_CNhs12896_12630-134F2_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay03BiolRep3H9EB3D3_CNhs12910_ctss_rev H9MelanocyticInduction_Day03Br3- H9 Embryoid body cells, melanocytic induction, day03, biol_rep3 (H9EB-3 d3)_CNhs12910_12825-136I8_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay03BiolRep3H9EB3D3_CNhs12910_ctss_fwd H9MelanocyticInduction_Day03Br3+ H9 Embryoid body cells, melanocytic induction, day03, biol_rep3 (H9EB-3 d3)_CNhs12910_12825-136I8_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay03BiolRep2H9EB2D3_CNhs12827_ctss_rev H9MelanocyticInduction_Day03Br2- H9 Embryoid body cells, melanocytic induction, day03, biol_rep2 (H9EB-2 d3)_CNhs12827_12727-135G9_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay03BiolRep2H9EB2D3_CNhs12827_ctss_fwd H9MelanocyticInduction_Day03Br2+ H9 Embryoid body cells, melanocytic induction, day03, biol_rep2 (H9EB-2 d3)_CNhs12827_12727-135G9_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay03BiolRep1H9EB1D3_CNhs12895_ctss_rev H9MelanocyticInduction_Day03Br1- H9 Embryoid body cells, melanocytic induction, day03, biol_rep1 (H9EB-1 d3)_CNhs12895_12629-134F1_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay03BiolRep1H9EB1D3_CNhs12895_ctss_fwd H9MelanocyticInduction_Day03Br1+ H9 Embryoid body cells, melanocytic induction, day03, biol_rep1 (H9EB-1 d3)_CNhs12895_12629-134F1_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay01BiolRep3H9EB3D1_CNhs12909_ctss_rev H9MelanocyticInduction_Day01Br3- H9 Embryoid body cells, melanocytic induction, day01, biol_rep3 (H9EB-3 d1)_CNhs12909_12824-136I7_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay01BiolRep3H9EB3D1_CNhs12909_ctss_fwd H9MelanocyticInduction_Day01Br3+ H9 Embryoid body cells, melanocytic induction, day01, biol_rep3 (H9EB-3 d1)_CNhs12909_12824-136I7_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay01BiolRep2H9EB2D1_CNhs12826_ctss_rev H9MelanocyticInduction_Day01Br2- H9 Embryoid body cells, melanocytic induction, day01, biol_rep2 (H9EB-2 d1)_CNhs12826_12726-135G8_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay01BiolRep2H9EB2D1_CNhs12826_ctss_fwd H9MelanocyticInduction_Day01Br2+ H9 Embryoid body cells, melanocytic induction, day01, biol_rep2 (H9EB-2 d1)_CNhs12826_12726-135G8_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay01BiolRep1H9EB1D1_CNhs12823_ctss_rev H9MelanocyticInduction_Day01Br1- H9 Embryoid body cells, melanocytic induction, day01, biol_rep1 (H9EB-1 d1)_CNhs12823_12628-134E9_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay01BiolRep1H9EB1D1_CNhs12823_ctss_fwd H9MelanocyticInduction_Day01Br1+ H9 Embryoid body cells, melanocytic induction, day01, biol_rep1 (H9EB-1 d1)_CNhs12823_12628-134E9_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay00BiolRep3H9EB3D0_CNhs12908_ctss_rev H9MelanocyticInduction_Day00Br3- H9 Embryoid body cells, melanocytic induction, day00, biol_rep3 (H9EB-3 d0)_CNhs12908_12823-136I6_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay00BiolRep3H9EB3D0_CNhs12908_ctss_fwd H9MelanocyticInduction_Day00Br3+ H9 Embryoid body cells, melanocytic induction, day00, biol_rep3 (H9EB-3 d0)_CNhs12908_12823-136I6_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay00BiolRep2H9EB2D0_CNhs12825_ctss_rev H9MelanocyticInduction_Day00Br2- H9 Embryoid body cells, melanocytic induction, day00, biol_rep2 (H9EB-2 d0)_CNhs12825_12725-135G7_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay00BiolRep2H9EB2D0_CNhs12825_ctss_fwd H9MelanocyticInduction_Day00Br2+ H9 Embryoid body cells, melanocytic induction, day00, biol_rep2 (H9EB-2 d0)_CNhs12825_12725-135G7_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay00BiolRep1H9EB1D0_CNhs12822_ctss_rev H9MelanocyticInduction_Day00Br1- H9 Embryoid body cells, melanocytic induction, day00, biol_rep1 (H9EB-1 d0)_CNhs12822_12627-134E8_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay00BiolRep1H9EB1D0_CNhs12822_ctss_fwd H9MelanocyticInduction_Day00Br1+ H9 Embryoid body cells, melanocytic induction, day00, biol_rep1 (H9EB-1 d0)_CNhs12822_12627-134E8_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay12BiolRep3_CNhs13736_ctss_rev Hes3-gfpCardiomyocyticInduction_Day12Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day12, biol_rep3_CNhs13736_13363-143F6_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay12BiolRep3_CNhs13736_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day12Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day12, biol_rep3_CNhs13736_13363-143F6_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay12BiolRep2_CNhs13724_ctss_rev Hes3-gfpCardiomyocyticInduction_Day12Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day12, biol_rep2_CNhs13724_13351-143E3_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay12BiolRep2_CNhs13724_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day12Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day12, biol_rep2_CNhs13724_13351-143E3_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay12BiolRep1_CNhs13711_ctss_rev Hes3-gfpCardiomyocyticInduction_Day12Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day12, biol_rep1_CNhs13711_13339-143C9_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay12BiolRep1_CNhs13711_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day12Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day12, biol_rep1_CNhs13711_13339-143C9_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay11BiolRep3_CNhs13735_ctss_rev Hes3-gfpCardiomyocyticInduction_Day11Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day11, biol_rep3_CNhs13735_13362-143F5_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay11BiolRep3_CNhs13735_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day11Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day11, biol_rep3_CNhs13735_13362-143F5_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay11BiolRep2_CNhs13723_ctss_rev Hes3-gfpCardiomyocyticInduction_Day11Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day11, biol_rep2_CNhs13723_13350-143E2_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay11BiolRep2_CNhs13723_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day11Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day11, biol_rep2_CNhs13723_13350-143E2_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay11BiolRep1_CNhs13710_ctss_rev Hes3-gfpCardiomyocyticInduction_Day11Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day11, biol_rep1_CNhs13710_13338-143C8_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay11BiolRep1_CNhs13710_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day11Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day11, biol_rep1_CNhs13710_13338-143C8_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay10BiolRep3_CNhs13734_ctss_rev Hes3-gfpCardiomyocyticInduction_Day10Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day10, biol_rep3_CNhs13734_13361-143F4_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay10BiolRep3_CNhs13734_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day10Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day10, biol_rep3_CNhs13734_13361-143F4_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay10BiolRep2_CNhs13722_ctss_rev Hes3-gfpCardiomyocyticInduction_Day10Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day10, biol_rep2_CNhs13722_13349-143E1_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay10BiolRep2_CNhs13722_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day10Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day10, biol_rep2_CNhs13722_13349-143E1_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay10BiolRep1_CNhs13662_ctss_rev Hes3-gfpCardiomyocyticInduction_Day10Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day10, biol_rep1_CNhs13662_13337-143C7_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay10BiolRep1_CNhs13662_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day10Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day10, biol_rep1_CNhs13662_13337-143C7_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay09BiolRep3_CNhs13733_ctss_rev Hes3-gfpCardiomyocyticInduction_Day09Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day09, biol_rep3_CNhs13733_13360-143F3_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay09BiolRep3_CNhs13733_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day09Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day09, biol_rep3_CNhs13733_13360-143F3_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay09BiolRep2_CNhs13721_ctss_rev Hes3-gfpCardiomyocyticInduction_Day09Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day09, biol_rep2_CNhs13721_13348-143D9_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay09BiolRep2_CNhs13721_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day09Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day09, biol_rep2_CNhs13721_13348-143D9_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay09BiolRep1_CNhs13661_ctss_rev Hes3-gfpCardiomyocyticInduction_Day09Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day09, biol_rep1_CNhs13661_13336-143C6_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay09BiolRep1_CNhs13661_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day09Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day09, biol_rep1_CNhs13661_13336-143C6_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay08BiolRep3_CNhs13732_ctss_rev Hes3-gfpCardiomyocyticInduction_Day08Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day08, biol_rep3_CNhs13732_13359-143F2_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay08BiolRep3_CNhs13732_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day08Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day08, biol_rep3_CNhs13732_13359-143F2_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay08BiolRep2_CNhs13720_ctss_rev Hes3-gfpCardiomyocyticInduction_Day08Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day08, biol_rep2_CNhs13720_13347-143D8_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay08BiolRep2_CNhs13720_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day08Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day08, biol_rep2_CNhs13720_13347-143D8_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay08BiolRep1_CNhs13660_ctss_rev Hes3-gfpCardiomyocyticInduction_Day08Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day08, biol_rep1_CNhs13660_13335-143C5_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay08BiolRep1_CNhs13660_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day08Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day08, biol_rep1_CNhs13660_13335-143C5_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay07BiolRep3_CNhs13731_ctss_rev Hes3-gfpCardiomyocyticInduction_Day07Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day07, biol_rep3_CNhs13731_13358-143F1_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay07BiolRep3_CNhs13731_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day07Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day07, biol_rep3_CNhs13731_13358-143F1_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay07BiolRep2_CNhs13719_ctss_rev Hes3-gfpCardiomyocyticInduction_Day07Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day07, biol_rep2_CNhs13719_13346-143D7_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay07BiolRep2_CNhs13719_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day07Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day07, biol_rep2_CNhs13719_13346-143D7_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay06BiolRep3_CNhs13730_ctss_rev Hes3-gfpCardiomyocyticInduction_Day06Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day06, biol_rep3_CNhs13730_13357-143E9_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay06BiolRep3_CNhs13730_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day06Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day06, biol_rep3_CNhs13730_13357-143E9_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay06BiolRep2_CNhs13718_ctss_rev Hes3-gfpCardiomyocyticInduction_Day06Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day06, biol_rep2_CNhs13718_13345-143D6_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay06BiolRep2_CNhs13718_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day06Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day06, biol_rep2_CNhs13718_13345-143D6_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay06BiolRep1_CNhs13658_ctss_rev Hes3-gfpCardiomyocyticInduction_Day06Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day06, biol_rep1_CNhs13658_13333-143C3_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay06BiolRep1_CNhs13658_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day06Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day06, biol_rep1_CNhs13658_13333-143C3_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay05BiolRep3_CNhs13729_ctss_rev Hes3-gfpCardiomyocyticInduction_Day05Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day05, biol_rep3_CNhs13729_13356-143E8_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay05BiolRep3_CNhs13729_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day05Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day05, biol_rep3_CNhs13729_13356-143E8_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay05BiolRep2_CNhs13717_ctss_rev Hes3-gfpCardiomyocyticInduction_Day05Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day05, biol_rep2_CNhs13717_13344-143D5_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay05BiolRep2_CNhs13717_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day05Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day05, biol_rep2_CNhs13717_13344-143D5_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay05BiolRep1_CNhs13657_ctss_rev Hes3-gfpCardiomyocyticInduction_Day05Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day05, biol_rep1_CNhs13657_13332-143C2_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay05BiolRep1_CNhs13657_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day05Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day05, biol_rep1_CNhs13657_13332-143C2_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay04BiolRep3_CNhs13728_ctss_rev Hes3-gfpCardiomyocyticInduction_Day04Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day04, biol_rep3_CNhs13728_13355-143E7_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay04BiolRep3_CNhs13728_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day04Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day04, biol_rep3_CNhs13728_13355-143E7_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay04BiolRep2_CNhs13716_ctss_rev Hes3-gfpCardiomyocyticInduction_Day04Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day04, biol_rep2_CNhs13716_13343-143D4_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay04BiolRep2_CNhs13716_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day04Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day04, biol_rep2_CNhs13716_13343-143D4_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay04BiolRep1_CNhs13656_ctss_rev Hes3-gfpCardiomyocyticInduction_Day04Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day04, biol_rep1_CNhs13656_13331-143C1_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay04BiolRep1_CNhs13656_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day04Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day04, biol_rep1_CNhs13656_13331-143C1_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay03BiolRep3_CNhs13727_ctss_rev Hes3-gfpCardiomyocyticInduction_Day03Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day03, biol_rep3_CNhs13727_13354-143E6_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay03BiolRep3_CNhs13727_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day03Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day03, biol_rep3_CNhs13727_13354-143E6_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay03BiolRep2_CNhs13715_ctss_rev Hes3-gfpCardiomyocyticInduction_Day03Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day03, biol_rep2_CNhs13715_13342-143D3_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay03BiolRep2_CNhs13715_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day03Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day03, biol_rep2_CNhs13715_13342-143D3_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay03BiolRep1_CNhs13655_ctss_rev Hes3-gfpCardiomyocyticInduction_Day03Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day03, biol_rep1_CNhs13655_13330-143B9_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay03BiolRep1_CNhs13655_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day03Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day03, biol_rep1_CNhs13655_13330-143B9_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay02BiolRep3_CNhs13726_ctss_rev Hes3-gfpCardiomyocyticInduction_Day02Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day02, biol_rep3_CNhs13726_13353-143E5_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay02BiolRep3_CNhs13726_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day02Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day02, biol_rep3_CNhs13726_13353-143E5_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay02BiolRep2_CNhs13714_ctss_rev Hes3-gfpCardiomyocyticInduction_Day02Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day02, biol_rep2_CNhs13714_13341-143D2_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay02BiolRep2_CNhs13714_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day02Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day02, biol_rep2_CNhs13714_13341-143D2_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay02BiolRep1_CNhs13654_ctss_rev Hes3-gfpCardiomyocyticInduction_Day02Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day02, biol_rep1_CNhs13654_13329-143B8_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay02BiolRep1_CNhs13654_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day02Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day02, biol_rep1_CNhs13654_13329-143B8_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay01BiolRep3_CNhs13725_ctss_rev Hes3-gfpCardiomyocyticInduction_Day01Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day01, biol_rep3_CNhs13725_13352-143E4_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay01BiolRep3_CNhs13725_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day01Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day01, biol_rep3_CNhs13725_13352-143E4_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay01BiolRep2_CNhs13712_ctss_rev Hes3-gfpCardiomyocyticInduction_Day01Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day01, biol_rep2_CNhs13712_13340-143D1_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay01BiolRep2_CNhs13712_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day01Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day01, biol_rep2_CNhs13712_13340-143D1_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay01BiolRep1_CNhs13653_ctss_rev Hes3-gfpCardiomyocyticInduction_Day01Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day01, biol_rep1_CNhs13653_13328-143B7_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay01BiolRep1_CNhs13653_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day01Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day01, biol_rep1_CNhs13653_13328-143B7_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay00BiolRep3UH3_CNhs13738_ctss_rev Hes3-gfpCardiomyocyticInduction_Day00Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day00, biol_rep3 (UH-3)_CNhs13738_13366-143F9_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay00BiolRep3UH3_CNhs13738_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day00Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day00, biol_rep3 (UH-3)_CNhs13738_13366-143F9_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay00BiolRep2UH2_CNhs13695_ctss_rev Hes3-gfpCardiomyocyticInduction_Day00Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day00, biol_rep2 (UH-2)_CNhs13695_13365-143F8_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay00BiolRep2UH2_CNhs13695_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day00Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day00, biol_rep2 (UH-2)_CNhs13695_13365-143F8_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay00BiolRep1UH1_CNhs13694_ctss_rev Hes3-gfpCardiomyocyticInduction_Day00Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day00, biol_rep1 (UH-1)_CNhs13694_13364-143F7_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay00BiolRep1UH1_CNhs13694_ctss_fwd Hes3-gfpCardiomyocyticInduction_Day00Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day00, biol_rep1 (UH-1)_CNhs13694_13364-143F7_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b06hrBiolRep3LK60_CNhs13586_ctss_rev AorticSmsToIL1b_06hrBr3- Aortic smooth muscle cell response to IL1b, 06hr, biol_rep3 (LK60)_CNhs13586_12857-137D4_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b06hrBiolRep3LK60_CNhs13586_ctss_fwd AorticSmsToIL1b_06hrBr3+ Aortic smooth muscle cell response to IL1b, 06hr, biol_rep3 (LK60)_CNhs13586_12857-137D4_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b06hrBiolRep2LK59_CNhs13378_ctss_rev AorticSmsToIL1b_06hrBr2- Aortic smooth muscle cell response to IL1b, 06hr, biol_rep2 (LK59)_CNhs13378_12759-136B5_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b06hrBiolRep2LK59_CNhs13378_ctss_fwd AorticSmsToIL1b_06hrBr2+ Aortic smooth muscle cell response to IL1b, 06hr, biol_rep2 (LK59)_CNhs13378_12759-136B5_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b06hrBiolRep1LK58_CNhs13357_ctss_rev AorticSmsToIL1b_06hrBr1- Aortic smooth muscle cell response to IL1b, 06hr, biol_rep1 (LK58)_CNhs13357_12661-134I6_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b06hrBiolRep1LK58_CNhs13357_ctss_fwd AorticSmsToIL1b_06hrBr1+ Aortic smooth muscle cell response to IL1b, 06hr, biol_rep1 (LK58)_CNhs13357_12661-134I6_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b05hrBiolRep2LK56_CNhs13377_ctss_rev AorticSmsToIL1b_05hrBr2- Aortic smooth muscle cell response to IL1b, 05hr, biol_rep2 (LK56)_CNhs13377_12758-136B4_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b05hrBiolRep2LK56_CNhs13377_ctss_fwd AorticSmsToIL1b_05hrBr2+ Aortic smooth muscle cell response to IL1b, 05hr, biol_rep2 (LK56)_CNhs13377_12758-136B4_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b05hrBiolRep1LK55_CNhs13356_ctss_rev AorticSmsToIL1b_05hrBr1- Aortic smooth muscle cell response to IL1b, 05hr, biol_rep1 (LK55)_CNhs13356_12660-134I5_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b05hrBiolRep1LK55_CNhs13356_ctss_fwd AorticSmsToIL1b_05hrBr1+ Aortic smooth muscle cell response to IL1b, 05hr, biol_rep1 (LK55)_CNhs13356_12660-134I5_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b04hrBiolRep3LK54_CNhs13584_ctss_rev AorticSmsToIL1b_04hrBr3- Aortic smooth muscle cell response to IL1b, 04hr, biol_rep3 (LK54)_CNhs13584_12855-137D2_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b04hrBiolRep3LK54_CNhs13584_ctss_fwd AorticSmsToIL1b_04hrBr3+ Aortic smooth muscle cell response to IL1b, 04hr, biol_rep3 (LK54)_CNhs13584_12855-137D2_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b04hrBiolRep2LK53_CNhs13376_ctss_rev AorticSmsToIL1b_04hrBr2- Aortic smooth muscle cell response to IL1b, 04hr, biol_rep2 (LK53)_CNhs13376_12757-136B3_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b04hrBiolRep2LK53_CNhs13376_ctss_fwd AorticSmsToIL1b_04hrBr2+ Aortic smooth muscle cell response to IL1b, 04hr, biol_rep2 (LK53)_CNhs13376_12757-136B3_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b04hrBiolRep1LK52_CNhs13682_ctss_rev AorticSmsToIL1b_04hrBr1- Aortic smooth muscle cell response to IL1b, 04hr, biol_rep1 (LK52)_CNhs13682_12659-134I4_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b04hrBiolRep1LK52_CNhs13682_ctss_fwd AorticSmsToIL1b_04hrBr1+ Aortic smooth muscle cell response to IL1b, 04hr, biol_rep1 (LK52)_CNhs13682_12659-134I4_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b03hrBiolRep2LK50_CNhs13375_ctss_rev AorticSmsToIL1b_03hrBr2- Aortic smooth muscle cell response to IL1b, 03hr, biol_rep2 (LK50)_CNhs13375_12756-136B2_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b03hrBiolRep2LK50_CNhs13375_ctss_fwd AorticSmsToIL1b_03hrBr2+ Aortic smooth muscle cell response to IL1b, 03hr, biol_rep2 (LK50)_CNhs13375_12756-136B2_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b03hrBiolRep1LK49_CNhs13355_ctss_rev AorticSmsToIL1b_03hrBr1- Aortic smooth muscle cell response to IL1b, 03hr, biol_rep1 (LK49)_CNhs13355_12658-134I3_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b03hrBiolRep1LK49_CNhs13355_ctss_fwd AorticSmsToIL1b_03hrBr1+ Aortic smooth muscle cell response to IL1b, 03hr, biol_rep1 (LK49)_CNhs13355_12658-134I3_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b02hrBiolRep3LK48_CNhs13582_ctss_rev AorticSmsToIL1b_02hrBr3- Aortic smooth muscle cell response to IL1b, 02hr, biol_rep3 (LK48)_CNhs13582_12853-137C9_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b02hrBiolRep3LK48_CNhs13582_ctss_fwd AorticSmsToIL1b_02hrBr3+ Aortic smooth muscle cell response to IL1b, 02hr, biol_rep3 (LK48)_CNhs13582_12853-137C9_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b02hrBiolRep2LK47_CNhs13374_ctss_rev AorticSmsToIL1b_02hrBr2- Aortic smooth muscle cell response to IL1b, 02hr, biol_rep2 (LK47)_CNhs13374_12755-136B1_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b02hrBiolRep2LK47_CNhs13374_ctss_fwd AorticSmsToIL1b_02hrBr2+ Aortic smooth muscle cell response to IL1b, 02hr, biol_rep2 (LK47)_CNhs13374_12755-136B1_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b01hrBiolRep2LK44_CNhs13373_ctss_rev AorticSmsToIL1b_01hrBr2- Aortic smooth muscle cell response to IL1b, 01hr, biol_rep2 (LK44)_CNhs13373_12754-136A9_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b01hrBiolRep2LK44_CNhs13373_ctss_fwd AorticSmsToIL1b_01hrBr2+ Aortic smooth muscle cell response to IL1b, 01hr, biol_rep2 (LK44)_CNhs13373_12754-136A9_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b01hrBiolRep1LK43_CNhs13353_ctss_rev AorticSmsToIL1b_01hrBr1- Aortic smooth muscle cell response to IL1b, 01hr, biol_rep1 (LK43)_CNhs13353_12656-134I1_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b01hrBiolRep1LK43_CNhs13353_ctss_fwd AorticSmsToIL1b_01hrBr1+ Aortic smooth muscle cell response to IL1b, 01hr, biol_rep1 (LK43)_CNhs13353_12656-134I1_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr45minBiolRep3LK42_CNhs13580_ctss_rev AorticSmsToIL1b_00hr45minBr3- Aortic smooth muscle cell response to IL1b, 00hr45min, biol_rep3 (LK42)_CNhs13580_12851-137C7_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr45minBiolRep3LK42_CNhs13580_ctss_fwd AorticSmsToIL1b_00hr45minBr3+ Aortic smooth muscle cell response to IL1b, 00hr45min, biol_rep3 (LK42)_CNhs13580_12851-137C7_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr45minBiolRep2LK41_CNhs13372_ctss_rev AorticSmsToIL1b_00hr45minBr2- Aortic smooth muscle cell response to IL1b, 00hr45min, biol_rep2 (LK41)_CNhs13372_12753-136A8_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr45minBiolRep2LK41_CNhs13372_ctss_fwd AorticSmsToIL1b_00hr45minBr2+ Aortic smooth muscle cell response to IL1b, 00hr45min, biol_rep2 (LK41)_CNhs13372_12753-136A8_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr45minBiolRep1LK40_CNhs13352_ctss_rev AorticSmsToIL1b_00hr45minBr1- Aortic smooth muscle cell response to IL1b, 00hr45min, biol_rep1 (LK40)_CNhs13352_12655-134H9_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr45minBiolRep1LK40_CNhs13352_ctss_fwd AorticSmsToIL1b_00hr45minBr1+ Aortic smooth muscle cell response to IL1b, 00hr45min, biol_rep1 (LK40)_CNhs13352_12655-134H9_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr30minBiolRep3LK39_CNhs13579_ctss_rev AorticSmsToIL1b_00hr30minBr3- Aortic smooth muscle cell response to IL1b, 00hr30min, biol_rep3 (LK39)_CNhs13579_12850-137C6_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr30minBiolRep3LK39_CNhs13579_ctss_fwd AorticSmsToIL1b_00hr30minBr3+ Aortic smooth muscle cell response to IL1b, 00hr30min, biol_rep3 (LK39)_CNhs13579_12850-137C6_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr30minBiolRep2LK38_CNhs13371_ctss_rev AorticSmsToIL1b_00hr30minBr2- Aortic smooth muscle cell response to IL1b, 00hr30min, biol_rep2 (LK38)_CNhs13371_12752-136A7_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr30minBiolRep2LK38_CNhs13371_ctss_fwd AorticSmsToIL1b_00hr30minBr2+ Aortic smooth muscle cell response to IL1b, 00hr30min, biol_rep2 (LK38)_CNhs13371_12752-136A7_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr30minBiolRep1LK37_CNhs13351_ctss_rev AorticSmsToIL1b_00hr30minBr1- Aortic smooth muscle cell response to IL1b, 00hr30min, biol_rep1 (LK37)_CNhs13351_12654-134H8_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr30minBiolRep1LK37_CNhs13351_ctss_fwd AorticSmsToIL1b_00hr30minBr1+ Aortic smooth muscle cell response to IL1b, 00hr30min, biol_rep1 (LK37)_CNhs13351_12654-134H8_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr15minBiolRep3LK36_CNhs13578_ctss_rev AorticSmsToIL1b_00hr15minBr3- Aortic smooth muscle cell response to IL1b, 00hr15min, biol_rep3 (LK36)_CNhs13578_12849-137C5_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr15minBiolRep3LK36_CNhs13578_ctss_fwd AorticSmsToIL1b_00hr15minBr3+ Aortic smooth muscle cell response to IL1b, 00hr15min, biol_rep3 (LK36)_CNhs13578_12849-137C5_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr15minBiolRep2LK35_CNhs13370_ctss_rev AorticSmsToIL1b_00hr15minBr2- Aortic smooth muscle cell response to IL1b, 00hr15min, biol_rep2 (LK35)_CNhs13370_12751-136A6_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr15minBiolRep2LK35_CNhs13370_ctss_fwd AorticSmsToIL1b_00hr15minBr2+ Aortic smooth muscle cell response to IL1b, 00hr15min, biol_rep2 (LK35)_CNhs13370_12751-136A6_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr15minBiolRep1LK34_CNhs13350_ctss_rev AorticSmsToIL1b_00hr15minBr1- Aortic smooth muscle cell response to IL1b, 00hr15min, biol_rep1 (LK34)_CNhs13350_12653-134H7_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr15minBiolRep1LK34_CNhs13350_ctss_fwd AorticSmsToIL1b_00hr15minBr1+ Aortic smooth muscle cell response to IL1b, 00hr15min, biol_rep1 (LK34)_CNhs13350_12653-134H7_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr00minBiolRep3LK33_CNhs13577_ctss_rev AorticSmsToIL1b_00hr00minBr3- Aortic smooth muscle cell response to IL1b, 00hr00min, biol_rep3 (LK33)_CNhs13577_12848-137C4_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr00minBiolRep3LK33_CNhs13577_ctss_fwd AorticSmsToIL1b_00hr00minBr3+ Aortic smooth muscle cell response to IL1b, 00hr00min, biol_rep3 (LK33)_CNhs13577_12848-137C4_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr00minBiolRep2LK32_CNhs13369_ctss_rev AorticSmsToIL1b_00hr00minBr2- Aortic smooth muscle cell response to IL1b, 00hr00min, biol_rep2 (LK32)_CNhs13369_12750-136A5_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr00minBiolRep2LK32_CNhs13369_ctss_fwd AorticSmsToIL1b_00hr00minBr2+ Aortic smooth muscle cell response to IL1b, 00hr00min, biol_rep2 (LK32)_CNhs13369_12750-136A5_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr00minBiolRep1LK31_CNhs13349_ctss_rev AorticSmsToIL1b_00hr00minBr1- Aortic smooth muscle cell response to IL1b, 00hr00min, biol_rep1 (LK31)_CNhs13349_12652-134H6_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr00minBiolRep1LK31_CNhs13349_ctss_fwd AorticSmsToIL1b_00hr00minBr1+ Aortic smooth muscle cell response to IL1b, 00hr00min, biol_rep1 (LK31)_CNhs13349_12652-134H6_forward Regulation 
AorticSmoothMuscleCellResponseToFGF206hrBiolRep3LK30_CNhs13576_ctss_rev AorticSmsToFgf2_06hrBr3- Aortic smooth muscle cell response to FGF2, 06hr, biol_rep3 (LK30)_CNhs13576_12847-137C3_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF206hrBiolRep3LK30_CNhs13576_ctss_fwd AorticSmsToFgf2_06hrBr3+ Aortic smooth muscle cell response to FGF2, 06hr, biol_rep3 (LK30)_CNhs13576_12847-137C3_forward Regulation 
AorticSmoothMuscleCellResponseToFGF206hrBiolRep2LK29_CNhs13368_ctss_rev AorticSmsToFgf2_06hrBr2- Aortic smooth muscle cell response to FGF2, 06hr, biol_rep2 (LK29)_CNhs13368_12749-136A4_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF206hrBiolRep2LK29_CNhs13368_ctss_fwd AorticSmsToFgf2_06hrBr2+ Aortic smooth muscle cell response to FGF2, 06hr, biol_rep2 (LK29)_CNhs13368_12749-136A4_forward Regulation 
AorticSmoothMuscleCellResponseToFGF206hrBiolRep1LK28_CNhs13348_ctss_rev AorticSmsToFgf2_06hrBr1- Aortic smooth muscle cell response to FGF2, 06hr, biol_rep1 (LK28)_CNhs13348_12651-134H5_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF206hrBiolRep1LK28_CNhs13348_ctss_fwd AorticSmsToFgf2_06hrBr1+ Aortic smooth muscle cell response to FGF2, 06hr, biol_rep1 (LK28)_CNhs13348_12651-134H5_forward Regulation 
AorticSmoothMuscleCellResponseToFGF205hrBiolRep3LK27_CNhs13575_ctss_rev AorticSmsToFgf2_05hrBr3- Aortic smooth muscle cell response to FGF2, 05hr, biol_rep3 (LK27)_CNhs13575_12846-137C2_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF205hrBiolRep3LK27_CNhs13575_ctss_fwd AorticSmsToFgf2_05hrBr3+ Aortic smooth muscle cell response to FGF2, 05hr, biol_rep3 (LK27)_CNhs13575_12846-137C2_forward Regulation 
AorticSmoothMuscleCellResponseToFGF205hrBiolRep2LK26_CNhs13367_ctss_rev AorticSmsToFgf2_05hrBr2- Aortic smooth muscle cell response to FGF2, 05hr, biol_rep2 (LK26)_CNhs13367_12748-136A3_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF205hrBiolRep2LK26_CNhs13367_ctss_fwd AorticSmsToFgf2_05hrBr2+ Aortic smooth muscle cell response to FGF2, 05hr, biol_rep2 (LK26)_CNhs13367_12748-136A3_forward Regulation 
AorticSmoothMuscleCellResponseToFGF205hrBiolRep1LK25_CNhs13347_ctss_rev AorticSmsToFgf2_05hrBr1- Aortic smooth muscle cell response to FGF2, 05hr, biol_rep1 (LK25)_CNhs13347_12650-134H4_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF205hrBiolRep1LK25_CNhs13347_ctss_fwd AorticSmsToFgf2_05hrBr1+ Aortic smooth muscle cell response to FGF2, 05hr, biol_rep1 (LK25)_CNhs13347_12650-134H4_forward Regulation 
AorticSmoothMuscleCellResponseToFGF203hrBiolRep3LK21_CNhs13573_ctss_rev AorticSmsToFgf2_03hrBr3- Aortic smooth muscle cell response to FGF2, 03hr, biol_rep3 (LK21)_CNhs13573_12844-137B9_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF203hrBiolRep3LK21_CNhs13573_ctss_fwd AorticSmsToFgf2_03hrBr3+ Aortic smooth muscle cell response to FGF2, 03hr, biol_rep3 (LK21)_CNhs13573_12844-137B9_forward Regulation 
AorticSmoothMuscleCellResponseToFGF203hrBiolRep2LK20_CNhs13364_ctss_rev AorticSmsToFgf2_03hrBr2- Aortic smooth muscle cell response to FGF2, 03hr, biol_rep2 (LK20)_CNhs13364_12746-136A1_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF203hrBiolRep2LK20_CNhs13364_ctss_fwd AorticSmsToFgf2_03hrBr2+ Aortic smooth muscle cell response to FGF2, 03hr, biol_rep2 (LK20)_CNhs13364_12746-136A1_forward Regulation 
AorticSmoothMuscleCellResponseToFGF203hrBiolRep1LK19_CNhs13345_ctss_rev AorticSmsToFgf2_03hrBr1- Aortic smooth muscle cell response to FGF2, 03hr, biol_rep1 (LK19)_CNhs13345_12648-134H2_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF203hrBiolRep1LK19_CNhs13345_ctss_fwd AorticSmsToFgf2_03hrBr1+ Aortic smooth muscle cell response to FGF2, 03hr, biol_rep1 (LK19)_CNhs13345_12648-134H2_forward Regulation 
AorticSmoothMuscleCellResponseToFGF202hrBiolRep3LK18_CNhs13572_ctss_rev AorticSmsToFgf2_02hrBr3- Aortic smooth muscle cell response to FGF2, 02hr, biol_rep3 (LK18)_CNhs13572_12843-137B8_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF202hrBiolRep3LK18_CNhs13572_ctss_fwd AorticSmsToFgf2_02hrBr3+ Aortic smooth muscle cell response to FGF2, 02hr, biol_rep3 (LK18)_CNhs13572_12843-137B8_forward Regulation 
AorticSmoothMuscleCellResponseToFGF202hrBiolRep2LK17_CNhs13363_ctss_rev AorticSmsToFgf2_02hrBr2- Aortic smooth muscle cell response to FGF2, 02hr, biol_rep2 (LK17)_CNhs13363_12745-135I9_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF202hrBiolRep2LK17_CNhs13363_ctss_fwd AorticSmsToFgf2_02hrBr2+ Aortic smooth muscle cell response to FGF2, 02hr, biol_rep2 (LK17)_CNhs13363_12745-135I9_forward Regulation 
AorticSmoothMuscleCellResponseToFGF202hrBiolRep1LK16_CNhs13344_ctss_rev AorticSmsToFgf2_02hrBr1- Aortic smooth muscle cell response to FGF2, 02hr, biol_rep1 (LK16)_CNhs13344_12647-134H1_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF202hrBiolRep1LK16_CNhs13344_ctss_fwd AorticSmsToFgf2_02hrBr1+ Aortic smooth muscle cell response to FGF2, 02hr, biol_rep1 (LK16)_CNhs13344_12647-134H1_forward Regulation 
AorticSmoothMuscleCellResponseToFGF201hrBiolRep3LK15_CNhs13683_ctss_rev AorticSmsToFgf2_01hrBr3- Aortic smooth muscle cell response to FGF2, 01hr, biol_rep3 (LK15)_CNhs13683_12842-137B7_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF201hrBiolRep3LK15_CNhs13683_ctss_fwd AorticSmsToFgf2_01hrBr3+ Aortic smooth muscle cell response to FGF2, 01hr, biol_rep3 (LK15)_CNhs13683_12842-137B7_forward Regulation 
AorticSmoothMuscleCellResponseToFGF201hrBiolRep1LK13_CNhs12741_ctss_rev AorticSmsToFgf2_01hrBr1- Aortic smooth muscle cell response to FGF2, 01hr, biol_rep1 (LK13)_CNhs12741_12646-134G9_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF201hrBiolRep1LK13_CNhs12741_ctss_fwd AorticSmsToFgf2_01hrBr1+ Aortic smooth muscle cell response to FGF2, 01hr, biol_rep1 (LK13)_CNhs12741_12646-134G9_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr45minBiolRep3LK12_CNhs13571_ctss_rev AorticSmsToFgf2_00hr45minBr3- Aortic smooth muscle cell response to FGF2, 00hr45min, biol_rep3 (LK12)_CNhs13571_12841-137B6_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr45minBiolRep3LK12_CNhs13571_ctss_fwd AorticSmsToFgf2_00hr45minBr3+ Aortic smooth muscle cell response to FGF2, 00hr45min, biol_rep3 (LK12)_CNhs13571_12841-137B6_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr45minBiolRep2LK11_CNhs13361_ctss_rev AorticSmsToFgf2_00hr45minBr2- Aortic smooth muscle cell response to FGF2, 00hr45min, biol_rep2 (LK11)_CNhs13361_12743-135I7_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr45minBiolRep2LK11_CNhs13361_ctss_fwd AorticSmsToFgf2_00hr45minBr2+ Aortic smooth muscle cell response to FGF2, 00hr45min, biol_rep2 (LK11)_CNhs13361_12743-135I7_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr45minBiolRep1LK10_CNhs13343_ctss_rev AorticSmsToFgf2_00hr45minBr1- Aortic smooth muscle cell response to FGF2, 00hr45min, biol_rep1 (LK10)_CNhs13343_12645-134G8_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr45minBiolRep1LK10_CNhs13343_ctss_fwd AorticSmsToFgf2_00hr45minBr1+ Aortic smooth muscle cell response to FGF2, 00hr45min, biol_rep1 (LK10)_CNhs13343_12645-134G8_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr30minBiolRep3LK9_CNhs13569_ctss_rev AorticSmsToFgf2_00hr30minBr3- Aortic smooth muscle cell response to FGF2, 00hr30min, biol_rep3 (LK9)_CNhs13569_12840-137B5_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr30minBiolRep3LK9_CNhs13569_ctss_fwd AorticSmsToFgf2_00hr30minBr3+ Aortic smooth muscle cell response to FGF2, 00hr30min, biol_rep3 (LK9)_CNhs13569_12840-137B5_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr30minBiolRep2LK8_CNhs13360_ctss_rev AorticSmsToFgf2_00hr30minBr2- Aortic smooth muscle cell response to FGF2, 00hr30min, biol_rep2 (LK8)_CNhs13360_12742-135I6_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr30minBiolRep2LK8_CNhs13360_ctss_fwd AorticSmsToFgf2_00hr30minBr2+ Aortic smooth muscle cell response to FGF2, 00hr30min, biol_rep2 (LK8)_CNhs13360_12742-135I6_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr30minBiolRep1LK7_CNhs13341_ctss_rev AorticSmsToFgf2_00hr30minBr1- Aortic smooth muscle cell response to FGF2, 00hr30min, biol_rep1 (LK7)_CNhs13341_12644-134G7_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr30minBiolRep1LK7_CNhs13341_ctss_fwd AorticSmsToFgf2_00hr30minBr1+ Aortic smooth muscle cell response to FGF2, 00hr30min, biol_rep1 (LK7)_CNhs13341_12644-134G7_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr15minBiolRep3LK6_CNhs13568_ctss_rev AorticSmsToFgf2_00hr15minBr3- Aortic smooth muscle cell response to FGF2, 00hr15min, biol_rep3 (LK6)_CNhs13568_12839-137B4_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr15minBiolRep3LK6_CNhs13568_ctss_fwd AorticSmsToFgf2_00hr15minBr3+ Aortic smooth muscle cell response to FGF2, 00hr15min, biol_rep3 (LK6)_CNhs13568_12839-137B4_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr15minBiolRep2LK5_CNhs13359_ctss_rev AorticSmsToFgf2_00hr15minBr2- Aortic smooth muscle cell response to FGF2, 00hr15min, biol_rep2 (LK5)_CNhs13359_12741-135I5_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr15minBiolRep2LK5_CNhs13359_ctss_fwd AorticSmsToFgf2_00hr15minBr2+ Aortic smooth muscle cell response to FGF2, 00hr15min, biol_rep2 (LK5)_CNhs13359_12741-135I5_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr15minBiolRep1LK4_CNhs13340_ctss_rev AorticSmsToFgf2_00hr15minBr1- Aortic smooth muscle cell response to FGF2, 00hr15min, biol_rep1 (LK4)_CNhs13340_12643-134G6_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr15minBiolRep1LK4_CNhs13340_ctss_fwd AorticSmsToFgf2_00hr15minBr1+ Aortic smooth muscle cell response to FGF2, 00hr15min, biol_rep1 (LK4)_CNhs13340_12643-134G6_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr00minBiolRep2LK2_CNhs13358_ctss_rev AorticSmsToFgf2_00hr00minBr2- Aortic smooth muscle cell response to FGF2, 00hr00min, biol_rep2 (LK2)_CNhs13358_12740-135I4_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr00minBiolRep2LK2_CNhs13358_ctss_fwd AorticSmsToFgf2_00hr00minBr2+ Aortic smooth muscle cell response to FGF2, 00hr00min, biol_rep2 (LK2)_CNhs13358_12740-135I4_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr00minBiolRep1LK1_CNhs13339_ctss_rev AorticSmsToFgf2_00hr00minBr1- Aortic smooth muscle cell response to FGF2, 00hr00min, biol_rep1 (LK1)_CNhs13339_12642-134G5_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr00minBiolRep1LK1_CNhs13339_ctss_fwd AorticSmsToFgf2_00hr00minBr1+ Aortic smooth muscle cell response to FGF2, 00hr00min, biol_rep1 (LK1)_CNhs13339_12642-134G5_forward Regulation 
cpgIslandExtUnmasked Unmasked CpG CpG Islands on All Sequence (Islands < 300 Bases are Light Green) Regulation Description CpG islands are associated with genes, particularly housekeeping genes, in vertebrates. CpG islands are typically common near transcription start sites and may be associated with promoter regions. Normally a C (cytosine) base followed immediately by a G (guanine) base (a CpG) is rare in vertebrate DNA because the Cs in such an arrangement tend to be methylated. This methylation helps distinguish the newly synthesized DNA strand from the parent strand, which aids in the final stages of DNA proofreading after duplication. However, over evolutionary time, methylated Cs tend to turn into Ts because of spontaneous deamination. The result is that CpGs are relatively rare unless there is selective pressure to keep them or a region is not methylated for some other reason, perhaps having to do with the regulation of gene expression. CpG islands are regions where CpGs are present at significantly higher levels than is typical for the genome as a whole. The unmasked version of the track displays potential CpG islands that exist in repeat regions and would otherwise not be visible in the repeat masked version. By default, only the masked version of the track is displayed. To view the unmasked version, change the visibility settings in the track controls at the top of this page. Methods CpG islands were predicted by searching the sequence one base at a time, scoring each dinucleotide (+17 for CG and -1 for others) and identifying maximally scoring segments. Each segment was then evaluated for the following criteria: GC content of 50% or greater length greater than 200 bp ratio greater than 0.6 of observed number of CG dinucleotides to the expected number on the basis of the number of Gs and Cs in the segment The entire genome sequence, masking areas included, was used for the construction of the track Unmasked CpG. The track CpG Islands is constructed on the sequence after all masked sequence is removed. The CpG count is the number of CG dinucleotides in the island. The Percentage CpG is the ratio of CpG nucleotide bases (twice the CpG count) to the length. The ratio of observed to expected CpG is calculated according to the formula (cited in Gardiner-Garden et al. (1987)): Obs/Exp CpG = Number of CpG * N / (Number of C * Number of G) where N = length of sequence. The calculation of the track data is performed by the following command sequence: twoBitToFa assembly.2bit stdout | maskOutFa stdin hard stdout \ | cpg_lh /dev/stdin 2&gt; cpg_lh.err \ | awk '{&dollar;2 = &dollar;2 - 1; width = &dollar;3 - &dollar;2; printf("%s\t%d\t%s\t%s %s\t%s\t%s\t%0.0f\t%0.1f\t%s\t%s\n", &dollar;1, &dollar;2, &dollar;3, &dollar;5, &dollar;6, width, &dollar;6, width*&dollar;7*0.01, 100.0*2*&dollar;6/width, &dollar;7, &dollar;9);}' \ | sort -k1,1 -k2,2n &gt; cpgIsland.bed The unmasked track data is constructed from twoBitToFa -noMask output for the twoBitToFa command. Data access CpG islands and its associated tables can be explored interactively using the REST API, the Table Browser or the Data Integrator. All the tables can also be queried directly from our public MySQL servers, with more information available on our help page as well as on our blog. The source for the cpg_lh program can be obtained from src/utils/cpgIslandExt/. The cpg_lh program binary can be obtained from: http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/cpg_lh (choose "save file") Credits This track was generated using a modification of a program developed by G. Miklem and L. Hillier (unpublished). References Gardiner-Garden M, Frommer M. CpG islands in vertebrate genomes. J Mol Biol. 1987 Jul 20;196(2):261-82. PMID: 3656447 
covidHgiGwas COVID GWAS v3 GWAS meta-analyses from the COVID-19 Host Genetics Initiative Phenotype and Literature Description This track set shows GWAS meta-analyses from the COVID-19 Host Genetics Initiative (HGI): a collaborative effort to facilitate the generation, analysis and sharing of COVID-19 host genetics research. The COVID-19 HGI organizes meta-analyses across multiple studies contributed by partners world-wide to identify the genetic determinants of SARS-CoV-2 infection susceptibility and disease severity and outcomes. Moreover, the COVID-19 HGI also aims to provide a platform for study partners to share analytical results in the form of summary statistics and/or individual level data where possible. The specific phenotypes studied by the COVID-19 HGI are those that benefit from maximal sample size: primary analysis on disease severity. Two meta-analyses are represented in this track: ANA_C2_V2: covid vs. population (6696 cases from 18 studies) ANA_B2_V2: hospitalized covid vs. population (3199 cases from 8 studies) Display Conventions Displayed items are colored by GWAS effect: red for positive, blue for negative. The height of the item reflects the effect size. The effect size, defined as the contribution of a SNP to the genetic variance of the trait, was measured as beta coefficient (beta). The higher the absolute value of the beta coefficient, the stronger the effect. The color saturation indicates statistical significance: p-values smaller than 1e-5 are brightly colored (bright red &nbsp;&nbsp; , bright blue &nbsp;&nbsp; ), those with less significance (p >= 1e-5) are paler (light red &nbsp;&nbsp; , light blue &nbsp;&nbsp; ). For better visualization of the data, only SNPs with p-values smaller than 1e-3 are displayed by default. Each track has separate display controls and data can be filtered according to the number of studies, minimum -log10 p-value, and the effect size (beta coefficient), using the track Configure options. Mouseover on items shows the rs ID (or chrom:pos if none assigned), both the non-effect and effect alleles, the effect size (beta coefficient), the p-value, and the number of studies. Additional information on each variant can be found on the details page by clicking on the item. Methods COVID-19 Host Genetics Initiative (HGI) GWAS meta-analysis round 3 (July 2020) results were used in this study. Each participating study partner submitted GWAS summary statistics for up to four of the COVID-19 phenotype definitions. Data were generated from genome-wide SNP array and whole exome and genome sequencing, leveraging the impact of both common and rare variants. The statistical analysis performed takes into account differences between sex, ancestry, and date of sample collection. Alleles were harmonized across studies and reported allele frequencies are based on gnomAD version 3.0 reference data. Most study partners used the SAIGE GWAS pipeline in order to generate summary statistics used for the COVID-19 HGI meta-analysis. The summary statistics of individual studies were manually examined for inflation, deflation, and excessive number of false positives. Qualifying summary statistics were filtered for INFO > 0.6 and MAF > 0.0001 prior to meta-analyzing the entirety of the data. The meta-analysis was done using inverse variance weighting of effects method, accounting for strand differences and allele flips in the individual studies. The meta-analysis results of variants appearing in at least three studies (analysis C2) or two studies (all other analyses) were made publicly available. The meta-analysis software and workflow are available here. More information about the prospective studies, processing pipeline, results and data sharing can be found here. Data Access The data underlying these tracks and summary statistics results are publicly available in COVID19-hg Release 3 (June 2020). The raw data can be explored interactively with the Table Browser, or the Data Integrator. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits Thanks to the COVID-19 Host Genetics Initiative contributors and project leads for making these data available, and in particular to Rachel Liao, Juha Karjalainen, and Kumar Veerapen at the Broad Institute for their review and input during browser track development. References COVID-19 Host Genetics Initiative. The COVID-19 Host Genetics Initiative, a global initiative to elucidate the role of host genetic factors in susceptibility and severity of the SARS-CoV-2 virus pandemic. Eur J Hum Genet. 2020 Jun;28(6):715-718. PMID: 32404885; PMC: PMC7220587 
covidHgiGwasB2 Hosp COVID GWAS Hospitalized COVID GWAS from the COVID-19 Host Genetics Initiative (3199 cases, 8 studies) Phenotype and Literature 
covidHgiGwasC2 COVID GWAS COVID GWAS from the COVID-19 Host Genetics Initiative (6696 cases, 18 studies) Phenotype and Literature 
covidMuts COVID Rare Harmful Var Rare variants underlying COVID-19 severity and susceptibility from the COVID Human Genetics Effort Phenotype and Literature Description This track shows rare variants associated with monogenic congenital defects of immunity to the SARS-CoV-2 virus identified by the COVID Human Genetic Effort. This international consortium aims to discover truly causative variations: those underlying severe forms of COVID-19 in previously healthy individuals, and those that make certain individuals resistant to infection by the SARS-CoV2 virus despite repeated exposure. The major feature of the small set of variants in this track is that they are functionally tested to be deleterious and genetically tested to be disease-causing. Specifically, rare variants were predicted to be loss-of-function at human loci known to govern interferon (IFN) immunity to influenza virus in patients with life-threatening COVID-19 pneumonia, relative to subjects with asymptomatic or benign infection. These genetic defects display incomplete penetrance for influenza respiratory distress and only appear clinically upon infection with the more virulent SARS-CoV-2. Display Conventions Only eight genes with 23 variants are contained in this track. Use the links below to navigate to the gene of interest or view all eight genes together using the following sessions for hg38 or hg19. Gene Name Human GRCh37/hg19 Assembly Human GRCh38/hg38 Assembly TLR3 chr4:186990309-187006252 chr4:186069152-186088069 IRF7 chr11:612555-615999 chr11:612591-615970 UNC93B1 chr11:67758575-67771593 chr11:67991100-68004097 TBK1 chr12:64845840-64895899 chr12:64452120-64502114 TICAM1 chr19:4815936-4831754 chr19:4815932-4831704 IRF3 chr19:50162826-50169132 chr19:49659570-49665875 IFNAR1 chr21:34697214-34732128 chr21:33324970-33359864 IFNAR2 chr21:34602231-34636820 chr21:33229974-33264525 Methods This track uses variant calls in autosomal IFN-related genes from whole exome and genome data with a MAF lower than 0.001 (gnomAD v2.1.1) and experimental demonstration of loss-of-function. The patient population studied consisted of 659 patients with life-threatening COVID-19 pneumonia relative to 534 subjects with asymptomatic or benign infection of varying ethnicities. Variants underlying autosomal-recessive or autosomal-dominant deficiencies were identified in 23 patients (3.5%) 17 to 77 years of age. The proportion of individuals carrying at least one variant was compared between severe cases and control cases by means of logistic regression with the likelihood ratio test. Principal Component Analysis (PCA) was conducted with Plink v1.9 software on whole exome and genome sequencing data with the 1000 Genomes (1kG) Project phase 3 public database as reference. Analysis of enrichment in rare synonymous variants of the genes was performed to check the calibration of the burden test. The odds ratio was also estimated by logistic regression and adjusted for ethnic heterogeneity. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits Thanks to the COVID Human Genetic Effort contributors for making these data available, and in particular to Qian Zhang at the Rockefeller University for review and input during browser track development. References Zhang Q, Bastard P, Liu Z, Le Pen J, Moncada-Velez M, Chen J, Ogishi M, Sabli IKD, Hodeib S, Korol C et al. Inborn errors of type I IFN immunity in patients with life-threatening COVID-19. Science. 2020 Sep 24;. PMID: 32972995 
cons30way Cons 30 Primates Mammals Multiz Alignment & Conservation (27 primates) Comparative Genomics Description This track shows multiple alignments of 30 species and measurements of evolutionary conservation using two methods (phastCons and phyloP) from the PHAST package, for all thirty species. The multiple alignments were generated using multiz and other tools in the UCSC/Penn State Bioinformatics comparative genomics alignment pipeline. Conserved elements identified by phastCons are also displayed in this track. PhastCons (which has been used in previous Conservation tracks) is a hidden Markov model-based method that estimates the probability that each nucleotide belongs to a conserved element, based on the multiple alignment. It considers not just each individual alignment column, but also its flanking columns. By contrast, phyloP separately measures conservation at individual columns, ignoring the effects of their neighbors. As a consequence, the phyloP plots have a less smooth appearance than the phastCons plots, with more "texture" at individual sites. The two methods have different strengths and weaknesses. PhastCons is sensitive to "runs" of conserved sites, and is therefore effective for picking out conserved elements. PhyloP, on the other hand, is more appropriate for evaluating signatures of selection at particular nucleotides or classes of nucleotides (e.g., third codon positions, or first positions of miRNA target sites). Another important difference is that phyloP can measure acceleration (faster evolution than expected under neutral drift) as well as conservation (slower than expected evolution). In the phyloP plots, sites predicted to be conserved are assigned positive scores (and shown in blue), while sites predicted to be fast-evolving are assigned negative scores (and shown in red). The absolute values of the scores represent -log p-values under a null hypothesis of neutral evolution. The phastCons scores, by contrast, represent probabilities of negative selection and range between 0 and 1. Both phastCons and phyloP treat alignment gaps and unaligned nucleotides as missing data. See also: lastz parameters and other details and chain minimum score and gap parameters used in these alignments. Missing sequence in the assemblies is highlighted in the track display by regions of yellow when zoomed out and Ns displayed at base level (see Gap Annotation, below). OrganismSpeciesRelease dateUCSC versionalignment type HumanHomo sapiens Dec. 2013 (GRCh38/hg38)Dec. 2013 (GRCh38/hg38)MAF Net ChimpPan troglodytes May 2016 (Pan_tro 3.0/panTro5)May 2016 (Pan_tro 3.0/panTro5)MAF Net BonoboPan paniscus Aug. 2015 (MPI-EVA panpan1.1/panPan2)Aug. 2015 (MPI-EVA panpan1.1/panPan2)MAF Net GorillaGorilla gorilla gorilla Mar. 2016 (GSMRT3/gorGor5)Mar. 2016 (GSMRT3/gorGor5)MAF Net OrangutanPongo pygmaeus abelii July 2007 (WUGSC 2.0.2/ponAbe2)July 2007 (WUGSC 2.0.2/ponAbe2)MAF Net GibbonNomascus leucogenys Oct. 2012 (GGSC Nleu3.0/nomLeu3)Oct. 2012 (GGSC Nleu3.0/nomLeu3)MAF Net RhesusMacaca mulatta Nov. 2015 (BCM Mmul_8.0.1/rheMac8)Nov. 2015 (BCM Mmul_8.0.1/rheMac8)MAF Net Crab-eating macaqueMacaca fascicularis Jun. 2013 (Macaca_fascicularis_5.0/macFas5)Jun. 2013 (Macaca_fascicularis_5.0/macFas5)MAF Net Pig-tailed macaqueMacaca nemestrina Mar. 2015 (Mnem_1.0/macNem1)Mar. 2015 (Mnem_1.0/macNem1)MAF Net Sooty mangabeyCercocebus atys Mar. 2015 (Caty_1.0/cerAty1)Mar. 2015 (Caty_1.0/cerAty1)MAF Net BaboonPapio anubis Feb. 2013 (Baylor Panu_2.0/papAnu3)Feb. 2013 (Baylor Panu_2.0/papAnu3)MAF Net Green monkeyChlorocebus sabaeus Mar. 2014 (Chlorocebus_sabeus 1.1/chlSab2)Mar. 2014 (Chlorocebus_sabeus 1.1/chlSab2)MAF Net DrillMandrillus leucophaeus Mar. 2015 (Mleu.le_1.0/manLeu1)Mar. 2015 (Mleu.le_1.0/manLeu1)MAF Net Proboscis monkeyNasalis larvatus Nov. 2014 (Charlie1.0/nasLar1)Nov. 2014 (Charlie1.0/nasLar1)MAF Net Angolan colobusColobus angolensis palliatus Mar. 2015 (Cang.pa_1.0/colAng1)Mar. 2015 (Cang.pa_1.0/colAng1)MAF Net Golden snub-nosed monkeyRhinopithecus roxellana Oct. 2014 (Rrox_v1/rhiRox1)Oct. 2014 (Rrox_v1/rhiRox1)MAF Net Black snub-nosed monkeyRhinopithecus bieti Aug. 2016 (ASM169854v1/rhiBie1)Aug. 2016 (ASM169854v1/rhiBie1)MAF Net MarmosetCallithrix jacchus March 2009 (WUGSC 3.2/calJac3)March 2009 (WUGSC 3.2/calJac3)MAF Net Squirrel monkeySaimiri boliviensis Oct. 2011 (Broad/saiBol1)Oct. 2011 (Broad/saiBol1)MAF Net White-faced sapajouCebus capucinus imitator Apr. 2016 (Cebus_imitator-1.0/cebCap1)Apr. 2016 (Cebus_imitator-1.0/cebCap1)MAF Net Ma's night monkeyAotus nancymaae Jun. 2017 (Anan_2.0/aotNan1)Jun. 2017 (Anan_2.0/aotNan1)MAF Net TarsierTarsius syrichta Sep. 2013 (Tarsius_syrichta-2.0.1/tarSyr2)Sep. 2013 (Tarsius_syrichta-2.0.1/tarSyr2)MAF Net Mouse lemurMicrocebus murinus Feb. 2017 (Mmur_3.0/micMur3)Feb. 2017 (Mmur_3.0/micMur3)MAF Net Coquerel's sifakaPropithecus coquereli Mar. 2015 (Pcoq_1.0/proCoq1)Mar. 2015 (Pcoq_1.0/proCoq1)MAF Net Black lemurEulemur macaco Aug. 2015 (Emacaco_refEf_BWA_oneround/eulMac1)Aug. 2015 (Emacaco_refEf_BWA_oneround/eulMac1)MAF Net Sclater's lemurEulemur flavifrons Aug. 2015 (Eflavifronsk33QCA/eulFla1)Aug. 2015 (Eflavifronsk33QCA/eulFla1)MAF Net BushbabyOtolemur garnettii Mar. 2011 (Broad/otoGar3)Mar. 2011 (Broad/otoGar3)MAF Net MouseMus musculus Dec. 2011 (GRCm38/mm10)Dec. 2011 (GRCm38/mm10)MAF Net DogCanis lupus familiaris Sep. 2011 (Broad CanFam3.1/canFam3)Sep. 2011 (Broad CanFam3.1/canFam3)MAF Net ArmadilloDasypus novemcinctus Dec. 2011 (Baylor/dasNov3)Dec. 2011 (Baylor/dasNov3)MAF Net Table 1. Genome assemblies included in the 30-way Conservation track. Downloads for data in this track are available: Multiz alignments (MAF format), and phylogenetic trees PhyloP conservation (WIG format) PhastCons conservation (WIG format) Display Conventions and Configuration In full and pack display modes, conservation scores are displayed as a wiggle track (histogram) in which the height reflects the value of the score. The conservation wiggles can be configured in a variety of ways to highlight different aspects of the displayed information. Click the Graph configuration help link for an explanation of the configuration options. Pairwise alignments of each species to the human genome are displayed below the conservation histogram as a grayscale density plot (in pack mode) or as a wiggle (in full mode) that indicates alignment quality. In dense display mode, conservation is shown in grayscale using darker values to indicate higher levels of overall conservation as scored by phastCons. Checkboxes on the track configuration page allow selection of the species to include in the pairwise display. Configuration buttons are available to select all of the species (Set all), deselect all of the species (Clear all), or use the default settings (Set defaults). Note that excluding species from the pairwise display does not alter the the conservation score display. To view detailed information about the alignments at a specific position, zoom the display in to 30,000 or fewer bases, then click on the alignment. Gap Annotation The Display chains between alignments configuration option enables display of gaps between alignment blocks in the pairwise alignments in a manner similar to the Chain track display. The following conventions are used: Single line: No bases in the aligned species. Possibly due to a lineage-specific insertion between the aligned blocks in the human genome or a lineage-specific deletion between the aligned blocks in the aligning species. Double line: Aligning species has one or more unalignable bases in the gap region. Possibly due to excessive evolutionary distance between species or independent indels in the region between the aligned blocks in both species. Pale yellow coloring: Aligning species has Ns in the gap region. Reflects uncertainty in the relationship between the DNA of both species, due to lack of sequence in relevant portions of the aligning species. Genomic Breaks Discontinuities in the genomic context (chromosome, scaffold or region) of the aligned DNA in the aligning species are shown as follows: Vertical blue bar: Represents a discontinuity that persists indefinitely on either side, e.g. a large region of DNA on either side of the bar comes from a different chromosome in the aligned species due to a large scale rearrangement. Green square brackets: Enclose shorter alignments consisting of DNA from one genomic context in the aligned species nested inside a larger chain of alignments from a different genomic context. The alignment within the brackets may represent a short misalignment, a lineage-specific insertion of a transposon in the human genome that aligns to a paralogous copy somewhere else in the aligned species, or other similar occurrence. Base Level When zoomed-in to the base-level display, the track shows the base composition of each alignment. The numbers and symbols on the Gaps line indicate the lengths of gaps in the human sequence at those alignment positions relative to the longest non-human sequence. If there is sufficient space in the display, the size of the gap is shown. If the space is insufficient and the gap size is a multiple of 3, a &quot;*&quot; is displayed; other gap sizes are indicated by &quot;+&quot;. Codon translation is available in base-level display mode if the displayed region is identified as a coding segment. To display this annotation, select the species for translation from the pull-down menu in the Codon Translation configuration section at the top of the page. Then, select one of the following modes: No codon translation: The gene annotation is not used; the bases are displayed without translation. Use default species reading frames for translation: The annotations from the genome displayed in the Default species to establish reading frame pull-down menu are used to translate all the aligned species present in the alignment. Use reading frames for species if available, otherwise no translation: Codon translation is performed only for those species where the region is annotated as protein coding. Use reading frames for species if available, otherwise use default species: Codon translation is done on those species that are annotated as being protein coding over the aligned region using species-specific annotation; the remaining species are translated using the default species annotation. Codon translation uses the following gene tracks as the basis for translation, depending on the species chosen (Table 2). Gene TrackSpecies Known Geneshuman, mouse Ensembl Genes v78baboon, bushbaby, chimp, dog, gorilla, marmoset, mouse lemur, orangutan, tree shrew RefSeqcrab-eating macaque, rhesus no annotationbonobo, green monkey, gibbon, proboscis monkey, golden snub-nosed monkey, squirrel monkey, tarsier Table 2. Gene tracks used for codon translation. Methods Pairwise alignments with the human genome were generated for each species using lastz from repeat-masked genomic sequence. Pairwise alignments were then linked into chains using a dynamic programming algorithm that finds maximally scoring chains of gapless subsections of the alignments organized in a kd-tree. The scoring matrix and parameters for pairwise alignment and chaining were tuned for each species based on phylogenetic distance from the reference. High-scoring chains were then placed along the genome, with gaps filled by lower-scoring chains, to produce an alignment net. For more information about the chaining and netting process and parameters for each species, see the description pages for the Chain and Net tracks. An additional filtering step was introduced in the generation of the 30-way conservation track to reduce the number of paralogs and pseudogenes from the high-quality assemblies and the suspect alignments from the low-quality assemblies. type of net alignmentSpecies Syntenic Netbaboon, chimp, dog, gibbon, green monkey, crab-eating macaque, marmoset, mouse, orangutan, rhesus Reciprocal best Netbushbaby, bonobo, gorilla, golden snub-nosed monkey, mouse lemur, proboscis monkey, squirrel monkey, tarsier, tree shrew Table 3. Type of Net alignment The resulting best-in-genome pairwise alignments were progressively aligned using multiz/autoMZ, following the tree topology diagrammed above, to produce multiple alignments. The multiple alignments were post-processed to add annotations indicating alignment gaps, genomic breaks, and base quality of the component sequences. The annotated multiple alignments, in MAF format, are available for bulk download. An alignment summary table containing an entry for each alignment block in each species was generated to improve track display performance at large scales. Framing tables were constructed to enable visualization of codons in the multiple alignment display. Phylogenetic Tree Model Both phastCons and phyloP are phylogenetic methods that rely on a tree model containing the tree topology, branch lengths representing evolutionary distance at neutrally evolving sites, the background distribution of nucleotides, and a substitution rate matrix. The all species tree model for this track was generated using the phyloFit program from the PHAST package (REV model, EM algorithm, medium precision) using multiple alignments of 4-fold degenerate sites extracted from the 30-way alignment (msa_view). The 4d sites were derived from the Xeno RefSeq gene set, filtered to select single-coverage long transcripts. This same tree model was used in the phyloP calculations, however their background frequencies were modified to maintain reversibility. The resulting tree model for all species. PhastCons Conservation The phastCons program computes conservation scores based on a phylo-HMM, a type of probabilistic model that describes both the process of DNA substitution at each site in a genome and the way this process changes from one site to the next (Felsenstein and Churchill 1996, Yang 1995, Siepel and Haussler 2005). PhastCons uses a two-state phylo-HMM, with a state for conserved regions and a state for non-conserved regions. The value plotted at each site is the posterior probability that the corresponding alignment column was "generated" by the conserved state of the phylo-HMM. These scores reflect the phylogeny (including branch lengths) of the species in question, a continuous-time Markov model of the nucleotide substitution process, and a tendency for conservation levels to be autocorrelated along the genome (i.e., to be similar at adjacent sites). The general reversible (REV) substitution model was used. Unlike many conservation-scoring programs, phastCons does not rely on a sliding window of fixed size; therefore, short highly-conserved regions and long moderately conserved regions can both obtain high scores. More information about phastCons can be found in Siepel et al. (2005). The phastCons parameters used were: expected-length=45, target-coverage=0.3, rho=0.3. PhyloP Conservation The phyloP program supports several different methods for computing p-values of conservation or acceleration, for individual nucleotides or larger elements (http://compgen.cshl.edu/phast/). Here it was used to produce separate scores at each base (--wig-scores option), considering all branches of the phylogeny rather than a particular subtree or lineage (i.e., the --subtree option was not used). The scores were computed by performing a likelihood ratio test at each alignment column (--method LRT), and scores for both conservation and acceleration were produced (--mode CONACC). Conserved Elements The conserved elements were predicted by running phastCons with the --viterbi option. The predicted elements are segments of the alignment that are likely to have been "generated" by the conserved state of the phylo-HMM. Each element is assigned a log-odds score equal to its log probability under the conserved model minus its log probability under the non-conserved model. The "score" field associated with this track contains transformed log-odds scores, taking values between 0 and 1000. (The scores are transformed using a monotonic function of the form a * log(x) + b.) The raw log odds scores are retained in the "name" field and can be seen on the details page or in the browser when the track's display mode is set to "pack" or "full". Credits This track was created using the following programs: Alignment tools: blastz and multiz by Minmei Hou, Scott Schwartz and Webb Miller of the Penn State Bioinformatics Group Chaining and Netting: axtChain, chainNet by Jim Kent at UCSC Conservation scoring: phastCons, phyloP, phyloFit, tree_doctor, msa_view and other programs in PHAST by Adam Siepel at Cold Spring Harbor Laboratory (original development done at the Haussler lab at UCSC). MAF Annotation tools: mafAddIRows by Brian Raney, UCSC; mafAddQRows by Richard Burhans, Penn State; genePredToMafFrames by Mark Diekhans, UCSC Tree image generator: phyloPng by Galt Barber, UCSC Conservation track display: Kate Rosenbloom, Hiram Clawson (wiggle display), and Brian Raney (gap annotation and codon framing) at UCSC The phylogenetic tree is based on Murphy et al. (2001) and general consensus in the vertebrate phylogeny community as of March 2007. References Phylo-HMMs, phastCons, and phyloP: Felsenstein J, Churchill GA. A Hidden Markov Model approach to variation among sites in rate of evolution. Mol Biol Evol. 1996 Jan;13(1):93-104. PMID: 8583911 Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 3010 Jan;30(1):110-21. PMID: 19858363; PMC: PMC2798823 Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, Clawson H, Spieth J, Hillier LW, Richards S, et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 2005 Aug;15(8):1034-50. PMID: 16024819; PMC: PMC1182216 Siepel A, Haussler D. Phylogenetic Hidden Markov Models. In: Nielsen R, editor. Statistical Methods in Molecular Evolution. New York: Springer; 2005. pp. 325-351 Yang Z. A space-time process model for the evolution of DNA sequences. Genetics. 1995 Feb;139(2):993-1005. PMID: 7713447; PMC: PMC1306396 Chain/Net: Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(30):11484-9. PMID: 14500911; PMC: PMC308784 Multiz: Blanchette M, Kent WJ, Riemer C, Elnitski L, Smit AF, Roskin KM, Baertsch R, Rosenbloom K, Clawson H, Green ED, et al. Aligning multiple genomic sequences with the threaded blockset aligner. Genome Res. 2004 Apr;14(4):708-15. PMID: 15060014; PMC: PMC383327 Harris RS. Improved pairwise alignment of genomic DNA. Ph.D. Thesis. Pennsylvania State University, USA. 2007. Blastz: Chiaromonte F, Yap VB, Miller W. Scoring pairwise genomic sequence alignments. Pac Symp Biocomput. 2002:115-26. PMID: 11928468 Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 Phylogenetic Tree: Murphy WJ, Eizirik E, O'Brien SJ, Madsen O, Scally M, Douady CJ, Teeling E, Ryder OA, Stanhope MJ, de Jong WW, Springer MS. Resolution of the early placental mammal radiation using Bayesian phylogenetics. Science. 2001 Dec 14;294(5550):2348-51. PMID: 12743200 
cons30wayViewalign Multiz Alignments Mammals Multiz Alignment & Conservation (27 primates) Comparative Genomics 
multiz30way Multiz Align Multiz Alignments of 30 mammals (27 primates) Comparative Genomics 
cons30wayViewphastcons Element Conservation (phastCons) Mammals Multiz Alignment & Conservation (27 primates) Comparative Genomics 
phastCons30way Cons 30 Mammals 30 mammals conservation by PhastCons (27 primates) Comparative Genomics 
cons30wayViewelements Conserved Elements Mammals Multiz Alignment & Conservation (27 primates) Comparative Genomics 
phastConsElements30way 30-way El 30 mammals Conserved Elements (27 primates) Comparative Genomics 
cons30wayViewphyloP Basewise Conservation (phyloP) Mammals Multiz Alignment & Conservation (27 primates) Comparative Genomics 
phyloP30way Cons 30 Mammals 30 mammals Basewise Conservation by PhyloP (27 primates) Comparative Genomics 
knownGeneV45 GENCODE V45 GENCODE V45 Genes and Gene Predictions Description The GENCODE Genes track (version 45, January 2024) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. By default, only the basic gene set is displayed, which is a subset of the comprehensive gene set. The basic set represents transcripts that GENCODE believes will be useful to the majority of users. The track includes protein-coding genes, non-coding RNA genes, and pseudo-genes, though pseudo-genes are not displayed by default. It contains annotations on the reference chromosomes as well as assembly patches and alternative loci (haplotypes). The following table provides statistics for the v45 release derived from the GTF file that contains annotations only on the main chromosomes. More information on how they were generated can be found in the GENCODE site. GENCODE v45 Release Stats GenesObservedTranscriptsObserved Protein-coding genes19,395Protein-coding transcripts89,110 Long non-coding RNA genes20,424- full length protein-coding64,028 Small non-coding RNA genes7,565- partial length protein-coding25,082 Pseudogenes14,719Nonsense mediated decay transcripts21,427 Immunoglobulin/T-cell receptor gene segments648Long non-coding RNA loci transcripts59,719 Total No of distinct translations65,357Genes that have more than one distinct translations13,600 For more information on the different gene tracks, see our Genes FAQ. Display Conventions and Configuration By default, this track displays only the basic GENCODE set, splice variants, and non-coding genes. It includes options to display the entire GENCODE set and pseudogenes. To customize these options, the respective boxes can be checked or unchecked at the top of this description page. This track also includes a variety of labels which identify the transcripts when visibility is set to &quot;full&quot; or &quot;pack&quot;. Gene symbols (e.g. NIPA1) are displayed by default, but additional options include GENCODE Transcript ID (ENST00000561183.5), UCSC Known Gene ID (uc001yve.4), UniProt Display ID (Q7RTP0). Additional information about gene and transcript names can be found in our FAQ. This track, in general, follows the display conventions for gene prediction tracks. The exons for putative non-coding genes and untranslated regions are represented by relatively thin blocks, while those for coding open reading frames are thicker. Coloring for the gene annotations is based on the annotation type: coding: protein coding transcripts, including polymorphic pseudogenes non-coding: non-protein coding transcripts pseudogene: pseudogene transcript annotations problem: problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) This track contains an optional codon coloring feature that allows users to quickly validate and compare gene predictions. There is also an option to display the data as a density graph, which can be helpful for visualizing the distribution of items over a region. Squishy-pack Display Within a gene using the pack display mode, transcripts below a specified rank will be condensed into a view similar to squish mode. The transcript ranking approach is preliminary and will change in future releases. The transcripts rankings are defined by the following criteria for protein-coding and non-coding genes: Protein_coding genes MANE or Ensembl canonical 1st: MANE Select / Ensembl canonical 2nd: MANE Plus Clinical Coding biotypes 1st: protein_coding and protein_coding_LoF 2nd: NMDs and NSDs 3rd: retained intron and protein_coding_CDS_not_defined Completeness 1st: full length 2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype 1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Methods The GENCODE v45 track was built from the GENCODE downloads file gencode.v45.chr_patch_hapl_scaff.annotation.gff3.gz. Data from other sources were correlated with the GENCODE data to build association tables. Related Data The GENCODE Genes transcripts are annotated in numerous tables, each of which is also available as a downloadable file. One can see a full list of the associated tables in the Table Browser by selecting GENCODE Genes from the track menu; this list is then available on the table menu. Data access GENCODE Genes and its associated tables can be explored interactively using the REST API, the Table Browser or the Data Integrator. The genePred format files for hg38 are available from our downloads directory or in our GTF download directory. All the tables can also be queried directly from our public MySQL servers, with more information available on our help page as well as on our blog. Credits The GENCODE Genes track was produced at UCSC from the GENCODE comprehensive gene set using a computational pipeline developed by Jim Kent and Brian Raney. This version of the track was generated by Jonathan Casper. References Frankish A, Carbonell-Sala S, Diekhans M, Jungreis I, Loveland JE, Mudge JM, Sisu C, Wright JC, Arnan C, Barnes I et al. GENCODE: reference annotation for the human and mouse genomes in 2023. Nucleic Acids Res. 2023 Jan 6;51(D1):D942-D949. PMID: 36420896; PMC: PMC9825462 A full list of GENCODE publications is available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
problematicGIAB GIAB Problematic Regions Difficult regions from GIAB via NCBI Mapping and Sequencing Description This container track helps call out sections of the genome that often cause problems or confusion when working with the genome. The hg19 genome has a track with the same name, but with many more subtracks, as the GeT-RM and Genome-in-a-Bottle artifact variants do not exist yet for hg38, to our knowledge. If you are missing a track here that you know from hg19 and have an idea how to add it hg38, do not hesitate to contact us. Problematic Regions The Problematic Regions track contains the following subtracks: The UCSC Unusual Regions subtrack contains annotations collected at UCSC, put together from other tracks, our experiences and support email list requests over the years. For example, it contains the most well-known gene clusters (IGH, IGL, PAR1/2, TCRA, TCRB, etc) and annotations for the GRC fixed sequences, alternate haplotypes, unplaced contigs, pseudo-autosomal regions, and mitochondria. These loci can yield alignments with low-quality mapping scores and discordant read pairs, especially for short-read sequencing data. This data set was manually curated, based on the Genome Browser's assembly description, the FAQs about assembly, and the NCBI RefSeq &quot;other&quot; annotations track data. The ENCODE Blacklist subtrack contains a comprehensive set of regions which are troublesome for high-throughput Next-Generation Sequencing (NGS) aligners. These regions tend to have a very high ratio of multi-mapping to unique mapping reads and high variance in mappability due to repetitive elements such as satellite, centromeric and telomeric repeats. The GRC Exclusions subtrack contains a set of regions that have been flagged by the GRC to contain false duplications or contamination sequences. The GRC has now removed these sequences from the files that it uses to generate the reference assembly, however, removing the sequences from the GRCh38/hg38 assembly would trigger the next major release of the human assembly. In order to help users recognize these regions and avoid them in their analyses, the GRC have produced a masking file to be used as a companion to GRCh38, and the BED file is available from the GenBank FTP site. Highly Reproducible Regions The Highly Reproducible Regions track highlights regions and variants from eight samples that can be used to assess variant detection pipelines. The &quot;Highly Reproducible Regions&quot; subtrack comprises the intersection of the reproducible regions across all eight samples, while the &quot;Variants&quot; subtracks contain the reproducible variants from each assayed sample. Both tracks contain data from the following samples: a Chinese Quartet, samples CQ-5, CQ-6, CQ-7, CQ-8 a HapMap Trio, samples NA10385, NA12248, NA12249 a Genome in a Bottle sample, NA12878s Please refer to the Pan et al reference for more information on how these regions were defined. GIAB Problematic Regions The Genome in a Bottle (GIAB) Problematic Regions tracks provide stratifications of the genome to evaluate variant calls in complex regions. It is designed for use with Global Alliance for Genomic Health (GA4GH) benchmarking tools like hap.py and includes regions with low complexity, segmental duplications, functional regions, and difficult-to-sequence areas. Developed in collaboration with GA4GH, the Genome in a Bottle (GIAB) consortium, and the Telomere-to-Telomere Consortium (T2T), the dataset aims to standardize the analysis of genetic variation by offering pre-defined BED files for stratifying true and false positives in genomic studies, facilitating accurate assessments in complex areas of the genome. The creation of the GIAB Problematic Regions tracks involves using a pipeline and configuration to generate stratification BED files that categorize genomic regions based on specific challenges, such as low complexity or difficult mapping, to facilitate accurate benchmarking of variant calls. For more information on the pipeline and configuration used, please visit the following webpage: https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/genome-stratifications/v3.5/README.md. If you have questions or comments, please write to Justin Zook (jzook@nist.gov). Display Conventions and Configuration Each track contains a set of regions of varying length with no special configuration options. The UCSC Unusual Regions track has a mouse-over description, all other tracks have at most a name field, which can be shown in pack mode. The tracks are usually kept in dense mode. The Hide empty subtracks control hides subtracks with no data in the browser window. Changing the browser window by zooming or scrolling may result in the display of a different selection of tracks. Data access The raw data can be explored interactively with the Table Browser or the Data Integrator. For automated download and analysis, the genome annotation is stored in bigBed files that can be downloaded from our download server. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/problematic/comments.bb -chrom=chr21 -start=0 -end=100000000 stdout Methods Files were downloaded from the respective databases and converted to bigBed format. The procedure is documented in our hg38 makeDoc file. Credits Thanks to Anna Benet-Pag&egrave;s, Max Haeussler, Angie Hinrichs, Daniel Schmelter, and Jairo Navarro at the UCSC Genome Browser for planning, building, and testing these tracks. The underlying data comes from the ENCODE Blacklist and some parts were copied manually from the HGNC and NCBI RefSeq tracks. References Amemiya HM, Kundaje A, Boyle AP. The ENCODE Blacklist: Identification of Problematic Regions of the Genome. Sci Rep. 2019 Jun 27;9(1):9354. PMID: 31249361; PMC: PMC6597582 Dwarshuis N, Kalra D, McDaniel J, Sanio P, Alvarez Jerez P, Jadhav B, Huang WE, Mondal R, Busby B, Olson ND et al. The GIAB genomic stratifications resource for human reference genomes. Nat Commun. 2024 Oct 19;15(1):9029. PMID: 39424793; PMC: PMC11489684 Krusche P, Trigg L, Boutros PC, Mason CE, De La Vega FM, Moore BL, Gonzalez-Porta M, Eberle MA, Tezak Z, Lababidi S et al. Best practices for benchmarking germline small-variant calls in human genomes. Nat Biotechnol. 2019 May;37(5):555-560. PMID: 30858580; PMC: PMC6699627 Pan B, Ren L, Onuchic V, Guan M, Kusko R, Bruinsma S, Trigg L, Scherer A, Ning B, Zhang C et al. Assessing reproducibility of inherited variants detected with short-read whole genome sequencing. Genome Biol. 2022 Jan 3;23(1):2. PMID: 34980216; PMC: PMC8722114 
notinalllowmapandsegdupregions Not lowMap+SegDup Genome In a Bottle: not lowMap+SegDup mapping regions Mapping and Sequencing 
notinalldifficultregions Not difficult regions Genome In a Bottle: not difficult regions Mapping and Sequencing 
alllowmapandsegdupregions LowMap+SegDup Genome In a Bottle: lowMap+SegDup regions Mapping and Sequencing 
alldifficultregions All difficult regions Genome In a Bottle: all difficult regions Mapping and Sequencing 
gtexImmuneAtlasFullDetails GTEx Immune Atlas GTEx single nuclei immune expression Single Cell RNA-seq Description This track collection shows data from Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. The dataset covers ~200,000 single nuclei from a total of 16 human donors across 25 samples, using 4 different sample preparation protocols followed by droplet based single-cell RNA-seq. The samples were obtained from frozen tissue as part of the Genotype-Tissue Expression (GTEx) project. Samples were taken from the esophagus, skeletal muscle, heart, lung, prostate, breast, and skin. The dataset includes 43 broad cell classes, some specific to certain tissues and some shared across all tissue types. This track collection contains three bar chart tracks of RNA expression. The first track, Cross Tissue Nuclei, allows cells to be grouped together and faceted on up to 4 categories: tissue, cell class, cell subclass, and cell type. The second track, Cross Tissue Details, allows cells to be grouped together and faceted on up to 7 categories: tissue, cell class, cell subclass, cell type, granular cell type, sex, and donor. The third track, GTEx Immune Atlas, allows cells to be grouped together and faceted on up to 5 categories: tissue, cell type, cell class, sex, and donor. Please see the GTEx portal for further interactive displays and additional data. Display Conventions and Configuration Tissue-cell type combinations in the Full and Combined tracks are colored by which cell type they belong to in the below table: Color Cell Type Endothelial Epithelial Glia Immune Neuron Stromal Other Tissue-cell type combinations in the Immune Atlas track are shaded according to the below table: Color Cell Type Inflammatory Macrophage Lung Macrophage Monocyte/Macrophage FCGR3A High Monocyte/Macrophage FCGR3A Low Macrophage HLAII High Macrophage LYVE1 High Proliferating Macrophage Dendritic Cell 1 Dendritic Cell 2 Mature Dendritic Cell Langerhans CD14+ Monocyte CD16+ Monocyte LAM-like Other Methods Using the previously collected tissue samples from the Genotype-Tissue Expression project, nuclei were isolated using four different protocols and sequenced using droplet based single cell RNA-seq. CellBender v2.1 and other standard quality control techniques were applied, resulting in 209,126 nuclei profiles across eight tissues, with a mean of 918 genes and 1519 transcripts per profile. Data from all samples was integrated with a conditional variation autoencoder in order to correct for multiple sources of variation like sex, and protocol while preserving tissue and cell type specific effects. For detailed methods, please refer to Eraslan et al, or the GTEx portal website. UCSC Methods The gene expression files were downloaded from the GTEx portal. The UCSC command line utilities matrixClusterColumns, matrixToBarChartBed, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions or our Data Access FAQ for more information. Credits Thanks to the GTEx Consortium for creating and analyzing these data. References Eraslan G, Drokhlyansky E, Anand S, Fiskin E, Subramanian A, Slyper M, Wang J, Van Wittenberghe N, Rouhana JM, Waldman J et al. Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. Science. 2022 May 13;376(6594):eabl4290. PMID: 35549429; PMC: PMC9383269 
microsat Microsatellite Microsatellites - Di-nucleotide and Tri-nucleotide Repeats Repeats Description This track displays regions that are likely to be useful as microsatellite markers. These are sequences of at least 15 perfect di-nucleotide and tri-nucleotide repeats and tend to be highly polymorphic in the population. Methods The data shown in this track are a subset of the Simple Repeats track, selecting only those repeats of period 2 and 3, with 100% identity and no indels and with at least 15 copies of the repeat. The Simple Repeats track is created using the Tandem Repeats Finder. For more information about this program, see Benson (1999). Credits Tandem Repeats Finder was written by Gary Benson. References Benson G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 1999 Jan 15;27(2):573-80. PMID: 9862982; PMC: PMC148217 
nuMtSeq NuMTs Sequence Nuclear mitochondrial DNA segments Repeats Description and display conventions Nuclear mitochondrial DNA segments (NUMTs) are a kind of insertion from the mitochondrion to the nucleus, which is an ongoing and frequent process that happens in all eukaryotes. In previous studies, NUMTs have been reported to increase genetic diversity, promote gene and genome evolution, and generate novel nuclear exons. NUMTs can also affect the accuracy when nuclear genomes are assembled. This track is a collection of Nuclear mitochondrial DNA segments, provided in BED format. Notice: Alignments to incompletely assembled or unmapped chromosome locations are omitted in this track. In this track, the BED score is calculated by -10log10(E-value), representing the alignment confidence and is reflected in the level of gray. Scores &gt;=100 (E-values &lt;= 1e-10) are colored black. It is important to note that when a NUMT is a merged result, the score is taken as the highest score among all results. Methods This dataset identifies nuclear mitochondrial genome segments (NUMTs) by comparing nuclear and mitochondrial genomes and proteins using LAST alignment tools. The method involves several steps: nuclear genome-mitochondrial genome comparison, nuclear genome-mitochondrial protein comparison, and exclusion of overlapping nuclear ribosomal RNA regions using maf-Bed and seg-suite tools. Results are merged if alignments are consistent across both comparisons, with sequences under 30bp excluded. Bedtools and LAST are used throughout the process for efficient alignment and merging. For more detailed information on the methods used for detecting NUMTs, please visit the following webpage: https://github.com/Koumokuyou/NUMTs Contact If you have questions or comments, please write to: Huang Muyao, &#50;&#49;&#55;&#49;27&#50;&#57;&#48;&#51;&#64;&#101;&#100;&#117;.&#107;.&#117;&#45;&#116;&#111;k&#121;&#111;.ac.&#106;&#112; References Kleine T, Maier UG, Leister D. DNA transfer from organelles to the nucleus: the idiosyncratic genetics of endosymbiosis. Annu Rev Plant Biol. 2009;60:115-38. DOI: 10.1146/annurev.arplant.043008.092119; PMID: 19014347 Zhang GJ, Dong R, Lan LN, Li SF, Gao WJ, Niu HX. Nuclear Integrants of Organellar DNA Contribute to Genome Structure and Evolution in Plants. Int J Mol Sci. 2020 Jan 21;21(3). DOI: 10.3390/ijms21030707; PMID: 31973163; PMC: PMC7037861 Yao Y, Frith MC. Improved DNA-Versus-Protein Homology Search for Protein Fossils. IEEE/ACM Trans Comput Biol Bioinform. 2023 May-Jun;20(3):1691-1699. DOI: 10.1109/TCBB.2022.3177855; PMID: 35617174 Frith MC. A simple method for finding related sequences by adding probabilities of alternative alignments. Genome Res. 2024 Sep 13;. DOI: 10.1101/gr.279464.124; PMID: 39152037 
omimLocation OMIM Cyto Loci OMIM Cytogenetic Loci Phenotypes - Gene Unknown Phenotype and Literature Description NOTE: OMIM is intended for use primarily by physicians and other professionals concerned with genetic disorders, by genetics researchers, and by advanced students in science and medicine. While the OMIM database is open to the public, users seeking information about a personal medical or genetic condition are urged to consult with a qualified physician for diagnosis and for answers to personal questions. Further, please be sure to click through to omim.org for the very latest, as they are continually updating data. NOTE ABOUT DOWNLOADS: OMIM is the property of Johns Hopkins University and is not available for download or mirroring by any third party without their permission. Please see OMIM for downloads. OMIM is a compendium of human genes and genetic phenotypes. The full-text, referenced overviews in OMIM contain information on all known Mendelian disorders and over 12,000 genes. OMIM is authored and edited at the McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, under the direction of Dr. Ada Hamosh. This database was initiated in the early 1960s by Dr. Victor A. McKusick as a catalog of Mendelian traits and disorders, entitled Mendelian Inheritance in Man (MIM). The OMIM data are separated into three separate tracks: OMIM Alleles &nbsp;&nbsp;&nbsp;&nbsp;Variants in the OMIM database that have associated dbSNP identifiers. This track is currently unavailable on the hg38 assembly, as it depends on dbSNP data that has not been released yet. OMIM Genes &nbsp;&nbsp;&nbsp;&nbsp;The genomic positions of gene entries in the OMIM database. The coloring indicates the associated OMIM phenotype map key. OMIM Phenotypes - Gene Unknown &nbsp;&nbsp;&nbsp;&nbsp;Regions known to be associated with a phenotype, but for which no specific gene is known to be causative. This track also includes known multi-gene syndromes. This track shows the cytogenetic locations of phenotype entries in the Online Mendelian Inheritance in Man (OMIM) database for which the gene is unknown. Display Conventions and Configuration Cytogenetic locations of OMIM entries are displayed as solid blocks. The entries are colored according to the OMIM phenotype map key of associated disorders: Lighter Green for phenotype map key 1 OMIM records - the disorder has been placed on the map based on its association with a gene, but the underlying defect is not known. Light Green for phenotype map key 2 OMIM records - the disorder has been placed on the map by linkage; no mutation has been found. Dark Green for phenotype map key 3 OMIM records - the molecular basis for the disorder is known; a mutation has been found in the gene. Purple for phenotype map key 4 OMIM records - a contiguous gene deletion or duplication syndrome; multiple genes are deleted or duplicated causing the phenotype. Gene symbols and disease information, when available, are displayed on the details pages. The descriptions of OMIM entries are shown on the main browser display when Full display mode is chosen. In Pack mode, the descriptions are shown when mousing over each entry. Items displayed can be filtered according to phenotype map key on the track controls page. Methods This track was constructed as follows: The data file genemap.txt from OMIM was loaded into the MySQL table omimGeneMap. Entries in genemap.txt having disorder info were parsed and loaded into the omimPhenotype table. The phenotype map keys (the numbers (1)(2)(3)(4) from the disorder columns) were placed into a separate field. The cytogenetic location data (from the location column in omimGeneMap) were parsed and converted into genomic start and end positions based on the cytoBand table. These genomic positions, together with the corresponding OMIM IDs, were loaded into the omimLocation table. All entries with no associated phenotype map key and all OMIM gene entries as reported in the &quot;OMIM Genes&quot; track were then excluded from the omimLocation table. Data Access Because OMIM has only allowed Data queries within individual chromosomes, no download files are available from the Genome Browser. Full genome datasets can be downloaded directly from the OMIM Downloads page. All genome-wide downloads are freely available from OMIM after registration. If you need the OMIM data in exactly the format of the UCSC Genome Browser, for example if you are running a UCSC Genome Browser local installation (a partial &quot;mirror&quot;), please create a user account on omim.org and contact OMIM via https://omim.org/contact. Send them your OMIM account name and request access to the UCSC Genome Browser 'entitlement'. They will then grant you access to a MySQL/MariaDB data dump that contains all UCSC Genome Browser OMIM tables. UCSC offers queries within chromosomes from Table Browser that include a variety of filtering options and cross-referencing other datasets using our Data Integrator tool. UCSC also has an API that can be used to retrieve data in JSON format from a particular chromosome range. Please refer to our searchable mailing list archives for more questions and example queries, or our Data Access FAQ for more information. Credits Thanks to OMIM and NCBI for the use of their data. This track was constructed by Fan Hsu, Robert Kuhn, and Brooke Rhead of the UCSC Genome Bioinformatics Group. References Amberger J, Bocchini CA, Scott AF, Hamosh A. McKusick's Online Mendelian Inheritance in Man (OMIM&#174;). Nucleic Acids Res. 2009 Jan;37(Database issue):D793-6. Epub 2008 Oct 8. Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005 Jan 1;33(Database issue):D514-7. 
recombMat Recomb. deCODE Mat Recombination rate: deCODE Genetics, maternal Mapping and Sequencing Description The recombination rate track represents calculated rates of recombination based on the genetic maps from deCODE (Halldorsson et al., 2019) and 1000 Genomes (2013 Phase 3 release, lifted from hg19). The deCODE map is more recent, has a higher resolution and was natively created on hg38 and therefore recommended. For the Recomb. deCODE average track, the recombination rates for chrX represent the female rate. This track also includes a subtrack with all the individual deCODE recombination events and another subtrack with several thousand de-novo mutations found in the deCODE sequencing data. These two tracks are hidden by default and have to be switched on explicitly on the configuration page. Display Conventions and Configuration This is a super track that contains different subtracks, three with the deCODE recombination rates (paternal, maternal and average) and one with the 1000 Genomes recombination rate (average). These tracks are in signal graph (wiggle) format. By default, to show most recombination hotspots, their maximum value is set to 100 cM, even though many regions have values higher than 100. The maximum value can be changed on the configuration pages of the tracks. There are two more tracks that show additional details provided by deCODE: one subtrack with the raw data of all cross-overs tagged with their proband ID and another one with around 8000 human de-novo mutation variants that are linked to cross-over changes. Methods The deCODE genetic map was created at deCODE Genetics. It is based on microarrays assaying 626,828 SNP markers that allowed to identify 1,476,140 crossovers in 56,321 paternal meioses and 3,055,395 crossovers in 70,086 maternal meioses. In total, the data is based on 4,531,535 crossovers in 126,427 meioses. By using WGS data with 9,305,070 SNPs, the boundaries for 761,981 crossovers were refined: 247,942 crossovers in 9423 paternal meioses and 514,039 crossovers in 11,750 maternal meioses. The average resolution of the genetic map is 682 base pairs (bp): 655 and 708 bp for the paternal and maternal maps, respectively. The 1000 Genomes genetic map is based on the IMPUTE genetic map based on 1000 Genomes Phase 3, on hg19 coordinates. It was converted to hg38 by Po-Ru Loh at the Broad Institute. After a run of liftOver, he post-processed the data to deal with situations in which consecutive map locations became much closer/farther after lifting. The heuristic used is sufficient for statistical phasing but may not be optimal for other analyses. For this reason, and because of its higher resolution, the DeCODE map is therefore recommended for hg38. As with all other tracks, the data conversion commands and pointers to the original data files are documented in the makeDoc file of this track. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated access, this track, like all others, is available via our API. However, for bulk processing, it is recommended to download the dataset. For automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed files that can be downloaded from our download server. Individual regions or the whole genome annotation can be obtained using our tools bigWigToWig or bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tools can also be used to obtain features confined to a given range, e.g., bigWigToBedGraph -chrom=chr17 -start=45941345 -end=45942345 http://hgdownload.soe.ucsc.edu/gbdb/hg38/recombRate/recombAvg.bw stdout Please refer to our Data Access FAQ for more information. Credits This track was produced at UCSC using data that are freely available for the deCODE and 1000 Genomes genetic maps. Thanks to Po-Ru Loh at the Broad Institute for providing the code to lift the hg19 1000 Genomes map data to hg38. References 1000 Genomes Project Consortium., Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, Hurles ME, McVean GA. A map of human genome variation from population-scale sequencing. Nature. 2010 Oct 28;467(7319):1061-73. PMID: 20981092; PMC: PMC3042601 Halldorsson BV, Palsson G, Stefansson OA, Jonsson H, Hardarson MT, Eggertsson HP, Gunnarsson B, Oddsson A, Halldorsson GH, Zink F et al. Characterizing mutagenic effects of recombination through a sequence-level genetic map. Science. 2019 Jan 25;363(6425). PMID: 30679340 
TSS_activity_TPM TSS activity (TPM) FANTOM5: TSS activity per sample (TPM) Regulation Description The FANTOM5 track shows mapped transcription start sites (TSS) and their usage in primary cells, cell lines, and tissues to produce a comprehensive overview of gene expression across the human body by using single molecule sequencing. Display Conventions and Configuration Items in this track are colored according to their strand orientation. Blue indicates alignment to the negative strand, and red indicates alignment to the positive strand. Methods Protocol Individual biological states are profiled by HeliScopeCAGE, which is a variation of the CAGE (Cap Analysis Gene Expression) protocol based on a single molecule sequencer. The standard protocol requiring 5 &micro;g of total RNA as a starting material is referred to as hCAGE, and an optimized version for a lower quantity (~ 100 ng) is referred to as LQhCAGE (Kanamori-Katyama et al. 2011). hCAGE LQhCAGE Samples Transcription start sites (TSSs) were mapped and their usage in human and mouse primary cells, cell lines, and tissues was to produce a comprehensive overview of mammalian gene expression across the human body. 5&prime;-end of the mapped CAGE reads are counted at a single base pair resolution (CTSS, CAGE tag starting sites) on the genomic coordinates, which represent TSS activities in the sample. Individual samples shown in "TSS activity" tracks are grouped as below. Primary cell Tissue Cell Line Time course Fractionation TSS peaks TSS (CAGE) peaks across the panel of the biological states (samples) are identified by DPI (decomposition based peak identification, Forrest et al. 2014), where each of the peaks consists of neighboring and related TSSs. The peaks are used as anchors to define promoters and units of promoter-level expression analysis. Two subsets of the peaks are defined based on evidence of read counts, depending on scopes of subsequent analyses, and the first subset (referred as a robust set of the peaks, thresholded for expression analysis is shown as TSS peaks. They are named "p#@GENE_SYMBOL" if associated with 5'-end of known genes, or "p@CHROM:START..END,STRAND" otherwise. The summary tracks consist of the TSS (CAGE) peaks and summary profiles of TSS activities (total and maximum values). The summary track consists of the following tracks. TSS (CAGE) peaks the robust peaks TSS summary profiles Total counts and TPM (tags per million) in all the samples Maximum counts and TPM among the samples TSS activity 5&prime;-end of the mapped CAGE reads are counted at a single base pair resolution (CTSS, CAGE tag starting sites) on the genomic coordinates, which represent TSS activities in the sample. The read counts tracks indicate raw counts of CAGE reads, and the TPM tracks indicate normalized counts as TPM (tags per million). Categories of individual samples - Cell Line hCAGE - Cell Line LQhCAGE - fractionation hCAGE - Primary cell hCAGE - Primary cell LQhCAGE - Time course hCAGE - Tissue hCAGE Data Access FANTOM5 data can be explored interactively with the Table Browser and cross-referenced with the Data Integrator. For programmatic access, the track can be accessed using the Genome Browser&apos;s REST API. ReMap annotations can be downloaded from the Genome Browser's download server as a bigBed file. This compressed binary format can be remotely queried through command line utilities. Please note that some of the download files can be quite large. The FANTOM5 reprocessed data can be found and downloaded on the FANTOM website. Credits Thanks to the FANTOM5 consortium, the Large Scale Data Managing Unit and Preventive Medicine and Applied Genomics Unit, the Center for Integrative Medical Sciences (IMS), and RIKEN for providing this data and its analysis. References FANTOM Consortium and the RIKEN PMI and CLST (DGT), Forrest AR, Kawaji H, Rehli M, Baillie JK, de Hoon MJ, Haberle V, Lassmann T, Kulakovskiy IV, Lizio M et al. A promoter-level mammalian expression atlas. Nature. 2014 Mar 27;507(7493):462-70. PMID: 24670764; PMC: PMC4529748 Kanamori-Katayama M, Itoh M, Kawaji H, Lassmann T, Katayama S, Kojima M, Bertin N, Kaiho A, Ninomiya N, Daub CO et al. Unamplified cap analysis of gene expression on a single-molecule sequencer. Genome Res. 2011 Jul;21(7):1150-9. PMID: 21596820; PMC: PMC3129257 Lizio M, Harshbarger J, Shimoji H, Severin J, Kasukawa T, Sahin S, Abugessaisa I, Fukuda S, Hori F, Ishikawa-Kato S et al. Gateways to the FANTOM5 promoter level mammalian expression atlas. Genome Biol. 2015 Jan 5;16(1):22. PMID: 25723102; PMC: PMC4310165 
VeinAdult_CNhs12844_tpm_rev VeinAdult- vein, adult_CNhs12844_10191-103E2_reverse Regulation 
VeinAdult_CNhs12844_tpm_fwd VeinAdult+ vein, adult_CNhs12844_10191-103E2_forward Regulation 
VaginaAdult_CNhs12854_tpm_rev VaginaAdult- vagina, adult_CNhs12854_10204-103F6_reverse Regulation 
VaginaAdult_CNhs12854_tpm_fwd VaginaAdult+ vagina, adult_CNhs12854_10204-103F6_forward Regulation 
UterusFetalDonor1_CNhs11763_tpm_rev UterusFetalD1- uterus, fetal, donor1_CNhs11763_10055-101H1_reverse Regulation 
UterusFetalDonor1_CNhs11763_tpm_fwd UterusFetalD1+ uterus, fetal, donor1_CNhs11763_10055-101H1_forward Regulation 
UterusAdultPool1_CNhs11676_tpm_rev UterusAdultPl1- uterus, adult, pool1_CNhs11676_10100-102D1_reverse Regulation 
UterusAdultPool1_CNhs11676_tpm_fwd UterusAdultPl1+ uterus, adult, pool1_CNhs11676_10100-102D1_forward Regulation 
UrethraDonor2_CNhs13464_tpm_rev UrethraD2- Urethra, donor2_CNhs13464_10319-105A4_reverse Regulation 
UrethraDonor2_CNhs13464_tpm_fwd UrethraD2+ Urethra, donor2_CNhs13464_10319-105A4_forward Regulation 
UniversalRNAHumanNormalTissuesBiochainPool1_CNhs10612_tpm_rev UniversalRnaNormalTissuesBiochainPl1- Universal RNA - Human Normal Tissues Biochain, pool1_CNhs10612_10007-101B4_reverse Regulation 
UniversalRNAHumanNormalTissuesBiochainPool1_CNhs10612_tpm_fwd UniversalRnaNormalTissuesBiochainPl1+ Universal RNA - Human Normal Tissues Biochain, pool1_CNhs10612_10007-101B4_forward Regulation 
UmbilicalCordFetalDonor1_CNhs11765_tpm_rev UmbilicalCordFetalD1- umbilical cord, fetal, donor1_CNhs11765_10057-101H3_reverse Regulation 
UmbilicalCordFetalDonor1_CNhs11765_tpm_fwd UmbilicalCordFetalD1+ umbilical cord, fetal, donor1_CNhs11765_10057-101H3_forward Regulation 
TracheaFetalDonor1_CNhs11766_tpm_rev TracheaFetalD1- trachea, fetal, donor1_CNhs11766_10058-101H4_reverse Regulation 
TracheaFetalDonor1_CNhs11766_tpm_fwd TracheaFetalD1+ trachea, fetal, donor1_CNhs11766_10058-101H4_forward Regulation 
TracheaAdultPool1_CNhs10635_tpm_rev TracheaAdultPl1- trachea, adult, pool1_CNhs10635_10029-101E2_reverse Regulation 
TracheaAdultPool1_CNhs10635_tpm_fwd TracheaAdultPl1+ trachea, adult, pool1_CNhs10635_10029-101E2_forward Regulation 
TonsilAdultPool1_CNhs10654_tpm_rev TonsilAdultPl1- tonsil, adult, pool1_CNhs10654_10047-101G2_reverse Regulation 
TonsilAdultPool1_CNhs10654_tpm_fwd TonsilAdultPl1+ tonsil, adult, pool1_CNhs10654_10047-101G2_forward Regulation 
TongueFetalDonor1_CNhs11768_tpm_rev TongueFetalD1- tongue, fetal, donor1_CNhs11768_10059-101H5_reverse Regulation 
TongueFetalDonor1_CNhs11768_tpm_fwd TongueFetalD1+ tongue, fetal, donor1_CNhs11768_10059-101H5_forward Regulation 
TongueEpidermisFungiformPapillaeDonor1_CNhs13460_tpm_rev TongueEpidermisD1- tongue epidermis (fungiform papillae), donor1_CNhs13460_10288-104F9_reverse Regulation 
TongueEpidermisFungiformPapillaeDonor1_CNhs13460_tpm_fwd TongueEpidermisD1+ tongue epidermis (fungiform papillae), donor1_CNhs13460_10288-104F9_forward Regulation 
TongueAdult_CNhs12853_tpm_rev TongueAdult- tongue, adult_CNhs12853_10203-103F5_reverse Regulation 
TongueAdult_CNhs12853_tpm_fwd TongueAdult+ tongue, adult_CNhs12853_10203-103F5_forward Regulation 
ThyroidFetalDonor1_CNhs11769_tpm_rev ThyroidFetalD1- thyroid, fetal, donor1_CNhs11769_10060-101H6_reverse Regulation 
ThyroidFetalDonor1_CNhs11769_tpm_fwd ThyroidFetalD1+ thyroid, fetal, donor1_CNhs11769_10060-101H6_forward Regulation 
ThyroidAdultPool1_CNhs10634_tpm_rev ThyroidAdultPl1- thyroid, adult, pool1_CNhs10634_10028-101E1_reverse Regulation 
ThyroidAdultPool1_CNhs10634_tpm_fwd ThyroidAdultPl1+ thyroid, adult, pool1_CNhs10634_10028-101E1_forward Regulation 
ThymusFetalPool1_CNhs10650_tpm_rev ThymusFetalPl1- thymus, fetal, pool1_CNhs10650_10043-101F7_reverse Regulation 
ThymusFetalPool1_CNhs10650_tpm_fwd ThymusFetalPl1+ thymus, fetal, pool1_CNhs10650_10043-101F7_forward Regulation 
ThymusAdultPool1_CNhs10633_tpm_rev ThymusAdultPl1- thymus, adult, pool1_CNhs10633_10027-101D9_reverse Regulation 
ThymusAdultPool1_CNhs10633_tpm_fwd ThymusAdultPl1+ thymus, adult, pool1_CNhs10633_10027-101D9_forward Regulation 
ThroatFetalDonor1_CNhs11770_tpm_rev ThroatFetalD1- throat, fetal, donor1_CNhs11770_10061-101H7_reverse Regulation 
ThroatFetalDonor1_CNhs11770_tpm_fwd ThroatFetalD1+ throat, fetal, donor1_CNhs11770_10061-101H7_forward Regulation 
ThroatAdult_CNhs12858_tpm_rev ThroatAdult- throat, adult_CNhs12858_10209-103G2_reverse Regulation 
ThroatAdult_CNhs12858_tpm_fwd ThroatAdult+ throat, adult_CNhs12858_10209-103G2_forward Regulation 
ThalamusNewbornDonor10223_CNhs14084_tpm_rev ThalamusNbD10223- thalamus, newborn, donor10223_CNhs14084_10366-105F6_reverse Regulation 
ThalamusNewbornDonor10223_CNhs14084_tpm_fwd ThalamusNbD10223+ thalamus, newborn, donor10223_CNhs14084_10366-105F6_forward Regulation 
ThalamusAdultDonor10258TechRep2_CNhs14551_tpm_rev ThalamusAdultD10258Tr2- thalamus, adult, donor10258, tech_rep2_CNhs14551_10370-105G1_reverse Regulation 
ThalamusAdultDonor10258TechRep2_CNhs14551_tpm_fwd ThalamusAdultD10258Tr2+ thalamus, adult, donor10258, tech_rep2_CNhs14551_10370-105G1_forward Regulation 
ThalamusAdultDonor10258TechRep1_CNhs14223_tpm_rev ThalamusAdultD10258Tr1- thalamus, adult, donor10258, tech_rep1_CNhs14223_10370-105G1_reverse Regulation 
ThalamusAdultDonor10258TechRep1_CNhs14223_tpm_fwd ThalamusAdultD10258Tr1+ thalamus, adult, donor10258, tech_rep1_CNhs14223_10370-105G1_forward Regulation 
ThalamusAdultDonor10252_CNhs12314_tpm_rev ThalamusAdultD10252- thalamus, adult, donor10252_CNhs12314_10154-103A1_reverse Regulation 
ThalamusAdultDonor10252_CNhs12314_tpm_fwd ThalamusAdultD10252+ thalamus, adult, donor10252_CNhs12314_10154-103A1_forward Regulation 
ThalamusAdultDonor10196_CNhs13794_tpm_rev ThalamusAdultD10196- thalamus - adult, donor10196_CNhs13794_10168-103B6_reverse Regulation 
ThalamusAdultDonor10196_CNhs13794_tpm_fwd ThalamusAdultD10196+ thalamus - adult, donor10196_CNhs13794_10168-103B6_forward Regulation 
TestisAdultPool2_CNhs12998_tpm_rev TestisAdultPl2- testis, adult, pool2_CNhs12998_10096-102C6_reverse Regulation 
TestisAdultPool2_CNhs12998_tpm_fwd TestisAdultPl2+ testis, adult, pool2_CNhs12998_10096-102C6_forward Regulation 
TestisAdultPool1_CNhs10632_tpm_rev TestisAdultPl1- testis, adult, pool1_CNhs10632_10026-101D8_reverse Regulation 
TestisAdultPool1_CNhs10632_tpm_fwd TestisAdultPl1+ testis, adult, pool1_CNhs10632_10026-101D8_forward Regulation 
TemporalLobeFetalDonor1TechRep2_CNhs12996_tpm_rev TemporalLobeFetalD1Tr2- temporal lobe, fetal, donor1, tech_rep2_CNhs12996_10063-101H9_reverse Regulation 
TemporalLobeFetalDonor1TechRep2_CNhs12996_tpm_fwd TemporalLobeFetalD1Tr2+ temporal lobe, fetal, donor1, tech_rep2_CNhs12996_10063-101H9_forward Regulation 
TemporalLobeFetalDonor1TechRep1_CNhs11772_tpm_rev TemporalLobeFetalD1Tr1- temporal lobe, fetal, donor1, tech_rep1_CNhs11772_10063-101H9_reverse Regulation 
TemporalLobeFetalDonor1TechRep1_CNhs11772_tpm_fwd TemporalLobeFetalD1Tr1+ temporal lobe, fetal, donor1, tech_rep1_CNhs11772_10063-101H9_forward Regulation 
TemporalLobeAdultPool1_CNhs10637_tpm_rev TemporalLobeAdultPl1- temporal lobe, adult, pool1_CNhs10637_10031-101E4_reverse Regulation 
TemporalLobeAdultPool1_CNhs10637_tpm_fwd TemporalLobeAdultPl1+ temporal lobe, adult, pool1_CNhs10637_10031-101E4_forward Regulation 
SubstantiaNigraNewbornDonor10223_CNhs14076_tpm_rev SubstantiaNigraNbD10223- substantia nigra, newborn, donor10223_CNhs14076_10358-105E7_reverse Regulation 
SubstantiaNigraNewbornDonor10223_CNhs14076_tpm_fwd SubstantiaNigraNbD10223+ substantia nigra, newborn, donor10223_CNhs14076_10358-105E7_forward Regulation 
SubstantiaNigraAdultDonor10258_CNhs14224_tpm_rev SubstantiaNigraAdultD10258- substantia nigra, adult, donor10258_CNhs14224_10371-105G2_reverse Regulation 
SubstantiaNigraAdultDonor10258_CNhs14224_tpm_fwd SubstantiaNigraAdultD10258+ substantia nigra, adult, donor10258_CNhs14224_10371-105G2_forward Regulation 
SubstantiaNigraAdultDonor10252_CNhs12318_tpm_rev SubstantiaNigraAdultD10252- substantia nigra, adult, donor10252_CNhs12318_10158-103A5_reverse Regulation 
SubstantiaNigraAdultDonor10252_CNhs12318_tpm_fwd SubstantiaNigraAdultD10252+ substantia nigra, adult, donor10252_CNhs12318_10158-103A5_forward Regulation 
SubstantiaNigraAdultDonor10196_CNhs13803_tpm_rev SubstantiaNigraAdultD10196- substantia nigra - adult, donor10196_CNhs13803_10178-103C7_reverse Regulation 
SubstantiaNigraAdultDonor10196_CNhs13803_tpm_fwd SubstantiaNigraAdultD10196+ substantia nigra - adult, donor10196_CNhs13803_10178-103C7_forward Regulation 
SubmaxillaryGlandAdult_CNhs12852_tpm_rev SubmaxillaryGlandAdult- submaxillary gland, adult_CNhs12852_10202-103F4_reverse Regulation 
SubmaxillaryGlandAdult_CNhs12852_tpm_fwd SubmaxillaryGlandAdult+ submaxillary gland, adult_CNhs12852_10202-103F4_forward Regulation 
StomachFetalDonor1_CNhs11771_tpm_rev StomachFetalD1- stomach, fetal, donor1_CNhs11771_10062-101H8_reverse Regulation 
StomachFetalDonor1_CNhs11771_tpm_fwd StomachFetalD1+ stomach, fetal, donor1_CNhs11771_10062-101H8_forward Regulation 
SpleenFetalPool1_CNhs10651_tpm_rev SpleenFetalPl1- spleen, fetal, pool1_CNhs10651_10044-101F8_reverse Regulation 
SpleenFetalPool1_CNhs10651_tpm_fwd SpleenFetalPl1+ spleen, fetal, pool1_CNhs10651_10044-101F8_forward Regulation 
SpleenAdultPool1_CNhs10631_tpm_rev SpleenAdultPl1- spleen, adult, pool1_CNhs10631_10025-101D7_reverse Regulation 
SpleenAdultPool1_CNhs10631_tpm_fwd SpleenAdultPl1+ spleen, adult, pool1_CNhs10631_10025-101D7_forward Regulation 
SpinalCordNewbornDonor10223_CNhs14077_tpm_rev SpinalCordNbD10223- spinal cord, newborn, donor10223_CNhs14077_10359-105E8_reverse Regulation 
SpinalCordNewbornDonor10223_CNhs14077_tpm_fwd SpinalCordNbD10223+ spinal cord, newborn, donor10223_CNhs14077_10359-105E8_forward Regulation 
SpinalCordFetalDonor1_CNhs11764_tpm_rev SpinalCordFetalD1- spinal cord, fetal, donor1_CNhs11764_10056-101H2_reverse Regulation 
SpinalCordFetalDonor1_CNhs11764_tpm_fwd SpinalCordFetalD1+ spinal cord, fetal, donor1_CNhs11764_10056-101H2_forward Regulation 
SpinalCordAdultDonor10258_CNhs14222_tpm_rev SpinalCordAdultD10258- spinal cord, adult, donor10258_CNhs14222_10369-105F9_reverse Regulation 
SpinalCordAdultDonor10258_CNhs14222_tpm_fwd SpinalCordAdultD10258+ spinal cord, adult, donor10258_CNhs14222_10369-105F9_forward Regulation 
SpinalCordAdultDonor10252_CNhs12227_tpm_rev SpinalCordAdultD10252- spinal cord, adult, donor10252_CNhs12227_10159-103A6_reverse Regulation 
SpinalCordAdultDonor10252_CNhs12227_tpm_fwd SpinalCordAdultD10252+ spinal cord, adult, donor10252_CNhs12227_10159-103A6_forward Regulation 
SpinalCordAdultDonor10196_CNhs13807_tpm_rev SpinalCordAdultD10196- spinal cord - adult, donor10196_CNhs13807_10181-103D1_reverse Regulation 
SpinalCordAdultDonor10196_CNhs13807_tpm_fwd SpinalCordAdultD10196+ spinal cord - adult, donor10196_CNhs13807_10181-103D1_forward Regulation 
SmoothMuscleAdultPool1_CNhs11755_tpm_rev SmoothMuscleAdultPl1- smooth muscle, adult, pool1_CNhs11755_10048-101G3_reverse Regulation 
SmoothMuscleAdultPool1_CNhs11755_tpm_fwd SmoothMuscleAdultPl1+ smooth muscle, adult, pool1_CNhs11755_10048-101G3_forward Regulation 
SmallIntestineFetalDonor1_CNhs11773_tpm_rev SmallIntestineFetalD1- small intestine, fetal, donor1_CNhs11773_10064-101I1_reverse Regulation 
SmallIntestineFetalDonor1_CNhs11773_tpm_fwd SmallIntestineFetalD1+ small intestine, fetal, donor1_CNhs11773_10064-101I1_forward Regulation 
SmallIntestineAdultPool1_CNhs10630_tpm_rev SmallIntestineAdultPl1- small intestine, adult, pool1_CNhs10630_10024-101D6_reverse Regulation 
SmallIntestineAdultPool1_CNhs10630_tpm_fwd SmallIntestineAdultPl1+ small intestine, adult, pool1_CNhs10630_10024-101D6_forward Regulation 
SkinPalmDonor1_CNhs13458_tpm_rev SkinPalmD1- Skin - palm, donor1_CNhs13458_10286-104F7_reverse Regulation 
SkinPalmDonor1_CNhs13458_tpm_fwd SkinPalmD1+ Skin - palm, donor1_CNhs13458_10286-104F7_forward Regulation 
SkinFetalDonor1_CNhs11774_tpm_rev SkinFetalD1- skin, fetal, donor1_CNhs11774_10065-101I2_reverse Regulation 
SkinFetalDonor1_CNhs11774_tpm_fwd SkinFetalD1+ skin, fetal, donor1_CNhs11774_10065-101I2_forward Regulation 
SkinAdultDonor1_CNhs11785_tpm_rev SkinAdultD1- skin, adult, donor1_CNhs11785_10074-102A2_reverse Regulation 
SkinAdultDonor1_CNhs11785_tpm_fwd SkinAdultD1+ skin, adult, donor1_CNhs11785_10074-102A2_forward Regulation 
SkeletalMuscleSoleusMuscleDonor1_CNhs13454_tpm_rev SkeletalMuscleSoleusMuscleD1- skeletal muscle - soleus muscle, donor1_CNhs13454_10282-104F3_reverse Regulation 
SkeletalMuscleSoleusMuscleDonor1_CNhs13454_tpm_fwd SkeletalMuscleSoleusMuscleD1+ skeletal muscle - soleus muscle, donor1_CNhs13454_10282-104F3_forward Regulation 
SkeletalMuscleFetalDonor1_CNhs11776_tpm_rev SkeletalMuscleFetalD1- skeletal muscle, fetal, donor1_CNhs11776_10066-101I3_reverse Regulation 
SkeletalMuscleFetalDonor1_CNhs11776_tpm_fwd SkeletalMuscleFetalD1+ skeletal muscle, fetal, donor1_CNhs11776_10066-101I3_forward Regulation 
SkeletalMuscleAdultPool1_CNhs10629_tpm_rev SkeletalMuscleAdultPl1- skeletal muscle, adult, pool1_CNhs10629_10023-101D5_reverse Regulation 
SkeletalMuscleAdultPool1_CNhs10629_tpm_fwd SkeletalMuscleAdultPl1+ skeletal muscle, adult, pool1_CNhs10629_10023-101D5_forward Regulation 
SeminalVesicleAdult_CNhs12851_tpm_rev SeminalVesicleAdult- seminal vesicle, adult_CNhs12851_10201-103F3_reverse Regulation 
SeminalVesicleAdult_CNhs12851_tpm_fwd SeminalVesicleAdult+ seminal vesicle, adult_CNhs12851_10201-103F3_forward Regulation 
SalivaryGlandAdultPool1_CNhs11677_tpm_rev SalivaryGlandAdultPl1- salivary gland, adult, pool1_CNhs11677_10093-102C3_reverse Regulation 
SalivaryGlandAdultPool1_CNhs11677_tpm_fwd SalivaryGlandAdultPl1+ salivary gland, adult, pool1_CNhs11677_10093-102C3_forward Regulation 
SABiosciencesXpressRefHumanUniversalTotalRNAPool1_CNhs10610_tpm_rev SabiosciencesXpressrefUniversalPl1- SABiosciences XpressRef Human Universal Total RNA, pool1_CNhs10610_10002-101A5_reverse Regulation 
SABiosciencesXpressRefHumanUniversalTotalRNAPool1_CNhs10610_tpm_fwd SabiosciencesXpressrefUniversalPl1+ SABiosciences XpressRef Human Universal Total RNA, pool1_CNhs10610_10002-101A5_forward Regulation 
RetinaAdultPool1_CNhs10636_tpm_rev RetinaAdultPl1- retina, adult, pool1_CNhs10636_10030-101E3_reverse Regulation 
RetinaAdultPool1_CNhs10636_tpm_fwd RetinaAdultPl1+ retina, adult, pool1_CNhs10636_10030-101E3_forward Regulation 
RectumFetalDonor1_CNhs11777_tpm_rev RectumFetalD1- rectum, fetal, donor1_CNhs11777_10067-101I4_reverse Regulation 
RectumFetalDonor1_CNhs11777_tpm_fwd RectumFetalD1+ rectum, fetal, donor1_CNhs11777_10067-101I4_forward Regulation 
PutamenNewbornDonor10223_CNhs14083_tpm_rev PutamenNbD10223- putamen, newborn, donor10223_CNhs14083_10365-105F5_reverse Regulation 
PutamenNewbornDonor10223_CNhs14083_tpm_fwd PutamenNbD10223+ putamen, newborn, donor10223_CNhs14083_10365-105F5_forward Regulation 
PutamenAdultDonor10258TechRep2_CNhs14618_tpm_rev PutamenAdultD10258Tr2- putamen, adult, donor10258, tech_rep2_CNhs14618_10372-105G3_reverse Regulation 
PutamenAdultDonor10258TechRep2_CNhs14618_tpm_fwd PutamenAdultD10258Tr2+ putamen, adult, donor10258, tech_rep2_CNhs14618_10372-105G3_forward Regulation 
PutamenAdultDonor10258TechRep1_CNhs14225_tpm_rev PutamenAdultD10258Tr1- putamen, adult, donor10258, tech_rep1_CNhs14225_10372-105G3_reverse Regulation 
PutamenAdultDonor10258TechRep1_CNhs14225_tpm_fwd PutamenAdultD10258Tr1+ putamen, adult, donor10258, tech_rep1_CNhs14225_10372-105G3_forward Regulation 
PutamenAdultDonor10252_CNhs13912_tpm_rev PutamenAdultD10252- putamen, adult, donor10252_CNhs13912_10152-102I8_reverse Regulation 
PutamenAdultDonor10252_CNhs13912_tpm_fwd PutamenAdultD10252+ putamen, adult, donor10252_CNhs13912_10152-102I8_forward Regulation 
PutamenAdultDonor10196_CNhs12324_tpm_rev PutamenAdultD10196- putamen, adult, donor10196_CNhs12324_10176-103C5_reverse Regulation 
PutamenAdultDonor10196_CNhs12324_tpm_fwd PutamenAdultD10196+ putamen, adult, donor10196_CNhs12324_10176-103C5_forward Regulation 
ProstateAdultPool1_CNhs10628_tpm_rev ProstateAdultPl1- prostate, adult, pool1_CNhs10628_10022-101D4_reverse Regulation 
ProstateAdultPool1_CNhs10628_tpm_fwd ProstateAdultPl1+ prostate, adult, pool1_CNhs10628_10022-101D4_forward Regulation 
PostcentralGyrusAdultPool1_CNhs10638_tpm_rev PostcentralGyrusAdultPl1- postcentral gyrus, adult, pool1_CNhs10638_10032-101E5_reverse Regulation 
PostcentralGyrusAdultPool1_CNhs10638_tpm_fwd PostcentralGyrusAdultPl1+ postcentral gyrus, adult, pool1_CNhs10638_10032-101E5_forward Regulation 
PonsAdultPool1_CNhs10640_tpm_rev PonsAdultPl1- pons, adult, pool1_CNhs10640_10033-101E6_reverse Regulation 
PonsAdultPool1_CNhs10640_tpm_fwd PonsAdultPl1+ pons, adult, pool1_CNhs10640_10033-101E6_forward Regulation 
PlacentaAdultPool1_CNhs10627_tpm_rev PlacentaAdultPl1- placenta, adult, pool1_CNhs10627_10021-101D3_reverse Regulation 
PlacentaAdultPool1_CNhs10627_tpm_fwd PlacentaAdultPl1+ placenta, adult, pool1_CNhs10627_10021-101D3_forward Regulation 
PituitaryGlandAdultDonor10258_CNhs14231_tpm_rev PituitaryGlandAdultD10258- pituitary gland, adult, donor10258_CNhs14231_10378-105G9_reverse Regulation 
PituitaryGlandAdultDonor10258_CNhs14231_tpm_fwd PituitaryGlandAdultD10258+ pituitary gland, adult, donor10258_CNhs14231_10378-105G9_forward Regulation 
PituitaryGlandAdultDonor10252_CNhs12229_tpm_rev PituitaryGlandAdultD10252- pituitary gland, adult, donor10252_CNhs12229_10162-103A9_reverse Regulation 
PituitaryGlandAdultDonor10252_CNhs12229_tpm_fwd PituitaryGlandAdultD10252+ pituitary gland, adult, donor10252_CNhs12229_10162-103A9_forward Regulation 
PituitaryGlandAdultDonor10196_CNhs13805_tpm_rev PituitaryGlandAdultD10196- pituitary gland - adult, donor10196_CNhs13805_10180-103C9_reverse Regulation 
PituitaryGlandAdultDonor10196_CNhs13805_tpm_fwd PituitaryGlandAdultD10196+ pituitary gland - adult, donor10196_CNhs13805_10180-103C9_forward Regulation 
PinealGlandAdultDonor10258_CNhs14230_tpm_rev PinealGlandAdultD10258- pineal gland, adult, donor10258_CNhs14230_10377-105G8_reverse Regulation 
PinealGlandAdultDonor10258_CNhs14230_tpm_fwd PinealGlandAdultD10258+ pineal gland, adult, donor10258_CNhs14230_10377-105G8_forward Regulation 
PinealGlandAdultDonor10252_CNhs12228_tpm_rev PinealGlandAdultD10252- pineal gland, adult, donor10252_CNhs12228_10160-103A7_reverse Regulation 
PinealGlandAdultDonor10252_CNhs12228_tpm_fwd PinealGlandAdultD10252+ pineal gland, adult, donor10252_CNhs12228_10160-103A7_forward Regulation 
PinealGlandAdultDonor10196_CNhs13804_tpm_rev PinealGlandAdultD10196- pineal gland - adult, donor10196_CNhs13804_10179-103C8_reverse Regulation 
PinealGlandAdultDonor10196_CNhs13804_tpm_fwd PinealGlandAdultD10196+ pineal gland - adult, donor10196_CNhs13804_10179-103C8_forward Regulation 
PenisAdult_CNhs12850_tpm_rev PenisAdult- penis, adult_CNhs12850_10200-103F2_reverse Regulation 
PenisAdult_CNhs12850_tpm_fwd PenisAdult+ penis, adult_CNhs12850_10200-103F2_forward Regulation 
ParotidGlandAdult_CNhs12849_tpm_rev ParotidGlandAdult- parotid gland, adult_CNhs12849_10199-103F1_reverse Regulation 
ParotidGlandAdult_CNhs12849_tpm_fwd ParotidGlandAdult+ parotid gland, adult_CNhs12849_10199-103F1_forward Regulation 
ParietalLobeNewbornDonor10223_CNhs14074_tpm_rev ParietalLobeNbD10223- parietal lobe, newborn, donor10223_CNhs14074_10356-105E5_reverse Regulation 
ParietalLobeNewbornDonor10223_CNhs14074_tpm_fwd ParietalLobeNbD10223+ parietal lobe, newborn, donor10223_CNhs14074_10356-105E5_forward Regulation 
ParietalLobeFetalDonor1_CNhs11782_tpm_rev ParietalLobeFetalD1- parietal lobe, fetal, donor1_CNhs11782_10072-101I9_reverse Regulation 
ParietalLobeFetalDonor1_CNhs11782_tpm_fwd ParietalLobeFetalD1+ parietal lobe, fetal, donor1_CNhs11782_10072-101I9_forward Regulation 
ParietalLobeAdultPool1_CNhs10641_tpm_rev ParietalLobeAdultPl1- parietal lobe, adult, pool1_CNhs10641_10034-101E7_reverse Regulation 
ParietalLobeAdultPool1_CNhs10641_tpm_fwd ParietalLobeAdultPl1+ parietal lobe, adult, pool1_CNhs10641_10034-101E7_forward Regulation 
ParietalLobeAdultDonor10252_CNhs12317_tpm_rev ParietalLobeAdultD10252- parietal lobe, adult, donor10252_CNhs12317_10157-103A4_reverse Regulation 
ParietalLobeAdultDonor10252_CNhs12317_tpm_fwd ParietalLobeAdultD10252+ parietal lobe, adult, donor10252_CNhs12317_10157-103A4_forward Regulation 
ParietalLobeAdultDonor10196_CNhs13797_tpm_rev ParietalLobeAdultD10196- parietal lobe - adult, donor10196_CNhs13797_10171-103B9_reverse Regulation 
ParietalLobeAdultDonor10196_CNhs13797_tpm_fwd ParietalLobeAdultD10196+ parietal lobe - adult, donor10196_CNhs13797_10171-103B9_forward Regulation 
ParietalCortexAdultDonor10258_CNhs14226_tpm_rev ParietalCortexAdultD10258- parietal cortex, adult, donor10258_CNhs14226_10373-105G4_reverse Regulation 
ParietalCortexAdultDonor10258_CNhs14226_tpm_fwd ParietalCortexAdultD10258+ parietal cortex, adult, donor10258_CNhs14226_10373-105G4_forward Regulation 
ParacentralGyrusAdultPool1_CNhs10642_tpm_rev ParacentralGyrusAdultPl1- paracentral gyrus, adult, pool1_CNhs10642_10035-101E8_reverse Regulation 
ParacentralGyrusAdultPool1_CNhs10642_tpm_fwd ParacentralGyrusAdultPl1+ paracentral gyrus, adult, pool1_CNhs10642_10035-101E8_forward Regulation 
PancreasAdultDonor1_CNhs11756_tpm_rev PancreasAdultD1- pancreas, adult, donor1_CNhs11756_10049-101G4_reverse Regulation 
PancreasAdultDonor1_CNhs11756_tpm_fwd PancreasAdultD1+ pancreas, adult, donor1_CNhs11756_10049-101G4_forward Regulation 
OvaryAdultPool1_CNhs10626_tpm_rev OvaryAdultPl1- ovary, adult, pool1_CNhs10626_10020-101D2_reverse Regulation 
OvaryAdultPool1_CNhs10626_tpm_fwd OvaryAdultPl1+ ovary, adult, pool1_CNhs10626_10020-101D2_forward Regulation 
OpticNerveDonor1_CNhs13449_tpm_rev OpticNerveD1- optic nerve, donor1_CNhs13449_10277-104E7_reverse Regulation 
OpticNerveDonor1_CNhs13449_tpm_fwd OpticNerveD1+ optic nerve, donor1_CNhs13449_10277-104E7_forward Regulation 
OlfactoryRegionAdult_CNhs12611_tpm_rev OlfactoryRegionAdult- olfactory region, adult_CNhs12611_10195-103E6_reverse Regulation 
OlfactoryRegionAdult_CNhs12611_tpm_fwd OlfactoryRegionAdult+ olfactory region, adult_CNhs12611_10195-103E6_forward Regulation 
OccipitalPoleAdultPool1_CNhs10643_tpm_rev OccipitalPoleAdultPl1- occipital pole, adult, pool1_CNhs10643_10036-101E9_reverse Regulation 
OccipitalPoleAdultPool1_CNhs10643_tpm_fwd OccipitalPoleAdultPl1+ occipital pole, adult, pool1_CNhs10643_10036-101E9_forward Regulation 
OccipitalLobeFetalDonor1_CNhs11784_tpm_rev OccipitalLobeFetalD1- occipital lobe, fetal, donor1_CNhs11784_10073-102A1_reverse Regulation 
OccipitalLobeFetalDonor1_CNhs11784_tpm_fwd OccipitalLobeFetalD1+ occipital lobe, fetal, donor1_CNhs11784_10073-102A1_forward Regulation 
OccipitalLobeAdultDonor1_CNhs11787_tpm_rev OccipitalLobeAdultD1- occipital lobe, adult, donor1_CNhs11787_10076-102A4_reverse Regulation 
OccipitalLobeAdultDonor1_CNhs11787_tpm_fwd OccipitalLobeAdultD1+ occipital lobe, adult, donor1_CNhs11787_10076-102A4_forward Regulation 
OccipitalCortexNewbornDonor10223_CNhs14073_tpm_rev OccipitalCortexNbD10223- occipital cortex, newborn, donor10223_CNhs14073_10355-105E4_reverse Regulation 
OccipitalCortexNewbornDonor10223_CNhs14073_tpm_fwd OccipitalCortexNbD10223+ occipital cortex, newborn, donor10223_CNhs14073_10355-105E4_forward Regulation 
OccipitalCortexAdultDonor10252_CNhs12320_tpm_rev OccipitalCortexAdultD10252- occipital cortex, adult, donor10252_CNhs12320_10163-103B1_reverse Regulation 
OccipitalCortexAdultDonor10252_CNhs12320_tpm_fwd OccipitalCortexAdultD10252+ occipital cortex, adult, donor10252_CNhs12320_10163-103B1_forward Regulation 
OccipitalCortexAdultDonor10196_CNhs13798_tpm_rev OccipitalCortexAdultD10196- occipital cortex - adult, donor10196_CNhs13798_10172-103C1_reverse Regulation 
OccipitalCortexAdultDonor10196_CNhs13798_tpm_fwd OccipitalCortexAdultD10196+ occipital cortex - adult, donor10196_CNhs13798_10172-103C1_forward Regulation 
NucleusAccumbensAdultPool1_CNhs10644_tpm_rev NucleusAccumbensAdultPl1- nucleus accumbens, adult, pool1_CNhs10644_10037-101F1_reverse Regulation 
NucleusAccumbensAdultPool1_CNhs10644_tpm_fwd NucleusAccumbensAdultPl1+ nucleus accumbens, adult, pool1_CNhs10644_10037-101F1_forward Regulation 
MedullaOblongataNewbornDonor10223_CNhs14079_tpm_rev MedullaOblongataNbD10223- medulla oblongata, newborn, donor10223_CNhs14079_10361-105F1_reverse Regulation 
MedullaOblongataNewbornDonor10223_CNhs14079_tpm_fwd MedullaOblongataNbD10223+ medulla oblongata, newborn, donor10223_CNhs14079_10361-105F1_forward Regulation 
MedullaOblongataAdultPool1_CNhs10645_tpm_rev MedullaOblongataAdultPl1- medulla oblongata, adult, pool1_CNhs10645_10038-101F2_reverse Regulation 
MedullaOblongataAdultPool1_CNhs10645_tpm_fwd MedullaOblongataAdultPl1+ medulla oblongata, adult, pool1_CNhs10645_10038-101F2_forward Regulation 
MedullaOblongataAdultDonor10252_CNhs12315_tpm_rev MedullaOblongataAdultD10252- medulla oblongata, adult, donor10252_CNhs12315_10155-103A2_reverse Regulation 
MedullaOblongataAdultDonor10252_CNhs12315_tpm_fwd MedullaOblongataAdultD10252+ medulla oblongata, adult, donor10252_CNhs12315_10155-103A2_forward Regulation 
MedullaOblongataAdultDonor10196_CNhs13800_tpm_rev MedullaOblongataAdultD10196- medulla oblongata - adult, donor10196_CNhs13800_10174-103C3_reverse Regulation 
MedullaOblongataAdultDonor10196_CNhs13800_tpm_fwd MedullaOblongataAdultD10196+ medulla oblongata - adult, donor10196_CNhs13800_10174-103C3_forward Regulation 
MedialTemporalGyrusNewbornDonor10223_CNhs14070_tpm_rev MedialTemporalGyrusNbD10223- medial temporal gyrus, newborn, donor10223_CNhs14070_10353-105E2_reverse Regulation 
MedialTemporalGyrusNewbornDonor10223_CNhs14070_tpm_fwd MedialTemporalGyrusNbD10223+ medial temporal gyrus, newborn, donor10223_CNhs14070_10353-105E2_forward Regulation 
MedialTemporalGyrusAdultDonor10258TechRep2_CNhs14552_tpm_rev MedialTemporalGyrusAdultD10258Tr2- medial temporal gyrus, adult, donor10258, tech_rep2_CNhs14552_10376-105G7_reverse Regulation 
MedialTemporalGyrusAdultDonor10258TechRep2_CNhs14552_tpm_fwd MedialTemporalGyrusAdultD10258Tr2+ medial temporal gyrus, adult, donor10258, tech_rep2_CNhs14552_10376-105G7_forward Regulation 
MedialTemporalGyrusAdultDonor10258TechRep1_CNhs14229_tpm_rev MedialTemporalGyrusAdultD10258Tr1- medial temporal gyrus, adult, donor10258, tech_rep1_CNhs14229_10376-105G7_reverse Regulation 
MedialTemporalGyrusAdultDonor10258TechRep1_CNhs14229_tpm_fwd MedialTemporalGyrusAdultD10258Tr1+ medial temporal gyrus, adult, donor10258, tech_rep1_CNhs14229_10376-105G7_forward Regulation 
MedialTemporalGyrusAdultDonor10252_CNhs12316_tpm_rev MedialTemporalGyrusAdultD10252- medial temporal gyrus, adult, donor10252_CNhs12316_10156-103A3_reverse Regulation 
MedialTemporalGyrusAdultDonor10252_CNhs12316_tpm_fwd MedialTemporalGyrusAdultD10252+ medial temporal gyrus, adult, donor10252_CNhs12316_10156-103A3_forward Regulation 
MedialTemporalGyrusAdultDonor10196_CNhs13809_tpm_rev MedialTemporalGyrusAdultD10196- medial temporal gyrus - adult, donor10196_CNhs13809_10183-103D3_reverse Regulation 
MedialTemporalGyrusAdultDonor10196_CNhs13809_tpm_fwd MedialTemporalGyrusAdultD10196+ medial temporal gyrus - adult, donor10196_CNhs13809_10183-103D3_forward Regulation 
MedialFrontalGyrusNewbornDonor10223_CNhs14069_tpm_rev MedialFrontalGyrusNbD10223- medial frontal gyrus, newborn, donor10223_CNhs14069_10352-105E1_reverse Regulation 
MedialFrontalGyrusNewbornDonor10223_CNhs14069_tpm_fwd MedialFrontalGyrusNbD10223+ medial frontal gyrus, newborn, donor10223_CNhs14069_10352-105E1_forward Regulation 
MedialFrontalGyrusAdultDonor10258_CNhs14221_tpm_rev MedialFrontalGyrusAdultD10258- medial frontal gyrus, adult, donor10258_CNhs14221_10368-105F8_reverse Regulation 
MedialFrontalGyrusAdultDonor10258_CNhs14221_tpm_fwd MedialFrontalGyrusAdultD10258+ medial frontal gyrus, adult, donor10258_CNhs14221_10368-105F8_forward Regulation 
MedialFrontalGyrusAdultDonor10252_CNhs12310_tpm_rev MedialFrontalGyrusAdultD10252- medial frontal gyrus, adult, donor10252_CNhs12310_10150-102I6_reverse Regulation 
MedialFrontalGyrusAdultDonor10252_CNhs12310_tpm_fwd MedialFrontalGyrusAdultD10252+ medial frontal gyrus, adult, donor10252_CNhs12310_10150-102I6_forward Regulation 
MedialFrontalGyrusAdultDonor10196_CNhs13796_tpm_rev MedialFrontalGyrusAdultD10196- medial frontal gyrus - adult, donor10196_CNhs13796_10170-103B8_reverse Regulation 
MedialFrontalGyrusAdultDonor10196_CNhs13796_tpm_fwd MedialFrontalGyrusAdultD10196+ medial frontal gyrus - adult, donor10196_CNhs13796_10170-103B8_forward Regulation 
LymphNodeAdultDonor1_CNhs11788_tpm_rev LymphNodeAdultD1- lymph node, adult, donor1_CNhs11788_10077-102A5_reverse Regulation 
LymphNodeAdultDonor1_CNhs11788_tpm_fwd LymphNodeAdultD1+ lymph node, adult, donor1_CNhs11788_10077-102A5_forward Regulation 
LungRightLowerLobeAdultDonor1_CNhs11786_tpm_rev LungRightLowerLobeAdultD1- lung, right lower lobe, adult, donor1_CNhs11786_10075-102A3_reverse Regulation 
LungRightLowerLobeAdultDonor1_CNhs11786_tpm_fwd LungRightLowerLobeAdultD1+ lung, right lower lobe, adult, donor1_CNhs11786_10075-102A3_forward Regulation 
LungFetalDonor1_CNhs11680_tpm_rev LungFetalD1- lung, fetal, donor1_CNhs11680_10068-101I5_reverse Regulation 
LungFetalDonor1_CNhs11680_tpm_fwd LungFetalD1+ lung, fetal, donor1_CNhs11680_10068-101I5_forward Regulation 
LungAdultPool1_CNhs10625_tpm_rev LungAdultPl1- lung, adult, pool1_CNhs10625_10019-101D1_reverse Regulation 
LungAdultPool1_CNhs10625_tpm_fwd LungAdultPl1+ lung, adult, pool1_CNhs10625_10019-101D1_forward Regulation 
LocusCoeruleusNewbornDonor10223_CNhs14080_tpm_rev LocusCoeruleusNbD10223- locus coeruleus, newborn, donor10223_CNhs14080_10362-105F2_reverse Regulation 
LocusCoeruleusNewbornDonor10223_CNhs14080_tpm_fwd LocusCoeruleusNbD10223+ locus coeruleus, newborn, donor10223_CNhs14080_10362-105F2_forward Regulation 
LocusCoeruleusAdultDonor10258_CNhs14550_tpm_rev LocusCoeruleusAdultD10258- locus coeruleus, adult, donor10258_CNhs14550_10375-105G6_reverse Regulation 
LocusCoeruleusAdultDonor10258_CNhs14550_tpm_fwd LocusCoeruleusAdultD10258+ locus coeruleus, adult, donor10258_CNhs14550_10375-105G6_forward Regulation 
LocusCoeruleusAdultDonor10252_CNhs12322_tpm_rev LocusCoeruleusAdultD10252- locus coeruleus, adult, donor10252_CNhs12322_10165-103B3_reverse Regulation 
LocusCoeruleusAdultDonor10252_CNhs12322_tpm_fwd LocusCoeruleusAdultD10252+ locus coeruleus, adult, donor10252_CNhs12322_10165-103B3_forward Regulation 
LocusCoeruleusAdultDonor10196_CNhs13808_tpm_rev LocusCoeruleusAdultD10196- locus coeruleus - adult, donor10196_CNhs13808_10182-103D2_reverse Regulation 
LocusCoeruleusAdultDonor10196_CNhs13808_tpm_fwd LocusCoeruleusAdultD10196+ locus coeruleus - adult, donor10196_CNhs13808_10182-103D2_forward Regulation 
LiverFetalPool1_CNhs11798_tpm_rev LiverFetalPl1- liver, fetal, pool1_CNhs11798_10086-102B5_reverse Regulation 
LiverFetalPool1_CNhs11798_tpm_fwd LiverFetalPl1+ liver, fetal, pool1_CNhs11798_10086-102B5_forward Regulation 
LiverAdultPool1_CNhs10624_tpm_rev LiverAdultPl1- liver, adult, pool1_CNhs10624_10018-101C9_reverse Regulation 
LiverAdultPool1_CNhs10624_tpm_fwd LiverAdultPl1+ liver, adult, pool1_CNhs10624_10018-101C9_forward Regulation 
LeftVentricleAdultDonor1_CNhs11789_tpm_rev LeftVentricleAdultD1- left ventricle, adult, donor1_CNhs11789_10078-102A6_reverse Regulation 
LeftVentricleAdultDonor1_CNhs11789_tpm_fwd LeftVentricleAdultD1+ left ventricle, adult, donor1_CNhs11789_10078-102A6_forward Regulation 
LeftAtriumAdultDonor1_CNhs11790_tpm_rev LeftAtriumAdultD1- left atrium, adult, donor1_CNhs11790_10079-102A7_reverse Regulation 
LeftAtriumAdultDonor1_CNhs11790_tpm_fwd LeftAtriumAdultD1+ left atrium, adult, donor1_CNhs11790_10079-102A7_forward Regulation 
KidneyFetalPool1_CNhs10652_tpm_rev KidneyFetalPl1- kidney, fetal, pool1_CNhs10652_10045-101F9_reverse Regulation 
KidneyFetalPool1_CNhs10652_tpm_fwd KidneyFetalPl1+ kidney, fetal, pool1_CNhs10652_10045-101F9_forward Regulation 
KidneyAdultPool1_CNhs10622_tpm_rev KidneyAdultPl1- kidney, adult, pool1_CNhs10622_10017-101C8_reverse Regulation 
KidneyAdultPool1_CNhs10622_tpm_fwd KidneyAdultPl1+ kidney, adult, pool1_CNhs10622_10017-101C8_forward Regulation 
InsulaAdultPool1_CNhs10646_tpm_rev InsulaAdultPl1- insula, adult, pool1_CNhs10646_10039-101F3_reverse Regulation 
InsulaAdultPool1_CNhs10646_tpm_fwd InsulaAdultPl1+ insula, adult, pool1_CNhs10646_10039-101F3_forward Regulation 
HippocampusNewbornDonor10223_CNhs14081_tpm_rev HippocampusNbD10223- hippocampus, newborn, donor10223_CNhs14081_10363-105F3_reverse Regulation 
HippocampusNewbornDonor10223_CNhs14081_tpm_fwd HippocampusNbD10223+ hippocampus, newborn, donor10223_CNhs14081_10363-105F3_forward Regulation 
HippocampusAdultDonor10258_CNhs14227_tpm_rev HippocampusAdultD10258- hippocampus, adult, donor10258_CNhs14227_10374-105G5_reverse Regulation 
HippocampusAdultDonor10258_CNhs14227_tpm_fwd HippocampusAdultD10258+ hippocampus, adult, donor10258_CNhs14227_10374-105G5_forward Regulation 
HippocampusAdultDonor10252_CNhs12312_tpm_rev HippocampusAdultD10252- hippocampus, adult, donor10252_CNhs12312_10153-102I9_reverse Regulation 
HippocampusAdultDonor10252_CNhs12312_tpm_fwd HippocampusAdultD10252+ hippocampus, adult, donor10252_CNhs12312_10153-102I9_forward Regulation 
HippocampusAdultDonor10196_CNhs13795_tpm_rev HippocampusAdultD10196- hippocampus - adult, donor10196_CNhs13795_10169-103B7_reverse Regulation 
HippocampusAdultDonor10196_CNhs13795_tpm_fwd HippocampusAdultD10196+ hippocampus - adult, donor10196_CNhs13795_10169-103B7_forward Regulation 
HeartTricuspidValveAdult_CNhs12857_tpm_rev HeartTricuspidValveAdult- heart - tricuspid valve, adult_CNhs12857_10207-103F9_reverse Regulation 
HeartTricuspidValveAdult_CNhs12857_tpm_fwd HeartTricuspidValveAdult+ heart - tricuspid valve, adult_CNhs12857_10207-103F9_forward Regulation 
HeartPulmonicValveAdult_CNhs12856_tpm_rev HeartPulmonicValveAdult- heart - pulmonic valve, adult_CNhs12856_10206-103F8_reverse Regulation 
HeartPulmonicValveAdult_CNhs12856_tpm_fwd HeartPulmonicValveAdult+ heart - pulmonic valve, adult_CNhs12856_10206-103F8_forward Regulation 
HeartMitralValveAdult_CNhs12855_tpm_rev HeartMitralValveAdult- heart - mitral valve, adult_CNhs12855_10205-103F7_reverse Regulation 
HeartMitralValveAdult_CNhs12855_tpm_fwd HeartMitralValveAdult+ heart - mitral valve, adult_CNhs12855_10205-103F7_forward Regulation 
HeartFetalPool1_CNhs10653_tpm_rev HeartFetalPl1- heart, fetal, pool1_CNhs10653_10046-101G1_reverse Regulation 
HeartFetalPool1_CNhs10653_tpm_fwd HeartFetalPl1+ heart, fetal, pool1_CNhs10653_10046-101G1_forward Regulation 
HeartAdultPool1_CNhs10621_tpm_rev HeartAdultPl1- heart, adult, pool1_CNhs10621_10016-101C7_reverse Regulation 
HeartAdultPool1_CNhs10621_tpm_fwd HeartAdultPl1+ heart, adult, pool1_CNhs10621_10016-101C7_forward Regulation 
HeartAdultDiseasedPostinfarctionDonor1_CNhs11757_tpm_rev HeartAdultDiseasedPost-infarctionD1- heart, adult, diseased post-infarction, donor1_CNhs11757_10050-101G5_reverse Regulation 
HeartAdultDiseasedPostinfarctionDonor1_CNhs11757_tpm_fwd HeartAdultDiseasedPost-infarctionD1+ heart, adult, diseased post-infarction, donor1_CNhs11757_10050-101G5_forward Regulation 
HeartAdultDiseasedDonor1_CNhs11758_tpm_rev HeartAdultDiseasedD1- heart, adult, diseased, donor1_CNhs11758_10051-101G6_reverse Regulation 
HeartAdultDiseasedDonor1_CNhs11758_tpm_fwd HeartAdultDiseasedD1+ heart, adult, diseased, donor1_CNhs11758_10051-101G6_forward Regulation 
GlobusPallidusNewbornDonor10223_CNhs14082_tpm_rev GlobusPallidusNbD10223- globus pallidus, newborn, donor10223_CNhs14082_10364-105F4_reverse Regulation 
GlobusPallidusNewbornDonor10223_CNhs14082_tpm_fwd GlobusPallidusNbD10223+ globus pallidus, newborn, donor10223_CNhs14082_10364-105F4_forward Regulation 
GlobusPallidusAdultDonor10258_CNhs14549_tpm_rev GlobusPallidusAdultD10258- globus pallidus, adult, donor10258_CNhs14549_10367-105F7_reverse Regulation 
GlobusPallidusAdultDonor10258_CNhs14549_tpm_fwd GlobusPallidusAdultD10258+ globus pallidus, adult, donor10258_CNhs14549_10367-105F7_forward Regulation 
GlobusPallidusAdultDonor10252_CNhs12319_tpm_rev GlobusPallidusAdultD10252- globus pallidus, adult, donor10252_CNhs12319_10161-103A8_reverse Regulation 
GlobusPallidusAdultDonor10252_CNhs12319_tpm_fwd GlobusPallidusAdultD10252+ globus pallidus, adult, donor10252_CNhs12319_10161-103A8_forward Regulation 
GlobusPallidusAdultDonor10196_CNhs13801_tpm_rev GlobusPallidusAdultD10196- globus pallidus - adult, donor10196_CNhs13801_10175-103C4_reverse Regulation 
GlobusPallidusAdultDonor10196_CNhs13801_tpm_fwd GlobusPallidusAdultD10196+ globus pallidus - adult, donor10196_CNhs13801_10175-103C4_forward Regulation 
GallBladderAdult_CNhs12848_tpm_rev GallBladderAdult- gall bladder, adult_CNhs12848_10198-103E9_reverse Regulation 
GallBladderAdult_CNhs12848_tpm_fwd GallBladderAdult+ gall bladder, adult_CNhs12848_10198-103E9_forward Regulation 
FrontalLobeAdultPool1_CNhs10647_tpm_rev FrontalLobeAdultPl1- frontal lobe, adult, pool1_CNhs10647_10040-101F4_reverse Regulation 
FrontalLobeAdultPool1_CNhs10647_tpm_fwd FrontalLobeAdultPl1+ frontal lobe, adult, pool1_CNhs10647_10040-101F4_forward Regulation 
FingernailIncludingNailPlateEponychiumAndHyponychiumDonor2_CNhs13445_tpm_rev FingernailD2- Fingernail (including nail plate, eponychium and hyponychium), donor2_CNhs13445_10301-104H4_reverse Regulation 
FingernailIncludingNailPlateEponychiumAndHyponychiumDonor2_CNhs13445_tpm_fwd FingernailD2+ Fingernail (including nail plate, eponychium and hyponychium), donor2_CNhs13445_10301-104H4_forward Regulation 
EyeVitreousHumorDonor1_CNhs13440_tpm_rev EyeVitreousHumorD1- eye - vitreous humor, donor1_CNhs13440_10268-104D7_reverse Regulation 
EyeVitreousHumorDonor1_CNhs13440_tpm_fwd EyeVitreousHumorD1+ eye - vitreous humor, donor1_CNhs13440_10268-104D7_forward Regulation 
EyeMuscleSuperiorDonor2_CNhs13441_tpm_rev EyeMuscleSuperiorD2- eye - muscle superior, donor2_CNhs13441_10297-104G9_reverse Regulation 
EyeMuscleSuperiorDonor2_CNhs13441_tpm_fwd EyeMuscleSuperiorD2+ eye - muscle superior, donor2_CNhs13441_10297-104G9_forward Regulation 
EyeMuscleMedialDonor2_CNhs13443_tpm_rev EyeMuscleMedialD2- eye - muscle medial, donor2_CNhs13443_10299-104H2_reverse Regulation 
EyeMuscleMedialDonor2_CNhs13443_tpm_fwd EyeMuscleMedialD2+ eye - muscle medial, donor2_CNhs13443_10299-104H2_forward Regulation 
EyeMuscleLateralDonor2_CNhs13442_tpm_rev EyeMuscleLateralD2- eye - muscle lateral, donor2_CNhs13442_10298-104H1_reverse Regulation 
EyeMuscleLateralDonor2_CNhs13442_tpm_fwd EyeMuscleLateralD2+ eye - muscle lateral, donor2_CNhs13442_10298-104H1_forward Regulation 
EyeMuscleInferiorRectusDonor1_CNhs13444_tpm_rev EyeMuscleInferiorRectusD1- eye - muscle inferior rectus, donor1_CNhs13444_10272-104E2_reverse Regulation 
EyeMuscleInferiorRectusDonor1_CNhs13444_tpm_fwd EyeMuscleInferiorRectusD1+ eye - muscle inferior rectus, donor1_CNhs13444_10272-104E2_forward Regulation 
EyeFetalDonor1_CNhs11762_tpm_rev EyeFetalD1- eye, fetal, donor1_CNhs11762_10054-101G9_reverse Regulation 
EyeFetalDonor1_CNhs11762_tpm_fwd EyeFetalD1+ eye, fetal, donor1_CNhs11762_10054-101G9_forward Regulation 
EsophagusAdultPool1_CNhs10620_tpm_rev EsophagusAdultPl1- esophagus, adult, pool1_CNhs10620_10015-101C6_reverse Regulation 
EsophagusAdultPool1_CNhs10620_tpm_fwd EsophagusAdultPl1+ esophagus, adult, pool1_CNhs10620_10015-101C6_forward Regulation 
EpididymisAdult_CNhs12847_tpm_rev EpididymisAdult- epididymis, adult_CNhs12847_10197-103E8_reverse Regulation 
EpididymisAdult_CNhs12847_tpm_fwd EpididymisAdult+ epididymis, adult_CNhs12847_10197-103E8_forward Regulation 
DuraMaterAdultDonor1_CNhs10648_tpm_rev DuraMaterAdultD1- dura mater, adult, donor1_CNhs10648_10041-101F5_reverse Regulation 
DuraMaterAdultDonor1_CNhs10648_tpm_fwd DuraMaterAdultD1+ dura mater, adult, donor1_CNhs10648_10041-101F5_forward Regulation 
DuodenumFetalDonor1TechRep2_CNhs12997_tpm_rev DuodenumFetalD1Tr2- duodenum, fetal, donor1, tech_rep2_CNhs12997_10071-101I8_reverse Regulation 
DuodenumFetalDonor1TechRep2_CNhs12997_tpm_fwd DuodenumFetalD1Tr2+ duodenum, fetal, donor1, tech_rep2_CNhs12997_10071-101I8_forward Regulation 
DuodenumFetalDonor1TechRep1_CNhs11781_tpm_rev DuodenumFetalD1Tr1- duodenum, fetal, donor1, tech_rep1_CNhs11781_10071-101I8_reverse Regulation 
DuodenumFetalDonor1TechRep1_CNhs11781_tpm_fwd DuodenumFetalD1Tr1+ duodenum, fetal, donor1, tech_rep1_CNhs11781_10071-101I8_forward Regulation 
DuctusDeferensAdult_CNhs12846_tpm_rev DuctusDeferensAdult- ductus deferens, adult_CNhs12846_10196-103E7_reverse Regulation 
DuctusDeferensAdult_CNhs12846_tpm_fwd DuctusDeferensAdult+ ductus deferens, adult_CNhs12846_10196-103E7_forward Regulation 
DiencephalonAdult_CNhs12610_tpm_rev DiencephalonAdult- diencephalon, adult_CNhs12610_10193-103E4_reverse Regulation 
DiencephalonAdult_CNhs12610_tpm_fwd DiencephalonAdult+ diencephalon, adult_CNhs12610_10193-103E4_forward Regulation 
DiaphragmFetalDonor1_CNhs11779_tpm_rev DiaphragmFetalD1- diaphragm, fetal, donor1_CNhs11779_10069-101I6_reverse Regulation 
DiaphragmFetalDonor1_CNhs11779_tpm_fwd DiaphragmFetalD1+ diaphragm, fetal, donor1_CNhs11779_10069-101I6_forward Regulation 
CruciateLigamentDonor2_CNhs13439_tpm_rev CruciateLigamentD2- cruciate ligament, donor2_CNhs13439_10295-104G7_reverse Regulation 
CruciateLigamentDonor2_CNhs13439_tpm_fwd CruciateLigamentD2+ cruciate ligament, donor2_CNhs13439_10295-104G7_forward Regulation 
CorpusCallosumAdultPool1_CNhs10649_tpm_rev CorpusCallosumAdultPl1- corpus callosum, adult, pool1_CNhs10649_10042-101F6_reverse Regulation 
CorpusCallosumAdultPool1_CNhs10649_tpm_fwd CorpusCallosumAdultPl1+ corpus callosum, adult, pool1_CNhs10649_10042-101F6_forward Regulation 
ColonFetalDonor1_CNhs11780_tpm_rev ColonFetalD1- colon, fetal, donor1_CNhs11780_10070-101I7_reverse Regulation 
ColonFetalDonor1_CNhs11780_tpm_fwd ColonFetalD1+ colon, fetal, donor1_CNhs11780_10070-101I7_forward Regulation 
ColonAdultPool1_CNhs10619_tpm_rev ColonAdultPl1- colon, adult, pool1_CNhs10619_10014-101C5_reverse Regulation 
ColonAdultPool1_CNhs10619_tpm_fwd ColonAdultPl1+ colon, adult, pool1_CNhs10619_10014-101C5_forward Regulation 
ColonAdultDonor1_CNhs11794_tpm_rev ColonAdultD1- colon, adult, donor1_CNhs11794_10082-102B1_reverse Regulation 
ColonAdultDonor1_CNhs11794_tpm_fwd ColonAdultD1+ colon, adult, donor1_CNhs11794_10082-102B1_forward Regulation 
ClontechHumanUniversalReferenceTotalRNAPool1_CNhs10608_tpm_rev ClontechUniversalReferencePl1- Clontech Human Universal Reference Total RNA, pool1_CNhs10608_10000-101A1_reverse Regulation 
ClontechHumanUniversalReferenceTotalRNAPool1_CNhs10608_tpm_fwd ClontechUniversalReferencePl1+ Clontech Human Universal Reference Total RNA, pool1_CNhs10608_10000-101A1_forward Regulation 
CervixAdultPool1_CNhs10618_tpm_rev CervixAdultPl1- cervix, adult, pool1_CNhs10618_10013-101C4_reverse Regulation 
CervixAdultPool1_CNhs10618_tpm_fwd CervixAdultPl1+ cervix, adult, pool1_CNhs10618_10013-101C4_forward Regulation 
CerebrospinalFluidDonor2_CNhs13437_tpm_rev CerebrospinalFluidD2- cerebrospinal fluid, donor2_CNhs13437_10294-104G6_reverse Regulation 
CerebrospinalFluidDonor2_CNhs13437_tpm_fwd CerebrospinalFluidD2+ cerebrospinal fluid, donor2_CNhs13437_10294-104G6_forward Regulation 
CerebralMeningesAdult_CNhs12840_tpm_rev CerebralMeningesAdult- cerebral meninges, adult_CNhs12840_10188-103D8_reverse Regulation 
CerebralMeningesAdult_CNhs12840_tpm_fwd CerebralMeningesAdult+ cerebral meninges, adult_CNhs12840_10188-103D8_forward Regulation 
CerebellumNewbornDonor10223_CNhs14075_tpm_rev CerebellumNbD10223- cerebellum, newborn, donor10223_CNhs14075_10357-105E6_reverse Regulation 
CerebellumNewbornDonor10223_CNhs14075_tpm_fwd CerebellumNbD10223+ cerebellum, newborn, donor10223_CNhs14075_10357-105E6_forward Regulation 
CerebellumAdultPool1_CNhs11795_tpm_rev CerebellumAdultPl1- cerebellum, adult, pool1_CNhs11795_10083-102B2_reverse Regulation 
CerebellumAdultPool1_CNhs11795_tpm_fwd CerebellumAdultPl1+ cerebellum, adult, pool1_CNhs11795_10083-102B2_forward Regulation 
CerebellumAdultDonor10252_CNhs12323_tpm_rev CerebellumAdultD10252- cerebellum, adult, donor10252_CNhs12323_10166-103B4_reverse Regulation 
CerebellumAdultDonor10252_CNhs12323_tpm_fwd CerebellumAdultD10252+ cerebellum, adult, donor10252_CNhs12323_10166-103B4_forward Regulation 
CerebellumAdultDonor10196_CNhs13799_tpm_rev CerebellumAdultD10196- cerebellum - adult, donor10196_CNhs13799_10173-103C2_reverse Regulation 
CerebellumAdultDonor10196_CNhs13799_tpm_fwd CerebellumAdultD10196+ cerebellum - adult, donor10196_CNhs13799_10173-103C2_forward Regulation 
CaudateNucleusNewbornDonor10223_CNhs14071_tpm_rev CaudateNucleusNbD10223- caudate nucleus, newborn, donor10223_CNhs14071_10354-105E3_reverse Regulation 
CaudateNucleusNewbornDonor10223_CNhs14071_tpm_fwd CaudateNucleusNbD10223+ caudate nucleus, newborn, donor10223_CNhs14071_10354-105E3_forward Regulation 
CaudateNucleusAdultDonor10258_CNhs14232_tpm_rev CaudateNucleusAdultD10258- caudate nucleus, adult, donor10258_CNhs14232_10379-105H1_reverse Regulation 
CaudateNucleusAdultDonor10258_CNhs14232_tpm_fwd CaudateNucleusAdultD10258+ caudate nucleus, adult, donor10258_CNhs14232_10379-105H1_forward Regulation 
CaudateNucleusAdultDonor10252_CNhs12321_tpm_rev CaudateNucleusAdultD10252- caudate nucleus, adult, donor10252_CNhs12321_10164-103B2_reverse Regulation 
CaudateNucleusAdultDonor10252_CNhs12321_tpm_fwd CaudateNucleusAdultD10252+ caudate nucleus, adult, donor10252_CNhs12321_10164-103B2_forward Regulation 
CaudateNucleusAdultDonor10196_CNhs13802_tpm_rev CaudateNucleusAdultD10196- caudate nucleus - adult, donor10196_CNhs13802_10177-103C6_reverse Regulation 
CaudateNucleusAdultDonor10196_CNhs13802_tpm_fwd CaudateNucleusAdultD10196+ caudate nucleus - adult, donor10196_CNhs13802_10177-103C6_forward Regulation 
BreastAdultDonor1_CNhs11792_tpm_rev BreastAdultD1- breast, adult, donor1_CNhs11792_10080-102A8_reverse Regulation 
BreastAdultDonor1_CNhs11792_tpm_fwd BreastAdultD1+ breast, adult, donor1_CNhs11792_10080-102A8_forward Regulation 
BrainFetalPool1_CNhs11797_tpm_rev BrainFetalPl1- brain, fetal, pool1_CNhs11797_10085-102B4_reverse Regulation 
BrainFetalPool1_CNhs11797_tpm_fwd BrainFetalPl1+ brain, fetal, pool1_CNhs11797_10085-102B4_forward Regulation 
BrainAdultPool1_CNhs10617_tpm_rev BrainAdultPl1- brain, adult, pool1_CNhs10617_10012-101C3_reverse Regulation 
BrainAdultPool1_CNhs10617_tpm_fwd BrainAdultPl1+ brain, adult, pool1_CNhs10617_10012-101C3_forward Regulation 
BrainAdultDonor1_CNhs11796_tpm_rev BrainAdultD1- brain, adult, donor1_CNhs11796_10084-102B3_reverse Regulation 
BrainAdultDonor1_CNhs11796_tpm_fwd BrainAdultD1+ brain, adult, donor1_CNhs11796_10084-102B3_forward Regulation 
BoneMarrowAdult_CNhs12845_tpm_rev BoneMarrowAdult- bone marrow, adult_CNhs12845_10192-103E3_reverse Regulation 
BoneMarrowAdult_CNhs12845_tpm_fwd BoneMarrowAdult+ bone marrow, adult_CNhs12845_10192-103E3_forward Regulation 
BloodAdultPool1_CNhs11761_tpm_rev BloodAdultPl1- blood, adult, pool1_CNhs11761_10053-101G8_reverse Regulation 
BloodAdultPool1_CNhs11761_tpm_fwd BloodAdultPl1+ blood, adult, pool1_CNhs11761_10053-101G8_forward Regulation 
BladderAdultPool1_CNhs10616_tpm_rev BladderAdultPl1- bladder, adult, pool1_CNhs10616_10011-101C2_reverse Regulation 
BladderAdultPool1_CNhs10616_tpm_fwd BladderAdultPl1+ bladder, adult, pool1_CNhs10616_10011-101C2_forward Regulation 
ArteryAdult_CNhs12843_tpm_rev ArteryAdult- artery, adult_CNhs12843_10190-103E1_reverse Regulation 
ArteryAdult_CNhs12843_tpm_fwd ArteryAdult+ artery, adult_CNhs12843_10190-103E1_forward Regulation 
AppendixAdult_CNhs12842_tpm_rev AppendixAdult- appendix, adult_CNhs12842_10189-103D9_reverse Regulation 
AppendixAdult_CNhs12842_tpm_fwd AppendixAdult+ appendix, adult_CNhs12842_10189-103D9_forward Regulation 
AortaAdultPool1_CNhs11760_tpm_rev AortaAdultPl1- aorta, adult, pool1_CNhs11760_10052-101G7_reverse Regulation 
AortaAdultPool1_CNhs11760_tpm_fwd AortaAdultPl1+ aorta, adult, pool1_CNhs11760_10052-101G7_forward Regulation 
AmygdalaNewbornDonor10223_CNhs14078_tpm_rev AmygdalaNbD1D10223- amygdala, newborn, donor10223_CNhs14078_10360-105E9_reverse Regulation 
AmygdalaNewbornDonor10223_CNhs14078_tpm_fwd AmygdalaNbD1D10223+ amygdala, newborn, donor10223_CNhs14078_10360-105E9_forward Regulation 
AmygdalaAdultDonor10252_CNhs12311_tpm_rev AmygdalaAdultD10252- amygdala, adult, donor10252_CNhs12311_10151-102I7_reverse Regulation 
AmygdalaAdultDonor10252_CNhs12311_tpm_fwd AmygdalaAdultD10252+ amygdala, adult, donor10252_CNhs12311_10151-102I7_forward Regulation 
AmygdalaAdultDonor10196_CNhs13793_tpm_rev AmygdalaAdultD10196- amygdala - adult, donor10196_CNhs13793_10167-103B5_reverse Regulation 
AmygdalaAdultDonor10196_CNhs13793_tpm_fwd AmygdalaAdultD10196+ amygdala - adult, donor10196_CNhs13793_10167-103B5_forward Regulation 
AdrenalGlandAdultPool1_CNhs11793_tpm_rev AdrenalGlandAdultPl1- adrenal gland, adult, pool1_CNhs11793_10081-102A9_reverse Regulation 
AdrenalGlandAdultPool1_CNhs11793_tpm_fwd AdrenalGlandAdultPl1+ adrenal gland, adult, pool1_CNhs11793_10081-102A9_forward Regulation 
AdiposeTissueAdultPool1_CNhs10615_tpm_rev AdiposeTissueAdultPl1- adipose tissue, adult, pool1_CNhs10615_10010-101C1_reverse Regulation 
AdiposeTissueAdultPool1_CNhs10615_tpm_fwd AdiposeTissueAdultPl1+ adipose tissue, adult, pool1_CNhs10615_10010-101C1_forward Regulation 
AdiposeDonor4_CNhs13975_tpm_rev AdiposeD4- adipose, donor4_CNhs13975_10187-103D7_reverse Regulation 
AdiposeDonor4_CNhs13975_tpm_fwd AdiposeD4+ adipose, donor4_CNhs13975_10187-103D7_forward Regulation 
AdiposeDonor3_CNhs13974_tpm_rev AdiposeD3- adipose, donor3_CNhs13974_10186-103D6_reverse Regulation 
AdiposeDonor3_CNhs13974_tpm_fwd AdiposeD3+ adipose, donor3_CNhs13974_10186-103D6_forward Regulation 
AdiposeDonor2_CNhs13973_tpm_rev AdiposeD2- adipose, donor2_CNhs13973_10185-103D5_reverse Regulation 
AdiposeDonor2_CNhs13973_tpm_fwd AdiposeD2+ adipose, donor2_CNhs13973_10185-103D5_forward Regulation 
AdiposeDonor1_CNhs13972_tpm_rev AdiposeD1- adipose, donor1_CNhs13972_10184-103D4_reverse Regulation 
AdiposeDonor1_CNhs13972_tpm_fwd AdiposeD1+ adipose, donor1_CNhs13972_10184-103D4_forward Regulation 
AchillesTendonDonor2_CNhs13435_tpm_rev AchillesTendonD2- achilles tendon, donor2_CNhs13435_10292-104G4_reverse Regulation 
AchillesTendonDonor2_CNhs13435_tpm_fwd AchillesTendonD2+ achilles tendon, donor2_CNhs13435_10292-104G4_forward Regulation 
Saos2OsteosarcomaCellLineUntreatedControlDay28BiolRep3B3T17_CNhs14196_tpm_rev Tc:Saos-2Untreated_Day28Br3- Saos-2 osteosarcoma cell line, untreated control, day28, biol_rep3 (B3 T17)_CNhs14196_12893-137H4_reverse Regulation 
Saos2OsteosarcomaCellLineUntreatedControlDay28BiolRep3B3T17_CNhs14196_tpm_fwd Tc:Saos-2Untreated_Day28Br3+ Saos-2 osteosarcoma cell line, untreated control, day28, biol_rep3 (B3 T17)_CNhs14196_12893-137H4_forward Regulation 
Saos2OsteosarcomaCellLineUntreatedControlDay28BiolRep2B2T17_CNhs14195_tpm_rev Tc:Saos-2Untreated_Day28Br2- Saos-2 osteosarcoma cell line, untreated control, day28, biol_rep2 (B2 T17)_CNhs14195_12795-136F5_reverse Regulation 
Saos2OsteosarcomaCellLineUntreatedControlDay28BiolRep2B2T17_CNhs14195_tpm_fwd Tc:Saos-2Untreated_Day28Br2+ Saos-2 osteosarcoma cell line, untreated control, day28, biol_rep2 (B2 T17)_CNhs14195_12795-136F5_forward Regulation 
Saos2OsteosarcomaCellLineUntreatedControlDay28BiolRep1B1T17_CNhs14194_tpm_rev Tc:Saos-2Untreated_Day28Br1- Saos-2 osteosarcoma cell line, untreated control, day28, biol_rep1 (B1 T17)_CNhs14194_12697-135D6_reverse Regulation 
Saos2OsteosarcomaCellLineUntreatedControlDay28BiolRep1B1T17_CNhs14194_tpm_fwd Tc:Saos-2Untreated_Day28Br1+ Saos-2 osteosarcoma cell line, untreated control, day28, biol_rep1 (B1 T17)_CNhs14194_12697-135D6_forward Regulation 
MesenchymalStemCellsAdiposeDerivedUndifferentiatedControlBiolRep3_CNhs13634_tpm_rev Tc:MscToAdiposeUndiffBr3- mesenchymal stem cells (adipose derived), undifferentiated control, biol_rep3_CNhs13634_13282-142F6_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedUndifferentiatedControlBiolRep3_CNhs13634_tpm_fwd Tc:MscToAdiposeUndiffBr3+ mesenchymal stem cells (adipose derived), undifferentiated control, biol_rep3_CNhs13634_13282-142F6_forward Regulation 
MesenchymalStemCellsAdiposeDerivedUndifferentiatedControlBiolRep2_CNhs13633_tpm_rev Tc:MscToAdiposeUndiffBr2- mesenchymal stem cells (adipose derived), undifferentiated control, biol_rep2_CNhs13633_13281-142F5_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedUndifferentiatedControlBiolRep2_CNhs13633_tpm_fwd Tc:MscToAdiposeUndiffBr2+ mesenchymal stem cells (adipose derived), undifferentiated control, biol_rep2_CNhs13633_13281-142F5_forward Regulation 
MesenchymalStemCellsAdiposeDerivedUndifferentiatedControlBiolRep1_CNhs13692_tpm_rev Tc:MscToAdiposeUndiffBr1- mesenchymal stem cells (adipose derived), undifferentiated control, biol_rep1_CNhs13692_13280-142F4_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedUndifferentiatedControlBiolRep1_CNhs13692_tpm_fwd Tc:MscToAdiposeUndiffBr1+ mesenchymal stem cells (adipose derived), undifferentiated control, biol_rep1_CNhs13692_13280-142F4_forward Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection00hr00minDonor1868_121MI_0h_CNhs13637_tpm_rev Tc:MdmToMock_00hr00minD1- Monocyte-derived macrophages response to mock influenza infection, 00hr00min, donor1 (868_121:MI_0h)_CNhs13637_13304-142I1_reverse Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection00hr00minDonor1868_121MI_0h_CNhs13637_tpm_fwd Tc:MdmToMock_00hr00minD1+ Monocyte-derived macrophages response to mock influenza infection, 00hr00min, donor1 (868_121:MI_0h)_CNhs13637_13304-142I1_forward Regulation 
MonocytederivedMacrophagesResponseToLPS16hrDonor1T20Subject1_CNhs12930_tpm_rev Tc:MdmToLps_16hrD1- Monocyte-derived macrophages response to LPS, 16hr, donor1 (t20 Subject1)_CNhs12930_12717-135F8_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS16hrDonor1T20Subject1_CNhs12930_tpm_fwd Tc:MdmToLps_16hrD1+ Monocyte-derived macrophages response to LPS, 16hr, donor1 (t20 Subject1)_CNhs12930_12717-135F8_forward Regulation 
MonocytederivedMacrophagesResponseToLPS10hrDonor1T17Subject1_CNhs12928_tpm_rev Tc:MdmToLps_10hrD1- Monocyte-derived macrophages response to LPS, 10hr, donor1 (t17 Subject1)_CNhs12928_12714-135F5_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS10hrDonor1T17Subject1_CNhs12928_tpm_fwd Tc:MdmToLps_10hrD1+ Monocyte-derived macrophages response to LPS, 10hr, donor1 (t17 Subject1)_CNhs12928_12714-135F5_forward Regulation 
MonocytederivedMacrophagesResponseToLPS07hrDonor3T15Subject3_CNhs13325_tpm_rev Tc:MdmToLps_07hrD3- Monocyte-derived macrophages response to LPS, 07hr, donor3 (t15 Subject3)_CNhs13325_12908-138A1_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS07hrDonor3T15Subject3_CNhs13325_tpm_fwd Tc:MdmToLps_07hrD3+ Monocyte-derived macrophages response to LPS, 07hr, donor3 (t15 Subject3)_CNhs13325_12908-138A1_forward Regulation 
MonocytederivedMacrophagesResponseToLPS07hrDonor2T15Subject2_CNhs13394_tpm_rev Tc:MdmToLps_07hrD2- Monocyte-derived macrophages response to LPS, 07hr, donor2 (t15 Subject2)_CNhs13394_12810-136H2_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS07hrDonor2T15Subject2_CNhs13394_tpm_fwd Tc:MdmToLps_07hrD2+ Monocyte-derived macrophages response to LPS, 07hr, donor2 (t15 Subject2)_CNhs13394_12810-136H2_forward Regulation 
MonocytederivedMacrophagesResponseToLPS07hrDonor1T15Subject1_CNhs12926_tpm_rev Tc:MdmToLps_07hrD1- Monocyte-derived macrophages response to LPS, 07hr, donor1 (t15 Subject1)_CNhs12926_12712-135F3_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS07hrDonor1T15Subject1_CNhs12926_tpm_fwd Tc:MdmToLps_07hrD1+ Monocyte-derived macrophages response to LPS, 07hr, donor1 (t15 Subject1)_CNhs12926_12712-135F3_forward Regulation 
MonocytederivedMacrophagesResponseToLPS06hrDonor3T14Subject3_CNhs13187_tpm_rev Tc:MdmToLps_06hrD3- Monocyte-derived macrophages response to LPS, 06hr, donor3 (t14 Subject3)_CNhs13187_12907-137I9_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS06hrDonor3T14Subject3_CNhs13187_tpm_fwd Tc:MdmToLps_06hrD3+ Monocyte-derived macrophages response to LPS, 06hr, donor3 (t14 Subject3)_CNhs13187_12907-137I9_forward Regulation 
MonocytederivedMacrophagesResponseToLPS06hrDonor2T14Subject2_CNhs13393_tpm_rev Tc:MdmToLps_06hrD2- Monocyte-derived macrophages response to LPS, 06hr, donor2 (t14 Subject2)_CNhs13393_12809-136H1_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS06hrDonor2T14Subject2_CNhs13393_tpm_fwd Tc:MdmToLps_06hrD2+ Monocyte-derived macrophages response to LPS, 06hr, donor2 (t14 Subject2)_CNhs13393_12809-136H1_forward Regulation 
MonocytederivedMacrophagesResponseToLPS06hrDonor1T14Subject1_CNhs12925_tpm_rev Tc:MdmToLps_06hrD1- Monocyte-derived macrophages response to LPS, 06hr, donor1 (t14 Subject1)_CNhs12925_12711-135F2_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS06hrDonor1T14Subject1_CNhs12925_tpm_fwd Tc:MdmToLps_06hrD1+ Monocyte-derived macrophages response to LPS, 06hr, donor1 (t14 Subject1)_CNhs12925_12711-135F2_forward Regulation 
MonocytederivedMacrophagesResponseToLPS04hrDonor1T12Subject1_CNhs13154_tpm_rev Tc:MdmToLps_04hrD1- Monocyte-derived macrophages response to LPS, 04hr, donor1 (t12 Subject1)_CNhs13154_12709-135E9_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS04hrDonor1T12Subject1_CNhs13154_tpm_fwd Tc:MdmToLps_04hrD1+ Monocyte-derived macrophages response to LPS, 04hr, donor1 (t12 Subject1)_CNhs13154_12709-135E9_forward Regulation 
MonocytederivedMacrophagesResponseToLPS03hr30minDonor1T11Subject1_CNhs13153_tpm_rev Tc:MdmToLps_03hr30minD1- Monocyte-derived macrophages response to LPS, 03hr30min, donor1 (t11 Subject1)_CNhs13153_12708-135E8_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS03hr30minDonor1T11Subject1_CNhs13153_tpm_fwd Tc:MdmToLps_03hr30minD1+ Monocyte-derived macrophages response to LPS, 03hr30min, donor1 (t11 Subject1)_CNhs13153_12708-135E8_forward Regulation 
MonocytederivedMacrophagesResponseToLPS02hr00minDonor1T8Subject1_CNhs13151_tpm_rev Tc:MdmToLps_02hr00minD1- Monocyte-derived macrophages response to LPS, 02hr00min, donor1 (t8 Subject1)_CNhs13151_12705-135E5_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS02hr00minDonor1T8Subject1_CNhs13151_tpm_fwd Tc:MdmToLps_02hr00minD1+ Monocyte-derived macrophages response to LPS, 02hr00min, donor1 (t8 Subject1)_CNhs13151_12705-135E5_forward Regulation 
MonocytederivedMacrophagesResponseToLPS01hr40minDonor3T7Subject3_CNhs13180_tpm_rev Tc:MdmToLps_01hr40minD3- Monocyte-derived macrophages response to LPS, 01hr40min, donor3 (t7 Subject3)_CNhs13180_12900-137I2_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS01hr40minDonor3T7Subject3_CNhs13180_tpm_fwd Tc:MdmToLps_01hr40minD3+ Monocyte-derived macrophages response to LPS, 01hr40min, donor3 (t7 Subject3)_CNhs13180_12900-137I2_forward Regulation 
MonocytederivedMacrophagesResponseToLPS01hr40minDonor2T7Subject2_CNhs13385_tpm_rev Tc:MdmToLps_01hr40minD2- Monocyte-derived macrophages response to LPS, 01hr40min, donor2 (t7 Subject2)_CNhs13385_12802-136G3_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS01hr40minDonor2T7Subject2_CNhs13385_tpm_fwd Tc:MdmToLps_01hr40minD2+ Monocyte-derived macrophages response to LPS, 01hr40min, donor2 (t7 Subject2)_CNhs13385_12802-136G3_forward Regulation 
MonocytederivedMacrophagesResponseToLPS01hr40minDonor1T7Subject1_CNhs13150_tpm_rev Tc:MdmToLps_01hr40minD1- Monocyte-derived macrophages response to LPS, 01hr40min, donor1 (t7 Subject1)_CNhs13150_12704-135E4_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS01hr40minDonor1T7Subject1_CNhs13150_tpm_fwd Tc:MdmToLps_01hr40minD1+ Monocyte-derived macrophages response to LPS, 01hr40min, donor1 (t7 Subject1)_CNhs13150_12704-135E4_forward Regulation 
MonocytederivedMacrophagesResponseToLPS01hr20minDonor1T6Subject1_CNhs13149_tpm_rev Tc:MdmToLps_01hr20minD1- Monocyte-derived macrophages response to LPS, 01hr20min, donor1 (t6 Subject1)_CNhs13149_12703-135E3_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS01hr20minDonor1T6Subject1_CNhs13149_tpm_fwd Tc:MdmToLps_01hr20minD1+ Monocyte-derived macrophages response to LPS, 01hr20min, donor1 (t6 Subject1)_CNhs13149_12703-135E3_forward Regulation 
MonocytederivedMacrophagesResponseToLPS01hr00minDonor1T5Subject1_CNhs13148_tpm_rev Tc:MdmToLps_01hr00minD1- Monocyte-derived macrophages response to LPS, 01hr00min, donor1 (t5 Subject1)_CNhs13148_12702-135E2_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS01hr00minDonor1T5Subject1_CNhs13148_tpm_fwd Tc:MdmToLps_01hr00minD1+ Monocyte-derived macrophages response to LPS, 01hr00min, donor1 (t5 Subject1)_CNhs13148_12702-135E2_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr45minDonor1T4Subject1_CNhs13147_tpm_rev Tc:MdmToLps_00hr45minD1- Monocyte-derived macrophages response to LPS, 00hr45min, donor1 (t4 Subject1)_CNhs13147_12701-135E1_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr45minDonor1T4Subject1_CNhs13147_tpm_fwd Tc:MdmToLps_00hr45minD1+ Monocyte-derived macrophages response to LPS, 00hr45min, donor1 (t4 Subject1)_CNhs13147_12701-135E1_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr30minDonor1T3Subject1_CNhs13146_tpm_rev Tc:MdmToLps_00hr30minD1- Monocyte-derived macrophages response to LPS, 00hr30min, donor1 (t3 Subject1)_CNhs13146_12700-135D9_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr30minDonor1T3Subject1_CNhs13146_tpm_fwd Tc:MdmToLps_00hr30minD1+ Monocyte-derived macrophages response to LPS, 00hr30min, donor1 (t3 Subject1)_CNhs13146_12700-135D9_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr15minDonor1T2Subject1_CNhs13145_tpm_rev Tc:MdmToLps_00hr15minD1- Monocyte-derived macrophages response to LPS, 00hr15min, donor1 (t2 Subject1)_CNhs13145_12699-135D8_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr15minDonor1T2Subject1_CNhs13145_tpm_fwd Tc:MdmToLps_00hr15minD1+ Monocyte-derived macrophages response to LPS, 00hr15min, donor1 (t2 Subject1)_CNhs13145_12699-135D8_forward Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay04BiolRep3_CNhs12804_tpm_rev Tc:K562ToHemin_Day04Br3- K562 erythroblastic leukemia response to hemin, day04, biol_rep3_CNhs12804_13228-141I6_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay04BiolRep3_CNhs12804_tpm_fwd Tc:K562ToHemin_Day04Br3+ K562 erythroblastic leukemia response to hemin, day04, biol_rep3_CNhs12804_13228-141I6_forward Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay04BiolRep2_CNhs12702_tpm_rev Tc:K562ToHemin_Day04Br2- K562 erythroblastic leukemia response to hemin, day04, biol_rep2_CNhs12702_13162-141B3_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay04BiolRep2_CNhs12702_tpm_fwd Tc:K562ToHemin_Day04Br2+ K562 erythroblastic leukemia response to hemin, day04, biol_rep2_CNhs12702_13162-141B3_forward Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay04BiolRep1_CNhs12474_tpm_rev Tc:K562ToHemin_Day04Br1- K562 erythroblastic leukemia response to hemin, day04, biol_rep1_CNhs12474_13096-140C9_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay04BiolRep1_CNhs12474_tpm_fwd Tc:K562ToHemin_Day04Br1+ K562 erythroblastic leukemia response to hemin, day04, biol_rep1_CNhs12474_13096-140C9_forward Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay03BiolRep3_CNhs12803_tpm_rev Tc:K562ToHemin_Day03Br3- K562 erythroblastic leukemia response to hemin, day03, biol_rep3_CNhs12803_13227-141I5_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay03BiolRep3_CNhs12803_tpm_fwd Tc:K562ToHemin_Day03Br3+ K562 erythroblastic leukemia response to hemin, day03, biol_rep3_CNhs12803_13227-141I5_forward Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay03BiolRep2_CNhs12701_tpm_rev Tc:K562ToHemin_Day03Br2- K562 erythroblastic leukemia response to hemin, day03, biol_rep2_CNhs12701_13161-141B2_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay03BiolRep2_CNhs12701_tpm_fwd Tc:K562ToHemin_Day03Br2+ K562 erythroblastic leukemia response to hemin, day03, biol_rep2_CNhs12701_13161-141B2_forward Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay03BiolRep1_CNhs12473_tpm_rev Tc:K562ToHemin_Day03Br1- K562 erythroblastic leukemia response to hemin, day03, biol_rep1_CNhs12473_13095-140C8_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay03BiolRep1_CNhs12473_tpm_fwd Tc:K562ToHemin_Day03Br1+ K562 erythroblastic leukemia response to hemin, day03, biol_rep1_CNhs12473_13095-140C8_forward Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay02BiolRep3_CNhs12802_tpm_rev Tc:K562ToHemin_Day02Br3- K562 erythroblastic leukemia response to hemin, day02, biol_rep3_CNhs12802_13226-141I4_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay02BiolRep3_CNhs12802_tpm_fwd Tc:K562ToHemin_Day02Br3+ K562 erythroblastic leukemia response to hemin, day02, biol_rep3_CNhs12802_13226-141I4_forward Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay02BiolRep2_CNhs12700_tpm_rev Tc:K562ToHemin_Day02Br2- K562 erythroblastic leukemia response to hemin, day02, biol_rep2_CNhs12700_13160-141B1_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay02BiolRep2_CNhs12700_tpm_fwd Tc:K562ToHemin_Day02Br2+ K562 erythroblastic leukemia response to hemin, day02, biol_rep2_CNhs12700_13160-141B1_forward Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay02BiolRep1_CNhs12472_tpm_rev Tc:K562ToHemin_Day02Br1- K562 erythroblastic leukemia response to hemin, day02, biol_rep1_CNhs12472_13094-140C7_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHeminDay02BiolRep1_CNhs12472_tpm_fwd Tc:K562ToHemin_Day02Br1+ K562 erythroblastic leukemia response to hemin, day02, biol_rep1_CNhs12472_13094-140C7_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin24hrBiolRep3_CNhs12801_tpm_rev Tc:K562ToHemin_24hrBr3- K562 erythroblastic leukemia response to hemin, 24hr, biol_rep3_CNhs12801_13225-141I3_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin24hrBiolRep3_CNhs12801_tpm_fwd Tc:K562ToHemin_24hrBr3+ K562 erythroblastic leukemia response to hemin, 24hr, biol_rep3_CNhs12801_13225-141I3_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin24hrBiolRep2_CNhs12699_tpm_rev Tc:K562ToHemin_24hrBr2- K562 erythroblastic leukemia response to hemin, 24hr, biol_rep2_CNhs12699_13159-141A9_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin24hrBiolRep2_CNhs12699_tpm_fwd Tc:K562ToHemin_24hrBr2+ K562 erythroblastic leukemia response to hemin, 24hr, biol_rep2_CNhs12699_13159-141A9_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin24hrBiolRep1_CNhs12471_tpm_rev Tc:K562ToHemin_24hrBr1- K562 erythroblastic leukemia response to hemin, 24hr, biol_rep1_CNhs12471_13093-140C6_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin24hrBiolRep1_CNhs12471_tpm_fwd Tc:K562ToHemin_24hrBr1+ K562 erythroblastic leukemia response to hemin, 24hr, biol_rep1_CNhs12471_13093-140C6_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin12hrBiolRep3_CNhs12800_tpm_rev Tc:K562ToHemin_12hrBr3- K562 erythroblastic leukemia response to hemin, 12hr, biol_rep3_CNhs12800_13224-141I2_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin12hrBiolRep3_CNhs12800_tpm_fwd Tc:K562ToHemin_12hrBr3+ K562 erythroblastic leukemia response to hemin, 12hr, biol_rep3_CNhs12800_13224-141I2_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin12hrBiolRep2_CNhs12698_tpm_rev Tc:K562ToHemin_12hrBr2- K562 erythroblastic leukemia response to hemin, 12hr, biol_rep2_CNhs12698_13158-141A8_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin12hrBiolRep2_CNhs12698_tpm_fwd Tc:K562ToHemin_12hrBr2+ K562 erythroblastic leukemia response to hemin, 12hr, biol_rep2_CNhs12698_13158-141A8_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin12hrBiolRep1_CNhs12470_tpm_rev Tc:K562ToHemin_12hrBr1- K562 erythroblastic leukemia response to hemin, 12hr, biol_rep1_CNhs12470_13092-140C5_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin12hrBiolRep1_CNhs12470_tpm_fwd Tc:K562ToHemin_12hrBr1+ K562 erythroblastic leukemia response to hemin, 12hr, biol_rep1_CNhs12470_13092-140C5_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin06hrBiolRep3_CNhs12799_tpm_rev Tc:K562ToHemin_06hrBr3- K562 erythroblastic leukemia response to hemin, 06hr, biol_rep3_CNhs12799_13223-141I1_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin06hrBiolRep3_CNhs12799_tpm_fwd Tc:K562ToHemin_06hrBr3+ K562 erythroblastic leukemia response to hemin, 06hr, biol_rep3_CNhs12799_13223-141I1_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin06hrBiolRep2_CNhs12697_tpm_rev Tc:K562ToHemin_06hrBr2- K562 erythroblastic leukemia response to hemin, 06hr, biol_rep2_CNhs12697_13157-141A7_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin06hrBiolRep2_CNhs12697_tpm_fwd Tc:K562ToHemin_06hrBr2+ K562 erythroblastic leukemia response to hemin, 06hr, biol_rep2_CNhs12697_13157-141A7_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin06hrBiolRep1_CNhs12469_tpm_rev Tc:K562ToHemin_06hrBr1- K562 erythroblastic leukemia response to hemin, 06hr, biol_rep1_CNhs12469_13091-140C4_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin06hrBiolRep1_CNhs12469_tpm_fwd Tc:K562ToHemin_06hrBr1+ K562 erythroblastic leukemia response to hemin, 06hr, biol_rep1_CNhs12469_13091-140C4_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin04hrBiolRep3_CNhs12798_tpm_rev Tc:K562ToHemin_04hrBr3- K562 erythroblastic leukemia response to hemin, 04hr, biol_rep3_CNhs12798_13222-141H9_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin04hrBiolRep3_CNhs12798_tpm_fwd Tc:K562ToHemin_04hrBr3+ K562 erythroblastic leukemia response to hemin, 04hr, biol_rep3_CNhs12798_13222-141H9_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin04hrBiolRep2_CNhs12696_tpm_rev Tc:K562ToHemin_04hrBr2- K562 erythroblastic leukemia response to hemin, 04hr, biol_rep2_CNhs12696_13156-141A6_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin04hrBiolRep2_CNhs12696_tpm_fwd Tc:K562ToHemin_04hrBr2+ K562 erythroblastic leukemia response to hemin, 04hr, biol_rep2_CNhs12696_13156-141A6_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin04hrBiolRep1_CNhs12468_tpm_rev Tc:K562ToHemin_04hrBr1- K562 erythroblastic leukemia response to hemin, 04hr, biol_rep1_CNhs12468_13090-140C3_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin04hrBiolRep1_CNhs12468_tpm_fwd Tc:K562ToHemin_04hrBr1+ K562 erythroblastic leukemia response to hemin, 04hr, biol_rep1_CNhs12468_13090-140C3_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr30minBiolRep3_CNhs12797_tpm_rev Tc:K562ToHemin_03hr30minBr3- K562 erythroblastic leukemia response to hemin, 03hr30min, biol_rep3_CNhs12797_13221-141H8_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr30minBiolRep3_CNhs12797_tpm_fwd Tc:K562ToHemin_03hr30minBr3+ K562 erythroblastic leukemia response to hemin, 03hr30min, biol_rep3_CNhs12797_13221-141H8_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr30minBiolRep2_CNhs12695_tpm_rev Tc:K562ToHemin_03hr30minBr2- K562 erythroblastic leukemia response to hemin, 03hr30min, biol_rep2_CNhs12695_13155-141A5_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr30minBiolRep2_CNhs12695_tpm_fwd Tc:K562ToHemin_03hr30minBr2+ K562 erythroblastic leukemia response to hemin, 03hr30min, biol_rep2_CNhs12695_13155-141A5_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr30minBiolRep1_CNhs12467_tpm_rev Tc:K562ToHemin_03hr30minBr1- K562 erythroblastic leukemia response to hemin, 03hr30min, biol_rep1_CNhs12467_13089-140C2_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr30minBiolRep1_CNhs12467_tpm_fwd Tc:K562ToHemin_03hr30minBr1+ K562 erythroblastic leukemia response to hemin, 03hr30min, biol_rep1_CNhs12467_13089-140C2_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr00minBiolRep3_CNhs12796_tpm_rev Tc:K562ToHemin_03hr00minBr3- K562 erythroblastic leukemia response to hemin, 03hr00min, biol_rep3_CNhs12796_13220-141H7_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr00minBiolRep3_CNhs12796_tpm_fwd Tc:K562ToHemin_03hr00minBr3+ K562 erythroblastic leukemia response to hemin, 03hr00min, biol_rep3_CNhs12796_13220-141H7_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr00minBiolRep2_CNhs12694_tpm_rev Tc:K562ToHemin_03hr00minBr2- K562 erythroblastic leukemia response to hemin, 03hr00min, biol_rep2_CNhs12694_13154-141A4_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr00minBiolRep2_CNhs12694_tpm_fwd Tc:K562ToHemin_03hr00minBr2+ K562 erythroblastic leukemia response to hemin, 03hr00min, biol_rep2_CNhs12694_13154-141A4_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr00minBiolRep1_CNhs12466_tpm_rev Tc:K562ToHemin_03hr00minBr1- K562 erythroblastic leukemia response to hemin, 03hr00min, biol_rep1_CNhs12466_13088-140C1_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin03hr00minBiolRep1_CNhs12466_tpm_fwd Tc:K562ToHemin_03hr00minBr1+ K562 erythroblastic leukemia response to hemin, 03hr00min, biol_rep1_CNhs12466_13088-140C1_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr30minBiolRep3_CNhs12795_tpm_rev Tc:K562ToHemin_02hr30minBr3- K562 erythroblastic leukemia response to hemin, 02hr30min, biol_rep3_CNhs12795_13219-141H6_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr30minBiolRep3_CNhs12795_tpm_fwd Tc:K562ToHemin_02hr30minBr3+ K562 erythroblastic leukemia response to hemin, 02hr30min, biol_rep3_CNhs12795_13219-141H6_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr30minBiolRep2_CNhs12693_tpm_rev Tc:K562ToHemin_02hr30minBr2- K562 erythroblastic leukemia response to hemin, 02hr30min, biol_rep2_CNhs12693_13153-141A3_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr30minBiolRep2_CNhs12693_tpm_fwd Tc:K562ToHemin_02hr30minBr2+ K562 erythroblastic leukemia response to hemin, 02hr30min, biol_rep2_CNhs12693_13153-141A3_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr30minBiolRep1_CNhs12465_tpm_rev Tc:K562ToHemin_02hr30minBr1- K562 erythroblastic leukemia response to hemin, 02hr30min, biol_rep1_CNhs12465_13087-140B9_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr30minBiolRep1_CNhs12465_tpm_fwd Tc:K562ToHemin_02hr30minBr1+ K562 erythroblastic leukemia response to hemin, 02hr30min, biol_rep1_CNhs12465_13087-140B9_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr00minBiolRep3_CNhs12794_tpm_rev Tc:K562ToHemin_02hr00minBr3- K562 erythroblastic leukemia response to hemin, 02hr00min, biol_rep3_CNhs12794_13218-141H5_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr00minBiolRep3_CNhs12794_tpm_fwd Tc:K562ToHemin_02hr00minBr3+ K562 erythroblastic leukemia response to hemin, 02hr00min, biol_rep3_CNhs12794_13218-141H5_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr00minBiolRep2_CNhs12692_tpm_rev Tc:K562ToHemin_02hr00minBr2- K562 erythroblastic leukemia response to hemin, 02hr00min, biol_rep2_CNhs12692_13152-141A2_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr00minBiolRep2_CNhs12692_tpm_fwd Tc:K562ToHemin_02hr00minBr2+ K562 erythroblastic leukemia response to hemin, 02hr00min, biol_rep2_CNhs12692_13152-141A2_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr00minBiolRep1_CNhs12737_tpm_rev Tc:K562ToHemin_02hr00minBr1- K562 erythroblastic leukemia response to hemin, 02hr00min, biol_rep1_CNhs12737_13086-140B8_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin02hr00minBiolRep1_CNhs12737_tpm_fwd Tc:K562ToHemin_02hr00minBr1+ K562 erythroblastic leukemia response to hemin, 02hr00min, biol_rep1_CNhs12737_13086-140B8_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr40minBiolRep3_CNhs12792_tpm_rev Tc:K562ToHemin_01hr40minBr3- K562 erythroblastic leukemia response to hemin, 01hr40min, biol_rep3_CNhs12792_13217-141H4_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr40minBiolRep3_CNhs12792_tpm_fwd Tc:K562ToHemin_01hr40minBr3+ K562 erythroblastic leukemia response to hemin, 01hr40min, biol_rep3_CNhs12792_13217-141H4_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr40minBiolRep2_CNhs12691_tpm_rev Tc:K562ToHemin_01hr40minBr2- K562 erythroblastic leukemia response to hemin, 01hr40min, biol_rep2_CNhs12691_13151-141A1_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr40minBiolRep2_CNhs12691_tpm_fwd Tc:K562ToHemin_01hr40minBr2+ K562 erythroblastic leukemia response to hemin, 01hr40min, biol_rep2_CNhs12691_13151-141A1_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr40minBiolRep1_CNhs12464_tpm_rev Tc:K562ToHemin_01hr40minBr1- K562 erythroblastic leukemia response to hemin, 01hr40min, biol_rep1_CNhs12464_13085-140B7_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr40minBiolRep1_CNhs12464_tpm_fwd Tc:K562ToHemin_01hr40minBr1+ K562 erythroblastic leukemia response to hemin, 01hr40min, biol_rep1_CNhs12464_13085-140B7_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr20minBiolRep3_CNhs12791_tpm_rev Tc:K562ToHemin_01hr20minBr3- K562 erythroblastic leukemia response to hemin, 01hr20min, biol_rep3_CNhs12791_13216-141H3_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr20minBiolRep3_CNhs12791_tpm_fwd Tc:K562ToHemin_01hr20minBr3+ K562 erythroblastic leukemia response to hemin, 01hr20min, biol_rep3_CNhs12791_13216-141H3_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr20minBiolRep2_CNhs12690_tpm_rev Tc:K562ToHemin_01hr20minBr2- K562 erythroblastic leukemia response to hemin, 01hr20min, biol_rep2_CNhs12690_13150-140I9_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr20minBiolRep2_CNhs12690_tpm_fwd Tc:K562ToHemin_01hr20minBr2+ K562 erythroblastic leukemia response to hemin, 01hr20min, biol_rep2_CNhs12690_13150-140I9_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr20minBiolRep1_CNhs12463_tpm_rev Tc:K562ToHemin_01hr20minBr1- K562 erythroblastic leukemia response to hemin, 01hr20min, biol_rep1_CNhs12463_13084-140B6_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr20minBiolRep1_CNhs12463_tpm_fwd Tc:K562ToHemin_01hr20minBr1+ K562 erythroblastic leukemia response to hemin, 01hr20min, biol_rep1_CNhs12463_13084-140B6_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr00minBiolRep3_CNhs12790_tpm_rev Tc:K562ToHemin_01hr00minBr3- K562 erythroblastic leukemia response to hemin, 01hr00min, biol_rep3_CNhs12790_13215-141H2_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr00minBiolRep3_CNhs12790_tpm_fwd Tc:K562ToHemin_01hr00minBr3+ K562 erythroblastic leukemia response to hemin, 01hr00min, biol_rep3_CNhs12790_13215-141H2_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr00minBiolRep2_CNhs12689_tpm_rev Tc:K562ToHemin_01hr00minBr2- K562 erythroblastic leukemia response to hemin, 01hr00min, biol_rep2_CNhs12689_13149-140I8_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr00minBiolRep2_CNhs12689_tpm_fwd Tc:K562ToHemin_01hr00minBr2+ K562 erythroblastic leukemia response to hemin, 01hr00min, biol_rep2_CNhs12689_13149-140I8_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr00minBiolRep1_CNhs12462_tpm_rev Tc:K562ToHemin_01hr00minBr1- K562 erythroblastic leukemia response to hemin, 01hr00min, biol_rep1_CNhs12462_13083-140B5_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin01hr00minBiolRep1_CNhs12462_tpm_fwd Tc:K562ToHemin_01hr00minBr1+ K562 erythroblastic leukemia response to hemin, 01hr00min, biol_rep1_CNhs12462_13083-140B5_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr45minBiolRep3_CNhs12789_tpm_rev Tc:K562ToHemin_00hr45minBr3- K562 erythroblastic leukemia response to hemin, 00hr45min, biol_rep3_CNhs12789_13214-141H1_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr45minBiolRep3_CNhs12789_tpm_fwd Tc:K562ToHemin_00hr45minBr3+ K562 erythroblastic leukemia response to hemin, 00hr45min, biol_rep3_CNhs12789_13214-141H1_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr45minBiolRep2_CNhs12688_tpm_rev Tc:K562ToHemin_00hr45minBr2- K562 erythroblastic leukemia response to hemin, 00hr45min, biol_rep2_CNhs12688_13148-140I7_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr45minBiolRep2_CNhs12688_tpm_fwd Tc:K562ToHemin_00hr45minBr2+ K562 erythroblastic leukemia response to hemin, 00hr45min, biol_rep2_CNhs12688_13148-140I7_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr45minBiolRep1_CNhs12461_tpm_rev Tc:K562ToHemin_00hr45minBr1- K562 erythroblastic leukemia response to hemin, 00hr45min, biol_rep1_CNhs12461_13082-140B4_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr45minBiolRep1_CNhs12461_tpm_fwd Tc:K562ToHemin_00hr45minBr1+ K562 erythroblastic leukemia response to hemin, 00hr45min, biol_rep1_CNhs12461_13082-140B4_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr30minBiolRep3_CNhs12788_tpm_rev Tc:K562ToHemin_00hr30minBr3- K562 erythroblastic leukemia response to hemin, 00hr30min, biol_rep3_CNhs12788_13213-141G9_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr30minBiolRep3_CNhs12788_tpm_fwd Tc:K562ToHemin_00hr30minBr3+ K562 erythroblastic leukemia response to hemin, 00hr30min, biol_rep3_CNhs12788_13213-141G9_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr30minBiolRep2_CNhs12687_tpm_rev Tc:K562ToHemin_00hr30minBr2- K562 erythroblastic leukemia response to hemin, 00hr30min, biol_rep2_CNhs12687_13147-140I6_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr30minBiolRep2_CNhs12687_tpm_fwd Tc:K562ToHemin_00hr30minBr2+ K562 erythroblastic leukemia response to hemin, 00hr30min, biol_rep2_CNhs12687_13147-140I6_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr30minBiolRep1_CNhs12460_tpm_rev Tc:K562ToHemin_00hr30minBr1- K562 erythroblastic leukemia response to hemin, 00hr30min, biol_rep1_CNhs12460_13081-140B3_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr30minBiolRep1_CNhs12460_tpm_fwd Tc:K562ToHemin_00hr30minBr1+ K562 erythroblastic leukemia response to hemin, 00hr30min, biol_rep1_CNhs12460_13081-140B3_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr15minBiolRep3_CNhs12787_tpm_rev Tc:K562ToHemin_00hr15minBr3- K562 erythroblastic leukemia response to hemin, 00hr15min, biol_rep3_CNhs12787_13212-141G8_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr15minBiolRep3_CNhs12787_tpm_fwd Tc:K562ToHemin_00hr15minBr3+ K562 erythroblastic leukemia response to hemin, 00hr15min, biol_rep3_CNhs12787_13212-141G8_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr15minBiolRep2_CNhs12686_tpm_rev Tc:K562ToHemin_00hr15minBr2- K562 erythroblastic leukemia response to hemin, 00hr15min, biol_rep2_CNhs12686_13146-140I5_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr15minBiolRep2_CNhs12686_tpm_fwd Tc:K562ToHemin_00hr15minBr2+ K562 erythroblastic leukemia response to hemin, 00hr15min, biol_rep2_CNhs12686_13146-140I5_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr15minBiolRep1_CNhs12459_tpm_rev Tc:K562ToHemin_00hr15minBr1- K562 erythroblastic leukemia response to hemin, 00hr15min, biol_rep1_CNhs12459_13080-140B2_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr15minBiolRep1_CNhs12459_tpm_fwd Tc:K562ToHemin_00hr15minBr1+ K562 erythroblastic leukemia response to hemin, 00hr15min, biol_rep1_CNhs12459_13080-140B2_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr00minBiolRep3_CNhs12786_tpm_rev Tc:K562ToHemin_00hr00minBr3- K562 erythroblastic leukemia response to hemin, 00hr00min, biol_rep3_CNhs12786_13211-141G7_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr00minBiolRep3_CNhs12786_tpm_fwd Tc:K562ToHemin_00hr00minBr3+ K562 erythroblastic leukemia response to hemin, 00hr00min, biol_rep3_CNhs12786_13211-141G7_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr00minBiolRep2_CNhs12684_tpm_rev Tc:K562ToHemin_00hr00minBr2- K562 erythroblastic leukemia response to hemin, 00hr00min, biol_rep2_CNhs12684_13145-140I4_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr00minBiolRep2_CNhs12684_tpm_fwd Tc:K562ToHemin_00hr00minBr2+ K562 erythroblastic leukemia response to hemin, 00hr00min, biol_rep2_CNhs12684_13145-140I4_forward Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr00minBiolRep1_CNhs12458_tpm_rev Tc:K562ToHemin_00hr00minBr1- K562 erythroblastic leukemia response to hemin, 00hr00min, biol_rep1_CNhs12458_13079-140B1_reverse Regulation 
K562ErythroblasticLeukemiaResponseToHemin00hr00minBiolRep1_CNhs12458_tpm_fwd Tc:K562ToHemin_00hr00minBr1+ K562 erythroblastic leukemia response to hemin, 00hr00min, biol_rep1_CNhs12458_13079-140B1_forward Regulation 
HIPSBiolRep3_CNhs14216_tpm_rev Tc:hIPSBr3- hIPS, biol_rep3_CNhs14216_14382-156B8_reverse Regulation 
HIPSBiolRep3_CNhs14216_tpm_fwd Tc:hIPSBr3+ hIPS, biol_rep3_CNhs14216_14382-156B8_forward Regulation 
HIPSBiolRep2_CNhs14215_tpm_rev Tc:hIPSBr2- hIPS, biol_rep2_CNhs14215_14381-156B7_reverse Regulation 
HIPSBiolRep2_CNhs14215_tpm_fwd Tc:hIPSBr2+ hIPS, biol_rep2_CNhs14215_14381-156B7_forward Regulation 
HIPSBiolRep1_CNhs14214_tpm_rev Tc:hIPSBr1- hIPS, biol_rep1_CNhs14214_14380-156B6_reverse Regulation 
HIPSBiolRep1_CNhs14214_tpm_fwd Tc:hIPSBr1+ hIPS, biol_rep1_CNhs14214_14380-156B6_forward Regulation 
HIPSCCl2BiolRep3_CNhs14219_tpm_rev Tc:hIPS+CCl2Br3- hIPS +CCl2, biol_rep3_CNhs14219_14385-156C2_reverse Regulation 
HIPSCCl2BiolRep3_CNhs14219_tpm_fwd Tc:hIPS+CCl2Br3+ hIPS +CCl2, biol_rep3_CNhs14219_14385-156C2_forward Regulation 
HIPSCCl2BiolRep2_CNhs14218_tpm_rev Tc:hIPS+CCl2Br2- hIPS +CCl2, biol_rep2_CNhs14218_14384-156C1_reverse Regulation 
HIPSCCl2BiolRep2_CNhs14218_tpm_fwd Tc:hIPS+CCl2Br2+ hIPS +CCl2, biol_rep2_CNhs14218_14384-156C1_forward Regulation 
HIPSCCl2BiolRep1_CNhs14217_tpm_rev Tc:hIPS+CCl2Br1- hIPS +CCl2, biol_rep1_CNhs14217_14383-156B9_reverse Regulation 
HIPSCCl2BiolRep1_CNhs14217_tpm_fwd Tc:hIPS+CCl2Br1+ hIPS +CCl2, biol_rep1_CNhs14217_14383-156B9_forward Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay09BiolRep3_CNhs13971_tpm_rev Tc:H1ToHsc_Day09Br3- H1 embryonic stem cells differentiation to CD34+ HSC, day09, biol_rep3_CNhs13971_13531-145G3_reverse Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay09BiolRep3_CNhs13971_tpm_fwd Tc:H1ToHsc_Day09Br3+ H1 embryonic stem cells differentiation to CD34+ HSC, day09, biol_rep3_CNhs13971_13531-145G3_forward Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay09BiolRep2_CNhs13970_tpm_rev Tc:H1ToHsc_Day09Br2- H1 embryonic stem cells differentiation to CD34+ HSC, day09, biol_rep2_CNhs13970_13530-145G2_reverse Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay09BiolRep2_CNhs13970_tpm_fwd Tc:H1ToHsc_Day09Br2+ H1 embryonic stem cells differentiation to CD34+ HSC, day09, biol_rep2_CNhs13970_13530-145G2_forward Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay09BiolRep1_CNhs13969_tpm_rev Tc:H1ToHsc_Day09Br1- H1 embryonic stem cells differentiation to CD34+ HSC, day09, biol_rep1_CNhs13969_13529-145G1_reverse Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay09BiolRep1_CNhs13969_tpm_fwd Tc:H1ToHsc_Day09Br1+ H1 embryonic stem cells differentiation to CD34+ HSC, day09, biol_rep1_CNhs13969_13529-145G1_forward Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay03BiolRep3_CNhs13968_tpm_rev Tc:H1ToHsc_Day03Br3- H1 embryonic stem cells differentiation to CD34+ HSC, day03, biol_rep3_CNhs13968_13528-145F9_reverse Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay03BiolRep3_CNhs13968_tpm_fwd Tc:H1ToHsc_Day03Br3+ H1 embryonic stem cells differentiation to CD34+ HSC, day03, biol_rep3_CNhs13968_13528-145F9_forward Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay03BiolRep2_CNhs13966_tpm_rev Tc:H1ToHsc_Day03Br2- H1 embryonic stem cells differentiation to CD34+ HSC, day03, biol_rep2_CNhs13966_13527-145F8_reverse Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay03BiolRep2_CNhs13966_tpm_fwd Tc:H1ToHsc_Day03Br2+ H1 embryonic stem cells differentiation to CD34+ HSC, day03, biol_rep2_CNhs13966_13527-145F8_forward Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay03BiolRep1_CNhs13965_tpm_rev Tc:H1ToHsc_Day03Br1- H1 embryonic stem cells differentiation to CD34+ HSC, day03, biol_rep1_CNhs13965_13526-145F7_reverse Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay03BiolRep1_CNhs13965_tpm_fwd Tc:H1ToHsc_Day03Br1+ H1 embryonic stem cells differentiation to CD34+ HSC, day03, biol_rep1_CNhs13965_13526-145F7_forward Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay00BiolRep3_CNhs13964_tpm_rev Tc:H1ToHsc_Day00Br3- H1 embryonic stem cells differentiation to CD34+ HSC, day00, biol_rep3_CNhs13964_13525-145F6_reverse Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay00BiolRep3_CNhs13964_tpm_fwd Tc:H1ToHsc_Day00Br3+ H1 embryonic stem cells differentiation to CD34+ HSC, day00, biol_rep3_CNhs13964_13525-145F6_forward Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay00BiolRep2_CNhs14068_tpm_rev Tc:H1ToHsc_Day00Br2- H1 embryonic stem cells differentiation to CD34+ HSC, day00, biol_rep2_CNhs14068_13524-145F5_reverse Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay00BiolRep2_CNhs14068_tpm_fwd Tc:H1ToHsc_Day00Br2+ H1 embryonic stem cells differentiation to CD34+ HSC, day00, biol_rep2_CNhs14068_13524-145F5_forward Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay00BiolRep1_CNhs14067_tpm_rev Tc:H1ToHsc_Day00Br1- H1 embryonic stem cells differentiation to CD34+ HSC, day00, biol_rep1_CNhs14067_13523-145F4_reverse Regulation 
H1EmbryonicStemCellsDifferentiationToCD34HSCDay00BiolRep1_CNhs14067_tpm_fwd Tc:H1ToHsc_Day00Br1+ H1 embryonic stem cells differentiation to CD34+ HSC, day00, biol_rep1_CNhs14067_13523-145F4_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha24hr00minBiolRep2_CNhs14536_tpm_rev Tc:ARPE-19Emt_24hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 24hr00min, biol_rep2_CNhs14536_13680-147E8_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha24hr00minBiolRep2_CNhs14536_tpm_fwd Tc:ARPE-19Emt_24hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 24hr00min, biol_rep2_CNhs14536_13680-147E8_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha06hr00minBiolRep2_CNhs14520_tpm_rev Tc:ARPE-19Emt_06hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 06hr00min, biol_rep2_CNhs14520_13665-147D2_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha06hr00minBiolRep2_CNhs14520_tpm_fwd Tc:ARPE-19Emt_06hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 06hr00min, biol_rep2_CNhs14520_13665-147D2_forward Regulation 
MyoblastDifferentiationToMyotubesDay10ControlDonor3_CNhs14584_tpm_rev MyoblastToMyotubes_Day10D3- Myoblast differentiation to myotubes, day10, control donor3_CNhs14584_13494-145C2_reverse Regulation 
MyoblastDifferentiationToMyotubesDay10ControlDonor3_CNhs14584_tpm_fwd MyoblastToMyotubes_Day10D3+ Myoblast differentiation to myotubes, day10, control donor3_CNhs14584_13494-145C2_forward Regulation 
MyoblastDifferentiationToMyotubesDay06DuchenneMuscularDystrophyDonor2_CNhs14601_tpm_rev MyoblastToMyotubes_Day06D2- Myoblast differentiation to myotubes, day06, Duchenne Muscular Dystrophy donor2_CNhs14601_13510-145D9_reverse Regulation 
MyoblastDifferentiationToMyotubesDay06DuchenneMuscularDystrophyDonor2_CNhs14601_tpm_fwd MyoblastToMyotubes_Day06D2+ Myoblast differentiation to myotubes, day06, Duchenne Muscular Dystrophy donor2_CNhs14601_13510-145D9_forward Regulation 
MyoblastDifferentiationToMyotubesDay01ControlDonor2_CNhs14568_tpm_rev MyoblastToMyotubes_Day01D2- Myoblast differentiation to myotubes, day01, control donor2_CNhs14568_13479-145A5_reverse Regulation 
MyoblastDifferentiationToMyotubesDay01ControlDonor2_CNhs14568_tpm_fwd MyoblastToMyotubes_Day01D2+ Myoblast differentiation to myotubes, day01, control donor2_CNhs14568_13479-145A5_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay14BiolRep2_CNhs13631_tpm_rev MscAdipogenicInduction_Day14Br2- mesenchymal stem cells (adipose derived), adipogenic induction, day14, biol_rep2_CNhs13631_13278-142F2_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay14BiolRep2_CNhs13631_tpm_fwd MscAdipogenicInduction_Day14Br2+ mesenchymal stem cells (adipose derived), adipogenic induction, day14, biol_rep2_CNhs13631_13278-142F2_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay04BiolRep2_CNhs13623_tpm_rev MscAdipogenicInduction_Day04Br2- mesenchymal stem cells (adipose derived), adipogenic induction, day04, biol_rep2_CNhs13623_13269-142E2_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay04BiolRep2_CNhs13623_tpm_fwd MscAdipogenicInduction_Day04Br2+ mesenchymal stem cells (adipose derived), adipogenic induction, day04, biol_rep2_CNhs13623_13269-142E2_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay01BiolRep1_CNhs13615_tpm_rev MscAdipogenicInduction_Day01Br1- mesenchymal stem cells (adipose derived), adipogenic induction, day01, biol_rep1_CNhs13615_13262-142D4_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay01BiolRep1_CNhs13615_tpm_fwd MscAdipogenicInduction_Day01Br1+ mesenchymal stem cells (adipose derived), adipogenic induction, day01, biol_rep1_CNhs13615_13262-142D4_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction12hr00minBiolRep3_CNhs13614_tpm_rev MscAdipogenicInduction_12hr00minBr3- mesenchymal stem cells (adipose derived), adipogenic induction, 12hr00min, biol_rep3_CNhs13614_13261-142D3_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction12hr00minBiolRep3_CNhs13614_tpm_fwd MscAdipogenicInduction_12hr00minBr3+ mesenchymal stem cells (adipose derived), adipogenic induction, 12hr00min, biol_rep3_CNhs13614_13261-142D3_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction12hr00minBiolRep2_CNhs13613_tpm_rev MscAdipogenicInduction_12hr00minBr2- mesenchymal stem cells (adipose derived), adipogenic induction, 12hr00min, biol_rep2_CNhs13613_13260-142D2_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction12hr00minBiolRep2_CNhs13613_tpm_fwd MscAdipogenicInduction_12hr00minBr2+ mesenchymal stem cells (adipose derived), adipogenic induction, 12hr00min, biol_rep2_CNhs13613_13260-142D2_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction12hr00minBiolRep1_CNhs13612_tpm_rev MscAdipogenicInduction_12hr00minBr1- mesenchymal stem cells (adipose derived), adipogenic induction, 12hr00min, biol_rep1_CNhs13612_13259-142D1_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction12hr00minBiolRep1_CNhs13612_tpm_fwd MscAdipogenicInduction_12hr00minBr1+ mesenchymal stem cells (adipose derived), adipogenic induction, 12hr00min, biol_rep1_CNhs13612_13259-142D1_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction03hr00minBiolRep2_CNhs13610_tpm_rev MscAdipogenicInduction_03hr00minBr2- mesenchymal stem cells (adipose derived), adipogenic induction, 03hr00min, biol_rep2_CNhs13610_13257-142C8_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction03hr00minBiolRep2_CNhs13610_tpm_fwd MscAdipogenicInduction_03hr00minBr2+ mesenchymal stem cells (adipose derived), adipogenic induction, 03hr00min, biol_rep2_CNhs13610_13257-142C8_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr30minBiolRep2_CNhs13607_tpm_rev MscAdipogenicInduction_02hr30minBr2- mesenchymal stem cells (adipose derived), adipogenic induction, 02hr30min, biol_rep2_CNhs13607_13254-142C5_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr30minBiolRep2_CNhs13607_tpm_fwd MscAdipogenicInduction_02hr30minBr2+ mesenchymal stem cells (adipose derived), adipogenic induction, 02hr30min, biol_rep2_CNhs13607_13254-142C5_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr00minBiolRep3_CNhs13605_tpm_rev MscAdipogenicInduction_02hr00minBr3- mesenchymal stem cells (adipose derived), adipogenic induction, 02hr00min, biol_rep3_CNhs13605_13252-142C3_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr00minBiolRep3_CNhs13605_tpm_fwd MscAdipogenicInduction_02hr00minBr3+ mesenchymal stem cells (adipose derived), adipogenic induction, 02hr00min, biol_rep3_CNhs13605_13252-142C3_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr20minBiolRep3_CNhs13599_tpm_rev MscAdipogenicInduction_01hr20minBr3- mesenchymal stem cells (adipose derived), adipogenic induction, 01hr20min, biol_rep3_CNhs13599_13246-142B6_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr20minBiolRep3_CNhs13599_tpm_fwd MscAdipogenicInduction_01hr20minBr3+ mesenchymal stem cells (adipose derived), adipogenic induction, 01hr20min, biol_rep3_CNhs13599_13246-142B6_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr20minBiolRep2_CNhs13598_tpm_rev MscAdipogenicInduction_01hr20minBr2- mesenchymal stem cells (adipose derived), adipogenic induction, 01hr20min, biol_rep2_CNhs13598_13245-142B5_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr20minBiolRep2_CNhs13598_tpm_fwd MscAdipogenicInduction_01hr20minBr2+ mesenchymal stem cells (adipose derived), adipogenic induction, 01hr20min, biol_rep2_CNhs13598_13245-142B5_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr20minBiolRep1_CNhs13434_tpm_rev MscAdipogenicInduction_01hr20minBr1- mesenchymal stem cells (adipose derived), adipogenic induction, 01hr20min, biol_rep1_CNhs13434_13244-142B4_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr20minBiolRep1_CNhs13434_tpm_fwd MscAdipogenicInduction_01hr20minBr1+ mesenchymal stem cells (adipose derived), adipogenic induction, 01hr20min, biol_rep1_CNhs13434_13244-142B4_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr30minBiolRep3_CNhs13427_tpm_rev MscAdipogenicInduction_00hr30minBr3- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr30min, biol_rep3_CNhs13427_13237-142A6_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr30minBiolRep3_CNhs13427_tpm_fwd MscAdipogenicInduction_00hr30minBr3+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr30min, biol_rep3_CNhs13427_13237-142A6_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection24hr00minDonor4227_121Ud_24h_CNhs13643_tpm_rev MonocyteMacrophageUdornInfluenza_24hr00minD4- Monocyte-derived macrophages response to udorn influenza infection, 24hr00min, donor4 (227_121:Ud_24h)_CNhs13643_13314-143A2_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection24hr00minDonor4227_121Ud_24h_CNhs13643_tpm_fwd MonocyteMacrophageUdornInfluenza_24hr00minD4+ Monocyte-derived macrophages response to udorn influenza infection, 24hr00min, donor4 (227_121:Ud_24h)_CNhs13643_13314-143A2_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection02hr00minDonor2150_120Ud_2h_CNhs13647_tpm_rev MonocyteMacrophageUdornInfluenza_02hr00minD2- Monocyte-derived macrophages response to udorn influenza infection, 02hr00min, donor2 (150_120:Ud_2h)_CNhs13647_13318-143A6_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection02hr00minDonor2150_120Ud_2h_CNhs13647_tpm_fwd MonocyteMacrophageUdornInfluenza_02hr00minD2+ Monocyte-derived macrophages response to udorn influenza infection, 02hr00min, donor2 (150_120:Ud_2h)_CNhs13647_13318-143A6_forward Regulation 
MelanocyteDonor3MC3_CNhs13406_tpm_rev MelanocyteD3- Melanocyte, donor3 (MC+3)_CNhs13406_12837-137B2_reverse Regulation 
MelanocyteDonor3MC3_CNhs13406_tpm_fwd MelanocyteD3+ Melanocyte, donor3 (MC+3)_CNhs13406_12837-137B2_forward Regulation 
MelanocyteDonor2MC2_CNhs13156_tpm_rev MelanocyteD2- Melanocyte, donor2 (MC+2)_CNhs13156_12739-135I3_reverse Regulation 
MelanocyteDonor2MC2_CNhs13156_tpm_fwd MelanocyteD2+ Melanocyte, donor2 (MC+2)_CNhs13156_12739-135I3_forward Regulation 
MelanocyteDonor1MC1_CNhs12816_tpm_rev MelanocyteD1- Melanocyte, donor1 (MC+1)_CNhs12816_12641-134G4_reverse Regulation 
MelanocyteDonor1MC1_CNhs12816_tpm_fwd MelanocyteD1+ Melanocyte, donor1 (MC+1)_CNhs12816_12641-134G4_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay07BiolRep1_CNhs13659_tpm_rev Hes3-gfpCardiomyocyticInduction_Day07Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day07, biol_rep1_CNhs13659_13334-143C4_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay07BiolRep1_CNhs13659_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day07Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day07, biol_rep1_CNhs13659_13334-143C4_forward Regulation 
H9EmbryonicStemCellsBiolRep3H9ES3_CNhs12837_tpm_rev H9EmbryonicStemCellsBr3- H9 Embryonic Stem cells, biol_rep3 (H9ES-3)_CNhs12837_12822-136I5_reverse Regulation 
H9EmbryonicStemCellsBiolRep3H9ES3_CNhs12837_tpm_fwd H9EmbryonicStemCellsBr3+ H9 Embryonic Stem cells, biol_rep3 (H9ES-3)_CNhs12837_12822-136I5_forward Regulation 
H9EmbryonicStemCellsBiolRep2H9ES2_CNhs12824_tpm_rev H9EmbryonicStemCellsBr2- H9 Embryonic Stem cells, biol_rep2 (H9ES-2)_CNhs12824_12724-135G6_reverse Regulation 
H9EmbryonicStemCellsBiolRep2H9ES2_CNhs12824_tpm_fwd H9EmbryonicStemCellsBr2+ H9 Embryonic Stem cells, biol_rep2 (H9ES-2)_CNhs12824_12724-135G6_forward Regulation 
H9EmbryonicStemCellsBiolRep1H9ES1_CNhs11917_tpm_rev H9EmbryonicStemCellsBr1- H9 Embryonic Stem cells, biol_rep1 (H9ES-1)_CNhs11917_12626-134E7_reverse Regulation 
H9EmbryonicStemCellsBiolRep1H9ES1_CNhs11917_tpm_fwd H9EmbryonicStemCellsBr1+ H9 Embryonic Stem cells, biol_rep1 (H9ES-1)_CNhs11917_12626-134E7_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b05hrBiolRep3LK57_CNhs13585_tpm_rev AorticSmsToIL1b_05hrBr3- Aortic smooth muscle cell response to IL1b, 05hr, biol_rep3 (LK57)_CNhs13585_12856-137D3_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b05hrBiolRep3LK57_CNhs13585_tpm_fwd AorticSmsToIL1b_05hrBr3+ Aortic smooth muscle cell response to IL1b, 05hr, biol_rep3 (LK57)_CNhs13585_12856-137D3_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b03hrBiolRep3LK51_CNhs13583_tpm_rev AorticSmsToIL1b_03hrBr3- Aortic smooth muscle cell response to IL1b, 03hr, biol_rep3 (LK51)_CNhs13583_12854-137D1_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b03hrBiolRep3LK51_CNhs13583_tpm_fwd AorticSmsToIL1b_03hrBr3+ Aortic smooth muscle cell response to IL1b, 03hr, biol_rep3 (LK51)_CNhs13583_12854-137D1_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b02hrBiolRep1LK46_CNhs13354_tpm_rev AorticSmsToIL1b_02hrBr1- Aortic smooth muscle cell response to IL1b, 02hr, biol_rep1 (LK46)_CNhs13354_12657-134I2_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b02hrBiolRep1LK46_CNhs13354_tpm_fwd AorticSmsToIL1b_02hrBr1+ Aortic smooth muscle cell response to IL1b, 02hr, biol_rep1 (LK46)_CNhs13354_12657-134I2_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b01hrBiolRep3LK45_CNhs13581_tpm_rev AorticSmsToIL1b_01hrBr3- Aortic smooth muscle cell response to IL1b, 01hr, biol_rep3 (LK45)_CNhs13581_12852-137C8_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b01hrBiolRep3LK45_CNhs13581_tpm_fwd AorticSmsToIL1b_01hrBr3+ Aortic smooth muscle cell response to IL1b, 01hr, biol_rep3 (LK45)_CNhs13581_12852-137C8_forward Regulation 
AorticSmoothMuscleCellResponseToFGF204hrBiolRep3LK24_CNhs13574_tpm_rev AorticSmsToFgf2_04hrBr3- Aortic smooth muscle cell response to FGF2, 04hr, biol_rep3 (LK24)_CNhs13574_12845-137C1_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF204hrBiolRep3LK24_CNhs13574_tpm_fwd AorticSmsToFgf2_04hrBr3+ Aortic smooth muscle cell response to FGF2, 04hr, biol_rep3 (LK24)_CNhs13574_12845-137C1_forward Regulation 
AorticSmoothMuscleCellResponseToFGF204hrBiolRep2LK23_CNhs13365_tpm_rev AorticSmsToFgf2_04hrBr2- Aortic smooth muscle cell response to FGF2, 04hr, biol_rep2 (LK23)_CNhs13365_12747-136A2_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF204hrBiolRep2LK23_CNhs13365_tpm_fwd AorticSmsToFgf2_04hrBr2+ Aortic smooth muscle cell response to FGF2, 04hr, biol_rep2 (LK23)_CNhs13365_12747-136A2_forward Regulation 
AorticSmoothMuscleCellResponseToFGF204hrBiolRep1LK22_CNhs13346_tpm_rev AorticSmsToFgf2_04hrBr1- Aortic smooth muscle cell response to FGF2, 04hr, biol_rep1 (LK22)_CNhs13346_12649-134H3_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF204hrBiolRep1LK22_CNhs13346_tpm_fwd AorticSmsToFgf2_04hrBr1+ Aortic smooth muscle cell response to FGF2, 04hr, biol_rep1 (LK22)_CNhs13346_12649-134H3_forward Regulation 
AorticSmoothMuscleCellResponseToFGF201hrBiolRep2LK14_CNhs13362_tpm_rev AorticSmsToFgf2_01hrBr2- Aortic smooth muscle cell response to FGF2, 01hr, biol_rep2 (LK14)_CNhs13362_12744-135I8_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF201hrBiolRep2LK14_CNhs13362_tpm_fwd AorticSmsToFgf2_01hrBr2+ Aortic smooth muscle cell response to FGF2, 01hr, biol_rep2 (LK14)_CNhs13362_12744-135I8_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr00minBiolRep3LK3_CNhs13567_tpm_rev AorticSmsToFgf2_00hr00minBr3- Aortic smooth muscle cell response to FGF2, 00hr00min, biol_rep3 (LK3)_CNhs13567_12838-137B3_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr00minBiolRep3LK3_CNhs13567_tpm_fwd AorticSmsToFgf2_00hr00minBr3+ Aortic smooth muscle cell response to FGF2, 00hr00min, biol_rep3 (LK3)_CNhs13567_12838-137B3_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr00minBiolRep1_CNhs12564_tpm_rev Mcf7ToEgf1_00hr00minBr1- MCF7 breast cancer cell line response to EGF1, 00hr00min, biol_rep1_CNhs12564_13031-139E7_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr00minBiolRep1_CNhs12564_tpm_fwd Mcf7ToEgf1_00hr00minBr1+ MCF7 breast cancer cell line response to EGF1, 00hr00min, biol_rep1_CNhs12564_13031-139E7_forward Regulation 
WholeBloodRibopureDonor090612Donation3_CNhs11949_tpm_rev WholeBloodD090612Dn3- Whole blood (ribopure), donor090612, donation3_CNhs11949_12184-129A6_reverse Regulation 
WholeBloodRibopureDonor090612Donation3_CNhs11949_tpm_fwd WholeBloodD090612Dn3+ Whole blood (ribopure), donor090612, donation3_CNhs11949_12184-129A6_forward Regulation 
WholeBloodRibopureDonor090612Donation2_CNhs11673_tpm_rev WholeBloodD090612Dn2- Whole blood (ribopure), donor090612, donation2_CNhs11673_12183-129A5_reverse Regulation 
WholeBloodRibopureDonor090612Donation2_CNhs11673_tpm_fwd WholeBloodD090612Dn2+ Whole blood (ribopure), donor090612, donation2_CNhs11673_12183-129A5_forward Regulation 
WholeBloodRibopureDonor090612Donation1_CNhs11672_tpm_rev WholeBloodD090612Dn1- Whole blood (ribopure), donor090612, donation1_CNhs11672_12182-129A4_reverse Regulation 
WholeBloodRibopureDonor090612Donation1_CNhs11672_tpm_fwd WholeBloodD090612Dn1+ Whole blood (ribopure), donor090612, donation1_CNhs11672_12182-129A4_forward Regulation 
WholeBloodRibopureDonor090325Donation2_CNhs11076_tpm_rev WholeBloodD090325Dn2- Whole blood (ribopure), donor090325, donation2_CNhs11076_12177-128I8_reverse Regulation 
WholeBloodRibopureDonor090325Donation2_CNhs11076_tpm_fwd WholeBloodD090325Dn2+ Whole blood (ribopure), donor090325, donation2_CNhs11076_12177-128I8_forward Regulation 
WholeBloodRibopureDonor090325Donation1_CNhs11075_tpm_rev WholeBloodD090325Dn1- Whole blood (ribopure), donor090325, donation1_CNhs11075_12176-128I7_reverse Regulation 
WholeBloodRibopureDonor090325Donation1_CNhs11075_tpm_fwd WholeBloodD090325Dn1+ Whole blood (ribopure), donor090325, donation1_CNhs11075_12176-128I7_forward Regulation 
WholeBloodRibopureDonor090309Donation3_CNhs11948_tpm_rev WholeBloodD090309Dn3- Whole blood (ribopure), donor090309, donation3_CNhs11948_12181-129A3_reverse Regulation 
WholeBloodRibopureDonor090309Donation3_CNhs11948_tpm_fwd WholeBloodD090309Dn3+ Whole blood (ribopure), donor090309, donation3_CNhs11948_12181-129A3_forward Regulation 
WholeBloodRibopureDonor090309Donation2_CNhs11671_tpm_rev WholeBloodD090309Dn2- Whole blood (ribopure), donor090309, donation2_CNhs11671_12180-129A2_reverse Regulation 
WholeBloodRibopureDonor090309Donation2_CNhs11671_tpm_fwd WholeBloodD090309Dn2+ Whole blood (ribopure), donor090309, donation2_CNhs11671_12180-129A2_forward Regulation 
WholeBloodRibopureDonor090309Donation1_CNhs11675_tpm_rev WholeBloodD090309Dn1- Whole blood (ribopure), donor090309, donation1_CNhs11675_12179-129A1_reverse Regulation 
WholeBloodRibopureDonor090309Donation1_CNhs11675_tpm_fwd WholeBloodD090309Dn1+ Whole blood (ribopure), donor090309, donation1_CNhs11675_12179-129A1_forward Regulation 
UrothelialCellsDonor3_CNhs12122_tpm_rev UrothelialCellsD3- Urothelial Cells, donor3_CNhs12122_11681-122H7_reverse Regulation 
UrothelialCellsDonor3_CNhs12122_tpm_fwd UrothelialCellsD3+ Urothelial Cells, donor3_CNhs12122_11681-122H7_forward Regulation 
UrothelialCellsDonor2_CNhs12091_tpm_rev UrothelialCellsD2- Urothelial Cells, donor2_CNhs12091_11600-120H7_reverse Regulation 
UrothelialCellsDonor2_CNhs12091_tpm_fwd UrothelialCellsD2+ Urothelial Cells, donor2_CNhs12091_11600-120H7_forward Regulation 
UrothelialCellsDonor1_CNhs11334_tpm_rev UrothelialCellsD1- Urothelial Cells, donor1_CNhs11334_11520-119H8_reverse Regulation 
UrothelialCellsDonor1_CNhs11334_tpm_fwd UrothelialCellsD1+ Urothelial Cells, donor1_CNhs11334_11520-119H8_forward Regulation 
UrothelialCellsDonor0_CNhs10843_tpm_rev UrothelialCellsD0- Urothelial cells, donor0_CNhs10843_11216-116B1_reverse Regulation 
UrothelialCellsDonor0_CNhs10843_tpm_fwd UrothelialCellsD0+ Urothelial cells, donor0_CNhs10843_11216-116B1_forward Regulation 
TrachealEpithelialCellsDonor3_CNhs12051_tpm_rev TrachealEpithelialCellsD3- Tracheal Epithelial Cells, donor3_CNhs12051_11441-118I1_reverse Regulation 
TrachealEpithelialCellsDonor3_CNhs12051_tpm_fwd TrachealEpithelialCellsD3+ Tracheal Epithelial Cells, donor3_CNhs12051_11441-118I1_forward Regulation 
TrachealEpithelialCellsDonor2_CNhs11993_tpm_rev TrachealEpithelialCellsD2- Tracheal Epithelial Cells, donor2_CNhs11993_11369-118A1_reverse Regulation 
TrachealEpithelialCellsDonor2_CNhs11993_tpm_fwd TrachealEpithelialCellsD2+ Tracheal Epithelial Cells, donor2_CNhs11993_11369-118A1_forward Regulation 
TrachealEpithelialCellsDonor1_CNhs11092_tpm_rev TrachealEpithelialCellsD1- Tracheal Epithelial Cells, donor1_CNhs11092_11292-117A5_reverse Regulation 
TrachealEpithelialCellsDonor1_CNhs11092_tpm_fwd TrachealEpithelialCellsD1+ Tracheal Epithelial Cells, donor1_CNhs11092_11292-117A5_forward Regulation 
TrabecularMeshworkCellsDonor3_CNhs12124_tpm_rev TrabecularMeshworkCellsD3- Trabecular Meshwork Cells, donor3_CNhs12124_11693-123A1_reverse Regulation 
TrabecularMeshworkCellsDonor3_CNhs12124_tpm_fwd TrabecularMeshworkCellsD3+ Trabecular Meshwork Cells, donor3_CNhs12124_11693-123A1_forward Regulation 
TrabecularMeshworkCellsDonor2_CNhs12097_tpm_rev TrabecularMeshworkCellsD2- Trabecular Meshwork Cells, donor2_CNhs12097_11612-122A1_reverse Regulation 
TrabecularMeshworkCellsDonor2_CNhs12097_tpm_fwd TrabecularMeshworkCellsD2+ Trabecular Meshwork Cells, donor2_CNhs12097_11612-122A1_forward Regulation 
TrabecularMeshworkCellsDonor1_CNhs11340_tpm_rev TrabecularMeshworkCellsD1- Trabecular Meshwork Cells, donor1_CNhs11340_11532-120A2_reverse Regulation 
TrabecularMeshworkCellsDonor1_CNhs11340_tpm_fwd TrabecularMeshworkCellsD1+ Trabecular Meshwork Cells, donor1_CNhs11340_11532-120A2_forward Regulation 
TenocyteDonor3_CNhs12641_tpm_rev TenocyteD3- tenocyte, donor3_CNhs12641_11768-123I4_reverse Regulation 
TenocyteDonor3_CNhs12641_tpm_fwd TenocyteD3+ tenocyte, donor3_CNhs12641_11768-123I4_forward Regulation 
TenocyteDonor2_CNhs12640_tpm_rev TenocyteD2- tenocyte, donor2_CNhs12640_11765-123I1_reverse Regulation 
TenocyteDonor2_CNhs12640_tpm_fwd TenocyteD2+ tenocyte, donor2_CNhs12640_11765-123I1_forward Regulation 
TenocyteDonor1_CNhs12639_tpm_rev TenocyteD1- tenocyte, donor1_CNhs12639_11763-123H8_reverse Regulation 
TenocyteDonor1_CNhs12639_tpm_fwd TenocyteD1+ tenocyte, donor1_CNhs12639_11763-123H8_forward Regulation 
SynoviocyteDonor3_CNhs12050_tpm_rev SynoviocyteD3- Synoviocyte, donor3_CNhs12050_11440-118H9_reverse Regulation 
SynoviocyteDonor3_CNhs12050_tpm_fwd SynoviocyteD3+ Synoviocyte, donor3_CNhs12050_11440-118H9_forward Regulation 
SynoviocyteDonor2_CNhs11992_tpm_rev SynoviocyteD2- Synoviocyte, donor2_CNhs11992_11368-117I9_reverse Regulation 
SynoviocyteDonor2_CNhs11992_tpm_fwd SynoviocyteD2+ Synoviocyte, donor2_CNhs11992_11368-117I9_forward Regulation 
SynoviocyteDonor1_CNhs11068_tpm_rev SynoviocyteD1- Synoviocyte, donor1_CNhs11068_11291-117A4_reverse Regulation 
SynoviocyteDonor1_CNhs11068_tpm_fwd SynoviocyteD1+ Synoviocyte, donor1_CNhs11068_11291-117A4_forward Regulation 
SmoothMuscleCellsUterineDonor3_CNhs11927_tpm_rev SmcUterineD3- Smooth Muscle Cells - Uterine, donor3_CNhs11927_11466-119B8_reverse Regulation 
SmoothMuscleCellsUterineDonor3_CNhs11927_tpm_fwd SmcUterineD3+ Smooth Muscle Cells - Uterine, donor3_CNhs11927_11466-119B8_forward Regulation 
SmoothMuscleCellsUterineDonor1_CNhs11921_tpm_rev SmcUterineD1- Smooth Muscle Cells - Uterine, donor1_CNhs11921_11258-116F7_reverse Regulation 
SmoothMuscleCellsUterineDonor1_CNhs11921_tpm_fwd SmcUterineD1+ Smooth Muscle Cells - Uterine, donor1_CNhs11921_11258-116F7_forward Regulation 
SmoothMuscleCellsUmbilicalVeinDonor3_CNhs13076_tpm_rev SmcUmbilicalVeinD3- Smooth Muscle Cells - Umbilical Vein, donor3_CNhs13076_11702-123B1_reverse Regulation 
SmoothMuscleCellsUmbilicalVeinDonor3_CNhs13076_tpm_fwd SmcUmbilicalVeinD3+ Smooth Muscle Cells - Umbilical Vein, donor3_CNhs13076_11702-123B1_forward Regulation 
SmoothMuscleCellsUmbilicalVeinDonor2_CNhs12569_tpm_rev SmcUmbilicalVeinD2- Smooth Muscle Cells - Umbilical Vein, donor2_CNhs12569_11621-122B1_reverse Regulation 
SmoothMuscleCellsUmbilicalVeinDonor2_CNhs12569_tpm_fwd SmcUmbilicalVeinD2+ Smooth Muscle Cells - Umbilical Vein, donor2_CNhs12569_11621-122B1_forward Regulation 
SmoothMuscleCellsUmbilicalVeinDonor1_CNhs12597_tpm_rev SmcUmbilicalVeinD1- Smooth Muscle Cells - Umbilical Vein, donor1_CNhs12597_11541-120B2_reverse Regulation 
SmoothMuscleCellsUmbilicalVeinDonor1_CNhs12597_tpm_fwd SmcUmbilicalVeinD1+ Smooth Muscle Cells - Umbilical Vein, donor1_CNhs12597_11541-120B2_forward Regulation 
SmoothMuscleCellsUmbilicalArteryDonor3_CNhs12049_tpm_rev SmcUmbilicalArteryD3- Smooth Muscle Cells - Umbilical Artery, donor3_CNhs12049_11439-118H8_reverse Regulation 
SmoothMuscleCellsUmbilicalArteryDonor3_CNhs12049_tpm_fwd SmcUmbilicalArteryD3+ Smooth Muscle Cells - Umbilical Artery, donor3_CNhs12049_11439-118H8_forward Regulation 
SmoothMuscleCellsUmbilicalArteryDonor2_CNhs11991_tpm_rev SmcUmbilicalArteryD2- Smooth Muscle Cells - Umbilical Artery, donor2_CNhs11991_11367-117I8_reverse Regulation 
SmoothMuscleCellsUmbilicalArteryDonor2_CNhs11991_tpm_fwd SmcUmbilicalArteryD2+ Smooth Muscle Cells - Umbilical Artery, donor2_CNhs11991_11367-117I8_forward Regulation 
SmoothMuscleCellsUmbilicalArteryDonor1_CNhs11091_tpm_rev SmcUmbilicalArteryD1- Smooth Muscle Cells - Umbilical Artery, donor1_CNhs11091_11290-117A3_reverse Regulation 
SmoothMuscleCellsUmbilicalArteryDonor1_CNhs11091_tpm_fwd SmcUmbilicalArteryD1+ Smooth Muscle Cells - Umbilical Artery, donor1_CNhs11091_11290-117A3_forward Regulation 
SmoothMuscleCellsUmbilicalArteryDonor0_CNhs10839_tpm_rev SmcUmbilicalArteryD0- Smooth Muscle Cells - Umbilical artery, donor0_CNhs10839_11212-116A6_reverse Regulation 
SmoothMuscleCellsUmbilicalArteryDonor0_CNhs10839_tpm_fwd SmcUmbilicalArteryD0+ Smooth Muscle Cells - Umbilical artery, donor0_CNhs10839_11212-116A6_forward Regulation 
SmoothMuscleCellsTrachealDonor3_CNhs12894_tpm_rev SmcTrachealD3- Smooth Muscle Cells - Tracheal, donor3_CNhs12894_11674-122G9_reverse Regulation 
SmoothMuscleCellsTrachealDonor3_CNhs12894_tpm_fwd SmcTrachealD3+ Smooth Muscle Cells - Tracheal, donor3_CNhs12894_11674-122G9_forward Regulation 
SmoothMuscleCellsTrachealDonor2_CNhs12567_tpm_rev SmcTrachealD2- Smooth Muscle Cells - Tracheal, donor2_CNhs12567_11593-120G9_reverse Regulation 
SmoothMuscleCellsTrachealDonor2_CNhs12567_tpm_fwd SmcTrachealD2+ Smooth Muscle Cells - Tracheal, donor2_CNhs12567_11593-120G9_forward Regulation 
SmoothMuscleCellsTrachealDonor1_CNhs11329_tpm_rev SmcTrachealD1- Smooth Muscle Cells - Tracheal, donor1_CNhs11329_11513-119H1_reverse Regulation 
SmoothMuscleCellsTrachealDonor1_CNhs11329_tpm_fwd SmcTrachealD1+ Smooth Muscle Cells - Tracheal, donor1_CNhs11329_11513-119H1_forward Regulation 
SmoothMuscleCellsSubclavianArteryDonor3_CNhs12048_tpm_rev SmcSubclavianArteryD3- Smooth Muscle Cells - Subclavian Artery, donor3_CNhs12048_11438-118H7_reverse Regulation 
SmoothMuscleCellsSubclavianArteryDonor3_CNhs12048_tpm_fwd SmcSubclavianArteryD3+ Smooth Muscle Cells - Subclavian Artery, donor3_CNhs12048_11438-118H7_forward Regulation 
SmoothMuscleCellsSubclavianArteryDonor2_CNhs11990_tpm_rev SmcSubclavianArteryD2- Smooth Muscle Cells - Subclavian Artery, donor2_CNhs11990_11366-117I7_reverse Regulation 
SmoothMuscleCellsSubclavianArteryDonor2_CNhs11990_tpm_fwd SmcSubclavianArteryD2+ Smooth Muscle Cells - Subclavian Artery, donor2_CNhs11990_11366-117I7_forward Regulation 
SmoothMuscleCellsSubclavianArteryDonor1_CNhs11090_tpm_rev SmcSubclavianArteryD1- Smooth Muscle Cells - Subclavian Artery, donor1_CNhs11090_11289-117A2_reverse Regulation 
SmoothMuscleCellsSubclavianArteryDonor1_CNhs11090_tpm_fwd SmcSubclavianArteryD1+ Smooth Muscle Cells - Subclavian Artery, donor1_CNhs11090_11289-117A2_forward Regulation 
SmoothMuscleCellsPulmonaryArteryDonor3_CNhs12047_tpm_rev SmcPulmonaryArteryD3- Smooth Muscle Cells - Pulmonary Artery, donor3_CNhs12047_11437-118H6_reverse Regulation 
SmoothMuscleCellsPulmonaryArteryDonor3_CNhs12047_tpm_fwd SmcPulmonaryArteryD3+ Smooth Muscle Cells - Pulmonary Artery, donor3_CNhs12047_11437-118H6_forward Regulation 
SmoothMuscleCellsPulmonaryArteryDonor2_CNhs11989_tpm_rev SmcPulmonaryArteryD2- Smooth Muscle Cells - Pulmonary Artery, donor2_CNhs11989_11365-117I6_reverse Regulation 
SmoothMuscleCellsPulmonaryArteryDonor2_CNhs11989_tpm_fwd SmcPulmonaryArteryD2+ Smooth Muscle Cells - Pulmonary Artery, donor2_CNhs11989_11365-117I6_forward Regulation 
SmoothMuscleCellsPulmonaryArteryDonor1_CNhs11089_tpm_rev SmcPulmonaryArteryD1- Smooth Muscle Cells - Pulmonary Artery, donor1_CNhs11089_11288-117A1_reverse Regulation 
SmoothMuscleCellsPulmonaryArteryDonor1_CNhs11089_tpm_fwd SmcPulmonaryArteryD1+ Smooth Muscle Cells - Pulmonary Artery, donor1_CNhs11089_11288-117A1_forward Regulation 
SmoothMuscleCellsProstateDonor3_CNhs11910_tpm_rev SmcProstateD3- Smooth Muscle Cells - Prostate, donor3_CNhs11910_11465-119B7_reverse Regulation 
SmoothMuscleCellsProstateDonor3_CNhs11910_tpm_fwd SmcProstateD3+ Smooth Muscle Cells - Prostate, donor3_CNhs11910_11465-119B7_forward Regulation 
SmoothMuscleCellsProstateDonor2_CNhs11976_tpm_rev SmcProstateD2- Smooth Muscle Cells - Prostate, donor2_CNhs11976_11335-117F3_reverse Regulation 
SmoothMuscleCellsProstateDonor2_CNhs11976_tpm_fwd SmcProstateD2+ Smooth Muscle Cells - Prostate, donor2_CNhs11976_11335-117F3_forward Regulation 
SmoothMuscleCellsProstateDonor1_CNhs11920_tpm_rev SmcProstateD1- Smooth Muscle Cells - Prostate, donor1_CNhs11920_11257-116F6_reverse Regulation 
SmoothMuscleCellsProstateDonor1_CNhs11920_tpm_fwd SmcProstateD1+ Smooth Muscle Cells - Prostate, donor1_CNhs11920_11257-116F6_forward Regulation 
SmoothMuscleCellsIntestinalDonor1_CNhs12595_tpm_rev SmcIntestinalD1- Smooth Muscle Cells - Intestinal, donor1_CNhs12595_11509-119G6_reverse Regulation 
SmoothMuscleCellsIntestinalDonor1_CNhs12595_tpm_fwd SmcIntestinalD1+ Smooth Muscle Cells - Intestinal, donor1_CNhs12595_11509-119G6_forward Regulation 
SmoothMuscleCellsInternalThoracicArteryDonor3_CNhs12046_tpm_rev SmcInternalThoracicArteryD3- Smooth Muscle Cells - Internal Thoracic Artery, donor3_CNhs12046_11436-118H5_reverse Regulation 
SmoothMuscleCellsInternalThoracicArteryDonor3_CNhs12046_tpm_fwd SmcInternalThoracicArteryD3+ Smooth Muscle Cells - Internal Thoracic Artery, donor3_CNhs12046_11436-118H5_forward Regulation 
SmoothMuscleCellsInternalThoracicArteryDonor2_CNhs11988_tpm_rev SmcInternalThoracicArteryD2- Smooth Muscle Cells - Internal Thoracic Artery, donor2_CNhs11988_11364-117I5_reverse Regulation 
SmoothMuscleCellsInternalThoracicArteryDonor2_CNhs11988_tpm_fwd SmcInternalThoracicArteryD2+ Smooth Muscle Cells - Internal Thoracic Artery, donor2_CNhs11988_11364-117I5_forward Regulation 
SmoothMuscleCellsInternalThoracicArteryDonor1_CNhs11067_tpm_rev SmcInternalThoracicArteryD1- Smooth Muscle Cells - Internal Thoracic Artery, donor1_CNhs11067_11287-116I9_reverse Regulation 
SmoothMuscleCellsInternalThoracicArteryDonor1_CNhs11067_tpm_fwd SmcInternalThoracicArteryD1+ Smooth Muscle Cells - Internal Thoracic Artery, donor1_CNhs11067_11287-116I9_forward Regulation 
SmoothMuscleCellsEsophagealDonor2_CNhs12727_tpm_rev SmcEsophagealD2- Smooth Muscle Cells - Esophageal, donor2_CNhs12727_11588-120G4_reverse Regulation 
SmoothMuscleCellsEsophagealDonor2_CNhs12727_tpm_fwd SmcEsophagealD2+ Smooth Muscle Cells - Esophageal, donor2_CNhs12727_11588-120G4_forward Regulation 
SmoothMuscleCellsEsophagealDonor1_CNhs11324_tpm_rev SmcEsophagealD1- Smooth Muscle Cells - Esophageal, donor1_CNhs11324_11508-119G5_reverse Regulation 
SmoothMuscleCellsEsophagealDonor1_CNhs11324_tpm_fwd SmcEsophagealD1+ Smooth Muscle Cells - Esophageal, donor1_CNhs11324_11508-119G5_forward Regulation 
SmoothMuscleCellsCoronaryArteryDonor3_CNhs12045_tpm_rev SmcCoronaryArteryD3- Smooth Muscle Cells - Coronary Artery, donor3_CNhs12045_11435-118H4_reverse Regulation 
SmoothMuscleCellsCoronaryArteryDonor3_CNhs12045_tpm_fwd SmcCoronaryArteryD3+ Smooth Muscle Cells - Coronary Artery, donor3_CNhs12045_11435-118H4_forward Regulation 
SmoothMuscleCellsCoronaryArteryDonor2_CNhs11987_tpm_rev SmcCoronaryArteryD2- Smooth Muscle Cells - Coronary Artery, donor2_CNhs11987_11363-117I4_reverse Regulation 
SmoothMuscleCellsCoronaryArteryDonor2_CNhs11987_tpm_fwd SmcCoronaryArteryD2+ Smooth Muscle Cells - Coronary Artery, donor2_CNhs11987_11363-117I4_forward Regulation 
SmoothMuscleCellsCoronaryArteryDonor1_CNhs11088_tpm_rev SmcCoronaryArteryD1- Smooth Muscle Cells - Coronary Artery, donor1_CNhs11088_11286-116I8_reverse Regulation 
SmoothMuscleCellsCoronaryArteryDonor1_CNhs11088_tpm_fwd SmcCoronaryArteryD1+ Smooth Muscle Cells - Coronary Artery, donor1_CNhs11088_11286-116I8_forward Regulation 
SmoothMuscleCellsColonicDonor3_CNhs12007_tpm_rev SmcColonicD3- Smooth Muscle Cells - Colonic, donor3_CNhs12007_11396-118D1_reverse Regulation 
SmoothMuscleCellsColonicDonor3_CNhs12007_tpm_fwd SmcColonicD3+ Smooth Muscle Cells - Colonic, donor3_CNhs12007_11396-118D1_forward Regulation 
SmoothMuscleCellsColonicDonor2_CNhs11963_tpm_rev SmcColonicD2- Smooth Muscle Cells - Colonic, donor2_CNhs11963_11320-117D6_reverse Regulation 
SmoothMuscleCellsColonicDonor2_CNhs11963_tpm_fwd SmcColonicD2+ Smooth Muscle Cells - Colonic, donor2_CNhs11963_11320-117D6_forward Regulation 
SmoothMuscleCellsColonicDonor1_CNhs10868_tpm_rev SmcColonicD1- Smooth Muscle Cells - Colonic, donor1_CNhs10868_11239-116D6_reverse Regulation 
SmoothMuscleCellsColonicDonor1_CNhs10868_tpm_fwd SmcColonicD1+ Smooth Muscle Cells - Colonic, donor1_CNhs10868_11239-116D6_forward Regulation 
SmoothMuscleCellsCarotidDonor3_CNhs12044_tpm_rev SmcCarotidD3- Smooth Muscle Cells - Carotid, donor3_CNhs12044_11434-118H3_reverse Regulation 
SmoothMuscleCellsCarotidDonor3_CNhs12044_tpm_fwd SmcCarotidD3+ Smooth Muscle Cells - Carotid, donor3_CNhs12044_11434-118H3_forward Regulation 
SmoothMuscleCellsCarotidDonor2_CNhs11986_tpm_rev SmcCarotidD2- Smooth Muscle Cells - Carotid, donor2_CNhs11986_11362-117I3_reverse Regulation 
SmoothMuscleCellsCarotidDonor2_CNhs11986_tpm_fwd SmcCarotidD2+ Smooth Muscle Cells - Carotid, donor2_CNhs11986_11362-117I3_forward Regulation 
SmoothMuscleCellsCarotidDonor1_CNhs11087_tpm_rev SmcCarotidD1- Smooth Muscle Cells - Carotid, donor1_CNhs11087_11285-116I7_reverse Regulation 
SmoothMuscleCellsCarotidDonor1_CNhs11087_tpm_fwd SmcCarotidD1+ Smooth Muscle Cells - Carotid, donor1_CNhs11087_11285-116I7_forward Regulation 
SmoothMuscleCellsBronchialDonor2_CNhs12348_tpm_rev SmcBronchialD2- Smooth Muscle Cells - Bronchial, donor2_CNhs12348_11592-120G8_reverse Regulation 
SmoothMuscleCellsBronchialDonor2_CNhs12348_tpm_fwd SmcBronchialD2+ Smooth Muscle Cells - Bronchial, donor2_CNhs12348_11592-120G8_forward Regulation 
SmoothMuscleCellsBronchialDonor1_CNhs11328_tpm_rev SmcBronchialD1- Smooth Muscle Cells - Bronchial, donor1_CNhs11328_11512-119G9_reverse Regulation 
SmoothMuscleCellsBronchialDonor1_CNhs11328_tpm_fwd SmcBronchialD1+ Smooth Muscle Cells - Bronchial, donor1_CNhs11328_11512-119G9_forward Regulation 
SmoothMuscleCellsBrainVascularDonor3_CNhs12004_tpm_rev SmcBrainVascularD3- Smooth Muscle Cells - Brain Vascular, donor3_CNhs12004_11391-118C5_reverse Regulation 
SmoothMuscleCellsBrainVascularDonor3_CNhs12004_tpm_fwd SmcBrainVascularD3+ Smooth Muscle Cells - Brain Vascular, donor3_CNhs12004_11391-118C5_forward Regulation 
SmoothMuscleCellsBrainVascularDonor2_CNhs11900_tpm_rev SmcBrainVascularD2- Smooth Muscle Cells - Brain Vascular, donor2_CNhs11900_11315-117D1_reverse Regulation 
SmoothMuscleCellsBrainVascularDonor2_CNhs11900_tpm_fwd SmcBrainVascularD2+ Smooth Muscle Cells - Brain Vascular, donor2_CNhs11900_11315-117D1_forward Regulation 
SmoothMuscleCellsBrainVascularDonor1_CNhs10863_tpm_rev SmcBrainVascularD1- Smooth Muscle Cells - Brain Vascular, donor1_CNhs10863_11234-116D1_reverse Regulation 
SmoothMuscleCellsBrainVascularDonor1_CNhs10863_tpm_fwd SmcBrainVascularD1+ Smooth Muscle Cells - Brain Vascular, donor1_CNhs10863_11234-116D1_forward Regulation 
SmoothMuscleCellsBrachiocephalicDonor3_CNhs12043_tpm_rev SmcBrachiocephalicD3- Smooth Muscle Cells - Brachiocephalic, donor3_CNhs12043_11433-118H2_reverse Regulation 
SmoothMuscleCellsBrachiocephalicDonor3_CNhs12043_tpm_fwd SmcBrachiocephalicD3+ Smooth Muscle Cells - Brachiocephalic, donor3_CNhs12043_11433-118H2_forward Regulation 
SmoothMuscleCellsBrachiocephalicDonor2_CNhs11985_tpm_rev SmcBrachiocephalicD2- Smooth Muscle Cells - Brachiocephalic, donor2_CNhs11985_11361-117I2_reverse Regulation 
SmoothMuscleCellsBrachiocephalicDonor2_CNhs11985_tpm_fwd SmcBrachiocephalicD2+ Smooth Muscle Cells - Brachiocephalic, donor2_CNhs11985_11361-117I2_forward Regulation 
SmoothMuscleCellsBrachiocephalicDonor1_CNhs11086_tpm_rev SmcBrachiocephalicD1- Smooth Muscle Cells - Brachiocephalic, donor1_CNhs11086_11284-116I6_reverse Regulation 
SmoothMuscleCellsBrachiocephalicDonor1_CNhs11086_tpm_fwd SmcBrachiocephalicD1+ Smooth Muscle Cells - Brachiocephalic, donor1_CNhs11086_11284-116I6_forward Regulation 
SmoothMuscleCellsBladderDonor1_CNhs12893_tpm_rev SmcBladderD1- Smooth Muscle Cells - Bladder, donor1_CNhs12893_11519-119H7_reverse Regulation 
SmoothMuscleCellsBladderDonor1_CNhs12893_tpm_fwd SmcBladderD1+ Smooth Muscle Cells - Bladder, donor1_CNhs12893_11519-119H7_forward Regulation 
SmoothMuscleCellsAorticDonor3_CNhs11309_tpm_rev SmcAorticCytofracD3- Smooth Muscle Cells - Aortic, donor3_CNhs11309_11432-118H1_reverse Regulation 
SmoothMuscleCellsAorticDonor3_CNhs11309_tpm_fwd SmcAorticCytofracD3+ Smooth Muscle Cells - Aortic, donor3_CNhs11309_11432-118H1_forward Regulation 
SmoothMuscleCellsAorticDonor2_CNhs11305_tpm_rev SmcAorticCytofracD2- Smooth Muscle Cells - Aortic, donor2_CNhs11305_11360-117I1_reverse Regulation 
SmoothMuscleCellsAorticDonor2_CNhs11305_tpm_fwd SmcAorticCytofracD2+ Smooth Muscle Cells - Aortic, donor2_CNhs11305_11360-117I1_forward Regulation 
SmoothMuscleCellsAorticDonor1_CNhs11085_tpm_rev SmcAorticCytofracD1- Smooth Muscle Cells - Aortic, donor1_CNhs11085_11283-116I5_reverse Regulation 
SmoothMuscleCellsAorticDonor1_CNhs11085_tpm_fwd SmcAorticCytofracD1+ Smooth Muscle Cells - Aortic, donor1_CNhs11085_11283-116I5_forward Regulation 
SmoothMuscleCellsAorticDonor0_CNhs10838_tpm_rev SmcAorticCytofracD0- Smooth Muscle Cells - Aortic, donor0_CNhs10838_11210-116A4_reverse Regulation 
SmoothMuscleCellsAorticDonor0_CNhs10838_tpm_fwd SmcAorticCytofracD0+ Smooth Muscle Cells - Aortic, donor0_CNhs10838_11210-116A4_forward Regulation 
SmoothMuscleCellsAirwayControlDonor4_CNhs14193_tpm_rev SmcAirwayControlD4- Smooth muscle cells - airway, control, donor4_CNhs14193_11969-126D7_reverse Regulation 
SmoothMuscleCellsAirwayControlDonor4_CNhs14193_tpm_fwd SmcAirwayControlD4+ Smooth muscle cells - airway, control, donor4_CNhs14193_11969-126D7_forward Regulation 
SmoothMuscleCellsAirwayControlDonor3_CNhs14192_tpm_rev SmcAirwayControlD3- Smooth muscle cells - airway, control, donor3_CNhs14192_11968-126D6_reverse Regulation 
SmoothMuscleCellsAirwayControlDonor3_CNhs14192_tpm_fwd SmcAirwayControlD3+ Smooth muscle cells - airway, control, donor3_CNhs14192_11968-126D6_forward Regulation 
SmoothMuscleCellsAirwayControlDonor2_CNhs14191_tpm_rev SmcAirwayControlD2- Smooth muscle cells - airway, control, donor2_CNhs14191_11967-126D5_reverse Regulation 
SmoothMuscleCellsAirwayControlDonor2_CNhs14191_tpm_fwd SmcAirwayControlD2+ Smooth muscle cells - airway, control, donor2_CNhs14191_11967-126D5_forward Regulation 
SmoothMuscleCellsAirwayControlDonor1_CNhs14190_tpm_rev SmcAirwayControlD1- Smooth muscle cells - airway, control, donor1_CNhs14190_11966-126D4_reverse Regulation 
SmoothMuscleCellsAirwayControlDonor1_CNhs14190_tpm_fwd SmcAirwayControlD1+ Smooth muscle cells - airway, control, donor1_CNhs14190_11966-126D4_forward Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor6_CNhs14189_tpm_rev SmcAirwayAsthmaD6- Smooth muscle cells - airway, asthmatic, donor6_CNhs14189_11965-126D3_reverse Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor6_CNhs14189_tpm_fwd SmcAirwayAsthmaD6+ Smooth muscle cells - airway, asthmatic, donor6_CNhs14189_11965-126D3_forward Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor5_CNhs14188_tpm_rev SmcAirwayAsthmaD5- Smooth muscle cells - airway, asthmatic, donor5_CNhs14188_11964-126D2_reverse Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor5_CNhs14188_tpm_fwd SmcAirwayAsthmaD5+ Smooth muscle cells - airway, asthmatic, donor5_CNhs14188_11964-126D2_forward Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor4_CNhs14187_tpm_rev SmcAirwayAsthmaD4- Smooth muscle cells - airway, asthmatic, donor4_CNhs14187_11963-126D1_reverse Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor4_CNhs14187_tpm_fwd SmcAirwayAsthmaD4+ Smooth muscle cells - airway, asthmatic, donor4_CNhs14187_11963-126D1_forward Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor3_CNhs14186_tpm_rev SmcAirwayAsthmaD3- Smooth muscle cells - airway, asthmatic, donor3_CNhs14186_11962-126C9_reverse Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor3_CNhs14186_tpm_fwd SmcAirwayAsthmaD3+ Smooth muscle cells - airway, asthmatic, donor3_CNhs14186_11962-126C9_forward Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor2_CNhs14184_tpm_rev SmcAirwayAsthmaD2- Smooth muscle cells - airway, asthmatic, donor2_CNhs14184_11961-126C8_reverse Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor2_CNhs14184_tpm_fwd SmcAirwayAsthmaD2+ Smooth muscle cells - airway, asthmatic, donor2_CNhs14184_11961-126C8_forward Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor1_CNhs14183_tpm_rev SmcAirwayAsthmaD1- Smooth muscle cells - airway, asthmatic, donor1_CNhs14183_11960-126C7_reverse Regulation 
SmoothMuscleCellsAirwayAsthmaticDonor1_CNhs14183_tpm_fwd SmcAirwayAsthmaD1+ Smooth muscle cells - airway, asthmatic, donor1_CNhs14183_11960-126C7_forward Regulation 
SmallAirwayEpithelialCellsDonor3_CNhs12016_tpm_rev SmallAirwayEpithelialCellsD3- Small Airway Epithelial Cells, donor3_CNhs12016_11406-118E2_reverse Regulation 
SmallAirwayEpithelialCellsDonor3_CNhs12016_tpm_fwd SmallAirwayEpithelialCellsD3+ Small Airway Epithelial Cells, donor3_CNhs12016_11406-118E2_forward Regulation 
SmallAirwayEpithelialCellsDonor2_CNhs11975_tpm_rev SmallAirwayEpithelialCellsD2- Small Airway Epithelial Cells, donor2_CNhs11975_11334-117F2_reverse Regulation 
SmallAirwayEpithelialCellsDonor2_CNhs11975_tpm_fwd SmallAirwayEpithelialCellsD2+ Small Airway Epithelial Cells, donor2_CNhs11975_11334-117F2_forward Regulation 
SmallAirwayEpithelialCellsDonor1_CNhs10884_tpm_rev SmallAirwayEpithelialCellsD1- Small Airway Epithelial Cells, donor1_CNhs10884_11256-116F5_reverse Regulation 
SmallAirwayEpithelialCellsDonor1_CNhs10884_tpm_fwd SmallAirwayEpithelialCellsD1+ Small Airway Epithelial Cells, donor1_CNhs10884_11256-116F5_forward Regulation 
SkeletalMuscleSatelliteCellsDonor3_CNhs12008_tpm_rev SkeletalMuscleSatelliteCellsD3- Skeletal Muscle Satellite Cells, donor3_CNhs12008_11397-118D2_reverse Regulation 
SkeletalMuscleSatelliteCellsDonor3_CNhs12008_tpm_fwd SkeletalMuscleSatelliteCellsD3+ Skeletal Muscle Satellite Cells, donor3_CNhs12008_11397-118D2_forward Regulation 
SkeletalMuscleSatelliteCellsDonor2_CNhs11964_tpm_rev SkeletalMuscleSatelliteCellsD2- Skeletal Muscle Satellite Cells, donor2_CNhs11964_11321-117D7_reverse Regulation 
SkeletalMuscleSatelliteCellsDonor2_CNhs11964_tpm_fwd SkeletalMuscleSatelliteCellsD2+ Skeletal Muscle Satellite Cells, donor2_CNhs11964_11321-117D7_forward Regulation 
SkeletalMuscleSatelliteCellsDonor1_CNhs10869_tpm_rev SkeletalMuscleSatelliteCellsD1- Skeletal Muscle Satellite Cells, donor1_CNhs10869_11240-116D7_reverse Regulation 
SkeletalMuscleSatelliteCellsDonor1_CNhs10869_tpm_fwd SkeletalMuscleSatelliteCellsD1+ Skeletal Muscle Satellite Cells, donor1_CNhs10869_11240-116D7_forward Regulation 
SkeletalMuscleCellsDifferentiatedIntoMyotubesMultinucleatedDonor3_CNhs12041_tpm_rev SkeletalMuscleCellsIntoMyotubesD3- Skeletal muscle cells differentiated into Myotubes - multinucleated, donor3_CNhs12041_11431-118G9_reverse Regulation 
SkeletalMuscleCellsDifferentiatedIntoMyotubesMultinucleatedDonor3_CNhs12041_tpm_fwd SkeletalMuscleCellsIntoMyotubesD3+ Skeletal muscle cells differentiated into Myotubes - multinucleated, donor3_CNhs12041_11431-118G9_forward Regulation 
SkeletalMuscleCellsDifferentiatedIntoMyotubesMultinucleatedDonor2_CNhs11984_tpm_rev SkeletalMuscleCellsIntoMyotubesD2- Skeletal muscle cells differentiated into Myotubes - multinucleated, donor2_CNhs11984_11359-117H9_reverse Regulation 
SkeletalMuscleCellsDifferentiatedIntoMyotubesMultinucleatedDonor2_CNhs11984_tpm_fwd SkeletalMuscleCellsIntoMyotubesD2+ Skeletal muscle cells differentiated into Myotubes - multinucleated, donor2_CNhs11984_11359-117H9_forward Regulation 
SkeletalMuscleCellsDifferentiatedIntoMyotubesMultinucleatedDonor1_CNhs11084_tpm_rev SkeletalMuscleCellsIntoMyotubesD1- Skeletal muscle cells differentiated into Myotubes - multinucleated, donor1_CNhs11084_11282-116I4_reverse Regulation 
SkeletalMuscleCellsDifferentiatedIntoMyotubesMultinucleatedDonor1_CNhs11084_tpm_fwd SkeletalMuscleCellsIntoMyotubesD1+ Skeletal muscle cells differentiated into Myotubes - multinucleated, donor1_CNhs11084_11282-116I4_forward Regulation 
SkeletalMuscleCellsDonor6_CNhs12060_tpm_rev SkeletalMuscleCellsD6- Skeletal Muscle Cells, donor6_CNhs12060_11459-119B1_reverse Regulation 
SkeletalMuscleCellsDonor6_CNhs12060_tpm_fwd SkeletalMuscleCellsD6+ Skeletal Muscle Cells, donor6_CNhs12060_11459-119B1_forward Regulation 
SkeletalMuscleCellsDonor5_CNhs12056_tpm_rev SkeletalMuscleCellsD5- Skeletal Muscle Cells, donor5_CNhs12056_11455-119A6_reverse Regulation 
SkeletalMuscleCellsDonor5_CNhs12056_tpm_fwd SkeletalMuscleCellsD5+ Skeletal Muscle Cells, donor5_CNhs12056_11455-119A6_forward Regulation 
SkeletalMuscleCellsDonor4_CNhs12053_tpm_rev SkeletalMuscleCellsD4- Skeletal Muscle Cells, donor4_CNhs12053_11451-119A2_reverse Regulation 
SkeletalMuscleCellsDonor4_CNhs12053_tpm_fwd SkeletalMuscleCellsD4+ Skeletal Muscle Cells, donor4_CNhs12053_11451-119A2_forward Regulation 
SkeletalMuscleCellsDonor3_CNhs12040_tpm_rev SkeletalMuscleCellsD3- Skeletal Muscle Cells, donor3_CNhs12040_11430-118G8_reverse Regulation 
SkeletalMuscleCellsDonor3_CNhs12040_tpm_fwd SkeletalMuscleCellsD3+ Skeletal Muscle Cells, donor3_CNhs12040_11430-118G8_forward Regulation 
SkeletalMuscleCellsDonor2_CNhs11983_tpm_rev SkeletalMuscleCellsD2- Skeletal Muscle Cells, donor2_CNhs11983_11358-117H8_reverse Regulation 
SkeletalMuscleCellsDonor2_CNhs11983_tpm_fwd SkeletalMuscleCellsD2+ Skeletal Muscle Cells, donor2_CNhs11983_11358-117H8_forward Regulation 
SkeletalMuscleCellsDonor1_CNhs11083_tpm_rev SkeletalMuscleCellsD1- Skeletal Muscle Cells, donor1_CNhs11083_11281-116I3_reverse Regulation 
SkeletalMuscleCellsDonor1_CNhs11083_tpm_fwd SkeletalMuscleCellsD1+ Skeletal Muscle Cells, donor1_CNhs11083_11281-116I3_forward Regulation 
SertoliCellsDonor2_CNhs11974_tpm_rev SertoliCellsD2- Sertoli Cells, donor2_CNhs11974_11333-117F1_reverse Regulation 
SertoliCellsDonor2_CNhs11974_tpm_fwd SertoliCellsD2+ Sertoli Cells, donor2_CNhs11974_11333-117F1_forward Regulation 
SertoliCellsDonor1_CNhs10851_tpm_rev SertoliCellsD1- Sertoli Cells, donor1_CNhs10851_11255-116F4_reverse Regulation 
SertoliCellsDonor1_CNhs10851_tpm_fwd SertoliCellsD1+ Sertoli Cells, donor1_CNhs10851_11255-116F4_forward Regulation 
SebocyteDonor3_CNhs11995_tpm_rev SebocyteD3- Sebocyte, donor3_CNhs11995_11378-118B1_reverse Regulation 
SebocyteDonor3_CNhs11995_tpm_fwd SebocyteD3+ Sebocyte, donor3_CNhs11995_11378-118B1_forward Regulation 
SebocyteDonor2_CNhs11951_tpm_rev SebocyteD2- Sebocyte, donor2_CNhs11951_11301-117B5_reverse Regulation 
SebocyteDonor2_CNhs11951_tpm_fwd SebocyteD2+ Sebocyte, donor2_CNhs11951_11301-117B5_forward Regulation 
SebocyteDonor1_CNhs10847_tpm_rev SebocyteD1- Sebocyte, donor1_CNhs10847_11220-116B5_reverse Regulation 
SebocyteDonor1_CNhs10847_tpm_fwd SebocyteD1+ Sebocyte, donor1_CNhs10847_11220-116B5_forward Regulation 
SchwannCellsDonor3_CNhs12621_tpm_rev SchwannCellsD3- Schwann Cells, donor3_CNhs12621_11659-122F3_reverse Regulation 
SchwannCellsDonor3_CNhs12621_tpm_fwd SchwannCellsD3+ Schwann Cells, donor3_CNhs12621_11659-122F3_forward Regulation 
SchwannCellsDonor2_CNhs12345_tpm_rev SchwannCellsD2- Schwann Cells, donor2_CNhs12345_11578-120F3_reverse Regulation 
SchwannCellsDonor2_CNhs12345_tpm_fwd SchwannCellsD2+ Schwann Cells, donor2_CNhs12345_11578-120F3_forward Regulation 
SchwannCellsDonor1_CNhs12073_tpm_rev SchwannCellsD1- Schwann Cells, donor1_CNhs12073_11498-119F4_reverse Regulation 
SchwannCellsDonor1_CNhs12073_tpm_fwd SchwannCellsD1+ Schwann Cells, donor1_CNhs12073_11498-119F4_forward Regulation 
SalivaryAcinarCellsDonor3_CNhs12812_tpm_rev SalivaryAcinarCellsD3- salivary acinar cells, donor3_CNhs12812_11773-123I9_reverse Regulation 
SalivaryAcinarCellsDonor3_CNhs12812_tpm_fwd SalivaryAcinarCellsD3+ salivary acinar cells, donor3_CNhs12812_11773-123I9_forward Regulation 
SalivaryAcinarCellsDonor2_CNhs12811_tpm_rev SalivaryAcinarCellsD2- salivary acinar cells, donor2_CNhs12811_11772-123I8_reverse Regulation 
SalivaryAcinarCellsDonor2_CNhs12811_tpm_fwd SalivaryAcinarCellsD2+ salivary acinar cells, donor2_CNhs12811_11772-123I8_forward Regulation 
SalivaryAcinarCellsDonor1_CNhs12810_tpm_rev SalivaryAcinarCellsD1- salivary acinar cells, donor1_CNhs12810_11771-123I7_reverse Regulation 
SalivaryAcinarCellsDonor1_CNhs12810_tpm_fwd SalivaryAcinarCellsD1+ salivary acinar cells, donor1_CNhs12810_11771-123I7_forward Regulation 
RenalProximalTubularEpithelialCellDonor3_CNhs12120_tpm_rev RptecD3- Renal Proximal Tubular Epithelial Cell, donor3_CNhs12120_11676-122H2_reverse Regulation 
RenalProximalTubularEpithelialCellDonor3_CNhs12120_tpm_fwd RptecD3+ Renal Proximal Tubular Epithelial Cell, donor3_CNhs12120_11676-122H2_forward Regulation 
RenalProximalTubularEpithelialCellDonor2_CNhs12087_tpm_rev RptecD2- Renal Proximal Tubular Epithelial Cell, donor2_CNhs12087_11595-120H2_reverse Regulation 
RenalProximalTubularEpithelialCellDonor2_CNhs12087_tpm_fwd RptecD2+ Renal Proximal Tubular Epithelial Cell, donor2_CNhs12087_11595-120H2_forward Regulation 
RenalProximalTubularEpithelialCellDonor1_CNhs11330_tpm_rev RptecD1- Renal Proximal Tubular Epithelial Cell, donor1_CNhs11330_11515-119H3_reverse Regulation 
RenalProximalTubularEpithelialCellDonor1_CNhs11330_tpm_fwd RptecD1+ Renal Proximal Tubular Epithelial Cell, donor1_CNhs11330_11515-119H3_forward Regulation 
RetinalPigmentEpithelialCellsDonor3_CNhs12733_tpm_rev RpecD3- Retinal Pigment Epithelial Cells, donor3_CNhs12733_11689-122I6_reverse Regulation 
RetinalPigmentEpithelialCellsDonor3_CNhs12733_tpm_fwd RpecD3+ Retinal Pigment Epithelial Cells, donor3_CNhs12733_11689-122I6_forward Regulation 
RetinalPigmentEpithelialCellsDonor2_CNhs12096_tpm_rev RpecD2- Retinal Pigment Epithelial Cells, donor2_CNhs12096_11608-120I6_reverse Regulation 
RetinalPigmentEpithelialCellsDonor2_CNhs12096_tpm_fwd RpecD2+ Retinal Pigment Epithelial Cells, donor2_CNhs12096_11608-120I6_forward Regulation 
RetinalPigmentEpithelialCellsDonor1_CNhs11338_tpm_rev RpecD1- Retinal Pigment Epithelial Cells, donor1_CNhs11338_11528-119I7_reverse Regulation 
RetinalPigmentEpithelialCellsDonor1_CNhs11338_tpm_fwd RpecD1+ Retinal Pigment Epithelial Cells, donor1_CNhs11338_11528-119I7_forward Regulation 
RetinalPigmentEpithelialCellsDonor0_CNhs10842_tpm_rev RpecD0- Retinal Pigment Epithelial Cells, donor0_CNhs10842_11215-116A9_reverse Regulation 
RetinalPigmentEpithelialCellsDonor0_CNhs10842_tpm_fwd RpecD0+ Retinal Pigment Epithelial Cells, donor0_CNhs10842_11215-116A9_forward Regulation 
RenalGlomerularEndothelialCellsDonor4_CNhs13080_tpm_rev RgecD4- Renal Glomerular Endothelial Cells, donor4_CNhs13080_11783-124B1_reverse Regulation 
RenalGlomerularEndothelialCellsDonor4_CNhs13080_tpm_fwd RgecD4+ Renal Glomerular Endothelial Cells, donor4_CNhs13080_11783-124B1_forward Regulation 
RenalGlomerularEndothelialCellsDonor3_CNhs12624_tpm_rev RgecD3- Renal Glomerular Endothelial Cells, donor3_CNhs12624_11675-122H1_reverse Regulation 
RenalGlomerularEndothelialCellsDonor3_CNhs12624_tpm_fwd RgecD3+ Renal Glomerular Endothelial Cells, donor3_CNhs12624_11675-122H1_forward Regulation 
RenalGlomerularEndothelialCellsDonor2_CNhs12086_tpm_rev RgecD2- Renal Glomerular Endothelial Cells, donor2_CNhs12086_11594-120H1_reverse Regulation 
RenalGlomerularEndothelialCellsDonor2_CNhs12086_tpm_fwd RgecD2+ Renal Glomerular Endothelial Cells, donor2_CNhs12086_11594-120H1_forward Regulation 
RenalGlomerularEndothelialCellsDonor1_CNhs12074_tpm_rev RgecD1- Renal Glomerular Endothelial Cells, donor1_CNhs12074_11514-119H2_reverse Regulation 
RenalGlomerularEndothelialCellsDonor1_CNhs12074_tpm_fwd RgecD1+ Renal Glomerular Endothelial Cells, donor1_CNhs12074_11514-119H2_forward Regulation 
RenalMesangialCellsDonor3_CNhs12121_tpm_rev RenalMesangialCellsD3- Renal Mesangial Cells, donor3_CNhs12121_11679-122H5_reverse Regulation 
RenalMesangialCellsDonor3_CNhs12121_tpm_fwd RenalMesangialCellsD3+ Renal Mesangial Cells, donor3_CNhs12121_11679-122H5_forward Regulation 
RenalMesangialCellsDonor2_CNhs12089_tpm_rev RenalMesangialCellsD2- Renal Mesangial Cells, donor2_CNhs12089_11598-120H5_reverse Regulation 
RenalMesangialCellsDonor2_CNhs12089_tpm_fwd RenalMesangialCellsD2+ Renal Mesangial Cells, donor2_CNhs12089_11598-120H5_forward Regulation 
RenalMesangialCellsDonor1_CNhs11333_tpm_rev RenalMesangialCellsD1- Renal Mesangial Cells, donor1_CNhs11333_11518-119H6_reverse Regulation 
RenalMesangialCellsDonor1_CNhs11333_tpm_fwd RenalMesangialCellsD1+ Renal Mesangial Cells, donor1_CNhs11333_11518-119H6_forward Regulation 
RenalEpithelialCellsDonor3_CNhs12732_tpm_rev RenalEpithelialCellsD3- Renal Epithelial Cells, donor3_CNhs12732_11678-122H4_reverse Regulation 
RenalEpithelialCellsDonor3_CNhs12732_tpm_fwd RenalEpithelialCellsD3+ Renal Epithelial Cells, donor3_CNhs12732_11678-122H4_forward Regulation 
RenalEpithelialCellsDonor2_CNhs12088_tpm_rev RenalEpithelialCellsD2- Renal Epithelial Cells, donor2_CNhs12088_11597-120H4_reverse Regulation 
RenalEpithelialCellsDonor2_CNhs12088_tpm_fwd RenalEpithelialCellsD2+ Renal Epithelial Cells, donor2_CNhs12088_11597-120H4_forward Regulation 
RenalEpithelialCellsDonor1_CNhs11332_tpm_rev RenalEpithelialCellsD1- Renal Epithelial Cells, donor1_CNhs11332_11517-119H5_reverse Regulation 
RenalEpithelialCellsDonor1_CNhs11332_tpm_fwd RenalEpithelialCellsD1+ Renal Epithelial Cells, donor1_CNhs11332_11517-119H5_forward Regulation 
RenalCorticalEpithelialCellsDonor2_CNhs12728_tpm_rev RcecD2- Renal Cortical Epithelial Cells, donor2_CNhs12728_11596-120H3_reverse Regulation 
RenalCorticalEpithelialCellsDonor2_CNhs12728_tpm_fwd RcecD2+ Renal Cortical Epithelial Cells, donor2_CNhs12728_11596-120H3_forward Regulation 
RenalCorticalEpithelialCellsDonor1_CNhs11331_tpm_rev RcecD1- Renal Cortical Epithelial Cells, donor1_CNhs11331_11516-119H4_reverse Regulation 
RenalCorticalEpithelialCellsDonor1_CNhs11331_tpm_fwd RcecD1+ Renal Cortical Epithelial Cells, donor1_CNhs11331_11516-119H4_forward Regulation 
ProstateStromalCellsDonor3_CNhs12015_tpm_rev ProstateStromalCellsD3- Prostate Stromal Cells, donor3_CNhs12015_11405-118E1_reverse Regulation 
ProstateStromalCellsDonor3_CNhs12015_tpm_fwd ProstateStromalCellsD3+ Prostate Stromal Cells, donor3_CNhs12015_11405-118E1_forward Regulation 
ProstateStromalCellsDonor2_CNhs11973_tpm_rev ProstateStromalCellsD2- Prostate Stromal Cells, donor2_CNhs11973_11332-117E9_reverse Regulation 
ProstateStromalCellsDonor2_CNhs11973_tpm_fwd ProstateStromalCellsD2+ Prostate Stromal Cells, donor2_CNhs11973_11332-117E9_forward Regulation 
ProstateStromalCellsDonor1_CNhs10883_tpm_rev ProstateStromalCellsD1- Prostate Stromal Cells, donor1_CNhs10883_11254-116F3_reverse Regulation 
ProstateStromalCellsDonor1_CNhs10883_tpm_fwd ProstateStromalCellsD1+ Prostate Stromal Cells, donor1_CNhs10883_11254-116F3_forward Regulation 
ProstateEpithelialCellsDonor3_CNhs12014_tpm_rev ProstateEpithelialCellsD3- Prostate Epithelial Cells, donor3_CNhs12014_11404-118D9_reverse Regulation 
ProstateEpithelialCellsDonor3_CNhs12014_tpm_fwd ProstateEpithelialCellsD3+ Prostate Epithelial Cells, donor3_CNhs12014_11404-118D9_forward Regulation 
ProstateEpithelialCellsDonor2_CNhs11972_tpm_rev ProstateEpithelialCellsD2- Prostate Epithelial Cells, donor2_CNhs11972_11331-117E8_reverse Regulation 
ProstateEpithelialCellsDonor2_CNhs11972_tpm_fwd ProstateEpithelialCellsD2+ Prostate Epithelial Cells, donor2_CNhs11972_11331-117E8_forward Regulation 
ProstateEpithelialCellsPolarizedDonor1_CNhs10882_tpm_rev ProstateEpithelialCellsD1- Prostate Epithelial Cells (polarized), donor1_CNhs10882_11253-116F2_reverse Regulation 
ProstateEpithelialCellsPolarizedDonor1_CNhs10882_tpm_fwd ProstateEpithelialCellsD1+ Prostate Epithelial Cells (polarized), donor1_CNhs10882_11253-116F2_forward Regulation 
PreadipocyteVisceralDonor3_CNhs12039_tpm_rev PreadipocyteVisceralD3- Preadipocyte - visceral, donor3_CNhs12039_11429-118G7_reverse Regulation 
PreadipocyteVisceralDonor3_CNhs12039_tpm_fwd PreadipocyteVisceralD3+ Preadipocyte - visceral, donor3_CNhs12039_11429-118G7_forward Regulation 
PreadipocyteVisceralDonor2_CNhs11982_tpm_rev PreadipocyteVisceralD2- Preadipocyte - visceral, donor2_CNhs11982_11357-117H7_reverse Regulation 
PreadipocyteVisceralDonor2_CNhs11982_tpm_fwd PreadipocyteVisceralD2+ Preadipocyte - visceral, donor2_CNhs11982_11357-117H7_forward Regulation 
PreadipocyteVisceralDonor1_CNhs11082_tpm_rev PreadipocyteVisceralD1- Preadipocyte - visceral, donor1_CNhs11082_11280-116I2_reverse Regulation 
PreadipocyteVisceralDonor1_CNhs11082_tpm_fwd PreadipocyteVisceralD1+ Preadipocyte - visceral, donor1_CNhs11082_11280-116I2_forward Regulation 
PreadipocyteSubcutaneousDonor3_CNhs12038_tpm_rev PreadipocyteSubcutaneousD3- Preadipocyte - subcutaneous, donor3_CNhs12038_11428-118G6_reverse Regulation 
PreadipocyteSubcutaneousDonor3_CNhs12038_tpm_fwd PreadipocyteSubcutaneousD3+ Preadipocyte - subcutaneous, donor3_CNhs12038_11428-118G6_forward Regulation 
PreadipocyteSubcutaneousDonor2_CNhs11981_tpm_rev PreadipocyteSubcutaneousD2- Preadipocyte - subcutaneous, donor2_CNhs11981_11356-117H6_reverse Regulation 
PreadipocyteSubcutaneousDonor2_CNhs11981_tpm_fwd PreadipocyteSubcutaneousD2+ Preadipocyte - subcutaneous, donor2_CNhs11981_11356-117H6_forward Regulation 
PreadipocyteSubcutaneousDonor1_CNhs11081_tpm_rev PreadipocyteSubcutaneousD1- Preadipocyte - subcutaneous, donor1_CNhs11081_11279-116I1_reverse Regulation 
PreadipocyteSubcutaneousDonor1_CNhs11081_tpm_fwd PreadipocyteSubcutaneousD1+ Preadipocyte - subcutaneous, donor1_CNhs11081_11279-116I1_forward Regulation 
PreadipocytePerirenalDonor1_CNhs12065_tpm_rev PreadipocytePerirenalD1- Preadipocyte - perirenal, donor1_CNhs12065_11469-119C2_reverse Regulation 
PreadipocytePerirenalDonor1_CNhs12065_tpm_fwd PreadipocytePerirenalD1+ Preadipocyte - perirenal, donor1_CNhs12065_11469-119C2_forward Regulation 
PreadipocyteOmentalDonor3_CNhs12013_tpm_rev PreadipocyteOmentalD3- Preadipocyte - omental, donor3_CNhs12013_11403-118D8_reverse Regulation 
PreadipocyteOmentalDonor3_CNhs12013_tpm_fwd PreadipocyteOmentalD3+ Preadipocyte - omental, donor3_CNhs12013_11403-118D8_forward Regulation 
PreadipocyteOmentalDonor2_CNhs11902_tpm_rev PreadipocyteOmentalD2- Preadipocyte - omental, donor2_CNhs11902_11329-117E6_reverse Regulation 
PreadipocyteOmentalDonor2_CNhs11902_tpm_fwd PreadipocyteOmentalD2+ Preadipocyte - omental, donor2_CNhs11902_11329-117E6_forward Regulation 
PreadipocyteOmentalDonor1_CNhs11065_tpm_rev PreadipocyteOmentalD1- Preadipocyte - omental, donor1_CNhs11065_11468-119C1_reverse Regulation 
PreadipocyteOmentalDonor1_CNhs11065_tpm_fwd PreadipocyteOmentalD1+ Preadipocyte - omental, donor1_CNhs11065_11468-119C1_forward Regulation 
PreadipocyteBreastDonor2_CNhs11971_tpm_rev PreadipocyteBreastD2- Preadipocyte - breast, donor2_CNhs11971_11328-117E5_reverse Regulation 
PreadipocyteBreastDonor2_CNhs11971_tpm_fwd PreadipocyteBreastD2+ Preadipocyte - breast, donor2_CNhs11971_11328-117E5_forward Regulation 
PreadipocyteBreastDonor1_CNhs11052_tpm_rev PreadipocyteBreastD1- Preadipocyte - breast, donor1_CNhs11052_11467-119B9_reverse Regulation 
PreadipocyteBreastDonor1_CNhs11052_tpm_fwd PreadipocyteBreastD1+ Preadipocyte - breast, donor1_CNhs11052_11467-119B9_forward Regulation 
PlacentalEpithelialCellsDonor3_CNhs12037_tpm_rev PlacentalEpithelialCellsD3- Placental Epithelial Cells, donor3_CNhs12037_11427-118G5_reverse Regulation 
PlacentalEpithelialCellsDonor3_CNhs12037_tpm_fwd PlacentalEpithelialCellsD3+ Placental Epithelial Cells, donor3_CNhs12037_11427-118G5_forward Regulation 
PlacentalEpithelialCellsDonor2_CNhs11386_tpm_rev PlacentalEpithelialCellsD2- Placental Epithelial Cells, donor2_CNhs11386_11355-117H5_reverse Regulation 
PlacentalEpithelialCellsDonor2_CNhs11386_tpm_fwd PlacentalEpithelialCellsD2+ Placental Epithelial Cells, donor2_CNhs11386_11355-117H5_forward Regulation 
PlacentalEpithelialCellsDonor1_CNhs11079_tpm_rev PlacentalEpithelialCellsD1- Placental Epithelial Cells, donor1_CNhs11079_11278-116H9_reverse Regulation 
PlacentalEpithelialCellsDonor1_CNhs11079_tpm_fwd PlacentalEpithelialCellsD1+ Placental Epithelial Cells, donor1_CNhs11079_11278-116H9_forward Regulation 
PeripheralBloodMononuclearCellsDonor3_CNhs12002_tpm_rev PeripheralBloodMononuclearCellsD3- Peripheral Blood Mononuclear Cells, donor3_CNhs12002_11388-118C2_reverse Regulation 
PeripheralBloodMononuclearCellsDonor3_CNhs12002_tpm_fwd PeripheralBloodMononuclearCellsD3+ Peripheral Blood Mononuclear Cells, donor3_CNhs12002_11388-118C2_forward Regulation 
PeripheralBloodMononuclearCellsDonor2_CNhs11958_tpm_rev PeripheralBloodMononuclearCellsD2- Peripheral Blood Mononuclear Cells, donor2_CNhs11958_11312-117C7_reverse Regulation 
PeripheralBloodMononuclearCellsDonor2_CNhs11958_tpm_fwd PeripheralBloodMononuclearCellsD2+ Peripheral Blood Mononuclear Cells, donor2_CNhs11958_11312-117C7_forward Regulation 
PeripheralBloodMononuclearCellsDonor1_CNhs10860_tpm_rev PeripheralBloodMononuclearCellsD1- Peripheral Blood Mononuclear Cells, donor1_CNhs10860_11231-116C7_reverse Regulation 
PeripheralBloodMononuclearCellsDonor1_CNhs10860_tpm_fwd PeripheralBloodMononuclearCellsD1+ Peripheral Blood Mononuclear Cells, donor1_CNhs10860_11231-116C7_forward Regulation 
PerineurialCellsDonor2_CNhs12590_tpm_rev PerineurialCellsD2- Perineurial Cells, donor2_CNhs12590_11579-120F4_reverse Regulation 
PerineurialCellsDonor2_CNhs12590_tpm_fwd PerineurialCellsD2+ Perineurial Cells, donor2_CNhs12590_11579-120F4_forward Regulation 
PerineurialCellsDonor1_CNhs12587_tpm_rev PerineurialCellsD1- Perineurial Cells, donor1_CNhs12587_11499-119F5_reverse Regulation 
PerineurialCellsDonor1_CNhs12587_tpm_fwd PerineurialCellsD1+ Perineurial Cells, donor1_CNhs12587_11499-119F5_forward Regulation 
PericytesDonor3_CNhs12116_tpm_rev PericytesD3- Pericytes, donor3_CNhs12116_11652-122E5_reverse Regulation 
PericytesDonor3_CNhs12116_tpm_fwd PericytesD3+ Pericytes, donor3_CNhs12116_11652-122E5_forward Regulation 
PericytesDonor2_CNhs12079_tpm_rev PericytesD2- Pericytes, donor2_CNhs12079_11571-120E5_reverse Regulation 
PericytesDonor2_CNhs12079_tpm_fwd PericytesD2+ Pericytes, donor2_CNhs12079_11571-120E5_forward Regulation 
PericytesDonor1_CNhs11317_tpm_rev PericytesD1- Pericytes, donor1_CNhs11317_11491-119E6_reverse Regulation 
PericytesDonor1_CNhs11317_tpm_fwd PericytesD1+ Pericytes, donor1_CNhs11317_11491-119E6_forward Regulation 
PancreaticStromalCellsDonor1_CNhs10877_tpm_rev PancreaticStromalCellsD1- Pancreatic stromal cells, donor1_CNhs10877_11249-116E7_reverse Regulation 
PancreaticStromalCellsDonor1_CNhs10877_tpm_fwd PancreaticStromalCellsD1+ Pancreatic stromal cells, donor1_CNhs10877_11249-116E7_forward Regulation 
OsteoblastDifferentiatedDonor3_CNhs12035_tpm_rev OsteoblastDifferentiatedD3- Osteoblast - differentiated, donor3_CNhs12035_11425-118G3_reverse Regulation 
OsteoblastDifferentiatedDonor3_CNhs12035_tpm_fwd OsteoblastDifferentiatedD3+ Osteoblast - differentiated, donor3_CNhs12035_11425-118G3_forward Regulation 
OsteoblastDifferentiatedDonor2_CNhs11980_tpm_rev OsteoblastDifferentiatedD2- Osteoblast - differentiated, donor2_CNhs11980_11353-117H3_reverse Regulation 
OsteoblastDifferentiatedDonor2_CNhs11980_tpm_fwd OsteoblastDifferentiatedD2+ Osteoblast - differentiated, donor2_CNhs11980_11353-117H3_forward Regulation 
OsteoblastDifferentiatedDonor1_CNhs11311_tpm_rev OsteoblastDifferentiatedD1- Osteoblast - differentiated, donor1_CNhs11311_11276-116H7_reverse Regulation 
OsteoblastDifferentiatedDonor1_CNhs11311_tpm_fwd OsteoblastDifferentiatedD1+ Osteoblast - differentiated, donor1_CNhs11311_11276-116H7_forward Regulation 
OsteoblastDonor3_CNhs12036_tpm_rev OsteoblastD3- Osteoblast, donor3_CNhs12036_11426-118G4_reverse Regulation 
OsteoblastDonor3_CNhs12036_tpm_fwd OsteoblastD3+ Osteoblast, donor3_CNhs12036_11426-118G4_forward Regulation 
OsteoblastDonor2_CNhs11385_tpm_rev OsteoblastD2- Osteoblast, donor2_CNhs11385_11354-117H4_reverse Regulation 
OsteoblastDonor2_CNhs11385_tpm_fwd OsteoblastD2+ Osteoblast, donor2_CNhs11385_11354-117H4_forward Regulation 
OsteoblastDonor1_CNhs11078_tpm_rev OsteoblastD1- Osteoblast, donor1_CNhs11078_11277-116H8_reverse Regulation 
OsteoblastDonor1_CNhs11078_tpm_fwd OsteoblastD1+ Osteoblast, donor1_CNhs11078_11277-116H8_forward Regulation 
OligodendrocytePrecursorsDonor1_CNhs12586_tpm_rev OligodendrocytePrecursorsD1- Oligodendrocyte - precursors, donor1_CNhs12586_11496-119F2_reverse Regulation 
OligodendrocytePrecursorsDonor1_CNhs12586_tpm_fwd OligodendrocytePrecursorsD1+ Oligodendrocyte - precursors, donor1_CNhs12586_11496-119F2_forward Regulation 
OlfactoryEpithelialCellsDonor4_CNhs13819_tpm_rev OlfactoryEpithelialCellsD4- Olfactory epithelial cells, donor4_CNhs13819_11936-126A1_reverse Regulation 
OlfactoryEpithelialCellsDonor4_CNhs13819_tpm_fwd OlfactoryEpithelialCellsD4+ Olfactory epithelial cells, donor4_CNhs13819_11936-126A1_forward Regulation 
OlfactoryEpithelialCellsDonor3_CNhs13818_tpm_rev OlfactoryEpithelialCellsD3- Olfactory epithelial cells, donor3_CNhs13818_11935-125I9_reverse Regulation 
OlfactoryEpithelialCellsDonor3_CNhs13818_tpm_fwd OlfactoryEpithelialCellsD3+ Olfactory epithelial cells, donor3_CNhs13818_11935-125I9_forward Regulation 
OlfactoryEpithelialCellsDonor2_CNhs13817_tpm_rev OlfactoryEpithelialCellsD2- Olfactory epithelial cells, donor2_CNhs13817_11934-125I8_reverse Regulation 
OlfactoryEpithelialCellsDonor2_CNhs13817_tpm_fwd OlfactoryEpithelialCellsD2+ Olfactory epithelial cells, donor2_CNhs13817_11934-125I8_forward Regulation 
OlfactoryEpithelialCellsDonor1_CNhs13816_tpm_rev OlfactoryEpithelialCellsD1- Olfactory epithelial cells, donor1_CNhs13816_11933-125I7_reverse Regulation 
OlfactoryEpithelialCellsDonor1_CNhs13816_tpm_fwd OlfactoryEpithelialCellsD1+ Olfactory epithelial cells, donor1_CNhs13816_11933-125I7_forward Regulation 
NucleusPulposusCellDonor3_CNhs12063_tpm_rev NucleusPulposusCellD3- Nucleus Pulposus Cell, donor3_CNhs12063_11462-119B4_reverse Regulation 
NucleusPulposusCellDonor3_CNhs12063_tpm_fwd NucleusPulposusCellD3+ Nucleus Pulposus Cell, donor3_CNhs12063_11462-119B4_forward Regulation 
NucleusPulposusCellDonor2_CNhs12019_tpm_rev NucleusPulposusCellD2- Nucleus Pulposus Cell, donor2_CNhs12019_11409-118E5_reverse Regulation 
NucleusPulposusCellDonor2_CNhs12019_tpm_fwd NucleusPulposusCellD2+ Nucleus Pulposus Cell, donor2_CNhs12019_11409-118E5_forward Regulation 
NucleusPulposusCellDonor1_CNhs10881_tpm_rev NucleusPulposusCellD1- Nucleus Pulposus Cell, donor1_CNhs10881_11252-116F1_reverse Regulation 
NucleusPulposusCellDonor1_CNhs10881_tpm_fwd NucleusPulposusCellD1+ Nucleus Pulposus Cell, donor1_CNhs10881_11252-116F1_forward Regulation 
NeutrophilsDonor3_CNhs11905_tpm_rev NeutrophilsD3- Neutrophils, donor3_CNhs11905_11390-118C4_reverse Regulation 
NeutrophilsDonor3_CNhs11905_tpm_fwd NeutrophilsD3+ Neutrophils, donor3_CNhs11905_11390-118C4_forward Regulation 
NeutrophilsDonor2_CNhs11959_tpm_rev NeutrophilsD2- Neutrophils, donor2_CNhs11959_11314-117C9_reverse Regulation 
NeutrophilsDonor2_CNhs11959_tpm_fwd NeutrophilsD2+ Neutrophils, donor2_CNhs11959_11314-117C9_forward Regulation 
NeutrophilsDonor1_CNhs10862_tpm_rev NeutrophilsD1- Neutrophils, donor1_CNhs10862_11233-116C9_reverse Regulation 
NeutrophilsDonor1_CNhs10862_tpm_fwd NeutrophilsD1+ Neutrophils, donor1_CNhs10862_11233-116C9_forward Regulation 
NeuronsDonor3_CNhs13815_tpm_rev NeuronsD3- Neurons, donor3_CNhs13815_11655-122E8_reverse Regulation 
NeuronsDonor3_CNhs13815_tpm_fwd NeuronsD3+ Neurons, donor3_CNhs13815_11655-122E8_forward Regulation 
NeuronsDonor2_CNhs12726_tpm_rev NeuronsD2- Neurons, donor2_CNhs12726_11574-120E8_reverse Regulation 
NeuronsDonor2_CNhs12726_tpm_fwd NeuronsD2+ Neurons, donor2_CNhs12726_11574-120E8_forward Regulation 
NeuronsDonor1_CNhs12338_tpm_rev NeuronsD1- Neurons, donor1_CNhs12338_11494-119E9_reverse Regulation 
NeuronsDonor1_CNhs12338_tpm_fwd NeuronsD1+ Neurons, donor1_CNhs12338_11494-119E9_forward Regulation 
NeuralStemCellsDonor2_CNhs11384_tpm_rev NeuralStemCellsD2- Neural stem cells, donor2_CNhs11384_11352-117H2_reverse Regulation 
NeuralStemCellsDonor2_CNhs11384_tpm_fwd NeuralStemCellsD2+ Neural stem cells, donor2_CNhs11384_11352-117H2_forward Regulation 
NeuralStemCellsDonor1_CNhs11063_tpm_rev NeuralStemCellsD1- Neural stem cells, donor1_CNhs11063_11275-116H6_reverse Regulation 
NeuralStemCellsDonor1_CNhs11063_tpm_fwd NeuralStemCellsD1+ Neural stem cells, donor1_CNhs11063_11275-116H6_forward Regulation 
NaturalKillerCellsDonor3_CNhs12001_tpm_rev NaturalKillerCellsD3- Natural Killer Cells, donor3_CNhs12001_11387-118C1_reverse Regulation 
NaturalKillerCellsDonor3_CNhs12001_tpm_fwd NaturalKillerCellsD3+ Natural Killer Cells, donor3_CNhs12001_11387-118C1_forward Regulation 
NaturalKillerCellsDonor2_CNhs11957_tpm_rev NaturalKillerCellsD2- Natural Killer Cells, donor2_CNhs11957_11311-117C6_reverse Regulation 
NaturalKillerCellsDonor2_CNhs11957_tpm_fwd NaturalKillerCellsD2+ Natural Killer Cells, donor2_CNhs11957_11311-117C6_forward Regulation 
NaturalKillerCellsDonor1_CNhs10859_tpm_rev NaturalKillerCellsD1- Natural Killer Cells, donor1_CNhs10859_11230-116C6_reverse Regulation 
NaturalKillerCellsDonor1_CNhs10859_tpm_fwd NaturalKillerCellsD1+ Natural Killer Cells, donor1_CNhs10859_11230-116C6_forward Regulation 
NasalEpithelialCellsDonor2_CNhs12574_tpm_rev NasalEpithelialCellsD2- nasal epithelial cells, donor2_CNhs12574_12227-129F4_reverse Regulation 
NasalEpithelialCellsDonor2_CNhs12574_tpm_fwd NasalEpithelialCellsD2+ nasal epithelial cells, donor2_CNhs12574_12227-129F4_forward Regulation 
NasalEpithelialCellsDonor1TechRep1_CNhs12589_tpm_rev NasalEpithelialCellsD1Tr1- nasal epithelial cells, donor1, tech_rep1_CNhs12589_12226-129F3_reverse Regulation 
NasalEpithelialCellsDonor1TechRep1_CNhs12589_tpm_fwd NasalEpithelialCellsD1Tr1+ nasal epithelial cells, donor1, tech_rep1_CNhs12589_12226-129F3_forward Regulation 
MyoblastDonor3_CNhs11908_tpm_rev MyoblastD3- Myoblast, donor3_CNhs11908_11398-118D3_reverse Regulation 
MyoblastDonor3_CNhs11908_tpm_fwd MyoblastD3+ Myoblast, donor3_CNhs11908_11398-118D3_forward Regulation 
MyoblastDonor2_CNhs11965_tpm_rev MyoblastD2- Myoblast, donor2_CNhs11965_11322-117D8_reverse Regulation 
MyoblastDonor2_CNhs11965_tpm_fwd MyoblastD2+ Myoblast, donor2_CNhs11965_11322-117D8_forward Regulation 
MyoblastDonor1_CNhs10870_tpm_rev MyoblastD1- Myoblast, donor1_CNhs10870_11241-116D8_reverse Regulation 
MyoblastDonor1_CNhs10870_tpm_fwd MyoblastD1+ Myoblast, donor1_CNhs10870_11241-116D8_forward Regulation 
MesenchymalStemCellsWhartonsJellyDonor1_CNhs11057_tpm_rev MscWharton'sJellyD1- Mesenchymal Stem Cells - Wharton's Jelly, donor1_CNhs11057_11548-120B9_reverse Regulation 
MesenchymalStemCellsWhartonsJellyDonor1_CNhs11057_tpm_fwd MscWharton'sJellyD1+ Mesenchymal Stem Cells - Wharton's Jelly, donor1_CNhs11057_11548-120B9_forward Regulation 
MesenchymalStemCellsVertebralDonor1_CNhs10846_tpm_rev MscVertebralD1- Mesenchymal Stem Cells - Vertebral, donor1_CNhs10846_11219-116B4_reverse Regulation 
MesenchymalStemCellsVertebralDonor1_CNhs10846_tpm_fwd MscVertebralD1+ Mesenchymal Stem Cells - Vertebral, donor1_CNhs10846_11219-116B4_forward Regulation 
MesenchymalStemCellsUmbilicalDonor3_CNhs12127_tpm_rev MscUmbilicalD3- Mesenchymal Stem Cells - umbilical, donor3_CNhs12127_11700-123A8_reverse Regulation 
MesenchymalStemCellsUmbilicalDonor3_CNhs12127_tpm_fwd MscUmbilicalD3+ Mesenchymal Stem Cells - umbilical, donor3_CNhs12127_11700-123A8_forward Regulation 
MesenchymalStemCellsUmbilicalDonor2_CNhs12102_tpm_rev MscUmbilicalD2- Mesenchymal Stem Cells - umbilical, donor2_CNhs12102_11619-122A8_reverse Regulation 
MesenchymalStemCellsUmbilicalDonor2_CNhs12102_tpm_fwd MscUmbilicalD2+ Mesenchymal Stem Cells - umbilical, donor2_CNhs12102_11619-122A8_forward Regulation 
MesenchymalStemCellsUmbilicalDonor1_CNhs11347_tpm_rev MscUmbilicalD1- Mesenchymal Stem Cells - umbilical, donor1_CNhs11347_11539-120A9_reverse Regulation 
MesenchymalStemCellsUmbilicalDonor1_CNhs11347_tpm_fwd MscUmbilicalD1+ Mesenchymal Stem Cells - umbilical, donor1_CNhs11347_11539-120A9_forward Regulation 
MesenchymalStemCellsUmbilicalDonor0_CNhs12492_tpm_rev MscUmbilicalD0- Mesenchymal stem cells - umbilical, donor0_CNhs12492_11214-116A8_reverse Regulation 
MesenchymalStemCellsUmbilicalDonor0_CNhs12492_tpm_fwd MscUmbilicalD0+ Mesenchymal stem cells - umbilical, donor0_CNhs12492_11214-116A8_forward Regulation 
MesenchymalStemCellsHepaticDonor2_CNhs12730_tpm_rev MscHepaticD2- Mesenchymal Stem Cells - hepatic, donor2_CNhs12730_11618-122A7_reverse Regulation 
MesenchymalStemCellsHepaticDonor2_CNhs12730_tpm_fwd MscHepaticD2+ Mesenchymal Stem Cells - hepatic, donor2_CNhs12730_11618-122A7_forward Regulation 
MesenchymalStemCellsHepaticDonor1_CNhs11346_tpm_rev MscHepaticD1- Mesenchymal Stem Cells - hepatic, donor1_CNhs11346_11538-120A8_reverse Regulation 
MesenchymalStemCellsHepaticDonor1_CNhs11346_tpm_fwd MscHepaticD1+ Mesenchymal Stem Cells - hepatic, donor1_CNhs11346_11538-120A8_forward Regulation 
MesenchymalStemCellsHepaticDonor0_CNhs10845_tpm_rev MscHepaticD0- Mesenchymal stem cells - hepatic, donor0_CNhs10845_11218-116B3_reverse Regulation 
MesenchymalStemCellsHepaticDonor0_CNhs10845_tpm_fwd MscHepaticD0+ Mesenchymal stem cells - hepatic, donor0_CNhs10845_11218-116B3_forward Regulation 
MesenchymalStemCellsBoneMarrowDonor4_CNhs11316_tpm_rev MscBoneMarrowD4- Mesenchymal Stem Cells - bone marrow, donor4_CNhs11316_11464-119B6_reverse Regulation 
MesenchymalStemCellsBoneMarrowDonor4_CNhs11316_tpm_fwd MscBoneMarrowD4+ Mesenchymal Stem Cells - bone marrow, donor4_CNhs11316_11464-119B6_forward Regulation 
MesenchymalStemCellsBoneMarrowDonor3_CNhs12126_tpm_rev MscBoneMarrowD3- Mesenchymal Stem Cells - bone marrow, donor3_CNhs12126_11697-123A5_reverse Regulation 
MesenchymalStemCellsBoneMarrowDonor3_CNhs12126_tpm_fwd MscBoneMarrowD3+ Mesenchymal Stem Cells - bone marrow, donor3_CNhs12126_11697-123A5_forward Regulation 
MesenchymalStemCellsBoneMarrowDonor2_CNhs12100_tpm_rev MscBoneMarrowD2- Mesenchymal Stem Cells - bone marrow, donor2_CNhs12100_11616-122A5_reverse Regulation 
MesenchymalStemCellsBoneMarrowDonor2_CNhs12100_tpm_fwd MscBoneMarrowD2+ Mesenchymal Stem Cells - bone marrow, donor2_CNhs12100_11616-122A5_forward Regulation 
MesenchymalStemCellsBoneMarrowDonor1_CNhs11344_tpm_rev MscBoneMarrowD1- Mesenchymal Stem Cells - bone marrow, donor1_CNhs11344_11536-120A6_reverse Regulation 
MesenchymalStemCellsBoneMarrowDonor1_CNhs11344_tpm_fwd MscBoneMarrowD1+ Mesenchymal Stem Cells - bone marrow, donor1_CNhs11344_11536-120A6_forward Regulation 
MesenchymalStemCellsAmnioticMembraneDonor2_CNhs12104_tpm_rev MscAmnioticMembraneD2- Mesenchymal Stem Cells - amniotic membrane, donor2_CNhs12104_11627-122B7_reverse Regulation 
MesenchymalStemCellsAmnioticMembraneDonor2_CNhs12104_tpm_fwd MscAmnioticMembraneD2+ Mesenchymal Stem Cells - amniotic membrane, donor2_CNhs12104_11627-122B7_forward Regulation 
MesenchymalStemCellsAmnioticMembraneDonor1_CNhs11349_tpm_rev MscAmnioticMembraneD1- Mesenchymal Stem Cells - amniotic membrane, donor1_CNhs11349_11547-120B8_reverse Regulation 
MesenchymalStemCellsAmnioticMembraneDonor1_CNhs11349_tpm_fwd MscAmnioticMembraneD1+ Mesenchymal Stem Cells - amniotic membrane, donor1_CNhs11349_11547-120B8_forward Regulation 
MesenchymalStemCellsAdiposeDonor3_CNhs12922_tpm_rev MscAdiposeD3- Mesenchymal Stem Cells - adipose, donor3_CNhs12922_11698-123A6_reverse Regulation 
MesenchymalStemCellsAdiposeDonor3_CNhs12922_tpm_fwd MscAdiposeD3+ Mesenchymal Stem Cells - adipose, donor3_CNhs12922_11698-123A6_forward Regulation 
MesenchymalStemCellsAdiposeDonor2_CNhs12101_tpm_rev MscAdiposeD2- Mesenchymal Stem Cells - adipose, donor2_CNhs12101_11617-122A6_reverse Regulation 
MesenchymalStemCellsAdiposeDonor2_CNhs12101_tpm_fwd MscAdiposeD2+ Mesenchymal Stem Cells - adipose, donor2_CNhs12101_11617-122A6_forward Regulation 
MesenchymalStemCellsAdiposeDonor1_CNhs11345_tpm_rev MscAdiposeD1- Mesenchymal Stem Cells - adipose, donor1_CNhs11345_11537-120A7_reverse Regulation 
MesenchymalStemCellsAdiposeDonor1_CNhs11345_tpm_fwd MscAdiposeD1+ Mesenchymal Stem Cells - adipose, donor1_CNhs11345_11537-120A7_forward Regulation 
MesenchymalStemCellsAdiposeDonor0_CNhs10844_tpm_rev MscAdiposeD0- Mesenchymal stem cells - adipose, donor0_CNhs10844_11217-116B2_reverse Regulation 
MesenchymalStemCellsAdiposeDonor0_CNhs10844_tpm_fwd MscAdiposeD0+ Mesenchymal stem cells - adipose, donor0_CNhs10844_11217-116B2_forward Regulation 
MesenchymalPrecursorCellOvarianCancerRightOvaryDonor4_CNhs13096_tpm_rev MpcOvarianCancerRightOvaryD4- mesenchymal precursor cell - ovarian cancer right ovary, donor4_CNhs13096_11837-124H1_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerRightOvaryDonor4_CNhs13096_tpm_fwd MpcOvarianCancerRightOvaryD4+ mesenchymal precursor cell - ovarian cancer right ovary, donor4_CNhs13096_11837-124H1_forward Regulation 
MesenchymalPrecursorCellOvarianCancerRightOvaryDonor3SOC5702G_CNhs13507_tpm_rev MpcOvarianCancerRightOvaryD3- mesenchymal precursor cell - ovarian cancer right ovary, donor3 (SOC-57-02-G)_CNhs13507_11842-124H6_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerRightOvaryDonor3SOC5702_CNhs12377_tpm_rev MpcOvarianCancerRightOvaryD3- mesenchymal precursor cell - ovarian cancer right ovary, donor3 (SOC-57-02)_CNhs12377_11761-123H6_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerRightOvaryDonor3SOC5702G_CNhs13507_tpm_fwd MpcOvarianCancerRightOvaryD3+ mesenchymal precursor cell - ovarian cancer right ovary, donor3 (SOC-57-02-G)_CNhs13507_11842-124H6_forward Regulation 
MesenchymalPrecursorCellOvarianCancerRightOvaryDonor3SOC5702_CNhs12377_tpm_fwd MpcOvarianCancerRightOvaryD3+ mesenchymal precursor cell - ovarian cancer right ovary, donor3 (SOC-57-02)_CNhs12377_11761-123H6_forward Regulation 
MesenchymalPrecursorCellOvarianCancerRightOvaryDonor2_CNhs12375_tpm_rev MpcOvarianCancerRightOvaryD2- mesenchymal precursor cell - ovarian cancer right ovary, donor2_CNhs12375_11759-123H4_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerRightOvaryDonor2_CNhs12375_tpm_fwd MpcOvarianCancerRightOvaryD2+ mesenchymal precursor cell - ovarian cancer right ovary, donor2_CNhs12375_11759-123H4_forward Regulation 
MesenchymalPrecursorCellOvarianCancerRightOvaryDonor1_CNhs12373_tpm_rev MpcOvarianCancerRightOvaryD1- mesenchymal precursor cell - ovarian cancer right ovary, donor1_CNhs12373_11757-123H2_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerRightOvaryDonor1_CNhs12373_tpm_fwd MpcOvarianCancerRightOvaryD1+ mesenchymal precursor cell - ovarian cancer right ovary, donor1_CNhs12373_11757-123H2_forward Regulation 
MesenchymalPrecursorCellOvarianCancerMetastasisDonor4_CNhs13097_tpm_rev MpcOvarianCancerMetastasisD4- mesenchymal precursor cell - ovarian cancer metastasis, donor4_CNhs13097_11838-124H2_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerMetastasisDonor4_CNhs13097_tpm_fwd MpcOvarianCancerMetastasisD4+ mesenchymal precursor cell - ovarian cancer metastasis, donor4_CNhs13097_11838-124H2_forward Regulation 
MesenchymalPrecursorCellOvarianCancerMetastasisDonor3_CNhs12378_tpm_rev MpcOvarianCancerMetastasisD3- mesenchymal precursor cell - ovarian cancer metastasis, donor3_CNhs12378_11762-123H7_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerMetastasisDonor3_CNhs12378_tpm_fwd MpcOvarianCancerMetastasisD3+ mesenchymal precursor cell - ovarian cancer metastasis, donor3_CNhs12378_11762-123H7_forward Regulation 
MesenchymalPrecursorCellOvarianCancerMetastasisDonor2_CNhs13093_tpm_rev MpcOvarianCancerMetastasisD2- mesenchymal precursor cell - ovarian cancer metastasis, donor2_CNhs13093_11835-124G8_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerMetastasisDonor2_CNhs13093_tpm_fwd MpcOvarianCancerMetastasisD2+ mesenchymal precursor cell - ovarian cancer metastasis, donor2_CNhs13093_11835-124G8_forward Regulation 
MesenchymalPrecursorCellOvarianCancerMetastasisDonor1_CNhs12374_tpm_rev MpcOvarianCancerMetastasisD1- mesenchymal precursor cell - ovarian cancer metastasis, donor1_CNhs12374_11758-123H3_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerMetastasisDonor1_CNhs12374_tpm_fwd MpcOvarianCancerMetastasisD1+ mesenchymal precursor cell - ovarian cancer metastasis, donor1_CNhs12374_11758-123H3_forward Regulation 
MesenchymalPrecursorCellOvarianCancerLeftOvaryDonor4_CNhs13094_tpm_rev MpcOvarianCancerLeftOvaryD4- mesenchymal precursor cell - ovarian cancer left ovary, donor4_CNhs13094_11836-124G9_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerLeftOvaryDonor4_CNhs13094_tpm_fwd MpcOvarianCancerLeftOvaryD4+ mesenchymal precursor cell - ovarian cancer left ovary, donor4_CNhs13094_11836-124G9_forward Regulation 
MesenchymalPrecursorCellOvarianCancerLeftOvaryDonor3_CNhs12376_tpm_rev MpcOvarianCancerLeftOvaryD3- mesenchymal precursor cell - ovarian cancer left ovary, donor3_CNhs12376_11760-123H5_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerLeftOvaryDonor3_CNhs12376_tpm_fwd MpcOvarianCancerLeftOvaryD3+ mesenchymal precursor cell - ovarian cancer left ovary, donor3_CNhs12376_11760-123H5_forward Regulation 
MesenchymalPrecursorCellOvarianCancerLeftOvaryDonor2_CNhs13092_tpm_rev MpcOvarianCancerLeftOvaryD2- mesenchymal precursor cell - ovarian cancer left ovary, donor2_CNhs13092_11833-124G6_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerLeftOvaryDonor2_CNhs13092_tpm_fwd MpcOvarianCancerLeftOvaryD2+ mesenchymal precursor cell - ovarian cancer left ovary, donor2_CNhs13092_11833-124G6_forward Regulation 
MesenchymalPrecursorCellOvarianCancerLeftOvaryDonor1_CNhs12372_tpm_rev MpcOvarianCancerLeftOvaryD1- mesenchymal precursor cell - ovarian cancer left ovary, donor1_CNhs12372_11756-123H1_reverse Regulation 
MesenchymalPrecursorCellOvarianCancerLeftOvaryDonor1_CNhs12372_tpm_fwd MpcOvarianCancerLeftOvaryD1+ mesenchymal precursor cell - ovarian cancer left ovary, donor1_CNhs12372_11756-123H1_forward Regulation 
MesenchymalPrecursorCellCardiacDonor4_CNhs12371_tpm_rev MpcCardiacD4- mesenchymal precursor cell - cardiac, donor4_CNhs12371_11755-123G9_reverse Regulation 
MesenchymalPrecursorCellCardiacDonor4_CNhs12371_tpm_fwd MpcCardiacD4+ mesenchymal precursor cell - cardiac, donor4_CNhs12371_11755-123G9_forward Regulation 
MesenchymalPrecursorCellCardiacDonor3_CNhs12370_tpm_rev MpcCardiacD3- mesenchymal precursor cell - cardiac, donor3_CNhs12370_11754-123G8_reverse Regulation 
MesenchymalPrecursorCellCardiacDonor3_CNhs12370_tpm_fwd MpcCardiacD3+ mesenchymal precursor cell - cardiac, donor3_CNhs12370_11754-123G8_forward Regulation 
MesenchymalPrecursorCellCardiacDonor2_CNhs12369_tpm_rev MpcCardiacD2- mesenchymal precursor cell - cardiac, donor2_CNhs12369_11753-123G7_reverse Regulation 
MesenchymalPrecursorCellCardiacDonor2_CNhs12369_tpm_fwd MpcCardiacD2+ mesenchymal precursor cell - cardiac, donor2_CNhs12369_11753-123G7_forward Regulation 
MesenchymalPrecursorCellCardiacDonor1_CNhs12368_tpm_rev MpcCardiacD1- mesenchymal precursor cell - cardiac, donor1_CNhs12368_11752-123G6_reverse Regulation 
MesenchymalPrecursorCellCardiacDonor1_CNhs12368_tpm_fwd MpcCardiacD1+ mesenchymal precursor cell - cardiac, donor1_CNhs12368_11752-123G6_forward Regulation 
MesenchymalPrecursorCellBoneMarrowDonor3_CNhs13098_tpm_rev MpcBoneMarrowD3- mesenchymal precursor cell - bone marrow, donor3_CNhs13098_11840-124H4_reverse Regulation 
MesenchymalPrecursorCellBoneMarrowDonor3_CNhs13098_tpm_fwd MpcBoneMarrowD3+ mesenchymal precursor cell - bone marrow, donor3_CNhs13098_11840-124H4_forward Regulation 
MesenchymalPrecursorCellBoneMarrowDonor2_CNhs12367_tpm_rev MpcBoneMarrowD2- mesenchymal precursor cell - bone marrow, donor2_CNhs12367_11751-123G5_reverse Regulation 
MesenchymalPrecursorCellBoneMarrowDonor2_CNhs12367_tpm_fwd MpcBoneMarrowD2+ mesenchymal precursor cell - bone marrow, donor2_CNhs12367_11751-123G5_forward Regulation 
MesenchymalPrecursorCellBoneMarrowDonor1_CNhs12366_tpm_rev MpcBoneMarrowD1- mesenchymal precursor cell - bone marrow, donor1_CNhs12366_11750-123G4_reverse Regulation 
MesenchymalPrecursorCellBoneMarrowDonor1_CNhs12366_tpm_fwd MpcBoneMarrowD1+ mesenchymal precursor cell - bone marrow, donor1_CNhs12366_11750-123G4_forward Regulation 
MesenchymalPrecursorCellAdiposeDonor3_CNhs12365_tpm_rev MpcAdiposeD3- mesenchymal precursor cell - adipose, donor3_CNhs12365_11749-123G3_reverse Regulation 
MesenchymalPrecursorCellAdiposeDonor3_CNhs12365_tpm_fwd MpcAdiposeD3+ mesenchymal precursor cell - adipose, donor3_CNhs12365_11749-123G3_forward Regulation 
MesenchymalPrecursorCellAdiposeDonor2_CNhs12364_tpm_rev MpcAdiposeD2- mesenchymal precursor cell - adipose, donor2_CNhs12364_11748-123G2_reverse Regulation 
MesenchymalPrecursorCellAdiposeDonor2_CNhs12364_tpm_fwd MpcAdiposeD2+ mesenchymal precursor cell - adipose, donor2_CNhs12364_11748-123G2_forward Regulation 
MesenchymalPrecursorCellAdiposeDonor1_CNhs12363_tpm_rev MpcAdiposeD1- mesenchymal precursor cell - adipose, donor1_CNhs12363_11747-123G1_reverse Regulation 
MesenchymalPrecursorCellAdiposeDonor1_CNhs12363_tpm_fwd MpcAdiposeD1+ mesenchymal precursor cell - adipose, donor1_CNhs12363_11747-123G1_forward Regulation 
MigratoryLangerhansCellsDonor3_CNhs13547_tpm_rev MigratoryLangerhansCellsD3- migratory langerhans cells, donor3_CNhs13547_11903-125F4_reverse Regulation 
MigratoryLangerhansCellsDonor3_CNhs13547_tpm_fwd MigratoryLangerhansCellsD3+ migratory langerhans cells, donor3_CNhs13547_11903-125F4_forward Regulation 
MigratoryLangerhansCellsDonor2_CNhs13536_tpm_rev MigratoryLangerhansCellsD2- migratory langerhans cells, donor2_CNhs13536_11902-125F3_reverse Regulation 
MigratoryLangerhansCellsDonor2_CNhs13536_tpm_fwd MigratoryLangerhansCellsD2+ migratory langerhans cells, donor2_CNhs13536_11902-125F3_forward Regulation 
MigratoryLangerhansCellsDonor1_CNhs13535_tpm_rev MigratoryLangerhansCellsD1- migratory langerhans cells, donor1_CNhs13535_11901-125F2_reverse Regulation 
MigratoryLangerhansCellsDonor1_CNhs13535_tpm_fwd MigratoryLangerhansCellsD1+ migratory langerhans cells, donor1_CNhs13535_11901-125F2_forward Regulation 
MesothelialCellsDonor3_CNhs12012_tpm_rev MesothelialCellsD3- Mesothelial Cells, donor3_CNhs12012_11402-118D7_reverse Regulation 
MesothelialCellsDonor3_CNhs12012_tpm_fwd MesothelialCellsD3+ Mesothelial Cells, donor3_CNhs12012_11402-118D7_forward Regulation 
MesothelialCellsDonor1_CNhs10850_tpm_rev MesothelialCellsD1- Mesothelial Cells, donor1_CNhs10850_11247-116E5_reverse Regulation 
MesothelialCellsDonor1_CNhs10850_tpm_fwd MesothelialCellsD1+ Mesothelial Cells, donor1_CNhs10850_11247-116E5_forward Regulation 
MeningealCellsDonor3_CNhs12731_tpm_rev MeningealCellsD3- Meningeal Cells, donor3_CNhs12731_11654-122E7_reverse Regulation 
MeningealCellsDonor3_CNhs12731_tpm_fwd MeningealCellsD3+ Meningeal Cells, donor3_CNhs12731_11654-122E7_forward Regulation 
MeningealCellsDonor2_CNhs12080_tpm_rev MeningealCellsD2- Meningeal Cells, donor2_CNhs12080_11573-120E7_reverse Regulation 
MeningealCellsDonor2_CNhs12080_tpm_fwd MeningealCellsD2+ Meningeal Cells, donor2_CNhs12080_11573-120E7_forward Regulation 
MeningealCellsDonor1_CNhs11320_tpm_rev MeningealCellsD1- Meningeal Cells, donor1_CNhs11320_11493-119E8_reverse Regulation 
MeningealCellsDonor1_CNhs11320_tpm_fwd MeningealCellsD1+ Meningeal Cells, donor1_CNhs11320_11493-119E8_forward Regulation 
MelanocyteLightDonor3_CNhs12033_tpm_rev MelanocyteLightD3- Melanocyte - light, donor3_CNhs12033_11423-118G1_reverse Regulation 
MelanocyteLightDonor3_CNhs12033_tpm_fwd MelanocyteLightD3+ Melanocyte - light, donor3_CNhs12033_11423-118G1_forward Regulation 
MelanocyteLightDonor2_CNhs11383_tpm_rev MelanocyteLightD2- Melanocyte - light, donor2_CNhs11383_11351-117H1_reverse Regulation 
MelanocyteLightDonor2_CNhs11383_tpm_fwd MelanocyteLightD2+ Melanocyte - light, donor2_CNhs11383_11351-117H1_forward Regulation 
MelanocyteLightDonor1_CNhs11303_tpm_rev MelanocyteLightD1- Melanocyte - light, donor1_CNhs11303_11274-116H5_reverse Regulation 
MelanocyteLightDonor1_CNhs11303_tpm_fwd MelanocyteLightD1+ Melanocyte - light, donor1_CNhs11303_11274-116H5_forward Regulation 
MelanocyteDarkDonor3_CNhs12570_tpm_rev MelanocyteDarkD3- Melanocyte - dark, donor3_CNhs12570_11663-122F7_reverse Regulation 
MelanocyteDarkDonor3_CNhs12570_tpm_fwd MelanocyteDarkD3+ Melanocyte - dark, donor3_CNhs12570_11663-122F7_forward Regulation 
MelanocyteDarkDonor2_CNhs12346_tpm_rev MelanocyteDarkD2- Melanocyte - dark, donor2_CNhs12346_11582-120F7_reverse Regulation 
MelanocyteDarkDonor2_CNhs12346_tpm_fwd MelanocyteDarkD2+ Melanocyte - dark, donor2_CNhs12346_11582-120F7_forward Regulation 
MelanocyteDarkDonor1_CNhs12591_tpm_rev MelanocyteDarkD1- Melanocyte - dark, donor1_CNhs12591_11502-119F8_reverse Regulation 
MelanocyteDarkDonor1_CNhs12591_tpm_fwd MelanocyteDarkD1+ Melanocyte - dark, donor1_CNhs12591_11502-119F8_forward Regulation 
MastCellStimulatedDonor1_CNhs11073_tpm_rev MastCellStimulatedD1- Mast cell - stimulated, donor1_CNhs11073_11487-119E2_reverse Regulation 
MastCellStimulatedDonor1_CNhs11073_tpm_fwd MastCellStimulatedD1+ Mast cell - stimulated, donor1_CNhs11073_11487-119E2_forward Regulation 
MastCellExpandedDonor8_CNhs13926_tpm_rev MastCellExpD8- Mast cell, expanded, donor8_CNhs13926_11941-126A6_reverse Regulation 
MastCellExpandedAndStimulatedDonor8_CNhs13927_tpm_rev MastCellExpD8- Mast cell, expanded and stimulated, donor8_CNhs13927_11942-126A7_reverse Regulation 
MastCellExpandedDonor8_CNhs13926_tpm_fwd MastCellExpD8+ Mast cell, expanded, donor8_CNhs13926_11941-126A6_forward Regulation 
MastCellExpandedAndStimulatedDonor8_CNhs13927_tpm_fwd MastCellExpD8+ Mast cell, expanded and stimulated, donor8_CNhs13927_11942-126A7_forward Regulation 
MastCellExpandedDonor5_CNhs13924_tpm_rev MastCellExpD5- Mast cell, expanded, donor5_CNhs13924_11939-126A4_reverse Regulation 
MastCellExpandedAndStimulatedDonor5_CNhs13925_tpm_rev MastCellExpD5- Mast cell, expanded and stimulated, donor5_CNhs13925_11940-126A5_reverse Regulation 
MastCellExpandedDonor5_CNhs13924_tpm_fwd MastCellExpD5+ Mast cell, expanded, donor5_CNhs13924_11939-126A4_forward Regulation 
MastCellExpandedAndStimulatedDonor5_CNhs13925_tpm_fwd MastCellExpD5+ Mast cell, expanded and stimulated, donor5_CNhs13925_11940-126A5_forward Regulation 
MastCellDonor4_CNhs12592_tpm_rev MastCellD4- Mast cell, donor4_CNhs12592_11567-120E1_reverse Regulation 
MastCellDonor4_CNhs12592_tpm_fwd MastCellD4+ Mast cell, donor4_CNhs12592_11567-120E1_forward Regulation 
MastCellDonor3_CNhs12593_tpm_rev MastCellD3- Mast cell, donor3_CNhs12593_11566-120D9_reverse Regulation 
MastCellDonor3_CNhs12593_tpm_fwd MastCellD3+ Mast cell, donor3_CNhs12593_11566-120D9_forward Regulation 
MastCellDonor2_CNhs12594_tpm_rev MastCellD2- Mast cell, donor2_CNhs12594_11565-120D8_reverse Regulation 
MastCellDonor2_CNhs12594_tpm_fwd MastCellD2+ Mast cell, donor2_CNhs12594_11565-120D8_forward Regulation 
MastCellDonor1_CNhs12566_tpm_rev MastCellD1- Mast cell, donor1_CNhs12566_11563-120D6_reverse Regulation 
MastCellDonor1_CNhs12566_tpm_fwd MastCellD1+ Mast cell, donor1_CNhs12566_11563-120D6_forward Regulation 
MammaryEpithelialCellDonor3_CNhs12032_tpm_rev MammaryEpithelialCellD3- Mammary Epithelial Cell, donor3_CNhs12032_11422-118F9_reverse Regulation 
MammaryEpithelialCellDonor3_CNhs12032_tpm_fwd MammaryEpithelialCellD3+ Mammary Epithelial Cell, donor3_CNhs12032_11422-118F9_forward Regulation 
MammaryEpithelialCellDonor2_CNhs11382_tpm_rev MammaryEpithelialCellD2- Mammary Epithelial Cell, donor2_CNhs11382_11350-117G9_reverse Regulation 
MammaryEpithelialCellDonor2_CNhs11382_tpm_fwd MammaryEpithelialCellD2+ Mammary Epithelial Cell, donor2_CNhs11382_11350-117G9_forward Regulation 
MammaryEpithelialCellDonor1_CNhs11077_tpm_rev MammaryEpithelialCellD1- Mammary Epithelial Cell, donor1_CNhs11077_11273-116H4_reverse Regulation 
MammaryEpithelialCellDonor1_CNhs11077_tpm_fwd MammaryEpithelialCellD1+ Mammary Epithelial Cell, donor1_CNhs11077_11273-116H4_forward Regulation 
MallassezderivedCellsDonor3_CNhs13551_tpm_rev MallassezCellsD3- Mallassez-derived cells, donor3_CNhs13551_11930-125I4_reverse Regulation 
MallassezderivedCellsDonor3_CNhs13551_tpm_fwd MallassezCellsD3+ Mallassez-derived cells, donor3_CNhs13551_11930-125I4_forward Regulation 
MallassezderivedCellsDonor2_CNhs13550_tpm_rev MallassezCellsD2- Mallassez-derived cells, donor2_CNhs13550_11929-125I3_reverse Regulation 
MallassezderivedCellsDonor2_CNhs13550_tpm_fwd MallassezCellsD2+ Mallassez-derived cells, donor2_CNhs13550_11929-125I3_forward Regulation 
MacrophageMonocyteDerivedDonor3_CNhs12003_tpm_rev MacrophageMonocyteD3- Macrophage - monocyte derived, donor3_CNhs12003_11389-118C3_reverse Regulation 
MacrophageMonocyteDerivedDonor3_CNhs12003_tpm_fwd MacrophageMonocyteD3+ Macrophage - monocyte derived, donor3_CNhs12003_11389-118C3_forward Regulation 
MacrophageMonocyteDerivedDonor2_CNhs11899_tpm_rev MacrophageMonocyteD2- Macrophage - monocyte derived, donor2_CNhs11899_11313-117C8_reverse Regulation 
MacrophageMonocyteDerivedDonor2_CNhs11899_tpm_fwd MacrophageMonocyteD2+ Macrophage - monocyte derived, donor2_CNhs11899_11313-117C8_forward Regulation 
MacrophageMonocyteDerivedDonor1_CNhs10861_tpm_rev MacrophageMonocyteD1- Macrophage - monocyte derived, donor1_CNhs10861_11232-116C8_reverse Regulation 
MacrophageMonocyteDerivedDonor1_CNhs10861_tpm_fwd MacrophageMonocyteD1+ Macrophage - monocyte derived, donor1_CNhs10861_11232-116C8_forward Regulation 
LensEpithelialCellsDonor3_CNhs12572_tpm_rev LensEpithelialCellsD3- Lens Epithelial Cells, donor3_CNhs12572_11690-122I7_reverse Regulation 
LensEpithelialCellsDonor3_CNhs12572_tpm_fwd LensEpithelialCellsD3+ Lens Epithelial Cells, donor3_CNhs12572_11690-122I7_forward Regulation 
LensEpithelialCellsDonor2_CNhs12568_tpm_rev LensEpithelialCellsD2- Lens Epithelial Cells, donor2_CNhs12568_11609-120I7_reverse Regulation 
LensEpithelialCellsDonor2_CNhs12568_tpm_fwd LensEpithelialCellsD2+ Lens Epithelial Cells, donor2_CNhs12568_11609-120I7_forward Regulation 
LensEpithelialCellsDonor1_CNhs12342_tpm_rev LensEpithelialCellsD1- Lens Epithelial Cells, donor1_CNhs12342_11529-119I8_reverse Regulation 
LensEpithelialCellsDonor1_CNhs12342_tpm_fwd LensEpithelialCellsD1+ Lens Epithelial Cells, donor1_CNhs12342_11529-119I8_forward Regulation 
KeratocytesDonor3_CNhs12921_tpm_rev KeratocytesD3- Keratocytes, donor3_CNhs12921_11688-122I5_reverse Regulation 
KeratocytesDonor3_CNhs12921_tpm_fwd KeratocytesD3+ Keratocytes, donor3_CNhs12921_11688-122I5_forward Regulation 
KeratocytesDonor2_CNhs12095_tpm_rev KeratocytesD2- Keratocytes, donor2_CNhs12095_11607-120I5_reverse Regulation 
KeratocytesDonor2_CNhs12095_tpm_fwd KeratocytesD2+ Keratocytes, donor2_CNhs12095_11607-120I5_forward Regulation 
KeratocytesDonor1_CNhs11337_tpm_rev KeratocytesD1- Keratocytes, donor1_CNhs11337_11527-119I6_reverse Regulation 
KeratocytesDonor1_CNhs11337_tpm_fwd KeratocytesD1+ Keratocytes, donor1_CNhs11337_11527-119I6_forward Regulation 
KeratinocyteOralDonor1_CNhs10879_tpm_rev KeratinocyteOralD1- Keratinocyte - oral, donor1_CNhs10879_11251-116E9_reverse Regulation 
KeratinocyteOralDonor1_CNhs10879_tpm_fwd KeratinocyteOralD1+ Keratinocyte - oral, donor1_CNhs10879_11251-116E9_forward Regulation 
KeratinocyteEpidermalDonor3_CNhs12031_tpm_rev KeratinocyteEpidermalD3- Keratinocyte - epidermal, donor3_CNhs12031_11421-118F8_reverse Regulation 
KeratinocyteEpidermalDonor3_CNhs12031_tpm_fwd KeratinocyteEpidermalD3+ Keratinocyte - epidermal, donor3_CNhs12031_11421-118F8_forward Regulation 
KeratinocyteEpidermalDonor2_CNhs11381_tpm_rev KeratinocyteEpidermalD2- Keratinocyte - epidermal, donor2_CNhs11381_11349-117G8_reverse Regulation 
KeratinocyteEpidermalDonor2_CNhs11381_tpm_fwd KeratinocyteEpidermalD2+ Keratinocyte - epidermal, donor2_CNhs11381_11349-117G8_forward Regulation 
KeratinocyteEpidermalDonor1_CNhs11064_tpm_rev KeratinocyteEpidermalD1- Keratinocyte - epidermal, donor1_CNhs11064_11272-116H3_reverse Regulation 
KeratinocyteEpidermalDonor1_CNhs11064_tpm_fwd KeratinocyteEpidermalD1+ Keratinocyte - epidermal, donor1_CNhs11064_11272-116H3_forward Regulation 
IrisPigmentEpithelialCellsDonor1_CNhs12596_tpm_rev IrisPigmentEpithelialCellsD1- Iris Pigment Epithelial Cells, donor1_CNhs12596_11530-119I9_reverse Regulation 
IrisPigmentEpithelialCellsDonor1_CNhs12596_tpm_fwd IrisPigmentEpithelialCellsD1+ Iris Pigment Epithelial Cells, donor1_CNhs12596_11530-119I9_forward Regulation 
IntestinalEpithelialCellsPolarizedDonor1_CNhs10875_tpm_rev IntestinalEpithelialCellsD1- Intestinal epithelial cells (polarized), donor1_CNhs10875_11246-116E4_reverse Regulation 
IntestinalEpithelialCellsPolarizedDonor1_CNhs10875_tpm_fwd IntestinalEpithelialCellsD1+ Intestinal epithelial cells (polarized), donor1_CNhs10875_11246-116E4_forward Regulation 
ImmatureLangerhansCellsDonor2_CNhs13480_tpm_rev ImmatureLangerhansCellsD2- immature langerhans cells, donor2_CNhs13480_11905-125F6_reverse Regulation 
ImmatureLangerhansCellsDonor2_CNhs13480_tpm_fwd ImmatureLangerhansCellsD2+ immature langerhans cells, donor2_CNhs13480_11905-125F6_forward Regulation 
ImmatureLangerhansCellsDonor1_CNhs13537_tpm_rev ImmatureLangerhansCellsD1- immature langerhans cells, donor1_CNhs13537_11904-125F5_reverse Regulation 
ImmatureLangerhansCellsDonor1_CNhs13537_tpm_fwd ImmatureLangerhansCellsD1+ immature langerhans cells, donor1_CNhs13537_11904-125F5_forward Regulation 
HepatocyteDonor3_CNhs12626_tpm_rev HepatocyteD3- Hepatocyte, donor3_CNhs12626_11684-122I1_reverse Regulation 
HepatocyteDonor3_CNhs12626_tpm_fwd HepatocyteD3+ Hepatocyte, donor3_CNhs12626_11684-122I1_forward Regulation 
HepatocyteDonor2_CNhs12349_tpm_rev HepatocyteD2- Hepatocyte, donor2_CNhs12349_11603-120I1_reverse Regulation 
HepatocyteDonor2_CNhs12349_tpm_fwd HepatocyteD2+ Hepatocyte, donor2_CNhs12349_11603-120I1_forward Regulation 
HepatocyteDonor1_CNhs12340_tpm_rev HepatocyteD1- Hepatocyte, donor1_CNhs12340_11523-119I2_reverse Regulation 
HepatocyteDonor1_CNhs12340_tpm_fwd HepatocyteD1+ Hepatocyte, donor1_CNhs12340_11523-119I2_forward Regulation 
HepaticStellateCellsLipocyteDonor3_CNhs12627_tpm_rev HepaticStellateCellsD3- Hepatic Stellate Cells (lipocyte), donor3_CNhs12627_11685-122I2_reverse Regulation 
HepaticStellateCellsLipocyteDonor3_CNhs12627_tpm_fwd HepaticStellateCellsD3+ Hepatic Stellate Cells (lipocyte), donor3_CNhs12627_11685-122I2_forward Regulation 
HepaticStellateCellsLipocyteDonor2_CNhs12093_tpm_rev HepaticStellateCellsD2- Hepatic Stellate Cells (lipocyte), donor2_CNhs12093_11604-120I2_reverse Regulation 
HepaticStellateCellsLipocyteDonor2_CNhs12093_tpm_fwd HepaticStellateCellsD2+ Hepatic Stellate Cells (lipocyte), donor2_CNhs12093_11604-120I2_forward Regulation 
HepaticStellateCellsLipocyteDonor1_CNhs11335_tpm_rev HepaticStellateCellsD1- Hepatic Stellate Cells (lipocyte), donor1_CNhs11335_11524-119I3_reverse Regulation 
HepaticStellateCellsLipocyteDonor1_CNhs11335_tpm_fwd HepaticStellateCellsD1+ Hepatic Stellate Cells (lipocyte), donor1_CNhs11335_11524-119I3_forward Regulation 
HepaticSinusoidalEndothelialCellsDonor3_CNhs12625_tpm_rev HepaticSinusoidalEndothelialCellsD3- Hepatic Sinusoidal Endothelial Cells, donor3_CNhs12625_11682-122H8_reverse Regulation 
HepaticSinusoidalEndothelialCellsDonor3_CNhs12625_tpm_fwd HepaticSinusoidalEndothelialCellsD3+ Hepatic Sinusoidal Endothelial Cells, donor3_CNhs12625_11682-122H8_forward Regulation 
HepaticSinusoidalEndothelialCellsDonor2_CNhs12092_tpm_rev HepaticSinusoidalEndothelialCellsD2- Hepatic Sinusoidal Endothelial Cells, donor2_CNhs12092_11601-120H8_reverse Regulation 
HepaticSinusoidalEndothelialCellsDonor2_CNhs12092_tpm_fwd HepaticSinusoidalEndothelialCellsD2+ Hepatic Sinusoidal Endothelial Cells, donor2_CNhs12092_11601-120H8_forward Regulation 
HepaticSinusoidalEndothelialCellsDonor1_CNhs12075_tpm_rev HepaticSinusoidalEndothelialCellsD1- Hepatic Sinusoidal Endothelial Cells, donor1_CNhs12075_11521-119H9_reverse Regulation 
HepaticSinusoidalEndothelialCellsDonor1_CNhs12075_tpm_fwd HepaticSinusoidalEndothelialCellsD1+ Hepatic Sinusoidal Endothelial Cells, donor1_CNhs12075_11521-119H9_forward Regulation 
HairFollicleOuterRootSheathCellsDonor2_CNhs12347_tpm_rev HairFollicleOuterRootSheathCellsD2- Hair Follicle Outer Root Sheath Cells, donor2_CNhs12347_11584-120F9_reverse Regulation 
HairFollicleOuterRootSheathCellsDonor2_CNhs12347_tpm_fwd HairFollicleOuterRootSheathCellsD2+ Hair Follicle Outer Root Sheath Cells, donor2_CNhs12347_11584-120F9_forward Regulation 
HairFollicleOuterRootSheathCellsDonor1_CNhs12339_tpm_rev HairFollicleOuterRootSheathCellsD1- Hair Follicle Outer Root Sheath Cells, donor1_CNhs12339_11504-119G1_reverse Regulation 
HairFollicleOuterRootSheathCellsDonor1_CNhs12339_tpm_fwd HairFollicleOuterRootSheathCellsD1+ Hair Follicle Outer Root Sheath Cells, donor1_CNhs12339_11504-119G1_forward Regulation 
HairFollicleDermalPapillaCellsDonor3_CNhs12030_tpm_rev HairFollicleDermalPapillaCellsD3- Hair Follicle Dermal Papilla Cells, donor3_CNhs12030_11420-118F7_reverse Regulation 
HairFollicleDermalPapillaCellsDonor3_CNhs12030_tpm_fwd HairFollicleDermalPapillaCellsD3+ Hair Follicle Dermal Papilla Cells, donor3_CNhs12030_11420-118F7_forward Regulation 
HairFollicleDermalPapillaCellsDonor2_CNhs11979_tpm_rev HairFollicleDermalPapillaCellsD2- Hair Follicle Dermal Papilla Cells, donor2_CNhs11979_11348-117G7_reverse Regulation 
HairFollicleDermalPapillaCellsDonor2_CNhs11979_tpm_fwd HairFollicleDermalPapillaCellsD2+ Hair Follicle Dermal Papilla Cells, donor2_CNhs11979_11348-117G7_forward Regulation 
HairFollicleDermalPapillaCellsDonor1_CNhs12501_tpm_rev HairFollicleDermalPapillaCellsD1- Hair Follicle Dermal Papilla Cells, donor1_CNhs12501_11271-116H2_reverse Regulation 
HairFollicleDermalPapillaCellsDonor1_CNhs12501_tpm_fwd HairFollicleDermalPapillaCellsD1+ Hair Follicle Dermal Papilla Cells, donor1_CNhs12501_11271-116H2_forward Regulation 
GingivalEpithelialCellsDonor3GEA15_CNhs11903_tpm_rev GingivalEpithelialCellsD3- Gingival epithelial cells, donor3 (GEA15)_CNhs11903_11379-118B2_reverse Regulation 
GingivalEpithelialCellsDonor3GEA15_CNhs11903_tpm_fwd GingivalEpithelialCellsD3+ Gingival epithelial cells, donor3 (GEA15)_CNhs11903_11379-118B2_forward Regulation 
GingivalEpithelialCellsDonor2GEA14_CNhs11896_tpm_rev GingivalEpithelialCellsD2- Gingival epithelial cells, donor2 (GEA14)_CNhs11896_11302-117B6_reverse Regulation 
GingivalEpithelialCellsDonor2GEA14_CNhs11896_tpm_fwd GingivalEpithelialCellsD2+ Gingival epithelial cells, donor2 (GEA14)_CNhs11896_11302-117B6_forward Regulation 
GingivalEpithelialCellsDonor1GEA11_CNhs11061_tpm_rev GingivalEpithelialCellsD1- Gingival epithelial cells, donor1 (GEA11)_CNhs11061_11221-116B6_reverse Regulation 
GingivalEpithelialCellsDonor1GEA11_CNhs11061_tpm_fwd GingivalEpithelialCellsD1+ Gingival epithelial cells, donor1 (GEA11)_CNhs11061_11221-116B6_forward Regulation 
GammaDeltaPositiveTCellsDonor2_CNhs13915_tpm_rev GammaDeltaTcellsD2- gamma delta positive T cells, donor2_CNhs13915_11938-126A3_reverse Regulation 
GammaDeltaPositiveTCellsDonor2_CNhs13915_tpm_fwd GammaDeltaTcellsD2+ gamma delta positive T cells, donor2_CNhs13915_11938-126A3_forward Regulation 
GammaDeltaPositiveTCellsDonor1_CNhs13914_tpm_rev GammaDeltaTcellsD1- gamma delta positive T cells, donor1_CNhs13914_11937-126A2_reverse Regulation 
GammaDeltaPositiveTCellsDonor1_CNhs13914_tpm_fwd GammaDeltaTcellsD1+ gamma delta positive T cells, donor1_CNhs13914_11937-126A2_forward Regulation 
FibroblastVillousMesenchymalDonor3_CNhs12920_tpm_rev FibroVillousMesenchymalD3- Fibroblast - Villous Mesenchymal, donor3_CNhs12920_11696-123A4_reverse Regulation 
FibroblastVillousMesenchymalDonor3_CNhs12920_tpm_fwd FibroVillousMesenchymalD3+ Fibroblast - Villous Mesenchymal, donor3_CNhs12920_11696-123A4_forward Regulation 
FibroblastVillousMesenchymalDonor2_CNhs12099_tpm_rev FibroVillousMesenchymalD2- Fibroblast - Villous Mesenchymal, donor2_CNhs12099_11615-122A4_reverse Regulation 
FibroblastVillousMesenchymalDonor2_CNhs12099_tpm_fwd FibroVillousMesenchymalD2+ Fibroblast - Villous Mesenchymal, donor2_CNhs12099_11615-122A4_forward Regulation 
FibroblastVillousMesenchymalDonor1_CNhs11343_tpm_rev FibroVillousMesenchymalD1- Fibroblast - Villous Mesenchymal, donor1_CNhs11343_11535-120A5_reverse Regulation 
FibroblastVillousMesenchymalDonor1_CNhs11343_tpm_fwd FibroVillousMesenchymalD1+ Fibroblast - Villous Mesenchymal, donor1_CNhs11343_11535-120A5_forward Regulation 
FibroblastSkinWalkerWarburgDonor1_CNhs11352_tpm_rev FibroSkinWalkerWarburgD1- Fibroblast - skin walker warburg, donor1_CNhs11352_11554-120C6_reverse Regulation 
FibroblastSkinWalkerWarburgDonor1_CNhs11352_tpm_fwd FibroSkinWalkerWarburgD1+ Fibroblast - skin walker warburg, donor1_CNhs11352_11554-120C6_forward Regulation 
FibroblastSkinSpinalMuscularAtrophyDonor3_CNhs11912_tpm_rev FibroSkinSpinalMuscularAtrophyNucfracD3- Fibroblast - skin spinal muscular atrophy, donor3_CNhs11912_11559-120D2_reverse Regulation 
FibroblastSkinSpinalMuscularAtrophyDonor3_CNhs11912_tpm_fwd FibroSkinSpinalMuscularAtrophyNucfracD3+ Fibroblast - skin spinal muscular atrophy, donor3_CNhs11912_11559-120D2_forward Regulation 
FibroblastSkinSpinalMuscularAtrophyDonor2_CNhs11911_tpm_rev FibroSkinSpinalMuscularAtrophyNucfracD2- Fibroblast - skin spinal muscular atrophy, donor2_CNhs11911_11558-120D1_reverse Regulation 
FibroblastSkinSpinalMuscularAtrophyDonor2_CNhs11911_tpm_fwd FibroSkinSpinalMuscularAtrophyNucfracD2+ Fibroblast - skin spinal muscular atrophy, donor2_CNhs11911_11558-120D1_forward Regulation 
FibroblastSkinSpinalMuscularAtrophyDonor1_CNhs11074_tpm_rev FibroSkinSpinalMuscularAtrophyNucfracD1- Fibroblast - skin spinal muscular atrophy, donor1_CNhs11074_11555-120C7_reverse Regulation 
FibroblastSkinSpinalMuscularAtrophyDonor1_CNhs11074_tpm_fwd FibroSkinSpinalMuscularAtrophyNucfracD1+ Fibroblast - skin spinal muscular atrophy, donor1_CNhs11074_11555-120C7_forward Regulation 
FibroblastSkinNormalDonor2_CNhs11914_tpm_rev FibroSkinNormalNucfracD2- Fibroblast - skin normal, donor2_CNhs11914_11561-120D4_reverse Regulation 
FibroblastSkinNormalDonor2_CNhs11914_tpm_fwd FibroSkinNormalNucfracD2+ Fibroblast - skin normal, donor2_CNhs11914_11561-120D4_forward Regulation 
FibroblastSkinNormalDonor1_CNhs11351_tpm_rev FibroSkinNormalNucfracD1- Fibroblast - skin normal, donor1_CNhs11351_11553-120C5_reverse Regulation 
FibroblastSkinNormalDonor1_CNhs11351_tpm_fwd FibroSkinNormalNucfracD1+ Fibroblast - skin normal, donor1_CNhs11351_11553-120C5_forward Regulation 
FibroblastSkinDystrophiaMyotonicaDonor3_CNhs11913_tpm_rev FibroSkinDystrophiaMyotonicaNucfracD3- Fibroblast - skin dystrophia myotonica, donor3_CNhs11913_11560-120D3_reverse Regulation 
FibroblastSkinDystrophiaMyotonicaDonor3_CNhs11913_tpm_fwd FibroSkinDystrophiaMyotonicaNucfracD3+ Fibroblast - skin dystrophia myotonica, donor3_CNhs11913_11560-120D3_forward Regulation 
FibroblastSkinDystrophiaMyotonicaDonor2_CNhs11354_tpm_rev FibroSkinDystrophiaMyotonicaNucfracD2- Fibroblast - skin dystrophia myotonica, donor2_CNhs11354_11557-120C9_reverse Regulation 
FibroblastSkinDystrophiaMyotonicaDonor2_CNhs11354_tpm_fwd FibroSkinDystrophiaMyotonicaNucfracD2+ Fibroblast - skin dystrophia myotonica, donor2_CNhs11354_11557-120C9_forward Regulation 
FibroblastSkinDystrophiaMyotonicaDonor1_CNhs11353_tpm_rev FibroSkinDystrophiaMyotonicaNucfracD1- Fibroblast - skin dystrophia myotonica, donor1_CNhs11353_11556-120C8_reverse Regulation 
FibroblastSkinDystrophiaMyotonicaDonor1_CNhs11353_tpm_fwd FibroSkinDystrophiaMyotonicaNucfracD1+ Fibroblast - skin dystrophia myotonica, donor1_CNhs11353_11556-120C8_forward Regulation 
FibroblastPulmonaryArteryDonor1_CNhs10878_tpm_rev FibroPulmonaryArteryD1- Fibroblast - Pulmonary Artery, donor1_CNhs10878_11250-116E8_reverse Regulation 
FibroblastPulmonaryArteryDonor1_CNhs10878_tpm_fwd FibroPulmonaryArteryD1+ Fibroblast - Pulmonary Artery, donor1_CNhs10878_11250-116E8_forward Regulation 
FibroblastPeriodontalLigamentDonor6PLH3_CNhs11996_tpm_rev FibroPeriodontalLigamentD6- Fibroblast - Periodontal Ligament, donor6 (PLH3)_CNhs11996_11380-118B3_reverse Regulation 
FibroblastPeriodontalLigamentDonor6PLH3_CNhs11996_tpm_fwd FibroPeriodontalLigamentD6+ Fibroblast - Periodontal Ligament, donor6 (PLH3)_CNhs11996_11380-118B3_forward Regulation 
FibroblastPeriodontalLigamentDonor5PL30_CNhs11953_tpm_rev FibroPeriodontalLigamentD5- Fibroblast - Periodontal Ligament, donor5 (PL30)_CNhs11953_11304-117B8_reverse Regulation 
FibroblastPeriodontalLigamentDonor5PL30_CNhs11953_tpm_fwd FibroPeriodontalLigamentD5+ Fibroblast - Periodontal Ligament, donor5 (PL30)_CNhs11953_11304-117B8_forward Regulation 
FibroblastPeriodontalLigamentDonor4PL29_CNhs12493_tpm_rev FibroPeriodontalLigamentD4- Fibroblast - Periodontal Ligament, donor4 (PL29)_CNhs12493_11223-116B8_reverse Regulation 
FibroblastPeriodontalLigamentDonor4PL29_CNhs12493_tpm_fwd FibroPeriodontalLigamentD4+ Fibroblast - Periodontal Ligament, donor4 (PL29)_CNhs12493_11223-116B8_forward Regulation 
FibroblastPeriodontalLigamentDonor3_CNhs11907_tpm_rev FibroPeriodontalLigamentD3- Fibroblast - Periodontal Ligament, donor3_CNhs11907_11395-118C9_reverse Regulation 
FibroblastPeriodontalLigamentDonor3_CNhs11907_tpm_fwd FibroPeriodontalLigamentD3+ Fibroblast - Periodontal Ligament, donor3_CNhs11907_11395-118C9_forward Regulation 
FibroblastPeriodontalLigamentDonor2_CNhs11962_tpm_rev FibroPeriodontalLigamentD2- Fibroblast - Periodontal Ligament, donor2_CNhs11962_11319-117D5_reverse Regulation 
FibroblastPeriodontalLigamentDonor2_CNhs11962_tpm_fwd FibroPeriodontalLigamentD2+ Fibroblast - Periodontal Ligament, donor2_CNhs11962_11319-117D5_forward Regulation 
FibroblastPeriodontalLigamentDonor1_CNhs10867_tpm_rev FibroPeriodontalLigamentD1- Fibroblast - Periodontal Ligament, donor1_CNhs10867_11238-116D5_reverse Regulation 
FibroblastPeriodontalLigamentDonor1_CNhs10867_tpm_fwd FibroPeriodontalLigamentD1+ Fibroblast - Periodontal Ligament, donor1_CNhs10867_11238-116D5_forward Regulation 
FibroblastMammaryDonor3_CNhs12128_tpm_rev FibroMammaryD3- Fibroblast - Mammary, donor3_CNhs12128_11701-123A9_reverse Regulation 
FibroblastMammaryDonor3_CNhs12128_tpm_fwd FibroMammaryD3+ Fibroblast - Mammary, donor3_CNhs12128_11701-123A9_forward Regulation 
FibroblastMammaryDonor2_CNhs12103_tpm_rev FibroMammaryD2- Fibroblast - Mammary, donor2_CNhs12103_11620-122A9_reverse Regulation 
FibroblastMammaryDonor2_CNhs12103_tpm_fwd FibroMammaryD2+ Fibroblast - Mammary, donor2_CNhs12103_11620-122A9_forward Regulation 
FibroblastMammaryDonor1_CNhs11348_tpm_rev FibroMammaryD1- Fibroblast - Mammary, donor1_CNhs11348_11540-120B1_reverse Regulation 
FibroblastMammaryDonor1_CNhs11348_tpm_fwd FibroMammaryD1+ Fibroblast - Mammary, donor1_CNhs11348_11540-120B1_forward Regulation 
FibroblastLymphaticDonor3_CNhs12118_tpm_rev FibroLymphaticD3- Fibroblast - Lymphatic, donor3_CNhs12118_11667-122G2_reverse Regulation 
FibroblastLymphaticDonor3_CNhs12118_tpm_fwd FibroLymphaticD3+ Fibroblast - Lymphatic, donor3_CNhs12118_11667-122G2_forward Regulation 
FibroblastLymphaticDonor2_CNhs12082_tpm_rev FibroLymphaticD2- Fibroblast - Lymphatic, donor2_CNhs12082_11586-120G2_reverse Regulation 
FibroblastLymphaticDonor2_CNhs12082_tpm_fwd FibroLymphaticD2+ Fibroblast - Lymphatic, donor2_CNhs12082_11586-120G2_forward Regulation 
FibroblastLymphaticDonor1_CNhs11322_tpm_rev FibroLymphaticD1- Fibroblast - Lymphatic, donor1_CNhs11322_11506-119G3_reverse Regulation 
FibroblastLymphaticDonor1_CNhs11322_tpm_fwd FibroLymphaticD1+ Fibroblast - Lymphatic, donor1_CNhs11322_11506-119G3_forward Regulation 
FibroblastLungDonor3_CNhs12029_tpm_rev FibroLungD3- Fibroblast - Lung, donor3_CNhs12029_11419-118F6_reverse Regulation 
FibroblastLungDonor3_CNhs12029_tpm_fwd FibroLungD3+ Fibroblast - Lung, donor3_CNhs12029_11419-118F6_forward Regulation 
FibroblastLungDonor2_CNhs11380_tpm_rev FibroLungD2- Fibroblast - Lung, donor2_CNhs11380_11347-117G6_reverse Regulation 
FibroblastLungDonor2_CNhs11380_tpm_fwd FibroLungD2+ Fibroblast - Lung, donor2_CNhs11380_11347-117G6_forward Regulation 
FibroblastLungDonor1_CNhs12500_tpm_rev FibroLungD1- Fibroblast - Lung, donor1_CNhs12500_11270-116H1_reverse Regulation 
FibroblastLungDonor1_CNhs12500_tpm_fwd FibroLungD1+ Fibroblast - Lung, donor1_CNhs12500_11270-116H1_forward Regulation 
FibroblastGingivalDonor9Control_CNhs14134_tpm_rev FibroGingivalD9- Fibroblast - Gingival, donor9 (control)_CNhs14134_11927-125I1_reverse Regulation 
FibroblastGingivalDonor9Control_CNhs14134_tpm_fwd FibroGingivalD9+ Fibroblast - Gingival, donor9 (control)_CNhs14134_11927-125I1_forward Regulation 
FibroblastGingivalDonor8Control_CNhs14133_tpm_rev FibroGingivalD8- Fibroblast - Gingival, donor8 (control)_CNhs14133_11926-125H9_reverse Regulation 
FibroblastGingivalDonor8ChronicPeriodontitis_CNhs14132_tpm_rev FibroGingivalD8- Fibroblast - Gingival, donor8 (chronic periodontitis)_CNhs14132_11925-125H8_reverse Regulation 
FibroblastGingivalDonor8ChronicPeriodontitis_CNhs14132_tpm_fwd FibroGingivalD8+ Fibroblast - Gingival, donor8 (chronic periodontitis)_CNhs14132_11925-125H8_forward Regulation 
FibroblastGingivalDonor8Control_CNhs14133_tpm_fwd FibroGingivalD8+ Fibroblast - Gingival, donor8 (control)_CNhs14133_11926-125H9_forward Regulation 
FibroblastGingivalDonor7Control_CNhs14131_tpm_rev FibroGingivalD7- Fibroblast - Gingival, donor7 (control)_CNhs14131_11924-125H7_reverse Regulation 
FibroblastGingivalDonor7AggressivePeriodontitis_CNhs14130_tpm_rev FibroGingivalD7- Fibroblast - Gingival, donor7 (aggressive periodontitis)_CNhs14130_11923-125H6_reverse Regulation 
FibroblastGingivalDonor7Control_CNhs14131_tpm_fwd FibroGingivalD7+ Fibroblast - Gingival, donor7 (control)_CNhs14131_11924-125H7_forward Regulation 
FibroblastGingivalDonor7AggressivePeriodontitis_CNhs14130_tpm_fwd FibroGingivalD7+ Fibroblast - Gingival, donor7 (aggressive periodontitis)_CNhs14130_11923-125H6_forward Regulation 
FibroblastGingivalDonor6AggressivePeriodontitis_CNhs14128_tpm_rev FibroGingivalD6- Fibroblast - Gingival, donor6 (aggressive periodontitis)_CNhs14128_11921-125H4_reverse Regulation 
FibroblastGingivalDonor6Control_CNhs14129_tpm_rev FibroGingivalD6- Fibroblast - Gingival, donor6 (control)_CNhs14129_11922-125H5_reverse Regulation 
FibroblastGingivalDonor6AggressivePeriodontitis_CNhs14128_tpm_fwd FibroGingivalD6+ Fibroblast - Gingival, donor6 (aggressive periodontitis)_CNhs14128_11921-125H4_forward Regulation 
FibroblastGingivalDonor6Control_CNhs14129_tpm_fwd FibroGingivalD6+ Fibroblast - Gingival, donor6 (control)_CNhs14129_11922-125H5_forward Regulation 
FibroblastGingivalDonor5GFH3_CNhs11952_tpm_rev FibroGingivalD5- Fibroblast - Gingival, donor5 (GFH3)_CNhs11952_11303-117B7_reverse Regulation 
FibroblastGingivalDonor5GFH3_CNhs11952_tpm_fwd FibroGingivalD5+ Fibroblast - Gingival, donor5 (GFH3)_CNhs11952_11303-117B7_forward Regulation 
FibroblastGingivalDonor4GFH2_CNhs10848_tpm_rev FibroGingivalD4- Fibroblast - Gingival, donor4 (GFH2)_CNhs10848_11222-116B7_reverse Regulation 
FibroblastGingivalDonor4GFH2_CNhs10848_tpm_fwd FibroGingivalD4+ Fibroblast - Gingival, donor4 (GFH2)_CNhs10848_11222-116B7_forward Regulation 
FibroblastGingivalDonor3_CNhs12006_tpm_rev FibroGingivalD3- Fibroblast - Gingival, donor3_CNhs12006_11394-118C8_reverse Regulation 
FibroblastGingivalDonor3_CNhs12006_tpm_fwd FibroGingivalD3+ Fibroblast - Gingival, donor3_CNhs12006_11394-118C8_forward Regulation 
FibroblastGingivalDonor2_CNhs11961_tpm_rev FibroGingivalD2- Fibroblast - Gingival, donor2_CNhs11961_11318-117D4_reverse Regulation 
FibroblastGingivalDonor2_CNhs11961_tpm_fwd FibroGingivalD2+ Fibroblast - Gingival, donor2_CNhs11961_11318-117D4_forward Regulation 
FibroblastGingivalDonor10Periodontitis_CNhs14135_tpm_rev FibroGingivalD10 (p- Fibroblast - Gingival, donor10 (periodontitis)_CNhs14135_11928-125I2_reverse Regulation 
FibroblastGingivalDonor10Periodontitis_CNhs14135_tpm_fwd FibroGingivalD10 (p+ Fibroblast - Gingival, donor10 (periodontitis)_CNhs14135_11928-125I2_forward Regulation 
FibroblastGingivalDonor1_CNhs10866_tpm_rev FibroGingivalD1- Fibroblast - Gingival, donor1_CNhs10866_11237-116D4_reverse Regulation 
FibroblastGingivalDonor1_CNhs10866_tpm_fwd FibroGingivalD1+ Fibroblast - Gingival, donor1_CNhs10866_11237-116D4_forward Regulation 
FibroblastDermalDonor6_CNhs12059_tpm_rev FibroDermalD6- Fibroblast - Dermal, donor6_CNhs12059_11458-119A9_reverse Regulation 
FibroblastDermalDonor6_CNhs12059_tpm_fwd FibroDermalD6+ Fibroblast - Dermal, donor6_CNhs12059_11458-119A9_forward Regulation 
FibroblastDermalDonor5_CNhs12055_tpm_rev FibroDermalD5- Fibroblast - Dermal, donor5_CNhs12055_11454-119A5_reverse Regulation 
FibroblastDermalDonor5_CNhs12055_tpm_fwd FibroDermalD5+ Fibroblast - Dermal, donor5_CNhs12055_11454-119A5_forward Regulation 
FibroblastDermalDonor4_CNhs12052_tpm_rev FibroDermalD4- Fibroblast - Dermal, donor4_CNhs12052_11450-119A1_reverse Regulation 
FibroblastDermalDonor4_CNhs12052_tpm_fwd FibroDermalD4+ Fibroblast - Dermal, donor4_CNhs12052_11450-119A1_forward Regulation 
FibroblastDermalDonor3_CNhs12028_tpm_rev FibroDermalD3- Fibroblast - Dermal, donor3_CNhs12028_11418-118F5_reverse Regulation 
FibroblastDermalDonor3_CNhs12028_tpm_fwd FibroDermalD3+ Fibroblast - Dermal, donor3_CNhs12028_11418-118F5_forward Regulation 
FibroblastDermalDonor2_CNhs11379_tpm_rev FibroDermalD2- Fibroblast - Dermal, donor2_CNhs11379_11346-117G5_reverse Regulation 
FibroblastDermalDonor2_CNhs11379_tpm_fwd FibroDermalD2+ Fibroblast - Dermal, donor2_CNhs11379_11346-117G5_forward Regulation 
FibroblastDermalDonor1_CNhs12499_tpm_rev FibroDermalD1- Fibroblast - Dermal, donor1_CNhs12499_11269-116G9_reverse Regulation 
FibroblastDermalDonor1_CNhs12499_tpm_fwd FibroDermalD1+ Fibroblast - Dermal, donor1_CNhs12499_11269-116G9_forward Regulation 
FibroblastConjunctivalDonor3_CNhs12734_tpm_rev FibroConjunctivalD3- Fibroblast - Conjunctival, donor3_CNhs12734_11692-122I9_reverse Regulation 
FibroblastConjunctivalDonor3_CNhs12734_tpm_fwd FibroConjunctivalD3+ Fibroblast - Conjunctival, donor3_CNhs12734_11692-122I9_forward Regulation 
FibroblastConjunctivalDonor1_CNhs11339_tpm_rev FibroConjunctivalD1- Fibroblast - Conjunctival, donor1_CNhs11339_11531-120A1_reverse Regulation 
FibroblastConjunctivalDonor1_CNhs11339_tpm_fwd FibroConjunctivalD1+ Fibroblast - Conjunctival, donor1_CNhs11339_11531-120A1_forward Regulation 
FibroblastChoroidPlexusDonor3_CNhs12620_tpm_rev FibroChoroidPlexusD3- Fibroblast - Choroid Plexus, donor3_CNhs12620_11653-122E6_reverse Regulation 
FibroblastChoroidPlexusDonor3_CNhs12620_tpm_fwd FibroChoroidPlexusD3+ Fibroblast - Choroid Plexus, donor3_CNhs12620_11653-122E6_forward Regulation 
FibroblastChoroidPlexusDonor2_CNhs12344_tpm_rev FibroChoroidPlexusD2- Fibroblast - Choroid Plexus, donor2_CNhs12344_11572-120E6_reverse Regulation 
FibroblastChoroidPlexusDonor2_CNhs12344_tpm_fwd FibroChoroidPlexusD2+ Fibroblast - Choroid Plexus, donor2_CNhs12344_11572-120E6_forward Regulation 
FibroblastChoroidPlexusDonor1_CNhs11319_tpm_rev FibroChoroidPlexusD1- Fibroblast - Choroid Plexus, donor1_CNhs11319_11492-119E7_reverse Regulation 
FibroblastChoroidPlexusDonor1_CNhs11319_tpm_fwd FibroChoroidPlexusD1+ Fibroblast - Choroid Plexus, donor1_CNhs11319_11492-119E7_forward Regulation 
FibroblastCardiacDonor6_CNhs12061_tpm_rev FibroCardiacD6- Fibroblast - Cardiac, donor6_CNhs12061_11460-119B2_reverse Regulation 
FibroblastCardiacDonor6_CNhs12061_tpm_fwd FibroCardiacD6+ Fibroblast - Cardiac, donor6_CNhs12061_11460-119B2_forward Regulation 
FibroblastCardiacDonor5_CNhs12057_tpm_rev FibroCardiacD5- Fibroblast - Cardiac, donor5_CNhs12057_11456-119A7_reverse Regulation 
FibroblastCardiacDonor5_CNhs12057_tpm_fwd FibroCardiacD5+ Fibroblast - Cardiac, donor5_CNhs12057_11456-119A7_forward Regulation 
FibroblastCardiacDonor4_CNhs11909_tpm_rev FibroCardiacD4- Fibroblast - Cardiac, donor4_CNhs11909_11452-119A3_reverse Regulation 
FibroblastCardiacDonor4_CNhs11909_tpm_fwd FibroCardiacD4+ Fibroblast - Cardiac, donor4_CNhs11909_11452-119A3_forward Regulation 
FibroblastCardiacDonor3_CNhs12027_tpm_rev FibroCardiacD3- Fibroblast - Cardiac, donor3_CNhs12027_11417-118F4_reverse Regulation 
FibroblastCardiacDonor3_CNhs12027_tpm_fwd FibroCardiacD3+ Fibroblast - Cardiac, donor3_CNhs12027_11417-118F4_forward Regulation 
FibroblastCardiacDonor2_CNhs11378_tpm_rev FibroCardiacD2- Fibroblast - Cardiac, donor2_CNhs11378_11345-117G4_reverse Regulation 
FibroblastCardiacDonor2_CNhs11378_tpm_fwd FibroCardiacD2+ Fibroblast - Cardiac, donor2_CNhs11378_11345-117G4_forward Regulation 
FibroblastCardiacDonor1_CNhs12498_tpm_rev FibroCardiacD1- Fibroblast - Cardiac, donor1_CNhs12498_11268-116G8_reverse Regulation 
FibroblastCardiacDonor1_CNhs12498_tpm_fwd FibroCardiacD1+ Fibroblast - Cardiac, donor1_CNhs12498_11268-116G8_forward Regulation 
FibroblastAorticAdventitialDonor3_CNhs12011_tpm_rev FibroAorticAdventitialD3- Fibroblast - Aortic Adventitial, donor3_CNhs12011_11401-118D6_reverse Regulation 
FibroblastAorticAdventitialDonor3_CNhs12011_tpm_fwd FibroAorticAdventitialD3+ Fibroblast - Aortic Adventitial, donor3_CNhs12011_11401-118D6_forward Regulation 
FibroblastAorticAdventitialDonor2_CNhs11968_tpm_rev FibroAorticAdventitialD2- Fibroblast - Aortic Adventitial, donor2_CNhs11968_11326-117E3_reverse Regulation 
FibroblastAorticAdventitialDonor2_CNhs11968_tpm_fwd FibroAorticAdventitialD2+ Fibroblast - Aortic Adventitial, donor2_CNhs11968_11326-117E3_forward Regulation 
FibroblastAorticAdventitialDonor1_CNhs10874_tpm_rev FibroAorticAdventitialD1- Fibroblast - Aortic Adventitial, donor1_CNhs10874_11245-116E3_reverse Regulation 
FibroblastAorticAdventitialDonor1_CNhs10874_tpm_fwd FibroAorticAdventitialD1+ Fibroblast - Aortic Adventitial, donor1_CNhs10874_11245-116E3_forward Regulation 
EsophagealEpithelialCellsDonor3_CNhs12622_tpm_rev EsophagealEpithelialCellsD3- Esophageal Epithelial Cells, donor3_CNhs12622_11668-122G3_reverse Regulation 
EsophagealEpithelialCellsDonor3_CNhs12622_tpm_fwd EsophagealEpithelialCellsD3+ Esophageal Epithelial Cells, donor3_CNhs12622_11668-122G3_forward Regulation 
EsophagealEpithelialCellsDonor2_CNhs12083_tpm_rev EsophagealEpithelialCellsD2- Esophageal Epithelial Cells, donor2_CNhs12083_11587-120G3_reverse Regulation 
EsophagealEpithelialCellsDonor2_CNhs12083_tpm_fwd EsophagealEpithelialCellsD2+ Esophageal Epithelial Cells, donor2_CNhs12083_11587-120G3_forward Regulation 
EsophagealEpithelialCellsDonor1_CNhs11323_tpm_rev EsophagealEpithelialCellsD1- Esophageal Epithelial Cells, donor1_CNhs11323_11507-119G4_reverse Regulation 
EsophagealEpithelialCellsDonor1_CNhs11323_tpm_fwd EsophagealEpithelialCellsD1+ Esophageal Epithelial Cells, donor1_CNhs11323_11507-119G4_forward Regulation 
EndothelialCellsVeinDonor3_CNhs12026_tpm_rev EndothelialCellsVeinD3- Endothelial Cells - Vein, donor3_CNhs12026_11416-118F3_reverse Regulation 
EndothelialCellsVeinDonor3_CNhs12026_tpm_fwd EndothelialCellsVeinD3+ Endothelial Cells - Vein, donor3_CNhs12026_11416-118F3_forward Regulation 
EndothelialCellsVeinDonor2_CNhs11377_tpm_rev EndothelialCellsVeinD2- Endothelial Cells - Vein, donor2_CNhs11377_11344-117G3_reverse Regulation 
EndothelialCellsVeinDonor2_CNhs11377_tpm_fwd EndothelialCellsVeinD2+ Endothelial Cells - Vein, donor2_CNhs11377_11344-117G3_forward Regulation 
EndothelialCellsVeinDonor1_CNhs12497_tpm_rev EndothelialCellsVeinD1- Endothelial Cells - Vein, donor1_CNhs12497_11267-116G7_reverse Regulation 
EndothelialCellsVeinDonor1_CNhs12497_tpm_fwd EndothelialCellsVeinD1+ Endothelial Cells - Vein, donor1_CNhs12497_11267-116G7_forward Regulation 
EndothelialCellsUmbilicalVeinDonor3_CNhs12010_tpm_rev EndothelialCellsUmbilicalVeinD3- Endothelial Cells - Umbilical vein, donor3_CNhs12010_11400-118D5_reverse Regulation 
EndothelialCellsUmbilicalVeinDonor3_CNhs12010_tpm_fwd EndothelialCellsUmbilicalVeinD3+ Endothelial Cells - Umbilical vein, donor3_CNhs12010_11400-118D5_forward Regulation 
EndothelialCellsUmbilicalVeinDonor2_CNhs11967_tpm_rev EndothelialCellsUmbilicalVeinD2- Endothelial Cells - Umbilical vein, donor2_CNhs11967_11324-117E1_reverse Regulation 
EndothelialCellsUmbilicalVeinDonor2_CNhs11967_tpm_fwd EndothelialCellsUmbilicalVeinD2+ Endothelial Cells - Umbilical vein, donor2_CNhs11967_11324-117E1_forward Regulation 
EndothelialCellsUmbilicalVeinDonor1_CNhs10872_tpm_rev EndothelialCellsUmbilicalVeinD1- Endothelial Cells - Umbilical vein, donor1_CNhs10872_11243-116E1_reverse Regulation 
EndothelialCellsUmbilicalVeinDonor1_CNhs10872_tpm_fwd EndothelialCellsUmbilicalVeinD1+ Endothelial Cells - Umbilical vein, donor1_CNhs10872_11243-116E1_forward Regulation 
EndothelialCellsThoracicDonor2_CNhs11978_tpm_rev EndothelialCellsThoracicD2- Endothelial Cells - Thoracic, donor2_CNhs11978_11343-117G2_reverse Regulation 
EndothelialCellsThoracicDonor2_CNhs11978_tpm_fwd EndothelialCellsThoracicD2+ Endothelial Cells - Thoracic, donor2_CNhs11978_11343-117G2_forward Regulation 
EndothelialCellsThoracicDonor1_CNhs11926_tpm_rev EndothelialCellsThoracicD1- Endothelial Cells - Thoracic, donor1_CNhs11926_11266-116G6_reverse Regulation 
EndothelialCellsThoracicDonor1_CNhs11926_tpm_fwd EndothelialCellsThoracicD1+ Endothelial Cells - Thoracic, donor1_CNhs11926_11266-116G6_forward Regulation 
EndothelialCellsMicrovascularDonor3_CNhs12024_tpm_rev EndothelialCellsMicrovascularD3- Endothelial Cells - Microvascular, donor3_CNhs12024_11414-118F1_reverse Regulation 
EndothelialCellsMicrovascularDonor3_CNhs12024_tpm_fwd EndothelialCellsMicrovascularD3+ Endothelial Cells - Microvascular, donor3_CNhs12024_11414-118F1_forward Regulation 
EndothelialCellsMicrovascularDonor2_CNhs11376_tpm_rev EndothelialCellsMicrovascularD2- Endothelial Cells - Microvascular, donor2_CNhs11376_11342-117G1_reverse Regulation 
EndothelialCellsMicrovascularDonor2_CNhs11376_tpm_fwd EndothelialCellsMicrovascularD2+ Endothelial Cells - Microvascular, donor2_CNhs11376_11342-117G1_forward Regulation 
EndothelialCellsMicrovascularDonor1_CNhs11925_tpm_rev EndothelialCellsMicrovascularD1- Endothelial Cells - Microvascular, donor1_CNhs11925_11265-116G5_reverse Regulation 
EndothelialCellsMicrovascularDonor1_CNhs11925_tpm_fwd EndothelialCellsMicrovascularD1+ Endothelial Cells - Microvascular, donor1_CNhs11925_11265-116G5_forward Regulation 
EndothelialCellsLymphaticDonor3_CNhs11906_tpm_rev EndothelialCellsLymphaticD3- Endothelial Cells - Lymphatic, donor3_CNhs11906_11393-118C7_reverse Regulation 
EndothelialCellsLymphaticDonor3_CNhs11906_tpm_fwd EndothelialCellsLymphaticD3+ Endothelial Cells - Lymphatic, donor3_CNhs11906_11393-118C7_forward Regulation 
EndothelialCellsLymphaticDonor2_CNhs11901_tpm_rev EndothelialCellsLymphaticD2- Endothelial Cells - Lymphatic, donor2_CNhs11901_11317-117D3_reverse Regulation 
EndothelialCellsLymphaticDonor2_CNhs11901_tpm_fwd EndothelialCellsLymphaticD2+ Endothelial Cells - Lymphatic, donor2_CNhs11901_11317-117D3_forward Regulation 
EndothelialCellsLymphaticDonor1_CNhs10865_tpm_rev EndothelialCellsLymphaticD1- Endothelial Cells - Lymphatic, donor1_CNhs10865_11236-116D3_reverse Regulation 
EndothelialCellsLymphaticDonor1_CNhs10865_tpm_fwd EndothelialCellsLymphaticD1+ Endothelial Cells - Lymphatic, donor1_CNhs10865_11236-116D3_forward Regulation 
EndothelialCellsArteryDonor3_CNhs12023_tpm_rev EndothelialCellsArteryD3- Endothelial Cells - Artery, donor3_CNhs12023_11413-118E9_reverse Regulation 
EndothelialCellsArteryDonor3_CNhs12023_tpm_fwd EndothelialCellsArteryD3+ Endothelial Cells - Artery, donor3_CNhs12023_11413-118E9_forward Regulation 
EndothelialCellsArteryDonor2_CNhs11977_tpm_rev EndothelialCellsArteryD2- Endothelial Cells - Artery, donor2_CNhs11977_11341-117F9_reverse Regulation 
EndothelialCellsArteryDonor2_CNhs11977_tpm_fwd EndothelialCellsArteryD2+ Endothelial Cells - Artery, donor2_CNhs11977_11341-117F9_forward Regulation 
EndothelialCellsArteryDonor1_CNhs12496_tpm_rev EndothelialCellsArteryD1- Endothelial Cells - Artery, donor1_CNhs12496_11264-116G4_reverse Regulation 
EndothelialCellsArteryDonor1_CNhs12496_tpm_fwd EndothelialCellsArteryD1+ Endothelial Cells - Artery, donor1_CNhs12496_11264-116G4_forward Regulation 
EndothelialCellsAorticDonor3_CNhs12022_tpm_rev EndothelialCellsAorticD3- Endothelial Cells - Aortic, donor3_CNhs12022_11412-118E8_reverse Regulation 
EndothelialCellsAorticDonor3_CNhs12022_tpm_fwd EndothelialCellsAorticD3+ Endothelial Cells - Aortic, donor3_CNhs12022_11412-118E8_forward Regulation 
EndothelialCellsAorticDonor2_CNhs11375_tpm_rev EndothelialCellsAorticD2- Endothelial Cells - Aortic, donor2_CNhs11375_11340-117F8_reverse Regulation 
EndothelialCellsAorticDonor2_CNhs11375_tpm_fwd EndothelialCellsAorticD2+ Endothelial Cells - Aortic, donor2_CNhs11375_11340-117F8_forward Regulation 
EndothelialCellsAorticDonor1_CNhs12495_tpm_rev EndothelialCellsAorticD1- Endothelial Cells - Aortic, donor1_CNhs12495_11263-116G3_reverse Regulation 
EndothelialCellsAorticDonor1_CNhs12495_tpm_fwd EndothelialCellsAorticD1+ Endothelial Cells - Aortic, donor1_CNhs12495_11263-116G3_forward Regulation 
EndothelialCellsAorticDonor0_CNhs10837_tpm_rev EndothelialCellsAorticD0- Endothelial Cells - Aortic, donor0_CNhs10837_11207-116A1_reverse Regulation 
EndothelialCellsAorticDonor0_CNhs10837_tpm_fwd EndothelialCellsAorticD0+ Endothelial Cells - Aortic, donor0_CNhs10837_11207-116A1_forward Regulation 
DendriticCellsPlasmacytoidDonor1_CNhs10857_tpm_rev DendriticCellsPlasmacytoidD1- Dendritic Cells - plasmacytoid, donor1_CNhs10857_11228-116C4_reverse Regulation 
DendriticCellsPlasmacytoidDonor1_CNhs10857_tpm_fwd DendriticCellsPlasmacytoidD1+ Dendritic Cells - plasmacytoid, donor1_CNhs10857_11228-116C4_forward Regulation 
DendriticCellsMonocyteImmatureDerivedDonor3_CNhs12000_tpm_rev DendriticCellsMonocyteImmatureD3- Dendritic Cells - monocyte immature derived, donor3_CNhs12000_11384-118B7_reverse Regulation 
DendriticCellsMonocyteImmatureDerivedDonor3_CNhs12000_tpm_fwd DendriticCellsMonocyteImmatureD3+ Dendritic Cells - monocyte immature derived, donor3_CNhs12000_11384-118B7_forward Regulation 
DendriticCellsMonocyteImmatureDerivedDonor1TechRep1_CNhs10855_tpm_rev DendriticCellsMonocyteImmatureD1Tr1- Dendritic Cells - monocyte immature derived, donor1, tech_rep1_CNhs10855_11227-116C3_reverse Regulation 
DendriticCellsMonocyteImmatureDerivedDonor1TechRep1_CNhs10855_tpm_fwd DendriticCellsMonocyteImmatureD1Tr1+ Dendritic Cells - monocyte immature derived, donor1, tech_rep1_CNhs10855_11227-116C3_forward Regulation 
CornealEpithelialCellsDonor3_CNhs12123_tpm_rev CornealEpithelialCellsD3- Corneal Epithelial Cells, donor3_CNhs12123_11687-122I4_reverse Regulation 
CornealEpithelialCellsDonor3_CNhs12123_tpm_fwd CornealEpithelialCellsD3+ Corneal Epithelial Cells, donor3_CNhs12123_11687-122I4_forward Regulation 
CornealEpithelialCellsDonor2_CNhs12094_tpm_rev CornealEpithelialCellsD2- Corneal Epithelial Cells, donor2_CNhs12094_11606-120I4_reverse Regulation 
CornealEpithelialCellsDonor2_CNhs12094_tpm_fwd CornealEpithelialCellsD2+ Corneal Epithelial Cells, donor2_CNhs12094_11606-120I4_forward Regulation 
CornealEpithelialCellsDonor1_CNhs11336_tpm_rev CornealEpithelialCellsD1- Corneal Epithelial Cells, donor1_CNhs11336_11526-119I5_reverse Regulation 
CornealEpithelialCellsDonor1_CNhs11336_tpm_fwd CornealEpithelialCellsD1+ Corneal Epithelial Cells, donor1_CNhs11336_11526-119I5_forward Regulation 
CiliaryEpithelialCellsDonor3_CNhs12009_tpm_rev CiliaryEpithelialCellsD3- Ciliary Epithelial Cells, donor3_CNhs12009_11399-118D4_reverse Regulation 
CiliaryEpithelialCellsDonor3_CNhs12009_tpm_fwd CiliaryEpithelialCellsD3+ Ciliary Epithelial Cells, donor3_CNhs12009_11399-118D4_forward Regulation 
CiliaryEpithelialCellsDonor2_CNhs11966_tpm_rev CiliaryEpithelialCellsD2- Ciliary Epithelial Cells, donor2_CNhs11966_11323-117D9_reverse Regulation 
CiliaryEpithelialCellsDonor2_CNhs11966_tpm_fwd CiliaryEpithelialCellsD2+ Ciliary Epithelial Cells, donor2_CNhs11966_11323-117D9_forward Regulation 
CiliaryEpithelialCellsDonor1_CNhs10871_tpm_rev CiliaryEpithelialCellsD1- Ciliary Epithelial Cells, donor1_CNhs10871_11242-116D9_reverse Regulation 
CiliaryEpithelialCellsDonor1_CNhs10871_tpm_fwd CiliaryEpithelialCellsD1+ Ciliary Epithelial Cells, donor1_CNhs10871_11242-116D9_forward Regulation 
ChorionicMembraneCellsDonor3_CNhs12380_tpm_rev ChorionicMembraneCellsD3- chorionic membrane cells, donor3_CNhs12380_12240-129G8_reverse Regulation 
ChorionicMembraneCellsDonor3_CNhs12380_tpm_fwd ChorionicMembraneCellsD3+ chorionic membrane cells, donor3_CNhs12380_12240-129G8_forward Regulation 
ChorionicMembraneCellsDonor2_CNhs12506_tpm_rev ChorionicMembraneCellsD2- chorionic membrane cells, donor2_CNhs12506_12239-129G7_reverse Regulation 
ChorionicMembraneCellsDonor2_CNhs12506_tpm_fwd ChorionicMembraneCellsD2+ chorionic membrane cells, donor2_CNhs12506_12239-129G7_forward Regulation 
ChorionicMembraneCellsDonor1_CNhs12504_tpm_rev ChorionicMembraneCellsD1- chorionic membrane cells, donor1_CNhs12504_12238-129G6_reverse Regulation 
ChorionicMembraneCellsDonor1_CNhs12504_tpm_fwd ChorionicMembraneCellsD1+ chorionic membrane cells, donor1_CNhs12504_12238-129G6_forward Regulation 
ChondrocyteReDiffDonor3_CNhs12021_tpm_rev ChondrocyteReDiffD3- Chondrocyte - re diff, donor3_CNhs12021_11411-118E7_reverse Regulation 
ChondrocyteReDiffDonor3_CNhs12021_tpm_fwd ChondrocyteReDiffD3+ Chondrocyte - re diff, donor3_CNhs12021_11411-118E7_forward Regulation 
ChondrocyteReDiffDonor2_CNhs11373_tpm_rev ChondrocyteReDiffD2- Chondrocyte - re diff, donor2_CNhs11373_11339-117F7_reverse Regulation 
ChondrocyteReDiffDonor2_CNhs11373_tpm_fwd ChondrocyteReDiffD2+ Chondrocyte - re diff, donor2_CNhs11373_11339-117F7_forward Regulation 
ChondrocyteDeDiffDonor3_CNhs12020_tpm_rev ChondrocyteDeDiffD3- Chondrocyte - de diff, donor3_CNhs12020_11410-118E6_reverse Regulation 
ChondrocyteDeDiffDonor3_CNhs12020_tpm_fwd ChondrocyteDeDiffD3+ Chondrocyte - de diff, donor3_CNhs12020_11410-118E6_forward Regulation 
ChondrocyteDeDiffDonor2_CNhs11372_tpm_rev ChondrocyteDeDiffD2- Chondrocyte - de diff, donor2_CNhs11372_11338-117F6_reverse Regulation 
ChondrocyteDeDiffDonor2_CNhs11372_tpm_fwd ChondrocyteDeDiffD2+ Chondrocyte - de diff, donor2_CNhs11372_11338-117F6_forward Regulation 
ChondrocyteDeDiffDonor1_CNhs11923_tpm_rev ChondrocyteDeDiffD1- Chondrocyte - de diff, donor1_CNhs11923_11261-116G1_reverse Regulation 
ChondrocyteDeDiffDonor1_CNhs11923_tpm_fwd ChondrocyteDeDiffD1+ Chondrocyte - de diff, donor1_CNhs11923_11261-116G1_forward Regulation 
CD8TCellsDonor3_CNhs11999_tpm_rev Cd8+TCellsD3- CD8+ T Cells, donor3_CNhs11999_11383-118B6_reverse Regulation 
CD8TCellsDonor3_CNhs11999_tpm_fwd Cd8+TCellsD3+ CD8+ T Cells, donor3_CNhs11999_11383-118B6_forward Regulation 
CD8TCellsDonor2_CNhs11956_tpm_rev Cd8+TCellsD2- CD8+ T Cells, donor2_CNhs11956_11307-117C2_reverse Regulation 
CD8TCellsDonor2_CNhs11956_tpm_fwd Cd8+TCellsD2+ CD8+ T Cells, donor2_CNhs11956_11307-117C2_forward Regulation 
CD8TCellsDonor1_CNhs10854_tpm_rev Cd8+TCellsD1- CD8+ T Cells, donor1_CNhs10854_11226-116C2_reverse Regulation 
CD8TCellsDonor1_CNhs10854_tpm_fwd Cd8+TCellsD1+ CD8+ T Cells, donor1_CNhs10854_11226-116C2_forward Regulation 
CD4TCellsDonor3_CNhs11998_tpm_rev Cd4+TCellsD3- CD4+ T Cells, donor3_CNhs11998_11382-118B5_reverse Regulation 
CD4TCellsDonor3_CNhs11998_tpm_fwd Cd4+TCellsD3+ CD4+ T Cells, donor3_CNhs11998_11382-118B5_forward Regulation 
CD4TCellsDonor2_CNhs11955_tpm_rev Cd4+TCellsD2- CD4+ T Cells, donor2_CNhs11955_11306-117C1_reverse Regulation 
CD4TCellsDonor2_CNhs11955_tpm_fwd Cd4+TCellsD2+ CD4+ T Cells, donor2_CNhs11955_11306-117C1_forward Regulation 
CD4TCellsDonor1_CNhs10853_tpm_rev Cd4+TCellsD1- CD4+ T Cells, donor1_CNhs10853_11225-116C1_reverse Regulation 
CD4TCellsDonor1_CNhs10853_tpm_fwd Cd4+TCellsD1+ CD4+ T Cells, donor1_CNhs10853_11225-116C1_forward Regulation 
CD4CD25CD45RAMemoryConventionalTCellsExpandedDonor3_CNhs13921_tpm_rev Cd4+cd25-cd45ra-ExpdD3- CD4+CD25-CD45RA- memory conventional T cells expanded, donor3_CNhs13921_11918-125H1_reverse Regulation 
CD4CD25CD45RAMemoryConventionalTCellsExpandedDonor3_CNhs13921_tpm_fwd Cd4+cd25-cd45ra-ExpdD3+ CD4+CD25-CD45RA- memory conventional T cells expanded, donor3_CNhs13921_11918-125H1_forward Regulation 
CD4CD25CD45RAMemoryConventionalTCellsExpandedDonor2_CNhs13920_tpm_rev Cd4+cd25-cd45ra-ExpdD2- CD4+CD25-CD45RA- memory conventional T cells expanded, donor2_CNhs13920_11914-125G6_reverse Regulation 
CD4CD25CD45RAMemoryConventionalTCellsExpandedDonor2_CNhs13920_tpm_fwd Cd4+cd25-cd45ra-ExpdD2+ CD4+CD25-CD45RA- memory conventional T cells expanded, donor2_CNhs13920_11914-125G6_forward Regulation 
CD4CD25CD45RAMemoryConventionalTCellsExpandedDonor1_CNhs13215_tpm_rev Cd4+cd25-cd45ra-ExpdD1- CD4+CD25-CD45RA- memory conventional T cells expanded, donor1_CNhs13215_11792-124C1_reverse Regulation 
CD4CD25CD45RAMemoryConventionalTCellsExpandedDonor1_CNhs13215_tpm_fwd Cd4+cd25-cd45ra-ExpdD1+ CD4+CD25-CD45RA- memory conventional T cells expanded, donor1_CNhs13215_11792-124C1_forward Regulation 
CD4CD25CD45RAMemoryConventionalTCellsDonor3_CNhs13539_tpm_rev Cd4+cd25-cd45ra-D3- CD4+CD25-CD45RA- memory conventional T cells, donor3_CNhs13539_11909-125G1_reverse Regulation 
CD4CD25CD45RAMemoryConventionalTCellsDonor3_CNhs13539_tpm_fwd Cd4+cd25-cd45ra-D3+ CD4+CD25-CD45RA- memory conventional T cells, donor3_CNhs13539_11909-125G1_forward Regulation 
CD4CD25CD45RANaiveConventionalTCellsExpandedDonor3_CNhs13814_tpm_rev Cd4+cd25-cd45ra+ExpdD3- CD4+CD25-CD45RA+ naive conventional T cells expanded, donor3_CNhs13814_11917-125G9_reverse Regulation 
CD4CD25CD45RANaiveConventionalTCellsExpandedDonor3_CNhs13814_tpm_fwd Cd4+cd25-cd45ra+ExpdD3+ CD4+CD25-CD45RA+ naive conventional T cells expanded, donor3_CNhs13814_11917-125G9_forward Regulation 
CD4CD25CD45RANaiveConventionalTCellsExpandedDonor2_CNhs13813_tpm_rev Cd4+cd25-cd45ra+ExpdD2- CD4+CD25-CD45RA+ naive conventional T cells expanded, donor2_CNhs13813_11913-125G5_reverse Regulation 
CD4CD25CD45RANaiveConventionalTCellsExpandedDonor2_CNhs13813_tpm_fwd Cd4+cd25-cd45ra+ExpdD2+ CD4+CD25-CD45RA+ naive conventional T cells expanded, donor2_CNhs13813_11913-125G5_forward Regulation 
CD4CD25CD45RANaiveConventionalTCellsExpandedDonor1_CNhs13202_tpm_rev Cd4+cd25-cd45ra+ExpdD1- CD4+CD25-CD45RA+ naive conventional T cells expanded, donor1_CNhs13202_11791-124B9_reverse Regulation 
CD4CD25CD45RANaiveConventionalTCellsExpandedDonor1_CNhs13202_tpm_fwd Cd4+cd25-cd45ra+ExpdD1+ CD4+CD25-CD45RA+ naive conventional T cells expanded, donor1_CNhs13202_11791-124B9_forward Regulation 
CD4CD25CD45RANaiveConventionalTCellsDonor3_CNhs13512_tpm_rev Cd4+cd25-cd45ra+D3- CD4+CD25-CD45RA+ naive conventional T cells, donor3_CNhs13512_11906-125F7_reverse Regulation 
CD4CD25CD45RANaiveConventionalTCellsDonor3_CNhs13512_tpm_fwd Cd4+cd25-cd45ra+D3+ CD4+CD25-CD45RA+ naive conventional T cells, donor3_CNhs13512_11906-125F7_forward Regulation 
CD4CD25CD45RANaiveConventionalTCellsDonor2_CNhs13205_tpm_rev Cd4+cd25-cd45ra+D2- CD4+CD25-CD45RA+ naive conventional T cells, donor2_CNhs13205_11795-124C4_reverse Regulation 
CD4CD25CD45RANaiveConventionalTCellsDonor2_CNhs13205_tpm_fwd Cd4+cd25-cd45ra+D2+ CD4+CD25-CD45RA+ naive conventional T cells, donor2_CNhs13205_11795-124C4_forward Regulation 
CD4CD25CD45RANaiveConventionalTCellsDonor1_CNhs13223_tpm_rev Cd4+cd25-cd45ra+D1- CD4+CD25-CD45RA+ naive conventional T cells, donor1_CNhs13223_11784-124B2_reverse Regulation 
CD4CD25CD45RANaiveConventionalTCellsDonor1_CNhs13223_tpm_fwd Cd4+cd25-cd45ra+D1+ CD4+CD25-CD45RA+ naive conventional T cells, donor1_CNhs13223_11784-124B2_forward Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsExpandedDonor3_CNhs13812_tpm_rev Cd4+cd25+cd45ra-ExpdD3- CD4+CD25+CD45RA- memory regulatory T cells expanded, donor3_CNhs13812_11920-125H3_reverse Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsExpandedDonor3_CNhs13812_tpm_fwd Cd4+cd25+cd45ra-ExpdD3+ CD4+CD25+CD45RA- memory regulatory T cells expanded, donor3_CNhs13812_11920-125H3_forward Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsExpandedDonor2_CNhs13811_tpm_rev Cd4+cd25+cd45ra-ExpdD2- CD4+CD25+CD45RA- memory regulatory T cells expanded, donor2_CNhs13811_11916-125G8_reverse Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsExpandedDonor2_CNhs13811_tpm_fwd Cd4+cd25+cd45ra-ExpdD2+ CD4+CD25+CD45RA- memory regulatory T cells expanded, donor2_CNhs13811_11916-125G8_forward Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsExpandedDonor1_CNhs13204_tpm_rev Cd4+cd25+cd45ra-ExpdD1- CD4+CD25+CD45RA- memory regulatory T cells expanded, donor1_CNhs13204_11794-124C3_reverse Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsExpandedDonor1_CNhs13204_tpm_fwd Cd4+cd25+cd45ra-ExpdD1+ CD4+CD25+CD45RA- memory regulatory T cells expanded, donor1_CNhs13204_11794-124C3_forward Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsDonor3_CNhs13538_tpm_rev Cd4+cd25+cd45ra-D3- CD4+CD25+CD45RA- memory regulatory T cells, donor3_CNhs13538_11908-125F9_reverse Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsDonor3_CNhs13538_tpm_fwd Cd4+cd25+cd45ra-D3+ CD4+CD25+CD45RA- memory regulatory T cells, donor3_CNhs13538_11908-125F9_forward Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsDonor2_CNhs13206_tpm_rev Cd4+cd25+cd45ra-D2- CD4+CD25+CD45RA- memory regulatory T cells, donor2_CNhs13206_11797-124C6_reverse Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsDonor2_CNhs13206_tpm_fwd Cd4+cd25+cd45ra-D2+ CD4+CD25+CD45RA- memory regulatory T cells, donor2_CNhs13206_11797-124C6_forward Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsDonor1_CNhs13195_tpm_rev Cd4+cd25+cd45ra-D1- CD4+CD25+CD45RA- memory regulatory T cells, donor1_CNhs13195_11782-124A9_reverse Regulation 
CD4CD25CD45RAMemoryRegulatoryTCellsDonor1_CNhs13195_tpm_fwd Cd4+cd25+cd45ra-D1+ CD4+CD25+CD45RA- memory regulatory T cells, donor1_CNhs13195_11782-124A9_forward Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsExpandedDonor3_CNhs13919_tpm_rev Cd4+cd25+cd45ra+ExpdD3- CD4+CD25+CD45RA+ naive regulatory T cells expanded, donor3_CNhs13919_11919-125H2_reverse Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsExpandedDonor3_CNhs13919_tpm_fwd Cd4+cd25+cd45ra+ExpdD3+ CD4+CD25+CD45RA+ naive regulatory T cells expanded, donor3_CNhs13919_11919-125H2_forward Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsExpandedDonor2_CNhs13918_tpm_rev Cd4+cd25+cd45ra+ExpdD2- CD4+CD25+CD45RA+ naive regulatory T cells expanded, donor2_CNhs13918_11915-125G7_reverse Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsExpandedDonor2_CNhs13918_tpm_fwd Cd4+cd25+cd45ra+ExpdD2+ CD4+CD25+CD45RA+ naive regulatory T cells expanded, donor2_CNhs13918_11915-125G7_forward Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsExpandedDonor1_CNhs13203_tpm_rev Cd4+cd25+cd45ra+ExpdD1- CD4+CD25+CD45RA+ naive regulatory T cells expanded, donor1_CNhs13203_11793-124C2_reverse Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsExpandedDonor1_CNhs13203_tpm_fwd Cd4+cd25+cd45ra+ExpdD1+ CD4+CD25+CD45RA+ naive regulatory T cells expanded, donor1_CNhs13203_11793-124C2_forward Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsDonor3_CNhs13513_tpm_rev Cd4+cd25+cd45ra+D3- CD4+CD25+CD45RA+ naive regulatory T cells, donor3_CNhs13513_11907-125F8_reverse Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsDonor3_CNhs13513_tpm_fwd Cd4+cd25+cd45ra+D3+ CD4+CD25+CD45RA+ naive regulatory T cells, donor3_CNhs13513_11907-125F8_forward Regulation 
CD34CellsDifferentiatedToErythrocyteLineageBiol_Rep2_CNhs13553_tpm_rev Cd34ErythrocyteBr2- CD34 cells differentiated to erythrocyte lineage, biol_ rep2_CNhs13553_11932-125I6_reverse Regulation 
CD34CellsDifferentiatedToErythrocyteLineageBiol_Rep2_CNhs13553_tpm_fwd Cd34ErythrocyteBr2+ CD34 cells differentiated to erythrocyte lineage, biol_ rep2_CNhs13553_11932-125I6_forward Regulation 
CD34CellsDifferentiatedToErythrocyteLineageBiol_Rep1_CNhs13552_tpm_rev Cd34ErythrocyteBr1- CD34 cells differentiated to erythrocyte lineage, biol_ rep1_CNhs13552_11931-125I5_reverse Regulation 
CD34CellsDifferentiatedToErythrocyteLineageBiol_Rep1_CNhs13552_tpm_fwd Cd34ErythrocyteBr1+ CD34 cells differentiated to erythrocyte lineage, biol_ rep1_CNhs13552_11931-125I5_forward Regulation 
CD34StemCellsAdultBoneMarrowDerivedDonor1TechRep1_CNhs12588_tpm_rev Cd34+StemCellsAdultBoneMarrowD1Tr1- CD34+ stem cells - adult bone marrow derived, donor1, tech_rep1_CNhs12588_12225-129F2_reverse Regulation 
CD34StemCellsAdultBoneMarrowDerivedDonor1TechRep1_CNhs12588_tpm_fwd Cd34+StemCellsAdultBoneMarrowD1Tr1+ CD34+ stem cells - adult bone marrow derived, donor1, tech_rep1_CNhs12588_12225-129F2_forward Regulation 
CD19BCellsDonor3_CNhs12354_tpm_rev Cd19+BCellsD3- CD19+ B Cells, donor3_CNhs12354_11705-123B4_reverse Regulation 
CD19BCellsDonor3_CNhs12354_tpm_fwd Cd19+BCellsD3+ CD19+ B Cells, donor3_CNhs12354_11705-123B4_forward Regulation 
CD19BCellsDonor2_CNhs12352_tpm_rev Cd19+BCellsD2- CD19+ B Cells, donor2_CNhs12352_11624-122B4_reverse Regulation 
CD19BCellsDonor2_CNhs12352_tpm_fwd Cd19+BCellsD2+ CD19+ B Cells, donor2_CNhs12352_11624-122B4_forward Regulation 
CD19BCellsDonor1_CNhs12343_tpm_rev Cd19+BCellsD1- CD19+ B Cells, donor1_CNhs12343_11544-120B5_reverse Regulation 
CD19BCellsDonor1_CNhs12343_tpm_fwd Cd19+BCellsD1+ CD19+ B Cells, donor1_CNhs12343_11544-120B5_forward Regulation 
CD14CD16MonocytesDonor3_CNhs13548_tpm_rev Cd14-cd16+MonocytesD3- CD14-CD16+ Monocytes, donor3_CNhs13548_11911-125G3_reverse Regulation 
CD14CD16MonocytesDonor3_CNhs13548_tpm_fwd Cd14-cd16+MonocytesD3+ CD14-CD16+ Monocytes, donor3_CNhs13548_11911-125G3_forward Regulation 
CD14CD16MonocytesDonor2_CNhs13207_tpm_rev Cd14-cd16+MonocytesD2- CD14-CD16+ Monocytes, donor2_CNhs13207_11800-124C9_reverse Regulation 
CD14CD16MonocytesDonor2_CNhs13207_tpm_fwd Cd14-cd16+MonocytesD2+ CD14-CD16+ Monocytes, donor2_CNhs13207_11800-124C9_forward Regulation 
CD14MonocytesTreatedWithTrehaloseDimycolateTDMDonor3_CNhs13544_tpm_rev Cd14+MoW/TrehaloseDimycolateD3- CD14+ monocytes - treated with Trehalose dimycolate (TDM), donor3_CNhs13544_11882-125D1_reverse Regulation 
CD14MonocytesTreatedWithTrehaloseDimycolateTDMDonor3_CNhs13544_tpm_fwd Cd14+MoW/TrehaloseDimycolateD3+ CD14+ monocytes - treated with Trehalose dimycolate (TDM), donor3_CNhs13544_11882-125D1_forward Regulation 
CD14MonocytesTreatedWithTrehaloseDimycolateTDMDonor2_CNhs13483_tpm_rev Cd14+MoW/TrehaloseDimycolateD2- CD14+ monocytes - treated with Trehalose dimycolate (TDM), donor2_CNhs13483_11872-125B9_reverse Regulation 
CD14MonocytesTreatedWithTrehaloseDimycolateTDMDonor2_CNhs13483_tpm_fwd Cd14+MoW/TrehaloseDimycolateD2+ CD14+ monocytes - treated with Trehalose dimycolate (TDM), donor2_CNhs13483_11872-125B9_forward Regulation 
CD14MonocytesTreatedWithTrehaloseDimycolateTDMDonor1_CNhs13467_tpm_rev Cd14+MoW/TrehaloseDimycolateD1- CD14+ monocytes - treated with Trehalose dimycolate (TDM), donor1_CNhs13467_11862-125A8_reverse Regulation 
CD14MonocytesTreatedWithTrehaloseDimycolateTDMDonor1_CNhs13467_tpm_fwd Cd14+MoW/TrehaloseDimycolateD1+ CD14+ monocytes - treated with Trehalose dimycolate (TDM), donor1_CNhs13467_11862-125A8_forward Regulation 
CD14MonocytesTreatedWithSalmonellaDonor3_CNhs13493_tpm_rev Cd14+MoW/SalmonellaD3- CD14+ monocytes - treated with Salmonella, donor3_CNhs13493_11886-125D5_reverse Regulation 
CD14MonocytesTreatedWithSalmonellaDonor3_CNhs13493_tpm_fwd Cd14+MoW/SalmonellaD3+ CD14+ monocytes - treated with Salmonella, donor3_CNhs13493_11886-125D5_forward Regulation 
CD14MonocytesTreatedWithSalmonellaDonor2_CNhs13485_tpm_rev Cd14+MoW/SalmonellaD2- CD14+ monocytes - treated with Salmonella, donor2_CNhs13485_11876-125C4_reverse Regulation 
CD14MonocytesTreatedWithSalmonellaDonor2_CNhs13485_tpm_fwd Cd14+MoW/SalmonellaD2+ CD14+ monocytes - treated with Salmonella, donor2_CNhs13485_11876-125C4_forward Regulation 
CD14MonocytesTreatedWithSalmonellaDonor1_CNhs13471_tpm_rev Cd14+MoW/SalmonellaD1- CD14+ monocytes - treated with Salmonella, donor1_CNhs13471_11866-125B3_reverse Regulation 
CD14MonocytesTreatedWithSalmonellaDonor1_CNhs13471_tpm_fwd Cd14+MoW/SalmonellaD1+ CD14+ monocytes - treated with Salmonella, donor1_CNhs13471_11866-125B3_forward Regulation 
CD14MonocytesTreatedWithLipopolysaccharideDonor3_CNhs13545_tpm_rev Cd14+MoW/LipopolysaccharideD3- CD14+ monocytes - treated with lipopolysaccharide, donor3_CNhs13545_11885-125D4_reverse Regulation 
CD14MonocytesTreatedWithLipopolysaccharideDonor3_CNhs13545_tpm_fwd Cd14+MoW/LipopolysaccharideD3+ CD14+ monocytes - treated with lipopolysaccharide, donor3_CNhs13545_11885-125D4_forward Regulation 
CD14MonocytesTreatedWithLipopolysaccharideDonor2_CNhs13533_tpm_rev Cd14+MoW/LipopolysaccharideD2- CD14+ monocytes - treated with lipopolysaccharide, donor2_CNhs13533_11875-125C3_reverse Regulation 
CD14MonocytesTreatedWithLipopolysaccharideDonor2_CNhs13533_tpm_fwd Cd14+MoW/LipopolysaccharideD2+ CD14+ monocytes - treated with lipopolysaccharide, donor2_CNhs13533_11875-125C3_forward Regulation 
CD14MonocytesTreatedWithLipopolysaccharideDonor1_CNhs13470_tpm_rev Cd14+MoW/LipopolysaccharideD1- CD14+ monocytes - treated with lipopolysaccharide, donor1_CNhs13470_11865-125B2_reverse Regulation 
CD14MonocytesTreatedWithLipopolysaccharideDonor1_CNhs13470_tpm_fwd Cd14+MoW/LipopolysaccharideD1+ CD14+ monocytes - treated with lipopolysaccharide, donor1_CNhs13470_11865-125B2_forward Regulation 
CD14MonocytesTreatedWithIFNNhexaneDonor3_CNhs13490_tpm_rev Cd14+MoW/Ifn+N-hexaneD3- CD14+ monocytes - treated with IFN + N-hexane, donor3_CNhs13490_11881-125C9_reverse Regulation 
CD14MonocytesTreatedWithIFNNhexaneDonor3_CNhs13490_tpm_fwd Cd14+MoW/Ifn+N-hexaneD3+ CD14+ monocytes - treated with IFN + N-hexane, donor3_CNhs13490_11881-125C9_forward Regulation 
CD14MonocytesTreatedWithIFNNhexaneDonor2_CNhs13476_tpm_rev Cd14+MoW/Ifn+N-hexaneD2- CD14+ monocytes - treated with IFN + N-hexane, donor2_CNhs13476_11871-125B8_reverse Regulation 
CD14MonocytesTreatedWithIFNNhexaneDonor2_CNhs13476_tpm_fwd Cd14+MoW/Ifn+N-hexaneD2+ CD14+ monocytes - treated with IFN + N-hexane, donor2_CNhs13476_11871-125B8_forward Regulation 
CD14MonocytesTreatedWithIFNNhexaneDonor1_CNhs13466_tpm_rev Cd14+MoW/Ifn+N-hexaneD1- CD14+ monocytes - treated with IFN + N-hexane, donor1_CNhs13466_11861-125A7_reverse Regulation 
CD14MonocytesTreatedWithIFNNhexaneDonor1_CNhs13466_tpm_fwd Cd14+MoW/Ifn+N-hexaneD1+ CD14+ monocytes - treated with IFN + N-hexane, donor1_CNhs13466_11861-125A7_forward Regulation 
CD14MonocytesTreatedWithGroupAStreptococciDonor3_CNhs13492_tpm_rev Cd14+MoW/GroupAStreptococciD3- CD14+ monocytes - treated with Group A streptococci, donor3_CNhs13492_11884-125D3_reverse Regulation 
CD14MonocytesTreatedWithGroupAStreptococciDonor3_CNhs13492_tpm_fwd Cd14+MoW/GroupAStreptococciD3+ CD14+ monocytes - treated with Group A streptococci, donor3_CNhs13492_11884-125D3_forward Regulation 
CD14MonocytesTreatedWithGroupAStreptococciDonor2_CNhs13532_tpm_rev Cd14+MoW/GroupAStreptococciD2- CD14+ monocytes - treated with Group A streptococci, donor2_CNhs13532_11874-125C2_reverse Regulation 
CD14MonocytesTreatedWithGroupAStreptococciDonor2_CNhs13532_tpm_fwd Cd14+MoW/GroupAStreptococciD2+ CD14+ monocytes - treated with Group A streptococci, donor2_CNhs13532_11874-125C2_forward Regulation 
CD14MonocytesTreatedWithGroupAStreptococciDonor1_CNhs13469_tpm_rev Cd14+MoW/GroupAStreptococciD1- CD14+ monocytes - treated with Group A streptococci, donor1_CNhs13469_11864-125B1_reverse Regulation 
CD14MonocytesTreatedWithGroupAStreptococciDonor1_CNhs13469_tpm_fwd Cd14+MoW/GroupAStreptococciD1+ CD14+ monocytes - treated with Group A streptococci, donor1_CNhs13469_11864-125B1_forward Regulation 
CD14MonocytesTreatedWithCryptococcusDonor3_CNhs13546_tpm_rev Cd14+MoW/CryptococcusD3- CD14+ monocytes - treated with Cryptococcus, donor3_CNhs13546_11887-125D6_reverse Regulation 
CD14MonocytesTreatedWithCryptococcusDonor3_CNhs13546_tpm_fwd Cd14+MoW/CryptococcusD3+ CD14+ monocytes - treated with Cryptococcus, donor3_CNhs13546_11887-125D6_forward Regulation 
CD14MonocytesTreatedWithCryptococcusDonor2_CNhs13487_tpm_rev Cd14+MoW/CryptococcusD2- CD14+ monocytes - treated with Cryptococcus, donor2_CNhs13487_11877-125C5_reverse Regulation 
CD14MonocytesTreatedWithCryptococcusDonor2_CNhs13487_tpm_fwd Cd14+MoW/CryptococcusD2+ CD14+ monocytes - treated with Cryptococcus, donor2_CNhs13487_11877-125C5_forward Regulation 
CD14MonocytesTreatedWithCryptococcusDonor1_CNhs13472_tpm_rev Cd14+MoW/CryptococcusD1- CD14+ monocytes - treated with Cryptococcus, donor1_CNhs13472_11867-125B4_reverse Regulation 
CD14MonocytesTreatedWithCryptococcusDonor1_CNhs13472_tpm_fwd Cd14+MoW/CryptococcusD1+ CD14+ monocytes - treated with Cryptococcus, donor1_CNhs13472_11867-125B4_forward Regulation 
CD14MonocytesTreatedWithCandidaDonor3_CNhs13494_tpm_rev Cd14+MoW/CandidaD3- CD14+ monocytes - treated with Candida, donor3_CNhs13494_11888-125D7_reverse Regulation 
CD14MonocytesTreatedWithCandidaDonor3_CNhs13494_tpm_fwd Cd14+MoW/CandidaD3+ CD14+ monocytes - treated with Candida, donor3_CNhs13494_11888-125D7_forward Regulation 
CD14MonocytesTreatedWithCandidaDonor2_CNhs13488_tpm_rev Cd14+MoW/CandidaD2- CD14+ monocytes - treated with Candida, donor2_CNhs13488_11878-125C6_reverse Regulation 
CD14MonocytesTreatedWithCandidaDonor2_CNhs13488_tpm_fwd Cd14+MoW/CandidaD2+ CD14+ monocytes - treated with Candida, donor2_CNhs13488_11878-125C6_forward Regulation 
CD14MonocytesTreatedWithCandidaDonor1_CNhs13473_tpm_rev Cd14+MoW/CandidaD1- CD14+ monocytes - treated with Candida, donor1_CNhs13473_11868-125B5_reverse Regulation 
CD14MonocytesTreatedWithCandidaDonor1_CNhs13473_tpm_fwd Cd14+MoW/CandidaD1+ CD14+ monocytes - treated with Candida, donor1_CNhs13473_11868-125B5_forward Regulation 
CD14MonocytesTreatedWithBCGDonor3_CNhs13543_tpm_rev Cd14+MoW/BcgD3- CD14+ monocytes - treated with BCG, donor3_CNhs13543_11880-125C8_reverse Regulation 
CD14MonocytesTreatedWithBCGDonor3_CNhs13543_tpm_fwd Cd14+MoW/BcgD3+ CD14+ monocytes - treated with BCG, donor3_CNhs13543_11880-125C8_forward Regulation 
CD14MonocytesTreatedWithBCGDonor2_CNhs13475_tpm_rev Cd14+MoW/BcgD2- CD14+ monocytes - treated with BCG, donor2_CNhs13475_11870-125B7_reverse Regulation 
CD14MonocytesTreatedWithBCGDonor2_CNhs13475_tpm_fwd Cd14+MoW/BcgD2+ CD14+ monocytes - treated with BCG, donor2_CNhs13475_11870-125B7_forward Regulation 
CD14MonocytesTreatedWithBCGDonor1_CNhs13465_tpm_rev Cd14+MoW/BcgD1- CD14+ monocytes - treated with BCG, donor1_CNhs13465_11860-125A6_reverse Regulation 
CD14MonocytesTreatedWithBCGDonor1_CNhs13465_tpm_fwd Cd14+MoW/BcgD1+ CD14+ monocytes - treated with BCG, donor1_CNhs13465_11860-125A6_forward Regulation 
CD14MonocytesTreatedWithBglucanDonor3_CNhs13495_tpm_rev Cd14+MoW/B-glucanD3- CD14+ monocytes - treated with B-glucan, donor3_CNhs13495_11889-125D8_reverse Regulation 
CD14MonocytesTreatedWithBglucanDonor3_CNhs13495_tpm_fwd Cd14+MoW/B-glucanD3+ CD14+ monocytes - treated with B-glucan, donor3_CNhs13495_11889-125D8_forward Regulation 
CD14MonocytesTreatedWithBglucanDonor2_CNhs13489_tpm_rev Cd14+MoW/B-glucanD2- CD14+ monocytes - treated with B-glucan, donor2_CNhs13489_11879-125C7_reverse Regulation 
CD14MonocytesTreatedWithBglucanDonor2_CNhs13489_tpm_fwd Cd14+MoW/B-glucanD2+ CD14+ monocytes - treated with B-glucan, donor2_CNhs13489_11879-125C7_forward Regulation 
CD14MonocytesTreatedWithBglucanDonor1_CNhs13474_tpm_rev Cd14+MoW/B-glucanD1- CD14+ monocytes - treated with B-glucan, donor1_CNhs13474_11869-125B6_reverse Regulation 
CD14MonocytesTreatedWithBglucanDonor1_CNhs13474_tpm_fwd Cd14+MoW/B-glucanD1+ CD14+ monocytes - treated with B-glucan, donor1_CNhs13474_11869-125B6_forward Regulation 
CD14MonocytesMockTreatedDonor3_CNhs13491_tpm_rev Cd14+MoMockTreatedD3- CD14+ monocytes - mock treated, donor3_CNhs13491_11883-125D2_reverse Regulation 
CD14MonocytesMockTreatedDonor3_CNhs13491_tpm_fwd Cd14+MoMockTreatedD3+ CD14+ monocytes - mock treated, donor3_CNhs13491_11883-125D2_forward Regulation 
CD14MonocytesMockTreatedDonor2_CNhs13484_tpm_rev Cd14+MoMockTreatedD2- CD14+ monocytes - mock treated, donor2_CNhs13484_11873-125C1_reverse Regulation 
CD14MonocytesMockTreatedDonor2_CNhs13484_tpm_fwd Cd14+MoMockTreatedD2+ CD14+ monocytes - mock treated, donor2_CNhs13484_11873-125C1_forward Regulation 
CD14MonocytesMockTreatedDonor1_CNhs13468_tpm_rev Cd14+MoMockTreatedD1- CD14+ monocytes - mock treated, donor1_CNhs13468_11863-125A9_reverse Regulation 
CD14MonocytesMockTreatedDonor1_CNhs13468_tpm_fwd Cd14+MoMockTreatedD1+ CD14+ monocytes - mock treated, donor1_CNhs13468_11863-125A9_forward Regulation 
CD14MonocyteDerivedEndothelialProgenitorCellsDonor3_CNhs11904_tpm_rev Cd14+MoEndothelialProgenitorCellsD3- CD14+ monocyte derived endothelial progenitor cells, donor3_CNhs11904_11386-118B9_reverse Regulation 
CD14MonocyteDerivedEndothelialProgenitorCellsDonor3_CNhs11904_tpm_fwd Cd14+MoEndothelialProgenitorCellsD3+ CD14+ monocyte derived endothelial progenitor cells, donor3_CNhs11904_11386-118B9_forward Regulation 
CD14MonocyteDerivedEndothelialProgenitorCellsDonor2_CNhs11897_tpm_rev Cd14+MoEndothelialProgenitorCellsD2- CD14+ monocyte derived endothelial progenitor cells, donor2_CNhs11897_11310-117C5_reverse Regulation 
CD14MonocyteDerivedEndothelialProgenitorCellsDonor2_CNhs11897_tpm_fwd Cd14+MoEndothelialProgenitorCellsD2+ CD14+ monocyte derived endothelial progenitor cells, donor2_CNhs11897_11310-117C5_forward Regulation 
CD14MonocyteDerivedEndothelialProgenitorCellsDonor1_CNhs10858_tpm_rev Cd14+MoEndothelialProgenitorCellsD1- CD14+ monocyte derived endothelial progenitor cells, donor1_CNhs10858_11229-116C5_reverse Regulation 
CD14MonocyteDerivedEndothelialProgenitorCellsDonor1_CNhs10858_tpm_fwd Cd14+MoEndothelialProgenitorCellsD1+ CD14+ monocyte derived endothelial progenitor cells, donor1_CNhs10858_11229-116C5_forward Regulation 
CD14MonocytesDonor3_CNhs11997_tpm_rev Cd14+MoD3- CD14+ Monocytes, donor3_CNhs11997_11381-118B4_reverse Regulation 
CD14MonocytesDonor3_CNhs11997_tpm_fwd Cd14+MoD3+ CD14+ Monocytes, donor3_CNhs11997_11381-118B4_forward Regulation 
CD14MonocytesDonor2_CNhs11954_tpm_rev Cd14+MoD2- CD14+ Monocytes, donor2_CNhs11954_11305-117B9_reverse Regulation 
CD14MonocytesDonor2_CNhs11954_tpm_fwd Cd14+MoD2+ CD14+ Monocytes, donor2_CNhs11954_11305-117B9_forward Regulation 
CD14MonocytesDonor1_CNhs10852_tpm_rev Cd14+MoD1- CD14+ Monocytes, donor1_CNhs10852_11224-116B9_reverse Regulation 
CD14MonocytesDonor1_CNhs10852_tpm_fwd Cd14+MoD1+ CD14+ Monocytes, donor1_CNhs10852_11224-116B9_forward Regulation 
CD14CD16MonocytesDonor3_CNhs13540_tpm_rev Cd14+cd16-MonocytesD3- CD14+CD16- Monocytes, donor3_CNhs13540_11910-125G2_reverse Regulation 
CD14CD16MonocytesDonor3_CNhs13540_tpm_fwd Cd14+cd16-MonocytesD3+ CD14+CD16- Monocytes, donor3_CNhs13540_11910-125G2_forward Regulation 
CD14CD16MonocytesDonor2_CNhs13216_tpm_rev Cd14+cd16-MonocytesD2- CD14+CD16- Monocytes, donor2_CNhs13216_11799-124C8_reverse Regulation 
CD14CD16MonocytesDonor2_CNhs13216_tpm_fwd Cd14+cd16-MonocytesD2+ CD14+CD16- Monocytes, donor2_CNhs13216_11799-124C8_forward Regulation 
CD14CD16MonocytesDonor1_CNhs13224_tpm_rev Cd14+cd16-MonocytesD1- CD14+CD16- Monocytes, donor1_CNhs13224_11788-124B6_reverse Regulation 
CD14CD16MonocytesDonor1_CNhs13224_tpm_fwd Cd14+cd16-MonocytesD1+ CD14+CD16- Monocytes, donor1_CNhs13224_11788-124B6_forward Regulation 
CD14CD16MonocytesDonor3_CNhs13549_tpm_rev Cd14+cd16+MonocytesD3- CD14+CD16+ Monocytes, donor3_CNhs13549_11912-125G4_reverse Regulation 
CD14CD16MonocytesDonor3_CNhs13549_tpm_fwd Cd14+cd16+MonocytesD3+ CD14+CD16+ Monocytes, donor3_CNhs13549_11912-125G4_forward Regulation 
CD14CD16MonocytesDonor2_CNhs13208_tpm_rev Cd14+cd16+MonocytesD2- CD14+CD16+ Monocytes, donor2_CNhs13208_11801-124D1_reverse Regulation 
CD14CD16MonocytesDonor2_CNhs13208_tpm_fwd Cd14+cd16+MonocytesD2+ CD14+CD16+ Monocytes, donor2_CNhs13208_11801-124D1_forward Regulation 
CD14CD16MonocytesDonor1_CNhs13541_tpm_rev Cd14+cd16+MonocytesD1- CD14+CD16+ Monocytes, donor1_CNhs13541_11789-124B7_reverse Regulation 
CD14CD16MonocytesDonor1_CNhs13541_tpm_fwd Cd14+cd16+MonocytesD1+ CD14+CD16+ Monocytes, donor1_CNhs13541_11789-124B7_forward Regulation 
MultipotentCordBloodUnrestrictedSomaticStemCellsDonor2_CNhs12105_tpm_rev CbStemCellsD2- Multipotent Cord Blood Unrestricted Somatic Stem Cells, donor2_CNhs12105_11629-122B9_reverse Regulation 
MultipotentCordBloodUnrestrictedSomaticStemCellsDonor2_CNhs12105_tpm_fwd CbStemCellsD2+ Multipotent Cord Blood Unrestricted Somatic Stem Cells, donor2_CNhs12105_11629-122B9_forward Regulation 
MultipotentCordBloodUnrestrictedSomaticStemCellsDonor1_CNhs11350_tpm_rev CbStemCellsD1- Multipotent Cord Blood Unrestricted Somatic Stem Cells, donor1_CNhs11350_11549-120C1_reverse Regulation 
MultipotentCordBloodUnrestrictedSomaticStemCellsDonor1_CNhs11350_tpm_fwd CbStemCellsD1+ Multipotent Cord Blood Unrestricted Somatic Stem Cells, donor1_CNhs11350_11549-120C1_forward Regulation 
CardiacMyocyteDonor3_CNhs12571_tpm_rev CardiacMyocyteD3- Cardiac Myocyte, donor3_CNhs12571_11686-122I3_reverse Regulation 
CardiacMyocyteDonor3_CNhs12571_tpm_fwd CardiacMyocyteD3+ Cardiac Myocyte, donor3_CNhs12571_11686-122I3_forward Regulation 
CardiacMyocyteDonor2_CNhs12350_tpm_rev CardiacMyocyteD2- Cardiac Myocyte, donor2_CNhs12350_11605-120I3_reverse Regulation 
CardiacMyocyteDonor2_CNhs12350_tpm_fwd CardiacMyocyteD2+ Cardiac Myocyte, donor2_CNhs12350_11605-120I3_forward Regulation 
CardiacMyocyteDonor1_CNhs12341_tpm_rev CardiacMyocyteD1- Cardiac Myocyte, donor1_CNhs12341_11525-119I4_reverse Regulation 
CardiacMyocyteDonor1_CNhs12341_tpm_fwd CardiacMyocyteD1+ Cardiac Myocyte, donor1_CNhs12341_11525-119I4_forward Regulation 
BronchialEpithelialCellDonor7_CNhs12642_tpm_rev BronchialEpithelialCellD7- Bronchial Epithelial Cell, donor7_CNhs12642_11769-123I5_reverse Regulation 
BronchialEpithelialCellDonor7_CNhs12642_tpm_fwd BronchialEpithelialCellD7+ Bronchial Epithelial Cell, donor7_CNhs12642_11769-123I5_forward Regulation 
BronchialEpithelialCellDonor6_CNhs12062_tpm_rev BronchialEpithelialCellD6- Bronchial Epithelial Cell, donor6_CNhs12062_11461-119B3_reverse Regulation 
BronchialEpithelialCellDonor6_CNhs12062_tpm_fwd BronchialEpithelialCellD6+ Bronchial Epithelial Cell, donor6_CNhs12062_11461-119B3_forward Regulation 
BronchialEpithelialCellDonor5_CNhs12058_tpm_rev BronchialEpithelialCellD5- Bronchial Epithelial Cell, donor5_CNhs12058_11457-119A8_reverse Regulation 
BronchialEpithelialCellDonor5_CNhs12058_tpm_fwd BronchialEpithelialCellD5+ Bronchial Epithelial Cell, donor5_CNhs12058_11457-119A8_forward Regulation 
BronchialEpithelialCellDonor4_CNhs12054_tpm_rev BronchialEpithelialCellD4- Bronchial Epithelial Cell, donor4_CNhs12054_11453-119A4_reverse Regulation 
BronchialEpithelialCellDonor4_CNhs12054_tpm_fwd BronchialEpithelialCellD4+ Bronchial Epithelial Cell, donor4_CNhs12054_11453-119A4_forward Regulation 
BronchialEpithelialCellDonor3_CNhs12623_tpm_rev BronchialEpithelialCellD3- Bronchial Epithelial Cell, donor3_CNhs12623_11672-122G7_reverse Regulation 
BronchialEpithelialCellDonor3_CNhs12623_tpm_fwd BronchialEpithelialCellD3+ Bronchial Epithelial Cell, donor3_CNhs12623_11672-122G7_forward Regulation 
BronchialEpithelialCellDonor2_CNhs12085_tpm_rev BronchialEpithelialCellD2- Bronchial Epithelial Cell, donor2_CNhs12085_11591-120G7_reverse Regulation 
BronchialEpithelialCellDonor2_CNhs12085_tpm_fwd BronchialEpithelialCellD2+ Bronchial Epithelial Cell, donor2_CNhs12085_11591-120G7_forward Regulation 
BronchialEpithelialCellDonor1_CNhs11327_tpm_rev BronchialEpithelialCellD1- Bronchial Epithelial Cell, donor1_CNhs11327_11511-119G8_reverse Regulation 
BronchialEpithelialCellDonor1_CNhs11327_tpm_fwd BronchialEpithelialCellD1+ Bronchial Epithelial Cell, donor1_CNhs11327_11511-119G8_forward Regulation 
BasophilsDonor3_CNhs12575_tpm_rev BasophilsD3- Basophils, donor3_CNhs12575_12243-129H2_reverse Regulation 
BasophilsDonor3_CNhs12575_tpm_fwd BasophilsD3+ Basophils, donor3_CNhs12575_12243-129H2_forward Regulation 
AstrocyteCerebralCortexDonor3_CNhs12005_tpm_rev AstrocyteCerebralCortexD3- Astrocyte - cerebral cortex, donor3_CNhs12005_11392-118C6_reverse Regulation 
AstrocyteCerebralCortexDonor3_CNhs12005_tpm_fwd AstrocyteCerebralCortexD3+ Astrocyte - cerebral cortex, donor3_CNhs12005_11392-118C6_forward Regulation 
AstrocyteCerebralCortexDonor2_CNhs11960_tpm_rev AstrocyteCerebralCortexD2- Astrocyte - cerebral cortex, donor2_CNhs11960_11316-117D2_reverse Regulation 
AstrocyteCerebralCortexDonor2_CNhs11960_tpm_fwd AstrocyteCerebralCortexD2+ Astrocyte - cerebral cortex, donor2_CNhs11960_11316-117D2_forward Regulation 
AstrocyteCerebralCortexDonor1_CNhs10864_tpm_rev AstrocyteCerebralCortexD1- Astrocyte - cerebral cortex, donor1_CNhs10864_11235-116D2_reverse Regulation 
AstrocyteCerebralCortexDonor1_CNhs10864_tpm_fwd AstrocyteCerebralCortexD1+ Astrocyte - cerebral cortex, donor1_CNhs10864_11235-116D2_forward Regulation 
AstrocyteCerebellumDonor3_CNhs12117_tpm_rev AstrocyteCerebellumD3- Astrocyte - cerebellum, donor3_CNhs12117_11661-122F5_reverse Regulation 
AstrocyteCerebellumDonor3_CNhs12117_tpm_fwd AstrocyteCerebellumD3+ Astrocyte - cerebellum, donor3_CNhs12117_11661-122F5_forward Regulation 
AstrocyteCerebellumDonor2_CNhs12081_tpm_rev AstrocyteCerebellumD2- Astrocyte - cerebellum, donor2_CNhs12081_11580-120F5_reverse Regulation 
AstrocyteCerebellumDonor2_CNhs12081_tpm_fwd AstrocyteCerebellumD2+ Astrocyte - cerebellum, donor2_CNhs12081_11580-120F5_forward Regulation 
AstrocyteCerebellumDonor1_CNhs11321_tpm_rev AstrocyteCerebellumD1- Astrocyte - cerebellum, donor1_CNhs11321_11500-119F6_reverse Regulation 
AstrocyteCerebellumDonor1_CNhs11321_tpm_fwd AstrocyteCerebellumD1+ Astrocyte - cerebellum, donor1_CNhs11321_11500-119F6_forward Regulation 
AnulusPulposusCellDonor2_CNhs12064_tpm_rev AnulusPulposusCellD2- Anulus Pulposus Cell, donor2_CNhs12064_11463-119B5_reverse Regulation 
AnulusPulposusCellDonor2_CNhs12064_tpm_fwd AnulusPulposusCellD2+ Anulus Pulposus Cell, donor2_CNhs12064_11463-119B5_forward Regulation 
AnulusPulposusCellDonor1_CNhs10876_tpm_rev AnulusPulposusCellD1- Anulus Pulposus Cell, donor1_CNhs10876_11248-116E6_reverse Regulation 
AnulusPulposusCellDonor1_CNhs10876_tpm_fwd AnulusPulposusCellD1+ Anulus Pulposus Cell, donor1_CNhs10876_11248-116E6_forward Regulation 
AmnioticMembraneCellsDonor3_CNhs12379_tpm_rev AmnioticMembraneCellsD3- amniotic membrane cells, donor3_CNhs12379_12237-129G5_reverse Regulation 
AmnioticMembraneCellsDonor3_CNhs12379_tpm_fwd AmnioticMembraneCellsD3+ amniotic membrane cells, donor3_CNhs12379_12237-129G5_forward Regulation 
AmnioticMembraneCellsDonor2_CNhs12503_tpm_rev AmnioticMembraneCellsD2- amniotic membrane cells, donor2_CNhs12503_12236-129G4_reverse Regulation 
AmnioticMembraneCellsDonor2_CNhs12503_tpm_fwd AmnioticMembraneCellsD2+ amniotic membrane cells, donor2_CNhs12503_12236-129G4_forward Regulation 
AmnioticMembraneCellsDonor1_CNhs12502_tpm_rev AmnioticMembraneCellsD1- amniotic membrane cells, donor1_CNhs12502_12235-129G3_reverse Regulation 
AmnioticMembraneCellsDonor1_CNhs12502_tpm_fwd AmnioticMembraneCellsD1+ amniotic membrane cells, donor1_CNhs12502_12235-129G3_forward Regulation 
AmnioticEpithelialCellsDonor3_CNhs12125_tpm_rev AmnioticEpithelialCellsD3- Amniotic Epithelial Cells, donor3_CNhs12125_11694-123A2_reverse Regulation 
AmnioticEpithelialCellsDonor3_CNhs12125_tpm_fwd AmnioticEpithelialCellsD3+ Amniotic Epithelial Cells, donor3_CNhs12125_11694-123A2_forward Regulation 
AmnioticEpithelialCellsDonor2_CNhs12098_tpm_rev AmnioticEpithelialCellsD2- Amniotic Epithelial Cells, donor2_CNhs12098_11613-122A2_reverse Regulation 
AmnioticEpithelialCellsDonor2_CNhs12098_tpm_fwd AmnioticEpithelialCellsD2+ Amniotic Epithelial Cells, donor2_CNhs12098_11613-122A2_forward Regulation 
AmnioticEpithelialCellsDonor1_CNhs11341_tpm_rev AmnioticEpithelialCellsD1- Amniotic Epithelial Cells, donor1_CNhs11341_11533-120A3_reverse Regulation 
AmnioticEpithelialCellsDonor1_CNhs11341_tpm_fwd AmnioticEpithelialCellsD1+ Amniotic Epithelial Cells, donor1_CNhs11341_11533-120A3_forward Regulation 
AlveolarEpithelialCellsDonor3_CNhs12119_tpm_rev AlveolarEpithelialCellsD3- Alveolar Epithelial Cells, donor3_CNhs12119_11671-122G6_reverse Regulation 
AlveolarEpithelialCellsDonor3_CNhs12119_tpm_fwd AlveolarEpithelialCellsD3+ Alveolar Epithelial Cells, donor3_CNhs12119_11671-122G6_forward Regulation 
AlveolarEpithelialCellsDonor2_CNhs12084_tpm_rev AlveolarEpithelialCellsD2- Alveolar Epithelial Cells, donor2_CNhs12084_11590-120G6_reverse Regulation 
AlveolarEpithelialCellsDonor2_CNhs12084_tpm_fwd AlveolarEpithelialCellsD2+ Alveolar Epithelial Cells, donor2_CNhs12084_11590-120G6_forward Regulation 
AlveolarEpithelialCellsDonor1_CNhs11325_tpm_rev AlveolarEpithelialCellsD1- Alveolar Epithelial Cells, donor1_CNhs11325_11510-119G7_reverse Regulation 
AlveolarEpithelialCellsDonor1_CNhs11325_tpm_fwd AlveolarEpithelialCellsD1+ Alveolar Epithelial Cells, donor1_CNhs11325_11510-119G7_forward Regulation 
AdipocyteSubcutaneousDonor3_CNhs12017_tpm_rev AdipocyteSubcutaneousD3- Adipocyte - subcutaneous, donor3_CNhs12017_11408-118E4_reverse Regulation 
AdipocyteSubcutaneousDonor3_CNhs12017_tpm_fwd AdipocyteSubcutaneousD3+ Adipocyte - subcutaneous, donor3_CNhs12017_11408-118E4_forward Regulation 
AdipocyteSubcutaneousDonor2_CNhs11371_tpm_rev AdipocyteSubcutaneousD2- Adipocyte - subcutaneous, donor2_CNhs11371_11336-117F4_reverse Regulation 
AdipocyteSubcutaneousDonor2_CNhs11371_tpm_fwd AdipocyteSubcutaneousD2+ Adipocyte - subcutaneous, donor2_CNhs11371_11336-117F4_forward Regulation 
AdipocyteSubcutaneousDonor1_CNhs12494_tpm_rev AdipocyteSubcutaneousD1- Adipocyte - subcutaneous, donor1_CNhs12494_11259-116F8_reverse Regulation 
AdipocyteSubcutaneousDonor1_CNhs12494_tpm_fwd AdipocyteSubcutaneousD1+ Adipocyte - subcutaneous, donor1_CNhs12494_11259-116F8_forward Regulation 
AdipocytePerirenalDonor1_CNhs12069_tpm_rev AdipocytePerirenalD1- Adipocyte - perirenal, donor1_CNhs12069_11476-119C9_reverse Regulation 
AdipocytePerirenalDonor1_CNhs12069_tpm_fwd AdipocytePerirenalD1+ Adipocyte - perirenal, donor1_CNhs12069_11476-119C9_forward Regulation 
AdipocyteOmentalDonor3_CNhs12068_tpm_rev AdipocyteOmentalD3- Adipocyte - omental, donor3_CNhs12068_11475-119C8_reverse Regulation 
AdipocyteOmentalDonor3_CNhs12068_tpm_fwd AdipocyteOmentalD3+ Adipocyte - omental, donor3_CNhs12068_11475-119C8_forward Regulation 
AdipocyteOmentalDonor2_CNhs12067_tpm_rev AdipocyteOmentalD2- Adipocyte - omental, donor2_CNhs12067_11474-119C7_reverse Regulation 
AdipocyteOmentalDonor2_CNhs12067_tpm_fwd AdipocyteOmentalD2+ Adipocyte - omental, donor2_CNhs12067_11474-119C7_forward Regulation 
AdipocyteOmentalDonor1_CNhs11054_tpm_rev AdipocyteOmentalD1- Adipocyte - omental, donor1_CNhs11054_11473-119C6_reverse Regulation 
AdipocyteOmentalDonor1_CNhs11054_tpm_fwd AdipocyteOmentalD1+ Adipocyte - omental, donor1_CNhs11054_11473-119C6_forward Regulation 
AdipocyteBreastDonor2_CNhs11969_tpm_rev AdipocyteBreastD2- Adipocyte - breast, donor2_CNhs11969_11327-117E4_reverse Regulation 
AdipocyteBreastDonor2_CNhs11969_tpm_fwd AdipocyteBreastD2+ Adipocyte - breast, donor2_CNhs11969_11327-117E4_forward Regulation 
AdipocyteBreastDonor1_CNhs11051_tpm_rev AdipocyteBreastD1- Adipocyte - breast, donor1_CNhs11051_11376-118A8_reverse Regulation 
AdipocyteBreastDonor1_CNhs11051_tpm_fwd AdipocyteBreastD1+ Adipocyte - breast, donor1_CNhs11051_11376-118A8_forward Regulation 
PromyelocytesmyelocytesPMCDonor3_CNhs12529_tpm_rev Promyelocytes/myelocytesPmcD3- promyelocytes/myelocytes PMC, donor3_CNhs12529_12140-128E7_reverse Regulation 
PromyelocytesmyelocytesPMCDonor3_CNhs12529_tpm_fwd Promyelocytes/myelocytesPmcD3+ promyelocytes/myelocytes PMC, donor3_CNhs12529_12140-128E7_forward Regulation 
PromyelocytesmyelocytesPMCDonor2_CNhs12525_tpm_rev Promyelocytes/myelocytesPmcD2- promyelocytes/myelocytes PMC, donor2_CNhs12525_12136-128E3_reverse Regulation 
PromyelocytesmyelocytesPMCDonor2_CNhs12525_tpm_fwd Promyelocytes/myelocytesPmcD2+ promyelocytes/myelocytes PMC, donor2_CNhs12525_12136-128E3_forward Regulation 
PromyelocytesmyelocytesPMCDonor1_CNhs12520_tpm_rev Promyelocytes/myelocytesPmcD1- promyelocytes/myelocytes PMC, donor1_CNhs12520_12132-128D8_reverse Regulation 
PromyelocytesmyelocytesPMCDonor1_CNhs12520_tpm_fwd Promyelocytes/myelocytesPmcD1+ promyelocytes/myelocytes PMC, donor1_CNhs12520_12132-128D8_forward Regulation 
NeutrophilPMNDonor3_CNhs12530_tpm_rev NeutrophilPmnD3- neutrophil PMN, donor3_CNhs12530_12141-128E8_reverse Regulation 
NeutrophilPMNDonor3_CNhs12530_tpm_fwd NeutrophilPmnD3+ neutrophil PMN, donor3_CNhs12530_12141-128E8_forward Regulation 
NeutrophilPMNDonor2_CNhs12526_tpm_rev NeutrophilPmnD2- neutrophil PMN, donor2_CNhs12526_12137-128E4_reverse Regulation 
NeutrophilPMNDonor2_CNhs12526_tpm_fwd NeutrophilPmnD2+ neutrophil PMN, donor2_CNhs12526_12137-128E4_forward Regulation 
NeutrophilPMNDonor1_CNhs12522_tpm_rev NeutrophilPmnD1- neutrophil PMN, donor1_CNhs12522_12133-128D9_reverse Regulation 
NeutrophilPMNDonor1_CNhs12522_tpm_fwd NeutrophilPmnD1+ neutrophil PMN, donor1_CNhs12522_12133-128D9_forward Regulation 
NasalEpithelialCellsDonor1TechRep2_CNhs12554_tpm_rev NasalEpithelialCellsD1Tr2- nasal epithelial cells, donor1, tech_rep2_CNhs12554_12226-129F3_reverse Regulation 
NasalEpithelialCellsDonor1TechRep2_CNhs12554_tpm_fwd NasalEpithelialCellsD1Tr2+ nasal epithelial cells, donor1, tech_rep2_CNhs12554_12226-129F3_forward Regulation 
MesothelialCellsDonor2_CNhs12197_tpm_rev MesothelialCellsD2- Mesothelial Cells, donor2_CNhs12197_12156-128G5_reverse Regulation 
MesothelialCellsDonor2_CNhs12197_tpm_fwd MesothelialCellsD2+ Mesothelial Cells, donor2_CNhs12197_12156-128G5_forward Regulation 
MatureAdipocyteDonor4_CNhs12562_tpm_rev MatureAdipocyteD4- mature adipocyte, donor4_CNhs12562_12234-129G2_reverse Regulation 
MatureAdipocyteDonor4_CNhs12562_tpm_fwd MatureAdipocyteD4+ mature adipocyte, donor4_CNhs12562_12234-129G2_forward Regulation 
MatureAdipocyteDonor3_CNhs12560_tpm_rev MatureAdipocyteD3- mature adipocyte, donor3_CNhs12560_12233-129G1_reverse Regulation 
MatureAdipocyteDonor3_CNhs12560_tpm_fwd MatureAdipocyteD3+ mature adipocyte, donor3_CNhs12560_12233-129G1_forward Regulation 
MatureAdipocyteDonor2_CNhs12559_tpm_rev MatureAdipocyteD2- mature adipocyte, donor2_CNhs12559_12232-129F9_reverse Regulation 
MatureAdipocyteDonor2_CNhs12559_tpm_fwd MatureAdipocyteD2+ mature adipocyte, donor2_CNhs12559_12232-129F9_forward Regulation 
MatureAdipocyteDonor1_CNhs12558_tpm_rev MatureAdipocyteD1- mature adipocyte, donor1_CNhs12558_12231-129F8_reverse Regulation 
MatureAdipocyteDonor1_CNhs12558_tpm_fwd MatureAdipocyteD1+ mature adipocyte, donor1_CNhs12558_12231-129F8_forward Regulation 
MallassezderivedCellsDonor1MZH3_CNhs12538_tpm_rev MallassezCellsD1- Mallassez-derived cells, donor1 (MZH3)_CNhs12538_12142-128E9_reverse Regulation 
MallassezderivedCellsDonor1MZH3_CNhs12538_tpm_fwd MallassezCellsD1+ Mallassez-derived cells, donor1 (MZH3)_CNhs12538_12142-128E9_forward Regulation 
GranulocyteMacrophageProgenitorDonor3_CNhs12528_tpm_rev GranulocyteMacrophageProgenitorD3- granulocyte macrophage progenitor, donor3_CNhs12528_12139-128E6_reverse Regulation 
GranulocyteMacrophageProgenitorDonor3_CNhs12528_tpm_fwd GranulocyteMacrophageProgenitorD3+ granulocyte macrophage progenitor, donor3_CNhs12528_12139-128E6_forward Regulation 
GranulocyteMacrophageProgenitorDonor2_CNhs12524_tpm_rev GranulocyteMacrophageProgenitorD2- granulocyte macrophage progenitor, donor2_CNhs12524_12135-128E2_reverse Regulation 
GranulocyteMacrophageProgenitorDonor2_CNhs12524_tpm_fwd GranulocyteMacrophageProgenitorD2+ granulocyte macrophage progenitor, donor2_CNhs12524_12135-128E2_forward Regulation 
GranulocyteMacrophageProgenitorDonor1_CNhs12519_tpm_rev GranulocyteMacrophageProgenitorD1- granulocyte macrophage progenitor, donor1_CNhs12519_12131-128D7_reverse Regulation 
GranulocyteMacrophageProgenitorDonor1_CNhs12519_tpm_fwd GranulocyteMacrophageProgenitorD1+ granulocyte macrophage progenitor, donor1_CNhs12519_12131-128D7_forward Regulation 
EosinophilsDonor3_CNhs12549_tpm_rev EosinophilsD3- Eosinophils, donor3_CNhs12549_12246-129H5_reverse Regulation 
EosinophilsDonor3_CNhs12549_tpm_fwd EosinophilsD3+ Eosinophils, donor3_CNhs12549_12246-129H5_forward Regulation 
EosinophilsDonor2_CNhs12548_tpm_rev EosinophilsD2- Eosinophils, donor2_CNhs12548_12245-129H4_reverse Regulation 
EosinophilsDonor2_CNhs12548_tpm_fwd EosinophilsD2+ Eosinophils, donor2_CNhs12548_12245-129H4_forward Regulation 
EosinophilsDonor1_CNhs12547_tpm_rev EosinophilsD1- Eosinophils, donor1_CNhs12547_12244-129H3_reverse Regulation 
EosinophilsDonor1_CNhs12547_tpm_fwd EosinophilsD1+ Eosinophils, donor1_CNhs12547_12244-129H3_forward Regulation 
DendriticCellsPlasmacytoidDonor3_CNhs12200_tpm_rev DendriticCellsPlasmacytoidD3- Dendritic Cells - plasmacytoid, donor3_CNhs12200_11385-118B8_reverse Regulation 
DendriticCellsPlasmacytoidDonor3_CNhs12200_tpm_fwd DendriticCellsPlasmacytoidD3+ Dendritic Cells - plasmacytoid, donor3_CNhs12200_11385-118B8_forward Regulation 
DendriticCellsPlasmacytoidDonor2_CNhs12196_tpm_rev DendriticCellsPlasmacytoidD2- Dendritic Cells - plasmacytoid, donor2_CNhs12196_11309-117C4_reverse Regulation 
DendriticCellsPlasmacytoidDonor2_CNhs12196_tpm_fwd DendriticCellsPlasmacytoidD2+ Dendritic Cells - plasmacytoid, donor2_CNhs12196_11309-117C4_forward Regulation 
DendriticCellsMonocyteImmatureDerivedDonor2_CNhs12195_tpm_rev DendriticCellsMonocyteImmatureD2- Dendritic Cells - monocyte immature derived, donor2_CNhs12195_11308-117C3_reverse Regulation 
DendriticCellsMonocyteImmatureDerivedDonor2_CNhs12195_tpm_fwd DendriticCellsMonocyteImmatureD2+ Dendritic Cells - monocyte immature derived, donor2_CNhs12195_11308-117C3_forward Regulation 
CommonMyeloidProgenitorCMPDonor2_CNhs12523_tpm_rev CommonMyeloidProgenitorCmpD2- common myeloid progenitor CMP, donor2_CNhs12523_12134-128E1_reverse Regulation 
CommonMyeloidProgenitorCMPDonor2_CNhs12523_tpm_fwd CommonMyeloidProgenitorCmpD2+ common myeloid progenitor CMP, donor2_CNhs12523_12134-128E1_forward Regulation 
CommonMyeloidProgenitorCMPDonor1_CNhs12518_tpm_rev CommonMyeloidProgenitorCmpD1- common myeloid progenitor CMP, donor1_CNhs12518_12130-128D6_reverse Regulation 
CommonMyeloidProgenitorCMPDonor1_CNhs12518_tpm_fwd CommonMyeloidProgenitorCmpD1+ common myeloid progenitor CMP, donor1_CNhs12518_12130-128D6_forward Regulation 
CD8TCellsPluriselectDonor090612Donation3_CNhs12187_tpm_rev Cd8+TCellsPluriD090612Dn3- CD8+ T Cells (pluriselect), donor090612, donation3_CNhs12187_12211-129D6_reverse Regulation 
CD8TCellsPluriselectDonor090612Donation3_CNhs12187_tpm_fwd Cd8+TCellsPluriD090612Dn3+ CD8+ T Cells (pluriselect), donor090612, donation3_CNhs12187_12211-129D6_forward Regulation 
CD8TCellsPluriselectDonor090612Donation2_CNhs12184_tpm_rev Cd8+TCellsPluriD090612Dn2- CD8+ T Cells (pluriselect), donor090612, donation2_CNhs12184_12206-129D1_reverse Regulation 
CD8TCellsPluriselectDonor090612Donation2_CNhs12184_tpm_fwd Cd8+TCellsPluriD090612Dn2+ CD8+ T Cells (pluriselect), donor090612, donation2_CNhs12184_12206-129D1_forward Regulation 
CD8TCellsPluriselectDonor090612Donation1_CNhs12182_tpm_rev Cd8+TCellsPluriD090612Dn1- CD8+ T Cells (pluriselect), donor090612, donation1_CNhs12182_12201-129C5_reverse Regulation 
CD8TCellsPluriselectDonor090612Donation1_CNhs12182_tpm_fwd Cd8+TCellsPluriD090612Dn1+ CD8+ T Cells (pluriselect), donor090612, donation1_CNhs12182_12201-129C5_forward Regulation 
CD8TCellsPluriselectDonor090325Donation2_CNhs12199_tpm_rev Cd8+TCellsPluriD090325Dn2- CD8+ T Cells (pluriselect), donor090325, donation2_CNhs12199_12171-128I2_reverse Regulation 
CD8TCellsPluriselectDonor090325Donation2_CNhs12199_tpm_fwd Cd8+TCellsPluriD090325Dn2+ CD8+ T Cells (pluriselect), donor090325, donation2_CNhs12199_12171-128I2_forward Regulation 
CD8TCellsPluriselectDonor090325Donation1_CNhs12201_tpm_rev Cd8+TCellsPluriD090325Dn1- CD8+ T Cells (pluriselect), donor090325, donation1_CNhs12201_12148-128F6_reverse Regulation 
CD8TCellsPluriselectDonor090325Donation1_CNhs12201_tpm_fwd Cd8+TCellsPluriD090325Dn1+ CD8+ T Cells (pluriselect), donor090325, donation1_CNhs12201_12148-128F6_forward Regulation 
CD8TCellsPluriselectDonor090309Donation3_CNhs12180_tpm_rev Cd8+TCellsPluriD090309Dn3- CD8+ T Cells (pluriselect), donor090309, donation3_CNhs12180_12196-129B9_reverse Regulation 
CD8TCellsPluriselectDonor090309Donation3_CNhs12180_tpm_fwd Cd8+TCellsPluriD090309Dn3+ CD8+ T Cells (pluriselect), donor090309, donation3_CNhs12180_12196-129B9_forward Regulation 
CD8TCellsPluriselectDonor090309Donation2_CNhs12178_tpm_rev Cd8+TCellsPluriD090309Dn2- CD8+ T Cells (pluriselect), donor090309, donation2_CNhs12178_12191-129B4_reverse Regulation 
CD8TCellsPluriselectDonor090309Donation2_CNhs12178_tpm_fwd Cd8+TCellsPluriD090309Dn2+ CD8+ T Cells (pluriselect), donor090309, donation2_CNhs12178_12191-129B4_forward Regulation 
CD8TCellsPluriselectDonor090309Donation1_CNhs12176_tpm_rev Cd8+TCellsPluriD090309Dn1- CD8+ T Cells (pluriselect), donor090309, donation1_CNhs12176_12186-129A8_reverse Regulation 
CD8TCellsPluriselectDonor090309Donation1_CNhs12176_tpm_fwd Cd8+TCellsPluriD090309Dn1+ CD8+ T Cells (pluriselect), donor090309, donation1_CNhs12176_12186-129A8_forward Regulation 
CD4CD25CD45RAMemoryConventionalTCellsDonor2_CNhs13237_tpm_rev Cd4+cd25-cd45ra-D2- CD4+CD25-CD45RA- memory conventional T cells, donor2_CNhs13237_11798-124C7_reverse Regulation 
CD4CD25CD45RAMemoryConventionalTCellsDonor2_CNhs13237_tpm_fwd Cd4+cd25-cd45ra-D2+ CD4+CD25-CD45RA- memory conventional T cells, donor2_CNhs13237_11798-124C7_forward Regulation 
CD4CD25CD45RAMemoryConventionalTCellsDonor1_CNhs13239_tpm_rev Cd4+cd25-cd45ra-D1- CD4+CD25-CD45RA- memory conventional T cells, donor1_CNhs13239_11786-124B4_reverse Regulation 
CD4CD25CD45RAMemoryConventionalTCellsDonor1_CNhs13239_tpm_fwd Cd4+cd25-cd45ra-D1+ CD4+CD25-CD45RA- memory conventional T cells, donor1_CNhs13239_11786-124B4_forward Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsDonor2_CNhs13235_tpm_rev Cd4+cd25+cd45ra+D2- CD4+CD25+CD45RA+ naive regulatory T cells, donor2_CNhs13235_11796-124C5_reverse Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsDonor2_CNhs13235_tpm_fwd Cd4+cd25+cd45ra+D2+ CD4+CD25+CD45RA+ naive regulatory T cells, donor2_CNhs13235_11796-124C5_forward Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsDonor1_CNhs13238_tpm_rev Cd4+cd25+cd45ra+D1- CD4+CD25+CD45RA+ naive regulatory T cells, donor1_CNhs13238_11780-124A7_reverse Regulation 
CD4CD25CD45RANaiveRegulatoryTCellsDonor1_CNhs13238_tpm_fwd Cd4+cd25+cd45ra+D1+ CD4+CD25+CD45RA+ naive regulatory T cells, donor1_CNhs13238_11780-124A7_forward Regulation 
CD34StemCellsAdultBoneMarrowDerivedDonor1TechRep2_CNhs12553_tpm_rev Cd34+StemCellsAdultBoneMarrowD1Tr2- CD34+ stem cells - adult bone marrow derived, donor1, tech_rep2_CNhs12553_12225-129F2_reverse Regulation 
CD34StemCellsAdultBoneMarrowDerivedDonor1TechRep2_CNhs12553_tpm_fwd Cd34+StemCellsAdultBoneMarrowD1Tr2+ CD34+ stem cells - adult bone marrow derived, donor1, tech_rep2_CNhs12553_12225-129F2_forward Regulation 
CD34ProgenitorsDonor2_CNhs12205_tpm_rev Cd34+ProgenitorsD2- CD34+ Progenitors, donor2_CNhs12205_11625-122B5_reverse Regulation 
CD34ProgenitorsDonor2_CNhs12205_tpm_fwd Cd34+ProgenitorsD2+ CD34+ Progenitors, donor2_CNhs12205_11625-122B5_forward Regulation 
CD34ProgenitorsDonor1_CNhs13227_tpm_rev Cd34+ProgenitorsD1- CD34+ Progenitors, donor1_CNhs13227_11545-120B6_reverse Regulation 
CD34ProgenitorsDonor1_CNhs13227_tpm_fwd Cd34+ProgenitorsD1+ CD34+ Progenitors, donor1_CNhs13227_11545-120B6_forward Regulation 
CD19BCellsPluriselectDonor090612Donation3_CNhs12188_tpm_rev Cd19+BCellsPluriD090612Dn3- CD19+ B Cells (pluriselect), donor090612, donation3_CNhs12188_12214-129D9_reverse Regulation 
CD19BCellsPluriselectDonor090612Donation3_CNhs12188_tpm_fwd Cd19+BCellsPluriD090612Dn3+ CD19+ B Cells (pluriselect), donor090612, donation3_CNhs12188_12214-129D9_forward Regulation 
CD19BCellsPluriselectDonor090612Donation2_CNhs12185_tpm_rev Cd19+BCellsPluriD090612Dn2- CD19+ B Cells (pluriselect), donor090612, donation2_CNhs12185_12209-129D4_reverse Regulation 
CD19BCellsPluriselectDonor090612Donation2_CNhs12185_tpm_fwd Cd19+BCellsPluriD090612Dn2+ CD19+ B Cells (pluriselect), donor090612, donation2_CNhs12185_12209-129D4_forward Regulation 
CD19BCellsPluriselectDonor090612Donation1_CNhs12183_tpm_rev Cd19+BCellsPluriD090612Dn1- CD19+ B Cells (pluriselect), donor090612, donation1_CNhs12183_12204-129C8_reverse Regulation 
CD19BCellsPluriselectDonor090612Donation1_CNhs12183_tpm_fwd Cd19+BCellsPluriD090612Dn1+ CD19+ B Cells (pluriselect), donor090612, donation1_CNhs12183_12204-129C8_forward Regulation 
CD19BCellsPluriselectDonor090325Donation2_CNhs12175_tpm_rev Cd19+BCellsPluriD090325Dn2- CD19+ B Cells (pluriselect), donor090325, donation2_CNhs12175_12174-128I5_reverse Regulation 
CD19BCellsPluriselectDonor090325Donation2_CNhs12175_tpm_fwd Cd19+BCellsPluriD090325Dn2+ CD19+ B Cells (pluriselect), donor090325, donation2_CNhs12175_12174-128I5_forward Regulation 
CD19BCellsPluriselectDonor090325Donation1_CNhs12531_tpm_rev Cd19+BCellsPluriD090325Dn1- CD19+ B Cells (pluriselect), donor090325, donation1_CNhs12531_12151-128F9_reverse Regulation 
CD19BCellsPluriselectDonor090325Donation1_CNhs12531_tpm_fwd Cd19+BCellsPluriD090325Dn1+ CD19+ B Cells (pluriselect), donor090325, donation1_CNhs12531_12151-128F9_forward Regulation 
CD19BCellsPluriselectDonor090309Donation3_CNhs12181_tpm_rev Cd19+BCellsPluriD090309Dn3- CD19+ B Cells (pluriselect), donor090309, donation3_CNhs12181_12199-129C3_reverse Regulation 
CD19BCellsPluriselectDonor090309Donation3_CNhs12181_tpm_fwd Cd19+BCellsPluriD090309Dn3+ CD19+ B Cells (pluriselect), donor090309, donation3_CNhs12181_12199-129C3_forward Regulation 
CD19BCellsPluriselectDonor090309Donation2_CNhs12179_tpm_rev Cd19+BCellsPluriD090309Dn2- CD19+ B Cells (pluriselect), donor090309, donation2_CNhs12179_12194-129B7_reverse Regulation 
CD19BCellsPluriselectDonor090309Donation2_CNhs12179_tpm_fwd Cd19+BCellsPluriD090309Dn2+ CD19+ B Cells (pluriselect), donor090309, donation2_CNhs12179_12194-129B7_forward Regulation 
CD19BCellsPluriselectDonor090309Donation1_CNhs12177_tpm_rev Cd19+BCellsPluriD090309Dn1- CD19+ B Cells (pluriselect), donor090309, donation1_CNhs12177_12189-129B2_reverse Regulation 
CD19BCellsPluriselectDonor090309Donation1_CNhs12177_tpm_fwd Cd19+BCellsPluriD090309Dn1+ CD19+ B Cells (pluriselect), donor090309, donation1_CNhs12177_12189-129B2_forward Regulation 
CD14CD16MonocytesDonor1_CNhs13229_tpm_rev Cd14-cd16+MonocytesD1- CD14-CD16+ Monocytes, donor1_CNhs13229_11790-124B8_reverse Regulation 
CD14CD16MonocytesDonor1_CNhs13229_tpm_fwd Cd14-cd16+MonocytesD1+ CD14-CD16+ Monocytes, donor1_CNhs13229_11790-124B8_forward Regulation 
CD133StemCellsCordBloodDerivedPool1_CNhs12545_tpm_rev Cd133+StemCellsCordBloodPl1- CD133+ stem cells - cord blood derived, pool1_CNhs12545_12223-129E9_reverse Regulation 
CD133StemCellsCordBloodDerivedPool1_CNhs12545_tpm_fwd Cd133+StemCellsCordBloodPl1+ CD133+ stem cells - cord blood derived, pool1_CNhs12545_12223-129E9_forward Regulation 
CD133StemCellsAdultBoneMarrowDerivedPool1_CNhs12552_tpm_rev Cd133+StemCellsAdultBoneMarrowPl1- CD133+ stem cells - adult bone marrow derived, pool1_CNhs12552_12224-129F1_reverse Regulation 
CD133StemCellsAdultBoneMarrowDerivedPool1_CNhs12552_tpm_fwd Cd133+StemCellsAdultBoneMarrowPl1+ CD133+ stem cells - adult bone marrow derived, pool1_CNhs12552_12224-129F1_forward Regulation 
BasophilsDonor2_CNhs12563_tpm_rev BasophilsD2- Basophils, donor2_CNhs12563_12242-129H1_reverse Regulation 
BasophilsDonor2_CNhs12563_tpm_fwd BasophilsD2+ Basophils, donor2_CNhs12563_12242-129H1_forward Regulation 
BasophilsDonor1_CNhs12546_tpm_rev BasophilsD1- Basophils, donor1_CNhs12546_12241-129G9_reverse Regulation 
BasophilsDonor1_CNhs12546_tpm_fwd BasophilsD1+ Basophils, donor1_CNhs12546_12241-129G9_forward Regulation 
SmoothMuscleCellsAorticDonor0NuclearFraction_CNhs12402_tpm_rev SmcAorticCytofracD0- Smooth Muscle Cells - Aortic, donor0 (nuclear fraction)_CNhs12402_14314-155D3_reverse Regulation 
SmoothMuscleCellsAorticDonor0CytoplasmicFraction_CNhs12401_tpm_rev SmcAorticCytofracD0- Smooth Muscle Cells - Aortic, donor0 (cytoplasmic fraction)_CNhs12401_14313-155D2_reverse Regulation 
SmoothMuscleCellsAorticDonor0NuclearFraction_CNhs12402_tpm_fwd SmcAorticCytofracD0+ Smooth Muscle Cells - Aortic, donor0 (nuclear fraction)_CNhs12402_14314-155D3_forward Regulation 
SmoothMuscleCellsAorticDonor0CytoplasmicFraction_CNhs12401_tpm_fwd SmcAorticCytofracD0+ Smooth Muscle Cells - Aortic, donor0 (cytoplasmic fraction)_CNhs12401_14313-155D2_forward Regulation 
SmallAirwayEpithelialCellsDonor3CytoplasmicFraction_CNhs14563_tpm_rev SmallAirwayEpithelialCellsD3- Small Airway Epithelial Cells donor3 (cytoplasmic fraction)_CNhs14563_14316-155D5_reverse Regulation 
SmallAirwayEpithelialCellsDonor3NuclearFraction_CNhs12583_tpm_rev SmallAirwayEpithelialCellsD3- Small Airway Epithelial Cells, donor3 (nuclear fraction)_CNhs12583_14317-155D6_reverse Regulation 
SmallAirwayEpithelialCellsDonor3CytoplasmicFraction_CNhs14563_tpm_fwd SmallAirwayEpithelialCellsD3+ Small Airway Epithelial Cells donor3 (cytoplasmic fraction)_CNhs14563_14316-155D5_forward Regulation 
SmallAirwayEpithelialCellsDonor3NuclearFraction_CNhs12583_tpm_fwd SmallAirwayEpithelialCellsD3+ Small Airway Epithelial Cells, donor3 (nuclear fraction)_CNhs12583_14317-155D6_forward Regulation 
SmallAirwayEpithelialCellsDonor2NuclearFraction_CNhs14565_tpm_rev SmallAirwayEpithelialCellsD2- Small Airway Epithelial Cells donor2 (nuclear fraction)_CNhs14565_14335-155F6_reverse Regulation 
SmallAirwayEpithelialCellsDonor2CytoplasmicFraction_CNhs14564_tpm_rev SmallAirwayEpithelialCellsD2- Small Airway Epithelial Cells donor2 (cytoplasmic fraction)_CNhs14564_14334-155F5_reverse Regulation 
SmallAirwayEpithelialCellsDonor2NuclearFraction_CNhs14565_tpm_fwd SmallAirwayEpithelialCellsD2+ Small Airway Epithelial Cells donor2 (nuclear fraction)_CNhs14565_14335-155F6_forward Regulation 
SmallAirwayEpithelialCellsDonor2CytoplasmicFraction_CNhs14564_tpm_fwd SmallAirwayEpithelialCellsD2+ Small Airway Epithelial Cells donor2 (cytoplasmic fraction)_CNhs14564_14334-155F5_forward Regulation 
PreadipocyteBreastDonor2CytoplasmicFraction_CNhs14562_tpm_rev PreadipocyteBreastD2- Preadipocyte - breast donor2 (cytoplasmic fraction)_CNhs14562_14319-155D8_reverse Regulation 
PreadipocyteBreastDonor2NuclearFraction_CNhs12584_tpm_rev PreadipocyteBreastD2- Preadipocyte - breast, donor2 (nuclear fraction)_CNhs12584_14320-155D9_reverse Regulation 
PreadipocyteBreastDonor2CytoplasmicFraction_CNhs14562_tpm_fwd PreadipocyteBreastD2+ Preadipocyte - breast donor2 (cytoplasmic fraction)_CNhs14562_14319-155D8_forward Regulation 
PreadipocyteBreastDonor2NuclearFraction_CNhs12584_tpm_fwd PreadipocyteBreastD2+ Preadipocyte - breast, donor2 (nuclear fraction)_CNhs12584_14320-155D9_forward Regulation 
FibroblastSkinNormalDonor2CytoplasmicFraction_CNhs14561_tpm_rev FibrosSkinD2- Fibroblast - skin, normal donor2 (cytoplasmic fraction)_CNhs14561_14301-155B8_reverse Regulation 
FibroblastSkinNormalDonor2CytoplasmicFraction_CNhs14561_tpm_fwd FibrosSkinD2+ Fibroblast - skin, normal donor2 (cytoplasmic fraction)_CNhs14561_14301-155B8_forward Regulation 
FibroblastSkinNormalDonor1CytoplasmicFraction_CNhs14560_tpm_rev FibrosSkinD1- Fibroblast - skin, normal donor1 (cytoplasmic fraction)_CNhs14560_14322-155E2_reverse Regulation 
FibroblastSkinNormalDonor1CytoplasmicFraction_CNhs14560_tpm_fwd FibrosSkinD1+ Fibroblast - skin, normal donor1 (cytoplasmic fraction)_CNhs14560_14322-155E2_forward Regulation 
FibroblastSkinSpinalMuscularAtrophyDonor3NuclearFraction_CNhs12398_tpm_rev FibroSkinSpinalMuscularAtrophyNucfracD3- Fibroblast - skin spinal muscular atrophy, donor3 (nuclear fraction)_CNhs12398_14305-155C3_reverse Regulation 
FibroblastSkinSpinalMuscularAtrophyDonor3NuclearFraction_CNhs12398_tpm_fwd FibroSkinSpinalMuscularAtrophyNucfracD3+ Fibroblast - skin spinal muscular atrophy, donor3 (nuclear fraction)_CNhs12398_14305-155C3_forward Regulation 
FibroblastSkinSpinalMuscularAtrophyDonor1NuclearFraction_CNhs12404_tpm_rev FibroSkinSpinalMuscularAtrophyNucfracD1- Fibroblast - skin spinal muscular atrophy, donor1 (nuclear fraction)_CNhs12404_14326-155E6_reverse Regulation 
FibroblastSkinSpinalMuscularAtrophyDonor1NuclearFraction_CNhs12404_tpm_fwd FibroSkinSpinalMuscularAtrophyNucfracD1+ Fibroblast - skin spinal muscular atrophy, donor1 (nuclear fraction)_CNhs12404_14326-155E6_forward Regulation 
FibroblastSkinNormalDonor2NuclearFraction_CNhs12582_tpm_rev FibroSkinNormalNucfracD2- Fibroblast - skin normal, donor2 (nuclear fraction)_CNhs12582_14302-155B9_reverse Regulation 
FibroblastSkinNormalDonor2NuclearFraction_CNhs12582_tpm_fwd FibroSkinNormalNucfracD2+ Fibroblast - skin normal, donor2 (nuclear fraction)_CNhs12582_14302-155B9_forward Regulation 
FibroblastSkinNormalDonor1NuclearFraction_CNhs12403_tpm_rev FibroSkinNormalNucfracD1- Fibroblast - skin normal, donor1 (nuclear fraction)_CNhs12403_14323-155E3_reverse Regulation 
FibroblastSkinNormalDonor1NuclearFraction_CNhs12403_tpm_fwd FibroSkinNormalNucfracD1+ Fibroblast - skin normal, donor1 (nuclear fraction)_CNhs12403_14323-155E3_forward Regulation 
FibroblastSkinDystrophiaMyotonicaDonor3NuclearFraction_CNhs12399_tpm_rev FibroSkinDystrophiaMyotonicaNucfracD3- Fibroblast - skin dystrophia myotonica, donor3 (nuclear fraction)_CNhs12399_14308-155C6_reverse Regulation 
FibroblastSkinDystrophiaMyotonicaDonor3NuclearFraction_CNhs12399_tpm_fwd FibroSkinDystrophiaMyotonicaNucfracD3+ Fibroblast - skin dystrophia myotonica, donor3 (nuclear fraction)_CNhs12399_14308-155C6_forward Regulation 
FibroblastSkinDystrophiaMyotonicaDonor1NuclearFraction_CNhs12405_tpm_rev FibroSkinDystrophiaMyotonicaNucfracD1- Fibroblast - skin dystrophia myotonica, donor1 (nuclear fraction)_CNhs12405_14329-155E9_reverse Regulation 
FibroblastSkinDystrophiaMyotonicaDonor1NuclearFraction_CNhs12405_tpm_fwd FibroSkinDystrophiaMyotonicaNucfracD1+ Fibroblast - skin dystrophia myotonica, donor1 (nuclear fraction)_CNhs12405_14329-155E9_forward Regulation 
FibroblastAorticAdventitialDonor3NuclearFraction_CNhs12400_tpm_rev FibroAorticAdventitialD3- Fibroblast - Aortic Adventitial, donor3 (nuclear fraction)_CNhs12400_14311-155C9_reverse Regulation 
FibroblastAorticAdventitialDonor3CytoplasmicFraction_CNhs14559_tpm_rev FibroAorticAdventitialD3- Fibroblast - Aortic Adventitial donor3 (cytoplasmic fraction)_CNhs14559_14310-155C8_reverse Regulation 
FibroblastAorticAdventitialDonor3NuclearFraction_CNhs12400_tpm_fwd FibroAorticAdventitialD3+ Fibroblast - Aortic Adventitial, donor3 (nuclear fraction)_CNhs12400_14311-155C9_forward Regulation 
FibroblastAorticAdventitialDonor3CytoplasmicFraction_CNhs14559_tpm_fwd FibroAorticAdventitialD3+ Fibroblast - Aortic Adventitial donor3 (cytoplasmic fraction)_CNhs14559_14310-155C8_forward Regulation 
FibroblastAorticAdventitialDonor2CytoplasmicFraction_CNhs14558_tpm_rev FibroAorticAdventitialD2- Fibroblast - Aortic Adventitial donor2 (cytoplasmic fraction)_CNhs14558_14331-155F2_reverse Regulation 
FibroblastAorticAdventitialDonor2NuclearFraction_CNhs12581_tpm_rev FibroAorticAdventitialD2- Fibroblast - Aortic Adventitial, donor2 (nuclear fraction)_CNhs12581_14332-155F3_reverse Regulation 
FibroblastAorticAdventitialDonor2CytoplasmicFraction_CNhs14558_tpm_fwd FibroAorticAdventitialD2+ Fibroblast - Aortic Adventitial donor2 (cytoplasmic fraction)_CNhs14558_14331-155F2_forward Regulation 
FibroblastAorticAdventitialDonor2NuclearFraction_CNhs12581_tpm_fwd FibroAorticAdventitialD2+ Fibroblast - Aortic Adventitial, donor2 (nuclear fraction)_CNhs12581_14332-155F3_forward Regulation 
AcuteMyeloidLeukemiaFABM5CellLineTHP1CytoplasmicFraction_CNhs14556_tpm_rev Cl:THP-1cyto- acute myeloid leukemia (FAB M5) cell line:THP-1 (cytoplasmic fraction)_CNhs14556_14298-155B5_reverse Regulation 
AcuteMyeloidLeukemiaFABM5CellLineTHP1CytoplasmicFraction_CNhs14556_tpm_fwd Cl:THP-1cyto+ acute myeloid leukemia (FAB M5) cell line:THP-1 (cytoplasmic fraction)_CNhs14556_14298-155B5_forward Regulation 
Hep2CellsTreatedWithStreptococciStrainJRS4BiolRep3_CNhs13499_tpm_rev Hep2W/StreptococciJrs4Br3- Hep-2 cells treated with Streptococci strain JRS4, biol_rep3_CNhs13499_11896-125E6_reverse Regulation 
Hep2CellsTreatedWithStreptococciStrainJRS4BiolRep3_CNhs13499_tpm_fwd Hep2W/StreptococciJrs4Br3+ Hep-2 cells treated with Streptococci strain JRS4, biol_rep3_CNhs13499_11896-125E6_forward Regulation 
Hep2CellsTreatedWithStreptococciStrainJRS4BiolRep2_CNhs13498_tpm_rev Hep2W/StreptococciJrs4Br2- Hep-2 cells treated with Streptococci strain JRS4, biol_rep2_CNhs13498_11895-125E5_reverse Regulation 
Hep2CellsTreatedWithStreptococciStrainJRS4BiolRep2_CNhs13498_tpm_fwd Hep2W/StreptococciJrs4Br2+ Hep-2 cells treated with Streptococci strain JRS4, biol_rep2_CNhs13498_11895-125E5_forward Regulation 
Hep2CellsTreatedWithStreptococciStrainJRS4BiolRep1_CNhs13478_tpm_rev Hep2W/StreptococciJrs4Br1- Hep-2 cells treated with Streptococci strain JRS4, biol_rep1_CNhs13478_11894-125E4_reverse Regulation 
Hep2CellsTreatedWithStreptococciStrainJRS4BiolRep1_CNhs13478_tpm_fwd Hep2W/StreptococciJrs4Br1+ Hep-2 cells treated with Streptococci strain JRS4, biol_rep1_CNhs13478_11894-125E4_forward Regulation 
Hep2CellsTreatedWithStreptococciStrain5448BiolRep3_CNhs13497_tpm_rev Hep2W/Streptococci5448Br3- Hep-2 cells treated with Streptococci strain 5448, biol_rep3_CNhs13497_11892-125E2_reverse Regulation 
Hep2CellsTreatedWithStreptococciStrain5448BiolRep3_CNhs13497_tpm_fwd Hep2W/Streptococci5448Br3+ Hep-2 cells treated with Streptococci strain 5448, biol_rep3_CNhs13497_11892-125E2_forward Regulation 
Hep2CellsTreatedWithStreptococciStrain5448BiolRep2_CNhs13496_tpm_rev Hep2W/Streptococci5448Br2- Hep-2 cells treated with Streptococci strain 5448, biol_rep2_CNhs13496_11891-125E1_reverse Regulation 
Hep2CellsTreatedWithStreptococciStrain5448BiolRep2_CNhs13496_tpm_fwd Hep2W/Streptococci5448Br2+ Hep-2 cells treated with Streptococci strain 5448, biol_rep2_CNhs13496_11891-125E1_forward Regulation 
Hep2CellsTreatedWithStreptococciStrain5448BiolRep1_CNhs13477_tpm_rev Hep2W/Streptococci5448Br1- Hep-2 cells treated with Streptococci strain 5448, biol_rep1_CNhs13477_11890-125D9_reverse Regulation 
Hep2CellsTreatedWithStreptococciStrain5448BiolRep1_CNhs13477_tpm_fwd Hep2W/Streptococci5448Br1+ Hep-2 cells treated with Streptococci strain 5448, biol_rep1_CNhs13477_11890-125D9_forward Regulation 
Hep2CellsMockTreatedBiolRep3_CNhs13501_tpm_rev Hep2MockTreatedBr3- Hep-2 cells mock treated, biol_rep3_CNhs13501_11900-125F1_reverse Regulation 
Hep2CellsMockTreatedBiolRep3_CNhs13501_tpm_fwd Hep2MockTreatedBr3+ Hep-2 cells mock treated, biol_rep3_CNhs13501_11900-125F1_forward Regulation 
Hep2CellsMockTreatedBiolRep2_CNhs13500_tpm_rev Hep2MockTreatedBr2- Hep-2 cells mock treated, biol_rep2_CNhs13500_11899-125E9_reverse Regulation 
Hep2CellsMockTreatedBiolRep2_CNhs13500_tpm_fwd Hep2MockTreatedBr2+ Hep-2 cells mock treated, biol_rep2_CNhs13500_11899-125E9_forward Regulation 
Hep2CellsMockTreatedBiolRep1_CNhs13479_tpm_rev Hep2MockTreatedBr1- Hep-2 cells mock treated, biol_rep1_CNhs13479_11898-125E8_reverse Regulation 
Hep2CellsMockTreatedBiolRep1_CNhs13479_tpm_fwd Hep2MockTreatedBr1+ Hep-2 cells mock treated, biol_rep1_CNhs13479_11898-125E8_forward Regulation 
RetinoblastomaCellLineY79_CNhs11267_tpm_rev Cl:Y79- retinoblastoma cell line:Y79_CNhs11267_10475-106I7_reverse Regulation 
RetinoblastomaCellLineY79_CNhs11267_tpm_fwd Cl:Y79+ retinoblastoma cell line:Y79_CNhs11267_10475-106I7_forward Regulation 
XerodermaPigentosumBCellLineXPL17_CNhs11813_tpm_rev Cl:XPL17- xeroderma pigentosum b cell line:XPL 17_CNhs11813_10563-108A5_reverse Regulation 
XerodermaPigentosumBCellLineXPL17_CNhs11813_tpm_fwd Cl:XPL17+ xeroderma pigentosum b cell line:XPL 17_CNhs11813_10563-108A5_forward Regulation 
HereditarySpherocyticAnemiaCellLineWIL2NS_CNhs11891_tpm_rev Cl:WIL2-NS- hereditary spherocytic anemia cell line:WIL2-NS_CNhs11891_10808-111A7_reverse Regulation 
HereditarySpherocyticAnemiaCellLineWIL2NS_CNhs11891_tpm_fwd Cl:WIL2-NS+ hereditary spherocytic anemia cell line:WIL2-NS_CNhs11891_10808-111A7_forward Regulation 
SmallCellLungCarcinomaCellLineWAhT_CNhs11812_tpm_rev Cl:WA-hT- small cell lung carcinoma cell line:WA-hT_CNhs11812_10562-108A4_reverse Regulation 
SmallCellLungCarcinomaCellLineWAhT_CNhs11812_tpm_fwd Cl:WA-hT+ small cell lung carcinoma cell line:WA-hT_CNhs11812_10562-108A4_forward Regulation 
AcuteMyeloidLeukemiaFABM5CellLineU937DE4_CNhs13058_tpm_rev Cl:U-937DE-4- acute myeloid leukemia (FAB M5) cell line:U-937 DE-4_CNhs13058_10834-111D6_reverse Regulation 
AcuteMyeloidLeukemiaFABM5CellLineU937DE4_CNhs13058_tpm_fwd Cl:U-937DE-4+ acute myeloid leukemia (FAB M5) cell line:U-937 DE-4_CNhs13058_10834-111D6_forward Regulation 
ThymicCarcinomaCellLineTy82_CNhs14139_tpm_rev Cl:Ty-82- thymic carcinoma cell line:Ty-82_CNhs14139_10803-111A2_reverse Regulation 
ThymicCarcinomaCellLineTy82_CNhs14139_tpm_fwd Cl:Ty-82+ thymic carcinoma cell line:Ty-82_CNhs14139_10803-111A2_forward Regulation 
RenalCellCarcinomaCellLineTUHR10TKB_CNhs11257_tpm_rev Cl:TUHR10TKB- renal cell carcinoma cell line:TUHR10TKB_CNhs11257_10471-106I3_reverse Regulation 
RenalCellCarcinomaCellLineTUHR10TKB_CNhs11257_tpm_fwd Cl:TUHR10TKB+ renal cell carcinoma cell line:TUHR10TKB_CNhs11257_10471-106I3_forward Regulation 
RectalCancerCellLineTT1TKB_CNhs11255_tpm_rev Cl:TT1TKB- rectal cancer cell line:TT1TKB_CNhs11255_10469-106I1_reverse Regulation 
RectalCancerCellLineTT1TKB_CNhs11255_tpm_fwd Cl:TT1TKB+ rectal cancer cell line:TT1TKB_CNhs11255_10469-106I1_forward Regulation 
AstrocytomaCellLineTM31_CNhs10742_tpm_rev Cl:TM-31- astrocytoma cell line:TM-31_CNhs10742_10425-106D2_reverse Regulation 
AstrocytomaCellLineTM31_CNhs10742_tpm_fwd Cl:TM-31+ astrocytoma cell line:TM-31_CNhs10742_10425-106D2_forward Regulation 
AcuteMyeloidLeukemiaFABM5CellLineTHP1Thawed_CNhs10724_tpm_rev Cl:THP-1thawed- acute myeloid leukemia (FAB M5) cell line:THP-1 (thawed)_CNhs10724_10405-106A9_reverse Regulation 
AcuteMyeloidLeukemiaFABM5CellLineTHP1Thawed_CNhs10724_tpm_fwd Cl:THP-1thawed+ acute myeloid leukemia (FAB M5) cell line:THP-1 (thawed)_CNhs10724_10405-106A9_forward Regulation 
AcuteMyeloidLeukemiaFABM5CellLineTHP1Revived_CNhs10723_tpm_rev Cl:THP-1revived- acute myeloid leukemia (FAB M5) cell line:THP-1 (revived)_CNhs10723_10400-106A4_reverse Regulation 
AcuteMyeloidLeukemiaFABM5CellLineTHP1Revived_CNhs10723_tpm_fwd Cl:THP-1revived+ acute myeloid leukemia (FAB M5) cell line:THP-1 (revived)_CNhs10723_10400-106A4_forward Regulation 
AcuteMyeloidLeukemiaFABM5CellLineTHP1Fresh_CNhs10722_tpm_rev Cl:THP-1fresh- acute myeloid leukemia (FAB M5) cell line:THP-1 (fresh)_CNhs10722_10399-106A3_reverse Regulation 
AcuteMyeloidLeukemiaFABM5CellLineTHP1Fresh_CNhs10722_tpm_fwd Cl:THP-1fresh+ acute myeloid leukemia (FAB M5) cell line:THP-1 (fresh)_CNhs10722_10399-106A3_forward Regulation 
GallBladderCarcinomaCellLineTGBC2TKB_CNhs10733_tpm_rev Cl:TGBC2TKB- gall bladder carcinoma cell line:TGBC2TKB_CNhs10733_10415-106C1_reverse Regulation 
GallBladderCarcinomaCellLineTGBC2TKB_CNhs10733_tpm_fwd Cl:TGBC2TKB+ gall bladder carcinoma cell line:TGBC2TKB_CNhs10733_10415-106C1_forward Regulation 
PapillotubularAdenocarcinomaCellLineTGBC18TKB_CNhs10734_tpm_rev Cl:TGBC18TKB- papillotubular adenocarcinoma cell line:TGBC18TKB_CNhs10734_10417-106C3_reverse Regulation 
PapillotubularAdenocarcinomaCellLineTGBC18TKB_CNhs10734_tpm_fwd Cl:TGBC18TKB+ papillotubular adenocarcinoma cell line:TGBC18TKB_CNhs10734_10417-106C3_forward Regulation 
GallBladderCarcinomaCellLineTGBC14TKB_CNhs11256_tpm_rev Cl:TGBC14TKB- gall bladder carcinoma cell line:TGBC14TKB_CNhs11256_10470-106I2_reverse Regulation 
GallBladderCarcinomaCellLineTGBC14TKB_CNhs11256_tpm_fwd Cl:TGBC14TKB+ gall bladder carcinoma cell line:TGBC14TKB_CNhs11256_10470-106I2_forward Regulation 
BileDuctCarcinomaCellLineTFK1_CNhs11265_tpm_rev Cl:TFK-1- bile duct carcinoma cell line:TFK-1_CNhs11265_10496-107C1_reverse Regulation 
BileDuctCarcinomaCellLineTFK1_CNhs11265_tpm_fwd Cl:TFK-1+ bile duct carcinoma cell line:TFK-1_CNhs11265_10496-107C1_forward Regulation 
ClearCellCarcinomaCellLineTEN_CNhs11930_tpm_rev Cl:TEN- clear cell carcinoma cell line:TEN_CNhs11930_10636-108I6_reverse Regulation 
ClearCellCarcinomaCellLineTEN_CNhs11930_tpm_fwd Cl:TEN+ clear cell carcinoma cell line:TEN_CNhs11930_10636-108I6_forward Regulation 
BasalCellCarcinomaCellLineTE354_T_CNhs11932_tpm_rev Cl:TE354_T- basal cell carcinoma cell line:TE 354_T_CNhs11932_10702-109G9_reverse Regulation 
BasalCellCarcinomaCellLineTE354_T_CNhs11932_tpm_fwd Cl:TE354_T+ basal cell carcinoma cell line:TE 354_T_CNhs11932_10702-109G9_forward Regulation 
ThyroidCarcinomaCellLineTCO1_CNhs11872_tpm_rev Cl:TCO-1- thyroid carcinoma cell line:TCO-1_CNhs11872_10783-110G9_reverse Regulation 
ThyroidCarcinomaCellLineTCO1_CNhs11872_tpm_fwd Cl:TCO-1+ thyroid carcinoma cell line:TCO-1_CNhs11872_10783-110G9_forward Regulation 
ArgyrophilSmallCellCarcinomaCellLineTCYIK_CNhs11725_tpm_rev Cl:TC-YIK- argyrophil small cell carcinoma cell line:TC-YIK_CNhs11725_10589-108D4_reverse Regulation 
ArgyrophilSmallCellCarcinomaCellLineTCYIK_CNhs11725_tpm_fwd Cl:TC-YIK+ argyrophil small cell carcinoma cell line:TC-YIK_CNhs11725_10589-108D4_forward Regulation 
NeuroectodermalTumorCellLineTASK1_CNhs11866_tpm_rev Cl:TASK1- neuroectodermal tumor cell line:TASK1_CNhs11866_10774-110F9_reverse Regulation 
NeuroectodermalTumorCellLineTASK1_CNhs11866_tpm_fwd Cl:TASK1+ neuroectodermal tumor cell line:TASK1_CNhs11866_10774-110F9_forward Regulation 
GlioblastomaCellLineT98G_CNhs11272_tpm_rev Cl:T98G- glioblastoma cell line:T98G_CNhs11272_10485-107A8_reverse Regulation 
GlioblastomaCellLineT98G_CNhs11272_tpm_fwd Cl:T98G+ glioblastoma cell line:T98G_CNhs11272_10485-107A8_forward Regulation 
SquamousCellCarcinomaCellLineT3M5_CNhs11739_tpm_rev Cl:T3M-5- squamous cell carcinoma cell line:T3M-5_CNhs11739_10616-108G4_reverse Regulation 
SquamousCellCarcinomaCellLineT3M5_CNhs11739_tpm_fwd Cl:T3M-5+ squamous cell carcinoma cell line:T3M-5_CNhs11739_10616-108G4_forward Regulation 
ChoriocarcinomaCellLineT3M3_CNhs11820_tpm_rev Cl:T3M-3- choriocarcinoma cell line:T3M-3_CNhs11820_10618-108G6_reverse Regulation 
ChoriocarcinomaCellLineT3M3_CNhs11820_tpm_fwd Cl:T3M-3+ choriocarcinoma cell line:T3M-3_CNhs11820_10618-108G6_forward Regulation 
LiposarcomaCellLineSW872_CNhs11851_tpm_rev Cl:SW872- liposarcoma cell line:SW 872_CNhs11851_10726-110A6_reverse Regulation 
LiposarcomaCellLineSW872_CNhs11851_tpm_fwd Cl:SW872+ liposarcoma cell line:SW 872_CNhs11851_10726-110A6_forward Regulation 
AlveolarCellCarcinomaCellLineSW1573_CNhs11838_tpm_rev Cl:SW1573- alveolar cell carcinoma cell line:SW 1573_CNhs11838_10708-109H6_reverse Regulation 
AlveolarCellCarcinomaCellLineSW1573_CNhs11838_tpm_fwd Cl:SW1573+ alveolar cell carcinoma cell line:SW 1573_CNhs11838_10708-109H6_forward Regulation 
ChondrosarcomaCellLineSW1353_CNhs11833_tpm_rev Cl:SW1353- chondrosarcoma cell line:SW 1353_CNhs11833_10700-109G7_reverse Regulation 
ChondrosarcomaCellLineSW1353_CNhs11833_tpm_fwd Cl:SW1353+ chondrosarcoma cell line:SW 1353_CNhs11833_10700-109G7_forward Regulation 
AdrenalCortexAdenocarcinomaCellLineSW13_CNhs11893_tpm_rev Cl:SW-13- adrenal cortex adenocarcinoma cell line:SW-13_CNhs11893_10810-111A9_reverse Regulation 
AdrenalCortexAdenocarcinomaCellLineSW13_CNhs11893_tpm_fwd Cl:SW-13+ adrenal cortex adenocarcinoma cell line:SW-13_CNhs11893_10810-111A9_forward Regulation 
TubularAdenocarcinomaCellLineSUIT2_CNhs11883_tpm_rev Cl:SUIT-2- tubular adenocarcinoma cell line:SUIT-2_CNhs11883_10797-110I5_reverse Regulation 
TubularAdenocarcinomaCellLineSUIT2_CNhs11883_tpm_fwd Cl:SUIT-2+ tubular adenocarcinoma cell line:SUIT-2_CNhs11883_10797-110I5_forward Regulation 
BoneMarrowStromalCellLineStromaNKtert_CNhs11931_tpm_rev Cl:StromaNKtert- bone marrow stromal cell line:StromaNKtert_CNhs11931_10686-109F2_reverse Regulation 
BoneMarrowStromalCellLineStromaNKtert_CNhs11931_tpm_fwd Cl:StromaNKtert+ bone marrow stromal cell line:StromaNKtert_CNhs11931_10686-109F2_forward Regulation 
LensEpithelialCellLineSRA0104_CNhs11750_tpm_rev Cl:SRA01/04- lens epithelial cell line:SRA 01/04_CNhs11750_10647-109A8_reverse Regulation 
LensEpithelialCellLineSRA0104_CNhs11750_tpm_fwd Cl:SRA01/04+ lens epithelial cell line:SRA 01/04_CNhs11750_10647-109A8_forward Regulation 
PleomorphicHepatocellularCarcinomaCellLineSNU387_CNhs11933_tpm_rev Cl:SNU-387- pleomorphic hepatocellular carcinoma cell line:SNU-387_CNhs11933_10706-109H4_reverse Regulation 
PleomorphicHepatocellularCarcinomaCellLineSNU387_CNhs11933_tpm_fwd Cl:SNU-387+ pleomorphic hepatocellular carcinoma cell line:SNU-387_CNhs11933_10706-109H4_forward Regulation 
SplenicLymphomaWithVillousLymphocytesCellLineSLVL_CNhs10741_tpm_rev Cl:SLVL- splenic lymphoma with villous lymphocytes cell line:SLVL_CNhs10741_10424-106D1_reverse Regulation 
SplenicLymphomaWithVillousLymphocytesCellLineSLVL_CNhs10741_tpm_fwd Cl:SLVL+ splenic lymphoma with villous lymphocytes cell line:SLVL_CNhs10741_10424-106D1_forward Regulation 
ChronicLymphocyticLeukemiaTCLLCellLineSKW3_CNhs11714_tpm_rev Cl:SKW-3- chronic lymphocytic leukemia (T-CLL) cell line:SKW-3_CNhs11714_10416-106C2_reverse Regulation 
ChronicLymphocyticLeukemiaTCLLCellLineSKW3_CNhs11714_tpm_fwd Cl:SKW-3+ chronic lymphocytic leukemia (T-CLL) cell line:SKW-3_CNhs11714_10416-106C2_forward Regulation 
MyelodysplasticSyndromeCellLineSKM1_CNhs11934_tpm_rev Cl:SKM-1- myelodysplastic syndrome cell line:SKM-1_CNhs11934_10772-110F7_reverse Regulation 
MyelodysplasticSyndromeCellLineSKM1_CNhs11934_tpm_fwd Cl:SKM-1+ myelodysplastic syndrome cell line:SKM-1_CNhs11934_10772-110F7_forward Regulation 
LargeCellNonkeratinizingSquamousCarcinomaCellLineSKGIISF_CNhs11825_tpm_rev Cl:SKG-II-SF- large cell non-keratinizing squamous carcinoma cell line:SKG-II-SF_CNhs11825_10692-109F8_reverse Regulation 
LargeCellNonkeratinizingSquamousCarcinomaCellLineSKGIISF_CNhs11825_tpm_fwd Cl:SKG-II-SF+ large cell non-keratinizing squamous carcinoma cell line:SKG-II-SF_CNhs11825_10692-109F8_forward Regulation 
CarcinoidCellLineSKPNDW_CNhs11846_tpm_rev Cl:SK-PN-DW- carcinoid cell line:SK-PN-DW_CNhs11846_10719-109I8_reverse Regulation 
CarcinoidCellLineSKPNDW_CNhs11846_tpm_fwd Cl:SK-PN-DW+ carcinoid cell line:SK-PN-DW_CNhs11846_10719-109I8_forward Regulation 
SerousAdenocarcinomaCellLineSKOV3RAfterCocultureWithSOC5702GBiolRep1_CNhs13508_tpm_rev Cl:SK-OV-3-RwithSOC-57-02-GBr1- serous adenocarcinoma cell line:SK-OV-3-R after co-culture with SOC-57-02-G, biol_rep1_CNhs13508_11843-124H7_reverse Regulation 
SerousAdenocarcinomaCellLineSKOV3RAfterCocultureWithSOC5702GBiolRep1_CNhs13508_tpm_fwd Cl:SK-OV-3-RwithSOC-57-02-GBr1+ serous adenocarcinoma cell line:SK-OV-3-R after co-culture with SOC-57-02-G, biol_rep1_CNhs13508_11843-124H7_forward Regulation 
SerousAdenocarcinomaCellLineSKOV3RBiolRep1_CNhs13099_tpm_rev Cl:SK-OV-3-RBr1- serous adenocarcinoma cell line:SK-OV-3-R, biol_rep1_CNhs13099_11841-124H5_reverse Regulation 
SerousAdenocarcinomaCellLineSKOV3RBiolRep1_CNhs13099_tpm_fwd Cl:SK-OV-3-RBr1+ serous adenocarcinoma cell line:SK-OV-3-R, biol_rep1_CNhs13099_11841-124H5_forward Regulation 
NeuroepitheliomaCellLineSKNMC_CNhs11853_tpm_rev Cl:SK-N-MC- neuroepithelioma cell line:SK-N-MC_CNhs11853_10728-110A8_reverse Regulation 
NeuroepitheliomaCellLineSKNMC_CNhs11853_tpm_fwd Cl:SK-N-MC+ neuroepithelioma cell line:SK-N-MC_CNhs11853_10728-110A8_forward Regulation 
ChoriocarcinomaCellLineSCH_CNhs11875_tpm_rev Cl:SCH- choriocarcinoma cell line:SCH_CNhs11875_10785-110H2_reverse Regulation 
ChoriocarcinomaCellLineSCH_CNhs11875_tpm_fwd Cl:SCH+ choriocarcinoma cell line:SCH_CNhs11875_10785-110H2_forward Regulation 
OralSquamousCellCarcinomaCellLineSAS_CNhs11810_tpm_rev Cl:SAS- oral squamous cell carcinoma cell line:SAS_CNhs11810_10544-107H4_reverse Regulation 
OralSquamousCellCarcinomaCellLineSAS_CNhs11810_tpm_fwd Cl:SAS+ oral squamous cell carcinoma cell line:SAS_CNhs11810_10544-107H4_forward Regulation 
AnaplasticSquamousCellCarcinomaCellLineRPMI2650_CNhs11889_tpm_rev Cl:RPMI2650- anaplastic squamous cell carcinoma cell line:RPMI 2650_CNhs11889_10805-111A4_reverse Regulation 
AnaplasticSquamousCellCarcinomaCellLineRPMI2650_CNhs11889_tpm_fwd Cl:RPMI2650+ anaplastic squamous cell carcinoma cell line:RPMI 2650_CNhs11889_10805-111A4_forward Regulation 
BCellLineRPMI1788_CNhs10744_tpm_rev Cl:RPMI1788- b cell line:RPMI1788_CNhs10744_10427-106D4_reverse Regulation 
BCellLineRPMI1788_CNhs10744_tpm_fwd Cl:RPMI1788+ b cell line:RPMI1788_CNhs10744_10427-106D4_forward Regulation 
RhabdomyosarcomaCellLineRMSYM_CNhs11269_tpm_rev Cl:RMS-YM- rhabdomyosarcoma cell line:RMS-YM_CNhs11269_10477-106I9_reverse Regulation 
RhabdomyosarcomaCellLineRMSYM_CNhs11269_tpm_fwd Cl:RMS-YM+ rhabdomyosarcoma cell line:RMS-YM_CNhs11269_10477-106I9_forward Regulation 
SquamousCellLungCarcinomaCellLineRERFLCAI_CNhs14240_tpm_rev Cl:RERF-LC-AI- squamous cell lung carcinoma cell line:RERF-LC-AI_CNhs14240_10501-107C6_reverse Regulation 
SquamousCellLungCarcinomaCellLineRERFLCAI_CNhs14240_tpm_fwd Cl:RERF-LC-AI+ squamous cell lung carcinoma cell line:RERF-LC-AI_CNhs14240_10501-107C6_forward Regulation 
BurkittsLymphomaCellLineRAJI_CNhs11268_tpm_rev Cl:RAJI- Burkitt's lymphoma cell line:RAJI_CNhs11268_10476-106I8_reverse Regulation 
BurkittsLymphomaCellLineRAJI_CNhs11268_tpm_fwd Cl:RAJI+ Burkitt's lymphoma cell line:RAJI_CNhs11268_10476-106I8_forward Regulation 
SomatostatinomaCellLineQGP1_CNhs11869_tpm_rev Cl:QGP-1- somatostatinoma cell line:QGP-1_CNhs11869_10781-110G7_reverse Regulation 
SomatostatinomaCellLineQGP1_CNhs11869_tpm_fwd Cl:QGP-1+ somatostatinoma cell line:QGP-1_CNhs11869_10781-110G7_forward Regulation 
MyelomaCellLinePCM6_CNhs11258_tpm_rev Cl:PCM6- myeloma cell line:PCM6_CNhs11258_10474-106I6_reverse Regulation 
MyelomaCellLinePCM6_CNhs11258_tpm_fwd Cl:PCM6+ myeloma cell line:PCM6_CNhs11258_10474-106I6_forward Regulation 
ProstateCancerCellLinePC3_CNhs11243_tpm_rev Cl:PC-3- prostate cancer cell line:PC-3_CNhs11243_10439-106E7_reverse Regulation 
ProstateCancerCellLinePC3_CNhs11243_tpm_fwd Cl:PC-3+ prostate cancer cell line:PC-3_CNhs11243_10439-106E7_forward Regulation 
LungAdenocarcinomaCellLinePC14_CNhs10726_tpm_rev Cl:PC-14- lung adenocarcinoma cell line:PC-14_CNhs10726_10408-106B3_reverse Regulation 
LungAdenocarcinomaCellLinePC14_CNhs10726_tpm_fwd Cl:PC-14+ lung adenocarcinoma cell line:PC-14_CNhs10726_10408-106B3_forward Regulation 
TeratocarcinomaCellLinePA1_CNhs11890_tpm_rev Cl:PA-1- teratocarcinoma cell line:PA-1_CNhs11890_10807-111A6_reverse Regulation 
TeratocarcinomaCellLinePA1_CNhs11890_tpm_fwd Cl:PA-1+ teratocarcinoma cell line:PA-1_CNhs11890_10807-111A6_forward Regulation 
AcuteMyeloidLeukemiaFABM5CellLineP31FUJ_CNhs13051_tpm_rev Cl:P31/FUJ- acute myeloid leukemia (FAB M5) cell line:P31/FUJ_CNhs13051_10770-110F5_reverse Regulation 
AcuteMyeloidLeukemiaFABM5CellLineP31FUJ_CNhs13051_tpm_fwd Cl:P31/FUJ+ acute myeloid leukemia (FAB M5) cell line:P31/FUJ_CNhs13051_10770-110F5_forward Regulation 
NonTNonBAcuteLymphoblasticLeukemiaALLCellLineP30OHK_CNhs10747_tpm_rev Cl:P30/OHK- non T non B acute lymphoblastic leukemia (ALL) cell line:P30/OHK_CNhs10747_10430-106D7_reverse Regulation 
NonTNonBAcuteLymphoblasticLeukemiaALLCellLineP30OHK_CNhs10747_tpm_fwd Cl:P30/OHK+ non T non B acute lymphoblastic leukemia (ALL) cell line:P30/OHK_CNhs10747_10430-106D7_forward Regulation 
RenalCellCarcinomaCellLineOSRC2_CNhs10729_tpm_rev Cl:OS-RC-2- renal cell carcinoma cell line:OS-RC-2_CNhs10729_10411-106B6_reverse Regulation 
RenalCellCarcinomaCellLineOSRC2_CNhs10729_tpm_fwd Cl:OS-RC-2+ renal cell carcinoma cell line:OS-RC-2_CNhs10729_10411-106B6_forward Regulation 
MedulloblastomaCellLineONS76_CNhs11861_tpm_rev Cl:ONS-76- medulloblastoma cell line:ONS-76_CNhs11861_10759-110E3_reverse Regulation 
MedulloblastomaCellLineONS76_CNhs11861_tpm_fwd Cl:ONS-76+ medulloblastoma cell line:ONS-76_CNhs11861_10759-110E3_forward Regulation 
MesotheliomaCellLineONE58_CNhs13075_tpm_rev Cl:ONE58- mesothelioma cell line:ONE58_CNhs13075_10858-111G3_reverse Regulation 
MesotheliomaCellLineONE58_CNhs13075_tpm_fwd Cl:ONE58+ mesothelioma cell line:ONE58_CNhs13075_10858-111G3_forward Regulation 
EndometrialStromalSarcomaCellLineOMC9_CNhs11249_tpm_rev Cl:OMC-9- endometrial stromal sarcoma cell line:OMC-9_CNhs11249_10448-106F7_reverse Regulation 
EndometrialStromalSarcomaCellLineOMC9_CNhs11249_tpm_fwd Cl:OMC-9+ endometrial stromal sarcoma cell line:OMC-9_CNhs11249_10448-106F7_forward Regulation 
EndometrialCarcinomaCellLineOMC2_CNhs11266_tpm_rev Cl:OMC-2- endometrial carcinoma cell line:OMC-2_CNhs11266_10497-107C2_reverse Regulation 
EndometrialCarcinomaCellLineOMC2_CNhs11266_tpm_fwd Cl:OMC-2+ endometrial carcinoma cell line:OMC-2_CNhs11266_10497-107C2_forward Regulation 
SignetRingCarcinomaCellLineNUGC4_CNhs11270_tpm_rev Cl:NUGC-4- signet ring carcinoma cell line:NUGC-4_CNhs11270_10483-107A6_reverse Regulation 
SignetRingCarcinomaCellLineNUGC4_CNhs11270_tpm_fwd Cl:NUGC-4+ signet ring carcinoma cell line:NUGC-4_CNhs11270_10483-107A6_forward Regulation 
PancreaticCarcinomaCellLineNORP1_CNhs11832_tpm_rev Cl:NOR-P1- pancreatic carcinoma cell line:NOR-P1_CNhs11832_10698-109G5_reverse Regulation 
PancreaticCarcinomaCellLineNORP1_CNhs11832_tpm_fwd Cl:NOR-P1+ pancreatic carcinoma cell line:NOR-P1_CNhs11832_10698-109G5_forward Regulation 
AcuteMyeloidLeukemiaFABM5CellLineNOMO1_CNhs13050_tpm_rev Cl:NOMO-1- acute myeloid leukemia (FAB M5) cell line:NOMO-1_CNhs13050_10764-110E8_reverse Regulation 
AcuteMyeloidLeukemiaFABM5CellLineNOMO1_CNhs13050_tpm_fwd Cl:NOMO-1+ acute myeloid leukemia (FAB M5) cell line:NOMO-1_CNhs13050_10764-110E8_forward Regulation 
MesotheliomaCellLineNo36_CNhs13074_tpm_rev Cl:No36- mesothelioma cell line:No36_CNhs13074_10857-111G2_reverse Regulation 
MesotheliomaCellLineNo36_CNhs13074_tpm_fwd Cl:No36+ mesothelioma cell line:No36_CNhs13074_10857-111G2_forward Regulation 
MyxofibrosarcomaCellLineNMFH1_CNhs11821_tpm_rev Cl:NMFH-1- myxofibrosarcoma cell line:NMFH-1_CNhs11821_10684-109E9_reverse Regulation 
MyxofibrosarcomaCellLineNMFH1_CNhs11821_tpm_fwd Cl:NMFH-1+ myxofibrosarcoma cell line:NMFH-1_CNhs11821_10684-109E9_forward Regulation 
AcuteMyeloidLeukemiaFABM2CellLineNKM1_CNhs11864_tpm_rev Cl:NKM-1- acute myeloid leukemia (FAB M2) cell line:NKM-1_CNhs11864_10765-110E9_reverse Regulation 
AcuteMyeloidLeukemiaFABM2CellLineNKM1_CNhs11864_tpm_fwd Cl:NKM-1+ acute myeloid leukemia (FAB M2) cell line:NKM-1_CNhs11864_10765-110E9_forward Regulation 
NeuroblastomaCellLineNH12_CNhs11811_tpm_rev Cl:NH-12- neuroblastoma cell line:NH-12_CNhs11811_10555-107I6_reverse Regulation 
NeuroblastomaCellLineNH12_CNhs11811_tpm_fwd Cl:NH-12+ neuroblastoma cell line:NH-12_CNhs11811_10555-107I6_forward Regulation 
TesticularGermCellEmbryonalCarcinomaCellLineNEC8_CNhs11726_tpm_rev Cl:NEC8- testicular germ cell embryonal carcinoma cell line:NEC8_CNhs11726_10590-108D5_reverse Regulation 
TesticularGermCellEmbryonalCarcinomaCellLineNEC8_CNhs11726_tpm_fwd Cl:NEC8+ testicular germ cell embryonal carcinoma cell line:NEC8_CNhs11726_10590-108D5_forward Regulation 
TesticularGermCellEmbryonalCarcinomaCellLineNEC15_CNhs12362_tpm_rev Cl:NEC15- testicular germ cell embryonal carcinoma cell line:NEC15_CNhs12362_10593-108D8_reverse Regulation 
TesticularGermCellEmbryonalCarcinomaCellLineNEC15_CNhs12362_tpm_fwd Cl:NEC15+ testicular germ cell embryonal carcinoma cell line:NEC15_CNhs12362_10593-108D8_forward Regulation 
TesticularGermCellEmbryonalCarcinomaCellLineNEC14_CNhs12351_tpm_rev Cl:NEC14- testicular germ cell embryonal carcinoma cell line:NEC14_CNhs12351_10591-108D6_reverse Regulation 
TesticularGermCellEmbryonalCarcinomaCellLineNEC14_CNhs12351_tpm_fwd Cl:NEC14+ testicular germ cell embryonal carcinoma cell line:NEC14_CNhs12351_10591-108D6_forward Regulation 
TeratocarcinomaCellLineNCRG1_CNhs11884_tpm_rev Cl:NCR-G1- teratocarcinoma cell line:NCR-G1_CNhs11884_10798-110I6_reverse Regulation 
TeratocarcinomaCellLineNCRG1_CNhs11884_tpm_fwd Cl:NCR-G1+ teratocarcinoma cell line:NCR-G1_CNhs11884_10798-110I6_forward Regulation 
SmallCellLungCarcinomaCellLineNCIH82_CNhs12809_tpm_rev Cl:NCI-H82- small cell lung carcinoma cell line:NCI-H82_CNhs12809_10842-111E5_reverse Regulation 
SmallCellLungCarcinomaCellLineNCIH82_CNhs12809_tpm_fwd Cl:NCI-H82+ small cell lung carcinoma cell line:NCI-H82_CNhs12809_10842-111E5_forward Regulation 
CarcinoidCellLineNCIH727_CNhs14244_tpm_rev Cl:NCI-H727- carcinoid cell line:NCI-H727_CNhs14244_10735-110B6_reverse Regulation 
CarcinoidCellLineNCIH727_CNhs14244_tpm_fwd Cl:NCI-H727+ carcinoid cell line:NCI-H727_CNhs14244_10735-110B6_forward Regulation 
BronchioalveolarCarcinomaCellLineNCIH650_CNhs14138_tpm_rev Cl:NCI-H650- bronchioalveolar carcinoma cell line:NCI-H650_CNhs14138_10715-109I4_reverse Regulation 
BronchioalveolarCarcinomaCellLineNCIH650_CNhs14138_tpm_fwd Cl:NCI-H650+ bronchioalveolar carcinoma cell line:NCI-H650_CNhs14138_10715-109I4_forward Regulation 
LargeCellLungCarcinomaCellLineNCIH460_CNhs12806_tpm_rev Cl:NCI-H460- large cell lung carcinoma cell line:NCI-H460_CNhs12806_10839-111E2_reverse Regulation 
LargeCellLungCarcinomaCellLineNCIH460_CNhs12806_tpm_fwd Cl:NCI-H460+ large cell lung carcinoma cell line:NCI-H460_CNhs12806_10839-111E2_forward Regulation 
LungAdenocarcinomaPapillaryCellLineNCIH441_CNhs14245_tpm_rev Cl:NCI-H441- lung adenocarcinoma, papillary cell line:NCI-H441_CNhs14245_10742-110C4_reverse Regulation 
LungAdenocarcinomaPapillaryCellLineNCIH441_CNhs14245_tpm_fwd Cl:NCI-H441+ lung adenocarcinoma, papillary cell line:NCI-H441_CNhs14245_10742-110C4_forward Regulation 
BronchioalveolarCarcinomaCellLineNCIH358_CNhs11840_tpm_rev Cl:NCI-H358- bronchioalveolar carcinoma cell line:NCI-H358_CNhs11840_10709-109H7_reverse Regulation 
BronchioalveolarCarcinomaCellLineNCIH358_CNhs11840_tpm_fwd Cl:NCI-H358+ bronchioalveolar carcinoma cell line:NCI-H358_CNhs11840_10709-109H7_forward Regulation 
MesotheliomaCellLineNCIH28_CNhs13061_tpm_rev Cl:NCI-H28- mesothelioma cell line:NCI-H28_CNhs13061_10845-111E8_reverse Regulation 
MesotheliomaCellLineNCIH28_CNhs13061_tpm_fwd Cl:NCI-H28+ mesothelioma cell line:NCI-H28_CNhs13061_10845-111E8_forward Regulation 
MesotheliomaCellLineNCIH2452_CNhs13064_tpm_rev Cl:NCI-H2452- mesothelioma cell line:NCI-H2452_CNhs13064_10848-111F2_reverse Regulation 
MesotheliomaCellLineNCIH2452_CNhs13064_tpm_fwd Cl:NCI-H2452+ mesothelioma cell line:NCI-H2452_CNhs13064_10848-111F2_forward Regulation 
MesotheliomaCellLineNCIH226_CNhs13062_tpm_rev Cl:NCI-H226- mesothelioma cell line:NCI-H226_CNhs13062_10846-111E9_reverse Regulation 
MesotheliomaCellLineNCIH226_CNhs13062_tpm_fwd Cl:NCI-H226+ mesothelioma cell line:NCI-H226_CNhs13062_10846-111E9_forward Regulation 
MesotheliomaCellLineNCIH2052_CNhs13063_tpm_rev Cl:NCI-H2052- mesothelioma cell line:NCI-H2052_CNhs13063_10847-111F1_reverse Regulation 
MesotheliomaCellLineNCIH2052_CNhs13063_tpm_fwd Cl:NCI-H2052+ mesothelioma cell line:NCI-H2052_CNhs13063_10847-111F1_forward Regulation 
CarcinoidCellLineNCIH1770_CNhs11834_tpm_rev Cl:NCI-H1770- carcinoid cell line:NCI-H1770_CNhs11834_10703-109H1_reverse Regulation 
CarcinoidCellLineNCIH1770_CNhs11834_tpm_fwd Cl:NCI-H1770+ carcinoid cell line:NCI-H1770_CNhs11834_10703-109H1_forward Regulation 
TeratocarcinomaCellLineNCCITA3_CNhs11878_tpm_rev Cl:NCC-IT-A3- teratocarcinoma cell line:NCC-IT-A3_CNhs11878_10790-110H7_reverse Regulation 
TeratocarcinomaCellLineNCCITA3_CNhs11878_tpm_fwd Cl:NCC-IT-A3+ teratocarcinoma cell line:NCC-IT-A3_CNhs11878_10790-110H7_forward Regulation 
NeuroblastomaCellLineNBsusSR_CNhs11818_tpm_rev Cl:NBsusSR- neuroblastoma cell line:NBsusSR_CNhs11818_10607-108F4_reverse Regulation 
NeuroblastomaCellLineNBsusSR_CNhs11818_tpm_fwd Cl:NBsusSR+ neuroblastoma cell line:NBsusSR_CNhs11818_10607-108F4_forward Regulation 
NeuroblastomaCellLineNB1_CNhs11284_tpm_rev Cl:NB-1- neuroblastoma cell line:NB-1_CNhs11284_10539-107G8_reverse Regulation 
NeuroblastomaCellLineNB1_CNhs11284_tpm_fwd Cl:NB-1+ neuroblastoma cell line:NB-1_CNhs11284_10539-107G8_forward Regulation 
AcuteLymphoblasticLeukemiaBALLCellLineNALM6_CNhs11282_tpm_rev Cl:NALM-6- acute lymphoblastic leukemia (B-ALL) cell line:NALM-6_CNhs11282_10534-107G3_reverse Regulation 
AcuteLymphoblasticLeukemiaBALLCellLineNALM6_CNhs11282_tpm_fwd Cl:NALM-6+ acute lymphoblastic leukemia (B-ALL) cell line:NALM-6_CNhs11282_10534-107G3_forward Regulation 
BiphenotypicBMyelomonocyticLeukemiaCellLineMV411_CNhs11845_tpm_rev Cl:MV-4-11- biphenotypic B myelomonocytic leukemia cell line:MV-4-11_CNhs11845_10718-109I7_reverse Regulation 
BiphenotypicBMyelomonocyticLeukemiaCellLineMV411_CNhs11845_tpm_fwd Cl:MV-4-11+ biphenotypic B myelomonocytic leukemia cell line:MV-4-11_CNhs11845_10718-109I7_forward Regulation 
MerkelCellCarcinomaCellLineMS1_CNhs12839_tpm_rev Cl:MS-1- merkel cell carcinoma cell line:MS-1_CNhs12839_10844-111E7_reverse Regulation 
MerkelCellCarcinomaCellLineMS1_CNhs12839_tpm_fwd Cl:MS-1+ merkel cell carcinoma cell line:MS-1_CNhs12839_10844-111E7_forward Regulation 
HairyCellLeukemiaCellLineMo_CNhs11843_tpm_rev Cl:Mo- hairy cell leukemia cell line:Mo_CNhs11843_10712-109I1_reverse Regulation 
HairyCellLeukemiaCellLineMo_CNhs11843_tpm_fwd Cl:Mo+ hairy cell leukemia cell line:Mo_CNhs11843_10712-109I1_forward Regulation 
LymphomaMalignantHairyBcellCellLineMLMA_CNhs11935_tpm_rev Cl:MLMA- lymphoma, malignant, hairy B-cell cell line:MLMA_CNhs11935_10775-110G1_reverse Regulation 
LymphomaMalignantHairyBcellCellLineMLMA_CNhs11935_tpm_fwd Cl:MLMA+ lymphoma, malignant, hairy B-cell cell line:MLMA_CNhs11935_10775-110G1_forward Regulation 
AcuteMyeloidLeukemiaFABM7CellLineMKPL1_CNhs11888_tpm_rev Cl:MKPL-1- acute myeloid leukemia (FAB M7) cell line:MKPL-1_CNhs11888_10802-111A1_reverse Regulation 
AcuteMyeloidLeukemiaFABM7CellLineMKPL1_CNhs11888_tpm_fwd Cl:MKPL-1+ acute myeloid leukemia (FAB M7) cell line:MKPL-1_CNhs11888_10802-111A1_forward Regulation 
GastricAdenocarcinomaCellLineMKN45_CNhs11819_tpm_rev Cl:MKN45- gastric adenocarcinoma cell line:MKN45_CNhs11819_10612-108F9_reverse Regulation 
GastricAdenocarcinomaCellLineMKN45_CNhs11819_tpm_fwd Cl:MKN45+ gastric adenocarcinoma cell line:MKN45_CNhs11819_10612-108F9_forward Regulation 
GastricAdenocarcinomaCellLineMKN1_CNhs11737_tpm_rev Cl:MKN1- gastric adenocarcinoma cell line:MKN1_CNhs11737_10614-108G2_reverse Regulation 
GastricAdenocarcinomaCellLineMKN1_CNhs11737_tpm_fwd Cl:MKN1+ gastric adenocarcinoma cell line:MKN1_CNhs11737_10614-108G2_forward Regulation 
MerkelCellCarcinomaCellLineMKL1_CNhs12838_tpm_rev Cl:MKL-1- merkel cell carcinoma cell line:MKL-1_CNhs12838_10843-111E6_reverse Regulation 
MerkelCellCarcinomaCellLineMKL1_CNhs12838_tpm_fwd Cl:MKL-1+ merkel cell carcinoma cell line:MKL-1_CNhs12838_10843-111E6_forward Regulation 
DuctalCellCarcinomaCellLineMIAPaca2_CNhs11259_tpm_rev Cl:MIAPaca2- ductal cell carcinoma cell line:MIA Paca2_CNhs11259_10488-107B2_reverse Regulation 
DuctalCellCarcinomaCellLineMIAPaca2_CNhs11259_tpm_fwd Cl:MIAPaca2+ ductal cell carcinoma cell line:MIA Paca2_CNhs11259_10488-107B2_forward Regulation 
MyxofibrosarcomaCellLineMFHino_CNhs11729_tpm_rev Cl:MFH-ino- myxofibrosarcoma cell line:MFH-ino_CNhs11729_10600-108E6_reverse Regulation 
MyxofibrosarcomaCellLineMFHino_CNhs11729_tpm_fwd Cl:MFH-ino+ myxofibrosarcoma cell line:MFH-ino_CNhs11729_10600-108E6_forward Regulation 
MesotheliomaCellLineMero95_CNhs13073_tpm_rev Cl:Mero-95- mesothelioma cell line:Mero-95_CNhs13073_10856-111G1_reverse Regulation 
MesotheliomaCellLineMero95_CNhs13073_tpm_fwd Cl:Mero-95+ mesothelioma cell line:Mero-95_CNhs13073_10856-111G1_forward Regulation 
MesotheliomaCellLineMero84_CNhs13072_tpm_rev Cl:Mero-84- mesothelioma cell line:Mero-84_CNhs13072_10855-111F9_reverse Regulation 
MesotheliomaCellLineMero84_CNhs13072_tpm_fwd Cl:Mero-84+ mesothelioma cell line:Mero-84_CNhs13072_10855-111F9_forward Regulation 
MesotheliomaCellLineMero83_CNhs13070_tpm_rev Cl:Mero-83- mesothelioma cell line:Mero-83_CNhs13070_10854-111F8_reverse Regulation 
MesotheliomaCellLineMero83_CNhs13070_tpm_fwd Cl:Mero-83+ mesothelioma cell line:Mero-83_CNhs13070_10854-111F8_forward Regulation 
MesotheliomaCellLineMero82_CNhs13069_tpm_rev Cl:Mero-82- mesothelioma cell line:Mero-82_CNhs13069_10853-111F7_reverse Regulation 
MesotheliomaCellLineMero82_CNhs13069_tpm_fwd Cl:Mero-82+ mesothelioma cell line:Mero-82_CNhs13069_10853-111F7_forward Regulation 
MesotheliomaCellLineMero48a_CNhs13068_tpm_rev Cl:Mero-48a- mesothelioma cell line:Mero-48a_CNhs13068_10852-111F6_reverse Regulation 
MesotheliomaCellLineMero48a_CNhs13068_tpm_fwd Cl:Mero-48a+ mesothelioma cell line:Mero-48a_CNhs13068_10852-111F6_forward Regulation 
MesotheliomaCellLineMero41_CNhs13067_tpm_rev Cl:Mero-41- mesothelioma cell line:Mero-41_CNhs13067_10851-111F5_reverse Regulation 
MesotheliomaCellLineMero41_CNhs13067_tpm_fwd Cl:Mero-41+ mesothelioma cell line:Mero-41_CNhs13067_10851-111F5_forward Regulation 
MesotheliomaCellLineMero25_CNhs13066_tpm_rev Cl:Mero-25- mesothelioma cell line:Mero-25_CNhs13066_10850-111F4_reverse Regulation 
MesotheliomaCellLineMero25_CNhs13066_tpm_fwd Cl:Mero-25+ mesothelioma cell line:Mero-25_CNhs13066_10850-111F4_forward Regulation 
MesotheliomaCellLineMero14TechRep1_CNhs13065_tpm_rev Cl:Mero-14Tr1- mesothelioma cell line:Mero-14, tech_rep1_CNhs13065_10849-111F3_reverse Regulation 
MesotheliomaCellLineMero14TechRep1_CNhs13065_tpm_fwd Cl:Mero-14Tr1+ mesothelioma cell line:Mero-14, tech_rep1_CNhs13065_10849-111F3_forward Regulation 
ChronicMyelogenousLeukemiaCMLCellLineMEGA2_CNhs11865_tpm_rev Cl:MEG-A2- chronic myelogenous leukemia (CML) cell line:MEG-A2_CNhs11865_10766-110F1_reverse Regulation 
ChronicMyelogenousLeukemiaCMLCellLineMEGA2_CNhs11865_tpm_fwd Cl:MEG-A2+ chronic myelogenous leukemia (CML) cell line:MEG-A2_CNhs11865_10766-110F1_forward Regulation 
LeukemiaChronicMegakaryoblasticCellLineMEG01_CNhs11859_tpm_rev Cl:MEG-01- leukemia, chronic megakaryoblastic cell line:MEG-01_CNhs11859_10752-110D5_reverse Regulation 
LeukemiaChronicMegakaryoblasticCellLineMEG01_CNhs11859_tpm_fwd Cl:MEG-01+ leukemia, chronic megakaryoblastic cell line:MEG-01_CNhs11859_10752-110D5_forward Regulation 
CervicalCancerCellLineME180_CNhs11289_tpm_rev Cl:ME-180- cervical cancer cell line:ME-180_CNhs11289_10553-107I4_reverse Regulation 
CervicalCancerCellLineME180_CNhs11289_tpm_fwd Cl:ME-180+ cervical cancer cell line:ME-180_CNhs11289_10553-107I4_forward Regulation 
BreastCarcinomaCellLineMDAMB453_CNhs10736_tpm_rev Cl:MDA-MB-453- breast carcinoma cell line:MDA-MB-453_CNhs10736_10419-106C5_reverse Regulation 
BreastCarcinomaCellLineMDAMB453_CNhs10736_tpm_fwd Cl:MDA-MB-453+ breast carcinoma cell line:MDA-MB-453_CNhs10736_10419-106C5_forward Regulation 
BreastCarcinomaCellLineMCF7_CNhs11943_tpm_rev Cl:MCF7- breast carcinoma cell line:MCF7_CNhs11943_10482-107A5_reverse Regulation 
BreastCarcinomaCellLineMCF7_CNhs11943_tpm_fwd Cl:MCF7+ breast carcinoma cell line:MCF7_CNhs11943_10482-107A5_forward Regulation 
MucinousCystadenocarcinomaCellLineMCAS_CNhs11873_tpm_rev Cl:MCAS- mucinous cystadenocarcinoma cell line:MCAS_CNhs11873_10784-110H1_reverse Regulation 
MucinousCystadenocarcinomaCellLineMCAS_CNhs11873_tpm_fwd Cl:MCAS+ mucinous cystadenocarcinoma cell line:MCAS_CNhs11873_10784-110H1_forward Regulation 
AcuteMyeloidLeukemiaFABM7CellLineMMOK_CNhs13049_tpm_rev Cl:M-MOK- acute myeloid leukemia (FAB M7) cell line:M-MOK_CNhs13049_10699-109G6_reverse Regulation 
AcuteMyeloidLeukemiaFABM7CellLineMMOK_CNhs13049_tpm_fwd Cl:M-MOK+ acute myeloid leukemia (FAB M7) cell line:M-MOK_CNhs13049_10699-109G6_forward Regulation 
GiantCellCarcinomaCellLineLu99B_CNhs10751_tpm_rev Cl:Lu99B- giant cell carcinoma cell line:Lu99B_CNhs10751_10433-106E1_reverse Regulation 
GiantCellCarcinomaCellLineLu99B_CNhs10751_tpm_fwd Cl:Lu99B+ giant cell carcinoma cell line:Lu99B_CNhs10751_10433-106E1_forward Regulation 
GiantCellCarcinomaCellLineLU65_CNhs11274_tpm_rev Cl:LU65- giant cell carcinoma cell line:LU65_CNhs11274_10487-107B1_reverse Regulation 
GiantCellCarcinomaCellLineLU65_CNhs11274_tpm_fwd Cl:LU65+ giant cell carcinoma cell line:LU65_CNhs11274_10487-107B1_forward Regulation 
SmallCellLungCarcinomaCellLineLK2_CNhs11285_tpm_rev Cl:LK-2- small cell lung carcinoma cell line:LK-2_CNhs11285_10541-107H1_reverse Regulation 
SmallCellLungCarcinomaCellLineLK2_CNhs11285_tpm_fwd Cl:LK-2+ small cell lung carcinoma cell line:LK-2_CNhs11285_10541-107H1_forward Regulation 
HepaticMesenchymalTumorCellLineLI90_CNhs11868_tpm_rev Cl:LI90- hepatic mesenchymal tumor cell line:LI90_CNhs11868_10778-110G4_reverse Regulation 
HepaticMesenchymalTumorCellLineLI90_CNhs11868_tpm_fwd Cl:LI90+ hepatic mesenchymal tumor cell line:LI90_CNhs11868_10778-110G4_forward Regulation 
HepatomaCellLineLi7_CNhs11271_tpm_rev Cl:Li-7- hepatoma cell line:Li-7_CNhs11271_10484-107A7_reverse Regulation 
HepatomaCellLineLi7_CNhs11271_tpm_fwd Cl:Li-7+ hepatoma cell line:Li-7_CNhs11271_10484-107A7_forward Regulation 
SquamousCellLungCarcinomaCellLineLC1F_CNhs14238_tpm_rev Cl:LC-1F- squamous cell lung carcinoma cell line:LC-1F_CNhs14238_10457-106G7_reverse Regulation 
SquamousCellLungCarcinomaCellLineLC1F_CNhs14238_tpm_fwd Cl:LC-1F+ squamous cell lung carcinoma cell line:LC-1F_CNhs14238_10457-106G7_forward Regulation 
RhabdomyosarcomaCellLineKYM1_CNhs11877_tpm_rev Cl:KYM-1- rhabdomyosarcoma cell line:KYM-1_CNhs11877_10787-110H4_reverse Regulation 
RhabdomyosarcomaCellLineKYM1_CNhs11877_tpm_fwd Cl:KYM-1+ rhabdomyosarcoma cell line:KYM-1_CNhs11877_10787-110H4_forward Regulation 
ChronicMyelogenousLeukemiaCellLineKU812_CNhs10727_tpm_rev Cl:KU812- chronic myelogenous leukemia cell line:KU812_CNhs10727_10409-106B4_reverse Regulation 
ChronicMyelogenousLeukemiaCellLineKU812_CNhs10727_tpm_fwd Cl:KU812+ chronic myelogenous leukemia cell line:KU812_CNhs10727_10409-106B4_forward Regulation 
PeripheralNeuroectodermalTumorCellLineKUSN_CNhs11830_tpm_rev Cl:KU-SN- peripheral neuroectodermal tumor cell line:KU-SN_CNhs11830_10697-109G4_reverse Regulation 
PeripheralNeuroectodermalTumorCellLineKUSN_CNhs11830_tpm_fwd Cl:KU-SN+ peripheral neuroectodermal tumor cell line:KU-SN_CNhs11830_10697-109G4_forward Regulation 
BronchialSquamousCellCarcinomaCellLineKNS62_CNhs11862_tpm_rev Cl:KNS-62- bronchial squamous cell carcinoma cell line:KNS-62_CNhs11862_10760-110E4_reverse Regulation 
BronchialSquamousCellCarcinomaCellLineKNS62_CNhs11862_tpm_fwd Cl:KNS-62+ bronchial squamous cell carcinoma cell line:KNS-62_CNhs11862_10760-110E4_forward Regulation 
LiposarcomaCellLineKMLS1_CNhs11870_tpm_rev Cl:KMLS-1- liposarcoma cell line:KMLS-1_CNhs11870_10782-110G8_reverse Regulation 
LiposarcomaCellLineKMLS1_CNhs11870_tpm_fwd Cl:KMLS-1+ liposarcoma cell line:KMLS-1_CNhs11870_10782-110G8_forward Regulation 
DuctalCellCarcinomaCellLineKLM1_CNhs11100_tpm_rev Cl:KLM-1- ductal cell carcinoma cell line:KLM-1_CNhs11100_10438-106E6_reverse Regulation 
DuctalCellCarcinomaCellLineKLM1_CNhs11100_tpm_fwd Cl:KLM-1+ ductal cell carcinoma cell line:KLM-1_CNhs11100_10438-106E6_forward Regulation 
AnaplasticLargeCellLymphomaCellLineKiJK_CNhs11881_tpm_rev Cl:Ki-JK- anaplastic large cell lymphoma cell line:Ki-JK_CNhs11881_10795-110I3_reverse Regulation 
AnaplasticLargeCellLymphomaCellLineKiJK_CNhs11881_tpm_fwd Cl:Ki-JK+ anaplastic large cell lymphoma cell line:Ki-JK_CNhs11881_10795-110I3_forward Regulation 
NKTCellLeukemiaCellLineKHYG1_CNhs11867_tpm_rev Cl:KHYG-1- NK T cell leukemia cell line:KHYG-1_CNhs11867_10777-110G3_reverse Regulation 
NKTCellLeukemiaCellLineKHYG1_CNhs11867_tpm_fwd Cl:KHYG-1+ NK T cell leukemia cell line:KHYG-1_CNhs11867_10777-110G3_forward Regulation 
ThyroidCarcinomaCellLineKHM5M_CNhs14140_tpm_rev Cl:KHM-5M- thyroid carcinoma cell line:KHM-5M_CNhs14140_10776-110G2_reverse Regulation 
ThyroidCarcinomaCellLineKHM5M_CNhs14140_tpm_fwd Cl:KHM-5M+ thyroid carcinoma cell line:KHM-5M_CNhs14140_10776-110G2_forward Regulation 
GranulosaCellTumorCellLineKGN_CNhs11740_tpm_rev Cl:KGN- granulosa cell tumor cell line:KGN_CNhs11740_10624-108H3_reverse Regulation 
GranulosaCellTumorCellLineKGN_CNhs11740_tpm_fwd Cl:KGN+ granulosa cell tumor cell line:KGN_CNhs11740_10624-108H3_forward Regulation 
AcuteMyeloidLeukemiaFABM0CellLineKG1_CNhs13053_tpm_rev Cl:KG-1- acute myeloid leukemia (FAB M0) cell line:KG-1_CNhs13053_10827-111C8_reverse Regulation 
AcuteMyeloidLeukemiaFABM0CellLineKG1_CNhs13053_tpm_fwd Cl:KG-1+ acute myeloid leukemia (FAB M0) cell line:KG-1_CNhs13053_10827-111C8_forward Regulation 
ChronicMyeloblasticLeukemiaCMLCellLineKCL22_CNhs11886_tpm_rev Cl:KCL-22- chronic myeloblastic leukemia (CML) cell line:KCL-22_CNhs11886_10801-110I9_reverse Regulation 
ChronicMyeloblasticLeukemiaCMLCellLineKCL22_CNhs11886_tpm_fwd Cl:KCL-22+ chronic myeloblastic leukemia (CML) cell line:KCL-22_CNhs11886_10801-110I9_forward Regulation 
SignetRingCarcinomaCellLineKatoIII_CNhs10753_tpm_rev Cl:KatoIII- signet ring carcinoma cell line:Kato III_CNhs10753_10436-106E4_reverse Regulation 
SignetRingCarcinomaCellLineKatoIII_CNhs10753_tpm_fwd Cl:KatoIII+ signet ring carcinoma cell line:Kato III_CNhs10753_10436-106E4_forward Regulation 
AcuteMyeloidLeukemiaFABM2CellLineKasumi6_CNhs13052_tpm_rev Cl:Kasumi-6- acute myeloid leukemia (FAB M2) cell line:Kasumi-6_CNhs13052_10792-110H9_reverse Regulation 
AcuteMyeloidLeukemiaFABM2CellLineKasumi6_CNhs13052_tpm_fwd Cl:Kasumi-6+ acute myeloid leukemia (FAB M2) cell line:Kasumi-6_CNhs13052_10792-110H9_forward Regulation 
AcuteMyeloidLeukemiaFABM2CellLineKasumi1_CNhs13502_tpm_rev Cl:Kasumi-1- acute myeloid leukemia (FAB M2) cell line:Kasumi-1_CNhs13502_10788-110H5_reverse Regulation 
AcuteMyeloidLeukemiaFABM2CellLineKasumi1_CNhs13502_tpm_fwd Cl:Kasumi-1+ acute myeloid leukemia (FAB M2) cell line:Kasumi-1_CNhs13502_10788-110H5_forward Regulation 
ChronicMyelogenousLeukemiaCellLineK562ENCODEBiolRep3_CNhs12336_tpm_rev Cl:K562Br3- chronic myelogenous leukemia cell line:K562 ENCODE, biol_rep3_CNhs12336_10826-111C7_reverse Regulation 
ChronicMyelogenousLeukemiaCellLineK562ENCODEBiolRep3_CNhs12336_tpm_fwd Cl:K562Br3+ chronic myelogenous leukemia cell line:K562 ENCODE, biol_rep3_CNhs12336_10826-111C7_forward Regulation 
ChronicMyelogenousLeukemiaCellLineK562ENCODEBiolRep2_CNhs12335_tpm_rev Cl:K562Br2- chronic myelogenous leukemia cell line:K562 ENCODE, biol_rep2_CNhs12335_10825-111C6_reverse Regulation 
ChronicMyelogenousLeukemiaCellLineK562ENCODEBiolRep2_CNhs12335_tpm_fwd Cl:K562Br2+ chronic myelogenous leukemia cell line:K562 ENCODE, biol_rep2_CNhs12335_10825-111C6_forward Regulation 
ChronicMyelogenousLeukemiaCellLineK562ENCODEBiolRep1_CNhs12334_tpm_rev Cl:K562Br1- chronic myelogenous leukemia cell line:K562 ENCODE, biol_rep1_CNhs12334_10824-111C5_reverse Regulation 
ChronicMyelogenousLeukemiaCellLineK562ENCODEBiolRep1_CNhs12334_tpm_fwd Cl:K562Br1+ chronic myelogenous leukemia cell line:K562 ENCODE, biol_rep1_CNhs12334_10824-111C5_forward Regulation 
ChronicMyelogenousLeukemiaCellLineK562_CNhs11250_tpm_rev Cl:K562- chronic myelogenous leukemia cell line:K562_CNhs11250_10454-106G4_reverse Regulation 
ChronicMyelogenousLeukemiaCellLineK562_CNhs11250_tpm_fwd Cl:K562+ chronic myelogenous leukemia cell line:K562_CNhs11250_10454-106G4_forward Regulation 
AcuteLymphoblasticLeukemiaTALLCellLineJurkat_CNhs11253_tpm_rev Cl:Jurkat- acute lymphoblastic leukemia (T-ALL) cell line:Jurkat_CNhs11253_10464-106H5_reverse Regulation 
AcuteLymphoblasticLeukemiaTALLCellLineJurkat_CNhs11253_tpm_fwd Cl:Jurkat+ acute lymphoblastic leukemia (T-ALL) cell line:Jurkat_CNhs11253_10464-106H5_forward Regulation 
TransitionalcellCarcinomaCellLineJMSU1_CNhs11261_tpm_rev Cl:JMSU1- transitional-cell carcinoma cell line:JMSU1_CNhs11261_10492-107B6_reverse Regulation 
TransitionalcellCarcinomaCellLineJMSU1_CNhs11261_tpm_fwd Cl:JMSU1+ transitional-cell carcinoma cell line:JMSU1_CNhs11261_10492-107B6_forward Regulation 
SquamousCellCarcinomaCellLineJHUSnk1_CNhs11749_tpm_rev Cl:JHUS-nk1- squamous cell carcinoma cell line:JHUS-nk1_CNhs11749_10646-109A7_reverse Regulation 
SquamousCellCarcinomaCellLineJHUSnk1_CNhs11749_tpm_fwd Cl:JHUS-nk1+ squamous cell carcinoma cell line:JHUS-nk1_CNhs11749_10646-109A7_forward Regulation 
EndometrioidAdenocarcinomaCellLineJHUEM1_CNhs11748_tpm_rev Cl:JHUEM-1- endometrioid adenocarcinoma cell line:JHUEM-1_CNhs11748_10643-109A4_reverse Regulation 
EndometrioidAdenocarcinomaCellLineJHUEM1_CNhs11748_tpm_fwd Cl:JHUEM-1+ endometrioid adenocarcinoma cell line:JHUEM-1_CNhs11748_10643-109A4_forward Regulation 
CarcinosarcomaCellLineJHUCS1_CNhs11747_tpm_rev Cl:JHUCS-1- carcinosarcoma cell line:JHUCS-1_CNhs11747_10642-109A3_reverse Regulation 
CarcinosarcomaCellLineJHUCS1_CNhs11747_tpm_fwd Cl:JHUCS-1+ carcinosarcoma cell line:JHUCS-1_CNhs11747_10642-109A3_forward Regulation 
SerousAdenocarcinomaCellLineJHOS2_CNhs11746_tpm_rev Cl:JHOS-2- serous adenocarcinoma cell line:JHOS-2_CNhs11746_10639-108I9_reverse Regulation 
SerousAdenocarcinomaCellLineJHOS2_CNhs11746_tpm_fwd Cl:JHOS-2+ serous adenocarcinoma cell line:JHOS-2_CNhs11746_10639-108I9_forward Regulation 
MucinousAdenocarcinomaCellLineJHOM1_CNhs11752_tpm_rev Cl:JHOM-1- mucinous adenocarcinoma cell line:JHOM-1_CNhs11752_10648-109A9_reverse Regulation 
MucinousAdenocarcinomaCellLineJHOM1_CNhs11752_tpm_fwd Cl:JHOM-1+ mucinous adenocarcinoma cell line:JHOM-1_CNhs11752_10648-109A9_forward Regulation 
ClearCellCarcinomaCellLineJHOC5_CNhs11745_tpm_rev Cl:JHOC-5- clear cell carcinoma cell line:JHOC-5_CNhs11745_10638-108I8_reverse Regulation 
ClearCellCarcinomaCellLineJHOC5_CNhs11745_tpm_fwd Cl:JHOC-5+ clear cell carcinoma cell line:JHOC-5_CNhs11745_10638-108I8_forward Regulation 
TesticularGermCellEmbryonalCarcinomaCellLineITOII_CNhs11876_tpm_rev Cl:ITO-II- testicular germ cell embryonal carcinoma cell line:ITO-II_CNhs11876_10786-110H3_reverse Regulation 
TesticularGermCellEmbryonalCarcinomaCellLineITOII_CNhs11876_tpm_fwd Cl:ITO-II+ testicular germ cell embryonal carcinoma cell line:ITO-II_CNhs11876_10786-110H3_forward Regulation 
AdenocarcinomaCellLineIM95m_CNhs11882_tpm_rev Cl:IM95m- adenocarcinoma cell line:IM95m_CNhs11882_10796-110I4_reverse Regulation 
AdenocarcinomaCellLineIM95m_CNhs11882_tpm_fwd Cl:IM95m+ adenocarcinoma cell line:IM95m_CNhs11882_10796-110I4_forward Regulation 
LargeCellLungCarcinomaCellLineIALM_CNhs11277_tpm_rev Cl:IA-LM- large cell lung carcinoma cell line:IA-LM_CNhs11277_10509-107D5_reverse Regulation 
LargeCellLungCarcinomaCellLineIALM_CNhs11277_tpm_fwd Cl:IA-LM+ large cell lung carcinoma cell line:IA-LM_CNhs11277_10509-107D5_forward Regulation 
AcuteMyeloidLeukemiaFABM1CellLineHYT1_CNhs13054_tpm_rev Cl:HYT-1- acute myeloid leukemia (FAB M1) cell line:HYT-1_CNhs13054_10828-111C9_reverse Regulation 
AcuteMyeloidLeukemiaFABM1CellLineHYT1_CNhs13054_tpm_fwd Cl:HYT-1+ acute myeloid leukemia (FAB M1) cell line:HYT-1_CNhs13054_10828-111C9_forward Regulation 
MycosisFungoidesTCellLymphomaCellLineHuT102TIB162_CNhs11858_tpm_rev Cl:HuT102TIB-162- mycosis fungoides, T cell lymphoma cell line:HuT 102 TIB-162_CNhs11858_10744-110C6_reverse Regulation 
MycosisFungoidesTCellLymphomaCellLineHuT102TIB162_CNhs11858_tpm_fwd Cl:HuT102TIB-162+ mycosis fungoides, T cell lymphoma cell line:HuT 102 TIB-162_CNhs11858_10744-110C6_forward Regulation 
HepatoblastomaCellLineHuH6_CNhs11742_tpm_rev Cl:HuH-6- hepatoblastoma cell line:HuH-6_CNhs11742_10633-108I3_reverse Regulation 
HepatoblastomaCellLineHuH6_CNhs11742_tpm_fwd Cl:HuH-6+ hepatoblastoma cell line:HuH-6_CNhs11742_10633-108I3_forward Regulation 
CholangiocellularCarcinomaCellLineHuH28_CNhs11283_tpm_rev Cl:HuH-28- cholangiocellular carcinoma cell line:HuH-28_CNhs11283_10536-107G5_reverse Regulation 
CholangiocellularCarcinomaCellLineHuH28_CNhs11283_tpm_fwd Cl:HuH-28+ cholangiocellular carcinoma cell line:HuH-28_CNhs11283_10536-107G5_forward Regulation 
BileDuctCarcinomaCellLineHuCCT1_CNhs10750_tpm_rev Cl:HuCCT1- bile duct carcinoma cell line:HuCCT1_CNhs10750_10432-106D9_reverse Regulation 
BileDuctCarcinomaCellLineHuCCT1_CNhs10750_tpm_fwd Cl:HuCCT1+ bile duct carcinoma cell line:HuCCT1_CNhs10750_10432-106D9_forward Regulation 
MesenchymalStemCellLineHu5E18_CNhs11718_tpm_rev Cl:Hu5/E18- mesenchymal stem cell line:Hu5/E18_CNhs11718_10568-108B1_reverse Regulation 
MesenchymalStemCellLineHu5E18_CNhs11718_tpm_fwd Cl:Hu5/E18+ mesenchymal stem cell line:Hu5/E18_CNhs11718_10568-108B1_forward Regulation 
SacrococcigealTeratomaCellLineHTST_CNhs11829_tpm_rev Cl:HTST- sacrococcigeal teratoma cell line:HTST_CNhs11829_10695-109G2_reverse Regulation 
SacrococcigealTeratomaCellLineHTST_CNhs11829_tpm_fwd Cl:HTST+ sacrococcigeal teratoma cell line:HTST_CNhs11829_10695-109G2_forward Regulation 
SerousCystadenocarcinomaCellLineHTOA_CNhs11827_tpm_rev Cl:HTOA- serous cystadenocarcinoma cell line:HTOA_CNhs11827_10693-109F9_reverse Regulation 
SerousCystadenocarcinomaCellLineHTOA_CNhs11827_tpm_fwd Cl:HTOA+ serous cystadenocarcinoma cell line:HTOA_CNhs11827_10693-109F9_forward Regulation 
MixedMullerianTumorCellLineHTMMT_CNhs11944_tpm_rev Cl:HTMMT- mixed mullerian tumor cell line:HTMMT_CNhs11944_10689-109F5_reverse Regulation 
MixedMullerianTumorCellLineHTMMT_CNhs11944_tpm_fwd Cl:HTMMT+ mixed mullerian tumor cell line:HTMMT_CNhs11944_10689-109F5_forward Regulation 
FibrosarcomaCellLineHT1080_CNhs11860_tpm_rev Cl:HT-1080- fibrosarcoma cell line:HT-1080_CNhs11860_10758-110E2_reverse Regulation 
FibrosarcomaCellLineHT1080_CNhs11860_tpm_fwd Cl:HT-1080+ fibrosarcoma cell line:HT-1080_CNhs11860_10758-110E2_forward Regulation 
MaxillarySinusTumorCellLineHSQ89_CNhs10732_tpm_rev Cl:HSQ-89- maxillary sinus tumor cell line:HSQ-89_CNhs10732_10414-106B9_reverse Regulation 
MaxillarySinusTumorCellLineHSQ89_CNhs10732_tpm_fwd Cl:HSQ-89+ maxillary sinus tumor cell line:HSQ-89_CNhs10732_10414-106B9_forward Regulation 
KrukenbergTumorCellLineHSKTC_CNhs11822_tpm_rev Cl:HSKTC- Krukenberg tumor cell line:HSKTC_CNhs11822_10687-109F3_reverse Regulation 
KrukenbergTumorCellLineHSKTC_CNhs11822_tpm_fwd Cl:HSKTC+ Krukenberg tumor cell line:HSKTC_CNhs11822_10687-109F3_forward Regulation 
OralSquamousCellCarcinomaCellLineHSC3_CNhs11717_tpm_rev Cl:HSC-3- oral squamous cell carcinoma cell line:HSC-3_CNhs11717_10545-107H5_reverse Regulation 
OralSquamousCellCarcinomaCellLineHSC3_CNhs11717_tpm_fwd Cl:HSC-3+ oral squamous cell carcinoma cell line:HSC-3_CNhs11717_10545-107H5_forward Regulation 
PagetoidSarcomaCellLineHs925_T_CNhs11856_tpm_rev Cl:Hs925_T- pagetoid sarcoma cell line:Hs 925_T_CNhs11856_10732-110B3_reverse Regulation 
PagetoidSarcomaCellLineHs925_T_CNhs11856_tpm_fwd Cl:Hs925_T+ pagetoid sarcoma cell line:Hs 925_T_CNhs11856_10732-110B3_forward Regulation 
EwingsSarcomaCellLineHs863_T_CNhs11836_tpm_rev Cl:Hs863_T- Ewing's sarcoma cell line:Hs 863_T_CNhs11836_10705-109H3_reverse Regulation 
EwingsSarcomaCellLineHs863_T_CNhs11836_tpm_fwd Cl:Hs863_T+ Ewing's sarcoma cell line:Hs 863_T_CNhs11836_10705-109H3_forward Regulation 
TransitionalCellCarcinomaCellLineHs769_T_CNhs11837_tpm_rev Cl:Hs769_T- transitional cell carcinoma cell line:Hs 769_T_CNhs11837_10707-109H5_reverse Regulation 
TransitionalCellCarcinomaCellLineHs769_T_CNhs11837_tpm_fwd Cl:Hs769_T+ transitional cell carcinoma cell line:Hs 769_T_CNhs11837_10707-109H5_forward Regulation 
OsteoclastomaCellLineHs706_T_CNhs11835_tpm_rev Cl:Hs706_T- osteoclastoma cell line:Hs 706_T_CNhs11835_10704-109H2_reverse Regulation 
OsteoclastomaCellLineHs706_T_CNhs11835_tpm_fwd Cl:Hs706_T+ osteoclastoma cell line:Hs 706_T_CNhs11835_10704-109H2_forward Regulation 
NeurofibromaCellLineHs53_T_CNhs11854_tpm_rev Cl:Hs53_T- neurofibroma cell line:Hs 53_T_CNhs11854_10729-110A9_reverse Regulation 
NeurofibromaCellLineHs53_T_CNhs11854_tpm_fwd Cl:Hs53_T+ neurofibroma cell line:Hs 53_T_CNhs11854_10729-110A9_forward Regulation 
SpindleCellSarcomaCellLineHs132_T_CNhs11857_tpm_rev Cl:Hs132_T- spindle cell sarcoma cell line:Hs 132_T_CNhs11857_10737-110B8_reverse Regulation 
SpindleCellSarcomaCellLineHs132_T_CNhs11857_tpm_fwd Cl:Hs132_T+ spindle cell sarcoma cell line:Hs 132_T_CNhs11857_10737-110B8_forward Regulation 
SynovialSarcomaCellLineHSSYII_CNhs11244_tpm_rev Cl:HS-SY-II- synovial sarcoma cell line:HS-SY-II_CNhs11244_10441-106E9_reverse Regulation 
SynovialSarcomaCellLineHSSYII_CNhs11244_tpm_fwd Cl:HS-SY-II+ synovial sarcoma cell line:HS-SY-II_CNhs11244_10441-106E9_forward Regulation 
SchwannomaCellLineHSPSSTechRep2_CNhs11245_tpm_rev Cl:HS-PSSTr2- schwannoma cell line:HS-PSS, tech_rep2_CNhs11245_10442-106F1_reverse Regulation 
SchwannomaCellLineHSPSSTechRep2_CNhs11245_tpm_fwd Cl:HS-PSSTr2+ schwannoma cell line:HS-PSS, tech_rep2_CNhs11245_10442-106F1_forward Regulation 
OsteosarcomaCellLineHSOs1_CNhs11290_tpm_rev Cl:HS-Os-1- osteosarcoma cell line:HS-Os-1_CNhs11290_10558-107I9_reverse Regulation 
OsteosarcomaCellLineHSOs1_CNhs11290_tpm_fwd Cl:HS-Os-1+ osteosarcoma cell line:HS-Os-1_CNhs11290_10558-107I9_forward Regulation 
EpithelioidSarcomaCellLineHSES2R_CNhs14239_tpm_rev Cl:HS-ES-2R- epithelioid sarcoma cell line:HS-ES-2R_CNhs14239_10495-107B9_reverse Regulation 
EpithelioidSarcomaCellLineHSES2R_CNhs14239_tpm_fwd Cl:HS-ES-2R+ epithelioid sarcoma cell line:HS-ES-2R_CNhs14239_10495-107B9_forward Regulation 
EpithelioidSarcomaCellLineHSES1_CNhs11247_tpm_rev Cl:HS-ES-1- epithelioid sarcoma cell line:HS-ES-1_CNhs11247_10443-106F2_reverse Regulation 
EpithelioidSarcomaCellLineHSES1_CNhs11247_tpm_fwd Cl:HS-ES-1+ epithelioid sarcoma cell line:HS-ES-1_CNhs11247_10443-106F2_forward Regulation 
AcuteLymphoblasticLeukemiaTALLCellLineHPBALL_CNhs10746_tpm_rev Cl:HPB-ALL- acute lymphoblastic leukemia (T-ALL) cell line:HPB-ALL_CNhs10746_10429-106D6_reverse Regulation 
AcuteLymphoblasticLeukemiaTALLCellLineHPBALL_CNhs10746_tpm_fwd Cl:HPB-ALL+ acute lymphoblastic leukemia (T-ALL) cell line:HPB-ALL_CNhs10746_10429-106D6_forward Regulation 
GlassyCellCarcinomaCellLineHOKUG_CNhs11824_tpm_rev Cl:HOKUG- glassy cell carcinoma cell line:HOKUG_CNhs11824_10688-109F4_reverse Regulation 
GlassyCellCarcinomaCellLineHOKUG_CNhs11824_tpm_fwd Cl:HOKUG+ glassy cell carcinoma cell line:HOKUG_CNhs11824_10688-109F4_forward Regulation 
OralSquamousCellCarcinomaCellLineHO1u1_CNhs11287_tpm_rev Cl:HO-1-u-1- oral squamous cell carcinoma cell line:HO-1-u-1_CNhs11287_10550-107I1_reverse Regulation 
OralSquamousCellCarcinomaCellLineHO1u1_CNhs11287_tpm_fwd Cl:HO-1-u-1+ oral squamous cell carcinoma cell line:HO-1-u-1_CNhs11287_10550-107I1_forward Regulation 
AcuteMyeloidLeukemiaFABM4CellLineHNT34_CNhs13504_tpm_rev Cl:HNT-34- acute myeloid leukemia (FAB M4) cell line:HNT-34_CNhs13504_10831-111D3_reverse Regulation 
AcuteMyeloidLeukemiaFABM4CellLineHNT34_CNhs13504_tpm_fwd Cl:HNT-34+ acute myeloid leukemia (FAB M4) cell line:HNT-34_CNhs13504_10831-111D3_forward Regulation 
AcuteMyeloidLeukemiaFABM3CellLineHL60_CNhs13055_tpm_rev Cl:HL60- acute myeloid leukemia (FAB M3) cell line:HL60_CNhs13055_10829-111D1_reverse Regulation 
AcuteMyeloidLeukemiaFABM3CellLineHL60_CNhs13055_tpm_fwd Cl:HL60+ acute myeloid leukemia (FAB M3) cell line:HL60_CNhs13055_10829-111D1_forward Regulation 
MeningiomaCellLineHKBMM_CNhs11945_tpm_rev Cl:HKBMM- meningioma cell line:HKBMM_CNhs11945_10691-109F7_reverse Regulation 
MeningiomaCellLineHKBMM_CNhs11945_tpm_fwd Cl:HKBMM+ meningioma cell line:HKBMM_CNhs11945_10691-109F7_forward Regulation 
KeratoacanthomaCellLineHKA1_CNhs11880_tpm_rev Cl:HKA-1- keratoacanthoma cell line:HKA-1_CNhs11880_10791-110H8_reverse Regulation 
KeratoacanthomaCellLineHKA1_CNhs11880_tpm_fwd Cl:HKA-1+ keratoacanthoma cell line:HKA-1_CNhs11880_10791-110H8_forward Regulation 
TridermalTeratomaCellLineHGRT_CNhs11828_tpm_rev Cl:HGRT- tridermal teratoma cell line:HGRT_CNhs11828_10694-109G1_reverse Regulation 
TridermalTeratomaCellLineHGRT_CNhs11828_tpm_fwd Cl:HGRT+ tridermal teratoma cell line:HGRT_CNhs11828_10694-109G1_forward Regulation 
WilmsTumorCellLineHFWT_CNhs11728_tpm_rev Cl:HFWT- Wilms' tumor cell line:HFWT_CNhs11728_10597-108E3_reverse Regulation 
WilmsTumorCellLineHFWT_CNhs11728_tpm_fwd Cl:HFWT+ Wilms' tumor cell line:HFWT_CNhs11728_10597-108E3_forward Regulation 
NormalEmbryonicPalatalMesenchymalCellLineHEPM_CNhs11894_tpm_rev Cl:HEPM- normal embryonic palatal mesenchymal cell line:HEPM_CNhs11894_10813-111B3_reverse Regulation 
NormalEmbryonicPalatalMesenchymalCellLineHEPM_CNhs11894_tpm_fwd Cl:HEPM+ normal embryonic palatal mesenchymal cell line:HEPM_CNhs11894_10813-111B3_forward Regulation 
HepatocellularCarcinomaCellLineHepG2ENCODEBiolRep3_CNhs12330_tpm_rev Cl:HepG2Br3- hepatocellular carcinoma cell line: HepG2 ENCODE, biol_rep3_CNhs12330_10820-111C1_reverse Regulation 
HepatocellularCarcinomaCellLineHepG2ENCODEBiolRep3_CNhs12330_tpm_fwd Cl:HepG2Br3+ hepatocellular carcinoma cell line: HepG2 ENCODE, biol_rep3_CNhs12330_10820-111C1_forward Regulation 
HepatocellularCarcinomaCellLineHepG2ENCODEBiolRep2_CNhs12329_tpm_rev Cl:HepG2Br2- hepatocellular carcinoma cell line: HepG2 ENCODE, biol_rep2_CNhs12329_10819-111B9_reverse Regulation 
HepatocellularCarcinomaCellLineHepG2ENCODEBiolRep2_CNhs12329_tpm_fwd Cl:HepG2Br2+ hepatocellular carcinoma cell line: HepG2 ENCODE, biol_rep2_CNhs12329_10819-111B9_forward Regulation 
HepatocellularCarcinomaCellLineHepG2ENCODEBiolRep1_CNhs12328_tpm_rev Cl:HepG2Br1- hepatocellular carcinoma cell line: HepG2 ENCODE, biol_rep1_CNhs12328_10818-111B8_reverse Regulation 
HepatocellularCarcinomaCellLineHepG2ENCODEBiolRep1_CNhs12328_tpm_fwd Cl:HepG2Br1+ hepatocellular carcinoma cell line: HepG2 ENCODE, biol_rep1_CNhs12328_10818-111B8_forward Regulation 
EpitheloidCarcinomaCellLineHelaS3ENCODEBiolRep3_CNhs12327_tpm_rev Cl:HelaS3Br3- epitheloid carcinoma cell line: HelaS3 ENCODE, biol_rep3_CNhs12327_10817-111B7_reverse Regulation 
EpitheloidCarcinomaCellLineHelaS3ENCODEBiolRep3_CNhs12327_tpm_fwd Cl:HelaS3Br3+ epitheloid carcinoma cell line: HelaS3 ENCODE, biol_rep3_CNhs12327_10817-111B7_forward Regulation 
EpitheloidCarcinomaCellLineHelaS3ENCODEBiolRep2_CNhs12326_tpm_rev Cl:HelaS3Br2- epitheloid carcinoma cell line: HelaS3 ENCODE, biol_rep2_CNhs12326_10816-111B6_reverse Regulation 
EpitheloidCarcinomaCellLineHelaS3ENCODEBiolRep2_CNhs12326_tpm_fwd Cl:HelaS3Br2+ epitheloid carcinoma cell line: HelaS3 ENCODE, biol_rep2_CNhs12326_10816-111B6_forward Regulation 
EpitheloidCarcinomaCellLineHelaS3ENCODEBiolRep1_CNhs12325_tpm_rev Cl:HelaS3Br1- epitheloid carcinoma cell line: HelaS3 ENCODE, biol_rep1_CNhs12325_10815-111B5_reverse Regulation 
EpitheloidCarcinomaCellLineHelaS3ENCODEBiolRep1_CNhs12325_tpm_fwd Cl:HelaS3Br1+ epitheloid carcinoma cell line: HelaS3 ENCODE, biol_rep1_CNhs12325_10815-111B5_forward Regulation 
EmbryonicKidneyCellLineHEK293SLAMUntreated_CNhs11046_tpm_rev Cl:HEK293/SLAMuntreated- embryonic kidney cell line: HEK293/SLAM untreated_CNhs11046_10450-106F9_reverse Regulation 
EmbryonicKidneyCellLineHEK293SLAMUntreated_CNhs11046_tpm_fwd Cl:HEK293/SLAMuntreated+ embryonic kidney cell line: HEK293/SLAM untreated_CNhs11046_10450-106F9_forward Regulation 
EmbryonicKidneyCellLineHEK293SLAMInfection24hr_CNhs11047_tpm_rev Cl:HEK293/SLAMinfection,24hr- embryonic kidney cell line: HEK293/SLAM infection, 24hr_CNhs11047_10451-106G1_reverse Regulation 
EmbryonicKidneyCellLineHEK293SLAMInfection24hr_CNhs11047_tpm_fwd Cl:HEK293/SLAMinfection,24hr+ embryonic kidney cell line: HEK293/SLAM infection, 24hr_CNhs11047_10451-106G1_forward Regulation 
HodgkinsLymphomaCellLineHDMar2_CNhs11715_tpm_rev Cl:HD-Mar2- Hodgkin's lymphoma cell line:HD-Mar2_CNhs11715_10435-106E3_reverse Regulation 
HodgkinsLymphomaCellLineHDMar2_CNhs11715_tpm_fwd Cl:HD-Mar2+ Hodgkin's lymphoma cell line:HD-Mar2_CNhs11715_10435-106E3_forward Regulation 
SmallCellCervicalCancerCellLineHCSC1_CNhs11885_tpm_rev Cl:HCSC-1- small cell cervical cancer cell line:HCSC-1_CNhs11885_10800-110I8_reverse Regulation 
SmallCellCervicalCancerCellLineHCSC1_CNhs11885_tpm_fwd Cl:HCSC-1+ small cell cervical cancer cell line:HCSC-1_CNhs11885_10800-110I8_forward Regulation 
AcantholyticSquamousCarcinomaCellLineHCC1806_CNhs11844_tpm_rev Cl:HCC1806- acantholytic squamous carcinoma cell line:HCC1806_CNhs11844_10717-109I6_reverse Regulation 
AcantholyticSquamousCarcinomaCellLineHCC1806_CNhs11844_tpm_fwd Cl:HCC1806+ acantholytic squamous carcinoma cell line:HCC1806_CNhs11844_10717-109I6_forward Regulation 
ExtraskeletalMyxoidChondrosarcomaCellLineHEMCSS_CNhs10728_tpm_rev Cl:H-EMC-SS- extraskeletal myxoid chondrosarcoma cell line:H-EMC-SS_CNhs10728_10410-106B5_reverse Regulation 
ExtraskeletalMyxoidChondrosarcomaCellLineHEMCSS_CNhs10728_tpm_fwd Cl:H-EMC-SS+ extraskeletal myxoid chondrosarcoma cell line:H-EMC-SS_CNhs10728_10410-106B5_forward Regulation 
GastricCancerCellLineGSS_CNhs14241_tpm_rev Cl:GSS- gastric cancer cell line:GSS_CNhs14241_10560-108A2_reverse Regulation 
GastricCancerCellLineGSS_CNhs14241_tpm_fwd Cl:GSS+ gastric cancer cell line:GSS_CNhs14241_10560-108A2_forward Regulation 
BLymphoblastoidCellLineGM12878ENCODEBiolRep3_CNhs12333_tpm_rev Cl:GM12878Br3- B lymphoblastoid cell line: GM12878 ENCODE, biol_rep3_CNhs12333_10823-111C4_reverse Regulation 
BLymphoblastoidCellLineGM12878ENCODEBiolRep3_CNhs12333_tpm_fwd Cl:GM12878Br3+ B lymphoblastoid cell line: GM12878 ENCODE, biol_rep3_CNhs12333_10823-111C4_forward Regulation 
BLymphoblastoidCellLineGM12878ENCODEBiolRep2_CNhs12332_tpm_rev Cl:GM12878Br2- B lymphoblastoid cell line: GM12878 ENCODE, biol_rep2_CNhs12332_10822-111C3_reverse Regulation 
BLymphoblastoidCellLineGM12878ENCODEBiolRep2_CNhs12332_tpm_fwd Cl:GM12878Br2+ B lymphoblastoid cell line: GM12878 ENCODE, biol_rep2_CNhs12332_10822-111C3_forward Regulation 
BLymphoblastoidCellLineGM12878ENCODEBiolRep1_CNhs12331_tpm_rev Cl:GM12878Br1- B lymphoblastoid cell line: GM12878 ENCODE, biol_rep1_CNhs12331_10821-111C2_reverse Regulation 
BLymphoblastoidCellLineGM12878ENCODEBiolRep1_CNhs12331_tpm_fwd Cl:GM12878Br1+ B lymphoblastoid cell line: GM12878 ENCODE, biol_rep1_CNhs12331_10821-111C2_forward Regulation 
GliomaCellLineGI1_CNhs10731_tpm_rev Cl:GI-1- glioma cell line:GI-1_CNhs10731_10413-106B8_reverse Regulation 
GliomaCellLineGI1_CNhs10731_tpm_fwd Cl:GI-1+ glioma cell line:GI-1_CNhs10731_10413-106B8_forward Regulation 
FibrousHistiocytomaCellLineGCTTIB223_CNhs11842_tpm_rev Cl:GCTTIB-223- fibrous histiocytoma cell line:GCT TIB-223_CNhs11842_10711-109H9_reverse Regulation 
FibrousHistiocytomaCellLineGCTTIB223_CNhs11842_tpm_fwd Cl:GCTTIB-223+ fibrous histiocytoma cell line:GCT TIB-223_CNhs11842_10711-109H9_forward Regulation 
LeiomyoblastomaCellLineG402_CNhs11848_tpm_rev Cl:G-402- leiomyoblastoma cell line:G-402_CNhs11848_10721-110A1_reverse Regulation 
LeiomyoblastomaCellLineG402_CNhs11848_tpm_fwd Cl:G-402+ leiomyoblastoma cell line:G-402_CNhs11848_10721-110A1_forward Regulation 
WilmsTumorCellLineG401_CNhs11892_tpm_rev Cl:G-401- Wilms' tumor cell line:G-401_CNhs11892_10809-111A8_reverse Regulation 
WilmsTumorCellLineG401_CNhs11892_tpm_fwd Cl:G-401+ Wilms' tumor cell line:G-401_CNhs11892_10809-111A8_forward Regulation 
MelanomaCellLineG361_CNhs11254_tpm_rev Cl:G-361- melanoma cell line:G-361_CNhs11254_10465-106H6_reverse Regulation 
MelanomaCellLineG361_CNhs11254_tpm_fwd Cl:G-361+ melanoma cell line:G-361_CNhs11254_10465-106H6_forward Regulation 
NeuroectodermalTumorCellLineFURPNT2_CNhs11753_tpm_rev Cl:FU-RPNT-2- neuroectodermal tumor cell line:FU-RPNT-2_CNhs11753_10663-109C6_reverse Regulation 
NeuroectodermalTumorCellLineFURPNT2_CNhs11753_tpm_fwd Cl:FU-RPNT-2+ neuroectodermal tumor cell line:FU-RPNT-2_CNhs11753_10663-109C6_forward Regulation 
NeuroectodermalTumorCellLineFURPNT1_CNhs11744_tpm_rev Cl:FU-RPNT-1- neuroectodermal tumor cell line:FU-RPNT-1_CNhs11744_10637-108I7_reverse Regulation 
NeuroectodermalTumorCellLineFURPNT1_CNhs11744_tpm_fwd Cl:FU-RPNT-1+ neuroectodermal tumor cell line:FU-RPNT-1_CNhs11744_10637-108I7_forward Regulation 
AcuteMyeloidLeukemiaFABM4CellLineFKH1_CNhs13503_tpm_rev Cl:FKH-1- acute myeloid leukemia (FAB M4) cell line:FKH-1_CNhs13503_10830-111D2_reverse Regulation 
AcuteMyeloidLeukemiaFABM4CellLineFKH1_CNhs13503_tpm_fwd Cl:FKH-1+ acute myeloid leukemia (FAB M4) cell line:FKH-1_CNhs13503_10830-111D2_forward Regulation 
NormalIntestinalEpithelialCellLineFHs74Int_CNhs11950_tpm_rev Cl:FHs74Int- normal intestinal epithelial cell line:FHs 74 Int_CNhs11950_10812-111B2_reverse Regulation 
NormalIntestinalEpithelialCellLineFHs74Int_CNhs11950_tpm_fwd Cl:FHs74Int+ normal intestinal epithelial cell line:FHs 74 Int_CNhs11950_10812-111B2_forward Regulation 
AcuteMyeloidLeukemiaFABM6CellLineF36P_CNhs13505_tpm_rev Cl:F-36P- acute myeloid leukemia (FAB M6) cell line:F-36P_CNhs13505_10837-111D9_reverse Regulation 
AcuteMyeloidLeukemiaFABM6CellLineF36P_CNhs13505_tpm_fwd Cl:F-36P+ acute myeloid leukemia (FAB M6) cell line:F-36P_CNhs13505_10837-111D9_forward Regulation 
AcuteMyeloidLeukemiaFABM6CellLineF36E_CNhs13060_tpm_rev Cl:F-36E- acute myeloid leukemia (FAB M6) cell line:F-36E_CNhs13060_10836-111D8_reverse Regulation 
AcuteMyeloidLeukemiaFABM6CellLineF36E_CNhs13060_tpm_fwd Cl:F-36E+ acute myeloid leukemia (FAB M6) cell line:F-36E_CNhs13060_10836-111D8_forward Regulation 
AcuteMyeloidLeukemiaFABM4eoCellLineEoL3_CNhs13057_tpm_rev Cl:EoL-3- acute myeloid leukemia (FAB M4eo) cell line:EoL-3_CNhs13057_10833-111D5_reverse Regulation 
AcuteMyeloidLeukemiaFABM4eoCellLineEoL3_CNhs13057_tpm_fwd Cl:EoL-3+ acute myeloid leukemia (FAB M4eo) cell line:EoL-3_CNhs13057_10833-111D5_forward Regulation 
AcuteMyeloidLeukemiaFABM4eoCellLineEoL1_CNhs13056_tpm_rev Cl:EoL-1- acute myeloid leukemia (FAB M4eo) cell line:EoL-1_CNhs13056_10832-111D4_reverse Regulation 
AcuteMyeloidLeukemiaFABM4eoCellLineEoL1_CNhs13056_tpm_fwd Cl:EoL-1+ acute myeloid leukemia (FAB M4eo) cell line:EoL-1_CNhs13056_10832-111D4_forward Regulation 
AcuteMyeloidLeukemiaFABM6CellLineEEB_CNhs13059_tpm_rev Cl:EEB- acute myeloid leukemia (FAB M6) cell line:EEB_CNhs13059_10835-111D7_reverse Regulation 
AcuteMyeloidLeukemiaFABM6CellLineEEB_CNhs13059_tpm_fwd Cl:EEB+ acute myeloid leukemia (FAB M6) cell line:EEB_CNhs13059_10835-111D7_forward Regulation 
SmallcellGastrointestinalCarcinomaCellLineECC4_CNhs11734_tpm_rev Cl:ECC4- small-cell gastrointestinal carcinoma cell line:ECC4_CNhs11734_10609-108F6_reverse Regulation 
SmallcellGastrointestinalCarcinomaCellLineECC4_CNhs11734_tpm_fwd Cl:ECC4+ small-cell gastrointestinal carcinoma cell line:ECC4_CNhs11734_10609-108F6_forward Regulation 
GastrointestinalCarcinomaCellLineECC12_CNhs11738_tpm_rev Cl:ECC12- gastrointestinal carcinoma cell line:ECC12_CNhs11738_10615-108G3_reverse Regulation 
GastrointestinalCarcinomaCellLineECC12_CNhs11738_tpm_fwd Cl:ECC12+ gastrointestinal carcinoma cell line:ECC12_CNhs11738_10615-108G3_forward Regulation 
SmallCellGastrointestinalCarcinomaCellLineECC10_CNhs11736_tpm_rev Cl:ECC10- small cell gastrointestinal carcinoma cell line:ECC10_CNhs11736_10610-108F7_reverse Regulation 
SmallCellGastrointestinalCarcinomaCellLineECC10_CNhs11736_tpm_fwd Cl:ECC10+ small cell gastrointestinal carcinoma cell line:ECC10_CNhs11736_10610-108F7_forward Regulation 
SquamousCellCarcinomaCellLineECGI10_CNhs11252_tpm_rev Cl:EC-GI-10- squamous cell carcinoma cell line:EC-GI-10_CNhs11252_10463-106H4_reverse Regulation 
SquamousCellCarcinomaCellLineECGI10_CNhs11252_tpm_fwd Cl:EC-GI-10+ squamous cell carcinoma cell line:EC-GI-10_CNhs11252_10463-106H4_forward Regulation 
SquamousCellLungCarcinomaCellLineEBC1_CNhs11273_tpm_rev Cl:EBC-1- squamous cell lung carcinoma cell line:EBC-1_CNhs11273_10486-107A9_reverse Regulation 
SquamousCellLungCarcinomaCellLineEBC1_CNhs11273_tpm_fwd Cl:EBC-1+ squamous cell lung carcinoma cell line:EBC-1_CNhs11273_10486-107A9_forward Regulation 
ProstateCancerCellLineDU145_CNhs11260_tpm_rev Cl:DU145- prostate cancer cell line:DU145_CNhs11260_10490-107B4_reverse Regulation 
ProstateCancerCellLineDU145_CNhs11260_tpm_fwd Cl:DU145+ prostate cancer cell line:DU145_CNhs11260_10490-107B4_forward Regulation 
LymphangiectasiaCellLineDS1_CNhs11852_tpm_rev Cl:DS-1- lymphangiectasia cell line:DS-1_CNhs11852_10727-110A7_reverse Regulation 
LymphangiectasiaCellLineDS1_CNhs11852_tpm_fwd Cl:DS-1+ lymphangiectasia cell line:DS-1_CNhs11852_10727-110A7_forward Regulation 
SmallCellLungCarcinomaCellLineDMS144_CNhs12808_tpm_rev Cl:DMS144- small cell lung carcinoma cell line:DMS 144_CNhs12808_10841-111E4_reverse Regulation 
SmallCellLungCarcinomaCellLineDMS144_CNhs12808_tpm_fwd Cl:DMS144+ small cell lung carcinoma cell line:DMS 144_CNhs12808_10841-111E4_forward Regulation 
MalignantTrichilemmalCystCellLineDJM1_CNhs10730_tpm_rev Cl:DJM-1- malignant trichilemmal cyst cell line:DJM-1_CNhs10730_10412-106B7_reverse Regulation 
MalignantTrichilemmalCystCellLineDJM1_CNhs10730_tpm_fwd Cl:DJM-1+ malignant trichilemmal cyst cell line:DJM-1_CNhs10730_10412-106B7_forward Regulation 
PharyngealCarcinomaCellLineDetroit562_CNhs11849_tpm_rev Cl:Detroit562- pharyngeal carcinoma cell line:Detroit 562_CNhs11849_10723-110A3_reverse Regulation 
PharyngealCarcinomaCellLineDetroit562_CNhs11849_tpm_fwd Cl:Detroit562+ pharyngeal carcinoma cell line:Detroit 562_CNhs11849_10723-110A3_forward Regulation 
BurkittsLymphomaCellLineDAUDI_CNhs10739_tpm_rev Cl:DAUDI- Burkitt's lymphoma cell line:DAUDI_CNhs10739_10422-106C8_reverse Regulation 
BurkittsLymphomaCellLineDAUDI_CNhs10739_tpm_fwd Cl:DAUDI+ Burkitt's lymphoma cell line:DAUDI_CNhs10739_10422-106C8_forward Regulation 
CervicalCancerCellLineD98AH2_CNhs11288_tpm_rev Cl:D98-AH2- cervical cancer cell line:D98-AH2_CNhs11288_10552-107I3_reverse Regulation 
CervicalCancerCellLineD98AH2_CNhs11288_tpm_fwd Cl:D98-AH2+ cervical cancer cell line:D98-AH2_CNhs11288_10552-107I3_forward Regulation 
MedulloblastomaCellLineD283Med_CNhs12805_tpm_rev Cl:D283Med- medulloblastoma cell line:D283 Med_CNhs12805_10838-111E1_reverse Regulation 
MedulloblastomaCellLineD283Med_CNhs12805_tpm_fwd Cl:D283Med+ medulloblastoma cell line:D283 Med_CNhs12805_10838-111E1_forward Regulation 
DiffuseLargeBcellLymphomaCellLineCTB1_CNhs11741_tpm_rev Cl:CTB-1- diffuse large B-cell lymphoma cell line:CTB-1_CNhs11741_10631-108I1_reverse Regulation 
DiffuseLargeBcellLymphomaCellLineCTB1_CNhs11741_tpm_fwd Cl:CTB-1+ diffuse large B-cell lymphoma cell line:CTB-1_CNhs11741_10631-108I1_forward Regulation 
MelanomaCellLineCOLO679_CNhs11281_tpm_rev Cl:COLO679- melanoma cell line:COLO 679_CNhs11281_10514-107E1_reverse Regulation 
MelanomaCellLineCOLO679_CNhs11281_tpm_fwd Cl:COLO679+ melanoma cell line:COLO 679_CNhs11281_10514-107E1_forward Regulation 
ColonCarcinomaCellLineCOLO320_CNhs10737_tpm_rev Cl:COLO-320- colon carcinoma cell line:COLO-320_CNhs10737_10420-106C6_reverse Regulation 
ColonCarcinomaCellLineCOLO320_CNhs10737_tpm_fwd Cl:COLO-320+ colon carcinoma cell line:COLO-320_CNhs10737_10420-106C6_forward Regulation 
CordBloodDerivedCellLineCOBLaUntreated_CNhs11045_tpm_rev Cl:COBL-auntreated- cord blood derived cell line:COBL-a untreated_CNhs11045_10449-106F8_reverse Regulation 
CordBloodDerivedCellLineCOBLaUntreated_CNhs11045_tpm_fwd Cl:COBL-auntreated+ cord blood derived cell line:COBL-a untreated_CNhs11045_10449-106F8_forward Regulation 
CordBloodDerivedCellLineCOBLa24hInfection_CNhs11050_tpm_rev Cl:COBL-a24hinfection- cord blood derived cell line:COBL-a 24h infection_CNhs11050_10453-106G3_reverse Regulation 
CordBloodDerivedCellLineCOBLa24hInfection_CNhs11050_tpm_fwd Cl:COBL-a24hinfection+ cord blood derived cell line:COBL-a 24h infection_CNhs11050_10453-106G3_forward Regulation 
CordBloodDerivedCellLineCOBLa24hInfectionC_CNhs11049_tpm_rev Cl:COBL-a24hinfection(-C)- cord blood derived cell line:COBL-a 24h infection(-C)_CNhs11049_10452-106G2_reverse Regulation 
CordBloodDerivedCellLineCOBLa24hInfectionC_CNhs11049_tpm_fwd Cl:COBL-a24hinfection(-C)+ cord blood derived cell line:COBL-a 24h infection(-C)_CNhs11049_10452-106G2_forward Regulation 
NeuroblastomaCellLineCHP134_CNhs11276_tpm_rev Cl:CHP-134- neuroblastoma cell line:CHP-134_CNhs11276_10508-107D4_reverse Regulation 
NeuroblastomaCellLineCHP134_CNhs11276_tpm_fwd Cl:CHP-134+ neuroblastoma cell line:CHP-134_CNhs11276_10508-107D4_forward Regulation 
BronchogenicCarcinomaCellLineChaGoK1_CNhs11841_tpm_rev Cl:ChaGo-K-1- bronchogenic carcinoma cell line:ChaGo-K-1_CNhs11841_10710-109H8_reverse Regulation 
BronchogenicCarcinomaCellLineChaGoK1_CNhs11841_tpm_fwd Cl:ChaGo-K-1+ bronchogenic carcinoma cell line:ChaGo-K-1_CNhs11841_10710-109H8_forward Regulation 
EpidermoidCarcinomaCellLineCaSki_CNhs10748_tpm_rev Cl:CaSki- epidermoid carcinoma cell line:Ca Ski_CNhs10748_10431-106D8_reverse Regulation 
EpidermoidCarcinomaCellLineCaSki_CNhs10748_tpm_fwd Cl:CaSki+ epidermoid carcinoma cell line:Ca Ski_CNhs10748_10431-106D8_forward Regulation 
ColonCarcinomaCellLineCACO2_CNhs11280_tpm_rev Cl:CACO-2- colon carcinoma cell line:CACO-2_CNhs11280_10513-107D9_reverse Regulation 
ColonCarcinomaCellLineCACO2_CNhs11280_tpm_fwd Cl:CACO-2+ colon carcinoma cell line:CACO-2_CNhs11280_10513-107D9_forward Regulation 
OralSquamousCellCarcinomaCellLineCa922_CNhs10752_tpm_rev Cl:Ca9-22- oral squamous cell carcinoma cell line:Ca9-22_CNhs10752_10434-106E2_reverse Regulation 
OralSquamousCellCarcinomaCellLineCa922_CNhs10752_tpm_fwd Cl:Ca9-22+ oral squamous cell carcinoma cell line:Ca9-22_CNhs10752_10434-106E2_forward Regulation 
ChoriocarcinomaCellLineBeWo_CNhs10740_tpm_rev Cl:BeWo- choriocarcinoma cell line:BeWo_CNhs10740_10423-106C9_reverse Regulation 
ChoriocarcinomaCellLineBeWo_CNhs10740_tpm_fwd Cl:BeWo+ choriocarcinoma cell line:BeWo_CNhs10740_10423-106C9_forward Regulation 
AcuteLymphoblasticLeukemiaBALLCellLineBALL1_CNhs11251_tpm_rev Cl:BALL-1- acute lymphoblastic leukemia (B-ALL) cell line:BALL-1_CNhs11251_10455-106G5_reverse Regulation 
AcuteLymphoblasticLeukemiaBALLCellLineBALL1_CNhs11251_tpm_fwd Cl:BALL-1+ acute lymphoblastic leukemia (B-ALL) cell line:BALL-1_CNhs11251_10455-106G5_forward Regulation 
GastricCancerCellLineAZ521_CNhs11286_tpm_rev Cl:AZ521- gastric cancer cell line:AZ521_CNhs11286_10549-107H9_reverse Regulation 
GastricCancerCellLineAZ521_CNhs11286_tpm_fwd Cl:AZ521+ gastric cancer cell line:AZ521_CNhs11286_10549-107H9_forward Regulation 
AdultTcellLeukemiaCellLineATN1_CNhs10738_tpm_rev Cl:ATN-1- adult T-cell leukemia cell line:ATN-1_CNhs10738_10421-106C7_reverse Regulation 
AdultTcellLeukemiaCellLineATN1_CNhs10738_tpm_fwd Cl:ATN-1+ adult T-cell leukemia cell line:ATN-1_CNhs10738_10421-106C7_forward Regulation 
PlasmaCellLeukemiaCellLineARH77_CNhs12807_tpm_rev Cl:ARH-77- plasma cell leukemia cell line:ARH-77_CNhs12807_10840-111E3_reverse Regulation 
PlasmaCellLeukemiaCellLineARH77_CNhs12807_tpm_fwd Cl:ARH-77+ plasma cell leukemia cell line:ARH-77_CNhs12807_10840-111E3_forward Regulation 
MesotheliomaCellLineACCMESO4_CNhs11264_tpm_rev Cl:ACC-MESO-4- mesothelioma cell line:ACC-MESO-4_CNhs11264_10494-107B8_reverse Regulation 
MesotheliomaCellLineACCMESO4_CNhs11264_tpm_fwd Cl:ACC-MESO-4+ mesothelioma cell line:ACC-MESO-4_CNhs11264_10494-107B8_forward Regulation 
MesotheliomaCellLineACCMESO1_CNhs11263_tpm_rev Cl:ACC-MESO-1- mesothelioma cell line:ACC-MESO-1_CNhs11263_10493-107B7_reverse Regulation 
MesotheliomaCellLineACCMESO1_CNhs11263_tpm_fwd Cl:ACC-MESO-1+ mesothelioma cell line:ACC-MESO-1_CNhs11263_10493-107B7_forward Regulation 
LungAdenocarcinomaCellLineA549_CNhs11275_tpm_rev Cl:A549- lung adenocarcinoma cell line:A549_CNhs11275_10499-107C4_reverse Regulation 
LungAdenocarcinomaCellLineA549_CNhs11275_tpm_fwd Cl:A549+ lung adenocarcinoma cell line:A549_CNhs11275_10499-107C4_forward Regulation 
EpidermoidCarcinomaCellLineA431_CNhs10743_tpm_rev Cl:A431- epidermoid carcinoma cell line:A431_CNhs10743_10426-106D3_reverse Regulation 
EpidermoidCarcinomaCellLineA431_CNhs10743_tpm_fwd Cl:A431+ epidermoid carcinoma cell line:A431_CNhs10743_10426-106D3_forward Regulation 
GlioblastomaCellLineA172TechRep2_CNhs11248_tpm_rev Cl:A172Tr2- glioblastoma cell line:A172, tech_rep2_CNhs11248_10444-106F3_reverse Regulation 
GlioblastomaCellLineA172TechRep2_CNhs11248_tpm_fwd Cl:A172Tr2+ glioblastoma cell line:A172, tech_rep2_CNhs11248_10444-106F3_forward Regulation 
PapillaryAdenocarcinomaCellLine8505C_CNhs11716_tpm_rev Cl:8505C- papillary adenocarcinoma cell line:8505C_CNhs11716_10437-106E5_reverse Regulation 
PapillaryAdenocarcinomaCellLine8505C_CNhs11716_tpm_fwd Cl:8505C+ papillary adenocarcinoma cell line:8505C_CNhs11716_10437-106E5_forward Regulation 
AnaplasticCarcinomaCellLine8305C_CNhs10745_tpm_rev Cl:8305C- anaplastic carcinoma cell line:8305C_CNhs10745_10428-106D5_reverse Regulation 
AnaplasticCarcinomaCellLine8305C_CNhs10745_tpm_fwd Cl:8305C+ anaplastic carcinoma cell line:8305C_CNhs10745_10428-106D5_forward Regulation 
TransitionalcellCarcinomaCellLine5637_CNhs10735_tpm_rev Cl:5637- transitional-cell carcinoma cell line:5637_CNhs10735_10418-106C4_reverse Regulation 
TransitionalcellCarcinomaCellLine5637_CNhs10735_tpm_fwd Cl:5637+ transitional-cell carcinoma cell line:5637_CNhs10735_10418-106C4_forward Regulation 
EmbryonicPancreasCellLine2C6_CNhs11814_tpm_rev Cl:2C6- embryonic pancreas cell line:2C6_CNhs11814_10603-108E9_reverse Regulation 
EmbryonicPancreasCellLine2C6_CNhs11814_tpm_fwd Cl:2C6+ embryonic pancreas cell line:2C6_CNhs11814_10603-108E9_forward Regulation 
EmbryonicPancreasCellLine1C3IKEI_CNhs11733_tpm_rev Cl:1C3IKEI- embryonic pancreas cell line:1C3IKEI_CNhs11733_10606-108F3_reverse Regulation 
EmbryonicPancreasCellLine1C3IKEI_CNhs11733_tpm_fwd Cl:1C3IKEI+ embryonic pancreas cell line:1C3IKEI_CNhs11733_10606-108F3_forward Regulation 
EmbryonicPancreasCellLine1C3D3_CNhs11732_tpm_rev Cl:1C3D3- embryonic pancreas cell line:1C3D3_CNhs11732_10605-108F2_reverse Regulation 
EmbryonicPancreasCellLine1C3D3_CNhs11732_tpm_fwd Cl:1C3D3+ embryonic pancreas cell line:1C3D3_CNhs11732_10605-108F2_forward Regulation 
EmbryonicPancreasCellLine1B2C6_CNhs11731_tpm_rev Cl:1B2C6- embryonic pancreas cell line:1B2C6_CNhs11731_10604-108F1_reverse Regulation 
EmbryonicPancreasCellLine1B2C6_CNhs11731_tpm_fwd Cl:1B2C6+ embryonic pancreas cell line:1B2C6_CNhs11731_10604-108F1_forward Regulation 
LeiomyomaCellLine15425_CNhs11724_tpm_rev Cl:15425- leiomyoma cell line:15425_CNhs11724_10571-108B4_reverse Regulation 
LeiomyomaCellLine15425_CNhs11724_tpm_fwd Cl:15425+ leiomyoma cell line:15425_CNhs11724_10571-108B4_forward Regulation 
LeiomyomaCellLine15242A_CNhs11723_tpm_rev Cl:15242A- leiomyoma cell line:15242A_CNhs11723_10570-108B3_reverse Regulation 
LeiomyomaCellLine15242A_CNhs11723_tpm_fwd Cl:15242A+ leiomyoma cell line:15242A_CNhs11723_10570-108B3_forward Regulation 
OsteosarcomaCellLine143BTKneoR_CNhs11279_tpm_rev Cl:143B/TK^(-)neo^(R)- osteosarcoma cell line:143B/TK^(-)neo^(R)_CNhs11279_10510-107D6_reverse Regulation 
OsteosarcomaCellLine143BTKneoR_CNhs11279_tpm_fwd Cl:143B/TK^(-)neo^(R)+ osteosarcoma cell line:143B/TK^(-)neo^(R)_CNhs11279_10510-107D6_forward Regulation 
LeiomyomaCellLine10964C_CNhs11722_tpm_rev Cl:10964C- leiomyoma cell line:10964C_CNhs11722_10569-108B2_reverse Regulation 
LeiomyomaCellLine10964C_CNhs11722_tpm_fwd Cl:10964C+ leiomyoma cell line:10964C_CNhs11722_10569-108B2_forward Regulation 
NonsmallCellLungCancerCellLineNCIH1385_CNhs12193_tpm_rev Cl:NCI-H1385- non-small cell lung cancer cell line:NCI-H1385_CNhs12193_10730-110B1_reverse Regulation 
NonsmallCellLungCancerCellLineNCIH1385_CNhs12193_tpm_fwd Cl:NCI-H1385+ non-small cell lung cancer cell line:NCI-H1385_CNhs12193_10730-110B1_forward Regulation 
MesotheliomaCellLineMero14TechRep2_CNhs14376_tpm_rev Cl:Mero-14Tr2- mesothelioma cell line:Mero-14, tech_rep2_CNhs14376_10849-111F3_reverse Regulation 
MesotheliomaCellLineMero14TechRep2_CNhs14376_tpm_fwd Cl:Mero-14Tr2+ mesothelioma cell line:Mero-14, tech_rep2_CNhs14376_10849-111F3_forward Regulation 
AcuteMyeloidLeukemiaFABM0CellLineKasumi3_CNhs13241_tpm_rev Cl:Kasumi-3- acute myeloid leukemia (FAB M0) cell line:Kasumi-3_CNhs13241_10789-110H6_reverse Regulation 
AcuteMyeloidLeukemiaFABM0CellLineKasumi3_CNhs13241_tpm_fwd Cl:Kasumi-3+ acute myeloid leukemia (FAB M0) cell line:Kasumi-3_CNhs13241_10789-110H6_forward Regulation 
LeiomyosarcomaCellLineHs5_T_CNhs12192_tpm_rev Cl:Hs5_T- leiomyosarcoma cell line:Hs 5_T_CNhs12192_10722-110A2_reverse Regulation 
LeiomyosarcomaCellLineHs5_T_CNhs12192_tpm_fwd Cl:Hs5_T+ leiomyosarcoma cell line:Hs 5_T_CNhs12192_10722-110A2_forward Regulation 
MesodermalTumorCellLineHIRSBM_CNhs12191_tpm_rev Cl:HIRS-BM- mesodermal tumor cell line:HIRS-BM_CNhs12191_10696-109G3_reverse Regulation 
MesodermalTumorCellLineHIRSBM_CNhs12191_tpm_fwd Cl:HIRS-BM+ mesodermal tumor cell line:HIRS-BM_CNhs12191_10696-109G3_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay28BiolRep3A3T17_CNhs12892_tpm_rev Saos-2W/AscorbicAcidBgp_Day28Br3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day28, biol_rep3 (A3 T17)_CNhs12892_12875-137F4_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay28BiolRep3A3T17_CNhs12892_tpm_fwd Saos-2W/AscorbicAcidBgp_Day28Br3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day28, biol_rep3 (A3 T17)_CNhs12892_12875-137F4_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay28BiolRep2A2T17_CNhs12876_tpm_rev Saos-2W/AscorbicAcidBgp_Day28Br2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day28, biol_rep2 (A2 T17)_CNhs12876_12777-136D5_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay28BiolRep2A2T17_CNhs12876_tpm_fwd Saos-2W/AscorbicAcidBgp_Day28Br2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day28, biol_rep2 (A2 T17)_CNhs12876_12777-136D5_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay28BiolRep1A1T17_CNhs11919_tpm_rev Saos-2W/AscorbicAcidBgp_Day28Br1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day28, biol_rep1 (A1 T17)_CNhs11919_12679-135B6_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay28BiolRep1A1T17_CNhs11919_tpm_fwd Saos-2W/AscorbicAcidBgp_Day28Br1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day28, biol_rep1 (A1 T17)_CNhs11919_12679-135B6_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay21BiolRep3A3T16_CNhs12891_tpm_rev Saos-2W/AscorbicAcidBgp_Day21Br3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day21, biol_rep3 (A3 T16)_CNhs12891_12874-137F3_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay21BiolRep3A3T16_CNhs12891_tpm_fwd Saos-2W/AscorbicAcidBgp_Day21Br3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day21, biol_rep3 (A3 T16)_CNhs12891_12874-137F3_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay21BiolRep2A2T16_CNhs12875_tpm_rev Saos-2W/AscorbicAcidBgp_Day21Br2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day21, biol_rep2 (A2 T16)_CNhs12875_12776-136D4_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay21BiolRep2A2T16_CNhs12875_tpm_fwd Saos-2W/AscorbicAcidBgp_Day21Br2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day21, biol_rep2 (A2 T16)_CNhs12875_12776-136D4_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay21BiolRep1A1T16_CNhs12397_tpm_rev Saos-2W/AscorbicAcidBgp_Day21Br1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day21, biol_rep1 (A1 T16)_CNhs12397_12678-135B5_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay21BiolRep1A1T16_CNhs12397_tpm_fwd Saos-2W/AscorbicAcidBgp_Day21Br1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day21, biol_rep1 (A1 T16)_CNhs12397_12678-135B5_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay14BiolRep3A3T15_CNhs12890_tpm_rev Saos-2W/AscorbicAcidBgp_Day14Br3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day14, biol_rep3 (A3 T15)_CNhs12890_12873-137F2_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay14BiolRep3A3T15_CNhs12890_tpm_fwd Saos-2W/AscorbicAcidBgp_Day14Br3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day14, biol_rep3 (A3 T15)_CNhs12890_12873-137F2_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay14BiolRep2A2T15_CNhs12953_tpm_rev Saos-2W/AscorbicAcidBgp_Day14Br2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day14, biol_rep2 (A2 T15)_CNhs12953_12775-136D3_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay14BiolRep2A2T15_CNhs12953_tpm_fwd Saos-2W/AscorbicAcidBgp_Day14Br2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day14, biol_rep2 (A2 T15)_CNhs12953_12775-136D3_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay14BiolRep1A1T15_CNhs12396_tpm_rev Saos-2W/AscorbicAcidBgp_Day14Br1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day14, biol_rep1 (A1 T15)_CNhs12396_12677-135B4_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay14BiolRep1A1T15_CNhs12396_tpm_fwd Saos-2W/AscorbicAcidBgp_Day14Br1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day14, biol_rep1 (A1 T15)_CNhs12396_12677-135B4_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay07BiolRep3A3T14_CNhs12888_tpm_rev Saos-2W/AscorbicAcidBgp_Day07Br3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day07, biol_rep3 (A3 T14)_CNhs12888_12872-137F1_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay07BiolRep3A3T14_CNhs12888_tpm_fwd Saos-2W/AscorbicAcidBgp_Day07Br3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day07, biol_rep3 (A3 T14)_CNhs12888_12872-137F1_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay07BiolRep2A2T14_CNhs12874_tpm_rev Saos-2W/AscorbicAcidBgp_Day07Br2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day07, biol_rep2 (A2 T14)_CNhs12874_12774-136D2_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay07BiolRep2A2T14_CNhs12874_tpm_fwd Saos-2W/AscorbicAcidBgp_Day07Br2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day07, biol_rep2 (A2 T14)_CNhs12874_12774-136D2_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay07BiolRep1A1T14_CNhs12395_tpm_rev Saos-2W/AscorbicAcidBgp_Day07Br1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day07, biol_rep1 (A1 T14)_CNhs12395_12676-135B3_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay07BiolRep1A1T14_CNhs12395_tpm_fwd Saos-2W/AscorbicAcidBgp_Day07Br1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day07, biol_rep1 (A1 T14)_CNhs12395_12676-135B3_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay04BiolRep3A3T13_CNhs12887_tpm_rev Saos-2W/AscorbicAcidBgp_Day04Br3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day04, biol_rep3 (A3 T13)_CNhs12887_12871-137E9_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay04BiolRep3A3T13_CNhs12887_tpm_fwd Saos-2W/AscorbicAcidBgp_Day04Br3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day04, biol_rep3 (A3 T13)_CNhs12887_12871-137E9_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay04BiolRep2A2T13_CNhs12873_tpm_rev Saos-2W/AscorbicAcidBgp_Day04Br2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day04, biol_rep2 (A2 T13)_CNhs12873_12773-136D1_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay04BiolRep2A2T13_CNhs12873_tpm_fwd Saos-2W/AscorbicAcidBgp_Day04Br2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day04, biol_rep2 (A2 T13)_CNhs12873_12773-136D1_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay04BiolRep1A1T13_CNhs12394_tpm_rev Saos-2W/AscorbicAcidBgp_Day04Br1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day04, biol_rep1 (A1 T13)_CNhs12394_12675-135B2_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcificationDay04BiolRep1A1T13_CNhs12394_tpm_fwd Saos-2W/AscorbicAcidBgp_Day04Br1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, day04, biol_rep1 (A1 T13)_CNhs12394_12675-135B2_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification24hrBiolRep3A3T12_CNhs12886_tpm_rev Saos-2W/AscorbicAcidBgp_24hrBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 24hr, biol_rep3 (A3 T12)_CNhs12886_12870-137E8_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification24hrBiolRep3A3T12_CNhs12886_tpm_fwd Saos-2W/AscorbicAcidBgp_24hrBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 24hr, biol_rep3 (A3 T12)_CNhs12886_12870-137E8_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification24hrBiolRep2A2T12_CNhs12872_tpm_rev Saos-2W/AscorbicAcidBgp_24hrBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 24hr, biol_rep2 (A2 T12)_CNhs12872_12772-136C9_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification24hrBiolRep2A2T12_CNhs12872_tpm_fwd Saos-2W/AscorbicAcidBgp_24hrBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 24hr, biol_rep2 (A2 T12)_CNhs12872_12772-136C9_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification24hrBiolRep1A1T12_CNhs12393_tpm_rev Saos-2W/AscorbicAcidBgp_24hrBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 24hr, biol_rep1 (A1 T12)_CNhs12393_12674-135B1_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification24hrBiolRep1A1T12_CNhs12393_tpm_fwd Saos-2W/AscorbicAcidBgp_24hrBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 24hr, biol_rep1 (A1 T12)_CNhs12393_12674-135B1_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification08hrBiolRep3A3T11_CNhs12885_tpm_rev Saos-2W/AscorbicAcidBgp_08hrBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 08hr, biol_rep3 (A3 T11)_CNhs12885_12869-137E7_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification08hrBiolRep3A3T11_CNhs12885_tpm_fwd Saos-2W/AscorbicAcidBgp_08hrBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 08hr, biol_rep3 (A3 T11)_CNhs12885_12869-137E7_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification08hrBiolRep2A2T11_CNhs12871_tpm_rev Saos-2W/AscorbicAcidBgp_08hrBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 08hr, biol_rep2 (A2 T11)_CNhs12871_12771-136C8_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification08hrBiolRep2A2T11_CNhs12871_tpm_fwd Saos-2W/AscorbicAcidBgp_08hrBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 08hr, biol_rep2 (A2 T11)_CNhs12871_12771-136C8_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification08hrBiolRep1A1T11_CNhs12392_tpm_rev Saos-2W/AscorbicAcidBgp_08hrBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 08hr, biol_rep1 (A1 T11)_CNhs12392_12673-135A9_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification08hrBiolRep1A1T11_CNhs12392_tpm_fwd Saos-2W/AscorbicAcidBgp_08hrBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 08hr, biol_rep1 (A1 T11)_CNhs12392_12673-135A9_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification04hrBiolRep3A3T10_CNhs12884_tpm_rev Saos-2W/AscorbicAcidBgp_04hrBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 04hr, biol_rep3 (A3 T10)_CNhs12884_12868-137E6_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification04hrBiolRep3A3T10_CNhs12884_tpm_fwd Saos-2W/AscorbicAcidBgp_04hrBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 04hr, biol_rep3 (A3 T10)_CNhs12884_12868-137E6_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification04hrBiolRep2A2T10_CNhs12870_tpm_rev Saos-2W/AscorbicAcidBgp_04hrBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 04hr, biol_rep2 (A2 T10)_CNhs12870_12770-136C7_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification04hrBiolRep2A2T10_CNhs12870_tpm_fwd Saos-2W/AscorbicAcidBgp_04hrBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 04hr, biol_rep2 (A2 T10)_CNhs12870_12770-136C7_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification04hrBiolRep1A1T10_CNhs12391_tpm_rev Saos-2W/AscorbicAcidBgp_04hrBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 04hr, biol_rep1 (A1 T10)_CNhs12391_12672-135A8_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification04hrBiolRep1A1T10_CNhs12391_tpm_fwd Saos-2W/AscorbicAcidBgp_04hrBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 04hr, biol_rep1 (A1 T10)_CNhs12391_12672-135A8_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification03hrBiolRep3A3T9_CNhs12883_tpm_rev Saos-2W/AscorbicAcidBgp_03hrBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 03hr, biol_rep3 (A3 T9)_CNhs12883_12867-137E5_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification03hrBiolRep3A3T9_CNhs12883_tpm_fwd Saos-2W/AscorbicAcidBgp_03hrBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 03hr, biol_rep3 (A3 T9)_CNhs12883_12867-137E5_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification03hrBiolRep2A2T9_CNhs12869_tpm_rev Saos-2W/AscorbicAcidBgp_03hrBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 03hr, biol_rep2 (A2 T9)_CNhs12869_12769-136C6_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification03hrBiolRep2A2T9_CNhs12869_tpm_fwd Saos-2W/AscorbicAcidBgp_03hrBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 03hr, biol_rep2 (A2 T9)_CNhs12869_12769-136C6_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification03hrBiolRep1A1T9_CNhs12390_tpm_rev Saos-2W/AscorbicAcidBgp_03hrBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 03hr, biol_rep1 (A1 T9)_CNhs12390_12671-135A7_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification03hrBiolRep1A1T9_CNhs12390_tpm_fwd Saos-2W/AscorbicAcidBgp_03hrBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 03hr, biol_rep1 (A1 T9)_CNhs12390_12671-135A7_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr30minBiolRep3A3T8_CNhs12882_tpm_rev Saos-2W/AscorbicAcidBgp_02hr30minBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr30min, biol_rep3 (A3 T8)_CNhs12882_12866-137E4_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr30minBiolRep3A3T8_CNhs12882_tpm_fwd Saos-2W/AscorbicAcidBgp_02hr30minBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr30min, biol_rep3 (A3 T8)_CNhs12882_12866-137E4_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr30minBiolRep2A2T8_CNhs12868_tpm_rev Saos-2W/AscorbicAcidBgp_02hr30minBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr30min, biol_rep2 (A2 T8)_CNhs12868_12768-136C5_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr30minBiolRep2A2T8_CNhs12868_tpm_fwd Saos-2W/AscorbicAcidBgp_02hr30minBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr30min, biol_rep2 (A2 T8)_CNhs12868_12768-136C5_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr30minBiolRep1A1T8_CNhs12389_tpm_rev Saos-2W/AscorbicAcidBgp_02hr30minBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr30min, biol_rep1 (A1 T8)_CNhs12389_12670-135A6_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr30minBiolRep1A1T8_CNhs12389_tpm_fwd Saos-2W/AscorbicAcidBgp_02hr30minBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr30min, biol_rep1 (A1 T8)_CNhs12389_12670-135A6_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr00minBiolRep3A3T7_CNhs12881_tpm_rev Saos-2W/AscorbicAcidBgp_02hr00minBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr00min, biol_rep3 (A3 T7)_CNhs12881_12865-137E3_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr00minBiolRep3A3T7_CNhs12881_tpm_fwd Saos-2W/AscorbicAcidBgp_02hr00minBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr00min, biol_rep3 (A3 T7)_CNhs12881_12865-137E3_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr00minBiolRep2A2T7_CNhs12867_tpm_rev Saos-2W/AscorbicAcidBgp_02hr00minBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr00min, biol_rep2 (A2 T7)_CNhs12867_12767-136C4_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr00minBiolRep2A2T7_CNhs12867_tpm_fwd Saos-2W/AscorbicAcidBgp_02hr00minBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr00min, biol_rep2 (A2 T7)_CNhs12867_12767-136C4_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr00minBiolRep1A1T7_CNhs12388_tpm_rev Saos-2W/AscorbicAcidBgp_02hr00minBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr00min, biol_rep1 (A1 T7)_CNhs12388_12669-135A5_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification02hr00minBiolRep1A1T7_CNhs12388_tpm_fwd Saos-2W/AscorbicAcidBgp_02hr00minBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 02hr00min, biol_rep1 (A1 T7)_CNhs12388_12669-135A5_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr40minBiolRep3A3T6_CNhs12880_tpm_rev Saos-2W/AscorbicAcidBgp_01hr40minBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr40min, biol_rep3 (A3 T6)_CNhs12880_12864-137E2_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr40minBiolRep3A3T6_CNhs12880_tpm_fwd Saos-2W/AscorbicAcidBgp_01hr40minBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr40min, biol_rep3 (A3 T6)_CNhs12880_12864-137E2_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr40minBiolRep2A2T6_CNhs12866_tpm_rev Saos-2W/AscorbicAcidBgp_01hr40minBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr40min, biol_rep2 (A2 T6)_CNhs12866_12766-136C3_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr40minBiolRep2A2T6_CNhs12866_tpm_fwd Saos-2W/AscorbicAcidBgp_01hr40minBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr40min, biol_rep2 (A2 T6)_CNhs12866_12766-136C3_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr40minBiolRep1A1T6_CNhs12387_tpm_rev Saos-2W/AscorbicAcidBgp_01hr40minBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr40min, biol_rep1 (A1 T6)_CNhs12387_12668-135A4_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr40minBiolRep1A1T6_CNhs12387_tpm_fwd Saos-2W/AscorbicAcidBgp_01hr40minBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr40min, biol_rep1 (A1 T6)_CNhs12387_12668-135A4_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr20minBiolRep3A3T5_CNhs12879_tpm_rev Saos-2W/AscorbicAcidBgp_01hr20minBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr20min, biol_rep3 (A3 T5)_CNhs12879_12863-137E1_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr20minBiolRep3A3T5_CNhs12879_tpm_fwd Saos-2W/AscorbicAcidBgp_01hr20minBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr20min, biol_rep3 (A3 T5)_CNhs12879_12863-137E1_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr20minBiolRep2A2T5_CNhs12864_tpm_rev Saos-2W/AscorbicAcidBgp_01hr20minBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr20min, biol_rep2 (A2 T5)_CNhs12864_12765-136C2_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr20minBiolRep2A2T5_CNhs12864_tpm_fwd Saos-2W/AscorbicAcidBgp_01hr20minBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr20min, biol_rep2 (A2 T5)_CNhs12864_12765-136C2_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr20minBiolRep1A1T5_CNhs12386_tpm_rev Saos-2W/AscorbicAcidBgp_01hr20minBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr20min, biol_rep1 (A1 T5)_CNhs12386_12667-135A3_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr20minBiolRep1A1T5_CNhs12386_tpm_fwd Saos-2W/AscorbicAcidBgp_01hr20minBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr20min, biol_rep1 (A1 T5)_CNhs12386_12667-135A3_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr00minBiolRep3A3T4_CNhs12955_tpm_rev Saos-2W/AscorbicAcidBgp_01hr00minBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr00min, biol_rep3 (A3 T4)_CNhs12955_12862-137D9_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr00minBiolRep3A3T4_CNhs12955_tpm_fwd Saos-2W/AscorbicAcidBgp_01hr00minBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr00min, biol_rep3 (A3 T4)_CNhs12955_12862-137D9_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr00minBiolRep2A2T4_CNhs12863_tpm_rev Saos-2W/AscorbicAcidBgp_01hr00minBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr00min, biol_rep2 (A2 T4)_CNhs12863_12764-136C1_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr00minBiolRep2A2T4_CNhs12863_tpm_fwd Saos-2W/AscorbicAcidBgp_01hr00minBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr00min, biol_rep2 (A2 T4)_CNhs12863_12764-136C1_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr00minBiolRep1A1T4_CNhs12384_tpm_rev Saos-2W/AscorbicAcidBgp_01hr00minBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr00min, biol_rep1 (A1 T4)_CNhs12384_12666-135A2_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification01hr00minBiolRep1A1T4_CNhs12384_tpm_fwd Saos-2W/AscorbicAcidBgp_01hr00minBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 01hr00min, biol_rep1 (A1 T4)_CNhs12384_12666-135A2_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr45minBiolRep3A3T3_CNhs12878_tpm_rev Saos-2W/AscorbicAcidBgp_00hr45minBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr45min, biol_rep3 (A3 T3)_CNhs12878_12861-137D8_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr45minBiolRep3A3T3_CNhs12878_tpm_fwd Saos-2W/AscorbicAcidBgp_00hr45minBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr45min, biol_rep3 (A3 T3)_CNhs12878_12861-137D8_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr45minBiolRep2A2T3_CNhs12862_tpm_rev Saos-2W/AscorbicAcidBgp_00hr45minBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr45min, biol_rep2 (A2 T3)_CNhs12862_12763-136B9_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr45minBiolRep2A2T3_CNhs12862_tpm_fwd Saos-2W/AscorbicAcidBgp_00hr45minBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr45min, biol_rep2 (A2 T3)_CNhs12862_12763-136B9_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr45minBiolRep1A1T3_CNhs12383_tpm_rev Saos-2W/AscorbicAcidBgp_00hr45minBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr45min, biol_rep1 (A1 T3)_CNhs12383_12665-135A1_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr45minBiolRep1A1T3_CNhs12383_tpm_fwd Saos-2W/AscorbicAcidBgp_00hr45minBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr45min, biol_rep1 (A1 T3)_CNhs12383_12665-135A1_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr30minBiolRep3A3T2_CNhs12954_tpm_rev Saos-2W/AscorbicAcidBgp_00hr30minBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr30min, biol_rep3 (A3 T2)_CNhs12954_12860-137D7_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr30minBiolRep3A3T2_CNhs12954_tpm_fwd Saos-2W/AscorbicAcidBgp_00hr30minBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr30min, biol_rep3 (A3 T2)_CNhs12954_12860-137D7_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr30minBiolRep2A2T2_CNhs12861_tpm_rev Saos-2W/AscorbicAcidBgp_00hr30minBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr30min, biol_rep2 (A2 T2)_CNhs12861_12762-136B8_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr30minBiolRep2A2T2_CNhs12861_tpm_fwd Saos-2W/AscorbicAcidBgp_00hr30minBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr30min, biol_rep2 (A2 T2)_CNhs12861_12762-136B8_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr30minBiolRep1A1T2_CNhs12382_tpm_rev Saos-2W/AscorbicAcidBgp_00hr30minBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr30min, biol_rep1 (A1 T2)_CNhs12382_12664-134I9_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr30minBiolRep1A1T2_CNhs12382_tpm_fwd Saos-2W/AscorbicAcidBgp_00hr30minBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr30min, biol_rep1 (A1 T2)_CNhs12382_12664-134I9_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr15minBiolRep3A3T1_CNhs12877_tpm_rev Saos-2W/AscorbicAcidBgp_00hr15minBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr15min, biol_rep3 (A3 T1)_CNhs12877_12859-137D6_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr15minBiolRep3A3T1_CNhs12877_tpm_fwd Saos-2W/AscorbicAcidBgp_00hr15minBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr15min, biol_rep3 (A3 T1)_CNhs12877_12859-137D6_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr15minBiolRep2A2T1_CNhs12860_tpm_rev Saos-2W/AscorbicAcidBgp_00hr15minBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr15min, biol_rep2 (A2 T1)_CNhs12860_12761-136B7_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr15minBiolRep2A2T1_CNhs12860_tpm_fwd Saos-2W/AscorbicAcidBgp_00hr15minBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr15min, biol_rep2 (A2 T1)_CNhs12860_12761-136B7_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr15minBiolRep1A1T1_CNhs12381_tpm_rev Saos-2W/AscorbicAcidBgp_00hr15minBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr15min, biol_rep1 (A1 T1)_CNhs12381_12663-134I8_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr15minBiolRep1A1T1_CNhs12381_tpm_fwd Saos-2W/AscorbicAcidBgp_00hr15minBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr15min, biol_rep1 (A1 T1)_CNhs12381_12663-134I8_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr00minBiolRep3A3T0_CNhs12952_tpm_rev Saos-2W/AscorbicAcidBgp_00hr00minBr3- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr00min, biol_rep3 (A3 T0)_CNhs12952_12858-137D5_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr00minBiolRep3A3T0_CNhs12952_tpm_fwd Saos-2W/AscorbicAcidBgp_00hr00minBr3+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr00min, biol_rep3 (A3 T0)_CNhs12952_12858-137D5_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr00minBiolRep2A2T0_CNhs12859_tpm_rev Saos-2W/AscorbicAcidBgp_00hr00minBr2- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr00min, biol_rep2 (A2 T0)_CNhs12859_12760-136B6_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr00minBiolRep2A2T0_CNhs12859_tpm_fwd Saos-2W/AscorbicAcidBgp_00hr00minBr2+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr00min, biol_rep2 (A2 T0)_CNhs12859_12760-136B6_forward Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr00minBiolRep1A1T0_CNhs11918_tpm_rev Saos-2W/AscorbicAcidBgp_00hr00minBr1- Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr00min, biol_rep1 (A1 T0)_CNhs11918_12662-134I7_reverse Regulation 
Saos2OsteosarcomaTreatedWithAscorbicAcidAndBGPToInduceCalcification00hr00minBiolRep1A1T0_CNhs11918_tpm_fwd Saos-2W/AscorbicAcidBgp_00hr00minBr1+ Saos-2 osteosarcoma treated with ascorbic acid and BGP to induce calcification, 00hr00min, biol_rep1 (A1 T0)_CNhs11918_12662-134I7_forward Regulation 
COBLaRinderpestInfection48hrBiolRep3_CNhs14434_tpm_rev Tc:COBL-aRinderpest_48hrBr3- COBL-a rinderpest infection, 48hr, biol_rep3_CNhs14434_13567-146B3_reverse Regulation 
COBLaRinderpestInfection48hrBiolRep3_CNhs14434_tpm_fwd Tc:COBL-aRinderpest_48hrBr3+ COBL-a rinderpest infection, 48hr, biol_rep3_CNhs14434_13567-146B3_forward Regulation 
COBLaRinderpestInfection48hrBiolRep2_CNhs14432_tpm_rev Tc:COBL-aRinderpest_48hrBr2- COBL-a rinderpest infection, 48hr, biol_rep2_CNhs14432_13566-146B2_reverse Regulation 
COBLaRinderpestInfection48hrBiolRep2_CNhs14432_tpm_fwd Tc:COBL-aRinderpest_48hrBr2+ COBL-a rinderpest infection, 48hr, biol_rep2_CNhs14432_13566-146B2_forward Regulation 
COBLaRinderpestInfection48hrBiolRep1_CNhs14431_tpm_rev Tc:COBL-aRinderpest_48hrBr1- COBL-a rinderpest infection, 48hr, biol_rep1_CNhs14431_13565-146B1_reverse Regulation 
COBLaRinderpestInfection48hrBiolRep1_CNhs14431_tpm_fwd Tc:COBL-aRinderpest_48hrBr1+ COBL-a rinderpest infection, 48hr, biol_rep1_CNhs14431_13565-146B1_forward Regulation 
COBLaRinderpestInfection24hrBiolRep3_CNhs14430_tpm_rev Tc:COBL-aRinderpest_24hrBr3- COBL-a rinderpest infection, 24hr, biol_rep3_CNhs14430_13564-146A9_reverse Regulation 
COBLaRinderpestInfection24hrBiolRep3_CNhs14430_tpm_fwd Tc:COBL-aRinderpest_24hrBr3+ COBL-a rinderpest infection, 24hr, biol_rep3_CNhs14430_13564-146A9_forward Regulation 
COBLaRinderpestInfection24hrBiolRep2_CNhs14429_tpm_rev Tc:COBL-aRinderpest_24hrBr2- COBL-a rinderpest infection, 24hr, biol_rep2_CNhs14429_13563-146A8_reverse Regulation 
COBLaRinderpestInfection24hrBiolRep2_CNhs14429_tpm_fwd Tc:COBL-aRinderpest_24hrBr2+ COBL-a rinderpest infection, 24hr, biol_rep2_CNhs14429_13563-146A8_forward Regulation 
COBLaRinderpestInfection24hrBiolRep1_CNhs14428_tpm_rev Tc:COBL-aRinderpest_24hrBr1- COBL-a rinderpest infection, 24hr, biol_rep1_CNhs14428_13562-146A7_reverse Regulation 
COBLaRinderpestInfection24hrBiolRep1_CNhs14428_tpm_fwd Tc:COBL-aRinderpest_24hrBr1+ COBL-a rinderpest infection, 24hr, biol_rep1_CNhs14428_13562-146A7_forward Regulation 
COBLaRinderpestInfection12hrBiolRep3_CNhs14427_tpm_rev Tc:COBL-aRinderpest_12hrBr3- COBL-a rinderpest infection, 12hr, biol_rep3_CNhs14427_13561-146A6_reverse Regulation 
COBLaRinderpestInfection12hrBiolRep3_CNhs14427_tpm_fwd Tc:COBL-aRinderpest_12hrBr3+ COBL-a rinderpest infection, 12hr, biol_rep3_CNhs14427_13561-146A6_forward Regulation 
COBLaRinderpestInfection12hrBiolRep2_CNhs14426_tpm_rev Tc:COBL-aRinderpest_12hrBr2- COBL-a rinderpest infection, 12hr, biol_rep2_CNhs14426_13560-146A5_reverse Regulation 
COBLaRinderpestInfection12hrBiolRep2_CNhs14426_tpm_fwd Tc:COBL-aRinderpest_12hrBr2+ COBL-a rinderpest infection, 12hr, biol_rep2_CNhs14426_13560-146A5_forward Regulation 
COBLaRinderpestInfection12hrBiolRep1_CNhs14425_tpm_rev Tc:COBL-aRinderpest_12hrBr1- COBL-a rinderpest infection, 12hr, biol_rep1_CNhs14425_13559-146A4_reverse Regulation 
COBLaRinderpestInfection12hrBiolRep1_CNhs14425_tpm_fwd Tc:COBL-aRinderpest_12hrBr1+ COBL-a rinderpest infection, 12hr, biol_rep1_CNhs14425_13559-146A4_forward Regulation 
COBLaRinderpestInfection06hrBiolRep3_CNhs14424_tpm_rev Tc:COBL-aRinderpest_06hrBr3- COBL-a rinderpest infection, 06hr, biol_rep3_CNhs14424_13558-146A3_reverse Regulation 
COBLaRinderpestInfection06hrBiolRep3_CNhs14424_tpm_fwd Tc:COBL-aRinderpest_06hrBr3+ COBL-a rinderpest infection, 06hr, biol_rep3_CNhs14424_13558-146A3_forward Regulation 
COBLaRinderpestInfection06hrBiolRep2_CNhs14423_tpm_rev Tc:COBL-aRinderpest_06hrBr2- COBL-a rinderpest infection, 06hr, biol_rep2_CNhs14423_13557-146A2_reverse Regulation 
COBLaRinderpestInfection06hrBiolRep2_CNhs14423_tpm_fwd Tc:COBL-aRinderpest_06hrBr2+ COBL-a rinderpest infection, 06hr, biol_rep2_CNhs14423_13557-146A2_forward Regulation 
COBLaRinderpestInfection06hrBiolRep1_CNhs14422_tpm_rev Tc:COBL-aRinderpest_06hrBr1- COBL-a rinderpest infection, 06hr, biol_rep1_CNhs14422_13556-146A1_reverse Regulation 
COBLaRinderpestInfection06hrBiolRep1_CNhs14422_tpm_fwd Tc:COBL-aRinderpest_06hrBr1+ COBL-a rinderpest infection, 06hr, biol_rep1_CNhs14422_13556-146A1_forward Regulation 
COBLaRinderpestInfection00hrBiolRep3_CNhs14421_tpm_rev Tc:COBL-aRinderpest_00hrBr3- COBL-a rinderpest infection, 00hr, biol_rep3_CNhs14421_13555-145I9_reverse Regulation 
COBLaRinderpestInfection00hrBiolRep3_CNhs14421_tpm_fwd Tc:COBL-aRinderpest_00hrBr3+ COBL-a rinderpest infection, 00hr, biol_rep3_CNhs14421_13555-145I9_forward Regulation 
COBLaRinderpestInfection00hrBiolRep2_CNhs14420_tpm_rev Tc:COBL-aRinderpest_00hrBr2- COBL-a rinderpest infection, 00hr, biol_rep2_CNhs14420_13554-145I8_reverse Regulation 
COBLaRinderpestInfection00hrBiolRep2_CNhs14420_tpm_fwd Tc:COBL-aRinderpest_00hrBr2+ COBL-a rinderpest infection, 00hr, biol_rep2_CNhs14420_13554-145I8_forward Regulation 
COBLaRinderpestInfection00hrBiolRep1_CNhs14419_tpm_rev Tc:COBL-aRinderpest_00hrBr1- COBL-a rinderpest infection, 00hr, biol_rep1_CNhs14419_13553-145I7_reverse Regulation 
COBLaRinderpestInfection00hrBiolRep1_CNhs14419_tpm_fwd Tc:COBL-aRinderpest_00hrBr1+ COBL-a rinderpest infection, 00hr, biol_rep1_CNhs14419_13553-145I7_forward Regulation 
COBLaRinderpestCInfection48hrBiolRep3_CNhs14446_tpm_rev Tc:COBL-aRinderpest(-C)_48hrBr3- COBL-a rinderpest(-C) infection, 48hr, biol_rep3_CNhs14446_13579-146C6_reverse Regulation 
COBLaRinderpestCInfection48hrBiolRep3_CNhs14446_tpm_fwd Tc:COBL-aRinderpest(-C)_48hrBr3+ COBL-a rinderpest(-C) infection, 48hr, biol_rep3_CNhs14446_13579-146C6_forward Regulation 
COBLaRinderpestCInfection48hrBiolRep2_CNhs14445_tpm_rev Tc:COBL-aRinderpest(-C)_48hrBr2- COBL-a rinderpest(-C) infection, 48hr, biol_rep2_CNhs14445_13578-146C5_reverse Regulation 
COBLaRinderpestCInfection48hrBiolRep2_CNhs14445_tpm_fwd Tc:COBL-aRinderpest(-C)_48hrBr2+ COBL-a rinderpest(-C) infection, 48hr, biol_rep2_CNhs14445_13578-146C5_forward Regulation 
COBLaRinderpestCInfection48hrBiolRep1_CNhs14444_tpm_rev Tc:COBL-aRinderpest(-C)_48hrBr1- COBL-a rinderpest(-C) infection, 48hr, biol_rep1_CNhs14444_13577-146C4_reverse Regulation 
COBLaRinderpestCInfection48hrBiolRep1_CNhs14444_tpm_fwd Tc:COBL-aRinderpest(-C)_48hrBr1+ COBL-a rinderpest(-C) infection, 48hr, biol_rep1_CNhs14444_13577-146C4_forward Regulation 
COBLaRinderpestCInfection24hrBiolRep3_CNhs14443_tpm_rev Tc:COBL-aRinderpest(-C)_24hrBr3- COBL-a rinderpest(-C) infection, 24hr, biol_rep3_CNhs14443_13576-146C3_reverse Regulation 
COBLaRinderpestCInfection24hrBiolRep3_CNhs14443_tpm_fwd Tc:COBL-aRinderpest(-C)_24hrBr3+ COBL-a rinderpest(-C) infection, 24hr, biol_rep3_CNhs14443_13576-146C3_forward Regulation 
COBLaRinderpestCInfection24hrBiolRep2_CNhs14442_tpm_rev Tc:COBL-aRinderpest(-C)_24hrBr2- COBL-a rinderpest(-C) infection, 24hr, biol_rep2_CNhs14442_13575-146C2_reverse Regulation 
COBLaRinderpestCInfection24hrBiolRep2_CNhs14442_tpm_fwd Tc:COBL-aRinderpest(-C)_24hrBr2+ COBL-a rinderpest(-C) infection, 24hr, biol_rep2_CNhs14442_13575-146C2_forward Regulation 
COBLaRinderpestCInfection24hrBiolRep1_CNhs14441_tpm_rev Tc:COBL-aRinderpest(-C)_24hrBr1- COBL-a rinderpest(-C) infection, 24hr, biol_rep1_CNhs14441_13574-146C1_reverse Regulation 
COBLaRinderpestCInfection24hrBiolRep1_CNhs14441_tpm_fwd Tc:COBL-aRinderpest(-C)_24hrBr1+ COBL-a rinderpest(-C) infection, 24hr, biol_rep1_CNhs14441_13574-146C1_forward Regulation 
COBLaRinderpestCInfection12hrBiolRep3_CNhs14440_tpm_rev Tc:COBL-aRinderpest(-C)_12hrBr3- COBL-a rinderpest(-C) infection, 12hr, biol_rep3_CNhs14440_13573-146B9_reverse Regulation 
COBLaRinderpestCInfection12hrBiolRep3_CNhs14440_tpm_fwd Tc:COBL-aRinderpest(-C)_12hrBr3+ COBL-a rinderpest(-C) infection, 12hr, biol_rep3_CNhs14440_13573-146B9_forward Regulation 
COBLaRinderpestCInfection12hrBiolRep2_CNhs14439_tpm_rev Tc:COBL-aRinderpest(-C)_12hrBr2- COBL-a rinderpest(-C) infection, 12hr, biol_rep2_CNhs14439_13572-146B8_reverse Regulation 
COBLaRinderpestCInfection12hrBiolRep2_CNhs14439_tpm_fwd Tc:COBL-aRinderpest(-C)_12hrBr2+ COBL-a rinderpest(-C) infection, 12hr, biol_rep2_CNhs14439_13572-146B8_forward Regulation 
COBLaRinderpestCInfection12hrBiolRep1_CNhs14438_tpm_rev Tc:COBL-aRinderpest(-C)_12hrBr1- COBL-a rinderpest(-C) infection, 12hr, biol_rep1_CNhs14438_13571-146B7_reverse Regulation 
COBLaRinderpestCInfection12hrBiolRep1_CNhs14438_tpm_fwd Tc:COBL-aRinderpest(-C)_12hrBr1+ COBL-a rinderpest(-C) infection, 12hr, biol_rep1_CNhs14438_13571-146B7_forward Regulation 
COBLaRinderpestCInfection06hrBiolRep3_CNhs14437_tpm_rev Tc:COBL-aRinderpest(-C)_06hrBr3- COBL-a rinderpest(-C) infection, 06hr, biol_rep3_CNhs14437_13570-146B6_reverse Regulation 
COBLaRinderpestCInfection06hrBiolRep3_CNhs14437_tpm_fwd Tc:COBL-aRinderpest(-C)_06hrBr3+ COBL-a rinderpest(-C) infection, 06hr, biol_rep3_CNhs14437_13570-146B6_forward Regulation 
COBLaRinderpestCInfection06hrBiolRep2_CNhs14436_tpm_rev Tc:COBL-aRinderpest(-C)_06hrBr2- COBL-a rinderpest(-C) infection, 06hr, biol_rep2_CNhs14436_13569-146B5_reverse Regulation 
COBLaRinderpestCInfection06hrBiolRep2_CNhs14436_tpm_fwd Tc:COBL-aRinderpest(-C)_06hrBr2+ COBL-a rinderpest(-C) infection, 06hr, biol_rep2_CNhs14436_13569-146B5_forward Regulation 
COBLaRinderpestCInfection06hrBiolRep1_CNhs14435_tpm_rev Tc:COBL-aRinderpest(-C)_06hrBr1- COBL-a rinderpest(-C) infection, 06hr, biol_rep1_CNhs14435_13568-146B4_reverse Regulation 
COBLaRinderpestCInfection06hrBiolRep1_CNhs14435_tpm_fwd Tc:COBL-aRinderpest(-C)_06hrBr1+ COBL-a rinderpest(-C) infection, 06hr, biol_rep1_CNhs14435_13568-146B4_forward Regulation 
293SLAMRinderpestInfection24hrBiolRep3_CNhs14418_tpm_rev Tc:293SlamRinderpest_24hrBr3- 293SLAM rinderpest infection, 24hr, biol_rep3_CNhs14418_13552-145I6_reverse Regulation 
293SLAMRinderpestInfection24hrBiolRep3_CNhs14418_tpm_fwd Tc:293SlamRinderpest_24hrBr3+ 293SLAM rinderpest infection, 24hr, biol_rep3_CNhs14418_13552-145I6_forward Regulation 
293SLAMRinderpestInfection24hrBiolRep2_CNhs14417_tpm_rev Tc:293SlamRinderpest_24hrBr2- 293SLAM rinderpest infection, 24hr, biol_rep2_CNhs14417_13551-145I5_reverse Regulation 
293SLAMRinderpestInfection24hrBiolRep2_CNhs14417_tpm_fwd Tc:293SlamRinderpest_24hrBr2+ 293SLAM rinderpest infection, 24hr, biol_rep2_CNhs14417_13551-145I5_forward Regulation 
293SLAMRinderpestInfection24hrBiolRep1_CNhs14416_tpm_rev Tc:293SlamRinderpest_24hrBr1- 293SLAM rinderpest infection, 24hr, biol_rep1_CNhs14416_13550-145I4_reverse Regulation 
293SLAMRinderpestInfection24hrBiolRep1_CNhs14416_tpm_fwd Tc:293SlamRinderpest_24hrBr1+ 293SLAM rinderpest infection, 24hr, biol_rep1_CNhs14416_13550-145I4_forward Regulation 
293SLAMRinderpestInfection12hrBiolRep3_CNhs14415_tpm_rev Tc:293SlamRinderpest_12hrBr3- 293SLAM rinderpest infection, 12hr, biol_rep3_CNhs14415_13549-145I3_reverse Regulation 
293SLAMRinderpestInfection12hrBiolRep3_CNhs14415_tpm_fwd Tc:293SlamRinderpest_12hrBr3+ 293SLAM rinderpest infection, 12hr, biol_rep3_CNhs14415_13549-145I3_forward Regulation 
293SLAMRinderpestInfection12hrBiolRep2_CNhs14414_tpm_rev Tc:293SlamRinderpest_12hrBr2- 293SLAM rinderpest infection, 12hr, biol_rep2_CNhs14414_13548-145I2_reverse Regulation 
293SLAMRinderpestInfection12hrBiolRep2_CNhs14414_tpm_fwd Tc:293SlamRinderpest_12hrBr2+ 293SLAM rinderpest infection, 12hr, biol_rep2_CNhs14414_13548-145I2_forward Regulation 
293SLAMRinderpestInfection12hrBiolRep1_CNhs14413_tpm_rev Tc:293SlamRinderpest_12hrBr1- 293SLAM rinderpest infection, 12hr, biol_rep1_CNhs14413_13547-145I1_reverse Regulation 
293SLAMRinderpestInfection12hrBiolRep1_CNhs14413_tpm_fwd Tc:293SlamRinderpest_12hrBr1+ 293SLAM rinderpest infection, 12hr, biol_rep1_CNhs14413_13547-145I1_forward Regulation 
293SLAMRinderpestInfection06hrBiolRep3_CNhs14412_tpm_rev Tc:293SlamRinderpest_06hrBr3- 293SLAM rinderpest infection, 06hr, biol_rep3_CNhs14412_13546-145H9_reverse Regulation 
293SLAMRinderpestInfection06hrBiolRep3_CNhs14412_tpm_fwd Tc:293SlamRinderpest_06hrBr3+ 293SLAM rinderpest infection, 06hr, biol_rep3_CNhs14412_13546-145H9_forward Regulation 
293SLAMRinderpestInfection06hrBiolRep2_CNhs14411_tpm_rev Tc:293SlamRinderpest_06hrBr2- 293SLAM rinderpest infection, 06hr, biol_rep2_CNhs14411_13545-145H8_reverse Regulation 
293SLAMRinderpestInfection06hrBiolRep2_CNhs14411_tpm_fwd Tc:293SlamRinderpest_06hrBr2+ 293SLAM rinderpest infection, 06hr, biol_rep2_CNhs14411_13545-145H8_forward Regulation 
293SLAMRinderpestInfection06hrBiolRep1_CNhs14410_tpm_rev Tc:293SlamRinderpest_06hrBr1- 293SLAM rinderpest infection, 06hr, biol_rep1_CNhs14410_13544-145H7_reverse Regulation 
293SLAMRinderpestInfection06hrBiolRep1_CNhs14410_tpm_fwd Tc:293SlamRinderpest_06hrBr1+ 293SLAM rinderpest infection, 06hr, biol_rep1_CNhs14410_13544-145H7_forward Regulation 
293SLAMRinderpestInfection00hrBiolRep3_CNhs14408_tpm_rev Tc:293SlamRinderpest_00hrBr3- 293SLAM rinderpest infection, 00hr, biol_rep3_CNhs14408_13543-145H6_reverse Regulation 
293SLAMRinderpestInfection00hrBiolRep3_CNhs14408_tpm_fwd Tc:293SlamRinderpest_00hrBr3+ 293SLAM rinderpest infection, 00hr, biol_rep3_CNhs14408_13543-145H6_forward Regulation 
293SLAMRinderpestInfection00hrBiolRep2_CNhs14407_tpm_rev Tc:293SlamRinderpest_00hrBr2- 293SLAM rinderpest infection, 00hr, biol_rep2_CNhs14407_13542-145H5_reverse Regulation 
293SLAMRinderpestInfection00hrBiolRep2_CNhs14407_tpm_fwd Tc:293SlamRinderpest_00hrBr2+ 293SLAM rinderpest infection, 00hr, biol_rep2_CNhs14407_13542-145H5_forward Regulation 
293SLAMRinderpestInfection00hrBiolRep1_CNhs14406_tpm_rev Tc:293SlamRinderpest_00hrBr1- 293SLAM rinderpest infection, 00hr, biol_rep1_CNhs14406_13541-145H4_reverse Regulation 
293SLAMRinderpestInfection00hrBiolRep1_CNhs14406_tpm_fwd Tc:293SlamRinderpest_00hrBr1+ 293SLAM rinderpest infection, 00hr, biol_rep1_CNhs14406_13541-145H4_forward Regulation 
AdipocyteDifferentiationDay12Donor4_CNhs13419_tpm_rev Tc:AdipoDiff_Day12D4- Adipocyte differentiation, day12, donor4_CNhs13419_13030-139E6_reverse Regulation 
AdipocyteDifferentiationDay12Donor4_CNhs13419_tpm_fwd Tc:AdipoDiff_Day12D4+ Adipocyte differentiation, day12, donor4_CNhs13419_13030-139E6_forward Regulation 
AdipocyteDifferentiationDay12Donor3_CNhs13416_tpm_rev Tc:AdipoDiff_Day12D3- Adipocyte differentiation, day12, donor3_CNhs13416_13027-139E3_reverse Regulation 
AdipocyteDifferentiationDay12Donor3_CNhs13416_tpm_fwd Tc:AdipoDiff_Day12D3+ Adipocyte differentiation, day12, donor3_CNhs13416_13027-139E3_forward Regulation 
AdipocyteDifferentiationDay12Donor2_CNhs13412_tpm_rev Tc:AdipoDiff_Day12D2- Adipocyte differentiation, day12, donor2_CNhs13412_13024-139D9_reverse Regulation 
AdipocyteDifferentiationDay12Donor2_CNhs13412_tpm_fwd Tc:AdipoDiff_Day12D2+ Adipocyte differentiation, day12, donor2_CNhs13412_13024-139D9_forward Regulation 
AdipocyteDifferentiationDay12Donor1_CNhs13336_tpm_rev Tc:AdipoDiff_Day12D1- Adipocyte differentiation, day12, donor1_CNhs13336_13021-139D6_reverse Regulation 
AdipocyteDifferentiationDay12Donor1_CNhs13336_tpm_fwd Tc:AdipoDiff_Day12D1+ Adipocyte differentiation, day12, donor1_CNhs13336_13021-139D6_forward Regulation 
AdipocyteDifferentiationDay08Donor4_CNhs13418_tpm_rev Tc:AdipoDiff_Day08D4- Adipocyte differentiation, day08, donor4_CNhs13418_13029-139E5_reverse Regulation 
AdipocyteDifferentiationDay08Donor4_CNhs13418_tpm_fwd Tc:AdipoDiff_Day08D4+ Adipocyte differentiation, day08, donor4_CNhs13418_13029-139E5_forward Regulation 
AdipocyteDifferentiationDay08Donor3_CNhs13415_tpm_rev Tc:AdipoDiff_Day08D3- Adipocyte differentiation, day08, donor3_CNhs13415_13026-139E2_reverse Regulation 
AdipocyteDifferentiationDay08Donor3_CNhs13415_tpm_fwd Tc:AdipoDiff_Day08D3+ Adipocyte differentiation, day08, donor3_CNhs13415_13026-139E2_forward Regulation 
AdipocyteDifferentiationDay08Donor2_CNhs13411_tpm_rev Tc:AdipoDiff_Day08D2- Adipocyte differentiation, day08, donor2_CNhs13411_13023-139D8_reverse Regulation 
AdipocyteDifferentiationDay08Donor2_CNhs13411_tpm_fwd Tc:AdipoDiff_Day08D2+ Adipocyte differentiation, day08, donor2_CNhs13411_13023-139D8_forward Regulation 
AdipocyteDifferentiationDay08Donor1_CNhs12517_tpm_rev Tc:AdipoDiff_Day08D1- Adipocyte differentiation, day08, donor1_CNhs12517_13020-139D5_reverse Regulation 
AdipocyteDifferentiationDay08Donor1_CNhs12517_tpm_fwd Tc:AdipoDiff_Day08D1+ Adipocyte differentiation, day08, donor1_CNhs12517_13020-139D5_forward Regulation 
AdipocyteDifferentiationDay04Donor4_CNhs13417_tpm_rev Tc:AdipoDiff_Day04D4- Adipocyte differentiation, day04, donor4_CNhs13417_13028-139E4_reverse Regulation 
AdipocyteDifferentiationDay04Donor4_CNhs13417_tpm_fwd Tc:AdipoDiff_Day04D4+ Adipocyte differentiation, day04, donor4_CNhs13417_13028-139E4_forward Regulation 
AdipocyteDifferentiationDay04Donor3_CNhs13413_tpm_rev Tc:AdipoDiff_Day04D3- Adipocyte differentiation, day04, donor3_CNhs13413_13025-139E1_reverse Regulation 
AdipocyteDifferentiationDay04Donor3_CNhs13413_tpm_fwd Tc:AdipoDiff_Day04D3+ Adipocyte differentiation, day04, donor3_CNhs13413_13025-139E1_forward Regulation 
AdipocyteDifferentiationDay04Donor2_CNhs13410_tpm_rev Tc:AdipoDiff_Day04D2- Adipocyte differentiation, day04, donor2_CNhs13410_13022-139D7_reverse Regulation 
AdipocyteDifferentiationDay04Donor2_CNhs13410_tpm_fwd Tc:AdipoDiff_Day04D2+ Adipocyte differentiation, day04, donor2_CNhs13410_13022-139D7_forward Regulation 
AdipocyteDifferentiationDay04Donor1_CNhs12516_tpm_rev Tc:AdipoDiff_Day04D1- Adipocyte differentiation, day04, donor1_CNhs12516_13019-139D4_reverse Regulation 
AdipocyteDifferentiationDay04Donor1_CNhs12516_tpm_fwd Tc:AdipoDiff_Day04D1+ Adipocyte differentiation, day04, donor1_CNhs12516_13019-139D4_forward Regulation 
MyoblastDifferentiationToMyotubesDay12DuchenneMuscularDystrophyDonor3_CNhs14613_tpm_rev MyoblastToMyotubes_Day12D3- Myoblast differentiation to myotubes, day12, Duchenne Muscular Dystrophy donor3_CNhs14613_13522-145F3_reverse Regulation 
MyoblastDifferentiationToMyotubesDay12ControlDonor3_CNhs14585_tpm_rev MyoblastToMyotubes_Day12D3- Myoblast differentiation to myotubes, day12, control donor3_CNhs14585_13495-145C3_reverse Regulation 
MyoblastDifferentiationToMyotubesDay12DuchenneMuscularDystrophyDonor3_CNhs14613_tpm_fwd MyoblastToMyotubes_Day12D3+ Myoblast differentiation to myotubes, day12, Duchenne Muscular Dystrophy donor3_CNhs14613_13522-145F3_forward Regulation 
MyoblastDifferentiationToMyotubesDay12ControlDonor3_CNhs14585_tpm_fwd MyoblastToMyotubes_Day12D3+ Myoblast differentiation to myotubes, day12, control donor3_CNhs14585_13495-145C3_forward Regulation 
MyoblastDifferentiationToMyotubesDay12DuchenneMuscularDystrophyDonor2_CNhs14604_tpm_rev MyoblastToMyotubes_Day12D2- Myoblast differentiation to myotubes, day12, Duchenne Muscular Dystrophy donor2_CNhs14604_13513-145E3_reverse Regulation 
MyoblastDifferentiationToMyotubesDay12ControlDonor2_CNhs14576_tpm_rev MyoblastToMyotubes_Day12D2- Myoblast differentiation to myotubes, day12, control donor2_CNhs14576_13486-145B3_reverse Regulation 
MyoblastDifferentiationToMyotubesDay12DuchenneMuscularDystrophyDonor2_CNhs14604_tpm_fwd MyoblastToMyotubes_Day12D2+ Myoblast differentiation to myotubes, day12, Duchenne Muscular Dystrophy donor2_CNhs14604_13513-145E3_forward Regulation 
MyoblastDifferentiationToMyotubesDay12ControlDonor2_CNhs14576_tpm_fwd MyoblastToMyotubes_Day12D2+ Myoblast differentiation to myotubes, day12, control donor2_CNhs14576_13486-145B3_forward Regulation 
MyoblastDifferentiationToMyotubesDay12ControlDonor1_CNhs14566_tpm_rev MyoblastToMyotubes_Day12D1- Myoblast differentiation to myotubes, day12, control donor1_CNhs14566_13477-145A3_reverse Regulation 
MyoblastDifferentiationToMyotubesDay12DuchenneMuscularDystrophyDonor1_CNhs14595_tpm_rev MyoblastToMyotubes_Day12D1- Myoblast differentiation to myotubes, day12, Duchenne Muscular Dystrophy donor1_CNhs14595_13504-145D3_reverse Regulation 
MyoblastDifferentiationToMyotubesDay12ControlDonor1_CNhs14566_tpm_fwd MyoblastToMyotubes_Day12D1+ Myoblast differentiation to myotubes, day12, control donor1_CNhs14566_13477-145A3_forward Regulation 
MyoblastDifferentiationToMyotubesDay12DuchenneMuscularDystrophyDonor1_CNhs14595_tpm_fwd MyoblastToMyotubes_Day12D1+ Myoblast differentiation to myotubes, day12, Duchenne Muscular Dystrophy donor1_CNhs14595_13504-145D3_forward Regulation 
MyoblastDifferentiationToMyotubesDay10DuchenneMuscularDystrophyDonor3_CNhs14612_tpm_rev MyoblastToMyotubes_Day10D3- Myoblast differentiation to myotubes, day10, Duchenne Muscular Dystrophy donor3_CNhs14612_13521-145F2_reverse Regulation 
MyoblastDifferentiationToMyotubesDay10DuchenneMuscularDystrophyDonor3_CNhs14612_tpm_fwd MyoblastToMyotubes_Day10D3+ Myoblast differentiation to myotubes, day10, Duchenne Muscular Dystrophy donor3_CNhs14612_13521-145F2_forward Regulation 
MyoblastDifferentiationToMyotubesDay10ControlDonor2_CNhs14575_tpm_rev MyoblastToMyotubes_Day10D2- Myoblast differentiation to myotubes, day10, control donor2_CNhs14575_13485-145B2_reverse Regulation 
MyoblastDifferentiationToMyotubesDay10DuchenneMuscularDystrophyDonor2_CNhs14603_tpm_rev MyoblastToMyotubes_Day10D2- Myoblast differentiation to myotubes, day10, Duchenne Muscular Dystrophy donor2_CNhs14603_13512-145E2_reverse Regulation 
MyoblastDifferentiationToMyotubesDay10ControlDonor2_CNhs14575_tpm_fwd MyoblastToMyotubes_Day10D2+ Myoblast differentiation to myotubes, day10, control donor2_CNhs14575_13485-145B2_forward Regulation 
MyoblastDifferentiationToMyotubesDay10DuchenneMuscularDystrophyDonor2_CNhs14603_tpm_fwd MyoblastToMyotubes_Day10D2+ Myoblast differentiation to myotubes, day10, Duchenne Muscular Dystrophy donor2_CNhs14603_13512-145E2_forward Regulation 
MyoblastDifferentiationToMyotubesDay10DuchenneMuscularDystrophyDonor1_CNhs14594_tpm_rev MyoblastToMyotubes_Day10D1- Myoblast differentiation to myotubes, day10, Duchenne Muscular Dystrophy donor1_CNhs14594_13503-145D2_reverse Regulation 
MyoblastDifferentiationToMyotubesDay10ControlDonor1_CNhs13854_tpm_rev MyoblastToMyotubes_Day10D1- Myoblast differentiation to myotubes, day10, control donor1_CNhs13854_13476-145A2_reverse Regulation 
MyoblastDifferentiationToMyotubesDay10DuchenneMuscularDystrophyDonor1_CNhs14594_tpm_fwd MyoblastToMyotubes_Day10D1+ Myoblast differentiation to myotubes, day10, Duchenne Muscular Dystrophy donor1_CNhs14594_13503-145D2_forward Regulation 
MyoblastDifferentiationToMyotubesDay10ControlDonor1_CNhs13854_tpm_fwd MyoblastToMyotubes_Day10D1+ Myoblast differentiation to myotubes, day10, control donor1_CNhs13854_13476-145A2_forward Regulation 
MyoblastDifferentiationToMyotubesDay08ControlDonor3_CNhs14583_tpm_rev MyoblastToMyotubes_Day08D3- Myoblast differentiation to myotubes, day08, control donor3_CNhs14583_13493-145C1_reverse Regulation 
MyoblastDifferentiationToMyotubesDay08DuchenneMuscularDystrophyDonor3_CNhs14611_tpm_rev MyoblastToMyotubes_Day08D3- Myoblast differentiation to myotubes, day08, Duchenne Muscular Dystrophy donor3_CNhs14611_13520-145F1_reverse Regulation 
MyoblastDifferentiationToMyotubesDay08ControlDonor3_CNhs14583_tpm_fwd MyoblastToMyotubes_Day08D3+ Myoblast differentiation to myotubes, day08, control donor3_CNhs14583_13493-145C1_forward Regulation 
MyoblastDifferentiationToMyotubesDay08DuchenneMuscularDystrophyDonor3_CNhs14611_tpm_fwd MyoblastToMyotubes_Day08D3+ Myoblast differentiation to myotubes, day08, Duchenne Muscular Dystrophy donor3_CNhs14611_13520-145F1_forward Regulation 
MyoblastDifferentiationToMyotubesDay08ControlDonor2_CNhs14574_tpm_rev MyoblastToMyotubes_Day08D2- Myoblast differentiation to myotubes, day08, control donor2_CNhs14574_13484-145B1_reverse Regulation 
MyoblastDifferentiationToMyotubesDay08DuchenneMuscularDystrophyDonor2_CNhs14602_tpm_rev MyoblastToMyotubes_Day08D2- Myoblast differentiation to myotubes, day08, Duchenne Muscular Dystrophy donor2_CNhs14602_13511-145E1_reverse Regulation 
MyoblastDifferentiationToMyotubesDay08ControlDonor2_CNhs14574_tpm_fwd MyoblastToMyotubes_Day08D2+ Myoblast differentiation to myotubes, day08, control donor2_CNhs14574_13484-145B1_forward Regulation 
MyoblastDifferentiationToMyotubesDay08DuchenneMuscularDystrophyDonor2_CNhs14602_tpm_fwd MyoblastToMyotubes_Day08D2+ Myoblast differentiation to myotubes, day08, Duchenne Muscular Dystrophy donor2_CNhs14602_13511-145E1_forward Regulation 
MyoblastDifferentiationToMyotubesDay08DuchenneMuscularDystrophyDonor1_CNhs14592_tpm_rev MyoblastToMyotubes_Day08D1- Myoblast differentiation to myotubes, day08, Duchenne Muscular Dystrophy donor1_CNhs14592_13502-145D1_reverse Regulation 
MyoblastDifferentiationToMyotubesDay08ControlDonor1_CNhs13853_tpm_rev MyoblastToMyotubes_Day08D1- Myoblast differentiation to myotubes, day08, control donor1_CNhs13853_13475-145A1_reverse Regulation 
MyoblastDifferentiationToMyotubesDay08DuchenneMuscularDystrophyDonor1_CNhs14592_tpm_fwd MyoblastToMyotubes_Day08D1+ Myoblast differentiation to myotubes, day08, Duchenne Muscular Dystrophy donor1_CNhs14592_13502-145D1_forward Regulation 
MyoblastDifferentiationToMyotubesDay08ControlDonor1_CNhs13853_tpm_fwd MyoblastToMyotubes_Day08D1+ Myoblast differentiation to myotubes, day08, control donor1_CNhs13853_13475-145A1_forward Regulation 
MyoblastDifferentiationToMyotubesDay06DuchenneMuscularDystrophyDonor3_CNhs14610_tpm_rev MyoblastToMyotubes_Day06D3- Myoblast differentiation to myotubes, day06, Duchenne Muscular Dystrophy donor3_CNhs14610_13519-145E9_reverse Regulation 
MyoblastDifferentiationToMyotubesDay06ControlDonor3_CNhs14582_tpm_rev MyoblastToMyotubes_Day06D3- Myoblast differentiation to myotubes, day06, control donor3_CNhs14582_13492-145B9_reverse Regulation 
MyoblastDifferentiationToMyotubesDay06DuchenneMuscularDystrophyDonor3_CNhs14610_tpm_fwd MyoblastToMyotubes_Day06D3+ Myoblast differentiation to myotubes, day06, Duchenne Muscular Dystrophy donor3_CNhs14610_13519-145E9_forward Regulation 
MyoblastDifferentiationToMyotubesDay06ControlDonor3_CNhs14582_tpm_fwd MyoblastToMyotubes_Day06D3+ Myoblast differentiation to myotubes, day06, control donor3_CNhs14582_13492-145B9_forward Regulation 
MyoblastDifferentiationToMyotubesDay06ControlDonor2_CNhs14573_tpm_rev MyoblastToMyotubes_Day06D2- Myoblast differentiation to myotubes, day06, control donor2_CNhs14573_13483-145A9_reverse Regulation 
MyoblastDifferentiationToMyotubesDay06ControlDonor2_CNhs14573_tpm_fwd MyoblastToMyotubes_Day06D2+ Myoblast differentiation to myotubes, day06, control donor2_CNhs14573_13483-145A9_forward Regulation 
MyoblastDifferentiationToMyotubesDay06DuchenneMuscularDystrophyDonor1_CNhs14591_tpm_rev MyoblastToMyotubes_Day06D1- Myoblast differentiation to myotubes, day06, Duchenne Muscular Dystrophy donor1_CNhs14591_13501-145C9_reverse Regulation 
MyoblastDifferentiationToMyotubesDay06ControlDonor1_CNhs13852_tpm_rev MyoblastToMyotubes_Day06D1- Myoblast differentiation to myotubes, day06, control donor1_CNhs13852_13474-144I9_reverse Regulation 
MyoblastDifferentiationToMyotubesDay06DuchenneMuscularDystrophyDonor1_CNhs14591_tpm_fwd MyoblastToMyotubes_Day06D1+ Myoblast differentiation to myotubes, day06, Duchenne Muscular Dystrophy donor1_CNhs14591_13501-145C9_forward Regulation 
MyoblastDifferentiationToMyotubesDay06ControlDonor1_CNhs13852_tpm_fwd MyoblastToMyotubes_Day06D1+ Myoblast differentiation to myotubes, day06, control donor1_CNhs13852_13474-144I9_forward Regulation 
MyoblastDifferentiationToMyotubesDay04ControlDonor3_CNhs14581_tpm_rev MyoblastToMyotubes_Day04D3- Myoblast differentiation to myotubes, day04, control donor3_CNhs14581_13491-145B8_reverse Regulation 
MyoblastDifferentiationToMyotubesDay04DuchenneMuscularDystrophyDonor3_CNhs14609_tpm_rev MyoblastToMyotubes_Day04D3- Myoblast differentiation to myotubes, day04, Duchenne Muscular Dystrophy donor3_CNhs14609_13518-145E8_reverse Regulation 
MyoblastDifferentiationToMyotubesDay04ControlDonor3_CNhs14581_tpm_fwd MyoblastToMyotubes_Day04D3+ Myoblast differentiation to myotubes, day04, control donor3_CNhs14581_13491-145B8_forward Regulation 
MyoblastDifferentiationToMyotubesDay04DuchenneMuscularDystrophyDonor3_CNhs14609_tpm_fwd MyoblastToMyotubes_Day04D3+ Myoblast differentiation to myotubes, day04, Duchenne Muscular Dystrophy donor3_CNhs14609_13518-145E8_forward Regulation 
MyoblastDifferentiationToMyotubesDay04DuchenneMuscularDystrophyDonor2_CNhs14600_tpm_rev MyoblastToMyotubes_Day04D2- Myoblast differentiation to myotubes, day04, Duchenne Muscular Dystrophy donor2_CNhs14600_13509-145D8_reverse Regulation 
MyoblastDifferentiationToMyotubesDay04ControlDonor2_CNhs14572_tpm_rev MyoblastToMyotubes_Day04D2- Myoblast differentiation to myotubes, day04, control donor2_CNhs14572_13482-145A8_reverse Regulation 
MyoblastDifferentiationToMyotubesDay04DuchenneMuscularDystrophyDonor2_CNhs14600_tpm_fwd MyoblastToMyotubes_Day04D2+ Myoblast differentiation to myotubes, day04, Duchenne Muscular Dystrophy donor2_CNhs14600_13509-145D8_forward Regulation 
MyoblastDifferentiationToMyotubesDay04ControlDonor2_CNhs14572_tpm_fwd MyoblastToMyotubes_Day04D2+ Myoblast differentiation to myotubes, day04, control donor2_CNhs14572_13482-145A8_forward Regulation 
MyoblastDifferentiationToMyotubesDay04ControlDonor1_CNhs13851_tpm_rev MyoblastToMyotubes_Day04D1- Myoblast differentiation to myotubes, day04, control donor1_CNhs13851_13473-144I8_reverse Regulation 
MyoblastDifferentiationToMyotubesDay04DuchenneMuscularDystrophyDonor1_CNhs14590_tpm_rev MyoblastToMyotubes_Day04D1- Myoblast differentiation to myotubes, day04, Duchenne Muscular Dystrophy donor1_CNhs14590_13500-145C8_reverse Regulation 
MyoblastDifferentiationToMyotubesDay04ControlDonor1_CNhs13851_tpm_fwd MyoblastToMyotubes_Day04D1+ Myoblast differentiation to myotubes, day04, control donor1_CNhs13851_13473-144I8_forward Regulation 
MyoblastDifferentiationToMyotubesDay04DuchenneMuscularDystrophyDonor1_CNhs14590_tpm_fwd MyoblastToMyotubes_Day04D1+ Myoblast differentiation to myotubes, day04, Duchenne Muscular Dystrophy donor1_CNhs14590_13500-145C8_forward Regulation 
MyoblastDifferentiationToMyotubesDay03ControlDonor3_CNhs14580_tpm_rev MyoblastToMyotubes_Day03D3- Myoblast differentiation to myotubes, day03, control donor3_CNhs14580_13490-145B7_reverse Regulation 
MyoblastDifferentiationToMyotubesDay03ControlDonor3_CNhs14580_tpm_fwd MyoblastToMyotubes_Day03D3+ Myoblast differentiation to myotubes, day03, control donor3_CNhs14580_13490-145B7_forward Regulation 
MyoblastDifferentiationToMyotubesDay03ControlDonor2_CNhs14571_tpm_rev MyoblastToMyotubes_Day03D2- Myoblast differentiation to myotubes, day03, control donor2_CNhs14571_13481-145A7_reverse Regulation 
MyoblastDifferentiationToMyotubesDay03DuchenneMuscularDystrophyDonor2_CNhs14599_tpm_rev MyoblastToMyotubes_Day03D2- Myoblast differentiation to myotubes, day03, Duchenne Muscular Dystrophy donor2_CNhs14599_13508-145D7_reverse Regulation 
MyoblastDifferentiationToMyotubesDay03ControlDonor2_CNhs14571_tpm_fwd MyoblastToMyotubes_Day03D2+ Myoblast differentiation to myotubes, day03, control donor2_CNhs14571_13481-145A7_forward Regulation 
MyoblastDifferentiationToMyotubesDay03DuchenneMuscularDystrophyDonor2_CNhs14599_tpm_fwd MyoblastToMyotubes_Day03D2+ Myoblast differentiation to myotubes, day03, Duchenne Muscular Dystrophy donor2_CNhs14599_13508-145D7_forward Regulation 
MyoblastDifferentiationToMyotubesDay03DuchenneMuscularDystrophyDonor1_CNhs14589_tpm_rev MyoblastToMyotubes_Day03D1- Myoblast differentiation to myotubes, day03, Duchenne Muscular Dystrophy donor1_CNhs14589_13499-145C7_reverse Regulation 
MyoblastDifferentiationToMyotubesDay03ControlDonor1_CNhs13850_tpm_rev MyoblastToMyotubes_Day03D1- Myoblast differentiation to myotubes, day03, control donor1_CNhs13850_13472-144I7_reverse Regulation 
MyoblastDifferentiationToMyotubesDay03DuchenneMuscularDystrophyDonor1_CNhs14589_tpm_fwd MyoblastToMyotubes_Day03D1+ Myoblast differentiation to myotubes, day03, Duchenne Muscular Dystrophy donor1_CNhs14589_13499-145C7_forward Regulation 
MyoblastDifferentiationToMyotubesDay03ControlDonor1_CNhs13850_tpm_fwd MyoblastToMyotubes_Day03D1+ Myoblast differentiation to myotubes, day03, control donor1_CNhs13850_13472-144I7_forward Regulation 
MyoblastDifferentiationToMyotubesDay02ControlDonor3_CNhs14579_tpm_rev MyoblastToMyotubes_Day02D3- Myoblast differentiation to myotubes, day02, control donor3_CNhs14579_13489-145B6_reverse Regulation 
MyoblastDifferentiationToMyotubesDay02DuchenneMuscularDystrophyDonor3_CNhs14607_tpm_rev MyoblastToMyotubes_Day02D3- Myoblast differentiation to myotubes, day02, Duchenne Muscular Dystrophy donor3_CNhs14607_13516-145E6_reverse Regulation 
MyoblastDifferentiationToMyotubesDay02ControlDonor3_CNhs14579_tpm_fwd MyoblastToMyotubes_Day02D3+ Myoblast differentiation to myotubes, day02, control donor3_CNhs14579_13489-145B6_forward Regulation 
MyoblastDifferentiationToMyotubesDay02DuchenneMuscularDystrophyDonor3_CNhs14607_tpm_fwd MyoblastToMyotubes_Day02D3+ Myoblast differentiation to myotubes, day02, Duchenne Muscular Dystrophy donor3_CNhs14607_13516-145E6_forward Regulation 
MyoblastDifferentiationToMyotubesDay02DuchenneMuscularDystrophyDonor2_CNhs14598_tpm_rev MyoblastToMyotubes_Day02D2- Myoblast differentiation to myotubes, day02, Duchenne Muscular Dystrophy donor2_CNhs14598_13507-145D6_reverse Regulation 
MyoblastDifferentiationToMyotubesDay02ControlDonor2_CNhs14570_tpm_rev MyoblastToMyotubes_Day02D2- Myoblast differentiation to myotubes, day02, control donor2_CNhs14570_13480-145A6_reverse Regulation 
MyoblastDifferentiationToMyotubesDay02DuchenneMuscularDystrophyDonor2_CNhs14598_tpm_fwd MyoblastToMyotubes_Day02D2+ Myoblast differentiation to myotubes, day02, Duchenne Muscular Dystrophy donor2_CNhs14598_13507-145D6_forward Regulation 
MyoblastDifferentiationToMyotubesDay02ControlDonor2_CNhs14570_tpm_fwd MyoblastToMyotubes_Day02D2+ Myoblast differentiation to myotubes, day02, control donor2_CNhs14570_13480-145A6_forward Regulation 
MyoblastDifferentiationToMyotubesDay02ControlDonor1_CNhs13849_tpm_rev MyoblastToMyotubes_Day02D1- Myoblast differentiation to myotubes, day02, control donor1_CNhs13849_13471-144I6_reverse Regulation 
MyoblastDifferentiationToMyotubesDay02DuchenneMuscularDystrophyDonor1_CNhs14588_tpm_rev MyoblastToMyotubes_Day02D1- Myoblast differentiation to myotubes, day02, Duchenne Muscular Dystrophy donor1_CNhs14588_13498-145C6_reverse Regulation 
MyoblastDifferentiationToMyotubesDay02ControlDonor1_CNhs13849_tpm_fwd MyoblastToMyotubes_Day02D1+ Myoblast differentiation to myotubes, day02, control donor1_CNhs13849_13471-144I6_forward Regulation 
MyoblastDifferentiationToMyotubesDay02DuchenneMuscularDystrophyDonor1_CNhs14588_tpm_fwd MyoblastToMyotubes_Day02D1+ Myoblast differentiation to myotubes, day02, Duchenne Muscular Dystrophy donor1_CNhs14588_13498-145C6_forward Regulation 
MyoblastDifferentiationToMyotubesDay01DuchenneMuscularDystrophyDonor3_CNhs14606_tpm_rev MyoblastToMyotubes_Day01D3- Myoblast differentiation to myotubes, day01, Duchenne Muscular Dystrophy donor3_CNhs14606_13515-145E5_reverse Regulation 
MyoblastDifferentiationToMyotubesDay01ControlDonor3_CNhs14578_tpm_rev MyoblastToMyotubes_Day01D3- Myoblast differentiation to myotubes, day01, control donor3_CNhs14578_13488-145B5_reverse Regulation 
MyoblastDifferentiationToMyotubesDay01DuchenneMuscularDystrophyDonor3_CNhs14606_tpm_fwd MyoblastToMyotubes_Day01D3+ Myoblast differentiation to myotubes, day01, Duchenne Muscular Dystrophy donor3_CNhs14606_13515-145E5_forward Regulation 
MyoblastDifferentiationToMyotubesDay01ControlDonor3_CNhs14578_tpm_fwd MyoblastToMyotubes_Day01D3+ Myoblast differentiation to myotubes, day01, control donor3_CNhs14578_13488-145B5_forward Regulation 
MyoblastDifferentiationToMyotubesDay01DuchenneMuscularDystrophyDonor2_CNhs14597_tpm_rev MyoblastToMyotubes_Day01D2- Myoblast differentiation to myotubes, day01, Duchenne Muscular Dystrophy donor2_CNhs14597_13506-145D5_reverse Regulation 
MyoblastDifferentiationToMyotubesDay01DuchenneMuscularDystrophyDonor2_CNhs14597_tpm_fwd MyoblastToMyotubes_Day01D2+ Myoblast differentiation to myotubes, day01, Duchenne Muscular Dystrophy donor2_CNhs14597_13506-145D5_forward Regulation 
MyoblastDifferentiationToMyotubesDay01DuchenneMuscularDystrophyDonor1_CNhs14587_tpm_rev MyoblastToMyotubes_Day01D1- Myoblast differentiation to myotubes, day01, Duchenne Muscular Dystrophy donor1_CNhs14587_13497-145C5_reverse Regulation 
MyoblastDifferentiationToMyotubesDay01ControlDonor1_CNhs13848_tpm_rev MyoblastToMyotubes_Day01D1- Myoblast differentiation to myotubes, day01, control donor1_CNhs13848_13470-144I5_reverse Regulation 
MyoblastDifferentiationToMyotubesDay01DuchenneMuscularDystrophyDonor1_CNhs14587_tpm_fwd MyoblastToMyotubes_Day01D1+ Myoblast differentiation to myotubes, day01, Duchenne Muscular Dystrophy donor1_CNhs14587_13497-145C5_forward Regulation 
MyoblastDifferentiationToMyotubesDay01ControlDonor1_CNhs13848_tpm_fwd MyoblastToMyotubes_Day01D1+ Myoblast differentiation to myotubes, day01, control donor1_CNhs13848_13470-144I5_forward Regulation 
MyoblastDifferentiationToMyotubesDay00DuchenneMuscularDystrophyDonor3_CNhs14605_tpm_rev MyoblastToMyotubes_Day00D3- Myoblast differentiation to myotubes, day00, Duchenne Muscular Dystrophy donor3_CNhs14605_13514-145E4_reverse Regulation 
MyoblastDifferentiationToMyotubesDay00ControlDonor3_CNhs14577_tpm_rev MyoblastToMyotubes_Day00D3- Myoblast differentiation to myotubes, day00, control donor3_CNhs14577_13487-145B4_reverse Regulation 
MyoblastDifferentiationToMyotubesDay00DuchenneMuscularDystrophyDonor3_CNhs14605_tpm_fwd MyoblastToMyotubes_Day00D3+ Myoblast differentiation to myotubes, day00, Duchenne Muscular Dystrophy donor3_CNhs14605_13514-145E4_forward Regulation 
MyoblastDifferentiationToMyotubesDay00ControlDonor3_CNhs14577_tpm_fwd MyoblastToMyotubes_Day00D3+ Myoblast differentiation to myotubes, day00, control donor3_CNhs14577_13487-145B4_forward Regulation 
MyoblastDifferentiationToMyotubesDay00ControlDonor2_CNhs14567_tpm_rev MyoblastToMyotubes_Day00D2- Myoblast differentiation to myotubes, day00, control donor2_CNhs14567_13478-145A4_reverse Regulation 
MyoblastDifferentiationToMyotubesDay00DuchenneMuscularDystrophyDonor2_CNhs14596_tpm_rev MyoblastToMyotubes_Day00D2- Myoblast differentiation to myotubes, day00, Duchenne Muscular Dystrophy donor2_CNhs14596_13505-145D4_reverse Regulation 
MyoblastDifferentiationToMyotubesDay00ControlDonor2_CNhs14567_tpm_fwd MyoblastToMyotubes_Day00D2+ Myoblast differentiation to myotubes, day00, control donor2_CNhs14567_13478-145A4_forward Regulation 
MyoblastDifferentiationToMyotubesDay00DuchenneMuscularDystrophyDonor2_CNhs14596_tpm_fwd MyoblastToMyotubes_Day00D2+ Myoblast differentiation to myotubes, day00, Duchenne Muscular Dystrophy donor2_CNhs14596_13505-145D4_forward Regulation 
MyoblastDifferentiationToMyotubesDay00ControlDonor1_CNhs13847_tpm_rev MyoblastToMyotubes_Day00D1- Myoblast differentiation to myotubes, day00, control donor1_CNhs13847_13469-144I4_reverse Regulation 
MyoblastDifferentiationToMyotubesDay00DuchenneMuscularDystrophyDonor1_CNhs14586_tpm_rev MyoblastToMyotubes_Day00D1- Myoblast differentiation to myotubes, day00, Duchenne Muscular Dystrophy donor1_CNhs14586_13496-145C4_reverse Regulation 
MyoblastDifferentiationToMyotubesDay00ControlDonor1_CNhs13847_tpm_fwd MyoblastToMyotubes_Day00D1+ Myoblast differentiation to myotubes, day00, control donor1_CNhs13847_13469-144I4_forward Regulation 
MyoblastDifferentiationToMyotubesDay00DuchenneMuscularDystrophyDonor1_CNhs14586_tpm_fwd MyoblastToMyotubes_Day00D1+ Myoblast differentiation to myotubes, day00, Duchenne Muscular Dystrophy donor1_CNhs14586_13496-145C4_forward Regulation 
MonocytederivedMacrophagesResponseToLPS48hrDonor2T26Subject2_CNhs13405_tpm_rev Tc:MdmToLps_48hrD2- Monocyte-derived macrophages response to LPS, 48hr, donor2 (t26 Subject2)_CNhs13405_12821-136I4_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS48hrDonor2T26Subject2_CNhs13405_tpm_fwd Tc:MdmToLps_48hrD2+ Monocyte-derived macrophages response to LPS, 48hr, donor2 (t26 Subject2)_CNhs13405_12821-136I4_forward Regulation 
MonocytederivedMacrophagesResponseToLPS48hrDonor1T26Subject1_CNhs11942_tpm_rev Tc:MdmToLps_48hrD1- Monocyte-derived macrophages response to LPS, 48hr, donor1 (t26 Subject1)_CNhs11942_12723-135G5_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS48hrDonor1T26Subject1_CNhs11942_tpm_fwd Tc:MdmToLps_48hrD1+ Monocyte-derived macrophages response to LPS, 48hr, donor1 (t26 Subject1)_CNhs11942_12723-135G5_forward Regulation 
MonocytederivedMacrophagesResponseToLPS36hrDonor3T25Subject3_CNhs13335_tpm_rev Tc:MdmToLps_36hrD3- Monocyte-derived macrophages response to LPS, 36hr, donor3 (t25 Subject3)_CNhs13335_12918-138B2_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS36hrDonor3T25Subject3_CNhs13335_tpm_fwd Tc:MdmToLps_36hrD3+ Monocyte-derived macrophages response to LPS, 36hr, donor3 (t25 Subject3)_CNhs13335_12918-138B2_forward Regulation 
MonocytederivedMacrophagesResponseToLPS36hrDonor2T25Subject2_CNhs13404_tpm_rev Tc:MdmToLps_36hrD2- Monocyte-derived macrophages response to LPS, 36hr, donor2 (t25 Subject2)_CNhs13404_12820-136I3_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS36hrDonor2T25Subject2_CNhs13404_tpm_fwd Tc:MdmToLps_36hrD2+ Monocyte-derived macrophages response to LPS, 36hr, donor2 (t25 Subject2)_CNhs13404_12820-136I3_forward Regulation 
MonocytederivedMacrophagesResponseToLPS36hrDonor1T25Subject1_CNhs12933_tpm_rev Tc:MdmToLps_36hrD1- Monocyte-derived macrophages response to LPS, 36hr, donor1 (t25 Subject1)_CNhs12933_12722-135G4_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS36hrDonor1T25Subject1_CNhs12933_tpm_fwd Tc:MdmToLps_36hrD1+ Monocyte-derived macrophages response to LPS, 36hr, donor1 (t25 Subject1)_CNhs12933_12722-135G4_forward Regulation 
MonocytederivedMacrophagesResponseToLPS24hrDonor3T24Subject3_CNhs13334_tpm_rev Tc:MdmToLps_24hrD3- Monocyte-derived macrophages response to LPS, 24hr, donor3 (t24 Subject3)_CNhs13334_12917-138B1_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS24hrDonor3T24Subject3_CNhs13334_tpm_fwd Tc:MdmToLps_24hrD3+ Monocyte-derived macrophages response to LPS, 24hr, donor3 (t24 Subject3)_CNhs13334_12917-138B1_forward Regulation 
MonocytederivedMacrophagesResponseToLPS24hrDonor2T24Subject2_CNhs13403_tpm_rev Tc:MdmToLps_24hrD2- Monocyte-derived macrophages response to LPS, 24hr, donor2 (t24 Subject2)_CNhs13403_12819-136I2_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS24hrDonor2T24Subject2_CNhs13403_tpm_fwd Tc:MdmToLps_24hrD2+ Monocyte-derived macrophages response to LPS, 24hr, donor2 (t24 Subject2)_CNhs13403_12819-136I2_forward Regulation 
MonocytederivedMacrophagesResponseToLPS24hrDonor1T24Subject1_CNhs12932_tpm_rev Tc:MdmToLps_24hrD1- Monocyte-derived macrophages response to LPS, 24hr, donor1 (t24 Subject1)_CNhs12932_12721-135G3_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS24hrDonor1T24Subject1_CNhs12932_tpm_fwd Tc:MdmToLps_24hrD1+ Monocyte-derived macrophages response to LPS, 24hr, donor1 (t24 Subject1)_CNhs12932_12721-135G3_forward Regulation 
MonocytederivedMacrophagesResponseToLPS22hrDonor3T23Subject3_CNhs13333_tpm_rev Tc:MdmToLps_22hrD3- Monocyte-derived macrophages response to LPS, 22hr, donor3 (t23 Subject3)_CNhs13333_12916-138A9_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS22hrDonor3T23Subject3_CNhs13333_tpm_fwd Tc:MdmToLps_22hrD3+ Monocyte-derived macrophages response to LPS, 22hr, donor3 (t23 Subject3)_CNhs13333_12916-138A9_forward Regulation 
MonocytederivedMacrophagesResponseToLPS22hrDonor2T23Subject2_CNhs13402_tpm_rev Tc:MdmToLps_22hrD2- Monocyte-derived macrophages response to LPS, 22hr, donor2 (t23 Subject2)_CNhs13402_12818-136I1_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS22hrDonor2T23Subject2_CNhs13402_tpm_fwd Tc:MdmToLps_22hrD2+ Monocyte-derived macrophages response to LPS, 22hr, donor2 (t23 Subject2)_CNhs13402_12818-136I1_forward Regulation 
MonocytederivedMacrophagesResponseToLPS22hrDonor1T23Subject1_CNhs12815_tpm_rev Tc:MdmToLps_22hrD1- Monocyte-derived macrophages response to LPS, 22hr, donor1 (t23 Subject1)_CNhs12815_12720-135G2_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS22hrDonor1T23Subject1_CNhs12815_tpm_fwd Tc:MdmToLps_22hrD1+ Monocyte-derived macrophages response to LPS, 22hr, donor1 (t23 Subject1)_CNhs12815_12720-135G2_forward Regulation 
MonocytederivedMacrophagesResponseToLPS20hrDonor3T22Subject3_CNhs13332_tpm_rev Tc:MdmToLps_20hrD3- Monocyte-derived macrophages response to LPS, 20hr, donor3 (t22 Subject3)_CNhs13332_12915-138A8_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS20hrDonor3T22Subject3_CNhs13332_tpm_fwd Tc:MdmToLps_20hrD3+ Monocyte-derived macrophages response to LPS, 20hr, donor3 (t22 Subject3)_CNhs13332_12915-138A8_forward Regulation 
MonocytederivedMacrophagesResponseToLPS20hrDonor2T22Subject2_CNhs13401_tpm_rev Tc:MdmToLps_20hrD2- Monocyte-derived macrophages response to LPS, 20hr, donor2 (t22 Subject2)_CNhs13401_12817-136H9_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS20hrDonor2T22Subject2_CNhs13401_tpm_fwd Tc:MdmToLps_20hrD2+ Monocyte-derived macrophages response to LPS, 20hr, donor2 (t22 Subject2)_CNhs13401_12817-136H9_forward Regulation 
MonocytederivedMacrophagesResponseToLPS20hrDonor1T22Subject1_CNhs12931_tpm_rev Tc:MdmToLps_20hrD1- Monocyte-derived macrophages response to LPS, 20hr, donor1 (t22 Subject1)_CNhs12931_12719-135G1_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS20hrDonor1T22Subject1_CNhs12931_tpm_fwd Tc:MdmToLps_20hrD1+ Monocyte-derived macrophages response to LPS, 20hr, donor1 (t22 Subject1)_CNhs12931_12719-135G1_forward Regulation 
MonocytederivedMacrophagesResponseToLPS18hrDonor3T21Subject3_CNhs13331_tpm_rev Tc:MdmToLps_18hrD3- Monocyte-derived macrophages response to LPS, 18hr, donor3 (t21 Subject3)_CNhs13331_12914-138A7_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS18hrDonor3T21Subject3_CNhs13331_tpm_fwd Tc:MdmToLps_18hrD3+ Monocyte-derived macrophages response to LPS, 18hr, donor3 (t21 Subject3)_CNhs13331_12914-138A7_forward Regulation 
MonocytederivedMacrophagesResponseToLPS18hrDonor2T21Subject2_CNhs13400_tpm_rev Tc:MdmToLps_18hrD2- Monocyte-derived macrophages response to LPS, 18hr, donor2 (t21 Subject2)_CNhs13400_12816-136H8_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS18hrDonor2T21Subject2_CNhs13400_tpm_fwd Tc:MdmToLps_18hrD2+ Monocyte-derived macrophages response to LPS, 18hr, donor2 (t21 Subject2)_CNhs13400_12816-136H8_forward Regulation 
MonocytederivedMacrophagesResponseToLPS18hrDonor1T21Subject1_CNhs12814_tpm_rev Tc:MdmToLps_18hrD1- Monocyte-derived macrophages response to LPS, 18hr, donor1 (t21 Subject1)_CNhs12814_12718-135F9_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS18hrDonor1T21Subject1_CNhs12814_tpm_fwd Tc:MdmToLps_18hrD1+ Monocyte-derived macrophages response to LPS, 18hr, donor1 (t21 Subject1)_CNhs12814_12718-135F9_forward Regulation 
MonocytederivedMacrophagesResponseToLPS16hrDonor3T20Subject3_CNhs13330_tpm_rev Tc:MdmToLps_16hrD3- Monocyte-derived macrophages response to LPS, 16hr, donor3 (t20 Subject3)_CNhs13330_12913-138A6_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS16hrDonor3T20Subject3_CNhs13330_tpm_fwd Tc:MdmToLps_16hrD3+ Monocyte-derived macrophages response to LPS, 16hr, donor3 (t20 Subject3)_CNhs13330_12913-138A6_forward Regulation 
MonocytederivedMacrophagesResponseToLPS16hrDonor2T20Subject2_CNhs13399_tpm_rev Tc:MdmToLps_16hrD2- Monocyte-derived macrophages response to LPS, 16hr, donor2 (t20 Subject2)_CNhs13399_12815-136H7_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS16hrDonor2T20Subject2_CNhs13399_tpm_fwd Tc:MdmToLps_16hrD2+ Monocyte-derived macrophages response to LPS, 16hr, donor2 (t20 Subject2)_CNhs13399_12815-136H7_forward Regulation 
MonocytederivedMacrophagesResponseToLPS14hrDonor3T19Subject3_CNhs13329_tpm_rev Tc:MdmToLps_14hrD3- Monocyte-derived macrophages response to LPS, 14hr, donor3 (t19 Subject3)_CNhs13329_12912-138A5_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS14hrDonor3T19Subject3_CNhs13329_tpm_fwd Tc:MdmToLps_14hrD3+ Monocyte-derived macrophages response to LPS, 14hr, donor3 (t19 Subject3)_CNhs13329_12912-138A5_forward Regulation 
MonocytederivedMacrophagesResponseToLPS14hrDonor2T19Subject2_CNhs13398_tpm_rev Tc:MdmToLps_14hrD2- Monocyte-derived macrophages response to LPS, 14hr, donor2 (t19 Subject2)_CNhs13398_12814-136H6_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS14hrDonor2T19Subject2_CNhs13398_tpm_fwd Tc:MdmToLps_14hrD2+ Monocyte-derived macrophages response to LPS, 14hr, donor2 (t19 Subject2)_CNhs13398_12814-136H6_forward Regulation 
MonocytederivedMacrophagesResponseToLPS14hrDonor1T19Subject1_CNhs12929_tpm_rev Tc:MdmToLps_14hrD1- Monocyte-derived macrophages response to LPS, 14hr, donor1 (t19 Subject1)_CNhs12929_12716-135F7_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS14hrDonor1T19Subject1_CNhs12929_tpm_fwd Tc:MdmToLps_14hrD1+ Monocyte-derived macrophages response to LPS, 14hr, donor1 (t19 Subject1)_CNhs12929_12716-135F7_forward Regulation 
MonocytederivedMacrophagesResponseToLPS12hrDonor3T18Subject3_CNhs13328_tpm_rev Tc:MdmToLps_12hrD3- Monocyte-derived macrophages response to LPS, 12hr, donor3 (t18 Subject3)_CNhs13328_12911-138A4_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS12hrDonor3T18Subject3_CNhs13328_tpm_fwd Tc:MdmToLps_12hrD3+ Monocyte-derived macrophages response to LPS, 12hr, donor3 (t18 Subject3)_CNhs13328_12911-138A4_forward Regulation 
MonocytederivedMacrophagesResponseToLPS12hrDonor2T18Subject2_CNhs13397_tpm_rev Tc:MdmToLps_12hrD2- Monocyte-derived macrophages response to LPS, 12hr, donor2 (t18 Subject2)_CNhs13397_12813-136H5_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS12hrDonor2T18Subject2_CNhs13397_tpm_fwd Tc:MdmToLps_12hrD2+ Monocyte-derived macrophages response to LPS, 12hr, donor2 (t18 Subject2)_CNhs13397_12813-136H5_forward Regulation 
MonocytederivedMacrophagesResponseToLPS12hrDonor1T18Subject1_CNhs12813_tpm_rev Tc:MdmToLps_12hrD1- Monocyte-derived macrophages response to LPS, 12hr, donor1 (t18 Subject1)_CNhs12813_12715-135F6_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS12hrDonor1T18Subject1_CNhs12813_tpm_fwd Tc:MdmToLps_12hrD1+ Monocyte-derived macrophages response to LPS, 12hr, donor1 (t18 Subject1)_CNhs12813_12715-135F6_forward Regulation 
MonocytederivedMacrophagesResponseToLPS10hrDonor3T17Subject3_CNhs13327_tpm_rev Tc:MdmToLps_10hrD3- Monocyte-derived macrophages response to LPS, 10hr, donor3 (t17 Subject3)_CNhs13327_12910-138A3_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS10hrDonor3T17Subject3_CNhs13327_tpm_fwd Tc:MdmToLps_10hrD3+ Monocyte-derived macrophages response to LPS, 10hr, donor3 (t17 Subject3)_CNhs13327_12910-138A3_forward Regulation 
MonocytederivedMacrophagesResponseToLPS10hrDonor2T17Subject2_CNhs13396_tpm_rev Tc:MdmToLps_10hrD2- Monocyte-derived macrophages response to LPS, 10hr, donor2 (t17 Subject2)_CNhs13396_12812-136H4_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS10hrDonor2T17Subject2_CNhs13396_tpm_fwd Tc:MdmToLps_10hrD2+ Monocyte-derived macrophages response to LPS, 10hr, donor2 (t17 Subject2)_CNhs13396_12812-136H4_forward Regulation 
MonocytederivedMacrophagesResponseToLPS08hrDonor3T16Subject3_CNhs13326_tpm_rev Tc:MdmToLps_08hrD3- Monocyte-derived macrophages response to LPS, 08hr, donor3 (t16 Subject3)_CNhs13326_12909-138A2_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS08hrDonor3T16Subject3_CNhs13326_tpm_fwd Tc:MdmToLps_08hrD3+ Monocyte-derived macrophages response to LPS, 08hr, donor3 (t16 Subject3)_CNhs13326_12909-138A2_forward Regulation 
MonocytederivedMacrophagesResponseToLPS08hrDonor2T16Subject2_CNhs13395_tpm_rev Tc:MdmToLps_08hrD2- Monocyte-derived macrophages response to LPS, 08hr, donor2 (t16 Subject2)_CNhs13395_12811-136H3_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS08hrDonor2T16Subject2_CNhs13395_tpm_fwd Tc:MdmToLps_08hrD2+ Monocyte-derived macrophages response to LPS, 08hr, donor2 (t16 Subject2)_CNhs13395_12811-136H3_forward Regulation 
MonocytederivedMacrophagesResponseToLPS08hrDonor1T16Subject1_CNhs12927_tpm_rev Tc:MdmToLps_08hrD1- Monocyte-derived macrophages response to LPS, 08hr, donor1 (t16 Subject1)_CNhs12927_12713-135F4_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS08hrDonor1T16Subject1_CNhs12927_tpm_fwd Tc:MdmToLps_08hrD1+ Monocyte-derived macrophages response to LPS, 08hr, donor1 (t16 Subject1)_CNhs12927_12713-135F4_forward Regulation 
MonocytederivedMacrophagesResponseToLPS05hrDonor3T13Subject3_CNhs13186_tpm_rev Tc:MdmToLps_05hrD3- Monocyte-derived macrophages response to LPS, 05hr, donor3 (t13 Subject3)_CNhs13186_12906-137I8_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS05hrDonor3T13Subject3_CNhs13186_tpm_fwd Tc:MdmToLps_05hrD3+ Monocyte-derived macrophages response to LPS, 05hr, donor3 (t13 Subject3)_CNhs13186_12906-137I8_forward Regulation 
MonocytederivedMacrophagesResponseToLPS05hrDonor2T13Subject2_CNhs13392_tpm_rev Tc:MdmToLps_05hrD2- Monocyte-derived macrophages response to LPS, 05hr, donor2 (t13 Subject2)_CNhs13392_12808-136G9_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS05hrDonor2T13Subject2_CNhs13392_tpm_fwd Tc:MdmToLps_05hrD2+ Monocyte-derived macrophages response to LPS, 05hr, donor2 (t13 Subject2)_CNhs13392_12808-136G9_forward Regulation 
MonocytederivedMacrophagesResponseToLPS05hrDonor1T13Subject1_CNhs13155_tpm_rev Tc:MdmToLps_05hrD1- Monocyte-derived macrophages response to LPS, 05hr, donor1 (t13 Subject1)_CNhs13155_12710-135F1_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS05hrDonor1T13Subject1_CNhs13155_tpm_fwd Tc:MdmToLps_05hrD1+ Monocyte-derived macrophages response to LPS, 05hr, donor1 (t13 Subject1)_CNhs13155_12710-135F1_forward Regulation 
MonocytederivedMacrophagesResponseToLPS04hrDonor3T12Subject3_CNhs13185_tpm_rev Tc:MdmToLps_04hrD3- Monocyte-derived macrophages response to LPS, 04hr, donor3 (t12 Subject3)_CNhs13185_12905-137I7_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS04hrDonor3T12Subject3_CNhs13185_tpm_fwd Tc:MdmToLps_04hrD3+ Monocyte-derived macrophages response to LPS, 04hr, donor3 (t12 Subject3)_CNhs13185_12905-137I7_forward Regulation 
MonocytederivedMacrophagesResponseToLPS04hrDonor2T12Subject2_CNhs13391_tpm_rev Tc:MdmToLps_04hrD2- Monocyte-derived macrophages response to LPS, 04hr, donor2 (t12 Subject2)_CNhs13391_12807-136G8_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS04hrDonor2T12Subject2_CNhs13391_tpm_fwd Tc:MdmToLps_04hrD2+ Monocyte-derived macrophages response to LPS, 04hr, donor2 (t12 Subject2)_CNhs13391_12807-136G8_forward Regulation 
MonocytederivedMacrophagesResponseToLPS03hr30minDonor3T11Subject3_CNhs13184_tpm_rev Tc:MdmToLps_03hr30minD3- Monocyte-derived macrophages response to LPS, 03hr30min, donor3 (t11 Subject3)_CNhs13184_12904-137I6_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS03hr30minDonor3T11Subject3_CNhs13184_tpm_fwd Tc:MdmToLps_03hr30minD3+ Monocyte-derived macrophages response to LPS, 03hr30min, donor3 (t11 Subject3)_CNhs13184_12904-137I6_forward Regulation 
MonocytederivedMacrophagesResponseToLPS03hr30minDonor2T11Subject2_CNhs13389_tpm_rev Tc:MdmToLps_03hr30minD2- Monocyte-derived macrophages response to LPS, 03hr30min, donor2 (t11 Subject2)_CNhs13389_12806-136G7_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS03hr30minDonor2T11Subject2_CNhs13389_tpm_fwd Tc:MdmToLps_03hr30minD2+ Monocyte-derived macrophages response to LPS, 03hr30min, donor2 (t11 Subject2)_CNhs13389_12806-136G7_forward Regulation 
MonocytederivedMacrophagesResponseToLPS03hr00minDonor3T10Subject3_CNhs13183_tpm_rev Tc:MdmToLps_03hr00minD3- Monocyte-derived macrophages response to LPS, 03hr00min, donor3 (t10 Subject3)_CNhs13183_12903-137I5_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS03hr00minDonor3T10Subject3_CNhs13183_tpm_fwd Tc:MdmToLps_03hr00minD3+ Monocyte-derived macrophages response to LPS, 03hr00min, donor3 (t10 Subject3)_CNhs13183_12903-137I5_forward Regulation 
MonocytederivedMacrophagesResponseToLPS03hr00minDonor2T10Subject2_CNhs13388_tpm_rev Tc:MdmToLps_03hr00minD2- Monocyte-derived macrophages response to LPS, 03hr00min, donor2 (t10 Subject2)_CNhs13388_12805-136G6_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS03hr00minDonor2T10Subject2_CNhs13388_tpm_fwd Tc:MdmToLps_03hr00minD2+ Monocyte-derived macrophages response to LPS, 03hr00min, donor2 (t10 Subject2)_CNhs13388_12805-136G6_forward Regulation 
MonocytederivedMacrophagesResponseToLPS03hr00minDonor1T10Subject1_CNhs12924_tpm_rev Tc:MdmToLps_03hr00minD1- Monocyte-derived macrophages response to LPS, 03hr00min, donor1 (t10 Subject1)_CNhs12924_12707-135E7_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS03hr00minDonor1T10Subject1_CNhs12924_tpm_fwd Tc:MdmToLps_03hr00minD1+ Monocyte-derived macrophages response to LPS, 03hr00min, donor1 (t10 Subject1)_CNhs12924_12707-135E7_forward Regulation 
MonocytederivedMacrophagesResponseToLPS02hr30minDonor3T9Subject3_CNhs13182_tpm_rev Tc:MdmToLps_02hr30minD3- Monocyte-derived macrophages response to LPS, 02hr30min, donor3 (t9 Subject3)_CNhs13182_12902-137I4_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS02hr30minDonor3T9Subject3_CNhs13182_tpm_fwd Tc:MdmToLps_02hr30minD3+ Monocyte-derived macrophages response to LPS, 02hr30min, donor3 (t9 Subject3)_CNhs13182_12902-137I4_forward Regulation 
MonocytederivedMacrophagesResponseToLPS02hr30minDonor2T9Subject2_CNhs13387_tpm_rev Tc:MdmToLps_02hr30minD2- Monocyte-derived macrophages response to LPS, 02hr30min, donor2 (t9 Subject2)_CNhs13387_12804-136G5_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS02hr30minDonor2T9Subject2_CNhs13387_tpm_fwd Tc:MdmToLps_02hr30minD2+ Monocyte-derived macrophages response to LPS, 02hr30min, donor2 (t9 Subject2)_CNhs13387_12804-136G5_forward Regulation 
MonocytederivedMacrophagesResponseToLPS02hr30minDonor1T9Subject1_CNhs13152_tpm_rev Tc:MdmToLps_02hr30minD1- Monocyte-derived macrophages response to LPS, 02hr30min, donor1 (t9 Subject1)_CNhs13152_12706-135E6_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS02hr30minDonor1T9Subject1_CNhs13152_tpm_fwd Tc:MdmToLps_02hr30minD1+ Monocyte-derived macrophages response to LPS, 02hr30min, donor1 (t9 Subject1)_CNhs13152_12706-135E6_forward Regulation 
MonocytederivedMacrophagesResponseToLPS02hr00minDonor3T8Subject3_CNhs13181_tpm_rev Tc:MdmToLps_02hr00minD3- Monocyte-derived macrophages response to LPS, 02hr00min, donor3 (t8 Subject3)_CNhs13181_12901-137I3_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS02hr00minDonor3T8Subject3_CNhs13181_tpm_fwd Tc:MdmToLps_02hr00minD3+ Monocyte-derived macrophages response to LPS, 02hr00min, donor3 (t8 Subject3)_CNhs13181_12901-137I3_forward Regulation 
MonocytederivedMacrophagesResponseToLPS02hr00minDonor2T8Subject2_CNhs13386_tpm_rev Tc:MdmToLps_02hr00minD2- Monocyte-derived macrophages response to LPS, 02hr00min, donor2 (t8 Subject2)_CNhs13386_12803-136G4_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS02hr00minDonor2T8Subject2_CNhs13386_tpm_fwd Tc:MdmToLps_02hr00minD2+ Monocyte-derived macrophages response to LPS, 02hr00min, donor2 (t8 Subject2)_CNhs13386_12803-136G4_forward Regulation 
MonocytederivedMacrophagesResponseToLPS01hr20minDonor3T6Subject3_CNhs13179_tpm_rev Tc:MdmToLps_01hr20minD3- Monocyte-derived macrophages response to LPS, 01hr20min, donor3 (t6 Subject3)_CNhs13179_12899-137I1_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS01hr20minDonor3T6Subject3_CNhs13179_tpm_fwd Tc:MdmToLps_01hr20minD3+ Monocyte-derived macrophages response to LPS, 01hr20min, donor3 (t6 Subject3)_CNhs13179_12899-137I1_forward Regulation 
MonocytederivedMacrophagesResponseToLPS01hr20minDonor2T6Subject2_CNhs13384_tpm_rev Tc:MdmToLps_01hr20minD2- Monocyte-derived macrophages response to LPS, 01hr20min, donor2 (t6 Subject2)_CNhs13384_12801-136G2_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS01hr20minDonor2T6Subject2_CNhs13384_tpm_fwd Tc:MdmToLps_01hr20minD2+ Monocyte-derived macrophages response to LPS, 01hr20min, donor2 (t6 Subject2)_CNhs13384_12801-136G2_forward Regulation 
MonocytederivedMacrophagesResponseToLPS01hr00minDonor3T5Subject3_CNhs13178_tpm_rev Tc:MdmToLps_01hr00minD3- Monocyte-derived macrophages response to LPS, 01hr00min, donor3 (t5 Subject3)_CNhs13178_12898-137H9_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS01hr00minDonor3T5Subject3_CNhs13178_tpm_fwd Tc:MdmToLps_01hr00minD3+ Monocyte-derived macrophages response to LPS, 01hr00min, donor3 (t5 Subject3)_CNhs13178_12898-137H9_forward Regulation 
MonocytederivedMacrophagesResponseToLPS01hr00minDonor2T5Subject2_CNhs13383_tpm_rev Tc:MdmToLps_01hr00minD2- Monocyte-derived macrophages response to LPS, 01hr00min, donor2 (t5 Subject2)_CNhs13383_12800-136G1_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS01hr00minDonor2T5Subject2_CNhs13383_tpm_fwd Tc:MdmToLps_01hr00minD2+ Monocyte-derived macrophages response to LPS, 01hr00min, donor2 (t5 Subject2)_CNhs13383_12800-136G1_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr45minDonor3T4Subject3_CNhs13177_tpm_rev Tc:MdmToLps_00hr45minD3- Monocyte-derived macrophages response to LPS, 00hr45min, donor3 (t4 Subject3)_CNhs13177_12897-137H8_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr45minDonor3T4Subject3_CNhs13177_tpm_fwd Tc:MdmToLps_00hr45minD3+ Monocyte-derived macrophages response to LPS, 00hr45min, donor3 (t4 Subject3)_CNhs13177_12897-137H8_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr45minDonor2T4Subject2_CNhs13382_tpm_rev Tc:MdmToLps_00hr45minD2- Monocyte-derived macrophages response to LPS, 00hr45min, donor2 (t4 Subject2)_CNhs13382_12799-136F9_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr45minDonor2T4Subject2_CNhs13382_tpm_fwd Tc:MdmToLps_00hr45minD2+ Monocyte-derived macrophages response to LPS, 00hr45min, donor2 (t4 Subject2)_CNhs13382_12799-136F9_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr30minDonor3T3Subject3_CNhs13176_tpm_rev Tc:MdmToLps_00hr30minD3- Monocyte-derived macrophages response to LPS, 00hr30min, donor3 (t3 Subject3)_CNhs13176_12896-137H7_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr30minDonor3T3Subject3_CNhs13176_tpm_fwd Tc:MdmToLps_00hr30minD3+ Monocyte-derived macrophages response to LPS, 00hr30min, donor3 (t3 Subject3)_CNhs13176_12896-137H7_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr30minDonor2T3Subject2_CNhs13381_tpm_rev Tc:MdmToLps_00hr30minD2- Monocyte-derived macrophages response to LPS, 00hr30min, donor2 (t3 Subject2)_CNhs13381_12798-136F8_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr30minDonor2T3Subject2_CNhs13381_tpm_fwd Tc:MdmToLps_00hr30minD2+ Monocyte-derived macrophages response to LPS, 00hr30min, donor2 (t3 Subject2)_CNhs13381_12798-136F8_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr15minDonor3T2Subject3_CNhs13175_tpm_rev Tc:MdmToLps_00hr15minD3- Monocyte-derived macrophages response to LPS, 00hr15min, donor3 (t2 Subject3)_CNhs13175_12895-137H6_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr15minDonor3T2Subject3_CNhs13175_tpm_fwd Tc:MdmToLps_00hr15minD3+ Monocyte-derived macrophages response to LPS, 00hr15min, donor3 (t2 Subject3)_CNhs13175_12895-137H6_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr15minDonor2T2Subject2_CNhs13380_tpm_rev Tc:MdmToLps_00hr15minD2- Monocyte-derived macrophages response to LPS, 00hr15min, donor2 (t2 Subject2)_CNhs13380_12797-136F7_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr15minDonor2T2Subject2_CNhs13380_tpm_fwd Tc:MdmToLps_00hr15minD2+ Monocyte-derived macrophages response to LPS, 00hr15min, donor2 (t2 Subject2)_CNhs13380_12797-136F7_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr00minDonor3T1Subject3_CNhs13174_tpm_rev Tc:MdmToLps_00hr00minD3- Monocyte-derived macrophages response to LPS, 00hr00min, donor3 (t1 Subject3)_CNhs13174_12894-137H5_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr00minDonor3T1Subject3_CNhs13174_tpm_fwd Tc:MdmToLps_00hr00minD3+ Monocyte-derived macrophages response to LPS, 00hr00min, donor3 (t1 Subject3)_CNhs13174_12894-137H5_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr00minDonor2T1Subject2_CNhs13379_tpm_rev Tc:MdmToLps_00hr00minD2- Monocyte-derived macrophages response to LPS, 00hr00min, donor2 (t1 Subject2)_CNhs13379_12796-136F6_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr00minDonor2T1Subject2_CNhs13379_tpm_fwd Tc:MdmToLps_00hr00minD2+ Monocyte-derived macrophages response to LPS, 00hr00min, donor2 (t1 Subject2)_CNhs13379_12796-136F6_forward Regulation 
MonocytederivedMacrophagesResponseToLPS00hr00minDonor1T1Subject1_CNhs11941_tpm_rev Tc:MdmToLps_00hr00minD1- Monocyte-derived macrophages response to LPS, 00hr00min, donor1 (t1 Subject1)_CNhs11941_12698-135D7_reverse Regulation 
MonocytederivedMacrophagesResponseToLPS00hr00minDonor1T1Subject1_CNhs11941_tpm_fwd Tc:MdmToLps_00hr00minD1+ Monocyte-derived macrophages response to LPS, 00hr00min, donor1 (t1 Subject1)_CNhs11941_12698-135D7_forward Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection24hr00minDonor4227_121MI_24h_CNhs13644_tpm_rev Tc:MdmToMock_24hr00minD4- Monocyte-derived macrophages response to mock influenza infection, 24hr00min, donor4 (227_121:MI_24h)_CNhs13644_13315-143A3_reverse Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection24hr00minDonor4227_121MI_24h_CNhs13644_tpm_fwd Tc:MdmToMock_24hr00minD4+ Monocyte-derived macrophages response to mock influenza infection, 24hr00min, donor4 (227_121:MI_24h)_CNhs13644_13315-143A3_forward Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection24hr00minDonor3536_119MI_24h_CNhs13652_tpm_rev Tc:MdmToMock_24hr00minD3- Monocyte-derived macrophages response to mock influenza infection, 24hr00min, donor3 (536_119:MI_24h)_CNhs13652_13327-143B6_reverse Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection24hr00minDonor3536_119MI_24h_CNhs13652_tpm_fwd Tc:MdmToMock_24hr00minD3+ Monocyte-derived macrophages response to mock influenza infection, 24hr00min, donor3 (536_119:MI_24h)_CNhs13652_13327-143B6_forward Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection24hr00minDonor2150_120MI_24h_CNhs13648_tpm_rev Tc:MdmToMock_24hr00minD2- Monocyte-derived macrophages response to mock influenza infection, 24hr00min, donor2 (150_120:MI_24h)_CNhs13648_13321-143A9_reverse Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection24hr00minDonor2150_120MI_24h_CNhs13648_tpm_fwd Tc:MdmToMock_24hr00minD2+ Monocyte-derived macrophages response to mock influenza infection, 24hr00min, donor2 (150_120:MI_24h)_CNhs13648_13321-143A9_forward Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection24hr00minDonor1868_121MI_24h_CNhs13693_tpm_rev Tc:MdmToMock_24hr00minD1- Monocyte-derived macrophages response to mock influenza infection, 24hr00min, donor1 (868_121:MI_24h)_CNhs13693_13309-142I6_reverse Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection24hr00minDonor1868_121MI_24h_CNhs13693_tpm_fwd Tc:MdmToMock_24hr00minD1+ Monocyte-derived macrophages response to mock influenza infection, 24hr00min, donor1 (868_121:MI_24h)_CNhs13693_13309-142I6_forward Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection00hr00minDonor4227_121MI_0h_CNhs13638_tpm_rev Tc:MdmToMock_00hr00minD4- Monocyte-derived macrophages response to mock influenza infection, 00hr00min, donor4 (227_121:MI_0h)_CNhs13638_13310-142I7_reverse Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection00hr00minDonor4227_121MI_0h_CNhs13638_tpm_fwd Tc:MdmToMock_00hr00minD4+ Monocyte-derived macrophages response to mock influenza infection, 00hr00min, donor4 (227_121:MI_0h)_CNhs13638_13310-142I7_forward Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection00hr00minDonor3536_119MI_0h_CNhs13649_tpm_rev Tc:MdmToMock_00hr00minD3- Monocyte-derived macrophages response to mock influenza infection, 00hr00min, donor3 (536_119:MI_0h)_CNhs13649_13322-143B1_reverse Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection00hr00minDonor3536_119MI_0h_CNhs13649_tpm_fwd Tc:MdmToMock_00hr00minD3+ Monocyte-derived macrophages response to mock influenza infection, 00hr00min, donor3 (536_119:MI_0h)_CNhs13649_13322-143B1_forward Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection00hr00minDonor2150_120MI_0h_CNhs13645_tpm_rev Tc:MdmToMock_00hr00minD2- Monocyte-derived macrophages response to mock influenza infection, 00hr00min, donor2 (150_120:MI_0h)_CNhs13645_13316-143A4_reverse Regulation 
MonocytederivedMacrophagesResponseToMockInfluenzaInfection00hr00minDonor2150_120MI_0h_CNhs13645_tpm_fwd Tc:MdmToMock_00hr00minD2+ Monocyte-derived macrophages response to mock influenza infection, 00hr00min, donor2 (150_120:MI_0h)_CNhs13645_13316-143A4_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection24hr00minDonor3536_119Ud_24h_CNhs13562_tpm_rev MonocyteMacrophageUdornInfluenza_24hr00minD3- Monocyte-derived macrophages response to udorn influenza infection, 24hr00min, donor3 (536_119:Ud_24h)_CNhs13562_13326-143B5_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection24hr00minDonor3536_119Ud_24h_CNhs13562_tpm_fwd MonocyteMacrophageUdornInfluenza_24hr00minD3+ Monocyte-derived macrophages response to udorn influenza infection, 24hr00min, donor3 (536_119:Ud_24h)_CNhs13562_13326-143B5_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection24hr00minDonor2150_120Ud_24h_CNhs13560_tpm_rev MonocyteMacrophageUdornInfluenza_24hr00minD2- Monocyte-derived macrophages response to udorn influenza infection, 24hr00min, donor2 (150_120:Ud_24h)_CNhs13560_13320-143A8_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection24hr00minDonor2150_120Ud_24h_CNhs13560_tpm_fwd MonocyteMacrophageUdornInfluenza_24hr00minD2+ Monocyte-derived macrophages response to udorn influenza infection, 24hr00min, donor2 (150_120:Ud_24h)_CNhs13560_13320-143A8_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection24hr00minDonor1868_121Ud_24h_CNhs13557_tpm_rev MonocyteMacrophageUdornInfluenza_24hr00minD1- Monocyte-derived macrophages response to udorn influenza infection, 24hr00min, donor1 (868_121:Ud_24h)_CNhs13557_13308-142I5_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection24hr00minDonor1868_121Ud_24h_CNhs13557_tpm_fwd MonocyteMacrophageUdornInfluenza_24hr00minD1+ Monocyte-derived macrophages response to udorn influenza infection, 24hr00min, donor1 (868_121:Ud_24h)_CNhs13557_13308-142I5_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection07hr00minDonor4227_121Ud_7h_CNhs13641_tpm_rev MonocyteMacrophageUdornInfluenza_07hr00minD4- Monocyte-derived macrophages response to udorn influenza infection, 07hr00min, donor4 (227_121:Ud_7h)_CNhs13641_13313-143A1_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection07hr00minDonor4227_121Ud_7h_CNhs13641_tpm_fwd MonocyteMacrophageUdornInfluenza_07hr00minD4+ Monocyte-derived macrophages response to udorn influenza infection, 07hr00min, donor4 (227_121:Ud_7h)_CNhs13641_13313-143A1_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection07hr00minDonor3536_119Ud_7h_CNhs13561_tpm_rev MonocyteMacrophageUdornInfluenza_07hr00minD3- Monocyte-derived macrophages response to udorn influenza infection, 07hr00min, donor3 (536_119:Ud_7h)_CNhs13561_13325-143B4_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection07hr00minDonor3536_119Ud_7h_CNhs13561_tpm_fwd MonocyteMacrophageUdornInfluenza_07hr00minD3+ Monocyte-derived macrophages response to udorn influenza infection, 07hr00min, donor3 (536_119:Ud_7h)_CNhs13561_13325-143B4_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection07hr00minDonor2150_120Ud_7h_CNhs13559_tpm_rev MonocyteMacrophageUdornInfluenza_07hr00minD2- Monocyte-derived macrophages response to udorn influenza infection, 07hr00min, donor2 (150_120:Ud_7h)_CNhs13559_13319-143A7_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection07hr00minDonor2150_120Ud_7h_CNhs13559_tpm_fwd MonocyteMacrophageUdornInfluenza_07hr00minD2+ Monocyte-derived macrophages response to udorn influenza infection, 07hr00min, donor2 (150_120:Ud_7h)_CNhs13559_13319-143A7_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection07hr00minDonor1868_121Ud_7h_CNhs13556_tpm_rev MonocyteMacrophageUdornInfluenza_07hr00minD1- Monocyte-derived macrophages response to udorn influenza infection, 07hr00min, donor1 (868_121:Ud_7h)_CNhs13556_13307-142I4_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection07hr00minDonor1868_121Ud_7h_CNhs13556_tpm_fwd MonocyteMacrophageUdornInfluenza_07hr00minD1+ Monocyte-derived macrophages response to udorn influenza infection, 07hr00min, donor1 (868_121:Ud_7h)_CNhs13556_13307-142I4_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection02hr00minDonor4227_121Ud_2h_CNhs13640_tpm_rev MonocyteMacrophageUdornInfluenza_02hr00minD4- Monocyte-derived macrophages response to udorn influenza infection, 02hr00min, donor4 (227_121:Ud_2h)_CNhs13640_13312-142I9_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection02hr00minDonor4227_121Ud_2h_CNhs13640_tpm_fwd MonocyteMacrophageUdornInfluenza_02hr00minD4+ Monocyte-derived macrophages response to udorn influenza infection, 02hr00min, donor4 (227_121:Ud_2h)_CNhs13640_13312-142I9_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection02hr00minDonor3536_119Ud_2h_CNhs13651_tpm_rev MonocyteMacrophageUdornInfluenza_02hr00minD3- Monocyte-derived macrophages response to udorn influenza infection, 02hr00min, donor3 (536_119:Ud_2h)_CNhs13651_13324-143B3_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection02hr00minDonor3536_119Ud_2h_CNhs13651_tpm_fwd MonocyteMacrophageUdornInfluenza_02hr00minD3+ Monocyte-derived macrophages response to udorn influenza infection, 02hr00min, donor3 (536_119:Ud_2h)_CNhs13651_13324-143B3_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection02hr00minDonor1868_121Ud_2h_CNhs13555_tpm_rev MonocyteMacrophageUdornInfluenza_02hr00minD1- Monocyte-derived macrophages response to udorn influenza infection, 02hr00min, donor1 (868_121:Ud_2h)_CNhs13555_13306-142I3_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection02hr00minDonor1868_121Ud_2h_CNhs13555_tpm_fwd MonocyteMacrophageUdornInfluenza_02hr00minD1+ Monocyte-derived macrophages response to udorn influenza infection, 02hr00min, donor1 (868_121:Ud_2h)_CNhs13555_13306-142I3_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection00hr00minDonor4227_121Ud_0h_CNhs13639_tpm_rev MonocyteMacrophageUdornInfluenza_00hr00minD4- Monocyte-derived macrophages response to udorn influenza infection, 00hr00min, donor4 (227_121:Ud_0h)_CNhs13639_13311-142I8_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection00hr00minDonor4227_121Ud_0h_CNhs13639_tpm_fwd MonocyteMacrophageUdornInfluenza_00hr00minD4+ Monocyte-derived macrophages response to udorn influenza infection, 00hr00min, donor4 (227_121:Ud_0h)_CNhs13639_13311-142I8_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection00hr00minDonor3536_119Ud_0h_CNhs13650_tpm_rev MonocyteMacrophageUdornInfluenza_00hr00minD3- Monocyte-derived macrophages response to udorn influenza infection, 00hr00min, donor3 (536_119:Ud_0h)_CNhs13650_13323-143B2_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection00hr00minDonor3536_119Ud_0h_CNhs13650_tpm_fwd MonocyteMacrophageUdornInfluenza_00hr00minD3+ Monocyte-derived macrophages response to udorn influenza infection, 00hr00min, donor3 (536_119:Ud_0h)_CNhs13650_13323-143B2_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection00hr00minDonor2150_120Ud_0h_CNhs13646_tpm_rev MonocyteMacrophageUdornInfluenza_00hr00minD2- Monocyte-derived macrophages response to udorn influenza infection, 00hr00min, donor2 (150_120:Ud_0h)_CNhs13646_13317-143A5_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection00hr00minDonor2150_120Ud_0h_CNhs13646_tpm_fwd MonocyteMacrophageUdornInfluenza_00hr00minD2+ Monocyte-derived macrophages response to udorn influenza infection, 00hr00min, donor2 (150_120:Ud_0h)_CNhs13646_13317-143A5_forward Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection00hr00minDonor1868_121Ud_0h_CNhs13554_tpm_rev MonocyteMacrophageUdornInfluenza_00hr00minD1- Monocyte-derived macrophages response to udorn influenza infection, 00hr00min, donor1 (868_121:Ud_0h)_CNhs13554_13305-142I2_reverse Regulation 
MonocytederivedMacrophagesResponseToUdornInfluenzaInfection00hr00minDonor1868_121Ud_0h_CNhs13554_tpm_fwd MonocyteMacrophageUdornInfluenza_00hr00minD1+ Monocyte-derived macrophages response to udorn influenza infection, 00hr00min, donor1 (868_121:Ud_0h)_CNhs13554_13305-142I2_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay14BiolRep3_CNhs13632_tpm_rev MscAdipogenicInduction_Day14Br3- mesenchymal stem cells (adipose derived), adipogenic induction, day14, biol_rep3_CNhs13632_13279-142F3_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay14BiolRep3_CNhs13632_tpm_fwd MscAdipogenicInduction_Day14Br3+ mesenchymal stem cells (adipose derived), adipogenic induction, day14, biol_rep3_CNhs13632_13279-142F3_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay14BiolRep1_CNhs13338_tpm_rev MscAdipogenicInduction_Day14Br1- mesenchymal stem cells (adipose derived), adipogenic induction, day14, biol_rep1_CNhs13338_13277-142F1_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay14BiolRep1_CNhs13338_tpm_fwd MscAdipogenicInduction_Day14Br1+ mesenchymal stem cells (adipose derived), adipogenic induction, day14, biol_rep1_CNhs13338_13277-142F1_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay12BiolRep3_CNhs13630_tpm_rev MscAdipogenicInduction_Day12Br3- mesenchymal stem cells (adipose derived), adipogenic induction, day12, biol_rep3_CNhs13630_13276-142E9_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay12BiolRep3_CNhs13630_tpm_fwd MscAdipogenicInduction_Day12Br3+ mesenchymal stem cells (adipose derived), adipogenic induction, day12, biol_rep3_CNhs13630_13276-142E9_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay12BiolRep2_CNhs13629_tpm_rev MscAdipogenicInduction_Day12Br2- mesenchymal stem cells (adipose derived), adipogenic induction, day12, biol_rep2_CNhs13629_13275-142E8_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay12BiolRep2_CNhs13629_tpm_fwd MscAdipogenicInduction_Day12Br2+ mesenchymal stem cells (adipose derived), adipogenic induction, day12, biol_rep2_CNhs13629_13275-142E8_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay12BiolRep1_CNhs13628_tpm_rev MscAdipogenicInduction_Day12Br1- mesenchymal stem cells (adipose derived), adipogenic induction, day12, biol_rep1_CNhs13628_13274-142E7_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay12BiolRep1_CNhs13628_tpm_fwd MscAdipogenicInduction_Day12Br1+ mesenchymal stem cells (adipose derived), adipogenic induction, day12, biol_rep1_CNhs13628_13274-142E7_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay08BiolRep3_CNhs13627_tpm_rev MscAdipogenicInduction_Day08Br3- mesenchymal stem cells (adipose derived), adipogenic induction, day08, biol_rep3_CNhs13627_13273-142E6_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay08BiolRep3_CNhs13627_tpm_fwd MscAdipogenicInduction_Day08Br3+ mesenchymal stem cells (adipose derived), adipogenic induction, day08, biol_rep3_CNhs13627_13273-142E6_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay08BiolRep2_CNhs13626_tpm_rev MscAdipogenicInduction_Day08Br2- mesenchymal stem cells (adipose derived), adipogenic induction, day08, biol_rep2_CNhs13626_13272-142E5_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay08BiolRep2_CNhs13626_tpm_fwd MscAdipogenicInduction_Day08Br2+ mesenchymal stem cells (adipose derived), adipogenic induction, day08, biol_rep2_CNhs13626_13272-142E5_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay08BiolRep1_CNhs13625_tpm_rev MscAdipogenicInduction_Day08Br1- mesenchymal stem cells (adipose derived), adipogenic induction, day08, biol_rep1_CNhs13625_13271-142E4_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay08BiolRep1_CNhs13625_tpm_fwd MscAdipogenicInduction_Day08Br1+ mesenchymal stem cells (adipose derived), adipogenic induction, day08, biol_rep1_CNhs13625_13271-142E4_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay04BiolRep3_CNhs13624_tpm_rev MscAdipogenicInduction_Day04Br3- mesenchymal stem cells (adipose derived), adipogenic induction, day04, biol_rep3_CNhs13624_13270-142E3_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay04BiolRep3_CNhs13624_tpm_fwd MscAdipogenicInduction_Day04Br3+ mesenchymal stem cells (adipose derived), adipogenic induction, day04, biol_rep3_CNhs13624_13270-142E3_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay04BiolRep1_CNhs13622_tpm_rev MscAdipogenicInduction_Day04Br1- mesenchymal stem cells (adipose derived), adipogenic induction, day04, biol_rep1_CNhs13622_13268-142E1_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay04BiolRep1_CNhs13622_tpm_fwd MscAdipogenicInduction_Day04Br1+ mesenchymal stem cells (adipose derived), adipogenic induction, day04, biol_rep1_CNhs13622_13268-142E1_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay02BiolRep3_CNhs13621_tpm_rev MscAdipogenicInduction_Day02Br3- mesenchymal stem cells (adipose derived), adipogenic induction, day02, biol_rep3_CNhs13621_13267-142D9_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay02BiolRep3_CNhs13621_tpm_fwd MscAdipogenicInduction_Day02Br3+ mesenchymal stem cells (adipose derived), adipogenic induction, day02, biol_rep3_CNhs13621_13267-142D9_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay02BiolRep2_CNhs13620_tpm_rev MscAdipogenicInduction_Day02Br2- mesenchymal stem cells (adipose derived), adipogenic induction, day02, biol_rep2_CNhs13620_13266-142D8_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay02BiolRep2_CNhs13620_tpm_fwd MscAdipogenicInduction_Day02Br2+ mesenchymal stem cells (adipose derived), adipogenic induction, day02, biol_rep2_CNhs13620_13266-142D8_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay02BiolRep1_CNhs13619_tpm_rev MscAdipogenicInduction_Day02Br1- mesenchymal stem cells (adipose derived), adipogenic induction, day02, biol_rep1_CNhs13619_13265-142D7_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay02BiolRep1_CNhs13619_tpm_fwd MscAdipogenicInduction_Day02Br1+ mesenchymal stem cells (adipose derived), adipogenic induction, day02, biol_rep1_CNhs13619_13265-142D7_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay01BiolRep3_CNhs13617_tpm_rev MscAdipogenicInduction_Day01Br3- mesenchymal stem cells (adipose derived), adipogenic induction, day01, biol_rep3_CNhs13617_13264-142D6_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay01BiolRep3_CNhs13617_tpm_fwd MscAdipogenicInduction_Day01Br3+ mesenchymal stem cells (adipose derived), adipogenic induction, day01, biol_rep3_CNhs13617_13264-142D6_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay01BiolRep2_CNhs13616_tpm_rev MscAdipogenicInduction_Day01Br2- mesenchymal stem cells (adipose derived), adipogenic induction, day01, biol_rep2_CNhs13616_13263-142D5_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInductionDay01BiolRep2_CNhs13616_tpm_fwd MscAdipogenicInduction_Day01Br2+ mesenchymal stem cells (adipose derived), adipogenic induction, day01, biol_rep2_CNhs13616_13263-142D5_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction03hr00minBiolRep3_CNhs13611_tpm_rev MscAdipogenicInduction_03hr00minBr3- mesenchymal stem cells (adipose derived), adipogenic induction, 03hr00min, biol_rep3_CNhs13611_13258-142C9_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction03hr00minBiolRep3_CNhs13611_tpm_fwd MscAdipogenicInduction_03hr00minBr3+ mesenchymal stem cells (adipose derived), adipogenic induction, 03hr00min, biol_rep3_CNhs13611_13258-142C9_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction03hr00minBiolRep1_CNhs13609_tpm_rev MscAdipogenicInduction_03hr00minBr1- mesenchymal stem cells (adipose derived), adipogenic induction, 03hr00min, biol_rep1_CNhs13609_13256-142C7_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction03hr00minBiolRep1_CNhs13609_tpm_fwd MscAdipogenicInduction_03hr00minBr1+ mesenchymal stem cells (adipose derived), adipogenic induction, 03hr00min, biol_rep1_CNhs13609_13256-142C7_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr30minBiolRep3_CNhs13608_tpm_rev MscAdipogenicInduction_02hr30minBr3- mesenchymal stem cells (adipose derived), adipogenic induction, 02hr30min, biol_rep3_CNhs13608_13255-142C6_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr30minBiolRep3_CNhs13608_tpm_fwd MscAdipogenicInduction_02hr30minBr3+ mesenchymal stem cells (adipose derived), adipogenic induction, 02hr30min, biol_rep3_CNhs13608_13255-142C6_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr30minBiolRep1_CNhs13606_tpm_rev MscAdipogenicInduction_02hr30minBr1- mesenchymal stem cells (adipose derived), adipogenic induction, 02hr30min, biol_rep1_CNhs13606_13253-142C4_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr30minBiolRep1_CNhs13606_tpm_fwd MscAdipogenicInduction_02hr30minBr1+ mesenchymal stem cells (adipose derived), adipogenic induction, 02hr30min, biol_rep1_CNhs13606_13253-142C4_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr00minBiolRep2_CNhs13604_tpm_rev MscAdipogenicInduction_02hr00minBr2- mesenchymal stem cells (adipose derived), adipogenic induction, 02hr00min, biol_rep2_CNhs13604_13251-142C2_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr00minBiolRep2_CNhs13604_tpm_fwd MscAdipogenicInduction_02hr00minBr2+ mesenchymal stem cells (adipose derived), adipogenic induction, 02hr00min, biol_rep2_CNhs13604_13251-142C2_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr00minBiolRep1_CNhs13603_tpm_rev MscAdipogenicInduction_02hr00minBr1- mesenchymal stem cells (adipose derived), adipogenic induction, 02hr00min, biol_rep1_CNhs13603_13250-142C1_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction02hr00minBiolRep1_CNhs13603_tpm_fwd MscAdipogenicInduction_02hr00minBr1+ mesenchymal stem cells (adipose derived), adipogenic induction, 02hr00min, biol_rep1_CNhs13603_13250-142C1_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr40minBiolRep3_CNhs13602_tpm_rev MscAdipogenicInduction_01hr40minBr3- mesenchymal stem cells (adipose derived), adipogenic induction, 01hr40min, biol_rep3_CNhs13602_13249-142B9_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr40minBiolRep3_CNhs13602_tpm_fwd MscAdipogenicInduction_01hr40minBr3+ mesenchymal stem cells (adipose derived), adipogenic induction, 01hr40min, biol_rep3_CNhs13602_13249-142B9_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr40minBiolRep2_CNhs13601_tpm_rev MscAdipogenicInduction_01hr40minBr2- mesenchymal stem cells (adipose derived), adipogenic induction, 01hr40min, biol_rep2_CNhs13601_13248-142B8_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr40minBiolRep2_CNhs13601_tpm_fwd MscAdipogenicInduction_01hr40minBr2+ mesenchymal stem cells (adipose derived), adipogenic induction, 01hr40min, biol_rep2_CNhs13601_13248-142B8_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr40minBiolRep1_CNhs13600_tpm_rev MscAdipogenicInduction_01hr40minBr1- mesenchymal stem cells (adipose derived), adipogenic induction, 01hr40min, biol_rep1_CNhs13600_13247-142B7_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr40minBiolRep1_CNhs13600_tpm_fwd MscAdipogenicInduction_01hr40minBr1+ mesenchymal stem cells (adipose derived), adipogenic induction, 01hr40min, biol_rep1_CNhs13600_13247-142B7_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr00minBiolRep3_CNhs13433_tpm_rev MscAdipogenicInduction_01hr00minBr3- mesenchymal stem cells (adipose derived), adipogenic induction, 01hr00min, biol_rep3_CNhs13433_13243-142B3_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr00minBiolRep3_CNhs13433_tpm_fwd MscAdipogenicInduction_01hr00minBr3+ mesenchymal stem cells (adipose derived), adipogenic induction, 01hr00min, biol_rep3_CNhs13433_13243-142B3_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr00minBiolRep2_CNhs13432_tpm_rev MscAdipogenicInduction_01hr00minBr2- mesenchymal stem cells (adipose derived), adipogenic induction, 01hr00min, biol_rep2_CNhs13432_13242-142B2_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr00minBiolRep2_CNhs13432_tpm_fwd MscAdipogenicInduction_01hr00minBr2+ mesenchymal stem cells (adipose derived), adipogenic induction, 01hr00min, biol_rep2_CNhs13432_13242-142B2_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr00minBiolRep1_CNhs13431_tpm_rev MscAdipogenicInduction_01hr00minBr1- mesenchymal stem cells (adipose derived), adipogenic induction, 01hr00min, biol_rep1_CNhs13431_13241-142B1_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction01hr00minBiolRep1_CNhs13431_tpm_fwd MscAdipogenicInduction_01hr00minBr1+ mesenchymal stem cells (adipose derived), adipogenic induction, 01hr00min, biol_rep1_CNhs13431_13241-142B1_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr45minBiolRep3_CNhs13430_tpm_rev MscAdipogenicInduction_00hr45minBr3- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr45min, biol_rep3_CNhs13430_13240-142A9_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr45minBiolRep3_CNhs13430_tpm_fwd MscAdipogenicInduction_00hr45minBr3+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr45min, biol_rep3_CNhs13430_13240-142A9_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr45minBiolRep2_CNhs13429_tpm_rev MscAdipogenicInduction_00hr45minBr2- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr45min, biol_rep2_CNhs13429_13239-142A8_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr45minBiolRep2_CNhs13429_tpm_fwd MscAdipogenicInduction_00hr45minBr2+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr45min, biol_rep2_CNhs13429_13239-142A8_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr45minBiolRep1_CNhs13428_tpm_rev MscAdipogenicInduction_00hr45minBr1- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr45min, biol_rep1_CNhs13428_13238-142A7_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr45minBiolRep1_CNhs13428_tpm_fwd MscAdipogenicInduction_00hr45minBr1+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr45min, biol_rep1_CNhs13428_13238-142A7_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr30minBiolRep2_CNhs13426_tpm_rev MscAdipogenicInduction_00hr30minBr2- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr30min, biol_rep2_CNhs13426_13236-142A5_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr30minBiolRep2_CNhs13426_tpm_fwd MscAdipogenicInduction_00hr30minBr2+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr30min, biol_rep2_CNhs13426_13236-142A5_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr30minBiolRep1_CNhs13425_tpm_rev MscAdipogenicInduction_00hr30minBr1- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr30min, biol_rep1_CNhs13425_13235-142A4_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr30minBiolRep1_CNhs13425_tpm_fwd MscAdipogenicInduction_00hr30minBr1+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr30min, biol_rep1_CNhs13425_13235-142A4_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr15minBiolRep3_CNhs13424_tpm_rev MscAdipogenicInduction_00hr15minBr3- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr15min, biol_rep3_CNhs13424_13234-142A3_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr15minBiolRep3_CNhs13424_tpm_fwd MscAdipogenicInduction_00hr15minBr3+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr15min, biol_rep3_CNhs13424_13234-142A3_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr15minBiolRep2_CNhs13423_tpm_rev MscAdipogenicInduction_00hr15minBr2- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr15min, biol_rep2_CNhs13423_13233-142A2_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr15minBiolRep2_CNhs13423_tpm_fwd MscAdipogenicInduction_00hr15minBr2+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr15min, biol_rep2_CNhs13423_13233-142A2_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr15minBiolRep1_CNhs13422_tpm_rev MscAdipogenicInduction_00hr15minBr1- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr15min, biol_rep1_CNhs13422_13232-142A1_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr15minBiolRep1_CNhs13422_tpm_fwd MscAdipogenicInduction_00hr15minBr1+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr15min, biol_rep1_CNhs13422_13232-142A1_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr00minBiolRep3_CNhs13421_tpm_rev MscAdipogenicInduction_00hr00minBr3- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr00min, biol_rep3_CNhs13421_13231-141I9_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr00minBiolRep3_CNhs13421_tpm_fwd MscAdipogenicInduction_00hr00minBr3+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr00min, biol_rep3_CNhs13421_13231-141I9_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr00minBiolRep2_CNhs13420_tpm_rev MscAdipogenicInduction_00hr00minBr2- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr00min, biol_rep2_CNhs13420_13230-141I8_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr00minBiolRep2_CNhs13420_tpm_fwd MscAdipogenicInduction_00hr00minBr2+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr00min, biol_rep2_CNhs13420_13230-141I8_forward Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr00minBiolRep1_CNhs13337_tpm_rev MscAdipogenicInduction_00hr00minBr1- mesenchymal stem cells (adipose derived), adipogenic induction, 00hr00min, biol_rep1_CNhs13337_13229-141I7_reverse Regulation 
MesenchymalStemCellsAdiposeDerivedAdipogenicInduction00hr00minBiolRep1_CNhs13337_tpm_fwd MscAdipogenicInduction_00hr00minBr1+ mesenchymal stem cells (adipose derived), adipogenic induction, 00hr00min, biol_rep1_CNhs13337_13229-141I7_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG08hrBiolRep3_CNhs12768_tpm_rev Tc:Mcf7ToHrg_08hrBr3- MCF7 breast cancer cell line response to HRG, 08hr, biol_rep3_CNhs12768_13194-141E8_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG08hrBiolRep3_CNhs12768_tpm_fwd Tc:Mcf7ToHrg_08hrBr3+ MCF7 breast cancer cell line response to HRG, 08hr, biol_rep3_CNhs12768_13194-141E8_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG08hrBiolRep2_CNhs12667_tpm_rev Tc:Mcf7ToHrg_08hrBr2- MCF7 breast cancer cell line response to HRG, 08hr, biol_rep2_CNhs12667_13128-140G5_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG08hrBiolRep2_CNhs12667_tpm_fwd Tc:Mcf7ToHrg_08hrBr2+ MCF7 breast cancer cell line response to HRG, 08hr, biol_rep2_CNhs12667_13128-140G5_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG08hrBiolRep1_CNhs12740_tpm_rev Tc:Mcf7ToHrg_08hrBr1- MCF7 breast cancer cell line response to HRG, 08hr, biol_rep1_CNhs12740_13062-139I2_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG08hrBiolRep1_CNhs12740_tpm_fwd Tc:Mcf7ToHrg_08hrBr1+ MCF7 breast cancer cell line response to HRG, 08hr, biol_rep1_CNhs12740_13062-139I2_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG07hrBiolRep3_CNhs12767_tpm_rev Tc:Mcf7ToHrg_07hrBr3- MCF7 breast cancer cell line response to HRG, 07hr, biol_rep3_CNhs12767_13193-141E7_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG07hrBiolRep3_CNhs12767_tpm_fwd Tc:Mcf7ToHrg_07hrBr3+ MCF7 breast cancer cell line response to HRG, 07hr, biol_rep3_CNhs12767_13193-141E7_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG07hrBiolRep2_CNhs12666_tpm_rev Tc:Mcf7ToHrg_07hrBr2- MCF7 breast cancer cell line response to HRG, 07hr, biol_rep2_CNhs12666_13127-140G4_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG07hrBiolRep2_CNhs12666_tpm_fwd Tc:Mcf7ToHrg_07hrBr2+ MCF7 breast cancer cell line response to HRG, 07hr, biol_rep2_CNhs12666_13127-140G4_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG07hrBiolRep1_CNhs12448_tpm_rev Tc:Mcf7ToHrg_07hrBr1- MCF7 breast cancer cell line response to HRG, 07hr, biol_rep1_CNhs12448_13061-139I1_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG07hrBiolRep1_CNhs12448_tpm_fwd Tc:Mcf7ToHrg_07hrBr1+ MCF7 breast cancer cell line response to HRG, 07hr, biol_rep1_CNhs12448_13061-139I1_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG06hrBiolRep3_CNhs12766_tpm_rev Tc:Mcf7ToHrg_06hrBr3- MCF7 breast cancer cell line response to HRG, 06hr, biol_rep3_CNhs12766_13192-141E6_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG06hrBiolRep3_CNhs12766_tpm_fwd Tc:Mcf7ToHrg_06hrBr3+ MCF7 breast cancer cell line response to HRG, 06hr, biol_rep3_CNhs12766_13192-141E6_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG06hrBiolRep2_CNhs12665_tpm_rev Tc:Mcf7ToHrg_06hrBr2- MCF7 breast cancer cell line response to HRG, 06hr, biol_rep2_CNhs12665_13126-140G3_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG06hrBiolRep2_CNhs12665_tpm_fwd Tc:Mcf7ToHrg_06hrBr2+ MCF7 breast cancer cell line response to HRG, 06hr, biol_rep2_CNhs12665_13126-140G3_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG06hrBiolRep1_CNhs12447_tpm_rev Tc:Mcf7ToHrg_06hrBr1- MCF7 breast cancer cell line response to HRG, 06hr, biol_rep1_CNhs12447_13060-139H9_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG06hrBiolRep1_CNhs12447_tpm_fwd Tc:Mcf7ToHrg_06hrBr1+ MCF7 breast cancer cell line response to HRG, 06hr, biol_rep1_CNhs12447_13060-139H9_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG05hrBiolRep3_CNhs12765_tpm_rev Tc:Mcf7ToHrg_05hrBr3- MCF7 breast cancer cell line response to HRG, 05hr, biol_rep3_CNhs12765_13191-141E5_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG05hrBiolRep3_CNhs12765_tpm_fwd Tc:Mcf7ToHrg_05hrBr3+ MCF7 breast cancer cell line response to HRG, 05hr, biol_rep3_CNhs12765_13191-141E5_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG05hrBiolRep2_CNhs12664_tpm_rev Tc:Mcf7ToHrg_05hrBr2- MCF7 breast cancer cell line response to HRG, 05hr, biol_rep2_CNhs12664_13125-140G2_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG05hrBiolRep2_CNhs12664_tpm_fwd Tc:Mcf7ToHrg_05hrBr2+ MCF7 breast cancer cell line response to HRG, 05hr, biol_rep2_CNhs12664_13125-140G2_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG05hrBiolRep1_CNhs12446_tpm_rev Tc:Mcf7ToHrg_05hrBr1- MCF7 breast cancer cell line response to HRG, 05hr, biol_rep1_CNhs12446_13059-139H8_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG05hrBiolRep1_CNhs12446_tpm_fwd Tc:Mcf7ToHrg_05hrBr1+ MCF7 breast cancer cell line response to HRG, 05hr, biol_rep1_CNhs12446_13059-139H8_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG04hrBiolRep3_CNhs12764_tpm_rev Tc:Mcf7ToHrg_04hrBr3- MCF7 breast cancer cell line response to HRG, 04hr, biol_rep3_CNhs12764_13190-141E4_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG04hrBiolRep3_CNhs12764_tpm_fwd Tc:Mcf7ToHrg_04hrBr3+ MCF7 breast cancer cell line response to HRG, 04hr, biol_rep3_CNhs12764_13190-141E4_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG04hrBiolRep2_CNhs12663_tpm_rev Tc:Mcf7ToHrg_04hrBr2- MCF7 breast cancer cell line response to HRG, 04hr, biol_rep2_CNhs12663_13124-140G1_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG04hrBiolRep2_CNhs12663_tpm_fwd Tc:Mcf7ToHrg_04hrBr2+ MCF7 breast cancer cell line response to HRG, 04hr, biol_rep2_CNhs12663_13124-140G1_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG04hrBiolRep1_CNhs12445_tpm_rev Tc:Mcf7ToHrg_04hrBr1- MCF7 breast cancer cell line response to HRG, 04hr, biol_rep1_CNhs12445_13058-139H7_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG04hrBiolRep1_CNhs12445_tpm_fwd Tc:Mcf7ToHrg_04hrBr1+ MCF7 breast cancer cell line response to HRG, 04hr, biol_rep1_CNhs12445_13058-139H7_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr30minBiolRep3_CNhs12763_tpm_rev Tc:Mcf7ToHrg_03hr30minBr3- MCF7 breast cancer cell line response to HRG, 03hr30min, biol_rep3_CNhs12763_13189-141E3_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr30minBiolRep3_CNhs12763_tpm_fwd Tc:Mcf7ToHrg_03hr30minBr3+ MCF7 breast cancer cell line response to HRG, 03hr30min, biol_rep3_CNhs12763_13189-141E3_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr30minBiolRep2_CNhs12662_tpm_rev Tc:Mcf7ToHrg_03hr30minBr2- MCF7 breast cancer cell line response to HRG, 03hr30min, biol_rep2_CNhs12662_13123-140F9_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr30minBiolRep2_CNhs12662_tpm_fwd Tc:Mcf7ToHrg_03hr30minBr2+ MCF7 breast cancer cell line response to HRG, 03hr30min, biol_rep2_CNhs12662_13123-140F9_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr30minBiolRep1_CNhs12444_tpm_rev Tc:Mcf7ToHrg_03hr30minBr1- MCF7 breast cancer cell line response to HRG, 03hr30min, biol_rep1_CNhs12444_13057-139H6_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr30minBiolRep1_CNhs12444_tpm_fwd Tc:Mcf7ToHrg_03hr30minBr1+ MCF7 breast cancer cell line response to HRG, 03hr30min, biol_rep1_CNhs12444_13057-139H6_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr00minBiolRep3_CNhs12762_tpm_rev Tc:Mcf7ToHrg_03hr00minBr3- MCF7 breast cancer cell line response to HRG, 03hr00min, biol_rep3_CNhs12762_13188-141E2_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr00minBiolRep3_CNhs12762_tpm_fwd Tc:Mcf7ToHrg_03hr00minBr3+ MCF7 breast cancer cell line response to HRG, 03hr00min, biol_rep3_CNhs12762_13188-141E2_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr00minBiolRep2_CNhs12660_tpm_rev Tc:Mcf7ToHrg_03hr00minBr2- MCF7 breast cancer cell line response to HRG, 03hr00min, biol_rep2_CNhs12660_13122-140F8_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr00minBiolRep2_CNhs12660_tpm_fwd Tc:Mcf7ToHrg_03hr00minBr2+ MCF7 breast cancer cell line response to HRG, 03hr00min, biol_rep2_CNhs12660_13122-140F8_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr00minBiolRep1_CNhs12443_tpm_rev Tc:Mcf7ToHrg_03hr00minBr1- MCF7 breast cancer cell line response to HRG, 03hr00min, biol_rep1_CNhs12443_13056-139H5_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG03hr00minBiolRep1_CNhs12443_tpm_fwd Tc:Mcf7ToHrg_03hr00minBr1+ MCF7 breast cancer cell line response to HRG, 03hr00min, biol_rep1_CNhs12443_13056-139H5_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr30minBiolRep3_CNhs12761_tpm_rev Tc:Mcf7ToHrg_02hr30minBr3- MCF7 breast cancer cell line response to HRG, 02hr30min, biol_rep3_CNhs12761_13187-141E1_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr30minBiolRep3_CNhs12761_tpm_fwd Tc:Mcf7ToHrg_02hr30minBr3+ MCF7 breast cancer cell line response to HRG, 02hr30min, biol_rep3_CNhs12761_13187-141E1_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr30minBiolRep2_CNhs12659_tpm_rev Tc:Mcf7ToHrg_02hr30minBr2- MCF7 breast cancer cell line response to HRG, 02hr30min, biol_rep2_CNhs12659_13121-140F7_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr30minBiolRep2_CNhs12659_tpm_fwd Tc:Mcf7ToHrg_02hr30minBr2+ MCF7 breast cancer cell line response to HRG, 02hr30min, biol_rep2_CNhs12659_13121-140F7_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr30minBiolRep1_CNhs12442_tpm_rev Tc:Mcf7ToHrg_02hr30minBr1- MCF7 breast cancer cell line response to HRG, 02hr30min, biol_rep1_CNhs12442_13055-139H4_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr30minBiolRep1_CNhs12442_tpm_fwd Tc:Mcf7ToHrg_02hr30minBr1+ MCF7 breast cancer cell line response to HRG, 02hr30min, biol_rep1_CNhs12442_13055-139H4_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr00minBiolRep3_CNhs12760_tpm_rev Tc:Mcf7ToHrg_02hr00minBr3- MCF7 breast cancer cell line response to HRG, 02hr00min, biol_rep3_CNhs12760_13186-141D9_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr00minBiolRep3_CNhs12760_tpm_fwd Tc:Mcf7ToHrg_02hr00minBr3+ MCF7 breast cancer cell line response to HRG, 02hr00min, biol_rep3_CNhs12760_13186-141D9_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr00minBiolRep2_CNhs12658_tpm_rev Tc:Mcf7ToHrg_02hr00minBr2- MCF7 breast cancer cell line response to HRG, 02hr00min, biol_rep2_CNhs12658_13120-140F6_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr00minBiolRep2_CNhs12658_tpm_fwd Tc:Mcf7ToHrg_02hr00minBr2+ MCF7 breast cancer cell line response to HRG, 02hr00min, biol_rep2_CNhs12658_13120-140F6_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr00minBiolRep1_CNhs12441_tpm_rev Tc:Mcf7ToHrg_02hr00minBr1- MCF7 breast cancer cell line response to HRG, 02hr00min, biol_rep1_CNhs12441_13054-139H3_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG02hr00minBiolRep1_CNhs12441_tpm_fwd Tc:Mcf7ToHrg_02hr00minBr1+ MCF7 breast cancer cell line response to HRG, 02hr00min, biol_rep1_CNhs12441_13054-139H3_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr40minBiolRep3_CNhs12759_tpm_rev Tc:Mcf7ToHrg_01hr40minBr3- MCF7 breast cancer cell line response to HRG, 01hr40min, biol_rep3_CNhs12759_13185-141D8_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr40minBiolRep3_CNhs12759_tpm_fwd Tc:Mcf7ToHrg_01hr40minBr3+ MCF7 breast cancer cell line response to HRG, 01hr40min, biol_rep3_CNhs12759_13185-141D8_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr40minBiolRep2_CNhs12657_tpm_rev Tc:Mcf7ToHrg_01hr40minBr2- MCF7 breast cancer cell line response to HRG, 01hr40min, biol_rep2_CNhs12657_13119-140F5_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr40minBiolRep2_CNhs12657_tpm_fwd Tc:Mcf7ToHrg_01hr40minBr2+ MCF7 breast cancer cell line response to HRG, 01hr40min, biol_rep2_CNhs12657_13119-140F5_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr40minBiolRep1_CNhs12440_tpm_rev Tc:Mcf7ToHrg_01hr40minBr1- MCF7 breast cancer cell line response to HRG, 01hr40min, biol_rep1_CNhs12440_13053-139H2_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr40minBiolRep1_CNhs12440_tpm_fwd Tc:Mcf7ToHrg_01hr40minBr1+ MCF7 breast cancer cell line response to HRG, 01hr40min, biol_rep1_CNhs12440_13053-139H2_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr20minBiolRep3_CNhs12758_tpm_rev Tc:Mcf7ToHrg_01hr20minBr3- MCF7 breast cancer cell line response to HRG, 01hr20min, biol_rep3_CNhs12758_13184-141D7_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr20minBiolRep3_CNhs12758_tpm_fwd Tc:Mcf7ToHrg_01hr20minBr3+ MCF7 breast cancer cell line response to HRG, 01hr20min, biol_rep3_CNhs12758_13184-141D7_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr20minBiolRep2_CNhs12656_tpm_rev Tc:Mcf7ToHrg_01hr20minBr2- MCF7 breast cancer cell line response to HRG, 01hr20min, biol_rep2_CNhs12656_13118-140F4_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr20minBiolRep2_CNhs12656_tpm_fwd Tc:Mcf7ToHrg_01hr20minBr2+ MCF7 breast cancer cell line response to HRG, 01hr20min, biol_rep2_CNhs12656_13118-140F4_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr20minBiolRep1_CNhs12439_tpm_rev Tc:Mcf7ToHrg_01hr20minBr1- MCF7 breast cancer cell line response to HRG, 01hr20min, biol_rep1_CNhs12439_13052-139H1_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr20minBiolRep1_CNhs12439_tpm_fwd Tc:Mcf7ToHrg_01hr20minBr1+ MCF7 breast cancer cell line response to HRG, 01hr20min, biol_rep1_CNhs12439_13052-139H1_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr00minBiolRep3_CNhs12757_tpm_rev Tc:Mcf7ToHrg_01hr00minBr3- MCF7 breast cancer cell line response to HRG, 01hr00min, biol_rep3_CNhs12757_13183-141D6_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr00minBiolRep3_CNhs12757_tpm_fwd Tc:Mcf7ToHrg_01hr00minBr3+ MCF7 breast cancer cell line response to HRG, 01hr00min, biol_rep3_CNhs12757_13183-141D6_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr00minBiolRep2_CNhs12655_tpm_rev Tc:Mcf7ToHrg_01hr00minBr2- MCF7 breast cancer cell line response to HRG, 01hr00min, biol_rep2_CNhs12655_13117-140F3_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr00minBiolRep2_CNhs12655_tpm_fwd Tc:Mcf7ToHrg_01hr00minBr2+ MCF7 breast cancer cell line response to HRG, 01hr00min, biol_rep2_CNhs12655_13117-140F3_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr00minBiolRep1_CNhs12438_tpm_rev Tc:Mcf7ToHrg_01hr00minBr1- MCF7 breast cancer cell line response to HRG, 01hr00min, biol_rep1_CNhs12438_13051-139G9_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG01hr00minBiolRep1_CNhs12438_tpm_fwd Tc:Mcf7ToHrg_01hr00minBr1+ MCF7 breast cancer cell line response to HRG, 01hr00min, biol_rep1_CNhs12438_13051-139G9_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr45minBiolRep3_CNhs12756_tpm_rev Tc:Mcf7ToHrg_00hr45minBr3- MCF7 breast cancer cell line response to HRG, 00hr45min, biol_rep3_CNhs12756_13182-141D5_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr45minBiolRep3_CNhs12756_tpm_fwd Tc:Mcf7ToHrg_00hr45minBr3+ MCF7 breast cancer cell line response to HRG, 00hr45min, biol_rep3_CNhs12756_13182-141D5_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr45minBiolRep2_CNhs12654_tpm_rev Tc:Mcf7ToHrg_00hr45minBr2- MCF7 breast cancer cell line response to HRG, 00hr45min, biol_rep2_CNhs12654_13116-140F2_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr45minBiolRep2_CNhs12654_tpm_fwd Tc:Mcf7ToHrg_00hr45minBr2+ MCF7 breast cancer cell line response to HRG, 00hr45min, biol_rep2_CNhs12654_13116-140F2_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr45minBiolRep1_CNhs12437_tpm_rev Tc:Mcf7ToHrg_00hr45minBr1- MCF7 breast cancer cell line response to HRG, 00hr45min, biol_rep1_CNhs12437_13050-139G8_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr45minBiolRep1_CNhs12437_tpm_fwd Tc:Mcf7ToHrg_00hr45minBr1+ MCF7 breast cancer cell line response to HRG, 00hr45min, biol_rep1_CNhs12437_13050-139G8_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr30minBiolRep3_CNhs12755_tpm_rev Tc:Mcf7ToHrg_00hr30minBr3- MCF7 breast cancer cell line response to HRG, 00hr30min, biol_rep3_CNhs12755_13181-141D4_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr30minBiolRep3_CNhs12755_tpm_fwd Tc:Mcf7ToHrg_00hr30minBr3+ MCF7 breast cancer cell line response to HRG, 00hr30min, biol_rep3_CNhs12755_13181-141D4_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr30minBiolRep2_CNhs12653_tpm_rev Tc:Mcf7ToHrg_00hr30minBr2- MCF7 breast cancer cell line response to HRG, 00hr30min, biol_rep2_CNhs12653_13115-140F1_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr30minBiolRep2_CNhs12653_tpm_fwd Tc:Mcf7ToHrg_00hr30minBr2+ MCF7 breast cancer cell line response to HRG, 00hr30min, biol_rep2_CNhs12653_13115-140F1_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr30minBiolRep1_CNhs12436_tpm_rev Tc:Mcf7ToHrg_00hr30minBr1- MCF7 breast cancer cell line response to HRG, 00hr30min, biol_rep1_CNhs12436_13049-139G7_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr30minBiolRep1_CNhs12436_tpm_fwd Tc:Mcf7ToHrg_00hr30minBr1+ MCF7 breast cancer cell line response to HRG, 00hr30min, biol_rep1_CNhs12436_13049-139G7_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr15minBiolRep3_CNhs12754_tpm_rev Tc:Mcf7ToHrg_00hr15minBr3- MCF7 breast cancer cell line response to HRG, 00hr15min, biol_rep3_CNhs12754_13180-141D3_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr15minBiolRep3_CNhs12754_tpm_fwd Tc:Mcf7ToHrg_00hr15minBr3+ MCF7 breast cancer cell line response to HRG, 00hr15min, biol_rep3_CNhs12754_13180-141D3_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr15minBiolRep2_CNhs12652_tpm_rev Tc:Mcf7ToHrg_00hr15minBr2- MCF7 breast cancer cell line response to HRG, 00hr15min, biol_rep2_CNhs12652_13114-140E9_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr15minBiolRep2_CNhs12652_tpm_fwd Tc:Mcf7ToHrg_00hr15minBr2+ MCF7 breast cancer cell line response to HRG, 00hr15min, biol_rep2_CNhs12652_13114-140E9_forward Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr15minBiolRep1_CNhs12435_tpm_rev Tc:Mcf7ToHrg_00hr15minBr1- MCF7 breast cancer cell line response to HRG, 00hr15min, biol_rep1_CNhs12435_13048-139G6_reverse Regulation 
MCF7BreastCancerCellLineResponseToHRG00hr15minBiolRep1_CNhs12435_tpm_fwd Tc:Mcf7ToHrg_00hr15minBr1+ MCF7 breast cancer cell line response to HRG, 00hr15min, biol_rep1_CNhs12435_13048-139G6_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF108hrBiolRep3_CNhs12753_tpm_rev Mcf7ToEgf1_08hrBr3- MCF7 breast cancer cell line response to EGF1, 08hr, biol_rep3_CNhs12753_13178-141D1_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF108hrBiolRep3_CNhs12753_tpm_fwd Mcf7ToEgf1_08hrBr3+ MCF7 breast cancer cell line response to EGF1, 08hr, biol_rep3_CNhs12753_13178-141D1_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF108hrBiolRep2_CNhs12491_tpm_rev Mcf7ToEgf1_08hrBr2- MCF7 breast cancer cell line response to EGF1, 08hr, biol_rep2_CNhs12491_13112-140E7_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF108hrBiolRep2_CNhs12491_tpm_fwd Mcf7ToEgf1_08hrBr2+ MCF7 breast cancer cell line response to EGF1, 08hr, biol_rep2_CNhs12491_13112-140E7_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF107hrBiolRep3_CNhs12752_tpm_rev Mcf7ToEgf1_07hrBr3- MCF7 breast cancer cell line response to EGF1, 07hr, biol_rep3_CNhs12752_13177-141C9_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF107hrBiolRep3_CNhs12752_tpm_fwd Mcf7ToEgf1_07hrBr3+ MCF7 breast cancer cell line response to EGF1, 07hr, biol_rep3_CNhs12752_13177-141C9_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF107hrBiolRep2_CNhs12490_tpm_rev Mcf7ToEgf1_07hrBr2- MCF7 breast cancer cell line response to EGF1, 07hr, biol_rep2_CNhs12490_13111-140E6_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF107hrBiolRep2_CNhs12490_tpm_fwd Mcf7ToEgf1_07hrBr2+ MCF7 breast cancer cell line response to EGF1, 07hr, biol_rep2_CNhs12490_13111-140E6_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF107hrBiolRep1_CNhs12434_tpm_rev Mcf7ToEgf1_07hrBr1- MCF7 breast cancer cell line response to EGF1, 07hr, biol_rep1_CNhs12434_13045-139G3_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF107hrBiolRep1_CNhs12434_tpm_fwd Mcf7ToEgf1_07hrBr1+ MCF7 breast cancer cell line response to EGF1, 07hr, biol_rep1_CNhs12434_13045-139G3_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF106hrBiolRep3_CNhs12751_tpm_rev Mcf7ToEgf1_06hrBr3- MCF7 breast cancer cell line response to EGF1, 06hr, biol_rep3_CNhs12751_13176-141C8_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF106hrBiolRep3_CNhs12751_tpm_fwd Mcf7ToEgf1_06hrBr3+ MCF7 breast cancer cell line response to EGF1, 06hr, biol_rep3_CNhs12751_13176-141C8_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF106hrBiolRep2_CNhs12489_tpm_rev Mcf7ToEgf1_06hrBr2- MCF7 breast cancer cell line response to EGF1, 06hr, biol_rep2_CNhs12489_13110-140E5_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF106hrBiolRep2_CNhs12489_tpm_fwd Mcf7ToEgf1_06hrBr2+ MCF7 breast cancer cell line response to EGF1, 06hr, biol_rep2_CNhs12489_13110-140E5_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF106hrBiolRep1_CNhs12432_tpm_rev Mcf7ToEgf1_06hrBr1- MCF7 breast cancer cell line response to EGF1, 06hr, biol_rep1_CNhs12432_13044-139G2_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF106hrBiolRep1_CNhs12432_tpm_fwd Mcf7ToEgf1_06hrBr1+ MCF7 breast cancer cell line response to EGF1, 06hr, biol_rep1_CNhs12432_13044-139G2_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF105hrBiolRep3_CNhs12750_tpm_rev Mcf7ToEgf1_05hrBr3- MCF7 breast cancer cell line response to EGF1, 05hr, biol_rep3_CNhs12750_13175-141C7_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF105hrBiolRep3_CNhs12750_tpm_fwd Mcf7ToEgf1_05hrBr3+ MCF7 breast cancer cell line response to EGF1, 05hr, biol_rep3_CNhs12750_13175-141C7_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF105hrBiolRep2_CNhs12488_tpm_rev Mcf7ToEgf1_05hrBr2- MCF7 breast cancer cell line response to EGF1, 05hr, biol_rep2_CNhs12488_13109-140E4_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF105hrBiolRep2_CNhs12488_tpm_fwd Mcf7ToEgf1_05hrBr2+ MCF7 breast cancer cell line response to EGF1, 05hr, biol_rep2_CNhs12488_13109-140E4_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF105hrBiolRep1_CNhs12431_tpm_rev Mcf7ToEgf1_05hrBr1- MCF7 breast cancer cell line response to EGF1, 05hr, biol_rep1_CNhs12431_13043-139G1_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF105hrBiolRep1_CNhs12431_tpm_fwd Mcf7ToEgf1_05hrBr1+ MCF7 breast cancer cell line response to EGF1, 05hr, biol_rep1_CNhs12431_13043-139G1_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF104hrBiolRep3_CNhs12749_tpm_rev Mcf7ToEgf1_04hrBr3- MCF7 breast cancer cell line response to EGF1, 04hr, biol_rep3_CNhs12749_13174-141C6_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF104hrBiolRep3_CNhs12749_tpm_fwd Mcf7ToEgf1_04hrBr3+ MCF7 breast cancer cell line response to EGF1, 04hr, biol_rep3_CNhs12749_13174-141C6_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF104hrBiolRep2_CNhs12487_tpm_rev Mcf7ToEgf1_04hrBr2- MCF7 breast cancer cell line response to EGF1, 04hr, biol_rep2_CNhs12487_13108-140E3_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF104hrBiolRep2_CNhs12487_tpm_fwd Mcf7ToEgf1_04hrBr2+ MCF7 breast cancer cell line response to EGF1, 04hr, biol_rep2_CNhs12487_13108-140E3_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF104hrBiolRep1_CNhs12430_tpm_rev Mcf7ToEgf1_04hrBr1- MCF7 breast cancer cell line response to EGF1, 04hr, biol_rep1_CNhs12430_13042-139F9_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF104hrBiolRep1_CNhs12430_tpm_fwd Mcf7ToEgf1_04hrBr1+ MCF7 breast cancer cell line response to EGF1, 04hr, biol_rep1_CNhs12430_13042-139F9_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr30minBiolRep3_CNhs12748_tpm_rev Mcf7ToEgf1_03hr30minBr3- MCF7 breast cancer cell line response to EGF1, 03hr30min, biol_rep3_CNhs12748_13173-141C5_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr30minBiolRep3_CNhs12748_tpm_fwd Mcf7ToEgf1_03hr30minBr3+ MCF7 breast cancer cell line response to EGF1, 03hr30min, biol_rep3_CNhs12748_13173-141C5_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr30minBiolRep2_CNhs12486_tpm_rev Mcf7ToEgf1_03hr30minBr2- MCF7 breast cancer cell line response to EGF1, 03hr30min, biol_rep2_CNhs12486_13107-140E2_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr30minBiolRep2_CNhs12486_tpm_fwd Mcf7ToEgf1_03hr30minBr2+ MCF7 breast cancer cell line response to EGF1, 03hr30min, biol_rep2_CNhs12486_13107-140E2_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr30minBiolRep1_CNhs12429_tpm_rev Mcf7ToEgf1_03hr30minBr1- MCF7 breast cancer cell line response to EGF1, 03hr30min, biol_rep1_CNhs12429_13041-139F8_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr30minBiolRep1_CNhs12429_tpm_fwd Mcf7ToEgf1_03hr30minBr1+ MCF7 breast cancer cell line response to EGF1, 03hr30min, biol_rep1_CNhs12429_13041-139F8_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr00minBiolRep3_CNhs12747_tpm_rev Mcf7ToEgf1_03hr00minBr3- MCF7 breast cancer cell line response to EGF1, 03hr00min, biol_rep3_CNhs12747_13172-141C4_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr00minBiolRep3_CNhs12747_tpm_fwd Mcf7ToEgf1_03hr00minBr3+ MCF7 breast cancer cell line response to EGF1, 03hr00min, biol_rep3_CNhs12747_13172-141C4_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr00minBiolRep2_CNhs12485_tpm_rev Mcf7ToEgf1_03hr00minBr2- MCF7 breast cancer cell line response to EGF1, 03hr00min, biol_rep2_CNhs12485_13106-140E1_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr00minBiolRep2_CNhs12485_tpm_fwd Mcf7ToEgf1_03hr00minBr2+ MCF7 breast cancer cell line response to EGF1, 03hr00min, biol_rep2_CNhs12485_13106-140E1_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr00minBiolRep1_CNhs12428_tpm_rev Mcf7ToEgf1_03hr00minBr1- MCF7 breast cancer cell line response to EGF1, 03hr00min, biol_rep1_CNhs12428_13040-139F7_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF103hr00minBiolRep1_CNhs12428_tpm_fwd Mcf7ToEgf1_03hr00minBr1+ MCF7 breast cancer cell line response to EGF1, 03hr00min, biol_rep1_CNhs12428_13040-139F7_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr30minBiolRep3_CNhs12746_tpm_rev Mcf7ToEgf1_02hr30minBr3- MCF7 breast cancer cell line response to EGF1, 02hr30min, biol_rep3_CNhs12746_13171-141C3_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr30minBiolRep3_CNhs12746_tpm_fwd Mcf7ToEgf1_02hr30minBr3+ MCF7 breast cancer cell line response to EGF1, 02hr30min, biol_rep3_CNhs12746_13171-141C3_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr30minBiolRep2_CNhs12484_tpm_rev Mcf7ToEgf1_02hr30minBr2- MCF7 breast cancer cell line response to EGF1, 02hr30min, biol_rep2_CNhs12484_13105-140D9_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr30minBiolRep2_CNhs12484_tpm_fwd Mcf7ToEgf1_02hr30minBr2+ MCF7 breast cancer cell line response to EGF1, 02hr30min, biol_rep2_CNhs12484_13105-140D9_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr30minBiolRep1_CNhs12427_tpm_rev Mcf7ToEgf1_02hr30minBr1- MCF7 breast cancer cell line response to EGF1, 02hr30min, biol_rep1_CNhs12427_13039-139F6_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr30minBiolRep1_CNhs12427_tpm_fwd Mcf7ToEgf1_02hr30minBr1+ MCF7 breast cancer cell line response to EGF1, 02hr30min, biol_rep1_CNhs12427_13039-139F6_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr00minBiolRep3_CNhs12744_tpm_rev Mcf7ToEgf1_02hr00minBr3- MCF7 breast cancer cell line response to EGF1, 02hr00min, biol_rep3_CNhs12744_13170-141C2_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr00minBiolRep3_CNhs12744_tpm_fwd Mcf7ToEgf1_02hr00minBr3+ MCF7 breast cancer cell line response to EGF1, 02hr00min, biol_rep3_CNhs12744_13170-141C2_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr00minBiolRep2_CNhs12483_tpm_rev Mcf7ToEgf1_02hr00minBr2- MCF7 breast cancer cell line response to EGF1, 02hr00min, biol_rep2_CNhs12483_13104-140D8_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr00minBiolRep2_CNhs12483_tpm_fwd Mcf7ToEgf1_02hr00minBr2+ MCF7 breast cancer cell line response to EGF1, 02hr00min, biol_rep2_CNhs12483_13104-140D8_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr00minBiolRep1_CNhs12426_tpm_rev Mcf7ToEgf1_02hr00minBr1- MCF7 breast cancer cell line response to EGF1, 02hr00min, biol_rep1_CNhs12426_13038-139F5_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF102hr00minBiolRep1_CNhs12426_tpm_fwd Mcf7ToEgf1_02hr00minBr1+ MCF7 breast cancer cell line response to EGF1, 02hr00min, biol_rep1_CNhs12426_13038-139F5_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr40minBiolRep3_CNhs12743_tpm_rev Mcf7ToEgf1_01hr40minBr3- MCF7 breast cancer cell line response to EGF1, 01hr40min, biol_rep3_CNhs12743_13169-141C1_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr40minBiolRep3_CNhs12743_tpm_fwd Mcf7ToEgf1_01hr40minBr3+ MCF7 breast cancer cell line response to EGF1, 01hr40min, biol_rep3_CNhs12743_13169-141C1_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr40minBiolRep2_CNhs12482_tpm_rev Mcf7ToEgf1_01hr40minBr2- MCF7 breast cancer cell line response to EGF1, 01hr40min, biol_rep2_CNhs12482_13103-140D7_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr40minBiolRep2_CNhs12482_tpm_fwd Mcf7ToEgf1_01hr40minBr2+ MCF7 breast cancer cell line response to EGF1, 01hr40min, biol_rep2_CNhs12482_13103-140D7_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr40minBiolRep1_CNhs12425_tpm_rev Mcf7ToEgf1_01hr40minBr1- MCF7 breast cancer cell line response to EGF1, 01hr40min, biol_rep1_CNhs12425_13037-139F4_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr40minBiolRep1_CNhs12425_tpm_fwd Mcf7ToEgf1_01hr40minBr1+ MCF7 breast cancer cell line response to EGF1, 01hr40min, biol_rep1_CNhs12425_13037-139F4_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr20minBiolRep3_CNhs12742_tpm_rev Mcf7ToEgf1_01hr20minBr3- MCF7 breast cancer cell line response to EGF1, 01hr20min, biol_rep3_CNhs12742_13168-141B9_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr20minBiolRep3_CNhs12742_tpm_fwd Mcf7ToEgf1_01hr20minBr3+ MCF7 breast cancer cell line response to EGF1, 01hr20min, biol_rep3_CNhs12742_13168-141B9_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr20minBiolRep2_CNhs12480_tpm_rev Mcf7ToEgf1_01hr20minBr2- MCF7 breast cancer cell line response to EGF1, 01hr20min, biol_rep2_CNhs12480_13102-140D6_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr20minBiolRep2_CNhs12480_tpm_fwd Mcf7ToEgf1_01hr20minBr2+ MCF7 breast cancer cell line response to EGF1, 01hr20min, biol_rep2_CNhs12480_13102-140D6_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr20minBiolRep1_CNhs12424_tpm_rev Mcf7ToEgf1_01hr20minBr1- MCF7 breast cancer cell line response to EGF1, 01hr20min, biol_rep1_CNhs12424_13036-139F3_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr20minBiolRep1_CNhs12424_tpm_fwd Mcf7ToEgf1_01hr20minBr1+ MCF7 breast cancer cell line response to EGF1, 01hr20min, biol_rep1_CNhs12424_13036-139F3_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr00minBiolRep3_CNhs12705_tpm_rev Mcf7ToEgf1_01hr00minBr3- MCF7 breast cancer cell line response to EGF1, 01hr00min, biol_rep3_CNhs12705_13167-141B8_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr00minBiolRep3_CNhs12705_tpm_fwd Mcf7ToEgf1_01hr00minBr3+ MCF7 breast cancer cell line response to EGF1, 01hr00min, biol_rep3_CNhs12705_13167-141B8_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr00minBiolRep2_CNhs12479_tpm_rev Mcf7ToEgf1_01hr00minBr2- MCF7 breast cancer cell line response to EGF1, 01hr00min, biol_rep2_CNhs12479_13101-140D5_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr00minBiolRep2_CNhs12479_tpm_fwd Mcf7ToEgf1_01hr00minBr2+ MCF7 breast cancer cell line response to EGF1, 01hr00min, biol_rep2_CNhs12479_13101-140D5_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr00minBiolRep1_CNhs12423_tpm_rev Mcf7ToEgf1_01hr00minBr1- MCF7 breast cancer cell line response to EGF1, 01hr00min, biol_rep1_CNhs12423_13035-139F2_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF101hr00minBiolRep1_CNhs12423_tpm_fwd Mcf7ToEgf1_01hr00minBr1+ MCF7 breast cancer cell line response to EGF1, 01hr00min, biol_rep1_CNhs12423_13035-139F2_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr45minBiolRep3_CNhs12739_tpm_rev Mcf7ToEgf1_00hr45minBr3- MCF7 breast cancer cell line response to EGF1, 00hr45min, biol_rep3_CNhs12739_13166-141B7_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr45minBiolRep3_CNhs12739_tpm_fwd Mcf7ToEgf1_00hr45minBr3+ MCF7 breast cancer cell line response to EGF1, 00hr45min, biol_rep3_CNhs12739_13166-141B7_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr45minBiolRep2_CNhs12478_tpm_rev Mcf7ToEgf1_00hr45minBr2- MCF7 breast cancer cell line response to EGF1, 00hr45min, biol_rep2_CNhs12478_13100-140D4_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr45minBiolRep2_CNhs12478_tpm_fwd Mcf7ToEgf1_00hr45minBr2+ MCF7 breast cancer cell line response to EGF1, 00hr45min, biol_rep2_CNhs12478_13100-140D4_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr45minBiolRep1_CNhs12422_tpm_rev Mcf7ToEgf1_00hr45minBr1- MCF7 breast cancer cell line response to EGF1, 00hr45min, biol_rep1_CNhs12422_13034-139F1_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr45minBiolRep1_CNhs12422_tpm_fwd Mcf7ToEgf1_00hr45minBr1+ MCF7 breast cancer cell line response to EGF1, 00hr45min, biol_rep1_CNhs12422_13034-139F1_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr30minBiolRep3_CNhs12738_tpm_rev Mcf7ToEgf1_00hr30minBr3- MCF7 breast cancer cell line response to EGF1, 00hr30min, biol_rep3_CNhs12738_13165-141B6_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr30minBiolRep3_CNhs12738_tpm_fwd Mcf7ToEgf1_00hr30minBr3+ MCF7 breast cancer cell line response to EGF1, 00hr30min, biol_rep3_CNhs12738_13165-141B6_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr30minBiolRep2_CNhs12477_tpm_rev Mcf7ToEgf1_00hr30minBr2- MCF7 breast cancer cell line response to EGF1, 00hr30min, biol_rep2_CNhs12477_13099-140D3_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr30minBiolRep2_CNhs12477_tpm_fwd Mcf7ToEgf1_00hr30minBr2+ MCF7 breast cancer cell line response to EGF1, 00hr30min, biol_rep2_CNhs12477_13099-140D3_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr30minBiolRep1_CNhs12421_tpm_rev Mcf7ToEgf1_00hr30minBr1- MCF7 breast cancer cell line response to EGF1, 00hr30min, biol_rep1_CNhs12421_13033-139E9_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr30minBiolRep1_CNhs12421_tpm_fwd Mcf7ToEgf1_00hr30minBr1+ MCF7 breast cancer cell line response to EGF1, 00hr30min, biol_rep1_CNhs12421_13033-139E9_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr15minBiolRep3_CNhs12704_tpm_rev Mcf7ToEgf1_00hr15minBr3- MCF7 breast cancer cell line response to EGF1, 00hr15min, biol_rep3_CNhs12704_13164-141B5_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr15minBiolRep3_CNhs12704_tpm_fwd Mcf7ToEgf1_00hr15minBr3+ MCF7 breast cancer cell line response to EGF1, 00hr15min, biol_rep3_CNhs12704_13164-141B5_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr15minBiolRep2_CNhs12476_tpm_rev Mcf7ToEgf1_00hr15minBr2- MCF7 breast cancer cell line response to EGF1, 00hr15min, biol_rep2_CNhs12476_13098-140D2_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr15minBiolRep2_CNhs12476_tpm_fwd Mcf7ToEgf1_00hr15minBr2+ MCF7 breast cancer cell line response to EGF1, 00hr15min, biol_rep2_CNhs12476_13098-140D2_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr15minBiolRep1_CNhs12420_tpm_rev Mcf7ToEgf1_00hr15minBr1- MCF7 breast cancer cell line response to EGF1, 00hr15min, biol_rep1_CNhs12420_13032-139E8_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr15minBiolRep1_CNhs12420_tpm_fwd Mcf7ToEgf1_00hr15minBr1+ MCF7 breast cancer cell line response to EGF1, 00hr15min, biol_rep1_CNhs12420_13032-139E8_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr00minBiolRep3_CNhs12703_tpm_rev Mcf7ToEgf1_00hr00minBr3- MCF7 breast cancer cell line response to EGF1, 00hr00min, biol_rep3_CNhs12703_13163-141B4_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr00minBiolRep3_CNhs12703_tpm_fwd Mcf7ToEgf1_00hr00minBr3+ MCF7 breast cancer cell line response to EGF1, 00hr00min, biol_rep3_CNhs12703_13163-141B4_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr00minBiolRep2_CNhs12475_tpm_rev Mcf7ToEgf1_00hr00minBr2- MCF7 breast cancer cell line response to EGF1, 00hr00min, biol_rep2_CNhs12475_13097-140D1_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF100hr00minBiolRep2_CNhs12475_tpm_fwd Mcf7ToEgf1_00hr00minBr2+ MCF7 breast cancer cell line response to EGF1, 00hr00min, biol_rep2_CNhs12475_13097-140D1_forward Regulation 
MCF7BreastCancerCellLineResponseToEGF108hrBiolRep1_CNhs12565_tpm_rev Mcf7ToEgf1_08hrBr1- MCF7 breast cancer cell line response to EGF1, 08hr, biol_rep1_CNhs12565_13046-139G4_reverse Regulation 
MCF7BreastCancerCellLineResponseToEGF108hrBiolRep1_CNhs12565_tpm_fwd Mcf7ToEgf1_08hrBr1+ MCF7 breast cancer cell line response to EGF1, 08hr, biol_rep1_CNhs12565_13046-139G4_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC08hrBiolRep3MMXXII16_CNhs13291_tpm_rev LymphaticEndothelialCellsToVegfc_08hrBr3- Lymphatic Endothelial cells response to VEGFC, 08hr, biol_rep3 (MM XXII - 16)_CNhs13291_12519-133B8_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC08hrBiolRep3MMXXII16_CNhs13291_tpm_fwd LymphaticEndothelialCellsToVegfc_08hrBr3+ Lymphatic Endothelial cells response to VEGFC, 08hr, biol_rep3 (MM XXII - 16)_CNhs13291_12519-133B8_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC08hrBiolRep2MMXIV16_CNhs13173_tpm_rev LymphaticEndothelialCellsToVegfc_08hrBr2- Lymphatic Endothelial cells response to VEGFC, 08hr, biol_rep2 (MM XIV - 16)_CNhs13173_12397-131G3_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC08hrBiolRep2MMXIV16_CNhs13173_tpm_fwd LymphaticEndothelialCellsToVegfc_08hrBr2+ Lymphatic Endothelial cells response to VEGFC, 08hr, biol_rep2 (MM XIV - 16)_CNhs13173_12397-131G3_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC08hrBiolRep1MMXIX16_CNhs11937_tpm_rev LymphaticEndothelialCellsToVegfc_08hrBr1- Lymphatic Endothelial cells response to VEGFC, 08hr, biol_rep1 (MM XIX - 16)_CNhs11937_12275-130B7_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC08hrBiolRep1MMXIX16_CNhs11937_tpm_fwd LymphaticEndothelialCellsToVegfc_08hrBr1+ Lymphatic Endothelial cells response to VEGFC, 08hr, biol_rep1 (MM XIX - 16)_CNhs11937_12275-130B7_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC07hrBiolRep3MMXXII15_CNhs13290_tpm_rev LymphaticEndothelialCellsToVegfc_07hrBr3- Lymphatic Endothelial cells response to VEGFC, 07hr, biol_rep3 (MM XXII - 15)_CNhs13290_12518-133B7_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC07hrBiolRep3MMXXII15_CNhs13290_tpm_fwd LymphaticEndothelialCellsToVegfc_07hrBr3+ Lymphatic Endothelial cells response to VEGFC, 07hr, biol_rep3 (MM XXII - 15)_CNhs13290_12518-133B7_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC07hrBiolRep2MMXIV15_CNhs13172_tpm_rev LymphaticEndothelialCellsToVegfc_07hrBr2- Lymphatic Endothelial cells response to VEGFC, 07hr, biol_rep2 (MM XIV - 15)_CNhs13172_12396-131G2_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC07hrBiolRep2MMXIV15_CNhs13172_tpm_fwd LymphaticEndothelialCellsToVegfc_07hrBr2+ Lymphatic Endothelial cells response to VEGFC, 07hr, biol_rep2 (MM XIV - 15)_CNhs13172_12396-131G2_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC07hrBiolRep1MMXIX15_CNhs13113_tpm_rev LymphaticEndothelialCellsToVegfc_07hrBr1- Lymphatic Endothelial cells response to VEGFC, 07hr, biol_rep1 (MM XIX - 15)_CNhs13113_12274-130B6_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC07hrBiolRep1MMXIX15_CNhs13113_tpm_fwd LymphaticEndothelialCellsToVegfc_07hrBr1+ Lymphatic Endothelial cells response to VEGFC, 07hr, biol_rep1 (MM XIX - 15)_CNhs13113_12274-130B6_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC06hrBiolRep3MMXXII14_CNhs13289_tpm_rev LymphaticEndothelialCellsToVegfc_06hrBr3- Lymphatic Endothelial cells response to VEGFC, 06hr, biol_rep3 (MM XXII - 14)_CNhs13289_12517-133B6_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC06hrBiolRep3MMXXII14_CNhs13289_tpm_fwd LymphaticEndothelialCellsToVegfc_06hrBr3+ Lymphatic Endothelial cells response to VEGFC, 06hr, biol_rep3 (MM XXII - 14)_CNhs13289_12517-133B6_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC06hrBiolRep2MMXIV14_CNhs13171_tpm_rev LymphaticEndothelialCellsToVegfc_06hrBr2- Lymphatic Endothelial cells response to VEGFC, 06hr, biol_rep2 (MM XIV - 14)_CNhs13171_12395-131G1_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC06hrBiolRep2MMXIV14_CNhs13171_tpm_fwd LymphaticEndothelialCellsToVegfc_06hrBr2+ Lymphatic Endothelial cells response to VEGFC, 06hr, biol_rep2 (MM XIV - 14)_CNhs13171_12395-131G1_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC06hrBiolRep1MMXIX14_CNhs13112_tpm_rev LymphaticEndothelialCellsToVegfc_06hrBr1- Lymphatic Endothelial cells response to VEGFC, 06hr, biol_rep1 (MM XIX - 14)_CNhs13112_12273-130B5_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC06hrBiolRep1MMXIX14_CNhs13112_tpm_fwd LymphaticEndothelialCellsToVegfc_06hrBr1+ Lymphatic Endothelial cells response to VEGFC, 06hr, biol_rep1 (MM XIX - 14)_CNhs13112_12273-130B5_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC05hrBiolRep3MMXXII13_CNhs13288_tpm_rev LymphaticEndothelialCellsToVegfc_05hrBr3- Lymphatic Endothelial cells response to VEGFC, 05hr, biol_rep3 (MM XXII - 13)_CNhs13288_12516-133B5_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC05hrBiolRep3MMXXII13_CNhs13288_tpm_fwd LymphaticEndothelialCellsToVegfc_05hrBr3+ Lymphatic Endothelial cells response to VEGFC, 05hr, biol_rep3 (MM XXII - 13)_CNhs13288_12516-133B5_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC05hrBiolRep2MMXIV13_CNhs13170_tpm_rev LymphaticEndothelialCellsToVegfc_05hrBr2- Lymphatic Endothelial cells response to VEGFC, 05hr, biol_rep2 (MM XIV - 13)_CNhs13170_12394-131F9_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC05hrBiolRep2MMXIV13_CNhs13170_tpm_fwd LymphaticEndothelialCellsToVegfc_05hrBr2+ Lymphatic Endothelial cells response to VEGFC, 05hr, biol_rep2 (MM XIV - 13)_CNhs13170_12394-131F9_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC05hrBiolRep1MMXIX13_CNhs13111_tpm_rev LymphaticEndothelialCellsToVegfc_05hrBr1- Lymphatic Endothelial cells response to VEGFC, 05hr, biol_rep1 (MM XIX - 13)_CNhs13111_12272-130B4_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC05hrBiolRep1MMXIX13_CNhs13111_tpm_fwd LymphaticEndothelialCellsToVegfc_05hrBr1+ Lymphatic Endothelial cells response to VEGFC, 05hr, biol_rep1 (MM XIX - 13)_CNhs13111_12272-130B4_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC04hrBiolRep3MMXXII12_CNhs13287_tpm_rev LymphaticEndothelialCellsToVegfc_04hrBr3- Lymphatic Endothelial cells response to VEGFC, 04hr, biol_rep3 (MM XXII - 12)_CNhs13287_12515-133B4_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC04hrBiolRep3MMXXII12_CNhs13287_tpm_fwd LymphaticEndothelialCellsToVegfc_04hrBr3+ Lymphatic Endothelial cells response to VEGFC, 04hr, biol_rep3 (MM XXII - 12)_CNhs13287_12515-133B4_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC04hrBiolRep2MMXIV12_CNhs13169_tpm_rev LymphaticEndothelialCellsToVegfc_04hrBr2- Lymphatic Endothelial cells response to VEGFC, 04hr, biol_rep2 (MM XIV - 12)_CNhs13169_12393-131F8_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC04hrBiolRep2MMXIV12_CNhs13169_tpm_fwd LymphaticEndothelialCellsToVegfc_04hrBr2+ Lymphatic Endothelial cells response to VEGFC, 04hr, biol_rep2 (MM XIV - 12)_CNhs13169_12393-131F8_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC04hrBiolRep1MMXIX12_CNhs13110_tpm_rev LymphaticEndothelialCellsToVegfc_04hrBr1- Lymphatic Endothelial cells response to VEGFC, 04hr, biol_rep1 (MM XIX - 12)_CNhs13110_12271-130B3_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC04hrBiolRep1MMXIX12_CNhs13110_tpm_fwd LymphaticEndothelialCellsToVegfc_04hrBr1+ Lymphatic Endothelial cells response to VEGFC, 04hr, biol_rep1 (MM XIX - 12)_CNhs13110_12271-130B3_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr30minBiolRep3MMXXII11_CNhs13286_tpm_rev LymphaticEndothelialCellsToVegfc_03hr30minBr3- Lymphatic Endothelial cells response to VEGFC, 03hr30min, biol_rep3 (MM XXII - 11)_CNhs13286_12514-133B3_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr30minBiolRep3MMXXII11_CNhs13286_tpm_fwd LymphaticEndothelialCellsToVegfc_03hr30minBr3+ Lymphatic Endothelial cells response to VEGFC, 03hr30min, biol_rep3 (MM XXII - 11)_CNhs13286_12514-133B3_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr30minBiolRep2MMXIV11_CNhs13168_tpm_rev LymphaticEndothelialCellsToVegfc_03hr30minBr2- Lymphatic Endothelial cells response to VEGFC, 03hr30min, biol_rep2 (MM XIV - 11)_CNhs13168_12392-131F7_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr30minBiolRep2MMXIV11_CNhs13168_tpm_fwd LymphaticEndothelialCellsToVegfc_03hr30minBr2+ Lymphatic Endothelial cells response to VEGFC, 03hr30min, biol_rep2 (MM XIV - 11)_CNhs13168_12392-131F7_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr30minBiolRep1MMXIX11_CNhs13109_tpm_rev LymphaticEndothelialCellsToVegfc_03hr30minBr1- Lymphatic Endothelial cells response to VEGFC, 03hr30min, biol_rep1 (MM XIX - 11)_CNhs13109_12270-130B2_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr30minBiolRep1MMXIX11_CNhs13109_tpm_fwd LymphaticEndothelialCellsToVegfc_03hr30minBr1+ Lymphatic Endothelial cells response to VEGFC, 03hr30min, biol_rep1 (MM XIX - 11)_CNhs13109_12270-130B2_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr00minBiolRep3MMXXII10_CNhs13285_tpm_rev LymphaticEndothelialCellsToVegfc_03hr00minBr3- Lymphatic Endothelial cells response to VEGFC, 03hr00min, biol_rep3 (MM XXII - 10)_CNhs13285_12513-133B2_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr00minBiolRep3MMXXII10_CNhs13285_tpm_fwd LymphaticEndothelialCellsToVegfc_03hr00minBr3+ Lymphatic Endothelial cells response to VEGFC, 03hr00min, biol_rep3 (MM XXII - 10)_CNhs13285_12513-133B2_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr00minBiolRep2MMXIV10_CNhs13166_tpm_rev LymphaticEndothelialCellsToVegfc_03hr00minBr2- Lymphatic Endothelial cells response to VEGFC, 03hr00min, biol_rep2 (MM XIV - 10)_CNhs13166_12391-131F6_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr00minBiolRep2MMXIV10_CNhs13166_tpm_fwd LymphaticEndothelialCellsToVegfc_03hr00minBr2+ Lymphatic Endothelial cells response to VEGFC, 03hr00min, biol_rep2 (MM XIV - 10)_CNhs13166_12391-131F6_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr00minBiolRep1MMXIX10_CNhs13108_tpm_rev LymphaticEndothelialCellsToVegfc_03hr00minBr1- Lymphatic Endothelial cells response to VEGFC, 03hr00min, biol_rep1 (MM XIX - 10)_CNhs13108_12269-130B1_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC03hr00minBiolRep1MMXIX10_CNhs13108_tpm_fwd LymphaticEndothelialCellsToVegfc_03hr00minBr1+ Lymphatic Endothelial cells response to VEGFC, 03hr00min, biol_rep1 (MM XIX - 10)_CNhs13108_12269-130B1_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr30minBiolRep3MMXXII9_CNhs13284_tpm_rev LymphaticEndothelialCellsToVegfc_02hr30minBr3- Lymphatic Endothelial cells response to VEGFC, 02hr30min, biol_rep3 (MM XXII - 9)_CNhs13284_12512-133B1_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr30minBiolRep3MMXXII9_CNhs13284_tpm_fwd LymphaticEndothelialCellsToVegfc_02hr30minBr3+ Lymphatic Endothelial cells response to VEGFC, 02hr30min, biol_rep3 (MM XXII - 9)_CNhs13284_12512-133B1_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr30minBiolRep2MMXIV9_CNhs13165_tpm_rev LymphaticEndothelialCellsToVegfc_02hr30minBr2- Lymphatic Endothelial cells response to VEGFC, 02hr30min, biol_rep2 (MM XIV - 9)_CNhs13165_12390-131F5_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr30minBiolRep2MMXIV9_CNhs13165_tpm_fwd LymphaticEndothelialCellsToVegfc_02hr30minBr2+ Lymphatic Endothelial cells response to VEGFC, 02hr30min, biol_rep2 (MM XIV - 9)_CNhs13165_12390-131F5_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr30minBiolRep1MMXIX9_CNhs13107_tpm_rev LymphaticEndothelialCellsToVegfc_02hr30minBr1- Lymphatic Endothelial cells response to VEGFC, 02hr30min, biol_rep1 (MM XIX - 9)_CNhs13107_12268-130A9_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr30minBiolRep1MMXIX9_CNhs13107_tpm_fwd LymphaticEndothelialCellsToVegfc_02hr30minBr1+ Lymphatic Endothelial cells response to VEGFC, 02hr30min, biol_rep1 (MM XIX - 9)_CNhs13107_12268-130A9_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr00minBiolRep3MMXXII8_CNhs13283_tpm_rev LymphaticEndothelialCellsToVegfc_02hr00minBr3- Lymphatic Endothelial cells response to VEGFC, 02hr00min, biol_rep3 (MM XXII - 8)_CNhs13283_12511-133A9_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr00minBiolRep3MMXXII8_CNhs13283_tpm_fwd LymphaticEndothelialCellsToVegfc_02hr00minBr3+ Lymphatic Endothelial cells response to VEGFC, 02hr00min, biol_rep3 (MM XXII - 8)_CNhs13283_12511-133A9_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr00minBiolRep2MMXIV8_CNhs13164_tpm_rev LymphaticEndothelialCellsToVegfc_02hr00minBr2- Lymphatic Endothelial cells response to VEGFC, 02hr00min, biol_rep2 (MM XIV - 8)_CNhs13164_12389-131F4_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr00minBiolRep2MMXIV8_CNhs13164_tpm_fwd LymphaticEndothelialCellsToVegfc_02hr00minBr2+ Lymphatic Endothelial cells response to VEGFC, 02hr00min, biol_rep2 (MM XIV - 8)_CNhs13164_12389-131F4_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr00minBiolRep1MMXIX8_CNhs13106_tpm_rev LymphaticEndothelialCellsToVegfc_02hr00minBr1- Lymphatic Endothelial cells response to VEGFC, 02hr00min, biol_rep1 (MM XIX - 8)_CNhs13106_12267-130A8_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC02hr00minBiolRep1MMXIX8_CNhs13106_tpm_fwd LymphaticEndothelialCellsToVegfc_02hr00minBr1+ Lymphatic Endothelial cells response to VEGFC, 02hr00min, biol_rep1 (MM XIX - 8)_CNhs13106_12267-130A8_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr40minBiolRep3MMXXII7_CNhs13282_tpm_rev LymphaticEndothelialCellsToVegfc_01hr40minBr3- Lymphatic Endothelial cells response to VEGFC, 01hr40min, biol_rep3 (MM XXII - 7)_CNhs13282_12510-133A8_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr40minBiolRep3MMXXII7_CNhs13282_tpm_fwd LymphaticEndothelialCellsToVegfc_01hr40minBr3+ Lymphatic Endothelial cells response to VEGFC, 01hr40min, biol_rep3 (MM XXII - 7)_CNhs13282_12510-133A8_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr40minBiolRep2MMXIV7_CNhs13163_tpm_rev LymphaticEndothelialCellsToVegfc_01hr40minBr2- Lymphatic Endothelial cells response to VEGFC, 01hr40min, biol_rep2 (MM XIV - 7)_CNhs13163_12388-131F3_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr40minBiolRep2MMXIV7_CNhs13163_tpm_fwd LymphaticEndothelialCellsToVegfc_01hr40minBr2+ Lymphatic Endothelial cells response to VEGFC, 01hr40min, biol_rep2 (MM XIV - 7)_CNhs13163_12388-131F3_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr40minBiolRep1MMXIX7_CNhs13105_tpm_rev LymphaticEndothelialCellsToVegfc_01hr40minBr1- Lymphatic Endothelial cells response to VEGFC, 01hr40min, biol_rep1 (MM XIX - 7)_CNhs13105_12266-130A7_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr40minBiolRep1MMXIX7_CNhs13105_tpm_fwd LymphaticEndothelialCellsToVegfc_01hr40minBr1+ Lymphatic Endothelial cells response to VEGFC, 01hr40min, biol_rep1 (MM XIX - 7)_CNhs13105_12266-130A7_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr20minBiolRep3MMXXII6_CNhs13281_tpm_rev LymphaticEndothelialCellsToVegfc_01hr20minBr3- Lymphatic Endothelial cells response to VEGFC, 01hr20min, biol_rep3 (MM XXII - 6)_CNhs13281_12509-133A7_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr20minBiolRep3MMXXII6_CNhs13281_tpm_fwd LymphaticEndothelialCellsToVegfc_01hr20minBr3+ Lymphatic Endothelial cells response to VEGFC, 01hr20min, biol_rep3 (MM XXII - 6)_CNhs13281_12509-133A7_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr20minBiolRep2MMXIV6_CNhs13162_tpm_rev LymphaticEndothelialCellsToVegfc_01hr20minBr2- Lymphatic Endothelial cells response to VEGFC, 01hr20min, biol_rep2 (MM XIV - 6)_CNhs13162_12387-131F2_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr20minBiolRep2MMXIV6_CNhs13162_tpm_fwd LymphaticEndothelialCellsToVegfc_01hr20minBr2+ Lymphatic Endothelial cells response to VEGFC, 01hr20min, biol_rep2 (MM XIV - 6)_CNhs13162_12387-131F2_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr20minBiolRep1MMXIX6_CNhs13104_tpm_rev LymphaticEndothelialCellsToVegfc_01hr20minBr1- Lymphatic Endothelial cells response to VEGFC, 01hr20min, biol_rep1 (MM XIX - 6)_CNhs13104_12265-130A6_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr20minBiolRep1MMXIX6_CNhs13104_tpm_fwd LymphaticEndothelialCellsToVegfc_01hr20minBr1+ Lymphatic Endothelial cells response to VEGFC, 01hr20min, biol_rep1 (MM XIX - 6)_CNhs13104_12265-130A6_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr00minBiolRep3MMXXII5_CNhs13280_tpm_rev LymphaticEndothelialCellsToVegfc_01hr00minBr3- Lymphatic Endothelial cells response to VEGFC, 01hr00min, biol_rep3 (MM XXII - 5)_CNhs13280_12508-133A6_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr00minBiolRep3MMXXII5_CNhs13280_tpm_fwd LymphaticEndothelialCellsToVegfc_01hr00minBr3+ Lymphatic Endothelial cells response to VEGFC, 01hr00min, biol_rep3 (MM XXII - 5)_CNhs13280_12508-133A6_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr00minBiolRep2MMXIV5_CNhs13161_tpm_rev LymphaticEndothelialCellsToVegfc_01hr00minBr2- Lymphatic Endothelial cells response to VEGFC, 01hr00min, biol_rep2 (MM XIV - 5)_CNhs13161_12386-131F1_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr00minBiolRep2MMXIV5_CNhs13161_tpm_fwd LymphaticEndothelialCellsToVegfc_01hr00minBr2+ Lymphatic Endothelial cells response to VEGFC, 01hr00min, biol_rep2 (MM XIV - 5)_CNhs13161_12386-131F1_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr00minBiolRep1MMXIX5_CNhs13103_tpm_rev LymphaticEndothelialCellsToVegfc_01hr00minBr1- Lymphatic Endothelial cells response to VEGFC, 01hr00min, biol_rep1 (MM XIX - 5)_CNhs13103_12264-130A5_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC01hr00minBiolRep1MMXIX5_CNhs13103_tpm_fwd LymphaticEndothelialCellsToVegfc_01hr00minBr1+ Lymphatic Endothelial cells response to VEGFC, 01hr00min, biol_rep1 (MM XIX - 5)_CNhs13103_12264-130A5_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr45minBiolRep3MMXXII4_CNhs13279_tpm_rev LymphaticEndothelialCellsToVegfc_00hr45minBr3- Lymphatic Endothelial cells response to VEGFC, 00hr45min, biol_rep3 (MM XXII - 4)_CNhs13279_12507-133A5_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr45minBiolRep3MMXXII4_CNhs13279_tpm_fwd LymphaticEndothelialCellsToVegfc_00hr45minBr3+ Lymphatic Endothelial cells response to VEGFC, 00hr45min, biol_rep3 (MM XXII - 4)_CNhs13279_12507-133A5_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr45minBiolRep2MMXIV4_CNhs13160_tpm_rev LymphaticEndothelialCellsToVegfc_00hr45minBr2- Lymphatic Endothelial cells response to VEGFC, 00hr45min, biol_rep2 (MM XIV - 4)_CNhs13160_12385-131E9_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr45minBiolRep2MMXIV4_CNhs13160_tpm_fwd LymphaticEndothelialCellsToVegfc_00hr45minBr2+ Lymphatic Endothelial cells response to VEGFC, 00hr45min, biol_rep2 (MM XIV - 4)_CNhs13160_12385-131E9_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr45minBiolRep1MMXIX4_CNhs13102_tpm_rev LymphaticEndothelialCellsToVegfc_00hr45minBr1- Lymphatic Endothelial cells response to VEGFC, 00hr45min, biol_rep1 (MM XIX - 4)_CNhs13102_12263-130A4_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr45minBiolRep1MMXIX4_CNhs13102_tpm_fwd LymphaticEndothelialCellsToVegfc_00hr45minBr1+ Lymphatic Endothelial cells response to VEGFC, 00hr45min, biol_rep1 (MM XIX - 4)_CNhs13102_12263-130A4_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr30minBiolRep3MMXXII3_CNhs13278_tpm_rev LymphaticEndothelialCellsToVegfc_00hr30minBr3- Lymphatic Endothelial cells response to VEGFC, 00hr30min, biol_rep3 (MM XXII - 3)_CNhs13278_12506-133A4_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr30minBiolRep3MMXXII3_CNhs13278_tpm_fwd LymphaticEndothelialCellsToVegfc_00hr30minBr3+ Lymphatic Endothelial cells response to VEGFC, 00hr30min, biol_rep3 (MM XXII - 3)_CNhs13278_12506-133A4_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr30minBiolRep2MMXIV3_CNhs13159_tpm_rev LymphaticEndothelialCellsToVegfc_00hr30minBr2- Lymphatic Endothelial cells response to VEGFC, 00hr30min, biol_rep2 (MM XIV - 3)_CNhs13159_12384-131E8_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr30minBiolRep2MMXIV3_CNhs13159_tpm_fwd LymphaticEndothelialCellsToVegfc_00hr30minBr2+ Lymphatic Endothelial cells response to VEGFC, 00hr30min, biol_rep2 (MM XIV - 3)_CNhs13159_12384-131E8_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr30minBiolRep1MMXIX3_CNhs13101_tpm_rev LymphaticEndothelialCellsToVegfc_00hr30minBr1- Lymphatic Endothelial cells response to VEGFC, 00hr30min, biol_rep1 (MM XIX - 3)_CNhs13101_12262-130A3_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr30minBiolRep1MMXIX3_CNhs13101_tpm_fwd LymphaticEndothelialCellsToVegfc_00hr30minBr1+ Lymphatic Endothelial cells response to VEGFC, 00hr30min, biol_rep1 (MM XIX - 3)_CNhs13101_12262-130A3_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr15minBiolRep3MMXXII2_CNhs13277_tpm_rev LymphaticEndothelialCellsToVegfc_00hr15minBr3- Lymphatic Endothelial cells response to VEGFC, 00hr15min, biol_rep3 (MM XXII - 2)_CNhs13277_12505-133A3_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr15minBiolRep3MMXXII2_CNhs13277_tpm_fwd LymphaticEndothelialCellsToVegfc_00hr15minBr3+ Lymphatic Endothelial cells response to VEGFC, 00hr15min, biol_rep3 (MM XXII - 2)_CNhs13277_12505-133A3_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr15minBiolRep2MMXIV2_CNhs13158_tpm_rev LymphaticEndothelialCellsToVegfc_00hr15minBr2- Lymphatic Endothelial cells response to VEGFC, 00hr15min, biol_rep2 (MM XIV - 2)_CNhs13158_12383-131E7_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr15minBiolRep2MMXIV2_CNhs13158_tpm_fwd LymphaticEndothelialCellsToVegfc_00hr15minBr2+ Lymphatic Endothelial cells response to VEGFC, 00hr15min, biol_rep2 (MM XIV - 2)_CNhs13158_12383-131E7_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr15minBiolRep1MMXIX2_CNhs13100_tpm_rev LymphaticEndothelialCellsToVegfc_00hr15minBr1- Lymphatic Endothelial cells response to VEGFC, 00hr15min, biol_rep1 (MM XIX - 2)_CNhs13100_12261-130A2_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr15minBiolRep1MMXIX2_CNhs13100_tpm_fwd LymphaticEndothelialCellsToVegfc_00hr15minBr1+ Lymphatic Endothelial cells response to VEGFC, 00hr15min, biol_rep1 (MM XIX - 2)_CNhs13100_12261-130A2_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr00minBiolRep3MMXXII1_CNhs13276_tpm_rev LymphaticEndothelialCellsToVegfc_00hr00minBr3- Lymphatic Endothelial cells response to VEGFC, 00hr00min, biol_rep3 (MM XXII - 1 )_CNhs13276_12504-133A2_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr00minBiolRep3MMXXII1_CNhs13276_tpm_fwd LymphaticEndothelialCellsToVegfc_00hr00minBr3+ Lymphatic Endothelial cells response to VEGFC, 00hr00min, biol_rep3 (MM XXII - 1 )_CNhs13276_12504-133A2_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr00minBiolRep2MMXIV1_CNhs13157_tpm_rev LymphaticEndothelialCellsToVegfc_00hr00minBr2- Lymphatic Endothelial cells response to VEGFC, 00hr00min, biol_rep2 (MM XIV - 1)_CNhs13157_12382-131E6_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr00minBiolRep2MMXIV1_CNhs13157_tpm_fwd LymphaticEndothelialCellsToVegfc_00hr00minBr2+ Lymphatic Endothelial cells response to VEGFC, 00hr00min, biol_rep2 (MM XIV - 1)_CNhs13157_12382-131E6_forward Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr00minBiolRep1MMXIX1_CNhs11936_tpm_rev LymphaticEndothelialCellsToVegfc_00hr00minBr1- Lymphatic Endothelial cells response to VEGFC, 00hr00min, biol_rep1 (MM XIX - 1)_CNhs11936_12260-130A1_reverse Regulation 
LymphaticEndothelialCellsResponseToVEGFC00hr00minBiolRep1MMXIX1_CNhs11936_tpm_fwd LymphaticEndothelialCellsToVegfc_00hr00minBr1+ Lymphatic Endothelial cells response to VEGFC, 00hr00min, biol_rep1 (MM XIX - 1)_CNhs11936_12260-130A1_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day18Rep3_CNhs14055_tpm_rev IpsToNeuronControlDnC11-CRL2429Day18R3- iPS differentiation to neuron, control donor C32-CRL1502, day18, rep3_CNhs14055_13444-144F6_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day18Rep3_CNhs14055_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day18R3+ iPS differentiation to neuron, control donor C32-CRL1502, day18, rep3_CNhs14055_13444-144F6_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day18Rep2_CNhs13842_tpm_rev IpsToNeuronControlDnC11-CRL2429Day18R2- iPS differentiation to neuron, control donor C32-CRL1502, day18, rep2_CNhs13842_13440-144F2_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day18Rep2_CNhs13842_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day18R2+ iPS differentiation to neuron, control donor C32-CRL1502, day18, rep2_CNhs13842_13440-144F2_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day18Rep1_CNhs13829_tpm_rev IpsToNeuronControlDnC11-CRL2429Day18R1- iPS differentiation to neuron, control donor C32-CRL1502, day18, rep1_CNhs13829_13436-144E7_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day18Rep1_CNhs13829_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day18R1+ iPS differentiation to neuron, control donor C32-CRL1502, day18, rep1_CNhs13829_13436-144E7_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day12Rep3_CNhs14054_tpm_rev IpsToNeuronControlDnC11-CRL2429Day12R3- iPS differentiation to neuron, control donor C32-CRL1502, day12, rep3_CNhs14054_13443-144F5_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day12Rep3_CNhs14054_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day12R3+ iPS differentiation to neuron, control donor C32-CRL1502, day12, rep3_CNhs14054_13443-144F5_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day12Rep2_CNhs13841_tpm_rev IpsToNeuronControlDnC11-CRL2429Day12R2- iPS differentiation to neuron, control donor C32-CRL1502, day12, rep2_CNhs13841_13439-144F1_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day12Rep2_CNhs13841_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day12R2+ iPS differentiation to neuron, control donor C32-CRL1502, day12, rep2_CNhs13841_13439-144F1_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day12Rep1_CNhs13828_tpm_rev IpsToNeuronControlDnC11-CRL2429Day12R1- iPS differentiation to neuron, control donor C32-CRL1502, day12, rep1_CNhs13828_13435-144E6_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day12Rep1_CNhs13828_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day12R1+ iPS differentiation to neuron, control donor C32-CRL1502, day12, rep1_CNhs13828_13435-144E6_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day06Rep3_CNhs14053_tpm_rev IpsToNeuronControlDnC11-CRL2429Day06R3- iPS differentiation to neuron, control donor C32-CRL1502, day06, rep3_CNhs14053_13442-144F4_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day06Rep3_CNhs14053_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day06R3+ iPS differentiation to neuron, control donor C32-CRL1502, day06, rep3_CNhs14053_13442-144F4_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day06Rep2_CNhs13840_tpm_rev IpsToNeuronControlDnC11-CRL2429Day06R2- iPS differentiation to neuron, control donor C32-CRL1502, day06, rep2_CNhs13840_13438-144E9_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day06Rep2_CNhs13840_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day06R2+ iPS differentiation to neuron, control donor C32-CRL1502, day06, rep2_CNhs13840_13438-144E9_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day06Rep1_CNhs13827_tpm_rev IpsToNeuronControlDnC11-CRL2429Day06R1- iPS differentiation to neuron, control donor C32-CRL1502, day06, rep1_CNhs13827_13434-144E5_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day06Rep1_CNhs13827_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day06R1+ iPS differentiation to neuron, control donor C32-CRL1502, day06, rep1_CNhs13827_13434-144E5_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day00Rep3_CNhs14052_tpm_rev IpsToNeuronControlDnC11-CRL2429Day00R3- iPS differentiation to neuron, control donor C32-CRL1502, day00, rep3_CNhs14052_13441-144F3_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day00Rep3_CNhs14052_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day00R3+ iPS differentiation to neuron, control donor C32-CRL1502, day00, rep3_CNhs14052_13441-144F3_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day00Rep2_CNhs13839_tpm_rev IpsToNeuronControlDnC11-CRL2429Day00R2- iPS differentiation to neuron, control donor C32-CRL1502, day00, rep2_CNhs13839_13437-144E8_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day00Rep2_CNhs13839_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day00R2+ iPS differentiation to neuron, control donor C32-CRL1502, day00, rep2_CNhs13839_13437-144E8_forward Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day00Rep1_CNhs13826_tpm_rev IpsToNeuronControlDnC11-CRL2429Day00R1- iPS differentiation to neuron, control donor C32-CRL1502, day00, rep1_CNhs13826_13433-144E4_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC32CRL1502Day00Rep1_CNhs13826_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day00R1+ iPS differentiation to neuron, control donor C32-CRL1502, day00, rep1_CNhs13826_13433-144E4_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day18Rep3_CNhs13917_tpm_rev IpsToNeuronControlDnC11-CRL2429Day18R3- iPS differentiation to neuron, control donor C11-CRL2429, day18, rep3_CNhs13917_13432-144E3_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day18Rep3_CNhs13917_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day18R3+ iPS differentiation to neuron, control donor C11-CRL2429, day18, rep3_CNhs13917_13432-144E3_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day18Rep2_CNhs13825_tpm_rev IpsToNeuronControlDnC11-CRL2429Day18R2- iPS differentiation to neuron, control donor C11-CRL2429, day18, rep2_CNhs13825_13428-144D8_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day18Rep2_CNhs13825_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day18R2+ iPS differentiation to neuron, control donor C11-CRL2429, day18, rep2_CNhs13825_13428-144D8_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day18Rep1_CNhs13916_tpm_rev IpsToNeuronControlDnC11-CRL2429Day18R1- iPS differentiation to neuron, control donor C11-CRL2429, day18, rep1_CNhs13916_13424-144D4_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day18Rep1_CNhs13916_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day18R1+ iPS differentiation to neuron, control donor C11-CRL2429, day18, rep1_CNhs13916_13424-144D4_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day12Rep3_CNhs14051_tpm_rev IpsToNeuronControlDnC11-CRL2429Day12R3- iPS differentiation to neuron, control donor C11-CRL2429, day12, rep3_CNhs14051_13431-144E2_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day12Rep3_CNhs14051_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day12R3+ iPS differentiation to neuron, control donor C11-CRL2429, day12, rep3_CNhs14051_13431-144E2_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day12Rep2_CNhs13824_tpm_rev IpsToNeuronControlDnC11-CRL2429Day12R2- iPS differentiation to neuron, control donor C11-CRL2429, day12, rep2_CNhs13824_13427-144D7_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day12Rep2_CNhs13824_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day12R2+ iPS differentiation to neuron, control donor C11-CRL2429, day12, rep2_CNhs13824_13427-144D7_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day12Rep1_CNhs14047_tpm_rev IpsToNeuronControlDnC11-CRL2429Day12R1- iPS differentiation to neuron, control donor C11-CRL2429, day12, rep1_CNhs14047_13423-144D3_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day12Rep1_CNhs14047_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day12R1+ iPS differentiation to neuron, control donor C11-CRL2429, day12, rep1_CNhs14047_13423-144D3_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day06Rep3_CNhs14050_tpm_rev IpsToNeuronControlDnC11-CRL2429Day06R3- iPS differentiation to neuron, control donor C11-CRL2429, day06, rep3_CNhs14050_13430-144E1_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day06Rep3_CNhs14050_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day06R3+ iPS differentiation to neuron, control donor C11-CRL2429, day06, rep3_CNhs14050_13430-144E1_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day06Rep2_CNhs13823_tpm_rev IpsToNeuronControlDnC11-CRL2429Day06R2- iPS differentiation to neuron, control donor C11-CRL2429, day06, rep2_CNhs13823_13426-144D6_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day06Rep2_CNhs13823_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day06R2+ iPS differentiation to neuron, control donor C11-CRL2429, day06, rep2_CNhs13823_13426-144D6_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day06Rep1_CNhs14046_tpm_rev IpsToNeuronControlDnC11-CRL2429Day06R1- iPS differentiation to neuron, control donor C11-CRL2429, day06, rep1_CNhs14046_13422-144D2_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day06Rep1_CNhs14046_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day06R1+ iPS differentiation to neuron, control donor C11-CRL2429, day06, rep1_CNhs14046_13422-144D2_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day00Rep3_CNhs14049_tpm_rev IpsToNeuronControlDnC11-CRL2429Day00R3- iPS differentiation to neuron, control donor C11-CRL2429, day00, rep3_CNhs14049_13429-144D9_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day00Rep3_CNhs14049_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day00R3+ iPS differentiation to neuron, control donor C11-CRL2429, day00, rep3_CNhs14049_13429-144D9_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day00Rep2_CNhs13822_tpm_rev IpsToNeuronControlDnC11-CRL2429Day00R2- iPS differentiation to neuron, control donor C11-CRL2429, day00, rep2_CNhs13822_13425-144D5_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day00Rep2_CNhs13822_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day00R2+ iPS differentiation to neuron, control donor C11-CRL2429, day00, rep2_CNhs13822_13425-144D5_forward Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day00Rep1_CNhs14045_tpm_rev IpsToNeuronControlDnC11-CRL2429Day00R1- iPS differentiation to neuron, control donor C11-CRL2429, day00, rep1_CNhs14045_13421-144D1_reverse Regulation 
IPSDifferentiationToNeuronControlDonorC11CRL2429Day00Rep1_CNhs14045_tpm_fwd IpsToNeuronControlDnC11-CRL2429Day00R1+ iPS differentiation to neuron, control donor C11-CRL2429, day00, rep1_CNhs14045_13421-144D1_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day18Rep3_CNhs14066_tpm_rev Tc:iPStoNeuronDs_Day18R3- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day18, rep3_CNhs14066_13468-144I3_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day18Rep3_CNhs14066_tpm_fwd Tc:iPStoNeuronDs_Day18R3+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day18, rep3_CNhs14066_13468-144I3_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day18Rep2_CNhs13922_tpm_rev Tc:iPStoNeuronDs_Day18R2- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day18, rep2_CNhs13922_13464-144H8_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day18Rep2_CNhs13922_tpm_fwd Tc:iPStoNeuronDs_Day18R2+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day18, rep2_CNhs13922_13464-144H8_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day18Rep1_CNhs13838_tpm_rev Tc:iPStoNeuronDs_Day18R1- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day18, rep1_CNhs13838_13460-144H4_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day18Rep1_CNhs13838_tpm_fwd Tc:iPStoNeuronDs_Day18R1+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day18, rep1_CNhs13838_13460-144H4_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day12Rep3_CNhs14065_tpm_rev Tc:iPStoNeuronDs_Day12R3- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day12, rep3_CNhs14065_13467-144I2_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day12Rep3_CNhs14065_tpm_fwd Tc:iPStoNeuronDs_Day12R3+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day12, rep3_CNhs14065_13467-144I2_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day12Rep2_CNhs14062_tpm_rev Tc:iPStoNeuronDs_Day12R2- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day12, rep2_CNhs14062_13463-144H7_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day12Rep2_CNhs14062_tpm_fwd Tc:iPStoNeuronDs_Day12R2+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day12, rep2_CNhs14062_13463-144H7_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day12Rep1_CNhs13837_tpm_rev Tc:iPStoNeuronDs_Day12R1- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day12, rep1_CNhs13837_13459-144H3_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day12Rep1_CNhs13837_tpm_fwd Tc:iPStoNeuronDs_Day12R1+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day12, rep1_CNhs13837_13459-144H3_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day06Rep3_CNhs14064_tpm_rev Tc:iPStoNeuronDs_Day06R3- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day06, rep3_CNhs14064_13466-144I1_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day06Rep3_CNhs14064_tpm_fwd Tc:iPStoNeuronDs_Day06R3+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day06, rep3_CNhs14064_13466-144I1_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day06Rep2_CNhs14061_tpm_rev Tc:iPStoNeuronDs_Day06R2- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day06, rep2_CNhs14061_13462-144H6_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day06Rep2_CNhs14061_tpm_fwd Tc:iPStoNeuronDs_Day06R2+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day06, rep2_CNhs14061_13462-144H6_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day06Rep1_CNhs13836_tpm_rev Tc:iPStoNeuronDs_Day06R1- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day06, rep1_CNhs13836_13458-144H2_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day06Rep1_CNhs13836_tpm_fwd Tc:iPStoNeuronDs_Day06R1+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day06, rep1_CNhs13836_13458-144H2_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day00Rep3_CNhs14063_tpm_rev Tc:iPStoNeuronDs_Day00R3- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day00, rep3_CNhs14063_13465-144H9_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day00Rep3_CNhs14063_tpm_fwd Tc:iPStoNeuronDs_Day00R3+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day00, rep3_CNhs14063_13465-144H9_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day00Rep2_CNhs14060_tpm_rev Tc:iPStoNeuronDs_Day00R2- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day00, rep2_CNhs14060_13461-144H5_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day00Rep2_CNhs14060_tpm_fwd Tc:iPStoNeuronDs_Day00R2+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day00, rep2_CNhs14060_13461-144H5_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day00Rep1_CNhs13835_tpm_rev Tc:iPStoNeuronDs_Day00R1- iPS differentiation to neuron, down-syndrome donor C18-CCL54, day00, rep1_CNhs13835_13457-144H1_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC18CCL54Day00Rep1_CNhs13835_tpm_fwd Tc:iPStoNeuronDs_Day00R1+ iPS differentiation to neuron, down-syndrome donor C18-CCL54, day00, rep1_CNhs13835_13457-144H1_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day18Rep3_CNhs14059_tpm_rev Tc:iPStoNeuronDs_Day18R3- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day18, rep3_CNhs14059_13456-144G9_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day18Rep3_CNhs14059_tpm_fwd Tc:iPStoNeuronDs_Day18R3+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day18, rep3_CNhs14059_13456-144G9_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day18Rep2_CNhs13846_tpm_rev Tc:iPStoNeuronDs_Day18R2- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day18, rep2_CNhs13846_13452-144G5_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day18Rep2_CNhs13846_tpm_fwd Tc:iPStoNeuronDs_Day18R2+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day18, rep2_CNhs13846_13452-144G5_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day18Rep1_CNhs13833_tpm_rev Tc:iPStoNeuronDs_Day18R1- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day18, rep1_CNhs13833_13448-144G1_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day18Rep1_CNhs13833_tpm_fwd Tc:iPStoNeuronDs_Day18R1+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day18, rep1_CNhs13833_13448-144G1_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day12Rep3_CNhs14058_tpm_rev Tc:iPStoNeuronDs_Day12R3- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day12, rep3_CNhs14058_13455-144G8_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day12Rep3_CNhs14058_tpm_fwd Tc:iPStoNeuronDs_Day12R3+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day12, rep3_CNhs14058_13455-144G8_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day12Rep2_CNhs13845_tpm_rev Tc:iPStoNeuronDs_Day12R2- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day12, rep2_CNhs13845_13451-144G4_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day12Rep2_CNhs13845_tpm_fwd Tc:iPStoNeuronDs_Day12R2+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day12, rep2_CNhs13845_13451-144G4_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day12Rep1_CNhs13832_tpm_rev Tc:iPStoNeuronDs_Day12R1- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day12, rep1_CNhs13832_13447-144F9_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day12Rep1_CNhs13832_tpm_fwd Tc:iPStoNeuronDs_Day12R1+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day12, rep1_CNhs13832_13447-144F9_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day06Rep3_CNhs14057_tpm_rev Tc:iPStoNeuronDs_Day06R3- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day06, rep3_CNhs14057_13454-144G7_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day06Rep3_CNhs14057_tpm_fwd Tc:iPStoNeuronDs_Day06R3+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day06, rep3_CNhs14057_13454-144G7_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day06Rep2_CNhs13844_tpm_rev Tc:iPStoNeuronDs_Day06R2- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day06, rep2_CNhs13844_13450-144G3_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day06Rep2_CNhs13844_tpm_fwd Tc:iPStoNeuronDs_Day06R2+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day06, rep2_CNhs13844_13450-144G3_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day06Rep1_CNhs13831_tpm_rev Tc:iPStoNeuronDs_Day06R1- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day06, rep1_CNhs13831_13446-144F8_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day06Rep1_CNhs13831_tpm_fwd Tc:iPStoNeuronDs_Day06R1+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day06, rep1_CNhs13831_13446-144F8_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day00Rep3_CNhs14056_tpm_rev Tc:iPStoNeuronDs_Day00R3- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day00, rep3_CNhs14056_13453-144G6_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day00Rep3_CNhs14056_tpm_fwd Tc:iPStoNeuronDs_Day00R3+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day00, rep3_CNhs14056_13453-144G6_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day00Rep2_CNhs13843_tpm_rev Tc:iPStoNeuronDs_Day00R2- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day00, rep2_CNhs13843_13449-144G2_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day00Rep2_CNhs13843_tpm_fwd Tc:iPStoNeuronDs_Day00R2+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day00, rep2_CNhs13843_13449-144G2_forward Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day00Rep1_CNhs13830_tpm_rev Tc:iPStoNeuronDs_Day00R1- iPS differentiation to neuron, down-syndrome donor C11-CCL54, day00, rep1_CNhs13830_13445-144F7_reverse Regulation 
IPSDifferentiationToNeuronDownsyndromeDonorC11CCL54Day00Rep1_CNhs13830_tpm_fwd Tc:iPStoNeuronDs_Day00R1+ iPS differentiation to neuron, down-syndrome donor C11-CCL54, day00, rep1_CNhs13830_13445-144F7_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha60hr00minBiolRep3_CNhs14543_tpm_rev Tc:ARPE-19Emt_60hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 60hr00min, biol_rep3_CNhs14543_13687-147F6_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha60hr00minBiolRep3_CNhs14543_tpm_fwd Tc:ARPE-19Emt_60hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 60hr00min, biol_rep3_CNhs14543_13687-147F6_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha60hr00minBiolRep2_CNhs14542_tpm_rev Tc:ARPE-19Emt_60hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 60hr00min, biol_rep2_CNhs14542_13686-147F5_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha60hr00minBiolRep2_CNhs14542_tpm_fwd Tc:ARPE-19Emt_60hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 60hr00min, biol_rep2_CNhs14542_13686-147F5_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha60hr00minBiolRep1_CNhs14541_tpm_rev Tc:ARPE-19Emt_60hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 60hr00min, biol_rep1_CNhs14541_13685-147F4_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha60hr00minBiolRep1_CNhs14541_tpm_fwd Tc:ARPE-19Emt_60hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 60hr00min, biol_rep1_CNhs14541_13685-147F4_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha42hr00minBiolRep3_CNhs14540_tpm_rev Tc:ARPE-19Emt_42hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 42hr00min, biol_rep3_CNhs14540_13684-147F3_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha42hr00minBiolRep3_CNhs14540_tpm_fwd Tc:ARPE-19Emt_42hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 42hr00min, biol_rep3_CNhs14540_13684-147F3_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha42hr00minBiolRep2_CNhs14539_tpm_rev Tc:ARPE-19Emt_42hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 42hr00min, biol_rep2_CNhs14539_13683-147F2_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha42hr00minBiolRep2_CNhs14539_tpm_fwd Tc:ARPE-19Emt_42hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 42hr00min, biol_rep2_CNhs14539_13683-147F2_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha42hr00minBiolRep1_CNhs14538_tpm_rev Tc:ARPE-19Emt_42hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 42hr00min, biol_rep1_CNhs14538_13682-147F1_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha42hr00minBiolRep1_CNhs14538_tpm_fwd Tc:ARPE-19Emt_42hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 42hr00min, biol_rep1_CNhs14538_13682-147F1_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha24hr00minBiolRep3_CNhs14537_tpm_rev Tc:ARPE-19Emt_24hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 24hr00min, biol_rep3_CNhs14537_13681-147E9_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha24hr00minBiolRep3_CNhs14537_tpm_fwd Tc:ARPE-19Emt_24hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 24hr00min, biol_rep3_CNhs14537_13681-147E9_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha24hr00minBiolRep1_CNhs14535_tpm_rev Tc:ARPE-19Emt_24hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 24hr00min, biol_rep1_CNhs14535_13679-147E7_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha24hr00minBiolRep1_CNhs14535_tpm_fwd Tc:ARPE-19Emt_24hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 24hr00min, biol_rep1_CNhs14535_13679-147E7_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha16hr00minBiolRep3_CNhs14534_tpm_rev Tc:ARPE-19Emt_16hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 16hr00min, biol_rep3_CNhs14534_13678-147E6_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha16hr00minBiolRep3_CNhs14534_tpm_fwd Tc:ARPE-19Emt_16hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 16hr00min, biol_rep3_CNhs14534_13678-147E6_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha16hr00minBiolRep2_CNhs14533_tpm_rev Tc:ARPE-19Emt_16hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 16hr00min, biol_rep2_CNhs14533_13677-147E5_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha16hr00minBiolRep2_CNhs14533_tpm_fwd Tc:ARPE-19Emt_16hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 16hr00min, biol_rep2_CNhs14533_13677-147E5_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha16hr00minBiolRep1_CNhs14532_tpm_rev Tc:ARPE-19Emt_16hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 16hr00min, biol_rep1_CNhs14532_13676-147E4_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha16hr00minBiolRep1_CNhs14532_tpm_fwd Tc:ARPE-19Emt_16hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 16hr00min, biol_rep1_CNhs14532_13676-147E4_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha12hr00minBiolRep3_CNhs14531_tpm_rev Tc:ARPE-19Emt_12hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 12hr00min, biol_rep3_CNhs14531_13675-147E3_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha12hr00minBiolRep3_CNhs14531_tpm_fwd Tc:ARPE-19Emt_12hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 12hr00min, biol_rep3_CNhs14531_13675-147E3_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha12hr00minBiolRep2_CNhs14530_tpm_rev Tc:ARPE-19Emt_12hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 12hr00min, biol_rep2_CNhs14530_13674-147E2_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha12hr00minBiolRep2_CNhs14530_tpm_fwd Tc:ARPE-19Emt_12hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 12hr00min, biol_rep2_CNhs14530_13674-147E2_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha08hr00minBiolRep3_CNhs14528_tpm_rev Tc:ARPE-19Emt_08hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 08hr00min, biol_rep3_CNhs14528_13672-147D9_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha08hr00minBiolRep3_CNhs14528_tpm_fwd Tc:ARPE-19Emt_08hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 08hr00min, biol_rep3_CNhs14528_13672-147D9_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha08hr00minBiolRep2_CNhs14527_tpm_rev Tc:ARPE-19Emt_08hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 08hr00min, biol_rep2_CNhs14527_13671-147D8_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha08hr00minBiolRep2_CNhs14527_tpm_fwd Tc:ARPE-19Emt_08hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 08hr00min, biol_rep2_CNhs14527_13671-147D8_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha08hr00minBiolRep1_CNhs14526_tpm_rev Tc:ARPE-19Emt_08hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 08hr00min, biol_rep1_CNhs14526_13670-147D7_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha08hr00minBiolRep1_CNhs14526_tpm_fwd Tc:ARPE-19Emt_08hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 08hr00min, biol_rep1_CNhs14526_13670-147D7_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha07hr00minBiolRep3_CNhs14525_tpm_rev Tc:ARPE-19Emt_07hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 07hr00min, biol_rep3_CNhs14525_13669-147D6_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha07hr00minBiolRep3_CNhs14525_tpm_fwd Tc:ARPE-19Emt_07hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 07hr00min, biol_rep3_CNhs14525_13669-147D6_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha07hr00minBiolRep2_CNhs14524_tpm_rev Tc:ARPE-19Emt_07hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 07hr00min, biol_rep2_CNhs14524_13668-147D5_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha07hr00minBiolRep2_CNhs14524_tpm_fwd Tc:ARPE-19Emt_07hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 07hr00min, biol_rep2_CNhs14524_13668-147D5_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha07hr00minBiolRep1_CNhs14523_tpm_rev Tc:ARPE-19Emt_07hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 07hr00min, biol_rep1_CNhs14523_13667-147D4_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha07hr00minBiolRep1_CNhs14523_tpm_fwd Tc:ARPE-19Emt_07hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 07hr00min, biol_rep1_CNhs14523_13667-147D4_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha06hr00minBiolRep3_CNhs14522_tpm_rev Tc:ARPE-19Emt_06hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 06hr00min, biol_rep3_CNhs14522_13666-147D3_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha06hr00minBiolRep3_CNhs14522_tpm_fwd Tc:ARPE-19Emt_06hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 06hr00min, biol_rep3_CNhs14522_13666-147D3_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha06hr00minBiolRep1_CNhs14519_tpm_rev Tc:ARPE-19Emt_06hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 06hr00min, biol_rep1_CNhs14519_13664-147D1_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha06hr00minBiolRep1_CNhs14519_tpm_fwd Tc:ARPE-19Emt_06hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 06hr00min, biol_rep1_CNhs14519_13664-147D1_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha05hr00minBiolRep3_CNhs14518_tpm_rev Tc:ARPE-19Emt_05hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 05hr00min, biol_rep3_CNhs14518_13663-147C9_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha05hr00minBiolRep3_CNhs14518_tpm_fwd Tc:ARPE-19Emt_05hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 05hr00min, biol_rep3_CNhs14518_13663-147C9_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha05hr00minBiolRep2_CNhs14501_tpm_rev Tc:ARPE-19Emt_05hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 05hr00min, biol_rep2_CNhs14501_13662-147C8_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha05hr00minBiolRep2_CNhs14501_tpm_fwd Tc:ARPE-19Emt_05hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 05hr00min, biol_rep2_CNhs14501_13662-147C8_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha05hr00minBiolRep1_CNhs14500_tpm_rev Tc:ARPE-19Emt_05hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 05hr00min, biol_rep1_CNhs14500_13661-147C7_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha05hr00minBiolRep1_CNhs14500_tpm_fwd Tc:ARPE-19Emt_05hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 05hr00min, biol_rep1_CNhs14500_13661-147C7_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha04hr00minBiolRep3_CNhs14499_tpm_rev Tc:ARPE-19Emt_04hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 04hr00min, biol_rep3_CNhs14499_13660-147C6_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha04hr00minBiolRep3_CNhs14499_tpm_fwd Tc:ARPE-19Emt_04hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 04hr00min, biol_rep3_CNhs14499_13660-147C6_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha04hr00minBiolRep2_CNhs14498_tpm_rev Tc:ARPE-19Emt_04hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 04hr00min, biol_rep2_CNhs14498_13659-147C5_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha04hr00minBiolRep2_CNhs14498_tpm_fwd Tc:ARPE-19Emt_04hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 04hr00min, biol_rep2_CNhs14498_13659-147C5_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha04hr00minBiolRep1_CNhs14497_tpm_rev Tc:ARPE-19Emt_04hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 04hr00min, biol_rep1_CNhs14497_13658-147C4_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha04hr00minBiolRep1_CNhs14497_tpm_fwd Tc:ARPE-19Emt_04hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 04hr00min, biol_rep1_CNhs14497_13658-147C4_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr30minBiolRep3_CNhs14496_tpm_rev Tc:ARPE-19Emt_03hr30minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr30min, biol_rep3_CNhs14496_13657-147C3_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr30minBiolRep3_CNhs14496_tpm_fwd Tc:ARPE-19Emt_03hr30minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr30min, biol_rep3_CNhs14496_13657-147C3_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr30minBiolRep2_CNhs14495_tpm_rev Tc:ARPE-19Emt_03hr30minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr30min, biol_rep2_CNhs14495_13656-147C2_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr30minBiolRep2_CNhs14495_tpm_fwd Tc:ARPE-19Emt_03hr30minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr30min, biol_rep2_CNhs14495_13656-147C2_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr30minBiolRep1_CNhs14494_tpm_rev Tc:ARPE-19Emt_03hr30minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr30min, biol_rep1_CNhs14494_13655-147C1_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr30minBiolRep1_CNhs14494_tpm_fwd Tc:ARPE-19Emt_03hr30minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr30min, biol_rep1_CNhs14494_13655-147C1_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr00minBiolRep3_CNhs14493_tpm_rev Tc:ARPE-19Emt_03hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr00min, biol_rep3_CNhs14493_13654-147B9_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr00minBiolRep3_CNhs14493_tpm_fwd Tc:ARPE-19Emt_03hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr00min, biol_rep3_CNhs14493_13654-147B9_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr00minBiolRep2_CNhs14492_tpm_rev Tc:ARPE-19Emt_03hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr00min, biol_rep2_CNhs14492_13653-147B8_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr00minBiolRep2_CNhs14492_tpm_fwd Tc:ARPE-19Emt_03hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr00min, biol_rep2_CNhs14492_13653-147B8_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr00minBiolRep1_CNhs14491_tpm_rev Tc:ARPE-19Emt_03hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr00min, biol_rep1_CNhs14491_13652-147B7_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha03hr00minBiolRep1_CNhs14491_tpm_fwd Tc:ARPE-19Emt_03hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 03hr00min, biol_rep1_CNhs14491_13652-147B7_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr30minBiolRep3_CNhs14490_tpm_rev Tc:ARPE-19Emt_02hr30minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr30min, biol_rep3_CNhs14490_13651-147B6_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr30minBiolRep3_CNhs14490_tpm_fwd Tc:ARPE-19Emt_02hr30minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr30min, biol_rep3_CNhs14490_13651-147B6_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr30minBiolRep2_CNhs14489_tpm_rev Tc:ARPE-19Emt_02hr30minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr30min, biol_rep2_CNhs14489_13650-147B5_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr30minBiolRep2_CNhs14489_tpm_fwd Tc:ARPE-19Emt_02hr30minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr30min, biol_rep2_CNhs14489_13650-147B5_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr30minBiolRep1_CNhs14488_tpm_rev Tc:ARPE-19Emt_02hr30minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr30min, biol_rep1_CNhs14488_13649-147B4_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr30minBiolRep1_CNhs14488_tpm_fwd Tc:ARPE-19Emt_02hr30minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr30min, biol_rep1_CNhs14488_13649-147B4_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr00minBiolRep3_CNhs14487_tpm_rev Tc:ARPE-19Emt_02hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr00min, biol_rep3_CNhs14487_13648-147B3_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr00minBiolRep3_CNhs14487_tpm_fwd Tc:ARPE-19Emt_02hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr00min, biol_rep3_CNhs14487_13648-147B3_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr00minBiolRep2_CNhs14486_tpm_rev Tc:ARPE-19Emt_02hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr00min, biol_rep2_CNhs14486_13647-147B2_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr00minBiolRep2_CNhs14486_tpm_fwd Tc:ARPE-19Emt_02hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr00min, biol_rep2_CNhs14486_13647-147B2_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr00minBiolRep1_CNhs14485_tpm_rev Tc:ARPE-19Emt_02hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr00min, biol_rep1_CNhs14485_13646-147B1_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha02hr00minBiolRep1_CNhs14485_tpm_fwd Tc:ARPE-19Emt_02hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 02hr00min, biol_rep1_CNhs14485_13646-147B1_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr40minBiolRep3_CNhs14484_tpm_rev Tc:ARPE-19Emt_01hr40minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr40min, biol_rep3_CNhs14484_13645-147A9_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr40minBiolRep3_CNhs14484_tpm_fwd Tc:ARPE-19Emt_01hr40minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr40min, biol_rep3_CNhs14484_13645-147A9_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr40minBiolRep2_CNhs14483_tpm_rev Tc:ARPE-19Emt_01hr40minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr40min, biol_rep2_CNhs14483_13644-147A8_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr40minBiolRep2_CNhs14483_tpm_fwd Tc:ARPE-19Emt_01hr40minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr40min, biol_rep2_CNhs14483_13644-147A8_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr40minBiolRep1_CNhs14482_tpm_rev Tc:ARPE-19Emt_01hr40minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr40min, biol_rep1_CNhs14482_13643-147A7_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr40minBiolRep1_CNhs14482_tpm_fwd Tc:ARPE-19Emt_01hr40minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr40min, biol_rep1_CNhs14482_13643-147A7_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr20minBiolRep3_CNhs14480_tpm_rev Tc:ARPE-19Emt_01hr20minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr20min, biol_rep3_CNhs14480_13642-147A6_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr20minBiolRep3_CNhs14480_tpm_fwd Tc:ARPE-19Emt_01hr20minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr20min, biol_rep3_CNhs14480_13642-147A6_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr20minBiolRep2_CNhs14479_tpm_rev Tc:ARPE-19Emt_01hr20minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr20min, biol_rep2_CNhs14479_13641-147A5_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr20minBiolRep2_CNhs14479_tpm_fwd Tc:ARPE-19Emt_01hr20minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr20min, biol_rep2_CNhs14479_13641-147A5_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr20minBiolRep1_CNhs14478_tpm_rev Tc:ARPE-19Emt_01hr20minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr20min, biol_rep1_CNhs14478_13640-147A4_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr20minBiolRep1_CNhs14478_tpm_fwd Tc:ARPE-19Emt_01hr20minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr20min, biol_rep1_CNhs14478_13640-147A4_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr00minBiolRep3_CNhs14477_tpm_rev Tc:ARPE-19Emt_01hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr00min, biol_rep3_CNhs14477_13639-147A3_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr00minBiolRep3_CNhs14477_tpm_fwd Tc:ARPE-19Emt_01hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr00min, biol_rep3_CNhs14477_13639-147A3_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr00minBiolRep2_CNhs14476_tpm_rev Tc:ARPE-19Emt_01hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr00min, biol_rep2_CNhs14476_13638-147A2_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr00minBiolRep2_CNhs14476_tpm_fwd Tc:ARPE-19Emt_01hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr00min, biol_rep2_CNhs14476_13638-147A2_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr00minBiolRep1_CNhs14475_tpm_rev Tc:ARPE-19Emt_01hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr00min, biol_rep1_CNhs14475_13637-147A1_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha01hr00minBiolRep1_CNhs14475_tpm_fwd Tc:ARPE-19Emt_01hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 01hr00min, biol_rep1_CNhs14475_13637-147A1_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr45minBiolRep3_CNhs14474_tpm_rev Tc:ARPE-19Emt_00hr45minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr45min, biol_rep3_CNhs14474_13636-146I9_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr45minBiolRep3_CNhs14474_tpm_fwd Tc:ARPE-19Emt_00hr45minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr45min, biol_rep3_CNhs14474_13636-146I9_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr45minBiolRep2_CNhs14473_tpm_rev Tc:ARPE-19Emt_00hr45minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr45min, biol_rep2_CNhs14473_13635-146I8_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr45minBiolRep2_CNhs14473_tpm_fwd Tc:ARPE-19Emt_00hr45minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr45min, biol_rep2_CNhs14473_13635-146I8_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr45minBiolRep1_CNhs14472_tpm_rev Tc:ARPE-19Emt_00hr45minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr45min, biol_rep1_CNhs14472_13634-146I7_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr45minBiolRep1_CNhs14472_tpm_fwd Tc:ARPE-19Emt_00hr45minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr45min, biol_rep1_CNhs14472_13634-146I7_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr30minBiolRep3_CNhs14471_tpm_rev Tc:ARPE-19Emt_00hr30minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr30min, biol_rep3_CNhs14471_13633-146I6_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr30minBiolRep3_CNhs14471_tpm_fwd Tc:ARPE-19Emt_00hr30minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr30min, biol_rep3_CNhs14471_13633-146I6_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr30minBiolRep2_CNhs14470_tpm_rev Tc:ARPE-19Emt_00hr30minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr30min, biol_rep2_CNhs14470_13632-146I5_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr30minBiolRep2_CNhs14470_tpm_fwd Tc:ARPE-19Emt_00hr30minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr30min, biol_rep2_CNhs14470_13632-146I5_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr30minBiolRep1_CNhs14469_tpm_rev Tc:ARPE-19Emt_00hr30minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr30min, biol_rep1_CNhs14469_13631-146I4_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr30minBiolRep1_CNhs14469_tpm_fwd Tc:ARPE-19Emt_00hr30minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr30min, biol_rep1_CNhs14469_13631-146I4_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr15minBiolRep3_CNhs14468_tpm_rev Tc:ARPE-19Emt_00hr15minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr15min, biol_rep3_CNhs14468_13630-146I3_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr15minBiolRep3_CNhs14468_tpm_fwd Tc:ARPE-19Emt_00hr15minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr15min, biol_rep3_CNhs14468_13630-146I3_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr15minBiolRep2_CNhs14467_tpm_rev Tc:ARPE-19Emt_00hr15minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr15min, biol_rep2_CNhs14467_13629-146I2_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr15minBiolRep2_CNhs14467_tpm_fwd Tc:ARPE-19Emt_00hr15minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr15min, biol_rep2_CNhs14467_13629-146I2_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr15minBiolRep1_CNhs14466_tpm_rev Tc:ARPE-19Emt_00hr15minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr15min, biol_rep1_CNhs14466_13628-146I1_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr15minBiolRep1_CNhs14466_tpm_fwd Tc:ARPE-19Emt_00hr15minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr15min, biol_rep1_CNhs14466_13628-146I1_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr00minBiolRep3_CNhs14465_tpm_rev Tc:ARPE-19Emt_00hr00minBr3- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr00min, biol_rep3_CNhs14465_13627-146H9_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr00minBiolRep3_CNhs14465_tpm_fwd Tc:ARPE-19Emt_00hr00minBr3+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr00min, biol_rep3_CNhs14465_13627-146H9_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr00minBiolRep2_CNhs14464_tpm_rev Tc:ARPE-19Emt_00hr00minBr2- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr00min, biol_rep2_CNhs14464_13626-146H8_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr00minBiolRep2_CNhs14464_tpm_fwd Tc:ARPE-19Emt_00hr00minBr2+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr00min, biol_rep2_CNhs14464_13626-146H8_forward Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr00minBiolRep1_CNhs14463_tpm_rev Tc:ARPE-19Emt_00hr00minBr1- ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr00min, biol_rep1_CNhs14463_13625-146H7_reverse Regulation 
ARPE19EMTInducedWithTGFbetaAndTNFalpha00hr00minBiolRep1_CNhs14463_tpm_fwd Tc:ARPE-19Emt_00hr00minBr1+ ARPE-19 EMT induced with TGF-beta and TNF-alpha, 00hr00min, biol_rep1_CNhs14463_13625-146H7_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay41BiolRep3H9EB3D41_CNhs12950_tpm_rev H9MelanocyticInduction_Day41Br3- H9 Embryoid body cells, melanocytic induction, day41, biol_rep3 (H9EB-3 d41)_CNhs12950_12836-137B1_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay41BiolRep3H9EB3D41_CNhs12950_tpm_fwd H9MelanocyticInduction_Day41Br3+ H9 Embryoid body cells, melanocytic induction, day41, biol_rep3 (H9EB-3 d41)_CNhs12950_12836-137B1_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay41BiolRep2H9EB2D41_CNhs12907_tpm_rev H9MelanocyticInduction_Day41Br2- H9 Embryoid body cells, melanocytic induction, day41, biol_rep2 (H9EB-2 d41)_CNhs12907_12738-135I2_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay41BiolRep2H9EB2D41_CNhs12907_tpm_fwd H9MelanocyticInduction_Day41Br2+ H9 Embryoid body cells, melanocytic induction, day41, biol_rep2 (H9EB-2 d41)_CNhs12907_12738-135I2_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay41BiolRep1H9EB1D41_CNhs12905_tpm_rev H9MelanocyticInduction_Day41Br1- H9 Embryoid body cells, melanocytic induction, day41, biol_rep1 (H9EB-1 d41)_CNhs12905_12640-134G3_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay41BiolRep1H9EB1D41_CNhs12905_tpm_fwd H9MelanocyticInduction_Day41Br1+ H9 Embryoid body cells, melanocytic induction, day41, biol_rep1 (H9EB-1 d41)_CNhs12905_12640-134G3_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay34BiolRep3H9EB3D34_CNhs12919_tpm_rev H9MelanocyticInduction_Day34Br3- H9 Embryoid body cells, melanocytic induction, day34, biol_rep3 (H9EB-3 d34)_CNhs12919_12835-137A9_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay34BiolRep3H9EB3D34_CNhs12919_tpm_fwd H9MelanocyticInduction_Day34Br3+ H9 Embryoid body cells, melanocytic induction, day34, biol_rep3 (H9EB-3 d34)_CNhs12919_12835-137A9_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay34BiolRep2H9EB2D34_CNhs12906_tpm_rev H9MelanocyticInduction_Day34Br2- H9 Embryoid body cells, melanocytic induction, day34, biol_rep2 (H9EB-2 d34)_CNhs12906_12737-135I1_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay34BiolRep2H9EB2D34_CNhs12906_tpm_fwd H9MelanocyticInduction_Day34Br2+ H9 Embryoid body cells, melanocytic induction, day34, biol_rep2 (H9EB-2 d34)_CNhs12906_12737-135I1_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay34BiolRep1H9EB1D34_CNhs12904_tpm_rev H9MelanocyticInduction_Day34Br1- H9 Embryoid body cells, melanocytic induction, day34, biol_rep1 (H9EB-1 d34)_CNhs12904_12639-134G2_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay34BiolRep1H9EB1D34_CNhs12904_tpm_fwd H9MelanocyticInduction_Day34Br1+ H9 Embryoid body cells, melanocytic induction, day34, biol_rep1 (H9EB-1 d34)_CNhs12904_12639-134G2_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay30BiolRep3H9EB3D30_CNhs12918_tpm_rev H9MelanocyticInduction_Day30Br3- H9 Embryoid body cells, melanocytic induction, day30, biol_rep3 (H9EB-3 d30)_CNhs12918_12834-137A8_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay30BiolRep3H9EB3D30_CNhs12918_tpm_fwd H9MelanocyticInduction_Day30Br3+ H9 Embryoid body cells, melanocytic induction, day30, biol_rep3 (H9EB-3 d30)_CNhs12918_12834-137A8_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay30BiolRep2H9EB2D30_CNhs12836_tpm_rev H9MelanocyticInduction_Day30Br2- H9 Embryoid body cells, melanocytic induction, day30, biol_rep2 (H9EB-2 d30)_CNhs12836_12736-135H9_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay30BiolRep2H9EB2D30_CNhs12836_tpm_fwd H9MelanocyticInduction_Day30Br2+ H9 Embryoid body cells, melanocytic induction, day30, biol_rep2 (H9EB-2 d30)_CNhs12836_12736-135H9_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay30BiolRep1H9EB1D30_CNhs12903_tpm_rev H9MelanocyticInduction_Day30Br1- H9 Embryoid body cells, melanocytic induction, day30, biol_rep1 (H9EB-1 d30)_CNhs12903_12638-134G1_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay30BiolRep1H9EB1D30_CNhs12903_tpm_fwd H9MelanocyticInduction_Day30Br1+ H9 Embryoid body cells, melanocytic induction, day30, biol_rep1 (H9EB-1 d30)_CNhs12903_12638-134G1_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay27BiolRep3H9EB3D27_CNhs12917_tpm_rev H9MelanocyticInduction_Day27Br3- H9 Embryoid body cells, melanocytic induction, day27, biol_rep3 (H9EB-3 d27)_CNhs12917_12833-137A7_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay27BiolRep3H9EB3D27_CNhs12917_tpm_fwd H9MelanocyticInduction_Day27Br3+ H9 Embryoid body cells, melanocytic induction, day27, biol_rep3 (H9EB-3 d27)_CNhs12917_12833-137A7_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay27BiolRep2H9EB2D27_CNhs12835_tpm_rev H9MelanocyticInduction_Day27Br2- H9 Embryoid body cells, melanocytic induction, day27, biol_rep2 (H9EB-2 d27)_CNhs12835_12735-135H8_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay27BiolRep2H9EB2D27_CNhs12835_tpm_fwd H9MelanocyticInduction_Day27Br2+ H9 Embryoid body cells, melanocytic induction, day27, biol_rep2 (H9EB-2 d27)_CNhs12835_12735-135H8_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay27BiolRep1H9EB1D27_CNhs12902_tpm_rev H9MelanocyticInduction_Day27Br1- H9 Embryoid body cells, melanocytic induction, day27, biol_rep1 (H9EB-1 d27)_CNhs12902_12637-134F9_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay27BiolRep1H9EB1D27_CNhs12902_tpm_fwd H9MelanocyticInduction_Day27Br1+ H9 Embryoid body cells, melanocytic induction, day27, biol_rep1 (H9EB-1 d27)_CNhs12902_12637-134F9_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay24BiolRep3H9EB3D24_CNhs12916_tpm_rev H9MelanocyticInduction_Day24Br3- H9 Embryoid body cells, melanocytic induction, day24, biol_rep3 (H9EB-3 d24)_CNhs12916_12832-137A6_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay24BiolRep3H9EB3D24_CNhs12916_tpm_fwd H9MelanocyticInduction_Day24Br3+ H9 Embryoid body cells, melanocytic induction, day24, biol_rep3 (H9EB-3 d24)_CNhs12916_12832-137A6_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay24BiolRep2H9EB2D24_CNhs12834_tpm_rev H9MelanocyticInduction_Day24Br2- H9 Embryoid body cells, melanocytic induction, day24, biol_rep2 (H9EB-2 d24)_CNhs12834_12734-135H7_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay24BiolRep2H9EB2D24_CNhs12834_tpm_fwd H9MelanocyticInduction_Day24Br2+ H9 Embryoid body cells, melanocytic induction, day24, biol_rep2 (H9EB-2 d24)_CNhs12834_12734-135H7_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay24BiolRep1H9EB1D24_CNhs12901_tpm_rev H9MelanocyticInduction_Day24Br1- H9 Embryoid body cells, melanocytic induction, day24, biol_rep1 (H9EB-1 d24)_CNhs12901_12636-134F8_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay24BiolRep1H9EB1D24_CNhs12901_tpm_fwd H9MelanocyticInduction_Day24Br1+ H9 Embryoid body cells, melanocytic induction, day24, biol_rep1 (H9EB-1 d24)_CNhs12901_12636-134F8_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay21BiolRep3H9EB3D21_CNhs12915_tpm_rev H9MelanocyticInduction_Day21Br3- H9 Embryoid body cells, melanocytic induction, day21, biol_rep3 (H9EB-3 d21)_CNhs12915_12831-137A5_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay21BiolRep3H9EB3D21_CNhs12915_tpm_fwd H9MelanocyticInduction_Day21Br3+ H9 Embryoid body cells, melanocytic induction, day21, biol_rep3 (H9EB-3 d21)_CNhs12915_12831-137A5_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay21BiolRep2H9EB2D21_CNhs12833_tpm_rev H9MelanocyticInduction_Day21Br2- H9 Embryoid body cells, melanocytic induction, day21, biol_rep2 (H9EB-2 d21)_CNhs12833_12733-135H6_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay21BiolRep2H9EB2D21_CNhs12833_tpm_fwd H9MelanocyticInduction_Day21Br2+ H9 Embryoid body cells, melanocytic induction, day21, biol_rep2 (H9EB-2 d21)_CNhs12833_12733-135H6_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay21BiolRep1H9EB1D21_CNhs12900_tpm_rev H9MelanocyticInduction_Day21Br1- H9 Embryoid body cells, melanocytic induction, day21, biol_rep1 (H9EB-1 d21)_CNhs12900_12635-134F7_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay21BiolRep1H9EB1D21_CNhs12900_tpm_fwd H9MelanocyticInduction_Day21Br1+ H9 Embryoid body cells, melanocytic induction, day21, biol_rep1 (H9EB-1 d21)_CNhs12900_12635-134F7_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay18BiolRep3H9EB3D18_CNhs12914_tpm_rev H9MelanocyticInduction_Day18Br3- H9 Embryoid body cells, melanocytic induction, day18, biol_rep3 (H9EB-3 d18)_CNhs12914_12830-137A4_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay18BiolRep3H9EB3D18_CNhs12914_tpm_fwd H9MelanocyticInduction_Day18Br3+ H9 Embryoid body cells, melanocytic induction, day18, biol_rep3 (H9EB-3 d18)_CNhs12914_12830-137A4_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay18BiolRep2H9EB2D18_CNhs12832_tpm_rev H9MelanocyticInduction_Day18Br2- H9 Embryoid body cells, melanocytic induction, day18, biol_rep2 (H9EB-2 d18)_CNhs12832_12732-135H5_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay18BiolRep2H9EB2D18_CNhs12832_tpm_fwd H9MelanocyticInduction_Day18Br2+ H9 Embryoid body cells, melanocytic induction, day18, biol_rep2 (H9EB-2 d18)_CNhs12832_12732-135H5_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay18BiolRep1H9EB1D18_CNhs12899_tpm_rev H9MelanocyticInduction_Day18Br1- H9 Embryoid body cells, melanocytic induction, day18, biol_rep1 (H9EB-1 d18)_CNhs12899_12634-134F6_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay18BiolRep1H9EB1D18_CNhs12899_tpm_fwd H9MelanocyticInduction_Day18Br1+ H9 Embryoid body cells, melanocytic induction, day18, biol_rep1 (H9EB-1 d18)_CNhs12899_12634-134F6_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay15BiolRep3H9EB3D15_CNhs12912_tpm_rev H9MelanocyticInduction_Day15Br3- H9 Embryoid body cells, melanocytic induction, day15, biol_rep3 (H9EB-3 d15)_CNhs12912_12829-137A3_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay15BiolRep3H9EB3D15_CNhs12912_tpm_fwd H9MelanocyticInduction_Day15Br3+ H9 Embryoid body cells, melanocytic induction, day15, biol_rep3 (H9EB-3 d15)_CNhs12912_12829-137A3_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay15BiolRep2H9EB2D15_CNhs12831_tpm_rev H9MelanocyticInduction_Day15Br2- H9 Embryoid body cells, melanocytic induction, day15, biol_rep2 (H9EB-2 d15)_CNhs12831_12731-135H4_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay15BiolRep2H9EB2D15_CNhs12831_tpm_fwd H9MelanocyticInduction_Day15Br2+ H9 Embryoid body cells, melanocytic induction, day15, biol_rep2 (H9EB-2 d15)_CNhs12831_12731-135H4_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay15BiolRep1H9EB1D15_CNhs12898_tpm_rev H9MelanocyticInduction_Day15Br1- H9 Embryoid body cells, melanocytic induction, day15, biol_rep1 (H9EB-1 d15)_CNhs12898_12633-134F5_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay15BiolRep1H9EB1D15_CNhs12898_tpm_fwd H9MelanocyticInduction_Day15Br1+ H9 Embryoid body cells, melanocytic induction, day15, biol_rep1 (H9EB-1 d15)_CNhs12898_12633-134F5_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay12BiolRep3H9EB3D12_CNhs12995_tpm_rev H9MelanocyticInduction_Day12Br3- H9 Embryoid body cells, melanocytic induction, day12, biol_rep3 (H9EB-3 d12)_CNhs12995_12828-137A2_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay12BiolRep3H9EB3D12_CNhs12949_tpm_rev H9MelanocyticInduction_Day12Br3- H9 Embryoid body cells, melanocytic induction, day12, biol_rep3 (H9EB-3 d12)_CNhs12949_12828-137A2_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay12BiolRep3H9EB3D12_CNhs12995_tpm_fwd H9MelanocyticInduction_Day12Br3+ H9 Embryoid body cells, melanocytic induction, day12, biol_rep3 (H9EB-3 d12)_CNhs12995_12828-137A2_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay12BiolRep3H9EB3D12_CNhs12949_tpm_fwd H9MelanocyticInduction_Day12Br3+ H9 Embryoid body cells, melanocytic induction, day12, biol_rep3 (H9EB-3 d12)_CNhs12949_12828-137A2_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay12BiolRep2H9EB2D12_CNhs12830_tpm_rev H9MelanocyticInduction_Day12Br2- H9 Embryoid body cells, melanocytic induction, day12, biol_rep2 (H9EB-2 d12)_CNhs12830_12730-135H3_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay12BiolRep2H9EB2D12_CNhs12830_tpm_fwd H9MelanocyticInduction_Day12Br2+ H9 Embryoid body cells, melanocytic induction, day12, biol_rep2 (H9EB-2 d12)_CNhs12830_12730-135H3_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay12BiolRep1H9EB1D12_CNhs12994_tpm_rev H9MelanocyticInduction_Day12Br1- H9 Embryoid body cells, melanocytic induction, day12, biol_rep1 (H9EB-1 d12)_CNhs12994_12632-134F4_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay12BiolRep1H9EB1D12_CNhs12948_tpm_rev H9MelanocyticInduction_Day12Br1- H9 Embryoid body cells, melanocytic induction, day12, biol_rep1 (H9EB-1 d12)_CNhs12948_12632-134F4_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay12BiolRep1H9EB1D12_CNhs12994_tpm_fwd H9MelanocyticInduction_Day12Br1+ H9 Embryoid body cells, melanocytic induction, day12, biol_rep1 (H9EB-1 d12)_CNhs12994_12632-134F4_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay12BiolRep1H9EB1D12_CNhs12948_tpm_fwd H9MelanocyticInduction_Day12Br1+ H9 Embryoid body cells, melanocytic induction, day12, biol_rep1 (H9EB-1 d12)_CNhs12948_12632-134F4_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay09BiolRep3H9EB3D9_CNhs12951_tpm_rev H9MelanocyticInduction_Day09Br3- H9 Embryoid body cells, melanocytic induction, day09, biol_rep3 (H9EB-3 d9)_CNhs12951_12827-137A1_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay09BiolRep3H9EB3D9_CNhs12951_tpm_fwd H9MelanocyticInduction_Day09Br3+ H9 Embryoid body cells, melanocytic induction, day09, biol_rep3 (H9EB-3 d9)_CNhs12951_12827-137A1_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay09BiolRep2H9EB2D9_CNhs12829_tpm_rev H9MelanocyticInduction_Day09Br2- H9 Embryoid body cells, melanocytic induction, day09, biol_rep2 (H9EB-2 d9)_CNhs12829_12729-135H2_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay09BiolRep2H9EB2D9_CNhs12829_tpm_fwd H9MelanocyticInduction_Day09Br2+ H9 Embryoid body cells, melanocytic induction, day09, biol_rep2 (H9EB-2 d9)_CNhs12829_12729-135H2_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay09BiolRep1H9EB1D9_CNhs12897_tpm_rev H9MelanocyticInduction_Day09Br1- H9 Embryoid body cells, melanocytic induction, day09, biol_rep1 (H9EB-1 d9)_CNhs12897_12631-134F3_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay09BiolRep1H9EB1D9_CNhs12897_tpm_fwd H9MelanocyticInduction_Day09Br1+ H9 Embryoid body cells, melanocytic induction, day09, biol_rep1 (H9EB-1 d9)_CNhs12897_12631-134F3_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay06BiolRep3H9EB3D6_CNhs12911_tpm_rev H9MelanocyticInduction_Day06Br3- H9 Embryoid body cells, melanocytic induction, day06, biol_rep3 (H9EB-3 d6)_CNhs12911_12826-136I9_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay06BiolRep3H9EB3D6_CNhs12911_tpm_fwd H9MelanocyticInduction_Day06Br3+ H9 Embryoid body cells, melanocytic induction, day06, biol_rep3 (H9EB-3 d6)_CNhs12911_12826-136I9_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay06BiolRep2H9EB2D6_CNhs12828_tpm_rev H9MelanocyticInduction_Day06Br2- H9 Embryoid body cells, melanocytic induction, day06, biol_rep2 (H9EB-2 d6)_CNhs12828_12728-135H1_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay06BiolRep2H9EB2D6_CNhs12828_tpm_fwd H9MelanocyticInduction_Day06Br2+ H9 Embryoid body cells, melanocytic induction, day06, biol_rep2 (H9EB-2 d6)_CNhs12828_12728-135H1_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay06BiolRep1H9EB1D6_CNhs12896_tpm_rev H9MelanocyticInduction_Day06Br1- H9 Embryoid body cells, melanocytic induction, day06, biol_rep1 (H9EB-1 d6)_CNhs12896_12630-134F2_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay06BiolRep1H9EB1D6_CNhs12896_tpm_fwd H9MelanocyticInduction_Day06Br1+ H9 Embryoid body cells, melanocytic induction, day06, biol_rep1 (H9EB-1 d6)_CNhs12896_12630-134F2_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay03BiolRep3H9EB3D3_CNhs12910_tpm_rev H9MelanocyticInduction_Day03Br3- H9 Embryoid body cells, melanocytic induction, day03, biol_rep3 (H9EB-3 d3)_CNhs12910_12825-136I8_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay03BiolRep3H9EB3D3_CNhs12910_tpm_fwd H9MelanocyticInduction_Day03Br3+ H9 Embryoid body cells, melanocytic induction, day03, biol_rep3 (H9EB-3 d3)_CNhs12910_12825-136I8_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay03BiolRep2H9EB2D3_CNhs12827_tpm_rev H9MelanocyticInduction_Day03Br2- H9 Embryoid body cells, melanocytic induction, day03, biol_rep2 (H9EB-2 d3)_CNhs12827_12727-135G9_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay03BiolRep2H9EB2D3_CNhs12827_tpm_fwd H9MelanocyticInduction_Day03Br2+ H9 Embryoid body cells, melanocytic induction, day03, biol_rep2 (H9EB-2 d3)_CNhs12827_12727-135G9_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay03BiolRep1H9EB1D3_CNhs12895_tpm_rev H9MelanocyticInduction_Day03Br1- H9 Embryoid body cells, melanocytic induction, day03, biol_rep1 (H9EB-1 d3)_CNhs12895_12629-134F1_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay03BiolRep1H9EB1D3_CNhs12895_tpm_fwd H9MelanocyticInduction_Day03Br1+ H9 Embryoid body cells, melanocytic induction, day03, biol_rep1 (H9EB-1 d3)_CNhs12895_12629-134F1_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay01BiolRep3H9EB3D1_CNhs12909_tpm_rev H9MelanocyticInduction_Day01Br3- H9 Embryoid body cells, melanocytic induction, day01, biol_rep3 (H9EB-3 d1)_CNhs12909_12824-136I7_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay01BiolRep3H9EB3D1_CNhs12909_tpm_fwd H9MelanocyticInduction_Day01Br3+ H9 Embryoid body cells, melanocytic induction, day01, biol_rep3 (H9EB-3 d1)_CNhs12909_12824-136I7_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay01BiolRep2H9EB2D1_CNhs12826_tpm_rev H9MelanocyticInduction_Day01Br2- H9 Embryoid body cells, melanocytic induction, day01, biol_rep2 (H9EB-2 d1)_CNhs12826_12726-135G8_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay01BiolRep2H9EB2D1_CNhs12826_tpm_fwd H9MelanocyticInduction_Day01Br2+ H9 Embryoid body cells, melanocytic induction, day01, biol_rep2 (H9EB-2 d1)_CNhs12826_12726-135G8_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay01BiolRep1H9EB1D1_CNhs12823_tpm_rev H9MelanocyticInduction_Day01Br1- H9 Embryoid body cells, melanocytic induction, day01, biol_rep1 (H9EB-1 d1)_CNhs12823_12628-134E9_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay01BiolRep1H9EB1D1_CNhs12823_tpm_fwd H9MelanocyticInduction_Day01Br1+ H9 Embryoid body cells, melanocytic induction, day01, biol_rep1 (H9EB-1 d1)_CNhs12823_12628-134E9_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay00BiolRep3H9EB3D0_CNhs12908_tpm_rev H9MelanocyticInduction_Day00Br3- H9 Embryoid body cells, melanocytic induction, day00, biol_rep3 (H9EB-3 d0)_CNhs12908_12823-136I6_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay00BiolRep3H9EB3D0_CNhs12908_tpm_fwd H9MelanocyticInduction_Day00Br3+ H9 Embryoid body cells, melanocytic induction, day00, biol_rep3 (H9EB-3 d0)_CNhs12908_12823-136I6_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay00BiolRep2H9EB2D0_CNhs12825_tpm_rev H9MelanocyticInduction_Day00Br2- H9 Embryoid body cells, melanocytic induction, day00, biol_rep2 (H9EB-2 d0)_CNhs12825_12725-135G7_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay00BiolRep2H9EB2D0_CNhs12825_tpm_fwd H9MelanocyticInduction_Day00Br2+ H9 Embryoid body cells, melanocytic induction, day00, biol_rep2 (H9EB-2 d0)_CNhs12825_12725-135G7_forward Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay00BiolRep1H9EB1D0_CNhs12822_tpm_rev H9MelanocyticInduction_Day00Br1- H9 Embryoid body cells, melanocytic induction, day00, biol_rep1 (H9EB-1 d0)_CNhs12822_12627-134E8_reverse Regulation 
H9EmbryoidBodyCellsMelanocyticInductionDay00BiolRep1H9EB1D0_CNhs12822_tpm_fwd H9MelanocyticInduction_Day00Br1+ H9 Embryoid body cells, melanocytic induction, day00, biol_rep1 (H9EB-1 d0)_CNhs12822_12627-134E8_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay12BiolRep3_CNhs13736_tpm_rev Hes3-gfpCardiomyocyticInduction_Day12Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day12, biol_rep3_CNhs13736_13363-143F6_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay12BiolRep3_CNhs13736_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day12Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day12, biol_rep3_CNhs13736_13363-143F6_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay12BiolRep2_CNhs13724_tpm_rev Hes3-gfpCardiomyocyticInduction_Day12Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day12, biol_rep2_CNhs13724_13351-143E3_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay12BiolRep2_CNhs13724_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day12Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day12, biol_rep2_CNhs13724_13351-143E3_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay12BiolRep1_CNhs13711_tpm_rev Hes3-gfpCardiomyocyticInduction_Day12Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day12, biol_rep1_CNhs13711_13339-143C9_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay12BiolRep1_CNhs13711_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day12Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day12, biol_rep1_CNhs13711_13339-143C9_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay11BiolRep3_CNhs13735_tpm_rev Hes3-gfpCardiomyocyticInduction_Day11Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day11, biol_rep3_CNhs13735_13362-143F5_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay11BiolRep3_CNhs13735_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day11Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day11, biol_rep3_CNhs13735_13362-143F5_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay11BiolRep2_CNhs13723_tpm_rev Hes3-gfpCardiomyocyticInduction_Day11Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day11, biol_rep2_CNhs13723_13350-143E2_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay11BiolRep2_CNhs13723_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day11Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day11, biol_rep2_CNhs13723_13350-143E2_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay11BiolRep1_CNhs13710_tpm_rev Hes3-gfpCardiomyocyticInduction_Day11Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day11, biol_rep1_CNhs13710_13338-143C8_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay11BiolRep1_CNhs13710_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day11Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day11, biol_rep1_CNhs13710_13338-143C8_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay10BiolRep3_CNhs13734_tpm_rev Hes3-gfpCardiomyocyticInduction_Day10Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day10, biol_rep3_CNhs13734_13361-143F4_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay10BiolRep3_CNhs13734_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day10Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day10, biol_rep3_CNhs13734_13361-143F4_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay10BiolRep2_CNhs13722_tpm_rev Hes3-gfpCardiomyocyticInduction_Day10Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day10, biol_rep2_CNhs13722_13349-143E1_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay10BiolRep2_CNhs13722_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day10Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day10, biol_rep2_CNhs13722_13349-143E1_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay10BiolRep1_CNhs13662_tpm_rev Hes3-gfpCardiomyocyticInduction_Day10Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day10, biol_rep1_CNhs13662_13337-143C7_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay10BiolRep1_CNhs13662_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day10Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day10, biol_rep1_CNhs13662_13337-143C7_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay09BiolRep3_CNhs13733_tpm_rev Hes3-gfpCardiomyocyticInduction_Day09Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day09, biol_rep3_CNhs13733_13360-143F3_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay09BiolRep3_CNhs13733_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day09Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day09, biol_rep3_CNhs13733_13360-143F3_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay09BiolRep2_CNhs13721_tpm_rev Hes3-gfpCardiomyocyticInduction_Day09Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day09, biol_rep2_CNhs13721_13348-143D9_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay09BiolRep2_CNhs13721_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day09Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day09, biol_rep2_CNhs13721_13348-143D9_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay09BiolRep1_CNhs13661_tpm_rev Hes3-gfpCardiomyocyticInduction_Day09Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day09, biol_rep1_CNhs13661_13336-143C6_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay09BiolRep1_CNhs13661_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day09Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day09, biol_rep1_CNhs13661_13336-143C6_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay08BiolRep3_CNhs13732_tpm_rev Hes3-gfpCardiomyocyticInduction_Day08Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day08, biol_rep3_CNhs13732_13359-143F2_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay08BiolRep3_CNhs13732_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day08Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day08, biol_rep3_CNhs13732_13359-143F2_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay08BiolRep2_CNhs13720_tpm_rev Hes3-gfpCardiomyocyticInduction_Day08Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day08, biol_rep2_CNhs13720_13347-143D8_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay08BiolRep2_CNhs13720_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day08Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day08, biol_rep2_CNhs13720_13347-143D8_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay08BiolRep1_CNhs13660_tpm_rev Hes3-gfpCardiomyocyticInduction_Day08Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day08, biol_rep1_CNhs13660_13335-143C5_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay08BiolRep1_CNhs13660_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day08Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day08, biol_rep1_CNhs13660_13335-143C5_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay07BiolRep3_CNhs13731_tpm_rev Hes3-gfpCardiomyocyticInduction_Day07Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day07, biol_rep3_CNhs13731_13358-143F1_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay07BiolRep3_CNhs13731_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day07Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day07, biol_rep3_CNhs13731_13358-143F1_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay07BiolRep2_CNhs13719_tpm_rev Hes3-gfpCardiomyocyticInduction_Day07Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day07, biol_rep2_CNhs13719_13346-143D7_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay07BiolRep2_CNhs13719_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day07Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day07, biol_rep2_CNhs13719_13346-143D7_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay06BiolRep3_CNhs13730_tpm_rev Hes3-gfpCardiomyocyticInduction_Day06Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day06, biol_rep3_CNhs13730_13357-143E9_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay06BiolRep3_CNhs13730_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day06Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day06, biol_rep3_CNhs13730_13357-143E9_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay06BiolRep2_CNhs13718_tpm_rev Hes3-gfpCardiomyocyticInduction_Day06Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day06, biol_rep2_CNhs13718_13345-143D6_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay06BiolRep2_CNhs13718_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day06Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day06, biol_rep2_CNhs13718_13345-143D6_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay06BiolRep1_CNhs13658_tpm_rev Hes3-gfpCardiomyocyticInduction_Day06Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day06, biol_rep1_CNhs13658_13333-143C3_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay06BiolRep1_CNhs13658_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day06Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day06, biol_rep1_CNhs13658_13333-143C3_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay05BiolRep3_CNhs13729_tpm_rev Hes3-gfpCardiomyocyticInduction_Day05Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day05, biol_rep3_CNhs13729_13356-143E8_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay05BiolRep3_CNhs13729_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day05Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day05, biol_rep3_CNhs13729_13356-143E8_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay05BiolRep2_CNhs13717_tpm_rev Hes3-gfpCardiomyocyticInduction_Day05Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day05, biol_rep2_CNhs13717_13344-143D5_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay05BiolRep2_CNhs13717_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day05Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day05, biol_rep2_CNhs13717_13344-143D5_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay05BiolRep1_CNhs13657_tpm_rev Hes3-gfpCardiomyocyticInduction_Day05Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day05, biol_rep1_CNhs13657_13332-143C2_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay05BiolRep1_CNhs13657_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day05Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day05, biol_rep1_CNhs13657_13332-143C2_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay04BiolRep3_CNhs13728_tpm_rev Hes3-gfpCardiomyocyticInduction_Day04Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day04, biol_rep3_CNhs13728_13355-143E7_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay04BiolRep3_CNhs13728_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day04Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day04, biol_rep3_CNhs13728_13355-143E7_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay04BiolRep2_CNhs13716_tpm_rev Hes3-gfpCardiomyocyticInduction_Day04Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day04, biol_rep2_CNhs13716_13343-143D4_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay04BiolRep2_CNhs13716_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day04Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day04, biol_rep2_CNhs13716_13343-143D4_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay04BiolRep1_CNhs13656_tpm_rev Hes3-gfpCardiomyocyticInduction_Day04Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day04, biol_rep1_CNhs13656_13331-143C1_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay04BiolRep1_CNhs13656_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day04Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day04, biol_rep1_CNhs13656_13331-143C1_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay03BiolRep3_CNhs13727_tpm_rev Hes3-gfpCardiomyocyticInduction_Day03Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day03, biol_rep3_CNhs13727_13354-143E6_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay03BiolRep3_CNhs13727_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day03Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day03, biol_rep3_CNhs13727_13354-143E6_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay03BiolRep2_CNhs13715_tpm_rev Hes3-gfpCardiomyocyticInduction_Day03Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day03, biol_rep2_CNhs13715_13342-143D3_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay03BiolRep2_CNhs13715_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day03Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day03, biol_rep2_CNhs13715_13342-143D3_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay03BiolRep1_CNhs13655_tpm_rev Hes3-gfpCardiomyocyticInduction_Day03Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day03, biol_rep1_CNhs13655_13330-143B9_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay03BiolRep1_CNhs13655_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day03Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day03, biol_rep1_CNhs13655_13330-143B9_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay02BiolRep3_CNhs13726_tpm_rev Hes3-gfpCardiomyocyticInduction_Day02Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day02, biol_rep3_CNhs13726_13353-143E5_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay02BiolRep3_CNhs13726_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day02Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day02, biol_rep3_CNhs13726_13353-143E5_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay02BiolRep2_CNhs13714_tpm_rev Hes3-gfpCardiomyocyticInduction_Day02Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day02, biol_rep2_CNhs13714_13341-143D2_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay02BiolRep2_CNhs13714_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day02Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day02, biol_rep2_CNhs13714_13341-143D2_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay02BiolRep1_CNhs13654_tpm_rev Hes3-gfpCardiomyocyticInduction_Day02Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day02, biol_rep1_CNhs13654_13329-143B8_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay02BiolRep1_CNhs13654_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day02Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day02, biol_rep1_CNhs13654_13329-143B8_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay01BiolRep3_CNhs13725_tpm_rev Hes3-gfpCardiomyocyticInduction_Day01Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day01, biol_rep3_CNhs13725_13352-143E4_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay01BiolRep3_CNhs13725_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day01Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day01, biol_rep3_CNhs13725_13352-143E4_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay01BiolRep2_CNhs13712_tpm_rev Hes3-gfpCardiomyocyticInduction_Day01Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day01, biol_rep2_CNhs13712_13340-143D1_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay01BiolRep2_CNhs13712_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day01Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day01, biol_rep2_CNhs13712_13340-143D1_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay01BiolRep1_CNhs13653_tpm_rev Hes3-gfpCardiomyocyticInduction_Day01Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day01, biol_rep1_CNhs13653_13328-143B7_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay01BiolRep1_CNhs13653_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day01Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day01, biol_rep1_CNhs13653_13328-143B7_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay00BiolRep3UH3_CNhs13738_tpm_rev Hes3-gfpCardiomyocyticInduction_Day00Br3- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day00, biol_rep3 (UH-3)_CNhs13738_13366-143F9_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay00BiolRep3UH3_CNhs13738_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day00Br3+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day00, biol_rep3 (UH-3)_CNhs13738_13366-143F9_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay00BiolRep2UH2_CNhs13695_tpm_rev Hes3-gfpCardiomyocyticInduction_Day00Br2- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day00, biol_rep2 (UH-2)_CNhs13695_13365-143F8_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay00BiolRep2UH2_CNhs13695_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day00Br2+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day00, biol_rep2 (UH-2)_CNhs13695_13365-143F8_forward Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay00BiolRep1UH1_CNhs13694_tpm_rev Hes3-gfpCardiomyocyticInduction_Day00Br1- HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day00, biol_rep1 (UH-1)_CNhs13694_13364-143F7_reverse Regulation 
HES3GFPEmbryonicStemCellsCardiomyocyticInductionDay00BiolRep1UH1_CNhs13694_tpm_fwd Hes3-gfpCardiomyocyticInduction_Day00Br1+ HES3-GFP Embryonic Stem cells, cardiomyocytic induction, day00, biol_rep1 (UH-1)_CNhs13694_13364-143F7_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b06hrBiolRep3LK60_CNhs13586_tpm_rev AorticSmsToIL1b_06hrBr3- Aortic smooth muscle cell response to IL1b, 06hr, biol_rep3 (LK60)_CNhs13586_12857-137D4_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b06hrBiolRep3LK60_CNhs13586_tpm_fwd AorticSmsToIL1b_06hrBr3+ Aortic smooth muscle cell response to IL1b, 06hr, biol_rep3 (LK60)_CNhs13586_12857-137D4_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b06hrBiolRep2LK59_CNhs13378_tpm_rev AorticSmsToIL1b_06hrBr2- Aortic smooth muscle cell response to IL1b, 06hr, biol_rep2 (LK59)_CNhs13378_12759-136B5_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b06hrBiolRep2LK59_CNhs13378_tpm_fwd AorticSmsToIL1b_06hrBr2+ Aortic smooth muscle cell response to IL1b, 06hr, biol_rep2 (LK59)_CNhs13378_12759-136B5_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b06hrBiolRep1LK58_CNhs13357_tpm_rev AorticSmsToIL1b_06hrBr1- Aortic smooth muscle cell response to IL1b, 06hr, biol_rep1 (LK58)_CNhs13357_12661-134I6_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b06hrBiolRep1LK58_CNhs13357_tpm_fwd AorticSmsToIL1b_06hrBr1+ Aortic smooth muscle cell response to IL1b, 06hr, biol_rep1 (LK58)_CNhs13357_12661-134I6_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b05hrBiolRep2LK56_CNhs13377_tpm_rev AorticSmsToIL1b_05hrBr2- Aortic smooth muscle cell response to IL1b, 05hr, biol_rep2 (LK56)_CNhs13377_12758-136B4_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b05hrBiolRep2LK56_CNhs13377_tpm_fwd AorticSmsToIL1b_05hrBr2+ Aortic smooth muscle cell response to IL1b, 05hr, biol_rep2 (LK56)_CNhs13377_12758-136B4_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b05hrBiolRep1LK55_CNhs13356_tpm_rev AorticSmsToIL1b_05hrBr1- Aortic smooth muscle cell response to IL1b, 05hr, biol_rep1 (LK55)_CNhs13356_12660-134I5_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b05hrBiolRep1LK55_CNhs13356_tpm_fwd AorticSmsToIL1b_05hrBr1+ Aortic smooth muscle cell response to IL1b, 05hr, biol_rep1 (LK55)_CNhs13356_12660-134I5_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b04hrBiolRep3LK54_CNhs13584_tpm_rev AorticSmsToIL1b_04hrBr3- Aortic smooth muscle cell response to IL1b, 04hr, biol_rep3 (LK54)_CNhs13584_12855-137D2_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b04hrBiolRep3LK54_CNhs13584_tpm_fwd AorticSmsToIL1b_04hrBr3+ Aortic smooth muscle cell response to IL1b, 04hr, biol_rep3 (LK54)_CNhs13584_12855-137D2_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b04hrBiolRep2LK53_CNhs13376_tpm_rev AorticSmsToIL1b_04hrBr2- Aortic smooth muscle cell response to IL1b, 04hr, biol_rep2 (LK53)_CNhs13376_12757-136B3_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b04hrBiolRep2LK53_CNhs13376_tpm_fwd AorticSmsToIL1b_04hrBr2+ Aortic smooth muscle cell response to IL1b, 04hr, biol_rep2 (LK53)_CNhs13376_12757-136B3_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b04hrBiolRep1LK52_CNhs13682_tpm_rev AorticSmsToIL1b_04hrBr1- Aortic smooth muscle cell response to IL1b, 04hr, biol_rep1 (LK52)_CNhs13682_12659-134I4_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b04hrBiolRep1LK52_CNhs13682_tpm_fwd AorticSmsToIL1b_04hrBr1+ Aortic smooth muscle cell response to IL1b, 04hr, biol_rep1 (LK52)_CNhs13682_12659-134I4_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b03hrBiolRep2LK50_CNhs13375_tpm_rev AorticSmsToIL1b_03hrBr2- Aortic smooth muscle cell response to IL1b, 03hr, biol_rep2 (LK50)_CNhs13375_12756-136B2_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b03hrBiolRep2LK50_CNhs13375_tpm_fwd AorticSmsToIL1b_03hrBr2+ Aortic smooth muscle cell response to IL1b, 03hr, biol_rep2 (LK50)_CNhs13375_12756-136B2_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b03hrBiolRep1LK49_CNhs13355_tpm_rev AorticSmsToIL1b_03hrBr1- Aortic smooth muscle cell response to IL1b, 03hr, biol_rep1 (LK49)_CNhs13355_12658-134I3_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b03hrBiolRep1LK49_CNhs13355_tpm_fwd AorticSmsToIL1b_03hrBr1+ Aortic smooth muscle cell response to IL1b, 03hr, biol_rep1 (LK49)_CNhs13355_12658-134I3_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b02hrBiolRep3LK48_CNhs13582_tpm_rev AorticSmsToIL1b_02hrBr3- Aortic smooth muscle cell response to IL1b, 02hr, biol_rep3 (LK48)_CNhs13582_12853-137C9_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b02hrBiolRep3LK48_CNhs13582_tpm_fwd AorticSmsToIL1b_02hrBr3+ Aortic smooth muscle cell response to IL1b, 02hr, biol_rep3 (LK48)_CNhs13582_12853-137C9_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b02hrBiolRep2LK47_CNhs13374_tpm_rev AorticSmsToIL1b_02hrBr2- Aortic smooth muscle cell response to IL1b, 02hr, biol_rep2 (LK47)_CNhs13374_12755-136B1_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b02hrBiolRep2LK47_CNhs13374_tpm_fwd AorticSmsToIL1b_02hrBr2+ Aortic smooth muscle cell response to IL1b, 02hr, biol_rep2 (LK47)_CNhs13374_12755-136B1_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b01hrBiolRep2LK44_CNhs13373_tpm_rev AorticSmsToIL1b_01hrBr2- Aortic smooth muscle cell response to IL1b, 01hr, biol_rep2 (LK44)_CNhs13373_12754-136A9_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b01hrBiolRep2LK44_CNhs13373_tpm_fwd AorticSmsToIL1b_01hrBr2+ Aortic smooth muscle cell response to IL1b, 01hr, biol_rep2 (LK44)_CNhs13373_12754-136A9_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b01hrBiolRep1LK43_CNhs13353_tpm_rev AorticSmsToIL1b_01hrBr1- Aortic smooth muscle cell response to IL1b, 01hr, biol_rep1 (LK43)_CNhs13353_12656-134I1_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b01hrBiolRep1LK43_CNhs13353_tpm_fwd AorticSmsToIL1b_01hrBr1+ Aortic smooth muscle cell response to IL1b, 01hr, biol_rep1 (LK43)_CNhs13353_12656-134I1_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr45minBiolRep3LK42_CNhs13580_tpm_rev AorticSmsToIL1b_00hr45minBr3- Aortic smooth muscle cell response to IL1b, 00hr45min, biol_rep3 (LK42)_CNhs13580_12851-137C7_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr45minBiolRep3LK42_CNhs13580_tpm_fwd AorticSmsToIL1b_00hr45minBr3+ Aortic smooth muscle cell response to IL1b, 00hr45min, biol_rep3 (LK42)_CNhs13580_12851-137C7_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr45minBiolRep2LK41_CNhs13372_tpm_rev AorticSmsToIL1b_00hr45minBr2- Aortic smooth muscle cell response to IL1b, 00hr45min, biol_rep2 (LK41)_CNhs13372_12753-136A8_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr45minBiolRep2LK41_CNhs13372_tpm_fwd AorticSmsToIL1b_00hr45minBr2+ Aortic smooth muscle cell response to IL1b, 00hr45min, biol_rep2 (LK41)_CNhs13372_12753-136A8_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr45minBiolRep1LK40_CNhs13352_tpm_rev AorticSmsToIL1b_00hr45minBr1- Aortic smooth muscle cell response to IL1b, 00hr45min, biol_rep1 (LK40)_CNhs13352_12655-134H9_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr45minBiolRep1LK40_CNhs13352_tpm_fwd AorticSmsToIL1b_00hr45minBr1+ Aortic smooth muscle cell response to IL1b, 00hr45min, biol_rep1 (LK40)_CNhs13352_12655-134H9_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr30minBiolRep3LK39_CNhs13579_tpm_rev AorticSmsToIL1b_00hr30minBr3- Aortic smooth muscle cell response to IL1b, 00hr30min, biol_rep3 (LK39)_CNhs13579_12850-137C6_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr30minBiolRep3LK39_CNhs13579_tpm_fwd AorticSmsToIL1b_00hr30minBr3+ Aortic smooth muscle cell response to IL1b, 00hr30min, biol_rep3 (LK39)_CNhs13579_12850-137C6_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr30minBiolRep2LK38_CNhs13371_tpm_rev AorticSmsToIL1b_00hr30minBr2- Aortic smooth muscle cell response to IL1b, 00hr30min, biol_rep2 (LK38)_CNhs13371_12752-136A7_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr30minBiolRep2LK38_CNhs13371_tpm_fwd AorticSmsToIL1b_00hr30minBr2+ Aortic smooth muscle cell response to IL1b, 00hr30min, biol_rep2 (LK38)_CNhs13371_12752-136A7_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr30minBiolRep1LK37_CNhs13351_tpm_rev AorticSmsToIL1b_00hr30minBr1- Aortic smooth muscle cell response to IL1b, 00hr30min, biol_rep1 (LK37)_CNhs13351_12654-134H8_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr30minBiolRep1LK37_CNhs13351_tpm_fwd AorticSmsToIL1b_00hr30minBr1+ Aortic smooth muscle cell response to IL1b, 00hr30min, biol_rep1 (LK37)_CNhs13351_12654-134H8_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr15minBiolRep3LK36_CNhs13578_tpm_rev AorticSmsToIL1b_00hr15minBr3- Aortic smooth muscle cell response to IL1b, 00hr15min, biol_rep3 (LK36)_CNhs13578_12849-137C5_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr15minBiolRep3LK36_CNhs13578_tpm_fwd AorticSmsToIL1b_00hr15minBr3+ Aortic smooth muscle cell response to IL1b, 00hr15min, biol_rep3 (LK36)_CNhs13578_12849-137C5_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr15minBiolRep2LK35_CNhs13370_tpm_rev AorticSmsToIL1b_00hr15minBr2- Aortic smooth muscle cell response to IL1b, 00hr15min, biol_rep2 (LK35)_CNhs13370_12751-136A6_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr15minBiolRep2LK35_CNhs13370_tpm_fwd AorticSmsToIL1b_00hr15minBr2+ Aortic smooth muscle cell response to IL1b, 00hr15min, biol_rep2 (LK35)_CNhs13370_12751-136A6_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr15minBiolRep1LK34_CNhs13350_tpm_rev AorticSmsToIL1b_00hr15minBr1- Aortic smooth muscle cell response to IL1b, 00hr15min, biol_rep1 (LK34)_CNhs13350_12653-134H7_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr15minBiolRep1LK34_CNhs13350_tpm_fwd AorticSmsToIL1b_00hr15minBr1+ Aortic smooth muscle cell response to IL1b, 00hr15min, biol_rep1 (LK34)_CNhs13350_12653-134H7_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr00minBiolRep3LK33_CNhs13577_tpm_rev AorticSmsToIL1b_00hr00minBr3- Aortic smooth muscle cell response to IL1b, 00hr00min, biol_rep3 (LK33)_CNhs13577_12848-137C4_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr00minBiolRep3LK33_CNhs13577_tpm_fwd AorticSmsToIL1b_00hr00minBr3+ Aortic smooth muscle cell response to IL1b, 00hr00min, biol_rep3 (LK33)_CNhs13577_12848-137C4_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr00minBiolRep2LK32_CNhs13369_tpm_rev AorticSmsToIL1b_00hr00minBr2- Aortic smooth muscle cell response to IL1b, 00hr00min, biol_rep2 (LK32)_CNhs13369_12750-136A5_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr00minBiolRep2LK32_CNhs13369_tpm_fwd AorticSmsToIL1b_00hr00minBr2+ Aortic smooth muscle cell response to IL1b, 00hr00min, biol_rep2 (LK32)_CNhs13369_12750-136A5_forward Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr00minBiolRep1LK31_CNhs13349_tpm_rev AorticSmsToIL1b_00hr00minBr1- Aortic smooth muscle cell response to IL1b, 00hr00min, biol_rep1 (LK31)_CNhs13349_12652-134H6_reverse Regulation 
AorticSmoothMuscleCellResponseToIL1b00hr00minBiolRep1LK31_CNhs13349_tpm_fwd AorticSmsToIL1b_00hr00minBr1+ Aortic smooth muscle cell response to IL1b, 00hr00min, biol_rep1 (LK31)_CNhs13349_12652-134H6_forward Regulation 
AorticSmoothMuscleCellResponseToFGF206hrBiolRep3LK30_CNhs13576_tpm_rev AorticSmsToFgf2_06hrBr3- Aortic smooth muscle cell response to FGF2, 06hr, biol_rep3 (LK30)_CNhs13576_12847-137C3_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF206hrBiolRep3LK30_CNhs13576_tpm_fwd AorticSmsToFgf2_06hrBr3+ Aortic smooth muscle cell response to FGF2, 06hr, biol_rep3 (LK30)_CNhs13576_12847-137C3_forward Regulation 
AorticSmoothMuscleCellResponseToFGF206hrBiolRep2LK29_CNhs13368_tpm_rev AorticSmsToFgf2_06hrBr2- Aortic smooth muscle cell response to FGF2, 06hr, biol_rep2 (LK29)_CNhs13368_12749-136A4_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF206hrBiolRep2LK29_CNhs13368_tpm_fwd AorticSmsToFgf2_06hrBr2+ Aortic smooth muscle cell response to FGF2, 06hr, biol_rep2 (LK29)_CNhs13368_12749-136A4_forward Regulation 
AorticSmoothMuscleCellResponseToFGF206hrBiolRep1LK28_CNhs13348_tpm_rev AorticSmsToFgf2_06hrBr1- Aortic smooth muscle cell response to FGF2, 06hr, biol_rep1 (LK28)_CNhs13348_12651-134H5_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF206hrBiolRep1LK28_CNhs13348_tpm_fwd AorticSmsToFgf2_06hrBr1+ Aortic smooth muscle cell response to FGF2, 06hr, biol_rep1 (LK28)_CNhs13348_12651-134H5_forward Regulation 
AorticSmoothMuscleCellResponseToFGF205hrBiolRep3LK27_CNhs13575_tpm_rev AorticSmsToFgf2_05hrBr3- Aortic smooth muscle cell response to FGF2, 05hr, biol_rep3 (LK27)_CNhs13575_12846-137C2_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF205hrBiolRep3LK27_CNhs13575_tpm_fwd AorticSmsToFgf2_05hrBr3+ Aortic smooth muscle cell response to FGF2, 05hr, biol_rep3 (LK27)_CNhs13575_12846-137C2_forward Regulation 
AorticSmoothMuscleCellResponseToFGF205hrBiolRep2LK26_CNhs13367_tpm_rev AorticSmsToFgf2_05hrBr2- Aortic smooth muscle cell response to FGF2, 05hr, biol_rep2 (LK26)_CNhs13367_12748-136A3_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF205hrBiolRep2LK26_CNhs13367_tpm_fwd AorticSmsToFgf2_05hrBr2+ Aortic smooth muscle cell response to FGF2, 05hr, biol_rep2 (LK26)_CNhs13367_12748-136A3_forward Regulation 
AorticSmoothMuscleCellResponseToFGF205hrBiolRep1LK25_CNhs13347_tpm_rev AorticSmsToFgf2_05hrBr1- Aortic smooth muscle cell response to FGF2, 05hr, biol_rep1 (LK25)_CNhs13347_12650-134H4_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF205hrBiolRep1LK25_CNhs13347_tpm_fwd AorticSmsToFgf2_05hrBr1+ Aortic smooth muscle cell response to FGF2, 05hr, biol_rep1 (LK25)_CNhs13347_12650-134H4_forward Regulation 
AorticSmoothMuscleCellResponseToFGF203hrBiolRep3LK21_CNhs13573_tpm_rev AorticSmsToFgf2_03hrBr3- Aortic smooth muscle cell response to FGF2, 03hr, biol_rep3 (LK21)_CNhs13573_12844-137B9_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF203hrBiolRep3LK21_CNhs13573_tpm_fwd AorticSmsToFgf2_03hrBr3+ Aortic smooth muscle cell response to FGF2, 03hr, biol_rep3 (LK21)_CNhs13573_12844-137B9_forward Regulation 
AorticSmoothMuscleCellResponseToFGF203hrBiolRep2LK20_CNhs13364_tpm_rev AorticSmsToFgf2_03hrBr2- Aortic smooth muscle cell response to FGF2, 03hr, biol_rep2 (LK20)_CNhs13364_12746-136A1_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF203hrBiolRep2LK20_CNhs13364_tpm_fwd AorticSmsToFgf2_03hrBr2+ Aortic smooth muscle cell response to FGF2, 03hr, biol_rep2 (LK20)_CNhs13364_12746-136A1_forward Regulation 
AorticSmoothMuscleCellResponseToFGF203hrBiolRep1LK19_CNhs13345_tpm_rev AorticSmsToFgf2_03hrBr1- Aortic smooth muscle cell response to FGF2, 03hr, biol_rep1 (LK19)_CNhs13345_12648-134H2_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF203hrBiolRep1LK19_CNhs13345_tpm_fwd AorticSmsToFgf2_03hrBr1+ Aortic smooth muscle cell response to FGF2, 03hr, biol_rep1 (LK19)_CNhs13345_12648-134H2_forward Regulation 
AorticSmoothMuscleCellResponseToFGF202hrBiolRep3LK18_CNhs13572_tpm_rev AorticSmsToFgf2_02hrBr3- Aortic smooth muscle cell response to FGF2, 02hr, biol_rep3 (LK18)_CNhs13572_12843-137B8_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF202hrBiolRep3LK18_CNhs13572_tpm_fwd AorticSmsToFgf2_02hrBr3+ Aortic smooth muscle cell response to FGF2, 02hr, biol_rep3 (LK18)_CNhs13572_12843-137B8_forward Regulation 
AorticSmoothMuscleCellResponseToFGF202hrBiolRep2LK17_CNhs13363_tpm_rev AorticSmsToFgf2_02hrBr2- Aortic smooth muscle cell response to FGF2, 02hr, biol_rep2 (LK17)_CNhs13363_12745-135I9_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF202hrBiolRep2LK17_CNhs13363_tpm_fwd AorticSmsToFgf2_02hrBr2+ Aortic smooth muscle cell response to FGF2, 02hr, biol_rep2 (LK17)_CNhs13363_12745-135I9_forward Regulation 
AorticSmoothMuscleCellResponseToFGF202hrBiolRep1LK16_CNhs13344_tpm_rev AorticSmsToFgf2_02hrBr1- Aortic smooth muscle cell response to FGF2, 02hr, biol_rep1 (LK16)_CNhs13344_12647-134H1_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF202hrBiolRep1LK16_CNhs13344_tpm_fwd AorticSmsToFgf2_02hrBr1+ Aortic smooth muscle cell response to FGF2, 02hr, biol_rep1 (LK16)_CNhs13344_12647-134H1_forward Regulation 
AorticSmoothMuscleCellResponseToFGF201hrBiolRep3LK15_CNhs13683_tpm_rev AorticSmsToFgf2_01hrBr3- Aortic smooth muscle cell response to FGF2, 01hr, biol_rep3 (LK15)_CNhs13683_12842-137B7_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF201hrBiolRep3LK15_CNhs13683_tpm_fwd AorticSmsToFgf2_01hrBr3+ Aortic smooth muscle cell response to FGF2, 01hr, biol_rep3 (LK15)_CNhs13683_12842-137B7_forward Regulation 
AorticSmoothMuscleCellResponseToFGF201hrBiolRep1LK13_CNhs12741_tpm_rev AorticSmsToFgf2_01hrBr1- Aortic smooth muscle cell response to FGF2, 01hr, biol_rep1 (LK13)_CNhs12741_12646-134G9_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF201hrBiolRep1LK13_CNhs12741_tpm_fwd AorticSmsToFgf2_01hrBr1+ Aortic smooth muscle cell response to FGF2, 01hr, biol_rep1 (LK13)_CNhs12741_12646-134G9_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr45minBiolRep3LK12_CNhs13571_tpm_rev AorticSmsToFgf2_00hr45minBr3- Aortic smooth muscle cell response to FGF2, 00hr45min, biol_rep3 (LK12)_CNhs13571_12841-137B6_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr45minBiolRep3LK12_CNhs13571_tpm_fwd AorticSmsToFgf2_00hr45minBr3+ Aortic smooth muscle cell response to FGF2, 00hr45min, biol_rep3 (LK12)_CNhs13571_12841-137B6_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr45minBiolRep2LK11_CNhs13361_tpm_rev AorticSmsToFgf2_00hr45minBr2- Aortic smooth muscle cell response to FGF2, 00hr45min, biol_rep2 (LK11)_CNhs13361_12743-135I7_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr45minBiolRep2LK11_CNhs13361_tpm_fwd AorticSmsToFgf2_00hr45minBr2+ Aortic smooth muscle cell response to FGF2, 00hr45min, biol_rep2 (LK11)_CNhs13361_12743-135I7_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr45minBiolRep1LK10_CNhs13343_tpm_rev AorticSmsToFgf2_00hr45minBr1- Aortic smooth muscle cell response to FGF2, 00hr45min, biol_rep1 (LK10)_CNhs13343_12645-134G8_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr45minBiolRep1LK10_CNhs13343_tpm_fwd AorticSmsToFgf2_00hr45minBr1+ Aortic smooth muscle cell response to FGF2, 00hr45min, biol_rep1 (LK10)_CNhs13343_12645-134G8_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr30minBiolRep3LK9_CNhs13569_tpm_rev AorticSmsToFgf2_00hr30minBr3- Aortic smooth muscle cell response to FGF2, 00hr30min, biol_rep3 (LK9)_CNhs13569_12840-137B5_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr30minBiolRep3LK9_CNhs13569_tpm_fwd AorticSmsToFgf2_00hr30minBr3+ Aortic smooth muscle cell response to FGF2, 00hr30min, biol_rep3 (LK9)_CNhs13569_12840-137B5_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr30minBiolRep2LK8_CNhs13360_tpm_rev AorticSmsToFgf2_00hr30minBr2- Aortic smooth muscle cell response to FGF2, 00hr30min, biol_rep2 (LK8)_CNhs13360_12742-135I6_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr30minBiolRep2LK8_CNhs13360_tpm_fwd AorticSmsToFgf2_00hr30minBr2+ Aortic smooth muscle cell response to FGF2, 00hr30min, biol_rep2 (LK8)_CNhs13360_12742-135I6_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr30minBiolRep1LK7_CNhs13341_tpm_rev AorticSmsToFgf2_00hr30minBr1- Aortic smooth muscle cell response to FGF2, 00hr30min, biol_rep1 (LK7)_CNhs13341_12644-134G7_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr30minBiolRep1LK7_CNhs13341_tpm_fwd AorticSmsToFgf2_00hr30minBr1+ Aortic smooth muscle cell response to FGF2, 00hr30min, biol_rep1 (LK7)_CNhs13341_12644-134G7_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr15minBiolRep3LK6_CNhs13568_tpm_rev AorticSmsToFgf2_00hr15minBr3- Aortic smooth muscle cell response to FGF2, 00hr15min, biol_rep3 (LK6)_CNhs13568_12839-137B4_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr15minBiolRep3LK6_CNhs13568_tpm_fwd AorticSmsToFgf2_00hr15minBr3+ Aortic smooth muscle cell response to FGF2, 00hr15min, biol_rep3 (LK6)_CNhs13568_12839-137B4_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr15minBiolRep2LK5_CNhs13359_tpm_rev AorticSmsToFgf2_00hr15minBr2- Aortic smooth muscle cell response to FGF2, 00hr15min, biol_rep2 (LK5)_CNhs13359_12741-135I5_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr15minBiolRep2LK5_CNhs13359_tpm_fwd AorticSmsToFgf2_00hr15minBr2+ Aortic smooth muscle cell response to FGF2, 00hr15min, biol_rep2 (LK5)_CNhs13359_12741-135I5_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr15minBiolRep1LK4_CNhs13340_tpm_rev AorticSmsToFgf2_00hr15minBr1- Aortic smooth muscle cell response to FGF2, 00hr15min, biol_rep1 (LK4)_CNhs13340_12643-134G6_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr15minBiolRep1LK4_CNhs13340_tpm_fwd AorticSmsToFgf2_00hr15minBr1+ Aortic smooth muscle cell response to FGF2, 00hr15min, biol_rep1 (LK4)_CNhs13340_12643-134G6_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr00minBiolRep2LK2_CNhs13358_tpm_rev AorticSmsToFgf2_00hr00minBr2- Aortic smooth muscle cell response to FGF2, 00hr00min, biol_rep2 (LK2)_CNhs13358_12740-135I4_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr00minBiolRep2LK2_CNhs13358_tpm_fwd AorticSmsToFgf2_00hr00minBr2+ Aortic smooth muscle cell response to FGF2, 00hr00min, biol_rep2 (LK2)_CNhs13358_12740-135I4_forward Regulation 
AorticSmoothMuscleCellResponseToFGF200hr00minBiolRep1LK1_CNhs13339_tpm_rev AorticSmsToFgf2_00hr00minBr1- Aortic smooth muscle cell response to FGF2, 00hr00min, biol_rep1 (LK1)_CNhs13339_12642-134G5_reverse Regulation 
AorticSmoothMuscleCellResponseToFGF200hr00minBiolRep1LK1_CNhs13339_tpm_fwd AorticSmsToFgf2_00hr00minBr1+ Aortic smooth muscle cell response to FGF2, 00hr00min, biol_rep1 (LK1)_CNhs13339_12642-134G5_forward Regulation 
gnomadGenomesVariantsV3_1_1 gnomAD v3.1.1 Genome Aggregation Database (gnomAD) Genome Variants v3.1.1 Variation Description With the gnomAD v4.1 data release, the v4 Pre-Release track has been replaced with the gnomAD v4.1 track. The v4.1 release includes a fix for the allele number issue. The v4.1 track shows variants from 807,162 individuals, including 730,947 exomes and 76,215 genomes. This includes the 76,156 genomes from the gnomAD v3.1.2 release as well as new exome data from 416,555 UK Biobank individuals. For more detailed information on gnomAD v4.1, see the related blog post. The gnomAD v3.1 track shows variants from 76,156 whole genomes (and no exomes), all mapped to the GRCh38/hg38 reference sequence. 4,454 genomes were added to the number of genomes in the previous v3 release. For more detailed information on gnomAD v3.1, see the related blog post. The gnomAD v3.1.1 track contains the same underlying data as v3.1, but with minor corrections to the VEP annotations and dbSNP rsIDs. On the UCSC side, we have now included the mitochondrial chromosome data that was released as part of gnomAD v3.1 (but after the UCSC version of the track was released). For more information about gnomAD v3.1.1, please see the related changelog. GnomAD Genome Mutational Constraint is based on v3.1.2 and is available only on hg38. It shows the reduced variation caused by purifying natural selection. This is similar to negative selection on loss-of-function (LoF) for genes, but can be calculated for non-coding regions too. Positive values are red and reflect stronger mutation constraint (and less variation), indicating higher natural selection pressure in a region. Negative values are green and reflect lower mutation constraint (and more variation), indicating less selection pressure and less functional effect. Briefly, for any 1kbp window in the genome, a model based on trinucleotide sequence context, base-level methylation, and regional genomic features predicts expected number of mutations, and compares this number to the observed number of mutations using a Z-score (see preprint in the Reference section for details). The chrX scores were added as received from the authors, as there are no de novo mutation data available on chrX (for estimating the effects of regional genomic features on mutation rates), they are more speculative than the ones on the autosomes. The gnomAD Predicted Constraint Metrics track contains metrics of pathogenicity per-gene as predicted for gnomAD v2.1.1 and identifies genes subject to strong selection against various classes of mutation. This includes data on both the gene and transcript level. The gnomAD v2 tracks show variants from 125,748 exomes and 15,708 whole genomes, all mapped to the GRCh37/hg19 reference sequence and lifted to the GRCh38/hg38 assembly. The data originate from 141,456 unrelated individuals sequenced as part of various population-genetic and disease-specific studies collected by the Genome Aggregation Database (gnomAD), release 2.1.1. Raw data from all studies have been reprocessed through a unified pipeline and jointly variant-called to increase consistency across projects. For more information on the processing pipeline and population annotations, see the following blog post and the 2.1.1 README. gnomAD v2 data are based on the GRCh37/hg19 assembly. These tracks display the GRCh38/hg38 lift-over provided by gnomAD on their downloads site. On hg38 only, a subtrack "Gnomad mutational constraint" aka "Genome non-coding constraint of haploinsufficient variation (Gnocchi)" captures the depletion of variation caused by purifying natural selection. This is similar to negative selection on loss-of-function (LoF) for genes, but can be calculated for non-coding regions, too. Briefly, for any 1kbp window in the genome, a model based on trinucleotide sequence context, base-level methylation, and regional genomic features predicts expected number of mutations, and compares this number to the observed number of mutations using a Z-score (see Chen et al 2024 in the Reference section for details). The chrX scores were added as received from the authors, as there are no mutations available for chrX, they are more speculative than the ones on the autosomes. For questions on the gnomAD data, also see the gnomAD FAQ. More details on the Variant type(s) can be found on the Sequence Ontology page. Display Conventions and Configuration gnomAD v4.1 The gnomAD v4.1 track version follows the same conventions and configuration as the v3.1.1 track, except for mouse hovering items. Mouse hover on an item will display the following details about each variant: Position Total Allele Frequency (TotalAF) Genes Annotation FILTER tags from VCF (FILTER) Population with maximum AF (PopMaxAF) Homozygous Individuals Homozygous Individuals in XX samples (chrX and chrY only) Hemizygous Individuals (chrX and chrY only) gnomAD v3.1.1 The gnomAD v3.1.1 track version follows the same conventions and configuration as the v3.1 track, except as noted below. There are additional FILTER field filters: AS_VQSR, indel_stack (chrM only), and npg (chrM only). Where possible, variants overlapping multiple transcripts/genes have been collapsed into one variant, with additional information available on the details page, which has roughly halved the number of items in the bigBed. The bigBed has been split into two files, one with the information necessary for the track display, and one with the information necessary for the details page. For more information on this data format, please see the Data Access section below. The VEP annotation is shown as a table instead of spread across multiple fields. Intergenic variants have not been pre-filtered. gnomAD v3.1 By default, a maximum of 50,000 variants can be displayed at a time (before applying the filters described below), before the track switches to dense display mode. Mouse hover on an item will display many details about each variant, including the affected gene(s), the variant type, and annotation (missense, synonymous, etc). Clicking on an item will display additional details on the variant, including a population frequency table showing allele count in each sub-population. Following the conventions on the gnomAD browser, items are shaded according to their Annotation type: pLoF Missense Synonymous Other Label Options To maintain consistency with the gnomAD website, variants are by default labeled according to their chromosomal start position followed by the reference and alternate alleles, for example &quot;chr1-1234-T-CAG&quot;. dbSNP rsID's are also available as an additional label, if the variant is present in dbSnp. Filtering Options Three filters are available for these tracks: FILTER: Used to exclude/include variants that failed Random Forest (RF), Inbreeding Coefficient (Inbreeding Coeff), or Allele Count (AC0) filters. The PASS option is used to include/exclude variants that pass all of the RF, InbreedingCoeff, and AC0 filters, as denoted in the original VCF. Annotation type: Used to exclude/include variants that are annotated as Probability Loss of Function (pLoF), Missense, Synonymous, or Other, as annotated by VEP version 85 (GENCODE v19). Variant Type: Used to exclude/include variants according to the type of variation, as annotated by VEP v85. There is one additional configurable filter on the minimum minor allele frequency. gnomAD v2.1.1 The gnomAD v2.1.1 track follows the standard display and configuration options available for VCF tracks, briefly explained below. In dense mode, a vertical line is drawn at the position of each variant. In pack mode, &quot;ref&quot; and &quot;alt&quot; alleles are displayed to the left of a vertical line with colored portions corresponding to allele counts. Hovering the mouse pointer over a variant pops up a display of alleles and counts. Filtering Options Four filters are available for these tracks, the same as the underlying VCF: AC0: Allele Count 0 after filtering out low confidence genotypes (GQ &lt; 20; DP &lt; 10; and AB &lt; 0.2 for het calls)) InbreedingCoeff: Inbreeding Coefficient &lt; -0.3 RF: Used to exclude/include variants that failed Random Forest filtering thresholds of 0.055272738028512555, 0.20641025579497013 (probabilities of being a true positive variant) for SNPs, indels) Pass: Variant passes all 3 filters There are two additional filters available, one for the minimum minor allele frequency, and a configurable filter on the QUAL score. UCSC Methods The gnomAD v3.1.1 and v4.1 data is unfiltered. For the v3.1 update only, in order to cut down on the amount of displayed data, the following variant types have been filtered out, but are still viewable in the gnomAD browser: Regulatory Region Variants Downstream/Upstream Gene Variants Transcription Factor Binding Site Variants For the full steps used to create the gnomAD tracks at UCSC, please see the hg38 gnomad makedoc. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API, and the genome annotations are stored in files that can be downloaded from our download server, subject to the conditions set forth by the gnomAD consortium (see below). Variant VCFs can be found in the vcf subdirectory. The v3.1, v3.1.1, and v4.1 variants can be found in a special directory as they have been transformed from the underlying VCF. For the v3.1.1 and v4.1 variants in particular, the underlying bigBed only contains enough information necessary to use the track in the browser. The extra data like VEP annotations and CADD scores are available in the same directory as the bigBed but in the files details.tab.gz and details.tab.gz.gzi. The details.tab.gz contains the gzip compressed extra data in JSON format, and the .gzi file is available to speed searching of this data. Each variant has an associated md5sum in the name field of the bigBed which can be used along with the _dataOffset and _dataLen fields to get the associated external data, as show below: # find item of interest: bigBedToBed genomes.bb stdout | head -4 | tail -1 chr1 12416 12417 854246d79dc5d02dcdbd5f5438542b6e [..omitted for brevity..] chr1-12417-G-A 67293 902 # use the final two fields, _dataOffset and _dataLen (add one to _dataLen to include a newline), to get the extra data: bgzip -b 67293 -s 903 gnomad.v3.1.1.details.tab.gz 854246d79dc5d02dcdbd5f5438542b6e {"DDX11L1": {"cons": ["non_coding_transcript_variant", [..omitted for brevity..] The data can also be found directly from the gnomAD downloads page. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. The mutational constraints score was updated in October 2022 from a previous, now deprecated, pre-publication version. The old version can be found in our archive directory on the download server. It can be loaded by copying the URL into our "Custom tracks" input box. Credits Thanks to the Genome Aggregation Database Consortium for making these data available. The data are released under the Creative Commons Zero Public Domain Dedication as described here. Please note that some annotations within the provided files may have restrictions on usage. See here for more information. References Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alfoldi J, Wang Q, Collins RL, Laricchia KM, Ganna A, Birnbaum DP et al. Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes. doi: https://doi.org/10.1101/531210. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill AJ, Cummings BB et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016 Aug 17;536(7616):285-91. PMID: 27535533; PMC: PMC5018207 Chen S, Francioli LC, Goodrich JK, Collins RL, Kanai M, Wang Q, Alf&#246;ldi J, Watts NA, Vittal C, Gauthier LD et al. A genomic mutational constraint map using variation in 76,156 human genomes. Nature. 2024 Jan;625(7993):92-100. PMID: 38057664 (We added the data in 2021, then later referenced the 2022 Biorxiv preprint, in which the track was not called "Gnocchi" yet) 
gnomadGenomesVariantsV3_1 gnomAD v3.1 Genome Aggregation Database (gnomAD) Genome Variants v3.1 Variation Description With the gnomAD v4.1 data release, the v4 Pre-Release track has been replaced with the gnomAD v4.1 track. The v4.1 release includes a fix for the allele number issue. The v4.1 track shows variants from 807,162 individuals, including 730,947 exomes and 76,215 genomes. This includes the 76,156 genomes from the gnomAD v3.1.2 release as well as new exome data from 416,555 UK Biobank individuals. For more detailed information on gnomAD v4.1, see the related blog post. The gnomAD v3.1 track shows variants from 76,156 whole genomes (and no exomes), all mapped to the GRCh38/hg38 reference sequence. 4,454 genomes were added to the number of genomes in the previous v3 release. For more detailed information on gnomAD v3.1, see the related blog post. The gnomAD v3.1.1 track contains the same underlying data as v3.1, but with minor corrections to the VEP annotations and dbSNP rsIDs. On the UCSC side, we have now included the mitochondrial chromosome data that was released as part of gnomAD v3.1 (but after the UCSC version of the track was released). For more information about gnomAD v3.1.1, please see the related changelog. GnomAD Genome Mutational Constraint is based on v3.1.2 and is available only on hg38. It shows the reduced variation caused by purifying natural selection. This is similar to negative selection on loss-of-function (LoF) for genes, but can be calculated for non-coding regions too. Positive values are red and reflect stronger mutation constraint (and less variation), indicating higher natural selection pressure in a region. Negative values are green and reflect lower mutation constraint (and more variation), indicating less selection pressure and less functional effect. Briefly, for any 1kbp window in the genome, a model based on trinucleotide sequence context, base-level methylation, and regional genomic features predicts expected number of mutations, and compares this number to the observed number of mutations using a Z-score (see preprint in the Reference section for details). The chrX scores were added as received from the authors, as there are no de novo mutation data available on chrX (for estimating the effects of regional genomic features on mutation rates), they are more speculative than the ones on the autosomes. The gnomAD Predicted Constraint Metrics track contains metrics of pathogenicity per-gene as predicted for gnomAD v2.1.1 and identifies genes subject to strong selection against various classes of mutation. This includes data on both the gene and transcript level. The gnomAD v2 tracks show variants from 125,748 exomes and 15,708 whole genomes, all mapped to the GRCh37/hg19 reference sequence and lifted to the GRCh38/hg38 assembly. The data originate from 141,456 unrelated individuals sequenced as part of various population-genetic and disease-specific studies collected by the Genome Aggregation Database (gnomAD), release 2.1.1. Raw data from all studies have been reprocessed through a unified pipeline and jointly variant-called to increase consistency across projects. For more information on the processing pipeline and population annotations, see the following blog post and the 2.1.1 README. gnomAD v2 data are based on the GRCh37/hg19 assembly. These tracks display the GRCh38/hg38 lift-over provided by gnomAD on their downloads site. On hg38 only, a subtrack "Gnomad mutational constraint" aka "Genome non-coding constraint of haploinsufficient variation (Gnocchi)" captures the depletion of variation caused by purifying natural selection. This is similar to negative selection on loss-of-function (LoF) for genes, but can be calculated for non-coding regions, too. Briefly, for any 1kbp window in the genome, a model based on trinucleotide sequence context, base-level methylation, and regional genomic features predicts expected number of mutations, and compares this number to the observed number of mutations using a Z-score (see Chen et al 2024 in the Reference section for details). The chrX scores were added as received from the authors, as there are no mutations available for chrX, they are more speculative than the ones on the autosomes. For questions on the gnomAD data, also see the gnomAD FAQ. More details on the Variant type(s) can be found on the Sequence Ontology page. Display Conventions and Configuration gnomAD v4.1 The gnomAD v4.1 track version follows the same conventions and configuration as the v3.1.1 track, except for mouse hovering items. Mouse hover on an item will display the following details about each variant: Position Total Allele Frequency (TotalAF) Genes Annotation FILTER tags from VCF (FILTER) Population with maximum AF (PopMaxAF) Homozygous Individuals Homozygous Individuals in XX samples (chrX and chrY only) Hemizygous Individuals (chrX and chrY only) gnomAD v3.1.1 The gnomAD v3.1.1 track version follows the same conventions and configuration as the v3.1 track, except as noted below. There are additional FILTER field filters: AS_VQSR, indel_stack (chrM only), and npg (chrM only). Where possible, variants overlapping multiple transcripts/genes have been collapsed into one variant, with additional information available on the details page, which has roughly halved the number of items in the bigBed. The bigBed has been split into two files, one with the information necessary for the track display, and one with the information necessary for the details page. For more information on this data format, please see the Data Access section below. The VEP annotation is shown as a table instead of spread across multiple fields. Intergenic variants have not been pre-filtered. gnomAD v3.1 By default, a maximum of 50,000 variants can be displayed at a time (before applying the filters described below), before the track switches to dense display mode. Mouse hover on an item will display many details about each variant, including the affected gene(s), the variant type, and annotation (missense, synonymous, etc). Clicking on an item will display additional details on the variant, including a population frequency table showing allele count in each sub-population. Following the conventions on the gnomAD browser, items are shaded according to their Annotation type: pLoF Missense Synonymous Other Label Options To maintain consistency with the gnomAD website, variants are by default labeled according to their chromosomal start position followed by the reference and alternate alleles, for example &quot;chr1-1234-T-CAG&quot;. dbSNP rsID's are also available as an additional label, if the variant is present in dbSnp. Filtering Options Three filters are available for these tracks: FILTER: Used to exclude/include variants that failed Random Forest (RF), Inbreeding Coefficient (Inbreeding Coeff), or Allele Count (AC0) filters. The PASS option is used to include/exclude variants that pass all of the RF, InbreedingCoeff, and AC0 filters, as denoted in the original VCF. Annotation type: Used to exclude/include variants that are annotated as Probability Loss of Function (pLoF), Missense, Synonymous, or Other, as annotated by VEP version 85 (GENCODE v19). Variant Type: Used to exclude/include variants according to the type of variation, as annotated by VEP v85. There is one additional configurable filter on the minimum minor allele frequency. gnomAD v2.1.1 The gnomAD v2.1.1 track follows the standard display and configuration options available for VCF tracks, briefly explained below. In dense mode, a vertical line is drawn at the position of each variant. In pack mode, &quot;ref&quot; and &quot;alt&quot; alleles are displayed to the left of a vertical line with colored portions corresponding to allele counts. Hovering the mouse pointer over a variant pops up a display of alleles and counts. Filtering Options Four filters are available for these tracks, the same as the underlying VCF: AC0: Allele Count 0 after filtering out low confidence genotypes (GQ &lt; 20; DP &lt; 10; and AB &lt; 0.2 for het calls)) InbreedingCoeff: Inbreeding Coefficient &lt; -0.3 RF: Used to exclude/include variants that failed Random Forest filtering thresholds of 0.055272738028512555, 0.20641025579497013 (probabilities of being a true positive variant) for SNPs, indels) Pass: Variant passes all 3 filters There are two additional filters available, one for the minimum minor allele frequency, and a configurable filter on the QUAL score. UCSC Methods The gnomAD v3.1.1 and v4.1 data is unfiltered. For the v3.1 update only, in order to cut down on the amount of displayed data, the following variant types have been filtered out, but are still viewable in the gnomAD browser: Regulatory Region Variants Downstream/Upstream Gene Variants Transcription Factor Binding Site Variants For the full steps used to create the gnomAD tracks at UCSC, please see the hg38 gnomad makedoc. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API, and the genome annotations are stored in files that can be downloaded from our download server, subject to the conditions set forth by the gnomAD consortium (see below). Variant VCFs can be found in the vcf subdirectory. The v3.1, v3.1.1, and v4.1 variants can be found in a special directory as they have been transformed from the underlying VCF. For the v3.1.1 and v4.1 variants in particular, the underlying bigBed only contains enough information necessary to use the track in the browser. The extra data like VEP annotations and CADD scores are available in the same directory as the bigBed but in the files details.tab.gz and details.tab.gz.gzi. The details.tab.gz contains the gzip compressed extra data in JSON format, and the .gzi file is available to speed searching of this data. Each variant has an associated md5sum in the name field of the bigBed which can be used along with the _dataOffset and _dataLen fields to get the associated external data, as show below: # find item of interest: bigBedToBed genomes.bb stdout | head -4 | tail -1 chr1 12416 12417 854246d79dc5d02dcdbd5f5438542b6e [..omitted for brevity..] chr1-12417-G-A 67293 902 # use the final two fields, _dataOffset and _dataLen (add one to _dataLen to include a newline), to get the extra data: bgzip -b 67293 -s 903 gnomad.v3.1.1.details.tab.gz 854246d79dc5d02dcdbd5f5438542b6e {"DDX11L1": {"cons": ["non_coding_transcript_variant", [..omitted for brevity..] The data can also be found directly from the gnomAD downloads page. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. The mutational constraints score was updated in October 2022 from a previous, now deprecated, pre-publication version. The old version can be found in our archive directory on the download server. It can be loaded by copying the URL into our "Custom tracks" input box. Credits Thanks to the Genome Aggregation Database Consortium for making these data available. The data are released under the Creative Commons Zero Public Domain Dedication as described here. Please note that some annotations within the provided files may have restrictions on usage. See here for more information. References Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alfoldi J, Wang Q, Collins RL, Laricchia KM, Ganna A, Birnbaum DP et al. Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes. doi: https://doi.org/10.1101/531210. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill AJ, Cummings BB et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016 Aug 17;536(7616):285-91. PMID: 27535533; PMC: PMC5018207 Chen S, Francioli LC, Goodrich JK, Collins RL, Kanai M, Wang Q, Alf&#246;ldi J, Watts NA, Vittal C, Gauthier LD et al. A genomic mutational constraint map using variation in 76,156 human genomes. Nature. 2024 Jan;625(7993):92-100. PMID: 38057664 (We added the data in 2021, then later referenced the 2022 Biorxiv preprint, in which the track was not called "Gnocchi" yet) 
gnomadGenomesVariantsV3 gnomAD v3 Genome Aggregation Database (gnomAD) Genome Variants v3 Variation 
knownGeneV44 GENCODE V44 GENCODE V44 Genes and Gene Predictions Description The GENCODE Genes track (version 44, July 2023) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. By default, only the basic gene set is displayed, which is a subset of the comprehensive gene set. The basic set represents transcripts that GENCODE believes will be useful to the majority of users. The track includes protein-coding genes, non-coding RNA genes, and pseudo-genes, though pseudo-genes are not displayed by default. It contains annotations on the reference chromosomes as well as assembly patches and alternative loci (haplotypes). The following table provides statistics for the v44 release derived from the GTF file that contains annotations only on the main chromosomes. More information on how they were generated can be found in the GENCODE site. GENCODE v44 Release Stats GenesObservedTranscriptsObserved Protein-coding genes19,396Protein-coding transcripts89,067 Long non-coding RNA genes19,922- full length protein-coding63,968 Small non-coding RNA genes7,566- partial length protein-coding25,099 Pseudogenes14,735Nonsense mediated decay transcripts21,384 Immunoglobulin/T-cell receptor gene segments647Long non-coding RNA loci transcripts58,246 Total No of distinct translations65,342Genes that have more than one distinct translations13,594 For more information on the different gene tracks, see our Genes FAQ. Display Conventions and Configuration By default, this track displays only the basic GENCODE set, splice variants, and non-coding genes. It includes options to display the entire GENCODE set and pseudogenes. To customize these options, the respective boxes can be checked or unchecked at the top of this description page. This track also includes a variety of labels which identify the transcripts when visibility is set to &quot;full&quot; or &quot;pack&quot;. Gene symbols (e.g. NIPA1) are displayed by default, but additional options include GENCODE Transcript ID (ENST00000561183.5), UCSC Known Gene ID (uc001yve.4), UniProt Display ID (Q7RTP0). Additional information about gene and transcript names can be found in our FAQ. This track, in general, follows the display conventions for gene prediction tracks. The exons for putative non-coding genes and untranslated regions are represented by relatively thin blocks, while those for coding open reading frames are thicker. Coloring for the gene annotations is based on the annotation type: coding: protein coding transcripts, including polymorphic pseudogenes non-coding: non-protein coding transcripts pseudogene: pseudogene transcript annotations problem: problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) This track contains an optional codon coloring feature that allows users to quickly validate and compare gene predictions. There is also an option to display the data as a density graph, which can be helpful for visualizing the distribution of items over a region. Squishy-pack Display Within a gene using the pack display mode, transcripts below a specified rank will be condensed into a view similar to squish mode. The transcript ranking approach is preliminary and will change in future releases. The transcripts rankings are defined by the following criteria for protein-coding and non-coding genes: Protein_coding genes MANE or Ensembl canonical 1st: MANE Select / Ensembl canonical 2nd: MANE Plus Clinical Coding biotypes 1st: protein_coding and protein_coding_LoF 2nd: NMDs and NSDs 3rd: retained intron and protein_coding_CDS_not_defined Completeness 1st: full length 2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype 1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Methods The GENCODE v44 track was built from the GENCODE downloads file gencode.v44.chr_patch_hapl_scaff.annotation.gff3.gz. Data from other sources were correlated with the GENCODE data to build association tables. Related Data The GENCODE Genes transcripts are annotated in numerous tables, each of which is also available as a downloadable file. One can see a full list of the associated tables in the Table Browser by selecting GENCODE Genes from the track menu; this list is then available on the table menu. Data access GENCODE Genes and its associated tables can be explored interactively using the REST API, the Table Browser or the Data Integrator. The genePred format files for hg38 are available from our downloads directory or in our GTF download directory. All the tables can also be queried directly from our public MySQL servers, with more information available on our help page as well as on our blog. Credits The GENCODE Genes track was produced at UCSC from the GENCODE comprehensive gene set using a computational pipeline developed by Jim Kent and Brian Raney. This version of the track was generated by Jonathan Casper. References Frankish A, Carbonell-Sala S, Diekhans M, Jungreis I, Loveland JE, Mudge JM, Sisu C, Wright JC, Arnan C, Barnes I et al. GENCODE: reference annotation for the human and mouse genomes in 2023. Nucleic Acids Res. 2023 Jan 6;51(D1):D942-D949. PMID: 36420896; PMC: PMC9825462 A full list of GENCODE publications is available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
gnomadVariantsV2 gnomAD v2 Genome Aggregation Database (gnomAD) Genome and Exome Variants v2.1 Variation Description With the gnomAD v4.1 data release, the v4 Pre-Release track has been replaced with the gnomAD v4.1 track. The v4.1 release includes a fix for the allele number issue. The v4.1 track shows variants from 807,162 individuals, including 730,947 exomes and 76,215 genomes. This includes the 76,156 genomes from the gnomAD v3.1.2 release as well as new exome data from 416,555 UK Biobank individuals. For more detailed information on gnomAD v4.1, see the related blog post. The gnomAD v3.1 track shows variants from 76,156 whole genomes (and no exomes), all mapped to the GRCh38/hg38 reference sequence. 4,454 genomes were added to the number of genomes in the previous v3 release. For more detailed information on gnomAD v3.1, see the related blog post. The gnomAD v3.1.1 track contains the same underlying data as v3.1, but with minor corrections to the VEP annotations and dbSNP rsIDs. On the UCSC side, we have now included the mitochondrial chromosome data that was released as part of gnomAD v3.1 (but after the UCSC version of the track was released). For more information about gnomAD v3.1.1, please see the related changelog. GnomAD Genome Mutational Constraint is based on v3.1.2 and is available only on hg38. It shows the reduced variation caused by purifying natural selection. This is similar to negative selection on loss-of-function (LoF) for genes, but can be calculated for non-coding regions too. Positive values are red and reflect stronger mutation constraint (and less variation), indicating higher natural selection pressure in a region. Negative values are green and reflect lower mutation constraint (and more variation), indicating less selection pressure and less functional effect. Briefly, for any 1kbp window in the genome, a model based on trinucleotide sequence context, base-level methylation, and regional genomic features predicts expected number of mutations, and compares this number to the observed number of mutations using a Z-score (see preprint in the Reference section for details). The chrX scores were added as received from the authors, as there are no de novo mutation data available on chrX (for estimating the effects of regional genomic features on mutation rates), they are more speculative than the ones on the autosomes. The gnomAD Predicted Constraint Metrics track contains metrics of pathogenicity per-gene as predicted for gnomAD v2.1.1 and identifies genes subject to strong selection against various classes of mutation. This includes data on both the gene and transcript level. The gnomAD v2 tracks show variants from 125,748 exomes and 15,708 whole genomes, all mapped to the GRCh37/hg19 reference sequence and lifted to the GRCh38/hg38 assembly. The data originate from 141,456 unrelated individuals sequenced as part of various population-genetic and disease-specific studies collected by the Genome Aggregation Database (gnomAD), release 2.1.1. Raw data from all studies have been reprocessed through a unified pipeline and jointly variant-called to increase consistency across projects. For more information on the processing pipeline and population annotations, see the following blog post and the 2.1.1 README. gnomAD v2 data are based on the GRCh37/hg19 assembly. These tracks display the GRCh38/hg38 lift-over provided by gnomAD on their downloads site. On hg38 only, a subtrack "Gnomad mutational constraint" aka "Genome non-coding constraint of haploinsufficient variation (Gnocchi)" captures the depletion of variation caused by purifying natural selection. This is similar to negative selection on loss-of-function (LoF) for genes, but can be calculated for non-coding regions, too. Briefly, for any 1kbp window in the genome, a model based on trinucleotide sequence context, base-level methylation, and regional genomic features predicts expected number of mutations, and compares this number to the observed number of mutations using a Z-score (see Chen et al 2024 in the Reference section for details). The chrX scores were added as received from the authors, as there are no mutations available for chrX, they are more speculative than the ones on the autosomes. For questions on the gnomAD data, also see the gnomAD FAQ. More details on the Variant type(s) can be found on the Sequence Ontology page. Display Conventions and Configuration gnomAD v4.1 The gnomAD v4.1 track version follows the same conventions and configuration as the v3.1.1 track, except for mouse hovering items. Mouse hover on an item will display the following details about each variant: Position Total Allele Frequency (TotalAF) Genes Annotation FILTER tags from VCF (FILTER) Population with maximum AF (PopMaxAF) Homozygous Individuals Homozygous Individuals in XX samples (chrX and chrY only) Hemizygous Individuals (chrX and chrY only) gnomAD v3.1.1 The gnomAD v3.1.1 track version follows the same conventions and configuration as the v3.1 track, except as noted below. There are additional FILTER field filters: AS_VQSR, indel_stack (chrM only), and npg (chrM only). Where possible, variants overlapping multiple transcripts/genes have been collapsed into one variant, with additional information available on the details page, which has roughly halved the number of items in the bigBed. The bigBed has been split into two files, one with the information necessary for the track display, and one with the information necessary for the details page. For more information on this data format, please see the Data Access section below. The VEP annotation is shown as a table instead of spread across multiple fields. Intergenic variants have not been pre-filtered. gnomAD v3.1 By default, a maximum of 50,000 variants can be displayed at a time (before applying the filters described below), before the track switches to dense display mode. Mouse hover on an item will display many details about each variant, including the affected gene(s), the variant type, and annotation (missense, synonymous, etc). Clicking on an item will display additional details on the variant, including a population frequency table showing allele count in each sub-population. Following the conventions on the gnomAD browser, items are shaded according to their Annotation type: pLoF Missense Synonymous Other Label Options To maintain consistency with the gnomAD website, variants are by default labeled according to their chromosomal start position followed by the reference and alternate alleles, for example &quot;chr1-1234-T-CAG&quot;. dbSNP rsID's are also available as an additional label, if the variant is present in dbSnp. Filtering Options Three filters are available for these tracks: FILTER: Used to exclude/include variants that failed Random Forest (RF), Inbreeding Coefficient (Inbreeding Coeff), or Allele Count (AC0) filters. The PASS option is used to include/exclude variants that pass all of the RF, InbreedingCoeff, and AC0 filters, as denoted in the original VCF. Annotation type: Used to exclude/include variants that are annotated as Probability Loss of Function (pLoF), Missense, Synonymous, or Other, as annotated by VEP version 85 (GENCODE v19). Variant Type: Used to exclude/include variants according to the type of variation, as annotated by VEP v85. There is one additional configurable filter on the minimum minor allele frequency. gnomAD v2.1.1 The gnomAD v2.1.1 track follows the standard display and configuration options available for VCF tracks, briefly explained below. In dense mode, a vertical line is drawn at the position of each variant. In pack mode, &quot;ref&quot; and &quot;alt&quot; alleles are displayed to the left of a vertical line with colored portions corresponding to allele counts. Hovering the mouse pointer over a variant pops up a display of alleles and counts. Filtering Options Four filters are available for these tracks, the same as the underlying VCF: AC0: Allele Count 0 after filtering out low confidence genotypes (GQ &lt; 20; DP &lt; 10; and AB &lt; 0.2 for het calls)) InbreedingCoeff: Inbreeding Coefficient &lt; -0.3 RF: Used to exclude/include variants that failed Random Forest filtering thresholds of 0.055272738028512555, 0.20641025579497013 (probabilities of being a true positive variant) for SNPs, indels) Pass: Variant passes all 3 filters There are two additional filters available, one for the minimum minor allele frequency, and a configurable filter on the QUAL score. UCSC Methods The gnomAD v3.1.1 and v4.1 data is unfiltered. For the v3.1 update only, in order to cut down on the amount of displayed data, the following variant types have been filtered out, but are still viewable in the gnomAD browser: Regulatory Region Variants Downstream/Upstream Gene Variants Transcription Factor Binding Site Variants For the full steps used to create the gnomAD tracks at UCSC, please see the hg38 gnomad makedoc. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API, and the genome annotations are stored in files that can be downloaded from our download server, subject to the conditions set forth by the gnomAD consortium (see below). Variant VCFs can be found in the vcf subdirectory. The v3.1, v3.1.1, and v4.1 variants can be found in a special directory as they have been transformed from the underlying VCF. For the v3.1.1 and v4.1 variants in particular, the underlying bigBed only contains enough information necessary to use the track in the browser. The extra data like VEP annotations and CADD scores are available in the same directory as the bigBed but in the files details.tab.gz and details.tab.gz.gzi. The details.tab.gz contains the gzip compressed extra data in JSON format, and the .gzi file is available to speed searching of this data. Each variant has an associated md5sum in the name field of the bigBed which can be used along with the _dataOffset and _dataLen fields to get the associated external data, as show below: # find item of interest: bigBedToBed genomes.bb stdout | head -4 | tail -1 chr1 12416 12417 854246d79dc5d02dcdbd5f5438542b6e [..omitted for brevity..] chr1-12417-G-A 67293 902 # use the final two fields, _dataOffset and _dataLen (add one to _dataLen to include a newline), to get the extra data: bgzip -b 67293 -s 903 gnomad.v3.1.1.details.tab.gz 854246d79dc5d02dcdbd5f5438542b6e {"DDX11L1": {"cons": ["non_coding_transcript_variant", [..omitted for brevity..] The data can also be found directly from the gnomAD downloads page. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. The mutational constraints score was updated in October 2022 from a previous, now deprecated, pre-publication version. The old version can be found in our archive directory on the download server. It can be loaded by copying the URL into our "Custom tracks" input box. Credits Thanks to the Genome Aggregation Database Consortium for making these data available. The data are released under the Creative Commons Zero Public Domain Dedication as described here. Please note that some annotations within the provided files may have restrictions on usage. See here for more information. References Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alfoldi J, Wang Q, Collins RL, Laricchia KM, Ganna A, Birnbaum DP et al. Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes. doi: https://doi.org/10.1101/531210. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill AJ, Cummings BB et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016 Aug 17;536(7616):285-91. PMID: 27535533; PMC: PMC5018207 Chen S, Francioli LC, Goodrich JK, Collins RL, Kanai M, Wang Q, Alf&#246;ldi J, Watts NA, Vittal C, Gauthier LD et al. A genomic mutational constraint map using variation in 76,156 human genomes. Nature. 2024 Jan;625(7993):92-100. PMID: 38057664 (We added the data in 2021, then later referenced the 2022 Biorxiv preprint, in which the track was not called "Gnocchi" yet) 
gnomadExomesVariantsV2 gnomAD Exome v2 Genome Aggregation Database (gnomAD) Exome Variants v2.1 Variation 
gnomadGenomesVariantsV2 gnomAD Genome v2 Genome Aggregation Database (gnomAD) Genome Variants v2.1 Variation 
recombEvents Recomb. deCODE Evts Recombination events in deCODE Genetic Map (zoom to < 10kbp to see the events) Mapping and Sequencing Description The recombination rate track represents calculated rates of recombination based on the genetic maps from deCODE (Halldorsson et al., 2019) and 1000 Genomes (2013 Phase 3 release, lifted from hg19). The deCODE map is more recent, has a higher resolution and was natively created on hg38 and therefore recommended. For the Recomb. deCODE average track, the recombination rates for chrX represent the female rate. This track also includes a subtrack with all the individual deCODE recombination events and another subtrack with several thousand de-novo mutations found in the deCODE sequencing data. These two tracks are hidden by default and have to be switched on explicitly on the configuration page. Display Conventions and Configuration This is a super track that contains different subtracks, three with the deCODE recombination rates (paternal, maternal and average) and one with the 1000 Genomes recombination rate (average). These tracks are in signal graph (wiggle) format. By default, to show most recombination hotspots, their maximum value is set to 100 cM, even though many regions have values higher than 100. The maximum value can be changed on the configuration pages of the tracks. There are two more tracks that show additional details provided by deCODE: one subtrack with the raw data of all cross-overs tagged with their proband ID and another one with around 8000 human de-novo mutation variants that are linked to cross-over changes. Methods The deCODE genetic map was created at deCODE Genetics. It is based on microarrays assaying 626,828 SNP markers that allowed to identify 1,476,140 crossovers in 56,321 paternal meioses and 3,055,395 crossovers in 70,086 maternal meioses. In total, the data is based on 4,531,535 crossovers in 126,427 meioses. By using WGS data with 9,305,070 SNPs, the boundaries for 761,981 crossovers were refined: 247,942 crossovers in 9423 paternal meioses and 514,039 crossovers in 11,750 maternal meioses. The average resolution of the genetic map is 682 base pairs (bp): 655 and 708 bp for the paternal and maternal maps, respectively. The 1000 Genomes genetic map is based on the IMPUTE genetic map based on 1000 Genomes Phase 3, on hg19 coordinates. It was converted to hg38 by Po-Ru Loh at the Broad Institute. After a run of liftOver, he post-processed the data to deal with situations in which consecutive map locations became much closer/farther after lifting. The heuristic used is sufficient for statistical phasing but may not be optimal for other analyses. For this reason, and because of its higher resolution, the DeCODE map is therefore recommended for hg38. As with all other tracks, the data conversion commands and pointers to the original data files are documented in the makeDoc file of this track. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated access, this track, like all others, is available via our API. However, for bulk processing, it is recommended to download the dataset. For automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed files that can be downloaded from our download server. Individual regions or the whole genome annotation can be obtained using our tools bigWigToWig or bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tools can also be used to obtain features confined to a given range, e.g., bigWigToBedGraph -chrom=chr17 -start=45941345 -end=45942345 http://hgdownload.soe.ucsc.edu/gbdb/hg38/recombRate/recombAvg.bw stdout Please refer to our Data Access FAQ for more information. Credits This track was produced at UCSC using data that are freely available for the deCODE and 1000 Genomes genetic maps. Thanks to Po-Ru Loh at the Broad Institute for providing the code to lift the hg19 1000 Genomes map data to hg38. References 1000 Genomes Project Consortium., Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, Hurles ME, McVean GA. A map of human genome variation from population-scale sequencing. Nature. 2010 Oct 28;467(7319):1061-73. PMID: 20981092; PMC: PMC3042601 Halldorsson BV, Palsson G, Stefansson OA, Jonsson H, Hardarson MT, Eggertsson HP, Gunnarsson B, Oddsson A, Halldorsson GH, Zink F et al. Characterizing mutagenic effects of recombination through a sequence-level genetic map. Science. 2019 Jan 25;363(6425). PMID: 30679340 
joinedRmsk RepeatMasker Viz. Detailed Visualization of RepeatMasker Annotations Repeats Description This track was created using Arian Smit's RepeatMasker program, which screens DNA sequences for interspersed repeats and low complexity DNA sequences. The program outputs a detailed annotation of the repeats that are present in the query sequence (represented by this track), as well as a modified version of the query sequence in which all the annotated repeats have been masked (generally available on the Downloads page). RepeatMasker uses a separately curated version of the Repbase Update repeat library from the Genetic Information Research Institute (GIRI). Repbase Update is described in Jurka (2000) in the References section below. Alternatively, RepeatMasker can use the new Dfam database of repeat profile HMMs. Profile HMMs provide a richer description of the repeat families and when used with RepeatMasker + nhmmer provide a more sensitive approach to identifying repeats. Dfam is described in Wheeler et al. (2012) in the References section below. Display Conventions and Configuration In dense display mode, a single line is displayed denoting the coverage of repeats using a series of black boxes. In full display mode, the track view is controlled by the scale of the view. At scales between 10 Mb and 30 kb, this track displays up to ten different classes of repeats (see below) one class per line. The repeat ranges are denoted as grayscale boxes, reflecting both the size of the repeat and the amount of base mismatch, base deletion, and base insertion associated with a repeat element. The higher the combined number of these, the lighter the shading. In full display mode and at scales less than 30 kb, a new detailed display mode is used. Repeats are displayed as arrow boxes, indicating the size and orientation of the repeat. The interior grayscale shading represents the divergence of the repeat (see above) while the outline color represents the class of the repeat. Dotted lines above the repeat and extending left or right indicate the length of unaligned repeat consensus sequence. If the length of the unaligned sequence is large, a double interruption line is used to indicate that the unaligned sequence is not to scale. For example, the following repeat is a SINE element in the forward orientation with average divergence. Only the 5' proximal fragment of the consensus sequence is aligned to the genome. The 3' unaligned length (384bp) is not drawn to scale and is instead displayed using a set of interruption lines along with the length of the unaligned sequence. Layer 1 384 Repeats that have been fragmented by insertions or large internal deletions are now represented by join lines. In the example below, a LINE element is found as two fragments. The solid connection lines indicate that there are no unaligned consensus bases between the two fragments. Also note these fragments represent the end of the repeat, as there is no unaligned consensus sequence following the last fragment. Layer 1 In cases where there is unaligned consensus sequence between the fragments, the repeat will look like the following. The dotted line indicates the length of the unaligned sequence between the two fragments. In this case the unaligned consensus is longer than the actual genomic distance between these two fragments. Layer 1 If there is consensus overlap between the two fragments, the joining lines will be drawn to indicate how much of the left fragment is repeated in the right fragment. Layer 1 The following table lists the repeat class colors: Color Repeat Class SINE - Short Interspersed Nuclear Element LINE - Long Interspersed Nuclear Element LTR - Long Terminal Repeat DNA - DNA Transposon Simple - Single Nucleotide Stretches and Tandem Repeats Low_complexity - Low Complexity DNA Satellite - Satellite Repeats RNA - RNA Repeats (including RNA, tRNA, rRNA, snRNA, scRNA, srpRNA) Other - Other Repeats (including class RC - Rolling Circle) Unknown - Unknown Classification A &quot;?&quot; at the end of the &quot;Family&quot; or &quot;Class&quot; (for example, DNA?) signifies that the curator was unsure of the classification. At some point in the future, either the &quot;?&quot; will be removed or the classification will be changed. Methods UCSC has used the most current versions of the RepeatMasker software and repeat libraries available to generate these data. Note that these versions may be newer than those that are publicly available on the Internet. Data are generated using the RepeatMasker -s flag. Additional flags may be used for certain organisms. Repeats are soft-masked. Alignments may extend through repeats, but are not permitted to initiate in them. See the FAQ for more information. Credits Thanks to Arian Smit, Robert Hubley and GIRI for providing the tools and repeat libraries used to generate this track. References Smit AFA, Hubley R, Green P. RepeatMasker Open-3.0. http://www.repeatmasker.org. 1996-2010. Dfam is described in: Wheeler TJ, Clements J, Eddy SR, Hubley R, Jones TA, Jurka J, Smit AF, Finn RD. Dfam: a database of repetitive DNA based on profile hidden Markov models. Nucleic Acids Res. 2013 Jan;41(Database issue):D70-82. PMID: 23203985; PMC: PMC3531169 Repbase Update is described in: Jurka J. Repbase Update: a database and an electronic journal of repetitive elements. Trends Genet. 2000 Sep;16(9):418-420. PMID: 10973072 For a discussion of repeats in mammalian genomes, see: Smit AF. Interspersed repeats and other mementos of transposable elements in mammalian genomes. Curr Opin Genet Dev. 1999 Dec;9(6):657-63. PMID: 10607616 Smit AF. The origin of interspersed repeats in the human genome. Curr Opin Genet Dev. 1996 Dec;6(6):743-8. PMID: 8994846 
rmskJoinedBaseline RepeatMasker Viz. RepeatMasker v3.0.1 db20100302 : Browser Baseline Dataset Repeats 
rmskJoinedCurrent RepeatMasker Viz. RepeatMasker v4.0.7 Dfam_2.0 : Current Dataset Repeats 
knownGeneV43 GENCODE V43 GENCODE V43 Genes and Gene Predictions Description The GENCODE Genes track (version 43, February 2023) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. By default, only the basic gene set is displayed, which is a subset of the comprehensive gene set. The basic set represents transcripts that GENCODE believes will be useful to the majority of users. The track includes protein-coding genes, non-coding RNA genes, and pseudo-genes, though pseudo-genes are not displayed by default. It contains annotations on the reference chromosomes as well as assembly patches and alternative loci (haplotypes). The following table provides statistics for the v43 release derived from the GTF file that contains annotations only on the main chromosomes. More information on how they were generated can be found in the GENCODE site. GENCODE v43 Release Stats GenesObservedTranscriptsObserved Protein-coding genes19,393Protein-coding transcripts89,411 Long non-coding RNA genes19,928- full length protein-coding64,004 Small non-coding RNA genes7,566- partial length protein-coding25,407 Pseudogenes14,737Nonsense mediated decay transcripts21,354 Immunoglobulin/T-cell receptor gene segments410Long non-coding RNA loci transcripts58,023 Total No of distinct translations65,519Genes that have more than one distinct translations13,618 For more information on the different gene tracks, see our Genes FAQ. Display Conventions and Configuration By default, this track displays only the basic GENCODE set, splice variants, and non-coding genes. It includes options to display the entire GENCODE set and pseudogenes. To customize these options, the respective boxes can be checked or unchecked at the top of this description page. This track also includes a variety of labels which identify the transcripts when visibility is set to &quot;full&quot; or &quot;pack&quot;. Gene symbols (e.g. NIPA1) are displayed by default, but additional options include GENCODE Transcript ID (ENST00000561183.5), UCSC Known Gene ID (uc001yve.4), UniProt Display ID (Q7RTP0). Additional information about gene and transcript names can be found in our FAQ. This track, in general, follows the display conventions for gene prediction tracks. The exons for putative non-coding genes and untranslated regions are represented by relatively thin blocks, while those for coding open reading frames are thicker. Coloring for the gene annotations is based on the annotation type: coding: protein coding transcripts, including polymorphic pseudogenes non-coding: non-protein coding transcripts pseudogene: pseudogene transcript annotations problem: problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) This track contains an optional codon coloring feature that allows users to quickly validate and compare gene predictions. There is also an option to display the data as a density graph, which can be helpful for visualizing the distribution of items over a region. Squishy-pack Display Within a gene using the pack display mode, transcripts below a specified rank will be condensed into a view similar to squish mode. The transcript ranking approach is preliminary and will change in future releases. The transcripts rankings are defined by the following criteria for protein-coding and non-coding genes: Protein_coding genes MANE or Ensembl canonical 1st: MANE Select / Ensembl canonical 2nd: MANE Plus Clinical Coding biotypes 1st: protein_coding and protein_coding_LoF 2nd: NMDs and NSDs 3rd: retained intron and protein_coding_CDS_not_defined Completeness 1st: full length 2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype 1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Methods The GENCODE v43 track was built from the GENCODE downloads file gencode.v43.chr_patch_hapl_scaff.annotation.gff3.gz. Data from other sources were correlated with the GENCODE data to build association tables. Related Data The GENCODE Genes transcripts are annotated in numerous tables, each of which is also available as a downloadable file. One can see a full list of the associated tables in the Table Browser by selecting GENCODE Genes from the track menu; this list is then available on the table menu. Data access GENCODE Genes and its associated tables can be explored interactively using the REST API, the Table Browser or the Data Integrator. The genePred format files for hg38 are available from our downloads directory or in our GTF download directory. All the tables can also be queried directly from our public MySQL servers, with more information available on our help page as well as on our blog. Credits The GENCODE Genes track was produced at UCSC from the GENCODE comprehensive gene set using a computational pipeline developed by Jim Kent and Brian Raney. References Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, Kokocinski F, Aken BL, Barrell D, Zadissa A, Searle S et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 2012 Sep;22(9):1760-74. PMID: 22955987; PMC: PMC3431492 Harrow J, Denoeud F, Frankish A, Reymond A, Chen CK, Chrast J, Lagarde J, Gilbert JG, Storey R, Swarbreck D et al. GENCODE: producing a reference annotation for ENCODE. Genome Biol. 2006;7 Suppl 1:S4.1-9. PMID: 16925838; PMC: PMC1810553 A full list of GENCODE publications is available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
recombDnm Recomb. deCODE Dmn Recombination rate: De-novo mutations found in deCODE samples Mapping and Sequencing Description The recombination rate track represents calculated rates of recombination based on the genetic maps from deCODE (Halldorsson et al., 2019) and 1000 Genomes (2013 Phase 3 release, lifted from hg19). The deCODE map is more recent, has a higher resolution and was natively created on hg38 and therefore recommended. For the Recomb. deCODE average track, the recombination rates for chrX represent the female rate. This track also includes a subtrack with all the individual deCODE recombination events and another subtrack with several thousand de-novo mutations found in the deCODE sequencing data. These two tracks are hidden by default and have to be switched on explicitly on the configuration page. Display Conventions and Configuration This is a super track that contains different subtracks, three with the deCODE recombination rates (paternal, maternal and average) and one with the 1000 Genomes recombination rate (average). These tracks are in signal graph (wiggle) format. By default, to show most recombination hotspots, their maximum value is set to 100 cM, even though many regions have values higher than 100. The maximum value can be changed on the configuration pages of the tracks. There are two more tracks that show additional details provided by deCODE: one subtrack with the raw data of all cross-overs tagged with their proband ID and another one with around 8000 human de-novo mutation variants that are linked to cross-over changes. Methods The deCODE genetic map was created at deCODE Genetics. It is based on microarrays assaying 626,828 SNP markers that allowed to identify 1,476,140 crossovers in 56,321 paternal meioses and 3,055,395 crossovers in 70,086 maternal meioses. In total, the data is based on 4,531,535 crossovers in 126,427 meioses. By using WGS data with 9,305,070 SNPs, the boundaries for 761,981 crossovers were refined: 247,942 crossovers in 9423 paternal meioses and 514,039 crossovers in 11,750 maternal meioses. The average resolution of the genetic map is 682 base pairs (bp): 655 and 708 bp for the paternal and maternal maps, respectively. The 1000 Genomes genetic map is based on the IMPUTE genetic map based on 1000 Genomes Phase 3, on hg19 coordinates. It was converted to hg38 by Po-Ru Loh at the Broad Institute. After a run of liftOver, he post-processed the data to deal with situations in which consecutive map locations became much closer/farther after lifting. The heuristic used is sufficient for statistical phasing but may not be optimal for other analyses. For this reason, and because of its higher resolution, the DeCODE map is therefore recommended for hg38. As with all other tracks, the data conversion commands and pointers to the original data files are documented in the makeDoc file of this track. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated access, this track, like all others, is available via our API. However, for bulk processing, it is recommended to download the dataset. For automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed files that can be downloaded from our download server. Individual regions or the whole genome annotation can be obtained using our tools bigWigToWig or bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tools can also be used to obtain features confined to a given range, e.g., bigWigToBedGraph -chrom=chr17 -start=45941345 -end=45942345 http://hgdownload.soe.ucsc.edu/gbdb/hg38/recombRate/recombAvg.bw stdout Please refer to our Data Access FAQ for more information. Credits This track was produced at UCSC using data that are freely available for the deCODE and 1000 Genomes genetic maps. Thanks to Po-Ru Loh at the Broad Institute for providing the code to lift the hg19 1000 Genomes map data to hg38. References 1000 Genomes Project Consortium., Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, Hurles ME, McVean GA. A map of human genome variation from population-scale sequencing. Nature. 2010 Oct 28;467(7319):1061-73. PMID: 20981092; PMC: PMC3042601 Halldorsson BV, Palsson G, Stefansson OA, Jonsson H, Hardarson MT, Eggertsson HP, Gunnarsson B, Oddsson A, Halldorsson GH, Zink F et al. Characterizing mutagenic effects of recombination through a sequence-level genetic map. Science. 2019 Jan 25;363(6425). PMID: 30679340 
genomicSuperDups Segmental Dups Duplications of >1000 Bases of Non-RepeatMasked Sequence Repeats Description This track shows regions detected as putative genomic duplications within the golden path. The following display conventions are used to distinguish levels of similarity: Light to dark gray: 90 - 98% similarity Light to dark yellow: 98 - 99% similarity Light to dark orange: greater than 99% similarity Red: duplications of greater than 98% similarity that lack sufficient Segmental Duplication Database evidence (most likely missed overlaps) For a region to be included in the track, at least 1 Kb of the total sequence (containing at least 500 bp of non-RepeatMasked sequence) had to align and a sequence identity of at least 90% was required. Methods Segmental duplications play an important role in both genomic disease and gene evolution. This track displays an analysis of the global organization of these long-range segments of identity in genomic sequence. Large recent duplications (&gt;= 1 kb and &gt;= 90% identity) were detected by identifying high-copy repeats, removing these repeats from the genomic sequence (&quot;fuguization&quot;) and searching all sequence for similarity. The repeats were then reinserted into the pairwise alignments, the ends of alignments trimmed, and global alignments were generated. For a full description of the &quot;fuguization&quot; detection method, see Bailey et al., 2001. This method has become known as WGAC (whole-genome assembly comparison); for example, see Bailey et al., 2002. Credits These data were provided by Ginger Cheng, Xinwei She, Archana Raja, Tin Louie and Evan Eichler at the University of Washington. References Bailey JA, Gu Z, Clark RA, Reinert K, Samonte RV, Schwartz S, Adams MD, Myers EW, Li PW, Eichler EE. Recent segmental duplications in the human genome. Science. 2002 Aug 9;297(5583):1003-7. PMID: 12169732 Bailey JA, Yavor AM, Massa HF, Trask BJ, Eichler EE. Segmental duplications: organization and impact within the current human genome project assembly. Genome Res. 2001 Jun;11(6):1005-17. PMID: 11381028; PMC: PMC311093 
knownGeneV39 GENCODE V39 GENCODE V39 Genes and Gene Predictions Description The GENCODE Genes track (version 39, December 2021) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. By default, only the basic gene set is displayed, which is a subset of the comprehensive gene set. The basic set represents transcripts that GENCODE believes will be useful to the majority of users. The track includes protein-coding genes, non-coding RNA genes, and pseudo-genes, though pseudo-genes are not displayed by default. It contains annotations on the reference chromosomes as well as assembly patches and alternative loci (haplotypes). The following table provides statistics for the v39 release derived from the GTF file that contains annotations only on the main chromosomes. More information on how they were generated can be found in the GENCODE site. GENCODE v39 Release Stats GenesObservedTranscriptsObserved Protein-coding genes19,982Protein-coding transcripts87,151 Long non-coding RNA genes18,811- full length protein-coding61,516 Small non-coding RNA genes7,567- partial length protein-coding25,635 Pseudogenes14,763Nonsense mediated decay transcripts19,762 Immunoglobulin/T-cell receptor gene segments409Long non-coding RNA loci transcripts53,009 For more information on the different gene tracks, see our Genes FAQ. Display Conventions and Configuration By default, this track displays only the basic GENCODE set, splice variants, and non-coding genes. It includes options to display the entire GENCODE set and pseudogenes. To customize these options, the respective boxes can be checked or unchecked at the top of this description page. This track also includes a variety of labels which identify the transcripts when visibility is set to &quot;full&quot; or &quot;pack&quot;. Gene symbols (e.g. NIPA1) are displayed by default, but additional options include GENCODE Transcript ID (ENST00000561183.5), UCSC Known Gene ID (uc001yve.4), UniProt Display ID (Q7RTP0). Additional information about gene and transcript names can be found in our FAQ. This track, in general, follows the display conventions for gene prediction tracks. The exons for putative non-coding genes and untranslated regions are represented by relatively thin blocks, while those for coding open reading frames are thicker. Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem This track contains an optional codon coloring feature that allows users to quickly validate and compare gene predictions. There is also an option to display the data as a density graph, which can be helpful for visualizing the distribution of items over a region. Methods The GENCODE v39 track was built from the GENCODE downloads file gencode.v39.chr_patch_hapl_scaff.annotation.gff3.gz. Data from other sources were correlated with the GENCODE data to build association tables. Related Data The GENCODE Genes transcripts are annotated in numerous tables, each of which is also available as a downloadable file. One can see a full list of the associated tables in the Table Browser by selecting GENCODE Genes from the track menu; this list is then available on the table menu. Data access GENCODE Genes and its associated tables can be explored interactively using the REST API, the Table Browser or the Data Integrator. The genePred format files for hg38 are available from our downloads directory or in our GTF download directory. All the tables can also be queried directly from our public MySQL servers, with more information available on our help page as well as on our blog. Credits The GENCODE Genes track was produced at UCSC from the GENCODE comprehensive gene set using a computational pipeline developed by Jim Kent and Brian Raney. References Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, Kokocinski F, Aken BL, Barrell D, Zadissa A, Searle S et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 2012 Sep;22(9):1760-74. PMID: 22955987; PMC: PMC3431492 Harrow J, Denoeud F, Frankish A, Reymond A, Chen CK, Chrast J, Lagarde J, Gilbert JG, Storey R, Swarbreck D et al. GENCODE: producing a reference annotation for ENCODE. Genome Biol. 2006;7 Suppl 1:S4.1-9. PMID: 16925838; PMC: PMC1810553 A full list of GENCODE publications is available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
recomb1000GAvg Recomb. 1k Genomes Recombination rate: 1000 Genomes, lifted from hg19 (PR Loh) Mapping and Sequencing Description The recombination rate track represents calculated rates of recombination based on the genetic maps from deCODE (Halldorsson et al., 2019) and 1000 Genomes (2013 Phase 3 release, lifted from hg19). The deCODE map is more recent, has a higher resolution and was natively created on hg38 and therefore recommended. For the Recomb. deCODE average track, the recombination rates for chrX represent the female rate. This track also includes a subtrack with all the individual deCODE recombination events and another subtrack with several thousand de-novo mutations found in the deCODE sequencing data. These two tracks are hidden by default and have to be switched on explicitly on the configuration page. Display Conventions and Configuration This is a super track that contains different subtracks, three with the deCODE recombination rates (paternal, maternal and average) and one with the 1000 Genomes recombination rate (average). These tracks are in signal graph (wiggle) format. By default, to show most recombination hotspots, their maximum value is set to 100 cM, even though many regions have values higher than 100. The maximum value can be changed on the configuration pages of the tracks. There are two more tracks that show additional details provided by deCODE: one subtrack with the raw data of all cross-overs tagged with their proband ID and another one with around 8000 human de-novo mutation variants that are linked to cross-over changes. Methods The deCODE genetic map was created at deCODE Genetics. It is based on microarrays assaying 626,828 SNP markers that allowed to identify 1,476,140 crossovers in 56,321 paternal meioses and 3,055,395 crossovers in 70,086 maternal meioses. In total, the data is based on 4,531,535 crossovers in 126,427 meioses. By using WGS data with 9,305,070 SNPs, the boundaries for 761,981 crossovers were refined: 247,942 crossovers in 9423 paternal meioses and 514,039 crossovers in 11,750 maternal meioses. The average resolution of the genetic map is 682 base pairs (bp): 655 and 708 bp for the paternal and maternal maps, respectively. The 1000 Genomes genetic map is based on the IMPUTE genetic map based on 1000 Genomes Phase 3, on hg19 coordinates. It was converted to hg38 by Po-Ru Loh at the Broad Institute. After a run of liftOver, he post-processed the data to deal with situations in which consecutive map locations became much closer/farther after lifting. The heuristic used is sufficient for statistical phasing but may not be optimal for other analyses. For this reason, and because of its higher resolution, the DeCODE map is therefore recommended for hg38. As with all other tracks, the data conversion commands and pointers to the original data files are documented in the makeDoc file of this track. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated access, this track, like all others, is available via our API. However, for bulk processing, it is recommended to download the dataset. For automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed files that can be downloaded from our download server. Individual regions or the whole genome annotation can be obtained using our tools bigWigToWig or bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tools can also be used to obtain features confined to a given range, e.g., bigWigToBedGraph -chrom=chr17 -start=45941345 -end=45942345 http://hgdownload.soe.ucsc.edu/gbdb/hg38/recombRate/recombAvg.bw stdout Please refer to our Data Access FAQ for more information. Credits This track was produced at UCSC using data that are freely available for the deCODE and 1000 Genomes genetic maps. Thanks to Po-Ru Loh at the Broad Institute for providing the code to lift the hg19 1000 Genomes map data to hg38. References 1000 Genomes Project Consortium., Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, Hurles ME, McVean GA. A map of human genome variation from population-scale sequencing. Nature. 2010 Oct 28;467(7319):1061-73. PMID: 20981092; PMC: PMC3042601 Halldorsson BV, Palsson G, Stefansson OA, Jonsson H, Hardarson MT, Eggertsson HP, Gunnarsson B, Oddsson A, Halldorsson GH, Zink F et al. Characterizing mutagenic effects of recombination through a sequence-level genetic map. Science. 2019 Jan 25;363(6425). PMID: 30679340 
chainSelf Self Alignment Human Chained Self Alignments Repeats Description This track shows alignments of the human genome with itself, using a gap scoring system that allows longer gaps than traditional affine gap scoring systems. The system can also tolerate gaps in both sets of sequence simultaneously. After filtering out the &quot;trivial&quot; alignments produced when identical locations of the genome map to one another (e.g. chrN mapping to chrN), the remaining alignments point out areas of duplication within the human genome. The pseudoautosomal regions of chrX and chrY are an exception: in this assembly, these regions have been copied from chrX into chrY, resulting in a large amount of self chains aligning in these positions on both chromosomes. The chain track displays boxes joined together by either single or double lines. The boxes represent aligning regions. Single lines indicate gaps that are largely due to a deletion in the query assembly or an insertion in the target assembly. Double lines represent more complex gaps that involve substantial sequence in both the query and target assemblies. This may result from inversions, overlapping deletions, an abundance of local mutation, or an unsequenced gap in one of the assemblies. In cases where multiple chains align over a particular region of the human genome, the chains with single-lined gaps are often due to processed pseudogenes, while chains with double-lined gaps are more often due to paralogs and unprocessed pseudogenes. Chains have both a score and a normalized score. The score is derived by comparing sequence similarity, while penalizing both mismatches and gaps in a per base fashion. This leads to longer chains having greater scores, even if a smaller chain provides a better match. The normalized score divides the score by the length of the alignment, providing a more comparable score value not dependent on the match length. Display Conventions and Configuration By default, the chains are colored by the normalized score. This can be changed to color based on which chromosome they map to in the aligning organism. There is also an option to color all the chains black. To display only the chains of one chromosome in the aligning organism, enter the name of that chromosome (e.g. chr4) in box next to: Filter by chromosome. By default, chains with a score of 20,000 or more are displayed. This default value provides a conservative cutoff, filtering out many false-positive alignments with low sequence similarity, or high penalties. It should be noted however, that alignments below this threshold may still be indicative of homology. In the "pack" and "full" display modes, the individual feature names indicate the chromosome, strand, and location (in thousands) of the match for each matching alignment. Methods The genome was aligned to itself using blastz. Trivial alignments were filtered out, and the remaining alignments were converted into axt format using the lavToAxt program. The axt alignments were fed into axtChain, which organizes all alignments between a single target chromosome and a single query chromosome into a group and creates a kd-tree out of the gapless subsections (blocks) of the alignments. A dynamic program was then run over the kd-trees to find the maximally scoring chains of these blocks. Chains scoring below a threshold were discarded; the remaining chains are displayed in this track. Credits Blastz was developed at Pennsylvania State University by Minmei Hou, Scott Schwartz, Zheng Zhang, and Webb Miller with advice from Ross Hardison. Lineage-specific repeats were identified by Arian Smit and his RepeatMasker program. The axtChain program was developed at the University of California at Santa Cruz by Jim Kent with advice from Webb Miller and David Haussler. The browser display and database storage of the chains were generated by Robert Baertsch and Jim Kent. References Chiaromonte F, Yap VB, Miller W. Scoring pairwise genomic sequence alignments. Pac Symp Biocomput 2002, 115-26 (2002). Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. 
knownGeneV38 GENCODE V38 GENCODE V38 Genes and Gene Predictions Description The GENCODE Genes track (version 38, May 2021) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. By default, only the basic gene set is displayed, which is a subset of the comprehensive gene set. The basic set represents transcripts that GENCODE believes will be useful to the majority of users. The track includes protein-coding genes, non-coding RNA genes, and pseudo-genes, though pseudo-genes are not displayed by default. It contains annotations on the reference chromosomes as well as assembly patches and alternative loci (haplotypes). The following table provides statistics for the v38 release derived from the GTF file that contains annotations only on the main chromosomes. More information on how they were generated can be found in the GENCODE site. GENCODE v38 Release Stats GenesObservedTranscriptsObserved Protein-coding genes19,955Protein-coding transcripts86,757 Long non-coding RNA genes17,944- full length protein-coding61,015 Small non-coding RNA genes7,567- partial length protein-coding25,742 Pseudogenes14,773Nonsense mediated decay transcripts18,881 Immunoglobulin/T-cell receptor gene segments409Long non-coding RNA loci transcripts48,752 For more information on the different gene tracks, see our Genes FAQ. Display Conventions and Configuration By default, this track displays only the basic GENCODE set, splice variants, and non-coding genes. It includes options to display the entire GENCODE set and pseudogenes. To customize these options, the respective boxes can be checked or unchecked at the top of this description page. This track also includes a variety of labels which identify the transcripts when visibility is set to &quot;full&quot; or &quot;pack&quot;. Gene symbols (e.g. NIPA1) are displayed by default, but additional options include GENCODE Transcript ID (ENST00000561183.5), UCSC Known Gene ID (uc001yve.4), UniProt Display ID (Q7RTP0). Additional information about gene and transcript names can be found in our FAQ. This track, in general, follows the display conventions for gene prediction tracks. The exons for putative non-coding genes and untranslated regions are represented by relatively thin blocks, while those for coding open reading frames are thicker. Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem This track contains an optional codon coloring feature that allows users to quickly validate and compare gene predictions. There is also an option to display the data as a density graph, which can be helpful for visualizing the distribution of items over a region. Methods The GENCODE v38 track was built from the GENCODE downloads file gencode.v38.chr_patch_hapl_scaff.annotation.gff3.gz. Data from other sources were correlated with the GENCODE data to build association tables. Related Data The GENCODE Genes transcripts are annotated in numerous tables, each of which is also available as a downloadable file. One can see a full list of the associated tables in the Table Browser by selecting GENCODE Genes from the track menu; this list is then available on the table menu. Data access GENCODE Genes and its associated tables can be explored interactively using the REST API, the Table Browser or the Data Integrator. The genePred format files for hg38 are available from our downloads directory or in our GTF download directory. All the tables can also be queried directly from our public MySQL servers, with more information available on our help page as well as on our blog. Credits The GENCODE Genes track was produced at UCSC from the GENCODE comprehensive gene set using a computational pipeline developed by Jim Kent and Brian Raney. References Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, Kokocinski F, Aken BL, Barrell D, Zadissa A, Searle S et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 2012 Sep;22(9):1760-74. PMID: 22955987; PMC: PMC3431492 Harrow J, Denoeud F, Frankish A, Reymond A, Chen CK, Chrast J, Lagarde J, Gilbert JG, Storey R, Swarbreck D et al. GENCODE: producing a reference annotation for ENCODE. Genome Biol. 2006;7 Suppl 1:S4.1-9. PMID: 16925838; PMC: PMC1810553 A full list of GENCODE publications is available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
primateChainNet Primate Chain/Net Primate Genomes, Chain and Net Alignments Comparative Genomics Description Chain Track The chain track shows alignments of human (Dec. 2013 (GRCh38/hg38)) to other genomes using a gap scoring system that allows longer gaps than traditional affine gap scoring systems. It can also tolerate gaps in both human and the other genome simultaneously. These &quot;double-sided&quot; gaps can be caused by local inversions and overlapping deletions in both species. The chain track displays boxes joined together by either single or double lines. The boxes represent aligning regions. Single lines indicate gaps that are largely due to a deletion in the other assembly or an insertion in the human assembly. Double lines represent more complex gaps that involve substantial sequence in both species. This may result from inversions, overlapping deletions, an abundance of local mutation, or an unsequenced gap in one species. In cases where multiple chains align over a particular region of the other genome, the chains with single-lined gaps are often due to processed pseudogenes, while chains with double-lined gaps are more often due to paralogs and unprocessed pseudogenes. In the &quot;pack&quot; and &quot;full&quot; display modes, the individual feature names indicate the chromosome, strand, and location (in thousands) of the match for each matching alignment. Net Track The net track shows only the alignments from the highest-scoring chain for each region of the human genome assembly. It is useful for finding orthologous regions and for studying genome rearrangement. The human sequence used in this annotation is from the Dec. 2013 (GRCh38/hg38) assembly. Display Conventions and Configuration Chain Track By default, the chains to chromosome-based assemblies are colored based on which chromosome they map to in the aligning organism. To turn off the coloring, check the &quot;off&quot; button next to: Color track based on chromosome. To display only the chains of one chromosome in the aligning organism, enter the name of that chromosome (e.g. chr4) in box next to: Filter by chromosome. Net Track In full display mode, the top-level (level 1) chains are the largest, highest-scoring chains that span this region. In many cases gaps exist in the top-level chain. When possible, these are filled in by other chains that are displayed at level 2. The gaps in level 2 chains may be filled by level 3 chains and so forth. In the graphical display, the boxes represent ungapped alignments; the lines represent gaps. Click on a box to view detailed information about the chain as a whole; click on a line to display information about the gap. The detailed information is useful in determining the cause of the gap or, for lower level chains, the genomic rearrangement. Individual items in the display are categorized as one of four types (other than gap): Top - the best, longest match. Displayed on level 1. Syn - line-ups on the same chromosome as the gap in the level above it. Inv - a line-up on the same chromosome as the gap above it, but in the opposite orientation. NonSyn - a match to a chromosome different from the gap in the level above. Methods Chain track The assemblies were examined for any transposons that had been inserted since the divergence of the two species. Any such transposons were removed before running the alignment. The abbreviated genomes were aligned with lastz, and the removed transposons were then added back in. The resulting alignments were converted into axt format using the lavToAxt program. The axt alignments were fed into axtChain, which organizes all alignments between a single human chromosome and a single chromosome from the other genome into a group and creates a kd-tree out of the gapless subsections (blocks) of the alignments. A dynamic program was then run over the kd-trees to find the maximally scoring chains of these blocks. The lastz matrices used for these alignments can be found in our download directory for the Dec. 2013 (GRCh38/hg38) assembly. See the README.txt file within the relevant vsAssembly directory for details (e.g., parameters for the alignment with tarSyr2 can be found in the vsTarSyr2/ subdirectory). For the alignments to Chimp and Rhesus, chains scoring below a minimum score of '5000' were discarded; the remaining chains are displayed in this track. The linear gap matrix used with axtChain: -linearGap=loose tablesize 11 smallSize 111 position 1 2 3 11 111 2111 12111 32111 72111 152111 252111 qGap 325 360 400 450 600 1100 3600 7600 15600 31600 56600 tGap 325 360 400 450 600 1100 3600 7600 15600 31600 56600 bothGap 625 660 700 750 900 1400 4000 8000 16000 32000 57000 For the alignments to Tarsier and Bonobo, chains scoring below a minimum score of '3000' were discarded; the remaining chains are displayed in this track. The same linear gap matrix shown above was used with axtChain. Chains for low-coverage assemblies for which no browser has been built are not available as browser tracks, but only from our downloads page. See also: lastz parameters and other details (e.g., update time) and chain minimum score and gap parameters used in these alignments. Net track Chains were derived from lastz alignments, using the methods described on the chain tracks description pages, and sorted with the highest-scoring chains in the genome ranked first. The program chainNet was then used to place the chains one at a time, trimming them as necessary to fit into sections not already covered by a higher-scoring chain. During this process, a natural hierarchy emerged in which a chain that filled a gap in a higher-scoring chain was placed underneath that chain. The program netSyntenic was used to fill in information about the relationship between higher- and lower-level chains, such as whether a lower-level chain was syntenic or inverted relative to the higher-level chain. The program netClass was then used to fill in how much of the gaps and chains contained Ns (sequencing gaps) in one or both species and how much was filled with transposons inserted before and after the two organisms diverged. Credits Harris, R.S. (2007) Improved pairwise alignment of genomic DNA. Ph.D. Thesis, The Pennsylvania State University. Lineage-specific repeats were identified by Arian Smit and his RepeatMasker program. The axtChain program was developed at the University of California at Santa Cruz by Jim Kent with advice from Webb Miller and David Haussler. The browser display and database storage of the chains and nets were created by Robert Baertsch and Jim Kent. The chainNet, netSyntenic, and netClass programs were developed at the University of California Santa Cruz by Jim Kent. References Chiaromonte F, Yap VB, Miller W. Scoring pairwise genomic sequence alignments. Pac Symp Biocomput. 2002:115-26. PMID: 11928468 Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 
primateChainNetViewnet Nets Primate Genomes, Chain and Net Alignments Comparative Genomics 
netOtoGar3 Bushbaby Net Bushbaby (Mar. 2011 (Broad/otoGar3)) Alignment Net Comparative Genomics 
netMicMur2 Mouse lemur Net Mouse lemur (May 2015 (Mouse lemur/micMur2)) Alignment Net Comparative Genomics 
netTarSyr2 Tarsier Net Tarsier (Sep. 2013 (Tarsius_syrichta-2.0.1/tarSyr2)) Alignment Net Comparative Genomics 
netCalJac4 Marmoset Net Marmoset (May 2020 (Callithrix_jacchus_cj1700_1.1/calJac4)) Alignment Net Comparative Genomics 
netSaiBol1 saiBol1 Net Squirrel monkey (Oct. 2011 (Broad/saiBol1)) Alignment Net Comparative Genomics 
netChlSab2 Green monkey Net Green monkey (Mar. 2014 (Chlorocebus_sabeus 1.1/chlSab2)) Alignment Net Comparative Genomics 
netPapAnu4 papAnu4 Net Baboon (Apr. 2017 (Panu_3.0/papAnu4)) Alignment Net Comparative Genomics 
netRheMac10 Rhesus Net Rhesus (Feb. 2019 (Mmul_10/rheMac10)) Alignment Net Comparative Genomics 
netMacFas5 Crab-eating macaque Net Crab-eating macaque (Jun. 2013 (Macaca_fascicularis_5.0/macFas5)) Alignment Net Comparative Genomics 
netRhiRox1 rhiRox1 Net Golden snub-nosed monkey (Oct. 2014 (Rrox_v1/rhiRox1)) Alignment Net Comparative Genomics 
netNasLar1 Proboscis monkey Net Proboscis monkey (Nov. 2014 (Charlie1.0/nasLar1)) Alignment Net Comparative Genomics 
netNomLeu3 Gibbon Net Gibbon (Oct. 2012 (GGSC Nleu3.0/nomLeu3)) Alignment Net Comparative Genomics 
netPonAbe3 Orangutan Net Orangutan (Jan. 2018 (Susie_PABv2/ponAbe3)) Alignment Net Comparative Genomics 
netGorGor6 Gorilla Net Gorilla (Aug. 2019 (Kamilah_GGO_v0/gorGor6)) Alignment Net Comparative Genomics 
netPanPan3 Bonobo Net Bonobo (May 2020 (Mhudiblu_PPA_v0/panPan3)) Alignment Net Comparative Genomics 
netPanTro6 Chimp Net Chimp (Jan. 2018 (Clint_PTRv2/panTro6)) Alignment Net Comparative Genomics 
primateChainNetViewchain Chains Primate Genomes, Chain and Net Alignments Comparative Genomics 
chainOtoGar3 Bushbaby Chain Bushbaby (Mar. 2011 (Broad/otoGar3)) Chained Alignments Comparative Genomics 
chainMicMur2 Mouse lemur Chain Mouse lemur (May 2015 (Mouse lemur/micMur2)) Chained Alignments Comparative Genomics 
chainTarSyr2 Tarsier Chain Tarsier (Sep. 2013 (Tarsius_syrichta-2.0.1/tarSyr2)) Chained Alignments Comparative Genomics 
chainCalJac4 Marmoset Chain Marmoset (May 2020 (Callithrix_jacchus_cj1700_1.1/calJac4)) Chained Alignments Comparative Genomics 
chainSaiBol1 saiBol1 Chain Squirrel monkey (Oct. 2011 (Broad/saiBol1)) Chained Alignments Comparative Genomics 
chainChlSab2 Green monkey Chain Green monkey (Mar. 2014 (Chlorocebus_sabeus 1.1/chlSab2)) Chained Alignments Comparative Genomics 
chainPapAnu4 papAnu4 Chain Baboon (Apr. 2017 (Panu_3.0/papAnu4)) Chained Alignments Comparative Genomics 
chainRheMac10 Rhesus Chain Rhesus (Feb. 2019 (Mmul_10/rheMac10)) Chained Alignments Comparative Genomics 
chainMacFas5 Crab-eating macaque Chain Crab-eating macaque (Jun. 2013 (Macaca_fascicularis_5.0/macFas5)) Chained Alignments Comparative Genomics 
chainRhiRox1 rhiRox1 Chain Golden snub-nosed monkey (Oct. 2014 (Rrox_v1/rhiRox1)) Chained Alignments Comparative Genomics 
chainNasLar1 Proboscis monkey Chain Proboscis monkey (Nov. 2014 (Charlie1.0/nasLar1)) Chained Alignments Comparative Genomics 
chainNomLeu3 Gibbon Chain Gibbon (Oct. 2012 (GGSC Nleu3.0/nomLeu3)) Chained Alignments Comparative Genomics 
chainPonAbe3 Orangutan Chain Orangutan (Jan. 2018 (Susie_PABv2/ponAbe3)) Chained Alignments Comparative Genomics 
chainGorGor6 Gorilla Chain Gorilla (Aug. 2019 (Kamilah_GGO_v0/gorGor6)) Chained Alignments Comparative Genomics 
chainPanPan3 Bonobo Chain Bonobo (May 2020 (Mhudiblu_PPA_v0/panPan3)) Chained Alignments Comparative Genomics 
chainPanTro6 Chimp Chain Chimp (Jan. 2018 (Clint_PTRv2/panTro6)) Chained Alignments Comparative Genomics 
simpleRepeat Simple Repeats Simple Tandem Repeats by TRF Repeats Description This track displays simple tandem repeats (possibly imperfect repeats) located by Tandem Repeats Finder (TRF) which is specialized for this purpose. These repeats can occur within coding regions of genes and may be quite polymorphic. Repeat expansions are sometimes associated with specific diseases. Methods For more information about the TRF program, see Benson (1999). Credits TRF was written by Gary Benson. References Benson G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 1999 Jan 15;27(2):573-80. PMID: 9862982; PMC: PMC148217 
knownGeneV36 GENCODE V36 GENCODE V36 Genes and Gene Predictions Description The GENCODE Genes track (version 36, Oct 2020) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. By default, only the basic gene set is displayed, which is a subset of the comprehensive gene set. The basic set represents transcripts that GENCODE believes will be useful to the majority of users. The track includes protein-coding genes, non-coding RNA genes, and pseudo-genes, though pseudo-genes are not displayed by default. It contains annotations on the reference chromosomes as well as assembly patches and alternative loci (haplotypes). The following table provides statistics for the v36 release derived from the GTF file that contains annotations only on the main chromosomes. More information on how they were generated can be found in the GENCODE site. GENCODE v36 Release Stats GenesObservedTranscriptsObserved Protein-coding genes19,965Protein-coding transcripts83,986 Long non-coding RNA genes17,910- full length protein-coding57,935 Small non-coding RNA genes7,576- partial length protein-coding26,051 Pseudogenes14,749Nonsense mediated decay transcripts15,811 Immunoglobulin/T-cell receptor gene segments645Long non-coding RNA loci transcripts48,351 For more information on the different gene tracks, see our Genes FAQ. Display Conventions and Configuration By default, this track displays only the basic GENCODE set, splice variants, and non-coding genes. It includes options to display the entire GENCODE set and pseudogenes. To customize these options, the respective boxes can be checked or unchecked at the top of this description page. This track also includes a variety of labels which identify the transcripts when visibility is set to &quot;full&quot; or &quot;pack&quot;. Gene symbols (e.g. NIPA1) are displayed by default, but additional options include GENCODE Transcript ID (ENST00000561183.5), UCSC Known Gene ID (uc001yve.4), UniProt Display ID (Q7RTP0). Additional information about gene and transcript names can be found in our FAQ. This track, in general, follows the display conventions for gene prediction tracks. The exons for putative non-coding genes and untranslated regions are represented by relatively thin blocks, while those for coding open reading frames are thicker. Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all 2-way pseudogenes all polyA annotations This track contains an optional codon coloring feature that allows users to quickly validate and compare gene predictions. There is also an option to display the data as a density graph, which can be helpful for visualizing the distribution of items over a region. Methods The GENCODE v36 track was built from the GENCODE downloads file gencode.v36.chr_patch_hapl_scaff.annotation.gff3.gz. Data from other sources were correlated with the GENCODE data to build association tables. Related Data The GENCODE Genes transcripts are annotated in numerous tables, each of which is also available as a downloadable file. One can see a full list of the associated tables in the Table Browser by selecting GENCODE Genes from the track menu; this list is then available on the table menu. Data access GENCODE Genes and its associated tables can be explored interactively using the REST API, the Table Browser or the Data Integrator. The genePred format files for hg38 are available from our downloads directory or in our GTF download directory. All the tables can also be queried directly from our public MySQL servers, with more information available on our help page as well as on our blog. Credits The GENCODE Genes track was produced at UCSC from the GENCODE comprehensive gene set using a computational pipeline developed by Jim Kent and Brian Raney. References Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, Kokocinski F, Aken BL, Barrell D, Zadissa A, Searle S et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 2012 Sep;22(9):1760-74. PMID: 22955987; PMC: PMC3431492 Harrow J, Denoeud F, Frankish A, Reymond A, Chen CK, Chrast J, Lagarde J, Gilbert JG, Storey R, Swarbreck D et al. GENCODE: producing a reference annotation for ENCODE. Genome Biol. 2006;7 Suppl 1:S4.1-9. PMID: 16925838; PMC: PMC1810553 A full list of GENCODE publications is available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
placentalChainNet Placental Chain/Net Non-primate Placental Mammal Genomes, Chain and Net Alignments Comparative Genomics Description Chain Track The chain track shows alignments of human (Dec. 2013 (GRCh38/hg38)) to other genomes using a gap scoring system that allows longer gaps than traditional affine gap scoring systems. It can also tolerate gaps in both human and the other genome simultaneously. These &quot;double-sided&quot; gaps can be caused by local inversions and overlapping deletions in both species. The chain track displays boxes joined together by either single or double lines. The boxes represent aligning regions. Single lines indicate gaps that are largely due to a deletion in the other assembly or an insertion in the human assembly. Double lines represent more complex gaps that involve substantial sequence in both species. This may result from inversions, overlapping deletions, an abundance of local mutation, or an unsequenced gap in one species. In cases where multiple chains align over a particular region of the other genome, the chains with single-lined gaps are often due to processed pseudogenes, while chains with double-lined gaps are more often due to paralogs and unprocessed pseudogenes. In the &quot;pack&quot; and &quot;full&quot; display modes, the individual feature names indicate the chromosome, strand, and location (in thousands) of the match for each matching alignment. Net Track The net track shows the best human/other chain for every part of the other genome. It is useful for finding orthologous regions and for studying genome rearrangement. The human sequence used in this annotation is from the Dec. 2013 (GRCh38/hg38) assembly. Display Conventions and Configuration Chain Track By default, the chains to chromosome-based assemblies are colored based on which chromosome they map to in the aligning organism. To turn off the coloring, check the &quot;off&quot; button next to: Color track based on chromosome. To display only the chains of one chromosome in the aligning organism, enter the name of that chromosome (e.g. chr4) in box next to: Filter by chromosome. Net Track In full display mode, the top-level (level 1) chains are the largest, highest-scoring chains that span this region. In many cases gaps exist in the top-level chain. When possible, these are filled in by other chains that are displayed at level 2. The gaps in level 2 chains may be filled by level 3 chains and so forth. In the graphical display, the boxes represent ungapped alignments; the lines represent gaps. Click on a box to view detailed information about the chain as a whole; click on a line to display information about the gap. The detailed information is useful in determining the cause of the gap or, for lower level chains, the genomic rearrangement. Individual items in the display are categorized as one of four types (other than gap): Top - the best, longest match. Displayed on level 1. Syn - line-ups on the same chromosome as the gap in the level above it. Inv - a line-up on the same chromosome as the gap above it, but in the opposite orientation. NonSyn - a match to a chromosome different from the gap in the level above. Methods Chain track Transposons that have been inserted since the human/other split were removed from the assemblies. The abbreviated genomes were aligned with lastz, and the transposons were added back in. The resulting alignments were converted into axt format using the lavToAxt program. The axt alignments were fed into axtChain, which organizes all alignments between a single human chromosome and a single chromosome from the other genome into a group and creates a kd-tree out of the gapless subsections (blocks) of the alignments. A dynamic program was then run over the kd-trees to find the maximally scoring chains of these blocks. Chains scoring below a minimum score of '5000' were discarded; the remaining chains are displayed in this track. The linear gap matrix used with axtChain: -linearGap=loose tablesize 11 smallSize 111 position 1 2 3 11 111 2111 12111 32111 72111 152111 252111 qGap 325 360 400 450 600 1100 3600 7600 15600 31600 56600 tGap 325 360 400 450 600 1100 3600 7600 15600 31600 56600 bothGap 625 660 700 750 900 1400 4000 8000 16000 32000 57000 See also: lastz parameters used in these alignments, and chain minimum score and gap parameters used in these alignments. Net track Chains were derived from lastz alignments, using the methods described on the chain tracks description pages, and sorted with the highest-scoring chains in the genome ranked first. The program chainNet was then used to place the chains one at a time, trimming them as necessary to fit into sections not already covered by a higher-scoring chain. During this process, a natural hierarchy emerged in which a chain that filled a gap in a higher-scoring chain was placed underneath that chain. The program netSyntenic was used to fill in information about the relationship between higher- and lower-level chains, such as whether a lower-level chain was syntenic or inverted relative to the higher-level chain. The program netClass was then used to fill in how much of the gaps and chains contained Ns (sequencing gaps) in one or both species and how much was filled with transposons inserted before and after the two organisms diverged. Credits Lastz (previously known as blastz) was developed at Pennsylvania State University by Minmei Hou, Scott Schwartz, Zheng Zhang, and Webb Miller with advice from Ross Hardison. Lineage-specific repeats were identified by Arian Smit and his RepeatMasker program. The axtChain program was developed at the University of California at Santa Cruz by Jim Kent with advice from Webb Miller and David Haussler. The browser display and database storage of the chains and nets were created by Robert Baertsch and Jim Kent. The chainNet, netSyntenic, and netClass programs were developed at the University of California Santa Cruz by Jim Kent. References Chiaromonte F, Yap VB, Miller W. Scoring pairwise genomic sequence alignments. Pac Symp Biocomput. 2002:115-26. PMID: 11928468 Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 
placentalChainNetViewnet Nets Non-primate Placental Mammal Genomes, Chain and Net Alignments Comparative Genomics 
netDasNov3 Armadillo Net Armadillo (Dec. 2011 (Baylor/dasNov3)) Alignment Net Comparative Genomics 
netEquCab3 Horse Net Horse (Jan. 2018 (EquCab3.0/equCab3)) Alignment Net Comparative Genomics 
netManPen1 Chinese pangolin Net Chinese pangolin (Aug 2014 (M_pentadactyla-1.1.1/manPen1)) Alignment Net Comparative Genomics 
netSusScr11 Pig Net Pig (Feb. 2017 (Sscrofa11.1/susScr11)) Alignment Net Comparative Genomics 
netOviAri4 Sheep Net Sheep (Nov. 2015 (Oar_v4.0/oviAri4)) Alignment Net Comparative Genomics 
netBosTau9 Cow Net Cow (Apr. 2018 (ARS-UCD1.2/bosTau9)) Alignment Net Comparative Genomics 
netNeoSch1 Hawaiian monk seal Net Hawaiian monk seal (Jun. 2017 (ASM220157v1/neoSch1)) Alignment Net Comparative Genomics 
netEnhLutNer1 Southern sea otter Net Southern sea otter (Jun. 2019 (ASM641071v1/enhLutNer1)) Alignment Net Comparative Genomics 
netFelCat9 Cat Net Cat (Nov. 2017 (Felis_catus_9.0/felCat9)) Alignment Net Comparative Genomics 
netCanFam4 Dog Net Dog (Mar. 2020 (UU_Cfam_GSD_1.0/canFam4)) Alignment Net Comparative Genomics 
netCanFam6 Dog Net Dog (Oct. 2020 (Dog10K_Boxer_Tasha/canFam6)) Alignment Net Comparative Genomics 
netRn6 Rat Net Rat (Jul. 2014 (RGSC 6.0/rn6)) Alignment Net Comparative Genomics 
netRn7 Rat Net Rat (Nov. 2020 (mRatBN7.2/rn7)) Alignment Net Comparative Genomics 
netMm10 Mouse Net Mouse (Dec. 2011 (GRCm38/mm10)) Alignment Net Comparative Genomics 
netMm39 Mouse Net Mouse (Jun. 2020 (GRCm39/mm39)) Alignment Net Comparative Genomics 
netGalVar1 Malayan flying lemur Net Malayan flying lemur (Jun. 2014 (G_variegatus-3.0.2/galVar1)) Alignment Net Comparative Genomics 
netCriGriChoV2 Chinese hamster Net Chinese hamster (Jun. 2017 (CHOK1S_HZDv1/criGriChoV2)) Alignment Net Comparative Genomics 
placentalChainNetViewchain Chains Non-primate Placental Mammal Genomes, Chain and Net Alignments Comparative Genomics 
chainDasNov3 Armadillo Chain Armadillo (Dec. 2011 (Baylor/dasNov3)) Chained Alignments Comparative Genomics 
chainEquCab3 Horse Chain Horse (Jan. 2018 (EquCab3.0/equCab3)) Chained Alignments Comparative Genomics 
chainManPen1 Chinese pangolin Chain Chinese pangolin (Aug 2014 (M_pentadactyla-1.1.1/manPen1)) Chained Alignments Comparative Genomics 
chainSusScr11 Pig Chain Pig (Feb. 2017 (Sscrofa11.1/susScr11)) Chained Alignments Comparative Genomics 
chainOviAri4 Sheep Chain Sheep (Nov. 2015 (Oar_v4.0/oviAri4)) Chained Alignments Comparative Genomics 
chainBosTau9 Cow Chain Cow (Apr. 2018 (ARS-UCD1.2/bosTau9)) Chained Alignments Comparative Genomics 
chainNeoSch1 Hawaiian monk seal Chain Hawaiian monk seal (Jun. 2017 (ASM220157v1/neoSch1)) Chained Alignments Comparative Genomics 
chainEnhLutNer1 Southern sea otter Chain Southern sea otter (Jun. 2019 (ASM641071v1/enhLutNer1)) Chained Alignments Comparative Genomics 
chainFelCat9 Cat Chain Cat (Nov. 2017 (Felis_catus_9.0/felCat9)) Chained Alignments Comparative Genomics 
chainCanFam4 Dog Chain Dog (Mar. 2020 (UU_Cfam_GSD_1.0/canFam4)) Chained Alignments Comparative Genomics 
chainCanFam6 Dog Chain Dog (Oct. 2020 (Dog10K_Boxer_Tasha/canFam6)) Chained Alignments Comparative Genomics 
chainRn6 Rat Chain Rat (Jul. 2014 (RGSC 6.0/rn6)) Chained Alignments Comparative Genomics 
chainRn7 Rat Chain Rat (Nov. 2020 (mRatBN7.2/rn7)) Chained Alignments Comparative Genomics 
chainMm10 Mouse Chain Mouse (Dec. 2011 (GRCm38/mm10)) Chained Alignments Comparative Genomics 
chainMm39 Mouse Chain Mouse (Jun. 2020 (GRCm39/mm39)) Chained Alignments Comparative Genomics 
chainGalVar1 Malayan flying lemur Chain Malayan flying lemur (Jun. 2014 (G_variegatus-3.0.2/galVar1)) Chained Alignments Comparative Genomics 
chainCriGriChoV2 Chinese hamster Chain Chinese hamster (Jun. 2017 (CHOK1S_HZDv1/criGriChoV2)) Chained Alignments Comparative Genomics 
windowmaskerSdust WM + SDust Genomic Intervals Masked by WindowMasker + SDust Repeats Description This track depicts masked sequence as determined by WindowMasker. The WindowMasker tool is included in the NCBI C++ toolkit. The source code for the entire toolkit is available from the NCBI FTP site. Methods To create this track, WindowMasker was run with the following parameters: windowmasker -mk_counts true -input hg38.fa -output wm_counts windowmasker -ustat wm_counts -sdust true -input hg38.fa -output repeats.bed The repeats.bed (BED3) file was loaded into the &quot;windowmaskerSdust&quot; table for this track. References Morgulis A, Gertz EM, Sch&auml;ffer AA, Agarwala R. WindowMasker: window-based masker for sequenced genomes. Bioinformatics. 2006 Jan 15;22(2):134-41. PMID: 16287941 
vertebrateChainNet Vertebrate Chain/Net Non-placental Vertebrate Genomes, Chain and Net Alignments Comparative Genomics Description Chain Track The chain track shows alignments of human (Dec. 2013 (GRCh38/hg38)) to other genomes using a gap scoring system that allows longer gaps than traditional affine gap scoring systems. It can also tolerate gaps in both human and the other genome simultaneously. These &quot;double-sided&quot; gaps can be caused by local inversions and overlapping deletions in both species. The chain track displays boxes joined together by either single or double lines. The boxes represent aligning regions. Single lines indicate gaps that are largely due to a deletion in the other assembly or an insertion in the human assembly. Double lines represent more complex gaps that involve substantial sequence in both species. This may result from inversions, overlapping deletions, an abundance of local mutation, or an unsequenced gap in one species. In cases where multiple chains align over a particular region of the other genome, the chains with single-lined gaps are often due to processed pseudogenes, while chains with double-lined gaps are more often due to paralogs and unprocessed pseudogenes. In the "pack" and "full" display modes, the individual feature names indicate the chromosome, strand, and location (in thousands) of the match for each matching alignment. Net Track The net track shows the best human/other chain for every part of the other genome. It is useful for finding orthologous regions and for studying genome rearrangement. The human sequence used in this annotation is from the Dec. 2013 (GRCh38/hg38) assembly. Display Conventions and Configuration Chain Track By default, the chains to chromosome-based assemblies are colored based on which chromosome they map to in the aligning organism. To turn off the coloring, check the &quot;off&quot; button next to: Color track based on chromosome. To display only the chains of one chromosome in the aligning organism, enter the name of that chromosome (e.g. chr4) in box next to: Filter by chromosome. Net Track In full display mode, the top-level (level 1) chains are the largest, highest-scoring chains that span this region. In many cases gaps exist in the top-level chain. When possible, these are filled in by other chains that are displayed at level 2. The gaps in level 2 chains may be filled by level 3 chains and so forth. In the graphical display, the boxes represent ungapped alignments; the lines represent gaps. Click on a box to view detailed information about the chain as a whole; click on a line to display information about the gap. The detailed information is useful in determining the cause of the gap or, for lower level chains, the genomic rearrangement. Individual items in the display are categorized as one of four types (other than gap): Top - the best, longest match. Displayed on level 1. Syn - line-ups on the same chromosome as the gap in the level above it. Inv - a line-up on the same chromosome as the gap above it, but in the opposite orientation. NonSyn - a match to a chromosome different from the gap in the level above. Methods Chain track Transposons that have been inserted since the human/other split were removed from the assemblies. The abbreviated genomes were aligned with lastz, and the transposons were added back in. The resulting alignments were converted into axt format using the lavToAxt program. The axt alignments were fed into axtChain, which organizes all alignments between a single human chromosome and a single chromosome from the other genome into a group and creates a kd-tree out of the gapless subsections (blocks) of the alignments. A dynamic program was then run over the kd-trees to find the maximally scoring chains of these blocks. The following lastz matrix was usedfor the alignments to: Wallaby, Tasmanian Devil &nbsp;ACGT A91-114-31-123 C-114100-125-31 G-31-125100-114 T-123-31-11491 &nbsp; The following lastz matrix was usedfor the alignments to: American Alligator, Medium Ground Finch, Opossum, Platypus, Chicken, Zebra Finch, Lizard, X. tropicalis, Stickleback, Fugu, Zebrafish, Tetraodon, Medaka, Lamprey &nbsp;ACGT A91-90-25-100 C-90100-100-25 G-25-100100-90 T-100-25-9091 For the Wallaby alignment, chains scoring below a minimum score of '3000' were discarded; the remaining chains are displayed in this track. The linear gap matrix used with axtChain: -linearGap=medium tableSize 11 smallSize 111 position 1 2 3 11 111 2111 12111 32111 72111 152111 252111 qGap 350 425 450 600 900 2900 22900 57900 117900 217900 317900 tGap 350 425 450 600 900 2900 22900 57900 117900 217900 317900 bothGap 750 825 850 1000 1300 3300 23300 58300 118300 218300 318300 For the alignments to: American Alligator, Medium Ground Finch, Tasmanian Devil, Opossum, Platypus, Chicken, Zebra Finch, Lizard, X. tropicalis, Stickleback, Fugu, Zebrafish, Tetraodon, Medaka and Lamprey, chains scoring below a minimum score of '5000' were discarded; the remaining chains are displayed in this track. The linear gap matrix used with axtChain: -linearGap=loose tablesize 11 smallSize 111 position 1 2 3 11 111 2111 12111 32111 72111 152111 252111 qGap 325 360 400 450 600 1100 3600 7600 15600 31600 56600 tGap 325 360 400 450 600 1100 3600 7600 15600 31600 56600 bothGap 625 660 700 750 900 1400 4000 8000 16000 32000 57000 See also: lastz parameters used in these alignments, and chain minimum score and gap parameters used in these alignments. Net track Chains were derived from lastz alignments, using the methods described on the chain tracks description pages, and sorted with the highest-scoring chains in the genome ranked first. The program chainNet was then used to place the chains one at a time, trimming them as necessary to fit into sections not already covered by a higher-scoring chain. During this process, a natural hierarchy emerged in which a chain that filled a gap in a higher-scoring chain was placed underneath that chain. The program netSyntenic was used to fill in information about the relationship between higher- and lower-level chains, such as whether a lower-level chain was syntenic or inverted relative to the higher-level chain. The program netClass was then used to fill in how much of the gaps and chains contained Ns (sequencing gaps) in one or both species and how much was filled with transposons inserted before and after the two organisms diverged. Credits Lastz (previously known as blastz) was developed at Pennsylvania State University by Minmei Hou, Scott Schwartz, Zheng Zhang, and Webb Miller with advice from Ross Hardison. Lineage-specific repeats were identified by Arian Smit and his RepeatMasker program. The axtChain program was developed at the University of California at Santa Cruz by Jim Kent with advice from Webb Miller and David Haussler. The browser display and database storage of the chains and nets were created by Robert Baertsch and Jim Kent. The chainNet, netSyntenic, and netClass programs were developed at the University of California Santa Cruz by Jim Kent. References Chiaromonte F, Yap VB, Miller W. Scoring pairwise genomic sequence alignments. Pac Symp Biocomput. 2002:115-26. PMID: 11928468 Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 
vertebrateChainNetViewnet Nets Non-placental Vertebrate Genomes, Chain and Net Alignments Comparative Genomics 
netPetMar3 Lamprey Net Lamprey (Dec. 2017 (Pmar_germline 1.0/petMar3)) Alignment Net Comparative Genomics 
netDanRer11 Zebrafish Net Zebrafish (May 2017 (GRCz11/danRer11)) Alignment Net Comparative Genomics 
netXenLae2 xenLae2 Net African clawed frog (Aug. 2016 (Xenopus_laevis_v2/xenLae2)) Alignment Net Comparative Genomics 
netXenTro10 xenTro10 Net X. tropicalis (Nov. 2019 (UCB_Xtro_10.0/xenTro10)) Alignment Net Comparative Genomics 
netThaSir1 thaSir1 Net Garter snake (Jun. 2015 (Thamnophis_sirtalis-6.0/thaSir1)) Alignment Net Comparative Genomics 
netAquChr2 aquChr2 Net Golden eagle (Oct. 2014 (aquChr-1.0.2/aquChr2)) Alignment Net Comparative Genomics 
netGalGal6 Chicken Net Chicken (Mar. 2018 (GRCg6a/galGal6)) Alignment Net Comparative Genomics 
netMelGal5 Turkey Net Turkey (Nov. 2014 (Turkey_5.0/melGal5)) Alignment Net Comparative Genomics 
netMonDom5 Opossum Net Opossum (Oct. 2006 (Broad/monDom5)) Alignment Net Comparative Genomics 
vertebrateChainNetViewchain Chains Non-placental Vertebrate Genomes, Chain and Net Alignments Comparative Genomics 
chainPetMar3 Lamprey Chain Lamprey (Dec. 2017 (Pmar_germline 1.0/petMar3)) Chained Alignments Comparative Genomics 
chainDanRer11 Zebrafish Chain Zebrafish (May 2017 (GRCz11/danRer11)) Chained Alignments Comparative Genomics 
chainXenLae2 xenLae2 Chain African clawed frog (Aug. 2016 (Xenopus_laevis_v2/xenLae2)) Chained Alignments Comparative Genomics 
chainXenTro10 xenTro10 Chain X. tropicalis (Nov. 2019 (UCB_Xtro_10.0/xenTro10)) Chained Alignments Comparative Genomics 
chainThaSir1 thaSir1 Chain Garter snake (Jun. 2015 (Thamnophis_sirtalis-6.0/thaSir1)) Chained Alignments Comparative Genomics 
chainAquChr2 aquChr2 Chain Golden eagle (Oct. 2014 (aquChr-1.0.2/aquChr2)) Chained Alignments Comparative Genomics 
chainGalGal6 Chicken Chain Chicken (Mar. 2018 (GRCg6a/galGal6)) Chained Alignments Comparative Genomics 
chainMelGal5 Turkey Chain Turkey (Nov. 2014 (Turkey_5.0/melGal5)) Chained Alignments Comparative Genomics 
chainMonDom5 Opossum Chain Opossum (Oct. 2006 (Broad/monDom5)) Chained Alignments Comparative Genomics 
gnomadConstraint gnomAD Mut Constraint Gnocchi: Genome Aggregation Database (gnomAD) non-coding constraint of haploinsufficient variation, includes chrX Variation Description With the gnomAD v4.1 data release, the v4 Pre-Release track has been replaced with the gnomAD v4.1 track. The v4.1 release includes a fix for the allele number issue. The v4.1 track shows variants from 807,162 individuals, including 730,947 exomes and 76,215 genomes. This includes the 76,156 genomes from the gnomAD v3.1.2 release as well as new exome data from 416,555 UK Biobank individuals. For more detailed information on gnomAD v4.1, see the related blog post. The gnomAD v3.1 track shows variants from 76,156 whole genomes (and no exomes), all mapped to the GRCh38/hg38 reference sequence. 4,454 genomes were added to the number of genomes in the previous v3 release. For more detailed information on gnomAD v3.1, see the related blog post. The gnomAD v3.1.1 track contains the same underlying data as v3.1, but with minor corrections to the VEP annotations and dbSNP rsIDs. On the UCSC side, we have now included the mitochondrial chromosome data that was released as part of gnomAD v3.1 (but after the UCSC version of the track was released). For more information about gnomAD v3.1.1, please see the related changelog. GnomAD Genome Mutational Constraint is based on v3.1.2 and is available only on hg38. It shows the reduced variation caused by purifying natural selection. This is similar to negative selection on loss-of-function (LoF) for genes, but can be calculated for non-coding regions too. Positive values are red and reflect stronger mutation constraint (and less variation), indicating higher natural selection pressure in a region. Negative values are green and reflect lower mutation constraint (and more variation), indicating less selection pressure and less functional effect. Briefly, for any 1kbp window in the genome, a model based on trinucleotide sequence context, base-level methylation, and regional genomic features predicts expected number of mutations, and compares this number to the observed number of mutations using a Z-score (see preprint in the Reference section for details). The chrX scores were added as received from the authors, as there are no de novo mutation data available on chrX (for estimating the effects of regional genomic features on mutation rates), they are more speculative than the ones on the autosomes. The gnomAD Predicted Constraint Metrics track contains metrics of pathogenicity per-gene as predicted for gnomAD v2.1.1 and identifies genes subject to strong selection against various classes of mutation. This includes data on both the gene and transcript level. The gnomAD v2 tracks show variants from 125,748 exomes and 15,708 whole genomes, all mapped to the GRCh37/hg19 reference sequence and lifted to the GRCh38/hg38 assembly. The data originate from 141,456 unrelated individuals sequenced as part of various population-genetic and disease-specific studies collected by the Genome Aggregation Database (gnomAD), release 2.1.1. Raw data from all studies have been reprocessed through a unified pipeline and jointly variant-called to increase consistency across projects. For more information on the processing pipeline and population annotations, see the following blog post and the 2.1.1 README. gnomAD v2 data are based on the GRCh37/hg19 assembly. These tracks display the GRCh38/hg38 lift-over provided by gnomAD on their downloads site. On hg38 only, a subtrack "Gnomad mutational constraint" aka "Genome non-coding constraint of haploinsufficient variation (Gnocchi)" captures the depletion of variation caused by purifying natural selection. This is similar to negative selection on loss-of-function (LoF) for genes, but can be calculated for non-coding regions, too. Briefly, for any 1kbp window in the genome, a model based on trinucleotide sequence context, base-level methylation, and regional genomic features predicts expected number of mutations, and compares this number to the observed number of mutations using a Z-score (see Chen et al 2024 in the Reference section for details). The chrX scores were added as received from the authors, as there are no mutations available for chrX, they are more speculative than the ones on the autosomes. For questions on the gnomAD data, also see the gnomAD FAQ. More details on the Variant type(s) can be found on the Sequence Ontology page. Display Conventions and Configuration gnomAD v4.1 The gnomAD v4.1 track version follows the same conventions and configuration as the v3.1.1 track, except for mouse hovering items. Mouse hover on an item will display the following details about each variant: Position Total Allele Frequency (TotalAF) Genes Annotation FILTER tags from VCF (FILTER) Population with maximum AF (PopMaxAF) Homozygous Individuals Homozygous Individuals in XX samples (chrX and chrY only) Hemizygous Individuals (chrX and chrY only) gnomAD v3.1.1 The gnomAD v3.1.1 track version follows the same conventions and configuration as the v3.1 track, except as noted below. There are additional FILTER field filters: AS_VQSR, indel_stack (chrM only), and npg (chrM only). Where possible, variants overlapping multiple transcripts/genes have been collapsed into one variant, with additional information available on the details page, which has roughly halved the number of items in the bigBed. The bigBed has been split into two files, one with the information necessary for the track display, and one with the information necessary for the details page. For more information on this data format, please see the Data Access section below. The VEP annotation is shown as a table instead of spread across multiple fields. Intergenic variants have not been pre-filtered. gnomAD v3.1 By default, a maximum of 50,000 variants can be displayed at a time (before applying the filters described below), before the track switches to dense display mode. Mouse hover on an item will display many details about each variant, including the affected gene(s), the variant type, and annotation (missense, synonymous, etc). Clicking on an item will display additional details on the variant, including a population frequency table showing allele count in each sub-population. Following the conventions on the gnomAD browser, items are shaded according to their Annotation type: pLoF Missense Synonymous Other Label Options To maintain consistency with the gnomAD website, variants are by default labeled according to their chromosomal start position followed by the reference and alternate alleles, for example &quot;chr1-1234-T-CAG&quot;. dbSNP rsID's are also available as an additional label, if the variant is present in dbSnp. Filtering Options Three filters are available for these tracks: FILTER: Used to exclude/include variants that failed Random Forest (RF), Inbreeding Coefficient (Inbreeding Coeff), or Allele Count (AC0) filters. The PASS option is used to include/exclude variants that pass all of the RF, InbreedingCoeff, and AC0 filters, as denoted in the original VCF. Annotation type: Used to exclude/include variants that are annotated as Probability Loss of Function (pLoF), Missense, Synonymous, or Other, as annotated by VEP version 85 (GENCODE v19). Variant Type: Used to exclude/include variants according to the type of variation, as annotated by VEP v85. There is one additional configurable filter on the minimum minor allele frequency. gnomAD v2.1.1 The gnomAD v2.1.1 track follows the standard display and configuration options available for VCF tracks, briefly explained below. In dense mode, a vertical line is drawn at the position of each variant. In pack mode, &quot;ref&quot; and &quot;alt&quot; alleles are displayed to the left of a vertical line with colored portions corresponding to allele counts. Hovering the mouse pointer over a variant pops up a display of alleles and counts. Filtering Options Four filters are available for these tracks, the same as the underlying VCF: AC0: Allele Count 0 after filtering out low confidence genotypes (GQ &lt; 20; DP &lt; 10; and AB &lt; 0.2 for het calls)) InbreedingCoeff: Inbreeding Coefficient &lt; -0.3 RF: Used to exclude/include variants that failed Random Forest filtering thresholds of 0.055272738028512555, 0.20641025579497013 (probabilities of being a true positive variant) for SNPs, indels) Pass: Variant passes all 3 filters There are two additional filters available, one for the minimum minor allele frequency, and a configurable filter on the QUAL score. UCSC Methods The gnomAD v3.1.1 and v4.1 data is unfiltered. For the v3.1 update only, in order to cut down on the amount of displayed data, the following variant types have been filtered out, but are still viewable in the gnomAD browser: Regulatory Region Variants Downstream/Upstream Gene Variants Transcription Factor Binding Site Variants For the full steps used to create the gnomAD tracks at UCSC, please see the hg38 gnomad makedoc. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API, and the genome annotations are stored in files that can be downloaded from our download server, subject to the conditions set forth by the gnomAD consortium (see below). Variant VCFs can be found in the vcf subdirectory. The v3.1, v3.1.1, and v4.1 variants can be found in a special directory as they have been transformed from the underlying VCF. For the v3.1.1 and v4.1 variants in particular, the underlying bigBed only contains enough information necessary to use the track in the browser. The extra data like VEP annotations and CADD scores are available in the same directory as the bigBed but in the files details.tab.gz and details.tab.gz.gzi. The details.tab.gz contains the gzip compressed extra data in JSON format, and the .gzi file is available to speed searching of this data. Each variant has an associated md5sum in the name field of the bigBed which can be used along with the _dataOffset and _dataLen fields to get the associated external data, as show below: # find item of interest: bigBedToBed genomes.bb stdout | head -4 | tail -1 chr1 12416 12417 854246d79dc5d02dcdbd5f5438542b6e [..omitted for brevity..] chr1-12417-G-A 67293 902 # use the final two fields, _dataOffset and _dataLen (add one to _dataLen to include a newline), to get the extra data: bgzip -b 67293 -s 903 gnomad.v3.1.1.details.tab.gz 854246d79dc5d02dcdbd5f5438542b6e {"DDX11L1": {"cons": ["non_coding_transcript_variant", [..omitted for brevity..] The data can also be found directly from the gnomAD downloads page. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. The mutational constraints score was updated in October 2022 from a previous, now deprecated, pre-publication version. The old version can be found in our archive directory on the download server. It can be loaded by copying the URL into our "Custom tracks" input box. Credits Thanks to the Genome Aggregation Database Consortium for making these data available. The data are released under the Creative Commons Zero Public Domain Dedication as described here. Please note that some annotations within the provided files may have restrictions on usage. See here for more information. References Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alfoldi J, Wang Q, Collins RL, Laricchia KM, Ganna A, Birnbaum DP et al. Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes. doi: https://doi.org/10.1101/531210. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill AJ, Cummings BB et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016 Aug 17;536(7616):285-91. PMID: 27535533; PMC: PMC5018207 Chen S, Francioli LC, Goodrich JK, Collins RL, Kanai M, Wang Q, Alf&#246;ldi J, Watts NA, Vittal C, Gauthier LD et al. A genomic mutational constraint map using variation in 76,156 human genomes. Nature. 2024 Jan;625(7993):92-100. PMID: 38057664 (We added the data in 2021, then later referenced the 2022 Biorxiv preprint, in which the track was not called "Gnocchi" yet) 
transMapEnsemblV5 TransMap Ensembl TransMap Ensembl and GENCODE Mappings Version 5 Genes and Gene Predictions Description This track contains GENCODE or Ensembl alignments produced by the TransMap cross-species alignment algorithm from other vertebrate species in the UCSC Genome Browser. GENCODE is Ensembl for human and mouse, for other Ensembl sources, only ones with full gene builds are used. Projection Ensembl gene annotations will not be used as sources. For closer evolutionary distances, the alignments are created using syntenically filtered BLASTZ alignment chains, resulting in a prediction of the orthologous genes in human. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. This track may also be configured to display codon coloring, a feature that allows the user to quickly compare cDNAs against the genomic sequence. For more information about this option, click here. Several types of alignment gap may also be colored; for more information, click here. Methods Source transcript alignments were obtained from vertebrate organisms in the UCSC Genome Browser Database. BLAT alignments of RefSeq Genes, GenBank mRNAs, and GenBank Spliced ESTs to the cognate genome, along with UCSC Genes, were used as available. For all vertebrate assemblies that had BLASTZ alignment chains and nets to the human (hg38) genome, a subset of the alignment chains were selected as follows: For organisms whose branch distance was no more than 0.5 (as computed by phyloFit, see Conservation track description for details), syntenic filtering was used. Reciprocal best nets were used if available; otherwise, nets were selected with the netfilter -syn command. The chains corresponding to the selected nets were used for mapping. For more distant species, where the determination of synteny is difficult, the full set of chains was used for mapping. This allows for more genes to map at the expense of some mapping to paralogous regions. The post-alignment filtering step removes some of the duplications. The pslMap program was used to do a base-level projection of the source transcript alignments via the selected chains to the human genome, resulting in pairwise alignments of the source transcripts to the genome. The resulting alignments were filtered with pslCDnaFilter with a global near-best criteria of 0.5% in finished genomes (human and mouse) and 1.0% in other genomes. Alignments where less than 20% of the transcript mapped were discarded. To ensure unique identifiers for each alignment, cDNA and gene accessions were made unique by appending a suffix for each location in the source genome and again for each mapped location in the destination genome. The format is: accession.version-srcUniq.destUniq Where srcUniq is a number added to make each source alignment unique, and destUniq is added to give the subsequent TransMap alignments unique identifiers. For example, in the cow genome, there are two alignments of mRNA BC149621.1. These are assigned the identifiers BC149621.1-1 and BC149621.1-2. When these are mapped to the human genome, BC149621.1-1 maps to a single location and is given the identifier BC149621.1-1.1. However, BC149621.1-2 maps to two locations, resulting in BC149621.1-2.1 and BC149621.1-2.2. Note that multiple TransMap mappings are usually the result of tandem duplications, where both chains are identified as syntenic. Data Access The raw data for these tracks can be accessed interactively through the Table Browser or the Data Integrator. For automated analysis, the annotations are stored in bigPsl files (containing a number of extra columns) and can be downloaded from our download server, or queried using our API. For more information on accessing track data see our Track Data Access FAQ. The files are associated with these tracks in the following way: TransMap Ensembl - hg38.ensembl.transMapV4.bigPsl TransMap RefGene - hg38.refseq.transMapV4.bigPsl TransMap RNA - hg38.rna.transMapV4.bigPsl TransMap ESTs - hg38.est.transMapV4.bigPsl Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/transMap/V4/hg38.refseq.transMapV4.bigPsl -chrom=chr6 -start=0 -end=1000000 stdout Credits This track was produced by Mark Diekhans at UCSC from cDNA and EST sequence data submitted to the international public sequence databases by scientists worldwide and annotations produced by the RefSeq, Ensembl, and GENCODE annotations projects. References Siepel A, Diekhans M, Brejov&#225; B, Langton L, Stevens M, Comstock CL, Davis C, Ewing B, Oommen S, Lau C et al. Targeted discovery of novel human exons by comparative genomics. Genome Res. 2007 Dec;17(12):1763-73. PMID: 17989246; PMC: PMC2099585 Stanke M, Diekhans M, Baertsch R, Haussler D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics. 2008 Mar 1;24(5):637-44. PMID: 18218656 Zhu J, Sanborn JZ, Diekhans M, Lowe CB, Pringle TH, Haussler D. Comparative genomics search for losses of long-established genes on the human lineage. PLoS Comput Biol. 2007 Dec;3(12):e247. PMID: 18085818; PMC: PMC2134963 
transMapV5 TransMap V5 TransMap Alignments Version 5 Genes and Gene Predictions Description These tracks contain cDNA and gene alignments produced by the TransMap cross-species alignment algorithm from other vertebrate species in the UCSC Genome Browser. For closer evolutionary distances, the alignments are created using syntenically filtered LASTZ or BLASTZ alignment chains, resulting in a prediction of the orthologous genes in human. For more distant organisms, reciprocal best alignments are used. TransMap maps genes and related annotations in one species to another using synteny-filtered pairwise genome alignments (chains and nets) to determine the most likely orthologs. For example, for the mRNA TransMap track on the human assembly, more than 400,000 mRNAs from 25 vertebrate species were aligned at high stringency to the native assembly using BLAT. The alignments were then mapped to the human assembly using the chain and net alignments produced using BLASTZ, which has higher sensitivity than BLAT for diverged organisms. Compared to translated BLAT, TransMap finds fewer paralogs and aligns more UTR bases. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. This track may also be configured to display codon coloring, a feature that allows the user to quickly compare cDNAs against the genomic sequence. For more information about this option, click here. Several types of alignment gap may also be colored; for more information, click here. Methods Source transcript alignments were obtained from vertebrate organisms in the UCSC Genome Browser Database. BLAT alignments of RefSeq Genes, GenBank mRNAs, and GenBank Spliced ESTs to the cognate genome, along with UCSC Genes, were used as available. For all vertebrate assemblies that had BLASTZ alignment chains and nets to the human (hg38) genome, a subset of the alignment chains were selected as follows: For organisms whose branch distance was no more than 0.5 (as computed by phyloFit, see Conservation track description for details), syntenic filtering was used. Reciprocal best nets were used if available; otherwise, nets were selected with the netfilter -syn command. The chains corresponding to the selected nets were used for mapping. For more distant species, where the determination of synteny is difficult, the full set of chains was used for mapping. This allows for more genes to map at the expense of some mapping to paralogous regions. The post-alignment filtering step removes some of the duplications. The pslMap program was used to do a base-level projection of the source transcript alignments via the selected chains to the human genome, resulting in pairwise alignments of the source transcripts to the genome. The resulting alignments were filtered with pslCDnaFilter with a global near-best criteria of 0.5% in finished genomes (human and mouse) and 1.0% in other genomes. Alignments where less than 20% of the transcript mapped were discarded. To ensure unique identifiers for each alignment, cDNA and gene accessions were made unique by appending a suffix for each location in the source genome and again for each mapped location in the destination genome. The format is: accession.version-srcUniq.destUniq Where srcUniq is a number added to make each source alignment unique, and destUniq is added to give the subsequent TransMap alignments unique identifiers. For example, in the cow genome, there are two alignments of mRNA BC149621.1. These are assigned the identifiers BC149621.1-1 and BC149621.1-2. When these are mapped to the human genome, BC149621.1-1 maps to a single location and is given the identifier BC149621.1-1.1. However, BC149621.1-2 maps to two locations, resulting in BC149621.1-2.1 and BC149621.1-2.2. Note that multiple TransMap mappings are usually the result of tandem duplications, where both chains are identified as syntenic. Data Access The raw data for these tracks can be accessed interactively through the Table Browser or the Data Integrator. For automated analysis, the annotations are stored in bigPsl files (containing a number of extra columns) and can be downloaded from our download server, or queried using our API. For more information on accessing track data see our Track Data Access FAQ. The files are associated with these tracks in the following way: TransMap Ensembl - hg38.ensembl.transMapV5.bigPsl TransMap RefGene - hg38.refseq.transMapV5.bigPsl TransMap RNA - hg38.rna.transMapV5.bigPsl TransMap ESTs - hg38.est.transMapV5.bigPsl Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed, which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/transMap/V5/hg38.refseq.transMapV5.bigPsl -chrom=chr6 -start=0 -end=1000000 stdout Credits This track was produced by Mark Diekhans at UCSC from cDNA and EST sequence data submitted to the international public sequence databases by scientists worldwide and annotations produced by the RefSeq, Ensembl, and GENCODE annotations projects. References Siepel A, Diekhans M, Brejov&#225; B, Langton L, Stevens M, Comstock CL, Davis C, Ewing B, Oommen S, Lau C et al. Targeted discovery of novel human exons by comparative genomics. Genome Res. 2007 Dec;17(12):1763-73. PMID: 17989246; PMC: PMC2099585 Stanke M, Diekhans M, Baertsch R, Haussler D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics. 2008 Mar 1;24(5):637-44. PMID: 18218656 Zhu J, Sanborn JZ, Diekhans M, Lowe CB, Pringle TH, Haussler D. Comparative genomics search for losses of long-established genes on the human lineage. PLoS Comput Biol. 2007 Dec;3(12):e247. PMID: 18085818; PMC: PMC2134963 
transMapRefSeqV5 TransMap RefGene TransMap RefSeq Gene Mappings Version 5 Genes and Gene Predictions Description This track contains RefSeq Gene alignments produced by the TransMap cross-species alignment algorithm from other vertebrate species in the UCSC Genome Browser. For closer evolutionary distances, the alignments are created using syntenically filtered BLASTZ alignment chains, resulting in a prediction of the orthologous genes in human. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. This track may also be configured to display codon coloring, a feature that allows the user to quickly compare cDNAs against the genomic sequence. For more information about this option, click here. Several types of alignment gap may also be colored; for more information, click here. Methods Source transcript alignments were obtained from vertebrate organisms in the UCSC Genome Browser Database. BLAT alignments of RefSeq Genes, GenBank mRNAs, and GenBank Spliced ESTs to the cognate genome, along with UCSC Genes, were used as available. For all vertebrate assemblies that had BLASTZ alignment chains and nets to the human (hg38) genome, a subset of the alignment chains were selected as follows: For organisms whose branch distance was no more than 0.5 (as computed by phyloFit, see Conservation track description for details), syntenic filtering was used. Reciprocal best nets were used if available; otherwise, nets were selected with the netfilter -syn command. The chains corresponding to the selected nets were used for mapping. For more distant species, where the determination of synteny is difficult, the full set of chains was used for mapping. This allows for more genes to map at the expense of some mapping to paralogous regions. The post-alignment filtering step removes some of the duplications. The pslMap program was used to do a base-level projection of the source transcript alignments via the selected chains to the human genome, resulting in pairwise alignments of the source transcripts to the genome. The resulting alignments were filtered with pslCDnaFilter with a global near-best criteria of 0.5% in finished genomes (human and mouse) and 1.0% in other genomes. Alignments where less than 20% of the transcript mapped were discarded. To ensure unique identifiers for each alignment, cDNA and gene accessions were made unique by appending a suffix for each location in the source genome and again for each mapped location in the destination genome. The format is: accession.version-srcUniq.destUniq Where srcUniq is a number added to make each source alignment unique, and destUniq is added to give the subsequent TransMap alignments unique identifiers. For example, in the cow genome, there are two alignments of mRNA BC149621.1. These are assigned the identifiers BC149621.1-1 and BC149621.1-2. When these are mapped to the human genome, BC149621.1-1 maps to a single location and is given the identifier BC149621.1-1.1. However, BC149621.1-2 maps to two locations, resulting in BC149621.1-2.1 and BC149621.1-2.2. Note that multiple TransMap mappings are usually the result of tandem duplications, where both chains are identified as syntenic. Data Access The raw data for these tracks can be accessed interactively through the Table Browser or the Data Integrator. For automated analysis, the annotations are stored in bigPsl files (containing a number of extra columns) and can be downloaded from our download server, or queried using our API. For more information on accessing track data see our Track Data Access FAQ. The files are associated with these tracks in the following way: TransMap Ensembl - hg38.ensembl.transMapV4.bigPsl TransMap RefGene - hg38.refseq.transMapV4.bigPsl TransMap RNA - hg38.rna.transMapV4.bigPsl TransMap ESTs - hg38.est.transMapV4.bigPsl Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/transMap/V4/hg38.refseq.transMapV4.bigPsl -chrom=chr6 -start=0 -end=1000000 stdout Credits This track was produced by Mark Diekhans at UCSC from cDNA and EST sequence data submitted to the international public sequence databases by scientists worldwide and annotations produced by the RefSeq, Ensembl, and GENCODE annotations projects. References Siepel A, Diekhans M, Brejov&#225; B, Langton L, Stevens M, Comstock CL, Davis C, Ewing B, Oommen S, Lau C et al. Targeted discovery of novel human exons by comparative genomics. Genome Res. 2007 Dec;17(12):1763-73. PMID: 17989246; PMC: PMC2099585 Stanke M, Diekhans M, Baertsch R, Haussler D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics. 2008 Mar 1;24(5):637-44. PMID: 18218656 Zhu J, Sanborn JZ, Diekhans M, Lowe CB, Pringle TH, Haussler D. Comparative genomics search for losses of long-established genes on the human lineage. PLoS Comput Biol. 2007 Dec;3(12):e247. PMID: 18085818; PMC: PMC2134963 
transMapRnaV5 TransMap RNA TransMap GenBank RNA Mappings Version 5 Genes and Gene Predictions Description This track contains GenBank mRNA alignments produced by the TransMap cross-species alignment algorithm from other vertebrate species in the UCSC Genome Browser. For closer evolutionary distances, the alignments are created using syntenically filtered BLASTZ alignment chains, resulting in a prediction of the orthologous genes in human. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. This track may also be configured to display codon coloring, a feature that allows the user to quickly compare cDNAs against the genomic sequence. For more information about this option, click here. Several types of alignment gap may also be colored; for more information, click here. Methods Source transcript alignments were obtained from vertebrate organisms in the UCSC Genome Browser Database. BLAT alignments of RefSeq Genes, GenBank mRNAs, and GenBank Spliced ESTs to the cognate genome, along with UCSC Genes, were used as available. For all vertebrate assemblies that had BLASTZ alignment chains and nets to the human (hg38) genome, a subset of the alignment chains were selected as follows: For organisms whose branch distance was no more than 0.5 (as computed by phyloFit, see Conservation track description for details), syntenic filtering was used. Reciprocal best nets were used if available; otherwise, nets were selected with the netfilter -syn command. The chains corresponding to the selected nets were used for mapping. For more distant species, where the determination of synteny is difficult, the full set of chains was used for mapping. This allows for more genes to map at the expense of some mapping to paralogous regions. The post-alignment filtering step removes some of the duplications. The pslMap program was used to do a base-level projection of the source transcript alignments via the selected chains to the human genome, resulting in pairwise alignments of the source transcripts to the genome. The resulting alignments were filtered with pslCDnaFilter with a global near-best criteria of 0.5% in finished genomes (human and mouse) and 1.0% in other genomes. Alignments where less than 20% of the transcript mapped were discarded. To ensure unique identifiers for each alignment, cDNA and gene accessions were made unique by appending a suffix for each location in the source genome and again for each mapped location in the destination genome. The format is: accession.version-srcUniq.destUniq Where srcUniq is a number added to make each source alignment unique, and destUniq is added to give the subsequent TransMap alignments unique identifiers. For example, in the cow genome, there are two alignments of mRNA BC149621.1. These are assigned the identifiers BC149621.1-1 and BC149621.1-2. When these are mapped to the human genome, BC149621.1-1 maps to a single location and is given the identifier BC149621.1-1.1. However, BC149621.1-2 maps to two locations, resulting in BC149621.1-2.1 and BC149621.1-2.2. Note that multiple TransMap mappings are usually the result of tandem duplications, where both chains are identified as syntenic. Data Access The raw data for these tracks can be accessed interactively through the Table Browser or the Data Integrator. For automated analysis, the annotations are stored in bigPsl files (containing a number of extra columns) and can be downloaded from our download server, or queried using our API. For more information on accessing track data see our Track Data Access FAQ. The files are associated with these tracks in the following way: TransMap Ensembl - hg38.ensembl.transMapV4.bigPsl TransMap RefGene - hg38.refseq.transMapV4.bigPsl TransMap RNA - hg38.rna.transMapV4.bigPsl TransMap ESTs - hg38.est.transMapV4.bigPsl Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/transMap/V4/hg38.refseq.transMapV4.bigPsl -chrom=chr6 -start=0 -end=1000000 stdout Credits This track was produced by Mark Diekhans at UCSC from cDNA and EST sequence data submitted to the international public sequence databases by scientists worldwide and annotations produced by the RefSeq, Ensembl, and GENCODE annotations projects. References Siepel A, Diekhans M, Brejov&#225; B, Langton L, Stevens M, Comstock CL, Davis C, Ewing B, Oommen S, Lau C et al. Targeted discovery of novel human exons by comparative genomics. Genome Res. 2007 Dec;17(12):1763-73. PMID: 17989246; PMC: PMC2099585 Stanke M, Diekhans M, Baertsch R, Haussler D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics. 2008 Mar 1;24(5):637-44. PMID: 18218656 Zhu J, Sanborn JZ, Diekhans M, Lowe CB, Pringle TH, Haussler D. Comparative genomics search for losses of long-established genes on the human lineage. PLoS Comput Biol. 2007 Dec;3(12):e247. PMID: 18085818; PMC: PMC2134963 
transMapEstV5 TransMap ESTs TransMap EST Mappings Version 5 Genes and Gene Predictions Description This track contains GenBank spliced EST alignments produced by the TransMap cross-species alignment algorithm from other vertebrate species in the UCSC Genome Browser. For closer evolutionary distances, the alignments are created using syntenically filtered BLASTZ alignment chains, resulting in a prediction of the orthologous genes in human. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. This track may also be configured to display codon coloring, a feature that allows the user to quickly compare cDNAs against the genomic sequence. For more information about this option, click here. Several types of alignment gap may also be colored; for more information, click here. Methods Source transcript alignments were obtained from vertebrate organisms in the UCSC Genome Browser Database. BLAT alignments of RefSeq Genes, GenBank mRNAs, and GenBank Spliced ESTs to the cognate genome, along with UCSC Genes, were used as available. For all vertebrate assemblies that had BLASTZ alignment chains and nets to the human (hg38) genome, a subset of the alignment chains were selected as follows: For organisms whose branch distance was no more than 0.5 (as computed by phyloFit, see Conservation track description for details), syntenic filtering was used. Reciprocal best nets were used if available; otherwise, nets were selected with the netfilter -syn command. The chains corresponding to the selected nets were used for mapping. For more distant species, where the determination of synteny is difficult, the full set of chains was used for mapping. This allows for more genes to map at the expense of some mapping to paralogous regions. The post-alignment filtering step removes some of the duplications. The pslMap program was used to do a base-level projection of the source transcript alignments via the selected chains to the human genome, resulting in pairwise alignments of the source transcripts to the genome. The resulting alignments were filtered with pslCDnaFilter with a global near-best criteria of 0.5% in finished genomes (human and mouse) and 1.0% in other genomes. Alignments where less than 20% of the transcript mapped were discarded. To ensure unique identifiers for each alignment, cDNA and gene accessions were made unique by appending a suffix for each location in the source genome and again for each mapped location in the destination genome. The format is: accession.version-srcUniq.destUniq Where srcUniq is a number added to make each source alignment unique, and destUniq is added to give the subsequent TransMap alignments unique identifiers. For example, in the cow genome, there are two alignments of mRNA BC149621.1. These are assigned the identifiers BC149621.1-1 and BC149621.1-2. When these are mapped to the human genome, BC149621.1-1 maps to a single location and is given the identifier BC149621.1-1.1. However, BC149621.1-2 maps to two locations, resulting in BC149621.1-2.1 and BC149621.1-2.2. Note that multiple TransMap mappings are usually the result of tandem duplications, where both chains are identified as syntenic. Data Access The raw data for these tracks can be accessed interactively through the Table Browser or the Data Integrator. For automated analysis, the annotations are stored in bigPsl files (containing a number of extra columns) and can be downloaded from our download server, or queried using our API. For more information on accessing track data see our Track Data Access FAQ. The files are associated with these tracks in the following way: TransMap Ensembl - hg38.ensembl.transMapV4.bigPsl TransMap RefGene - hg38.refseq.transMapV4.bigPsl TransMap RNA - hg38.rna.transMapV4.bigPsl TransMap ESTs - hg38.est.transMapV4.bigPsl Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/transMap/V4/hg38.refseq.transMapV4.bigPsl -chrom=chr6 -start=0 -end=1000000 stdout Credits This track was produced by Mark Diekhans at UCSC from cDNA and EST sequence data submitted to the international public sequence databases by scientists worldwide and annotations produced by the RefSeq, Ensembl, and GENCODE annotations projects. References Siepel A, Diekhans M, Brejov&#225; B, Langton L, Stevens M, Comstock CL, Davis C, Ewing B, Oommen S, Lau C et al. Targeted discovery of novel human exons by comparative genomics. Genome Res. 2007 Dec;17(12):1763-73. PMID: 17989246; PMC: PMC2099585 Stanke M, Diekhans M, Baertsch R, Haussler D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics. 2008 Mar 1;24(5):637-44. PMID: 18218656 Zhu J, Sanborn JZ, Diekhans M, Lowe CB, Pringle TH, Haussler D. Comparative genomics search for losses of long-established genes on the human lineage. PLoS Comput Biol. 2007 Dec;3(12):e247. PMID: 18085818; PMC: PMC2134963 
gtexCov GTEx RNA-Seq Coverage GTEx V8 RNA-Seq Read Coverage by Tissue Expression Description The NIH Genotype-Tissue Expression (GTEx) project determined genetic variation and gene expression in 52 tissues and 2 cell lines using RNA-seq data (V8, August 2019), on 17,382 samples from 948 adults. This track focuses on the gene expression part. It shows read coverage, from one single sample per tissue, selected for high-quality and high read depth. The data is summarized to one number per base pair, the number of sequencing reads that cover this position. The plot allows finding out if a given exon is transcribed primarily in certain tissues and also whether transcription is uniform over the length of a single exon. Display Conventions This track follows the display conventions for composite &quot;wiggle&quot; tracks. The subtracks, one per tissue, of this track may be configured in a variety of ways to highlight different aspects of the displayed data. The graphical configuration options are shown at the top of the track description page, followed by a list of subtracks. To display only selected subtracks, uncheck the boxes next to the tracks you wish to hide. For more information about the graphical configuration options, click the Graph configuration help link. Tissue colors were assigned to conform to the GTEx Consortium publication conventions. In Dense mode, the darkness of the grayscale rectangle displayed for the gene reflects the absolute read count. Methods For background information about GTEx sample selection, see our GTEx gene expression track. In short, samples were sequenced with the Illumina TrueSeq protocol on unstranded polyA+ librarires to obtain 76-bp paired end reads with HiSeq 2000 and 2500 machines. Sequence reads were aligned to the hg38/GRCh38 human genome using STAR v2.5.3a and the GENCODE 26 transcriptome. The alignment pipeline is available here. For further method details, see the GTEx Portal Documentation page. To obtain read coverage, the GTEx Laboratory, Data Analysis and Coordinating Center (LDACC) at the Broad Institute decided to select a single, high-quality representative sample for each tissue type, since aggregated tracks may obscure certain features or even introduce some artifacts (e.g. intronic coverage). For each tissue, the selected sample has the highest RIN value with a high coverage (&gt;80M reads) and exonic rate (&gt;85%). The alignment-to-coverage pipeline is available from Github: Python script, Docker file and Pipeline WDL description. To show the exact GTEx sample that was used for each tissue, click the &quot;Schema&quot; link on the track configuration page (above), the filename under &quot;bigDataUrl&quot; includes the identifier. Subject and Sample Characteristics The scientific goal of the GTEx project required that the donors and their biospecimen present with no evidence of disease. The tissue types collected were chosen based on their clinical significance, logistical feasibility and their relevance to the scientific goal of the project and the research community. Summary plots of GTEx sample characteristics are available at the GTEx Portal Tissue Summary page. Data Access The raw data for the GTEx Read Coverage track can be accessed interactively through the Table Browser. For automated analysis and downloads, the track data files can be downloaded from our downloads server or the JSON API. Individual regions or the whole genome annotation can be accessed as text using our utility bigBedToBed. Instructions for downloading the utility can be found here. That utility can also be used to obtain features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/gtex/gtexGeneV8.bb -chrom=chr21 -start=0 -end=100000000 stdout Data can also be obtained directly from GTEx at the following link: https://gtexportal.org/home/datasets Credits Statistical analysis and data interpretation was performed by The GTEx Consortium Analysis Working Group. Data was provided by the GTEx LDACC at The Broad Institute of MIT and Harvard. References GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science. 2020 Sep 11;369(6509):1318-1330. PMID: 32913098; PMC: PMC7737656 GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013 Jun;45(6):580-5. PMID: 23715323; PMC: PMC4010069 Carithers LJ, Ardlie K, Barcus M, Branton PA, Britton A, Buia SA, Compton CC, DeLuca DS, Peter-Demchok J, Gelfand ET et al. A Novel Approach to High-Quality Postmortem Tissue Procurement: The GTEx Project. Biopreserv Biobank. 2015 Oct;13(5):311-9. PMID: 26484571; PMC: PMC4675181 Mel&#233; M, Ferreira PG, Reverter F, DeLuca DS, Monlong J, Sammeth M, Young TR, Goldmann JM, Pervouchine DD, Sullivan TJ et al. Human genomics. The human transcriptome across tissues and individuals. Science. 2015 May 8;348(6235):660-5. PMID: 25954002; PMC: PMC4547472 DeLuca DS, Levin JZ, Sivachenko A, Fennell T, Nazaire MD, Williams C, Reich M, Winckler W, Getz G. RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics. 2012 Jun 1;28(11):1530-2. PMID: 22539670; PMC: PMC3356847 
gtexCovWholeBlood Whole Blood Whole Blood Expression 
gtexCovVagina Vagina Vagina Expression 
gtexCovUterus Uterus Uterus Expression 
gtexCovThyroid Thyroid Thyroid Expression 
gtexCovTestis Testis Testis Expression 
gtexCovStomach Stomach Stomach Expression 
gtexCovSpleen Spleen Spleen Expression 
gtexCovSmallIntestineTerminalIleum Small Intestine Small Intestine Terminal Ileum Expression 
gtexCovSkinSunExposedLowerleg Skin sun exp Skin Sun Exposed Lower leg Expression 
gtexCovSkinNotSunExposedSuprapubic Skin not sun exp Skin Not Sun Exposed Suprapubic Expression 
gtexCovProstate Prostate Prostate Expression 
gtexCovPituitary Pituitary Pituitary Expression 
gtexCovPancreas Pancreas Pancreas Expression 
gtexCovOvary Ovary Ovary Expression 
gtexCovNerveTibial Nerve Tibial Nerve Tibial Expression 
gtexCovMuscleSkeletal Muscle Skeletal Muscle Skeletal Expression 
gtexCovMinorSalivaryGland Minor Saliv Gland Minor Salivary Gland Expression 
gtexCovLung Lung Lung Expression 
gtexCovLiver Liver Liver Expression 
gtexCovKidneyMedulla Kidney Medulla Kidney Medulla Expression 
gtexCovKidneyCortex Kidney Cortex Kidney Cortex Expression 
gtexCovHeartLeftVentricle Heart Left Ventr Heart Left Ventricle Expression 
gtexCovHeartAtrialAppendage Heart Atr Append Heart Atrial Appendage Expression 
gtexCovFallopianTube Fallopian Tube Fallopian Tube Expression 
gtexCovEsophagusMuscularis Esoph Muscularis Esophagus Muscularis Expression 
gtexCovEsophagusMucosa Esoph Mucosa Esophagus Mucosa Expression 
gtexCovEsophagusGastroesophagealJunction Esoph Gastroes Junc Esophagus Gastroesophageal Junction Expression 
gtexCovColonTransverse Colon Transverse Colon Transverse Expression 
gtexCovColonSigmoid Colon Sigmoid Colon Sigmoid Expression 
gtexCovCervixEndocervix Cervix Endocerv Cervix Endocervix Expression 
gtexCovCervixEctocervix Cervix Ectocerv Cervix Ectocervix Expression 
gtexCovCellsCulturedfibroblasts Cells fibrobl cult Cells Cultured fibroblasts Expression 
gtexCovCellsEBV-transformedlymphocytes Cells EBV lymphoc Cells EBV-transformed lymphocytes Expression 
gtexCovBreastMammaryTissue Breast Mammary Breast Mammary Tissue Expression 
gtexCovBrainSubstantianigra Brain Subst nigr Brain Substantia nigra Expression 
gtexCovBrainSpinalcordcervicalc-1 Brain Spinal cord cerv Brain Spinal cord cervical c-1 Expression 
gtexCovBrainPutamenbasalganglia Brain Put bas gang Brain Putamen basal ganglia Expression 
gtexCovBrainNucleusaccumbensbasalganglia Brain Nucl acc bas gang Brain Nucleus accumbens basal ganglia Expression 
gtexCovBrainHypothalamus Brain Hypothal Brain Hypothalamus Expression 
gtexCovBrainHippocampus Brain Hippocamp Brain Hippocampus Expression 
gtexCovBrainFrontalCortexBA9 Brain Front Cortex Brain Frontal Cortex BA9 Expression 
gtexCovBrainCortex Brain Cortex Brain Cortex Expression 
gtexCovBrainCerebellarHemisphere Brain Cereb Hemisph Brain Cerebellar Hemisphere Expression 
gtexCovBrainCerebellum Brain Cereb Brain Cerebellum Expression 
gtexCovBrainCaudatebasalganglia Brain Caud bas gangl Brain Caudate basal ganglia Expression 
gtexCovBrainAnteriorcingulatecortexBA24 Brain Ant cin cort Brain Anterior cingulate cortex BA24 Expression 
gtexCovBrainAmygdala Brain Amygd Brain Amygdala Expression 
gtexCovBladder Bladder Bladder Expression 
gtexCovArteryTibial Artery Tibia Artery Tibial Expression 
gtexCovArteryCoronary Artery Coron Artery Coronary Expression 
gtexCovArteryAorta Artery Aorta Artery Aorta Expression 
gtexCovAdrenalGland Adren Gland Adrenal Gland Expression 
gtexCovAdiposeVisceralOmentum Adip Visc Om Adipose Visceral Omentum - GTEX-14BMU-0626-SM-73KZ6 Expression 
gtexCovAdiposeSubcutaneous Adip Subcut Adipose Subcutaneous Expression 
ukbDepletion UKB Depl. Rank Score UK Biobank / deCODE Genetics Depletion Rank Score Phenotype and Literature Description The "Constraint scores" container track includes several subtracks showing the results of constraint prediction algorithms. These try to find regions of negative selection, where variations likely have functional impact. The algorithms do not use multi-species alignments to derive evolutionary constraint, but use primarily human variation, usually from variants collected by gnomAD (see the gnomAD V2 or V3 tracks on hg19 and hg38) or TOPMED (contained in our dbSNP tracks and available as a filter). One of the subtracks is based on UK Biobank variants, which are not available publicly, so we have no track with the raw data. The number of human genomes that are used as the input for these scores are 76k, 53k and 110k for gnomAD, TOPMED and UK Biobank, respectively. Note that another important constraint score, gnomAD constraint, is not part of this container track but can be found in the hg38 gnomAD track. The algorithms included in this track are: JARVIS - "Junk" Annotation genome-wide Residual Variation Intolerance Score: JARVIS scores were created by first scanning the entire genome with a sliding-window approach (using a 1-nucleotide step), recording the number of all TOPMED variants and common variants, irrespective of their predicted effect, within each window, to eventually calculate a single-nucleotide resolution genome-wide residual variation intolerance score (gwRVIS). That score, gwRVIS was then combined with primary genomic sequence context, and additional genomic annotations with a multi-module deep learning framework to infer pathogenicity of noncoding regions that still remains naive to existing phylogenetic conservation metrics. The higher the score, the more deleterious the prediction. This score covers the entire genome, except the gaps. HMC - Homologous Missense Constraint: Homologous Missense Constraint (HMC) is a amino acid level measure of genetic intolerance of missense variants within human populations. For all assessable amino-acid positions in Pfam domains, the number of missense substitutions directly observed in gnomAD (Observed) was counted and compared to the expected value under a neutral evolution model (Expected). The upper limit of a 95% confidence interval for the Observed/Expected ratio is defined as the HMC score. Missense variants disrupting the amino-acid positions with HMC&lt;0.8 are predicted to be likely deleterious. This score only covers PFAM domains within coding regions. MetaDome - Tolerance Landscape Score (hg19 only): MetaDome Tolerance Landscape scores are computed as a missense over synonymous variant count ratio, which is calculated in a sliding window (with a size of 21 codons/residues) to provide a per-position indication of regional tolerance to missense variation. The variant database was gnomAD and the score corrected for codon composition. Scores &lt;0.7 are considered intolerant. This score covers only coding regions. MTR - Missense Tolerance Ratio (hg19 only): Missense Tolerance Ratio (MTR) scores aim to quantify the amount of purifying selection acting specifically on missense variants in a given window of protein-coding sequence. It is estimated across sliding windows of 31 codons (default) and uses observed standing variation data from the WES component of gnomAD / the Exome Aggregation Consortium Database (ExAC), version 2.0. Scores were computed using Ensembl v95 release. The number of gnomAD 2 exomes used here is higher than the number of gnomAD 3 samples (125 exoms versus 76k full genomes), but this score only covers coding regions. UK Biobank depletion rank score (hg38 only): Halldorsson et al. tabulated the number of UK Biobank variants in each 500bp window of the genome and compared this number to an expected number given the heptamer nucleotide composition of the window and the fraction of heptamers with a sequence variant across the genome and their mutational classes. A variant depletion score was computed for every overlapping set of 500-bp windows in the genome with a 50-bp step size. They then assigned a rank (depletion rank (DR)) from 0 (most depletion) to 100 (least depletion) for each 500-bp window. Since the windows are overlapping, we plot the value only in the central 50bp of the 500bp window, following advice from the author of the score, Hakon Jonsson, deCODE Genetics. He suggested that the value of the central window, rather than the worst possible score of all overlapping windows, is the most informative for a position. This score covers almost the entire genome, only very few regions were excluded, where the genome sequence had too many gap characters. Display Conventions and Configuration JARVIS JARVIS scores are shown as a signal ("wiggle") track, with one score per genome position. Mousing over the bars displays the exact values. The scores were downloaded and converted to a single bigWig file. Move the mouse over the bars to display the exact values. A horizontal line is shown at the 0.733 value which signifies the 90th percentile. See hg19 makeDoc and hg38 makeDoc. Interpretation: The authors offer a suggested guideline of > 0.9998 for identifying higher confidence calls and minimizing false positives. In addition to that strict threshold, the following two more relaxed cutoffs can be used to explore additional hits. Note that these thresholds are offered as guidelines and are not necessarily representative of pathogenicity. PercentileJARVIS score threshold 99th0.9998 95th0.9826 90th0.7338 HMC HMC scores are displayed as a signal ("wiggle") track, with one score per genome position. Mousing over the bars displays the exact values. The highly-constrained cutoff of 0.8 is indicated with a line. Interpretation: A protein residue with HMC score &lt;1 indicates that missense variants affecting the homologous residues are significantly under negative selection (P-value &lt; 0.05) and likely to be deleterious. A more stringent score threshold of HMC&lt;0.8 is recommended to prioritize predicted disease-associated variants. MetaDome MetaDome data can be found on two tracks, MetaDome and MetaDome All Data. The MetaDome track should be used by default for data exploration. In this track the raw data containing the MetaDome tolerance scores were converted into a signal ("wiggle") track. Since this data was computed on the proteome, there was a small amount of coordinate overlap, roughly 0.42%. In these regions the lowest possible score was chosen for display in the track to maintain sensitivity. For this reason, if a protein variant is being evaluated, the MetaDome All Data track can be used to validate the score. More information on this data can be found in the MetaDome FAQ. Interpretation: The authors suggest the following guidelines for evaluating intolerance. By default, the MetaDome track displays a horizontal line at 0.7 which signifies the first intolerant bin. For more information see the MetaDome publication. ClassificationMetaDome Tolerance Score Highly intolerant&le; 0.175 Intolerant&le; 0.525 Slightly intolerant&le; 0.7 MTR MTR data can be found on two tracks, MTR All data and MTR Scores. In the MTR Scores track the data has been converted into 4 separate signal tracks representing each base pair mutation, with the lowest possible score shown when multiple transcripts overlap at a position. Overlaps can happen since this score is derived from transcripts and multiple transcripts can overlap. A horizontal line is drawn on the 0.8 score line to roughly represent the 25th percentile, meaning the items below may be of particular interest. It is recommended that the data be explored using this version of the track, as it condenses the information substantially while retaining the magnitude of the data. Any specific point mutations of interest can then be researched in the MTR All data track. This track contains all of the information from MTRV2 including more than 3 possible scores per base when transcripts overlap. A mouse-over on this track shows the ref and alt allele, as well as the MTR score and the MTR score percentile. Filters are available for MTR score, False Discovery Rate (FDR), MTR percentile, and variant consequence. By default, only items in the bottom 25 percentile are shown. Items in the track are colored according to their MTR percentile: Green items MTR percentiles over 75 Black items MTR percentiles between 25 and 75 Red items MTR percentiles below 25 Blue items No MTR score Interpretation: Regions with low MTR scores were seen to be enriched with pathogenic variants. For example, ClinVar pathogenic variants were seen to have an average score of 0.77 whereas ClinVar benign variants had an average score of 0.92. Further validation using the FATHMM cancer-associated training dataset saw that scores less than 0.5 contained 8.6% of the pathogenic variants while only containing 0.9% of neutral variants. In summary, lower scores are more likely to represent pathogenic variants whereas higher scores could be pathogenic, but have a higher chance to be a false positive. For more information see the MTR-Viewer publication. Methods JARVIS Scores were downloaded and converted to a single bigWig file. See the hg19 makeDoc and the hg38 makeDoc for more info. HMC Scores were downloaded and converted to .bedGraph files with a custom Python script. The bedGraph files were then converted to bigWig files, as documented in our makeDoc hg19 build log. MetaDome The authors provided a bed file containing codon coordinates along with the scores. This file was parsed with a python script to create the two tracks. For the first track the scores were aggregated for each coordinate, then the lowest score chosen for any overlaps and the result written out to bedGraph format. The file was then converted to bigWig with the bedGraphToBigWig utility. For the second track the file was reorganized into a bed 4+3 and conveted to bigBed with the bedToBigBed utility. See the hg19 makeDoc for details including the build script. The raw MetaDome data can also be accessed via their Zenodo handle. MTR V2 file was downloaded and columns were reshuffled as well as itemRgb added for the MTR All data track. For the MTR Scores track the file was parsed with a python script to pull out the highest possible MTR score for each of the 3 possible mutations at each base pair and 4 tracks built out of these values representing each mutation. See the hg19 makeDoc entry on MTR for more info. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated access, this track, like all others, is available via our API. However, for bulk processing, it is recommended to download the dataset. For automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed files that can be downloaded from our download server. Individual regions or the whole genome annotation can be obtained using our tools bigWigToWig or bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tools can also be used to obtain features confined to a given range, e.g., bigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/hmc/hmc.bw stdout Please refer to our Data Access FAQ for more information. Credits Thanks to Jean-Madeleine Desainteagathe (APHP Paris, France) for suggesting the JARVIS, MTR, HMC tracks. Thanks to Xialei Zhang for providing the HMC data file and to Dimitrios Vitsios and Slave Petrovski for helping clean up the hg38 JARVIS files for providing guidance on interpretation. Additional thanks to Laurens van de Wiel for providing the MetaDome data as well as guidance on the track development and interpretation. References Vitsios D, Dhindsa RS, Middleton L, Gussow AB, Petrovski S. Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning. Nat Commun. 2021 Mar 8;12(1):1504. PMID: 33686085; PMC: PMC7940646 Xiaolei Zhang, Pantazis I. Theotokis, Nicholas Li, the SHaRe Investigators, Caroline F. Wright, Kaitlin E. Samocha, Nicola Whiffin, James S. Ware Genetic constraint at single amino acid resolution improves missense variant prioritisation and gene discovery. Medrxiv 2022.02.16.22271023 Wiel L, Baakman C, Gilissen D, Veltman JA, Vriend G, Gilissen C. MetaDome: Pathogenicity analysis of genetic variants through aggregation of homologous human protein domains. Hum Mutat. 2019 Aug;40(8):1030-1038. PMID: 31116477; PMC: PMC6772141 Silk M, Petrovski S, Ascher DB. MTR-Viewer: identifying regions within genes under purifying selection. Nucleic Acids Res. 2019 Jul 2;47(W1):W121-W126. PMID: 31170280; PMC: PMC6602522 Halldorsson BV, Eggertsson HP, Moore KHS, Hauswedell H, Eiriksson O, Ulfarsson MO, Palsson G, Hardarson MT, Oddsson A, Jensson BO et al. The sequences of 150,119 genomes in the UK Biobank. Nature. 2022 Jul;607(7920):732-740. PMID: 35859178; PMC: PMC9329122 
wgEncodeGencodeV47 All GENCODE V47 All GENCODE annotations from V47 (Ensembl 113) Genes and Gene Predictions Description The GENCODE Genes track (version 47, Oct 2024) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The 47 annotation was carried out on genome assembly GRCh38 (hg38). The Ensembl human and mouse data sets are the same gene annotations as GENCODE for the corresponding release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 47 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 47 corresponds to Ensembl 113. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeSuper GENCODE Versions Container of all new and previous GENCODE releases Genes and Gene Predictions Description The aim of the GENCODE Genes project (Harrow et al., 2006) is to produce a set of highly accurate annotations of evidence-based gene features on the human reference genome. This includes the identification of all protein-coding loci with associated alternative splice variants, non-coding with transcript evidence in the public databases (NCBI/EMBL/DDBJ) and pseudogenes. A high quality set of gene structures is necessary for many research studies such as comparative or evolutionary analyses, or for experimental design and interpretation of the results. The GENCODE Genes tracks display the high-quality manual annotations merged with evidence-based automated annotations across the entire human genome. The GENCODE gene set presents a full merge between HAVANA manual annotation and Ensembl automatic annotation. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. With each release, there is an increase in the number of annotations that have undergone manual curation. This annotation was carried out on the GRCh38 (hg38) genome assembly. For more information on the different gene tracks, see our Genes FAQ. Display Conventions These are multi-view composite tracks that contain differing data sets (views). Instructions for configuring multi-view tracks are here. Only some subtracks are shown by default. The user can select which subtracks are displayed via the display controls on the track details pages. Further details on display conventions and data interpretation are available in the track descriptions. Data access GENCODE Genes and its associated tables can be explored interactively using the REST API, the Table Browser or the Data Integrator. The GENCODE data files for hg38 are available in our downloads directory as wgEncodeGencode* files in genePred format. All the tables can also be queried directly from our public MySQL servers, with instructions on this method available on our MySQL help page as well as on our blog. Release Notes GENCODE version 47 corresponds to Ensembl 113. GENCODE version 46 corresponds to Ensembl 112. GENCODE version 45 corresponds to Ensembl 111. GENCODE version 44 corresponds to Ensembl 110. GENCODE version 43 corresponds to Ensembl 109. GENCODE version 42 corresponds to Ensembl 108. GENCODE version 41 corresponds to Ensembl 107. GENCODE version 40 corresponds to Ensembl 106. GENCODE version 39 corresponds to Ensembl 105. GENCODE version 38 corresponds to Ensembl 104. GENCODE version 37 corresponds to Ensembl 103. GENCODE version 36 corresponds to Ensembl 102. GENCODE version 35 corresponds to Ensembl 101. GENCODE version 34 corresponds to Ensembl 100. GENCODE version 33 corresponds to Ensembl 99. GENCODE version 30 corresponds to Ensembl 96. GENCODE version 29 corresponds to Ensembl 94. GENCODE version 28 corresponds to Ensembl 92. GENCODE version 27 corresponds to Ensembl 90. GENCODE version 26 corresponds to Ensembl 88. GENCODE version 24 corresponds to Ensembl 84. GENCODE version 23 corresponds to Ensembl 81. GENCODE version 22 corresponds to Ensembl 79. GENCODE version 20 corresponds to Ensembl 76. See also: The GENCODE Project Release History. Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV47ViewPolya PolyA All GENCODE annotations from V47 (Ensembl 113) Genes and Gene Predictions 
wgEncodeGencodePolyaV47 PolyA PolyA Transcript Annotation Set from GENCODE Version 47 (Ensembl 113) Genes and Gene Predictions 
wgEncodeGencodeV47ViewGenes Genes All GENCODE annotations from V47 (Ensembl 113) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV47 Pseudogenes Pseudogene Annotation Set from GENCODE Version 47 (Ensembl 113) Genes and Gene Predictions 
wgEncodeGencodeCompV47 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 47 (Ensembl 113) Genes and Gene Predictions 
wgEncodeGencodeBasicV47 Basic Basic Gene Annotation Set from GENCODE Version 47 (Ensembl 113) Genes and Gene Predictions 
wgEncodeGencodeV46 All GENCODE V46 All GENCODE annotations from V46 (Ensembl 112) Genes and Gene Predictions Description The GENCODE Genes track (version 46, May 2024) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The 46 annotation was carried out on genome assembly GRCh38 (hg38). The Ensembl human and mouse data sets are the same gene annotations as GENCODE for the corresponding release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 46 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 46 corresponds to Ensembl 112. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV46ViewPolya PolyA All GENCODE annotations from V46 (Ensembl 112) Genes and Gene Predictions 
wgEncodeGencodePolyaV46 PolyA PolyA Transcript Annotation Set from GENCODE Version 46 (Ensembl 112) Genes and Gene Predictions 
wgEncodeGencodeV46ViewGenes Genes All GENCODE annotations from V46 (Ensembl 112) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV46 Pseudogenes Pseudogene Annotation Set from GENCODE Version 46 (Ensembl 112) Genes and Gene Predictions 
wgEncodeGencodeCompV46 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 46 (Ensembl 112) Genes and Gene Predictions 
wgEncodeGencodeBasicV46 Basic Basic Gene Annotation Set from GENCODE Version 46 (Ensembl 112) Genes and Gene Predictions 
wgEncodeGencodeV45 All GENCODE V45 All GENCODE annotations from V45 (Ensembl 111) Genes and Gene Predictions Description The GENCODE Genes track (version 45, Jan 2024) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The 45 annotation was carried out on genome assembly GRCh38 (hg38). The Ensembl human and mouse data sets are the same gene annotations as GENCODE for the corresponding release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 45 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 45 corresponds to Ensembl 111. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV45ViewPolya PolyA All GENCODE annotations from V45 (Ensembl 111) Genes and Gene Predictions 
wgEncodeGencodePolyaV45 PolyA PolyA Transcript Annotation Set from GENCODE Version 45 (Ensembl 111) Genes and Gene Predictions 
wgEncodeGencodeV45ViewGenes Genes All GENCODE annotations from V45 (Ensembl 111) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV45 Pseudogenes Pseudogene Annotation Set from GENCODE Version 45 (Ensembl 111) Genes and Gene Predictions 
wgEncodeGencodeCompV45 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 45 (Ensembl 111) Genes and Gene Predictions 
wgEncodeGencodeBasicV45 Basic Basic Gene Annotation Set from GENCODE Version 45 (Ensembl 111) Genes and Gene Predictions 
wgEncodeGencodeV44 All GENCODE V44 All GENCODE annotations from V44 (Ensembl 110) Genes and Gene Predictions Description The GENCODE Genes track (version 44, July 2023) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The 44 annotation was carried out on genome assembly GRCh38 (hg38). The Ensembl human and mouse data sets are the same gene annotations as GENCODE for the corresponding release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 44 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 44 corresponds to Ensembl 110. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV44ViewPolya PolyA All GENCODE annotations from V44 (Ensembl 110) Genes and Gene Predictions 
wgEncodeGencodePolyaV44 PolyA PolyA Transcript Annotation Set from GENCODE Version 44 (Ensembl 110) Genes and Gene Predictions 
wgEncodeGencodeV44ViewGenes Genes All GENCODE annotations from V44 (Ensembl 110) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV44 Pseudogenes Pseudogene Annotation Set from GENCODE Version 44 (Ensembl 110) Genes and Gene Predictions 
wgEncodeGencodeCompV44 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 44 (Ensembl 110) Genes and Gene Predictions 
wgEncodeGencodeBasicV44 Basic Basic Gene Annotation Set from GENCODE Version 44 (Ensembl 110) Genes and Gene Predictions 
wgEncodeGencodeV43 All GENCODE V43 All GENCODE annotations from V43 (Ensembl 109) Genes and Gene Predictions Description The GENCODE Genes track (version 43, Feb 2023) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The 43 annotation was carried out on genome assembly GRCh38 (hg38). The Ensembl human and mouse data sets are the same gene annotations as GENCODE for the corresponding release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 43 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 43 corresponds to Ensembl 109. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV43ViewPolya PolyA All GENCODE annotations from V43 (Ensembl 109) Genes and Gene Predictions 
wgEncodeGencodePolyaV43 PolyA PolyA Transcript Annotation Set from GENCODE Version 43 (Ensembl 109) Genes and Gene Predictions 
wgEncodeGencodeV43ViewGenes Genes All GENCODE annotations from V43 (Ensembl 109) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV43 Pseudogenes Pseudogene Annotation Set from GENCODE Version 43 (Ensembl 109) Genes and Gene Predictions 
wgEncodeGencodeCompV43 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 43 (Ensembl 109) Genes and Gene Predictions 
wgEncodeGencodeBasicV43 Basic Basic Gene Annotation Set from GENCODE Version 43 (Ensembl 109) Genes and Gene Predictions 
wgEncodeGencodeV42 All GENCODE V42 All GENCODE annotations from V42 (Ensembl 108) Genes and Gene Predictions Description The GENCODE Genes track (version 42, Oct 2022) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The 42 annotation was carried out on genome assembly GRCh38 (hg38). The Ensembl human and mouse data sets are the same gene annotations as GENCODE for the corresponding release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 42 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 42 corresponds to Ensembl 108. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV42ViewPolya PolyA All GENCODE annotations from V42 (Ensembl 108) Genes and Gene Predictions 
wgEncodeGencodePolyaV42 PolyA PolyA Transcript Annotation Set from GENCODE Version 42 (Ensembl 108) Genes and Gene Predictions 
wgEncodeGencodeV42ViewGenes Genes All GENCODE annotations from V42 (Ensembl 108) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV42 Pseudogenes Pseudogene Annotation Set from GENCODE Version 42 (Ensembl 108) Genes and Gene Predictions 
wgEncodeGencodeCompV42 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 42 (Ensembl 108) Genes and Gene Predictions 
wgEncodeGencodeBasicV42 Basic Basic Gene Annotation Set from GENCODE Version 42 (Ensembl 108) Genes and Gene Predictions 
wgEncodeGencodeV41 All GENCODE V41 All GENCODE annotations from V41 (Ensembl 107) Genes and Gene Predictions Description The GENCODE Genes track (version 41, July 2022) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The 41 annotation was carried out on genome assembly GRCh38 (hg38). The Ensembl human and mouse data sets are the same gene annotations as GENCODE for the corresponding release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 41 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 41 corresponds to Ensembl 107. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV41ViewPolya PolyA All GENCODE annotations from V41 (Ensembl 107) Genes and Gene Predictions 
wgEncodeGencodePolyaV41 PolyA PolyA Transcript Annotation Set from GENCODE Version 41 (Ensembl 107) Genes and Gene Predictions 
wgEncodeGencodeV41ViewGenes Genes All GENCODE annotations from V41 (Ensembl 107) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV41 Pseudogenes Pseudogene Annotation Set from GENCODE Version 41 (Ensembl 107) Genes and Gene Predictions 
wgEncodeGencodeCompV41 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 41 (Ensembl 107) Genes and Gene Predictions 
wgEncodeGencodeBasicV41 Basic Basic Gene Annotation Set from GENCODE Version 41 (Ensembl 107) Genes and Gene Predictions 
wgEncodeGencodeV41View2Way 2-Way All GENCODE annotations from V41 (Ensembl 107) Genes and Gene Predictions 
wgEncodeGencode2wayConsPseudoV41 2-way Pseudogenes 2-way Pseudogene Annotation Set from GENCODE Version 41 (Ensembl 107) Genes and Gene Predictions 
wgEncodeGencodeV40 All GENCODE V40 All GENCODE annotations from V40 (Ensembl 106) Genes and Gene Predictions Description The GENCODE Genes track (version 40, Feb 2022) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The 40 annotation was carried out on genome assembly GRCh38 (hg38). The Ensembl human and mouse data sets are the same gene annotations as GENCODE for the corresponding release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 40 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 40 corresponds to Ensembl 106. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV40ViewPolya PolyA All GENCODE annotations from V40 (Ensembl 106) Genes and Gene Predictions 
wgEncodeGencodePolyaV40 PolyA PolyA Transcript Annotation Set from GENCODE Version 40 (Ensembl 106) Genes and Gene Predictions 
wgEncodeGencodeV40ViewGenes Genes All GENCODE annotations from V40 (Ensembl 106) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV40 Pseudogenes Pseudogene Annotation Set from GENCODE Version 40 (Ensembl 106) Genes and Gene Predictions 
wgEncodeGencodeCompV40 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 40 (Ensembl 106) Genes and Gene Predictions 
wgEncodeGencodeBasicV40 Basic Basic Gene Annotation Set from GENCODE Version 40 (Ensembl 106) Genes and Gene Predictions 
wgEncodeGencodeV40View2Way 2-Way All GENCODE annotations from V40 (Ensembl 106) Genes and Gene Predictions 
wgEncodeGencode2wayConsPseudoV40 2-way Pseudogenes 2-way Pseudogene Annotation Set from GENCODE Version 40 (Ensembl 106) Genes and Gene Predictions 
wgEncodeGencodeV39 All GENCODE V39 All GENCODE annotations from V39 (Ensembl 105) Genes and Gene Predictions Description The GENCODE Genes track (version 39, Oct 2021) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The 39 annotation was carried out on genome assembly GRCh38 (hg38). The Ensembl human and mouse data sets are the same gene annotations as GENCODE for the corresponding release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 39 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 39 corresponds to Ensembl 105. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV39ViewPolya PolyA All GENCODE annotations from V39 (Ensembl 105) Genes and Gene Predictions 
wgEncodeGencodePolyaV39 PolyA PolyA Transcript Annotation Set from GENCODE Version 39 (Ensembl 105) Genes and Gene Predictions 
wgEncodeGencodeV39ViewGenes Genes All GENCODE annotations from V39 (Ensembl 105) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV39 Pseudogenes Pseudogene Annotation Set from GENCODE Version 39 (Ensembl 105) Genes and Gene Predictions 
wgEncodeGencodeCompV39 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 39 (Ensembl 105) Genes and Gene Predictions 
wgEncodeGencodeBasicV39 Basic Basic Gene Annotation Set from GENCODE Version 39 (Ensembl 105) Genes and Gene Predictions 
wgEncodeGencodeV39View2Way 2-Way All GENCODE annotations from V39 (Ensembl 105) Genes and Gene Predictions 
wgEncodeGencode2wayConsPseudoV39 2-way Pseudogenes 2-way Pseudogene Annotation Set from GENCODE Version 39 (Ensembl 105) Genes and Gene Predictions 
wgEncodeGencodeV38 All GENCODE V38 All GENCODE annotations from V38 (Ensembl 104) Genes and Gene Predictions Description The GENCODE Genes track (version 38, May 2021) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The 38 annotation was carried out on genome assembly GRCh38 (hg38). The Ensembl human and mouse data sets are the same gene annotations as GENCODE for the corresponding release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 38 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 38 corresponds to Ensembl 104. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV38ViewPolya PolyA All GENCODE annotations from V38 (Ensembl 104) Genes and Gene Predictions 
wgEncodeGencodePolyaV38 PolyA PolyA Transcript Annotation Set from GENCODE Version 38 (Ensembl 104) Genes and Gene Predictions 
wgEncodeGencodeV38ViewGenes Genes All GENCODE annotations from V38 (Ensembl 104) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV38 Pseudogenes Pseudogene Annotation Set from GENCODE Version 38 (Ensembl 104) Genes and Gene Predictions 
wgEncodeGencodeCompV38 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 38 (Ensembl 104) Genes and Gene Predictions 
wgEncodeGencodeBasicV38 Basic Basic Gene Annotation Set from GENCODE Version 38 (Ensembl 104) Genes and Gene Predictions 
wgEncodeGencodeV38View2Way 2-Way All GENCODE annotations from V38 (Ensembl 104) Genes and Gene Predictions 
wgEncodeGencode2wayConsPseudoV38 2-way Pseudogenes 2-way Pseudogene Annotation Set from GENCODE Version 38 (Ensembl 104) Genes and Gene Predictions 
wgEncodeGencodeV37 All GENCODE V37 All GENCODE annotations from V37 (Ensembl 103) Genes and Gene Predictions Description The GENCODE Genes track (version 37, Feb 2021) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The 37 annotation was carried out on genome assembly GRCh38 (hg38). The Ensembl human and mouse data sets are the same gene annotations as GENCODE for the corresponding release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 37 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 37 corresponds to Ensembl 103. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV37ViewPolya PolyA All GENCODE annotations from V37 (Ensembl 103) Genes and Gene Predictions 
wgEncodeGencodePolyaV37 PolyA PolyA Transcript Annotation Set from GENCODE Version 37 (Ensembl 103) Genes and Gene Predictions 
wgEncodeGencodeV37ViewGenes Genes All GENCODE annotations from V37 (Ensembl 103) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV37 Pseudogenes Pseudogene Annotation Set from GENCODE Version 37 (Ensembl 103) Genes and Gene Predictions 
wgEncodeGencodeCompV37 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 37 (Ensembl 103) Genes and Gene Predictions 
wgEncodeGencodeBasicV37 Basic Basic Gene Annotation Set from GENCODE Version 37 (Ensembl 103) Genes and Gene Predictions 
wgEncodeGencodeV37View2Way 2-Way All GENCODE annotations from V37 (Ensembl 103) Genes and Gene Predictions 
wgEncodeGencode2wayConsPseudoV37 2-way Pseudogenes 2-way Pseudogene Annotation Set from GENCODE Version 37 (Ensembl 103) Genes and Gene Predictions 
wgEncodeGencodeV36 All GENCODE V36 All GENCODE annotations from V36 (Ensembl 102) Genes and Gene Predictions Description The GENCODE Genes track (version 36, Nov 2020) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The 36 annotation was carried out on genome assembly GRCh38 (hg38). The Ensembl human and mouse data sets are the same gene annotations as GENCODE for the corresponding release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 36 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 36 corresponds to Ensembl 102. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV36ViewPolya PolyA All GENCODE annotations from V36 (Ensembl 102) Genes and Gene Predictions 
wgEncodeGencodePolyaV36 PolyA PolyA Transcript Annotation Set from GENCODE Version 36 (Ensembl 102) Genes and Gene Predictions 
wgEncodeGencodeV36ViewGenes Genes All GENCODE annotations from V36 (Ensembl 102) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV36 Pseudogenes Pseudogene Annotation Set from GENCODE Version 36 (Ensembl 102) Genes and Gene Predictions 
wgEncodeGencodeCompV36 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 36 (Ensembl 102) Genes and Gene Predictions 
wgEncodeGencodeBasicV36 Basic Basic Gene Annotation Set from GENCODE Version 36 (Ensembl 102) Genes and Gene Predictions 
wgEncodeGencodeV36View2Way 2-Way All GENCODE annotations from V36 (Ensembl 102) Genes and Gene Predictions 
wgEncodeGencode2wayConsPseudoV36 2-way Pseudogenes 2-way Pseudogene Annotation Set from GENCODE Version 36 (Ensembl 102) Genes and Gene Predictions 
wgEncodeGencodeV35 All GENCODE V35 All GENCODE annotations from V35 (Ensembl 101) Genes and Gene Predictions Description The GENCODE Genes track (version 35, Aug 2020) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The 35 annotation was carried out on genome assembly GRCh38 (hg38). The Ensembl human and mouse data sets are the same gene annotations as GENCODE for the corresponding release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 35 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 35 corresponds to Ensembl 101. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV35ViewPolya PolyA All GENCODE annotations from V35 (Ensembl 101) Genes and Gene Predictions 
wgEncodeGencodePolyaV35 PolyA PolyA Transcript Annotation Set from GENCODE Version 35 (Ensembl 101) Genes and Gene Predictions 
wgEncodeGencodeV35ViewGenes Genes All GENCODE annotations from V35 (Ensembl 101) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV35 Pseudogenes Pseudogene Annotation Set from GENCODE Version 35 (Ensembl 101) Genes and Gene Predictions 
wgEncodeGencodeCompV35 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 35 (Ensembl 101) Genes and Gene Predictions 
wgEncodeGencodeBasicV35 Basic Basic Gene Annotation Set from GENCODE Version 35 (Ensembl 101) Genes and Gene Predictions 
wgEncodeGencodeV35View2Way 2-Way All GENCODE annotations from V35 (Ensembl 101) Genes and Gene Predictions 
wgEncodeGencode2wayConsPseudoV35 2-way Pseudogenes 2-way Pseudogene Annotation Set from GENCODE Version 35 (Ensembl 101) Genes and Gene Predictions 
wgEncodeGencodeV34 All GENCODE V34 All GENCODE annotations from V34 (Ensembl 100) Genes and Gene Predictions Description The GENCODE Genes track (version 34, April 2020) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The 34 annotation was carried out on genome assembly GRCh38 (hg38). The Ensembl human and mouse data sets are the same gene annotations as GENCODE for the corresponding release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 34 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 34 corresponds to Ensembl 100. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV34ViewPolya PolyA All GENCODE annotations from V34 (Ensembl 100) Genes and Gene Predictions 
wgEncodeGencodePolyaV34 PolyA PolyA Transcript Annotation Set from GENCODE Version 34 (Ensembl 100) Genes and Gene Predictions 
wgEncodeGencodeV34ViewGenes Genes All GENCODE annotations from V34 (Ensembl 100) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV34 Pseudogenes Pseudogene Annotation Set from GENCODE Version 34 (Ensembl 100) Genes and Gene Predictions 
wgEncodeGencodeCompV34 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 34 (Ensembl 100) Genes and Gene Predictions 
wgEncodeGencodeBasicV34 Basic Basic Gene Annotation Set from GENCODE Version 34 (Ensembl 100) Genes and Gene Predictions 
wgEncodeGencodeV34View2Way 2-Way All GENCODE annotations from V34 (Ensembl 100) Genes and Gene Predictions 
wgEncodeGencode2wayConsPseudoV34 2-way Pseudogenes 2-way Pseudogene Annotation Set from GENCODE Version 34 (Ensembl 100) Genes and Gene Predictions 
wgEncodeGencodeV33 All GENCODE V33 All GENCODE annotations from V33 (Ensembl 99) Genes and Gene Predictions Description The GENCODE Genes track (version 33, Jan 2020) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The 33 annotation was carried out on genome assembly GRCh38 (hg38). The Ensembl human and mouse data sets are the same gene annotations as GENCODE for the corresponding release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 33 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 33 corresponds to Ensembl 99. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV33ViewPolya PolyA All GENCODE annotations from V33 (Ensembl 99) Genes and Gene Predictions 
wgEncodeGencodePolyaV33 PolyA PolyA Transcript Annotation Set from GENCODE Version 33 (Ensembl 99) Genes and Gene Predictions 
wgEncodeGencodeV33ViewGenes Genes All GENCODE annotations from V33 (Ensembl 99) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV33 Pseudogenes Pseudogene Annotation Set from GENCODE Version 33 (Ensembl 99) Genes and Gene Predictions 
wgEncodeGencodeCompV33 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 33 (Ensembl 99) Genes and Gene Predictions 
wgEncodeGencodeBasicV33 Basic Basic Gene Annotation Set from GENCODE Version 33 (Ensembl 99) Genes and Gene Predictions 
wgEncodeGencodeV33View2Way 2-Way All GENCODE annotations from V33 (Ensembl 99) Genes and Gene Predictions 
wgEncodeGencode2wayConsPseudoV33 2-way Pseudogenes 2-way Pseudogene Annotation Set from GENCODE Version 33 (Ensembl 99) Genes and Gene Predictions 
wgEncodeGencodeV32 All GENCODE V32 All GENCODE annotations from V32 (Ensembl 98) Genes and Gene Predictions Description The GENCODE Genes track (version 32, Sept 2019) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The 32 annotation was carried out on genome assembly GRCh38 (hg38). The Ensembl human and mouse data sets are the same gene annotations as GENCODE for the corresponding release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 32 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 32 corresponds to Ensembl 98. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV32ViewPolya PolyA All GENCODE annotations from V32 (Ensembl 98) Genes and Gene Predictions 
wgEncodeGencodePolyaV32 PolyA PolyA Transcript Annotation Set from GENCODE Version 32 (Ensembl 98) Genes and Gene Predictions 
wgEncodeGencodeV32ViewGenes Genes All GENCODE annotations from V32 (Ensembl 98) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV32 Pseudogenes Pseudogene Annotation Set from GENCODE Version 32 (Ensembl 98) Genes and Gene Predictions 
wgEncodeGencodeCompV32 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 32 (Ensembl 98) Genes and Gene Predictions 
wgEncodeGencodeBasicV32 Basic Basic Gene Annotation Set from GENCODE Version 32 (Ensembl 98) Genes and Gene Predictions 
wgEncodeGencodeV32View2Way 2-Way All GENCODE annotations from V32 (Ensembl 98) Genes and Gene Predictions 
wgEncodeGencode2wayConsPseudoV32 2-way Pseudogenes 2-way Pseudogene Annotation Set from GENCODE Version 32 (Ensembl 98) Genes and Gene Predictions 
wgEncodeGencodeV31 All GENCODE V31 All GENCODE annotations from V31 (Ensembl 97) Genes and Gene Predictions Description The GENCODE Genes track (version 31, June 2019) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The 31 annotation was carried out on genome assembly GRCh38 (hg38). The Ensembl human and mouse data sets are the same gene annotations as GENCODE for the corresponding release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 31 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 31 corresponds to Ensembl 97. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV31ViewPolya PolyA All GENCODE annotations from V31 (Ensembl 97) Genes and Gene Predictions 
wgEncodeGencodePolyaV31 PolyA PolyA Transcript Annotation Set from GENCODE Version 31 (Ensembl 97) Genes and Gene Predictions 
wgEncodeGencodeV31ViewGenes Genes All GENCODE annotations from V31 (Ensembl 97) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV31 Pseudogenes Pseudogene Annotation Set from GENCODE Version 31 (Ensembl 97) Genes and Gene Predictions 
wgEncodeGencodeCompV31 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 31 (Ensembl 97) Genes and Gene Predictions 
wgEncodeGencodeBasicV31 Basic Basic Gene Annotation Set from GENCODE Version 31 (Ensembl 97) Genes and Gene Predictions 
wgEncodeGencodeV31View2Way 2-Way All GENCODE annotations from V31 (Ensembl 97) Genes and Gene Predictions 
wgEncodeGencode2wayConsPseudoV31 2-way Pseudogenes 2-way Pseudogene Annotation Set from GENCODE Version 31 (Ensembl 97) Genes and Gene Predictions 
wgEncodeGencodeV30 All GENCODE V30 All GENCODE annotations from V30 (Ensembl 96) Genes and Gene Predictions Description The GENCODE Genes track (version 30, Apr 2019) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The 30 annotation was carried out on genome assembly GRCh38 (hg38). The Ensembl human and mouse data sets are the same gene annotations as GENCODE for the corresponding release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 30 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 30 corresponds to Ensembl 96. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV30ViewPolya PolyA All GENCODE annotations from V30 (Ensembl 96) Genes and Gene Predictions 
wgEncodeGencodePolyaV30 PolyA PolyA Transcript Annotation Set from GENCODE Version 30 (Ensembl 96) Genes and Gene Predictions 
wgEncodeGencodeV30ViewGenes Genes All GENCODE annotations from V30 (Ensembl 96) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV30 Pseudogenes Pseudogene Annotation Set from GENCODE Version 30 (Ensembl 96) Genes and Gene Predictions 
wgEncodeGencodeCompV30 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 30 (Ensembl 96) Genes and Gene Predictions 
wgEncodeGencodeBasicV30 Basic Basic Gene Annotation Set from GENCODE Version 30 (Ensembl 96) Genes and Gene Predictions 
wgEncodeGencodeV30View2Way 2-Way All GENCODE annotations from V30 (Ensembl 96) Genes and Gene Predictions 
wgEncodeGencode2wayConsPseudoV30 2-way Pseudogenes 2-way Pseudogene Annotation Set from GENCODE Version 30 (Ensembl 96) Genes and Gene Predictions 
wgEncodeGencodeV29 All GENCODE V29 All GENCODE annotations from V29 (Ensembl 94) Genes and Gene Predictions Description The GENCODE Genes track (version 29, Oct 2018) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The 29 annotation was carried out on genome assembly GRCh38 (hg38). The Ensembl human and mouse data sets are the same gene annotations as GENCODE for the corresponding release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 29 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 29 corresponds to Ensembl 94. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV29ViewPolya PolyA All GENCODE annotations from V29 (Ensembl 94) Genes and Gene Predictions 
wgEncodeGencodePolyaV29 PolyA PolyA Transcript Annotation Set from GENCODE Version 29 (Ensembl 94) Genes and Gene Predictions 
wgEncodeGencodeV29ViewGenes Genes All GENCODE annotations from V29 (Ensembl 94) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV29 Pseudogenes Pseudogene Annotation Set from GENCODE Version 29 (Ensembl 94) Genes and Gene Predictions 
wgEncodeGencodeCompV29 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 29 (Ensembl 94) Genes and Gene Predictions 
wgEncodeGencodeBasicV29 Basic Basic Gene Annotation Set from GENCODE Version 29 (Ensembl 94) Genes and Gene Predictions 
wgEncodeGencodeV29View2Way 2-Way All GENCODE annotations from V29 (Ensembl 94) Genes and Gene Predictions 
wgEncodeGencode2wayConsPseudoV29 2-way Pseudogenes 2-way Pseudogene Annotation Set from GENCODE Version 29 (Ensembl 94) Genes and Gene Predictions 
wgEncodeGencodeV28 All GENCODE V28 All GENCODE annotations from V28 (Ensembl 92) Genes and Gene Predictions Description The GENCODE Genes track (version 28, Apr 2018) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The 28 annotation was carried out on genome assembly GRCh38 (hg38). The Ensembl human and mouse data sets are the same gene annotations as GENCODE for the corresponding release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 28 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 28 corresponds to Ensembl 92. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV28ViewPolya PolyA All GENCODE annotations from V28 (Ensembl 92) Genes and Gene Predictions 
wgEncodeGencodePolyaV28 PolyA PolyA Transcript Annotation Set from GENCODE Version 28 (Ensembl 92) Genes and Gene Predictions 
wgEncodeGencodeV28ViewGenes Genes All GENCODE annotations from V28 (Ensembl 92) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV28 Pseudogenes Pseudogene Annotation Set from GENCODE Version 28 (Ensembl 92) Genes and Gene Predictions 
wgEncodeGencodeCompV28 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 28 (Ensembl 92) Genes and Gene Predictions 
wgEncodeGencodeBasicV28 Basic Basic Gene Annotation Set from GENCODE Version 28 (Ensembl 92) Genes and Gene Predictions 
wgEncodeGencodeV28View2Way 2-Way All GENCODE annotations from V28 (Ensembl 92) Genes and Gene Predictions 
wgEncodeGencode2wayConsPseudoV28 2-way Pseudogenes 2-way Pseudogene Annotation Set from GENCODE Version 28 (Ensembl 92) Genes and Gene Predictions 
wgEncodeGencodeV27 All GENCODE V27 All GENCODE annotations from V27 (Ensembl 90) Genes and Gene Predictions Description The GENCODE Genes track (version 27, Aug 2017) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The 27 annotation was carried out on genome assembly GRCh38 (hg38). The Ensembl human and mouse data sets are the same gene annotations as GENCODE for the corresponding release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 27 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 27 corresponds to Ensembl 90. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV27ViewPolya PolyA All GENCODE annotations from V27 (Ensembl 90) Genes and Gene Predictions 
wgEncodeGencodePolyaV27 PolyA PolyA Transcript Annotation Set from GENCODE Version 27 (Ensembl 90) Genes and Gene Predictions 
wgEncodeGencodeV27ViewGenes Genes All GENCODE annotations from V27 (Ensembl 90) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV27 Pseudogenes Pseudogene Annotation Set from GENCODE Version 27 (Ensembl 90) Genes and Gene Predictions 
wgEncodeGencodeCompV27 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 27 (Ensembl 90) Genes and Gene Predictions 
wgEncodeGencodeBasicV27 Basic Basic Gene Annotation Set from GENCODE Version 27 (Ensembl 90) Genes and Gene Predictions 
wgEncodeGencodeV27View2Way 2-Way All GENCODE annotations from V27 (Ensembl 90) Genes and Gene Predictions 
wgEncodeGencode2wayConsPseudoV27 2-way Pseudogenes 2-way Pseudogene Annotation Set from GENCODE Version 27 (Ensembl 90) Genes and Gene Predictions 
wgEncodeGencodeV26 All GENCODE V26 All GENCODE annotations from V26 (Ensembl 88) Genes and Gene Predictions Description The GENCODE Genes track (version 26, March 2017) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The 26 annotation was carried out on genome assembly GRCh38 (hg38). The Ensembl human and mouse data sets are the same gene annotations as GENCODE for the corresponding release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 26 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 26 corresponds to Ensembl 88. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV26ViewPolya PolyA All GENCODE annotations from V26 (Ensembl 88) Genes and Gene Predictions 
wgEncodeGencodePolyaV26 PolyA PolyA Transcript Annotation Set from GENCODE Version 26 (Ensembl 88) Genes and Gene Predictions 
wgEncodeGencodeV26ViewGenes Genes All GENCODE annotations from V26 (Ensembl 88) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV26 Pseudogenes Pseudogene Annotation Set from GENCODE Version 26 (Ensembl 88) Genes and Gene Predictions 
wgEncodeGencodeCompV26 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 26 (Ensembl 88) Genes and Gene Predictions 
wgEncodeGencodeBasicV26 Basic Basic Gene Annotation Set from GENCODE Version 26 (Ensembl 88) Genes and Gene Predictions 
wgEncodeGencodeV26View2Way 2-Way All GENCODE annotations from V26 (Ensembl 88) Genes and Gene Predictions 
wgEncodeGencode2wayConsPseudoV26 2-way Pseudogenes 2-way Pseudogene Annotation Set from GENCODE Version 26 (Ensembl 88) Genes and Gene Predictions 
wgEncodeGencodeV25 All GENCODE V25 All GENCODE transcripts including comprehensive set V25 Genes and Gene Predictions Description The GENCODE Genes track (version 25, July 2016) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The annotation was carried out on genome assembly GRCh38 (hg38). As of GENCODE Version 11, Ensembl and GENCODE have converged. The gene annotations in the GENCODE comprehensive set are the same as the corresponding Ensembl release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 25 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. --> GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 25 corresponds to Ensembl 85. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV25ViewPolya PolyA All GENCODE transcripts including comprehensive set V25 Genes and Gene Predictions 
wgEncodeGencodePolyaV25 PolyA PolyA Transcript Annotation Set from GENCODE Version 25 (Ensembl 85) Genes and Gene Predictions 
wgEncodeGencodeV25ViewGenes Genes All GENCODE transcripts including comprehensive set V25 Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV25 Pseudogenes Pseudogene Annotation Set from GENCODE Version 25 (Ensembl 85) Genes and Gene Predictions 
wgEncodeGencodeCompV25 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 25 (Ensembl 85) Genes and Gene Predictions 
wgEncodeGencodeBasicV25 Basic Basic Gene Annotation Set from GENCODE Version 25 (Ensembl 85) Genes and Gene Predictions 
wgEncodeGencodeV25View2Way 2-Way All GENCODE transcripts including comprehensive set V25 Genes and Gene Predictions 
wgEncodeGencode2wayConsPseudoV25 2-way Pseudogenes 2-way Pseudogene Annotation Set from GENCODE Version 25 (Ensembl 85) Genes and Gene Predictions 
wgEncodeGencodeV24 All GENCODE V24 All GENCODE transcripts including comprehensive set V24 Genes and Gene Predictions Description The GENCODE Genes track (version 24, December 2015) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The annotation was carried out on genome assembly GRCh38 (hg38). As of GENCODE Version 11, Ensembl and GENCODE have converged. The gene annotations in the GENCODE comprehensive set are the same as the corresponding Ensembl release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 24 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. --> GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 24 corresponds to Ensembl 84. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV24ViewPolya PolyA All GENCODE transcripts including comprehensive set V24 Genes and Gene Predictions 
wgEncodeGencodePolyaV24 PolyA PolyA Transcript Annotation Set from GENCODE Version 24 (Ensembl 83) Genes and Gene Predictions 
wgEncodeGencodeV24ViewGenes Genes All GENCODE transcripts including comprehensive set V24 Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV24 Pseudogenes Pseudogene Annotation Set from GENCODE Version 24 (Ensembl 83) Genes and Gene Predictions 
wgEncodeGencodeCompV24 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 24 (Ensembl 83) Genes and Gene Predictions 
wgEncodeGencodeBasicV24 Basic Basic Gene Annotation Set from GENCODE Version 24 (Ensembl 83) Genes and Gene Predictions 
wgEncodeGencodeV24View2Way 2-Way All GENCODE transcripts including comprehensive set V24 Genes and Gene Predictions 
wgEncodeGencode2wayConsPseudoV24 2-way Pseudogenes 2-way Pseudogene Annotation Set from GENCODE Version 24 (Ensembl 83) Genes and Gene Predictions 
wgEncodeGencodeV23 All GENCODE V23 All GENCODE transcripts including comprehensive set V23 Genes and Gene Predictions Description The GENCODE Genes track (version 23, March 2015) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The annotation was carried out on genome assembly GRCh38 (hg38). As of GENCODE Version 11, Ensembl and GENCODE have converged. The gene annotations in the GENCODE comprehensive set are the same as the corresponding Ensembl release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Downloads GENCODE GFF3 and GTF files are available from the GENCODE release 23 site. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 23 corresponds to Ensembl 81 and 82. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV23ViewPolya PolyA All GENCODE transcripts including comprehensive set V23 Genes and Gene Predictions 
wgEncodeGencodePolyaV23 PolyA PolyA Transcript Annotation Set from GENCODE Version 23 (Ensembl 81) Genes and Gene Predictions 
wgEncodeGencodeV23ViewGenes Genes All GENCODE transcripts including comprehensive set V23 Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV23 Pseudogenes Pseudogene Annotation Set from GENCODE Version 23 (Ensembl 81) Genes and Gene Predictions 
wgEncodeGencodeCompV23 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 23 (Ensembl 81) Genes and Gene Predictions 
wgEncodeGencodeBasicV23 Basic Basic Gene Annotation Set from GENCODE Version 23 (Ensembl 81) Genes and Gene Predictions 
wgEncodeGencodeV23View2Way 2-Way All GENCODE transcripts including comprehensive set V23 Genes and Gene Predictions 
wgEncodeGencode2wayConsPseudoV23 2-way Pseudogenes 2-way Pseudogene Annotation Set from GENCODE Version 23 (Ensembl 81) Genes and Gene Predictions 
wgEncodeGencodeV22 All GENCODE V22 All GENCODE transcripts including comprehensive set V22 Genes and Gene Predictions Description The GENCODE Genes track (version 22, March 2015) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The annotation was carried out on genome assembly GRCh38 (hg38). As of GENCODE Version 11, Ensembl and GENCODE have converged. The gene annotations in the GENCODE comprehensive set are the same as the corresponding Ensembl release. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. --> GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 22 corresponds to Ensembl 79. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV22ViewPolya PolyA All GENCODE transcripts including comprehensive set V22 Genes and Gene Predictions 
wgEncodeGencodePolyaV22 PolyA PolyA Transcript Annotation Set from GENCODE Version 22 (Ensembl 79) Genes and Gene Predictions 
wgEncodeGencodeV22ViewGenes Genes All GENCODE transcripts including comprehensive set V22 Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV22 Pseudogenes Pseudogene Annotation Set from GENCODE Version 22 (Ensembl 79) Genes and Gene Predictions 
wgEncodeGencodeCompV22 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 22 (Ensembl 79) Genes and Gene Predictions 
wgEncodeGencodeBasicV22 Basic Basic Gene Annotation Set from GENCODE Version 22 (Ensembl 79) Genes and Gene Predictions 
wgEncodeGencodeV22View2Way 2-Way All GENCODE transcripts including comprehensive set V22 Genes and Gene Predictions 
wgEncodeGencode2wayConsPseudoV22 2-way Pseudogenes 2-way Pseudogene Annotation Set from GENCODE Version 22 (Ensembl 79) Genes and Gene Predictions 
wgEncodeGencodeV20 GENCODE V20 (Ensembl 76) Gene Annotations from GENCODE Version 20 (Ensembl 76) Genes and Gene Predictions Description The GENCODE Genes track (version 20, August 2014) shows high-quality manual annotations merged with evidence-based automated annotations across the entire human genome generated by the GENCODE project. The GENCODE gene set presents a full merge between HAVANA manual annotation process and Ensembl automatic annotation pipeline. Priority is given to the manually curated HAVANA annotation using predicted Ensembl annotations when there are no corresponding manual annotations. The annotation was carried out on genome assembly GRCh38 (hg38). As of GENCODE Version 11, Ensembl and GENCODE have converged. The gene annotations in the GENCODE comprehensive set are the same as the corresponding Ensembl release. UCSC will continue to provide a separate Ensembl track on Human in the same format as the Ensembl tracks on other organisms. Display Conventions and Configuration This track is a multi-view composite track that contains differing data sets (views). Instructions for configuring multi-view tracks are here. To show only selected subtracks, uncheck the boxes next to the tracks that you wish to hide. Views available on this track are: Genes The gene annotations in this view are divided into three subtracks: GENCODE Basic set is a subset of the Comprehensive set. The selection criteria are described in the methods section. GENCODE Comprehensive set contains all GENCODE coding and non-coding transcript annotations, including polymorphic pseudogenes. This includes both manual and automatic annotations. This is a super-set of the Basic set. GENCODE Pseudogenes include all annotations except polymorphic pseudogenes. PolyA GENCODE PolyA contains polyA signals and sites manually annotated on the genome based on transcribed evidence (ESTs and cDNAs) of 3' end of transcripts containing at least 3 A's not matching the genome. Maximum number of transcripts to display is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks. Starting with the GENCODE human V42 and mouse VM31 releases, transcripts are assigned rank within the gene. The ranks may be used to filter the number of transcripts displayed in a principled manner. Transcript ranking is not available in the lift37 releases. See Methods for details of rank assignment. Filtering is available for the items in the GENCODE Basic, Comprehensive and Pseudogene tracks using the following criteria: Transcript class: filter by the basic biological function of a transcript annotation All - don't filter by transcript class coding - display protein coding transcripts, including polymorphic pseudogenes nonCoding - display non-protein coding transcripts pseudo - display pseudogene transcript annotations problem - display problem transcripts (Biotypes of retained_intron, TEC, or disrupted_domain) Transcript Annotation Method: filter by the method used to create the annotation All - don't filter by transcript class manual - display manually created annotations, including those that are also created automatically automatic - display automatically created annotations, including those that are also created manually manual_only - display manually created annotations that were not annotated by the automatic method automatic_only - display automatically created annotations that were not annotated by the manual method Transcript Biotype: filter transcripts by Biotype Support Level: filter transcripts by transcription support level Coloring for the gene annotations is based on the annotation type: coding non-coding pseudogene problem all polyA annotations Methods The GENCODE project aims to annotate all evidence-based gene features on the human and mouse reference sequence with high accuracy by integrating computational approaches (including comparative methods), manual annotation and targeted experimental verification. This goal includes identifying all protein-coding loci with associated alternative variants, non-coding loci which have transcript evidence, and pseudogenes. For a detailed description of the methods and references used, see Harrow et al. (2006). GENCODE Basic Set selection: The GENCODE Basic Set is intended to provide a simplified subset of the GENCODE transcript annotations that will be useful to the majority of users. The goal was to have a high-quality basic set that also covered all loci. Selection of GENCODE annotations for inclusion in the basic set was determined independently for the coding and non-coding transcripts at each gene locus. Criteria for selection of coding transcripts (including polymorphic pseudogenes) at a given locus: All full-length coding transcripts (except problem transcripts or transcripts that are nonsense-mediated decay) were included in the basic set. If there were no transcripts meeting the above criteria, then the partial coding transcript with the largest CDS was included in the basic set (excluding problem transcripts). Criteria for selection of non-coding transcripts at a given locus: All full-length non-coding transcripts (except problem transcripts) with a well characterized Biotype (see below) were included in the basic set. If there were no transcripts meeting the above criteria, then the largest non-coding transcript was included in the basic set (excluding problem transcripts). If no transcripts were included by either of the above criteria, the longest problem transcript is included. Non-coding transcript categorization: Non-coding transcripts are categorized using their biotype and the following criteria: well characterized: antisense, Mt_rRNA, Mt_tRNA, miRNA, rRNA, snRNA, snoRNA poorly characterized: 3prime_overlapping_ncrna, lincRNA, misc_RNA, non_coding, processed_transcript, sense_intronic, sense_overlapping Transcript ranking: Within each gene, transcripts have been ranked according to the following criteria. The ranking approach is preliminary and will change is future releases. Protein_coding genes MANE or Ensembl canonical -1st: MANE Select / Ensembl canonical -2nd: MANE Plus Clinical Coding biotypes -1st: protein_coding and protein_coding_LoF -2nd: NMDs and NSDs -3rd: retained intron and protein_coding_CDS_not_defined Completeness -1st: full length -2nd: CDS start/end not found CARS score (only for coding transcripts) Transcript genomic span and length (only for non-coding transcripts) Non-coding genes Transcript biotype -1st: transcript biotype identical to gene biotype Ensembl canonical GENCODE basic Transcript genomic span Transcript length Transcription Support Level (TSL): It is important that users understand how to assess transcript annotations that they see in GENCODE. While some transcript models have a high level of support through the full length of their exon structure, there are also transcripts that are poorly supported and that should be considered speculative. The Transcription Support Level (TSL) is a method to highlight the well-supported and poorly-supported transcript models for users. The method relies on the primary data that can support full-length transcript structure: mRNA and EST alignments supplied by UCSC and Ensembl. The mRNA and EST alignments are compared to the GENCODE transcripts and the transcripts are scored according to how well the alignment matches over its full length. The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in mouse. Mouse transcript sequences from the International Nucleotide Sequence Database Collaboration (GenBank, ENA, and DDBJ) are used as the evidence for this analysis. Exonerate RNA alignments from Ensembl, BLAT RNA and EST alignments from the UCSC Genome Browser Database are used in the analysis. Erroneous transcripts and libraries identified in lists maintained by the Ensembl, UCSC, HAVANA and RefSeq groups are flagged as suspect. GENCODE annotations for protein-coding and non-protein-coding transcripts are compared with the evidence alignments. Annotations in the MHC region and other immunological genes are not evaluated, as automatic alignments tend to be very problematic. Methods for evaluating single-exon genes are still being developed and they are not included in the current analysis. Multi-exon GENCODE annotations are evaluated using the criteria that all introns are supported by an evidence alignment and the evidence alignment does not indicate that there are unannotated exons. Small insertions and deletions in evidence alignments are assumed to be due to polymorphisms and not considered as differing from the annotations. All intron boundaries must match exactly. The transcript start and end locations are allowed to differ. The following categories are assigned to each of the evaluated annotations: tsl1 - all splice junctions of the transcript are supported by at least one non-suspect mRNA tsl2 - the best supporting mRNA is flagged as suspect or the support is from multiple ESTs tsl3 - the only support is from a single EST tsl4 - the best supporting EST is flagged as suspect tsl5 - no single transcript supports the model structure tslNA - the transcript was not analyzed for one of the following reasons: pseudogene annotation, including transcribed pseudogenes immunoglobin gene transcript T-cell receptor transcript single-exon transcript (will be included in a future version) APPRIS is a system to annotate alternatively spliced transcripts based on a range of computational methods. It provides value to the annotations of the human, mouse, zebrafish, rat, and pig genomes. APPRIS has selected a single CDS variant for each gene as the 'PRINCIPAL' isoform. Principal isoforms are tagged with the numbers 1 to 5, with 1 being the most reliable. PRINCIPAL:1 - Transcript(s) expected to code for the main functional isoform based solely on the core modules in the APPRIS. PRINCIPAL:2 - Where the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the database chooses two or more of the CDS variants as "candidates" to be the principal variant. PRINCIPAL:3 - Where the APPRIS core modules are unable to choose a clear principal variant and more than one of the variants have distinct CCDS identifiers, APPRIS selects the variant with lowest CCDS identifier as the principal variant. The lower the CCDS identifier, the earlier it was annotated. PRINCIPAL:4 - Where the APPRIS core modules are unable to choose a clear principal CDS and there is more than one variant with distinct (but consecutive) CCDS identifiers, APPRIS selects the longest CCDS isoform as the principal variant. PRINCIPAL:5 - Where the APPRIS core modules are unable to choose a clear principal variant and none of the candidate variants are annotated by CCDS, APPRIS selects the longest of the candidate isoforms as the principal variant. For genes in which the APPRIS core modules are unable to choose a clear principal variant (approximately 25% of human protein coding genes), the "candidate" variants not chosen as principal are labeled in the following way: ALTERNATIVE:1 - Candidate transcript(s) models that are conserved in at least three tested species. ALTERNATIVE:2 - Candidate transcript(s) models that appear to be conserved in fewer than three tested species. Non-candidate transcripts are not tagged and are considered as "Minor" transcripts. Further information and additional web services can be found at the APPRIS website. Verification Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. Those experiments can be found at GEO: GSE30619:[E-MTAB-612] - Batch I is based on annotation from July 2008 (without pseudogenes). GSE25711:[E-MTAB-407] - Batch II is based on annotation from April 2009. GSE30612:[E-MTAB-533] - Batch III is verifying RGASP models for c.elegans and human. GSE34797:[E-MTAB-684] - Batch IV is based on chromosome 3, 4 and 5 annotations from GENCODE 4 (January 2010). GSE34820:[E-MTAB-737] - Batch V is based on annotations from GENCODE 6 (November 2010). GSE34821:[E-MTAB-831] - Batch VI is based on annotations from GENCODE 6 (November 2010) as well as transcript models predicted by the Ensembl Genebuild group based on the Illumina Human BodyMap 2.0 data. See Harrow et al. (2006) for information on verification techniques. Release Notes GENCODE version 20 corresponds to Ensembl 76 and Vega 56. See also: The GENCODE Project Credits The GENCODE project is an international collaboration funded by NIH/NHGRI grant U41HG007234. More information is available at www.gencodegenes.org, Participating GENCODE institutions and personnel can be found here. References Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I et al. GENCODE 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D916-D923. PMID: 33270111; PMC: PMC7778937; DOI: 10.1093/nar/gkaa1087 A full list of GENCODE publications are available at The GENCODE Project web site. Data Release Policy GENCODE data are available for use without restrictions. 
wgEncodeGencodeV20ViewPolya PolyA Gene Annotations from GENCODE Version 20 (Ensembl 76) Genes and Gene Predictions 
wgEncodeGencodePolyaV20 PolyA PolyA Transcript Annotation Set from GENCODE Version 20 (Ensembl 76) Genes and Gene Predictions 
wgEncodeGencodeV20ViewGenes Genes Gene Annotations from GENCODE Version 20 (Ensembl 76) Genes and Gene Predictions 
wgEncodeGencodePseudoGeneV20 Pseudogenes Pseudogene Annotation Set from GENCODE Version 20 (Ensembl 76) Genes and Gene Predictions 
wgEncodeGencodeCompV20 Comprehensive Comprehensive Gene Annotation Set from GENCODE Version 20 (Ensembl 76) Genes and Gene Predictions 
wgEncodeGencodeBasicV20 Basic Basic Gene Annotation Set from GENCODE Version 20 (Ensembl 76) Genes and Gene Predictions 
wgEncodeGencodeV20View2Way 2-Way Gene Annotations from GENCODE Version 20 (Ensembl 76) Genes and Gene Predictions 
wgEncodeGencode2wayConsPseudoV20 2-way Pseudogenes 2-way Pseudogene Annotation Set from GENCODE Version 20 (Ensembl 76) Genes and Gene Predictions 
tgpTrios 1000 Genomes Trios Thousand Genomes Project Family VCF Trios Variation Description This track shows approximately 4.5 million single nucleotide variants (SNVs) and 0.6 million short insertions/deletions (indels) from 7 different parent/child trios as produced by the International Genome Sample Resource (IGSR), from sequence data generated by the 1000 Genomes Project in its Phase 3 sequencing of 2,504 genomes from 16 populations worldwide. Variants were called on the autosomes (chromosomes 1 through 22) and on the Pseudo-Autosomal Regions (PARs) of chromosome X. Therefore this track has no annotations on alternate haplotype sequences, fix patches, chromosome Y, or the non-PAR portion (the majority) of chromosome X. The variant genotypes have been phased (i.e., the two alleles of each diploid genotype have been assigned to two haplotypes, one inherited from each parent). This information allows us to illustrate which haplotypes in the child have been inherited from which parent. Trios from six different populations are available, including: YRI - Yoruban from Idaban, Nigeria KHV - Kinh in Ho Chi Minh City, Vietnam PUR - Puerto Ricans from Puerto Rico CEU - CEPH Utah CHS - Southern Han Chinese MXL - Mexican Ancestry from Los Angeles Display Conventions and Configuration This track illustrates the vcfPhasedTrio track type, where two lines, one for each chromosome in the diploid genome, is drawn per sample in the underlying VCF. Variants in the window are then drawn on the haplotype line corresponding to which haplotype they belong to, such that variants on the same line were likely inherited together. The sorting routine is the same as what is used to draw the haplotype sorted display in the non-trio 1000 Genomes track, and is described here. The child haplotypes are drawn in the center of each group, flanked above and below by parent haplotypes, and variants are sorted to show the transmitted alleles: parent 1 untransmitted haploytpe parent 1 transmitted haplotype child haplotype inherited from parent 1 child haplotype inherited from parent 2 parent 2 transmitted haplotype parent 2 untransmitted haploytpe Track configuration options include: Showing the child haplotypes below the parent(s) Toggling the haplotype labels with mother/father/child or VCF sample IDs Hiding the parent samples Allele coloring options include: No shading - the default option Shading by functional effect of the variant relative to NCBI RefSeq Curated Transcripts: reference alleles invisible alternate alleles in red for non-synonymous alternate alleles in green for synonymous alternate alleles in blue for UTR/noncoding alternate alleles in black otherwise Child de novo alleles in red - all alternate alleles black except for cases where the child has an allele not present in either parent Child alleles that are &quot;inconsistent&quot; with phasing in red - all alternate alleles black except for cases where the &quot;inherited&quot; child allele does not match the &quot;transmitted&quot; parent allele. Note that as the genomic location changes, and thus the alleles present to use for sorting change, whether an allele is marked as inconsistent can change as well. Because all the variants present in the window are considered a haplotype, what haplotypes are considered &quot;inherited&quot; and &quot;transmitted&quot; varies as the viewing location changes From the subtrack configure menu, there is the option to manually rearrange the family order for each trio by dragging haplotypes. Clicking on a variant takes one to a details page with the standard VCF details, including INFO column annotations, the REF and ALT alleles, and the genotypes from all three samples. Methods The genomes of 2,504 individuals were sequenced using both whole-genome sequencing (mean depth = 7.4x) and targeted exome sequencing (mean depth = 65.7x). Sequence reads were aligned to the reference genome using alt-aware BWA-MEM (Zheng-Bradley et al.). Variant discovery and quality control were performed as described in Lowy-Gallego et al. See also: 1000 Genomes Project - Analysis overview IGSR/1000 Genomes Frequently Asked Questions (FAQ) UCSC Methods Trio samples were extracted out of both the main 1000 Genomes set, and the related samples using the pedigree information from 1000 Genomes. Variants that were homozygous reference across all three samples were removed. Data Access Trio VCFs are available for download from our download server. Credits Thanks to the International Genome Sample Resource (IGSR) for making these variant calls freely available. References Zheng-Bradley X, Streeter I, Fairley S, Richardson D, Clarke L, Flicek P, 1000 Genomes Project Consortium. Alignment of 1000 Genomes Project reads to reference assembly GRCh38. Gigascience. 2017 Jul 1;6(7):1-8. PMID: 28531267; PMC: PMC5522380 Fairley S, Lowy-Gallego E, Perry E, Flicek P. The International Genome Sample Resource (IGSR) collection of open human genomic variation resources. Nucleic Acids Res. 2019 Oct 4. PMID: 31584097 Lowy-Gallego E, Fairley S, Zheng-Bradley X, Ruffier M, Clarke L, Flicek P, 1000 Genomes Project Consortium. Variant calling on the GRCh38 assembly with the data from phase three of the 1000 Genomes Project [version 1; peer review: 2 not approved]. Wellcome Open Research. 2019 Mar. 11. 1000 Genomes Project Consortium, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, Marchini JL, McCarthy S, McVean GA et al. A global reference for human genetic variation. Nature. 2015 Oct 1;526(7571):68-74. PMID: 26432245 
tgpArchive 1000 Genomes 1000 Genomes Phase 3 Variation Description This supertrack is a collection of tracks from the 1000 Genomes Project showing paired-end accessible regions and integrated variant calls. More information about display conventions, methods, credits, and references can be found on each subtrack's description page. For more details, see: 1000 Genomes Frequently Asked Questions (FAQ) 1000 Genomes Project - Analysis overview Credits Thanks to the International Genome Sample Resource (IGSR) for making these variant calls freely available. 
tgpNA19240_Y117_YRI Y117 YRI Trio 1000 Genomes Yoruban in Ibadan, Nigeria Trio Variation 
tgpHG02024_VN049_KHV VN049 KHV Trio 1000 Genomes Kinh in Ho Chi Minh City, Vietnam Trio Variation 
tgpHG00702_SH089_CHS SH089 CHS Trio 1000 Genomes Southern Han Chinese Trio Variation 
tgpHG00733_PR05_PUR PR05 PUR Trio 1000 Genomes Puerto Ricans from Puerto Rico Trio Variation 
tgpNA19685_m011_MXL m011 MXL Trio 1000 Genomes m011 Mexican Ancestry from Los Angeles Trio Variation 
tgpNA19675_m004_MXL m004 MXL Trio 1000 Genomes m004 Mexican Ancestry from Los Angeles Trio Variation 
tgpNA12878_1463_CEU 1463 CEU Trio 1000 Genomes Utah CEPH Trio Variation 
tgpPhase3 1000G Ph3 Vars 1000 Genomes Phase 3 Integrated Variant Calls from IGSR: SNVs and Indels Variation Description This track shows approximately 73 million single nucleotide variants (SNVs) and 5 million short insertions/deletions (indels) produced by the International Genome Sample Resource (IGSR) from sequence data generated by the 1000 Genomes Project in its Phase 3 sequencing of 2,504 genomes from 16 populations worldwide. Variants were called on the autosomes (chromosomes 1 through 22) and on the Pseudo-Autosomal Regions (PARs) of chromosome X. Therefore this track has no annotations on alternate haplotype sequences, fix patches, chromosome Y, or the non-PAR portion (the majority) of chromosome X. The variant genotypes have been phased (i.e., the two alleles of each diploid genotype have been assigned to two haplotypes, one inherited from each parent). This extra information enables a clustering of independent haplotypes by local similarity for display. Display Conventions In &quot;dense&quot; mode, a vertical line is drawn at the position of each variant. In &quot;pack&quot; mode, since these variants have been phased, the display shows a clustering of haplotypes in the viewed range, sorted by similarity of alleles weighted by proximity to a central variant. The clustering view can highlight local patterns of linkage. In the clustering display, each sample's phased diploid genotype is split into two independent haplotypes. Each haplotype is placed in a horizontal row of pixels; when the number of haplotypes exceeds the number of vertical pixels for the track, multiple haplotypes fall in the same pixel row and pixels are averaged across haplotypes. Each variant is a vertical bar with white (invisible) representing the reference allele and black representing the non-reference allele(s). Tick marks are drawn at the top and bottom of each variant's vertical bar to make the bar more visible when most alleles are reference alleles. The vertical bar for the central variant used in clustering is outlined in purple. In order to avoid long compute times, the range of alleles used in clustering may be limited; alleles used in clustering have purple tick marks at the top and bottom. The clustering tree is displayed to the left of the main image. It does not represent relatedness of individuals; it simply shows the arrangement of local haplotypes by similarity. When a rightmost branch is purple, it means that all haplotypes in that branch are identical, at least within the range of variants used in clustering. Methods The genomes of 2,504 individuals were sequenced using both whole-genome sequencing (mean depth = 7.4x) and targeted exome sequencing (mean depth = 65.7x). Sequence reads were aligned to the reference genome using alt-aware BWA-MEM (Zheng-Bradley et al.). Variant discovery and quality control were performed as described in (Lowy-Gallego et al.). See also: 1000 Genomes Project - Analysis overview IGSR/1000 Genomes Frequently Asked Questions (FAQ) Data Access VCF files were downloaded from EBI and are also available for download from UCSC. Credits Thanks to the International Genome Sample Resource (IGSR) for making these variant calls freely available. References Zheng-Bradley X, Streeter I, Fairley S, Richardson D, Clarke L, Flicek P, 1000 Genomes Project Consortium. Alignment of 1000 Genomes Project reads to reference assembly GRCh38. Gigascience. 2017 Jul 1;6(7):1-8. PMID: 28531267; PMC: PMC5522380 Fairley S, Lowy-Gallego E, Perry E, Flicek P. The International Genome Sample Resource (IGSR) collection of open human genomic variation resources. Nucleic Acids Res. 2019 Oct 4. PMID: 31584097 Lowy-Gallego E, Fairley S, Zheng-Bradley X, Ruffier M, Clarke L, Flicek P, 1000 Genomes Project Consortium. Variant calling on the GRCh38 assembly with the data from phase three of the 1000 Genomes Project [version 1; peer review: 2 not approved]. Wellcome Open Research. 2019 Mar. 11. 1000 Genomes Project Consortium, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, Marchini JL, McCarthy S, McVean GA et al. A global reference for human genetic variation. Nature. 2015 Oct 1;526(7571):68-74. PMID: 26432245 
abSplice AbSplice Scores Aberrant Splicing Prediction Scores Phenotype and Literature Description AbSplice is a method that predicts aberrant splicing across human tissues, as described in Wagner, &Ccedil;elik et al., 2023. This track displays precomputed AbSplice scores for all possible single-nucleotide variants genome-wide. The scores represent the probability that a given variant causes aberrant splicing in a given tissue. AbSplice scores can be computed from VCF files and are based on quantitative tissue-specific splice site annotations (SpliceMaps). While SpliceMaps can be generated for any tissue of interest from a cohort of RNA-seq samples, this track includes 49 tissues available from the Genotype-Tissue Expression (GTEx) dataset. Display Conventions The AbSplice score is a probability estimate of how likely aberrant splicing of some sort takes place in a given tissue. The authors suggest three cutoffs which are represented by color in the track. High (red) - An AbSplice score over 0.2 indicates a high likelihood of aberrant splicing in at least one tissue. Medium (orange) - A score between 0.05 and 0.2 indicates a medium likelihood. Low (blue) - A score between 0.01 and 0.05 indicates a low likelihood. Scores below 0.01 are not displayed. Mouseover on items shows the gene name, maximum score, and tissues that had this score. Clicking on any item brings up a table with scores for all 49 GTEX tissues. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Precomputed AbSplice-DNA scores in all 49 GTEx tissues are available at Zenodo. Methods Data was converted from the files (AbSplice_DNA_ hg38 _snvs_high_scores.zip) provided by the authors at zenodo.org. Files in the score_cutoff=0.01 directory were concatenated. To convert the data to bigBed format, scores and their tissues were selected from the AbSplice_DNA fields and maximum scores, and then calculated using a custom Python script, which can be found in the makeDoc from our GitHub repository. Credits Thanks to Nils Wagner for helpful comments and suggestions. References Wagner N, &#199;elik MH, H&#246;lzlwimmer FR, Mertes C, Prokisch H, Y&#233;pez VA, Gagneur J. Aberrant splicing prediction across human tissues. Nat Genet. 2023 May;55(5):861-870. PMID: 37142848 
affyGnf1h Affy GNF1H Alignments of Affymetrix Consensus/Exemplars from GNF1H Expression Description This track shows the location of the sequences used for the selection of probes on the Affymetrix GNF1H chips. This contains 11406 predicted genes that do not overlap with the Affy U133A chip. Methods The sequences were mapped to the genome using blat followed by pslReps with the parameters: -minCover=0.3 -minAli=0.95 -nearTop=0.005 Credits Thanks to the Genomics Institute of the Novartis Research Foundation (GNF) for the data underlying this track. References Su AI, Wiltshire T, Batalov S, Lapp H, Ching KA, Block D, Zhang J, Soden R, Hayakawa M, Kreiman G et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci U S A. 2004 Apr 20;101(16):6062-7. PMID: 15075390; PMC: PMC395923 
affyArchive Affy Archive Affymetrix Archive Expression Description This supertrack is a collection of Affymetrix tracks showing the location of the consensus and exemplar sequences used for the selection of probes on the Affymetrix chips. Credits Thanks to Affymetrix for the data underlying these tracks. 
affyU133 Affy U133 Alignments of Affymetrix Consensus/Exemplars from HG-U133 Expression Description This track shows the location of the consensus and exemplar sequences used for the selection of probes on the Affymetrix HG-U133A and HG-U133B chips. Methods Consensus and exemplar sequences were downloaded from the Affymetrix Product Support and mapped to the genome using blat followed by pslReps with the parameters: -minCover=0.5 -minAli=0.97 -nearTop=0.005 Credits Thanks to Affymetrix for the data underlying this track. 
affyU95 Affy U95 Alignments of Affymetrix Consensus/Exemplars from HG-U95 Expression Description This track shows the location of the consensus and exemplar sequences used for the selection of probes on the Affymetrix HG-U95Av2 chip. For this chip, probes are predominantly designed from consensus sequences. Methods Consensus and exemplar sequences were downloaded from the Affymetrix Product Support and mapped to the genome using blat followed by pslReps with the parameters: -minCover=0.3 -minAli=0.95 -nearTop=0.005 Credits Thanks to Affymetrix for the data underlying this track. 
altSeqLiftOverPsl Alt Haplotypes Reference Assembly Alternate Haplotype Sequence Alignments Mapping and Sequencing Description This track shows alignments of alternate locus (also known as &quot;alternate haplotype&quot;) reference sequences to main chromosome sequences in the reference genome assembly. Some loci in the genome are highly variable, with sets of variants that tend to segregate into distinct haplotypes. Only one haplotype can be included in a reference assembly chromosome sequence. Instead of providing a separate complete chromosome sequence for each haplotype, which could cause confusion with divergent chromosome coordinates and ambiguity about which sequence is the official reference, the Genome Reference Consortium (GRC) adds alternate locus sequences, ranging from tens of thousands of bases up to low millions of bases in size, to represent the distinct haplotypes. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. Mismatching bases are highlighted in red. Several types of alignment gap may also be colored; for more information, see Alignment Insertion/Deletion Display Options. Credits The alignments were provided by NCBI as GFF files and translated into the PSL representation for browser display by UCSC. 
genotypeArrays Array Probesets Microarray Probesets Variation Description Agilent Arrays The arrays listed in this track are probes from the Agilent Catalog Oligonucleotide Microarrays. Please note that more microarray tracks are available on the hg19 genome assembly. To view those tracks, please click this link for hg19 microarrays. Microarrays that are not listed can be added as Custom Tracks with data from the companies. Agilent GenetiSure Cyto Agilent's oligonucleotide CGH (Comparative Genomic Hybridization) platform enables the study of genome-wide DNA copy number changes at a high resolution. The CGH probes on Agilent CGH microarrays are 60-mer oligonucleotides synthesized in situ using Agilent's inkjet SurePrint technology. The probes represented on the Agilent CGH microarrays have been selected using algorithms developed specifically for the CGH application, assuring optimal performance of these probes in detecting DNA copy number changes. Illumina 450k and 850k Methylation Arrays With the Infinium MethylationEPIC BeadChip Kit, researchers can interrogate over 850,000 methylation sites quantitatively across the genome at single-nucleotide resolution. Multiple samples, including FFPE, can be analyzed in parallel to deliver high-throughput power while minimizing the cost per sample. These tracks show positions being measured on the Illumina 450k and 850k (EPIC) microarray tracks. More information about the arrays can be found on the Infinium MethylationEPIC Kit website. Illumina CytoSNP 850K Probe Array The Infinium CytoSNP-850K v1.2 BeadChip provides comprehensive coverage of cytogenetically relevant genes on a proven platform, helping researchers find valuable information that may be missed by other technologies. It contains approximately 850,000 empirically selected single nucleotide polymorphisms (SNPs) spanning the entire genome with enriched coverage for 3,262 genes of known cytogenetics relevance in both constitutional and cancer applications. Affymetrix Cytoscan HD GeneChip Array The CytoScan HD Array, which is included in the CytoScan HD Suite, provides the broadest coverage and highest performance for detecting chromosomal aberrations. CytoScan HD Suite has greater than 99% sensitivity and can reliably detect 25-50kb copy number changes across the genome at high specificity with single-nucleotide polymorphism (SNP) allelic corroboration. With more than 2.6 million copy number markers, CytoScan HD Suite covers all OMIM and RefSeq genes. Display Conventions and Configuration Items in this track are colored according to their strand orientation. Blue indicates alignment to the negative strand, and red indicates alignment to the positive strand. Methods The Agilent arrays were downloaded from their Agilent SureDesign website tool on March 2022. The Illumina 450k and 850k (EPIC) tracks were created using a few columns from the Infinium MethylationEPIC v1.0 B5 Manifest File (CSV Format) and was then converted into a bigBed. The Illumina CytoSNP-850K track was created by downloading the CytoSNP-850K v1.2 Manifest File (CSV Format) (GRCh38) file and then converted into a bigBed file. The Affymetrix Cytoscan HD GeneChip Array track was created by converting the CytoScanHD_Accel_Array.na36.bed.zip into a bigBed file. Data Access The raw data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API or downloaded from our Downloads site. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits Thanks to the Aliglent and Illumina support teams for sharing the data and the UCSC Genome Browser engineers for configuring the data. 
affyCytoScanHD Affy CytoScan HD Affymetrix Cytoscan HD GeneChip Array Variation Description Agilent Arrays The arrays listed in this track are probes from the Agilent Catalog Oligonucleotide Microarrays. Please note that more microarray tracks are available on the hg19 genome assembly. To view those tracks, please click this link for hg19 microarrays. Microarrays that are not listed can be added as Custom Tracks with data from the companies. Agilent GenetiSure Cyto Agilent's oligonucleotide CGH (Comparative Genomic Hybridization) platform enables the study of genome-wide DNA copy number changes at a high resolution. The CGH probes on Agilent CGH microarrays are 60-mer oligonucleotides synthesized in situ using Agilent's inkjet SurePrint technology. The probes represented on the Agilent CGH microarrays have been selected using algorithms developed specifically for the CGH application, assuring optimal performance of these probes in detecting DNA copy number changes. Illumina 450k and 850k Methylation Arrays With the Infinium MethylationEPIC BeadChip Kit, researchers can interrogate over 850,000 methylation sites quantitatively across the genome at single-nucleotide resolution. Multiple samples, including FFPE, can be analyzed in parallel to deliver high-throughput power while minimizing the cost per sample. These tracks show positions being measured on the Illumina 450k and 850k (EPIC) microarray tracks. More information about the arrays can be found on the Infinium MethylationEPIC Kit website. Illumina CytoSNP 850K Probe Array The Infinium CytoSNP-850K v1.2 BeadChip provides comprehensive coverage of cytogenetically relevant genes on a proven platform, helping researchers find valuable information that may be missed by other technologies. It contains approximately 850,000 empirically selected single nucleotide polymorphisms (SNPs) spanning the entire genome with enriched coverage for 3,262 genes of known cytogenetics relevance in both constitutional and cancer applications. Affymetrix Cytoscan HD GeneChip Array The CytoScan HD Array, which is included in the CytoScan HD Suite, provides the broadest coverage and highest performance for detecting chromosomal aberrations. CytoScan HD Suite has greater than 99% sensitivity and can reliably detect 25-50kb copy number changes across the genome at high specificity with single-nucleotide polymorphism (SNP) allelic corroboration. With more than 2.6 million copy number markers, CytoScan HD Suite covers all OMIM and RefSeq genes. Display Conventions and Configuration Items in this track are colored according to their strand orientation. Blue indicates alignment to the negative strand, and red indicates alignment to the positive strand. Methods The Agilent arrays were downloaded from their Agilent SureDesign website tool on March 2022. The Illumina 450k and 850k (EPIC) tracks were created using a few columns from the Infinium MethylationEPIC v1.0 B5 Manifest File (CSV Format) and was then converted into a bigBed. The Illumina CytoSNP-850K track was created by downloading the CytoSNP-850K v1.2 Manifest File (CSV Format) (GRCh38) file and then converted into a bigBed file. The Affymetrix Cytoscan HD GeneChip Array track was created by converting the CytoScanHD_Accel_Array.na36.bed.zip into a bigBed file. Data Access The raw data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API or downloaded from our Downloads site. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits Thanks to the Aliglent and Illumina support teams for sharing the data and the UCSC Genome Browser engineers for configuring the data. 
snpArrayCytoSnp850k CytoSNP 850k Illumina 850k CytoSNP Array Variation Description Agilent Arrays The arrays listed in this track are probes from the Agilent Catalog Oligonucleotide Microarrays. Please note that more microarray tracks are available on the hg19 genome assembly. To view those tracks, please click this link for hg19 microarrays. Microarrays that are not listed can be added as Custom Tracks with data from the companies. Agilent GenetiSure Cyto Agilent's oligonucleotide CGH (Comparative Genomic Hybridization) platform enables the study of genome-wide DNA copy number changes at a high resolution. The CGH probes on Agilent CGH microarrays are 60-mer oligonucleotides synthesized in situ using Agilent's inkjet SurePrint technology. The probes represented on the Agilent CGH microarrays have been selected using algorithms developed specifically for the CGH application, assuring optimal performance of these probes in detecting DNA copy number changes. Illumina 450k and 850k Methylation Arrays With the Infinium MethylationEPIC BeadChip Kit, researchers can interrogate over 850,000 methylation sites quantitatively across the genome at single-nucleotide resolution. Multiple samples, including FFPE, can be analyzed in parallel to deliver high-throughput power while minimizing the cost per sample. These tracks show positions being measured on the Illumina 450k and 850k (EPIC) microarray tracks. More information about the arrays can be found on the Infinium MethylationEPIC Kit website. Illumina CytoSNP 850K Probe Array The Infinium CytoSNP-850K v1.2 BeadChip provides comprehensive coverage of cytogenetically relevant genes on a proven platform, helping researchers find valuable information that may be missed by other technologies. It contains approximately 850,000 empirically selected single nucleotide polymorphisms (SNPs) spanning the entire genome with enriched coverage for 3,262 genes of known cytogenetics relevance in both constitutional and cancer applications. Affymetrix Cytoscan HD GeneChip Array The CytoScan HD Array, which is included in the CytoScan HD Suite, provides the broadest coverage and highest performance for detecting chromosomal aberrations. CytoScan HD Suite has greater than 99% sensitivity and can reliably detect 25-50kb copy number changes across the genome at high specificity with single-nucleotide polymorphism (SNP) allelic corroboration. With more than 2.6 million copy number markers, CytoScan HD Suite covers all OMIM and RefSeq genes. Display Conventions and Configuration Items in this track are colored according to their strand orientation. Blue indicates alignment to the negative strand, and red indicates alignment to the positive strand. Methods The Agilent arrays were downloaded from their Agilent SureDesign website tool on March 2022. The Illumina 450k and 850k (EPIC) tracks were created using a few columns from the Infinium MethylationEPIC v1.0 B5 Manifest File (CSV Format) and was then converted into a bigBed. The Illumina CytoSNP-850K track was created by downloading the CytoSNP-850K v1.2 Manifest File (CSV Format) (GRCh38) file and then converted into a bigBed file. The Affymetrix Cytoscan HD GeneChip Array track was created by converting the CytoScanHD_Accel_Array.na36.bed.zip into a bigBed file. Data Access The raw data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API or downloaded from our Downloads site. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits Thanks to the Aliglent and Illumina support teams for sharing the data and the UCSC Genome Browser engineers for configuring the data. 
snpArrayIllumina850k Illumina 850k Illumina 850k EPIC Methylation Array Variation Description Agilent Arrays The arrays listed in this track are probes from the Agilent Catalog Oligonucleotide Microarrays. Please note that more microarray tracks are available on the hg19 genome assembly. To view those tracks, please click this link for hg19 microarrays. Microarrays that are not listed can be added as Custom Tracks with data from the companies. Agilent GenetiSure Cyto Agilent's oligonucleotide CGH (Comparative Genomic Hybridization) platform enables the study of genome-wide DNA copy number changes at a high resolution. The CGH probes on Agilent CGH microarrays are 60-mer oligonucleotides synthesized in situ using Agilent's inkjet SurePrint technology. The probes represented on the Agilent CGH microarrays have been selected using algorithms developed specifically for the CGH application, assuring optimal performance of these probes in detecting DNA copy number changes. Illumina 450k and 850k Methylation Arrays With the Infinium MethylationEPIC BeadChip Kit, researchers can interrogate over 850,000 methylation sites quantitatively across the genome at single-nucleotide resolution. Multiple samples, including FFPE, can be analyzed in parallel to deliver high-throughput power while minimizing the cost per sample. These tracks show positions being measured on the Illumina 450k and 850k (EPIC) microarray tracks. More information about the arrays can be found on the Infinium MethylationEPIC Kit website. Illumina CytoSNP 850K Probe Array The Infinium CytoSNP-850K v1.2 BeadChip provides comprehensive coverage of cytogenetically relevant genes on a proven platform, helping researchers find valuable information that may be missed by other technologies. It contains approximately 850,000 empirically selected single nucleotide polymorphisms (SNPs) spanning the entire genome with enriched coverage for 3,262 genes of known cytogenetics relevance in both constitutional and cancer applications. Affymetrix Cytoscan HD GeneChip Array The CytoScan HD Array, which is included in the CytoScan HD Suite, provides the broadest coverage and highest performance for detecting chromosomal aberrations. CytoScan HD Suite has greater than 99% sensitivity and can reliably detect 25-50kb copy number changes across the genome at high specificity with single-nucleotide polymorphism (SNP) allelic corroboration. With more than 2.6 million copy number markers, CytoScan HD Suite covers all OMIM and RefSeq genes. Display Conventions and Configuration Items in this track are colored according to their strand orientation. Blue indicates alignment to the negative strand, and red indicates alignment to the positive strand. Methods The Agilent arrays were downloaded from their Agilent SureDesign website tool on March 2022. The Illumina 450k and 850k (EPIC) tracks were created using a few columns from the Infinium MethylationEPIC v1.0 B5 Manifest File (CSV Format) and was then converted into a bigBed. The Illumina CytoSNP-850K track was created by downloading the CytoSNP-850K v1.2 Manifest File (CSV Format) (GRCh38) file and then converted into a bigBed file. The Affymetrix Cytoscan HD GeneChip Array track was created by converting the CytoScanHD_Accel_Array.na36.bed.zip into a bigBed file. Data Access The raw data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API or downloaded from our Downloads site. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits Thanks to the Aliglent and Illumina support teams for sharing the data and the UCSC Genome Browser engineers for configuring the data. 
snpArrayIllumina450k Illumina 450k Illumina 450k Methylation Array Variation Description Agilent Arrays The arrays listed in this track are probes from the Agilent Catalog Oligonucleotide Microarrays. Please note that more microarray tracks are available on the hg19 genome assembly. To view those tracks, please click this link for hg19 microarrays. Microarrays that are not listed can be added as Custom Tracks with data from the companies. Agilent GenetiSure Cyto Agilent's oligonucleotide CGH (Comparative Genomic Hybridization) platform enables the study of genome-wide DNA copy number changes at a high resolution. The CGH probes on Agilent CGH microarrays are 60-mer oligonucleotides synthesized in situ using Agilent's inkjet SurePrint technology. The probes represented on the Agilent CGH microarrays have been selected using algorithms developed specifically for the CGH application, assuring optimal performance of these probes in detecting DNA copy number changes. Illumina 450k and 850k Methylation Arrays With the Infinium MethylationEPIC BeadChip Kit, researchers can interrogate over 850,000 methylation sites quantitatively across the genome at single-nucleotide resolution. Multiple samples, including FFPE, can be analyzed in parallel to deliver high-throughput power while minimizing the cost per sample. These tracks show positions being measured on the Illumina 450k and 850k (EPIC) microarray tracks. More information about the arrays can be found on the Infinium MethylationEPIC Kit website. Illumina CytoSNP 850K Probe Array The Infinium CytoSNP-850K v1.2 BeadChip provides comprehensive coverage of cytogenetically relevant genes on a proven platform, helping researchers find valuable information that may be missed by other technologies. It contains approximately 850,000 empirically selected single nucleotide polymorphisms (SNPs) spanning the entire genome with enriched coverage for 3,262 genes of known cytogenetics relevance in both constitutional and cancer applications. Affymetrix Cytoscan HD GeneChip Array The CytoScan HD Array, which is included in the CytoScan HD Suite, provides the broadest coverage and highest performance for detecting chromosomal aberrations. CytoScan HD Suite has greater than 99% sensitivity and can reliably detect 25-50kb copy number changes across the genome at high specificity with single-nucleotide polymorphism (SNP) allelic corroboration. With more than 2.6 million copy number markers, CytoScan HD Suite covers all OMIM and RefSeq genes. Display Conventions and Configuration Items in this track are colored according to their strand orientation. Blue indicates alignment to the negative strand, and red indicates alignment to the positive strand. Methods The Agilent arrays were downloaded from their Agilent SureDesign website tool on March 2022. The Illumina 450k and 850k (EPIC) tracks were created using a few columns from the Infinium MethylationEPIC v1.0 B5 Manifest File (CSV Format) and was then converted into a bigBed. The Illumina CytoSNP-850K track was created by downloading the CytoSNP-850K v1.2 Manifest File (CSV Format) (GRCh38) file and then converted into a bigBed file. The Affymetrix Cytoscan HD GeneChip Array track was created by converting the CytoScanHD_Accel_Array.na36.bed.zip into a bigBed file. Data Access The raw data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API or downloaded from our Downloads site. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits Thanks to the Aliglent and Illumina support teams for sharing the data and the UCSC Genome Browser engineers for configuring the data. 
genetiSureCytoCghSnp8x60 Agilent GenetiSure Cyto CGH 8x60 Agilent GenetiSure Cyto CGH 8x60K 085590 20200302 Variation 
genetiSureCytoCgh4x180 Agilent GenetiSure Cyto CGH 4x180K Agilent GenetiSure Cyto CGH 4x180K 085589 20200302 Variation 
genetiSureCytoCghSnp Agilent GenetiSure Cyto CGH+SNP Agilent GenetiSure Cyto CGH+SNP 4x180K 085591 20200302 Variation 
gold Assembly Assembly from Fragments Mapping and Sequencing Description This track shows the contigs used to construct the GRCh38 (hg38) genome assembly, as defined in the AGP file delivered with the sequence. For information on the AGP file format, see the NCBI AGP Specification. The NCBI website also provides an overview of genome assembly procedures, as well as specific information about the hg38 assembly. In dense mode, this track depicts the contigs that make up the currently viewed scaffold. Contig boundaries are distinguished by the use of alternating gold and brown coloration. Where gaps exist between contigs, spaces are shown between the gold and brown blocks. The relative order and orientation of the contigs within a scaffold is always known; therefore, a line is drawn in the graphical display to bridge the blocks. Component types found in this track (with counts of that type in parenthesis): F - finished sequence (35,798) O - other sequence (8,536) W - whole genome shotgun (764) P - pre draft (16) D - draft sequence (8) A - active finishing (8) In addition to the standard nucleotide codes, the raw sequence files from NCBI also include IUPAC ambiguity codes for bases that could not be positively identified as A, C, G or T (see Wikipedia's IUPAC notation article for more information). As part of the UCSC assembly creation process, all IUPAC ambiguity characters are converted to Ns. The FASTA files available for download from UCSC reflect this. The raw data files containing the original IUPAC characters can be downloaded from the NCBI FTP site. The following table lists the counts by chromosome of the various IUPAC ambiguity characters in the original NCBI data files: chromosome 1 2 3 6 7 9 10 12 13 16 17 21 22 X Y Total code B 1 1 2 K 1 4 1 2 8 M 1 1 3 1 2 8 R 1 1 1 1 1 13 1 3 1 2 1 1 27 S 1 1 1 1 1 5 W 2 2 6 1 1 1 1 14 Y 4 3 1 2 2 8 2 2 5 2 2 2 35 Total 2 9 7 1 4 3 36 3 3 1 12 3 5 5 5 99 
augustusGene AUGUSTUS AUGUSTUS ab initio gene predictions v3.1 Genes and Gene Predictions Description This track shows ab initio predictions from the program AUGUSTUS (version 3.1). The predictions are based on the genome sequence alone. For more information on the different gene tracks, see our Genes FAQ. Methods Statistical signal models were built for splice sites, branch-point patterns, translation start sites, and the poly-A signal. Furthermore, models were built for the sequence content of protein-coding and non-coding regions as well as for the length distributions of different exon and intron types. Detailed descriptions of most of these different models can be found in Mario Stanke's dissertation. This track shows the most likely gene structure according to a Semi-Markov Conditional Random Field model. Alternative splicing transcripts were obtained with a sampling algorithm (--alternatives-from-sampling=true --sample=100 --minexonintronprob=0.2 --minmeanexonintronprob=0.5 --maxtracks=3 --temperature=2). The different models used by Augustus were trained on a number of different species-specific gene sets, which included 1000-2000 training gene structures. The --species option allows one to choose the species used for training the models. Different training species were used for the --species option when generating these predictions for different groups of assemblies. Assembly Group Training Species Fish zebrafish Birds chicken Human and all other vertebrates human Nematodes caenorhabditis Drosophila fly A. mellifera honeybee1 A. gambiae culex S. cerevisiae saccharomyces This table describes which training species was used for a particular group of assemblies. When available, the closest related training species was used. Credits Thanks to the Stanke lab for providing the AUGUSTUS program. The training for the chicken version was done by Stefanie K&ouml;nig and the training for the human and zebrafish versions was done by Mario Stanke. References Stanke M, Diekhans M, Baertsch R, Haussler D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics. 2008 Mar 1;24(5):637-44. PMID: 18218656 Stanke M, Waack S. Gene prediction with a hidden Markov model and a new intron submodel. Bioinformatics. 2003 Oct;19 Suppl 2:ii215-25. PMID: 14534192 
genePredArchive Prediction Archive Gene Prediction Archive Genes and Gene Predictions Description This supertrack is a collection of gene prediction tracks and is composed of the following tracks: AUGUSTUS shows ab initio predictions from the program AUGUSTUS (version 3.1). The predictions are based on the genome sequence alone. Geneid Genes shows gene predictions from the geneid program. Geneid is a program to predict genes in anonymous genomic sequences designed with a hierarchical structure. Genscan Genes shows predictions from the Genscan program. The predictions are based on transcriptional, translational and donor/acceptor splicing signals as well as the length and compositional distributions of exons, introns and intergenic regions. SGP Genes shows gene predictions from the SGP2 homology-based gene prediction program. To predict genes in a genomic query, SGP2 combines geneid predictions with tblastx comparisons of the genome of the target species against genomic sequences of other species (reference genomes) deemed to be at an appropriate evolutionary distance from the target. SIB Genes a transcript-based set of gene predictions based on data from RefSeq and EMBL/GenBank. The track includes both protein-coding and non-coding transcripts. The coding regions are predicted using ESTScan. More information about display conventions, methods, credits, and references can be found on each subtrack's description page. 
avada Avada Variants Avada Variants extracted from full text publications Phenotype and Literature Description This track shows the genomic positions of variants in the AVADA database. AVADA is a database of variants built by a machine learning software that analyzes full text research articles to find the gene mentions in the text that look like they are most relevant for monogenic (non-cancer) genetic diagnosis, finds variant descriptions and uses the genes to map the variants to the genome. For details see the AVADA paper. As the data is automatically extracted from full-text publications, it includes some false positives. In the original study, out of 200 randomly selected articles, only 99 were considered relevant after manual curation. However, this share is very high compared to the Genomenom track. Ideally, the track is used in combination with variants found in human patients, to find relevant literature, or with Genome Browser tracks of variant databases that curated a single study for each variant, like our tracks for HGMD or LOVD. Display Conventions and Configuration Genomic locations of a variants are labeled with the variant description in the original text. This is not a normalized HGVS string, but the original text as the authors of the study described it. The Pubmed ID, gene and transcript for each variant are shown on the variant's details page, as well as the PubMed title, authors, and abstract. Mouse over the variants to show the gene, variant, first author, year, and title. The data has been lifted from hg19 to hg38. Data access The raw data can be explored interactively with the Table Browser, for download, intersection or correlations with other tracks. To join this track with others based on the chromosome positions, use the Data Integrator. For automated download and analysis, the genome annotation is stored in a bigBed file that can be downloaded from our download server. The file for this track is called avada.bb. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg19/bbi/avada.bb -chrom=chr21 -start=0 -end=100000000 stdout For automated access, this track like all others, is also available via our API. However, for bulk processing in pipelines, downloading the data and/or using bigBed files as described above is usually faster. Methods The AVADA VCF file was reformatted at UCSC to the bigBed format. The program that performs the conversion is available on Github. The paper reference information was added from MEDLINE and is used Courtesy of the U.S. National Library of Medicine, according to its Terms and Conditions. Credits Thanks to Gill Bejerano and Johannes Birgmeier for making the data available. References Johannes Birgmeier, Cole A. Deisseroth, Laura E. Hayward, Luisa M. T. Galhardo, Andrew P. Tierno, Karthik A. Jagadeesh, Peter D. Stenson, David N. Cooper, Jonathan A. Bernstein, Maximilian Haeussler, and Gill Bejerano. AVADA: Towards Automated Pathogenic Variant Evidence Retrieval Directly from the Full Text Literature. . Genetics in Medicine. 2019. PMID: 31467448 
varsInPubs Variants in Papers Genetic Variants mentioned in scientific publications Phenotype and Literature Description The tracks that are listed here contain genetic variants and links to scientific publications that mention them. The Mastermind track was created by Genomenom, a company that analyzes fulltext of publications with their own proprietary software with an unknown false positive rate. The AVADA track was created in the Bejerano lab at Stanford by J. Birgmeier also on fulltext papers, using sophisticated machine learning methods and was evaluated to have a false positive rate of around 50% in their study. The PubTator rsIDs track was created using PubTator 3 data. For additional information please click on the hyperlink of the respective track above. Display conventions By default, each variant is labeled with the nucleotide change. Hover over the feature to see more information, explained on the track details page of the particular track or when clicking onto the feature. Credits For data provenance, access and descriptions, please click the documentation via the link above. 
bismap Bismap Single-read and multi-read mappability after bisulfite conversion Mapping and Sequencing Description These tracks indicate regions with uniquely mappable reads of particular lengths before and after bisulfite conversion. Both Umap and Bismap tracks contain single-read mappability and multi-read mappability tracks for four different read lengths: 24 bp, 36 bp, 50 bp, and 100 bp. You can use these tracks for many purposes, including filtering unreliable signal from sequencing assays. The Bismap track can help filter unreliable signal from sequencing assays involving bisulfite conversion, such as whole-genome bisulfite sequencing or reduced representation bisulfite sequencing. Bismap single-read and multi-read mappability Bismap single-read mappability These tracks mark any region of the bisulfite-converted genome that is uniquely mappable by at least one k-mer on the specified strand. Mappability of the forward strand was generated by converting all instances of cytosine to thymine. Similarly, mappability of the reverse strand was generated by converting all instances of guanine to adenine. To calculate the single-read mappability, you must find the overlap of a given region with the region that is uniquely mappable on both strands. Regions not uniquely mappable on both strands or have a low multi-read mappability might bias the downstream analysis. Bismap multi-read mappability These tracks represent the probability that a randomly selected k-mer which overlaps with a given position is uniquely mappable. Multi-read mappability track is calculated for k-mers that are uniquely mappable on both strands, and thus there is no strand specification. Umap single-read and multi-read mappability Umap single-read mappability These tracks mark any region of the genome that is uniquely mappable by at least one k-mer. To calculate the single-read mappability, you must find the overlap of a given region with this track. Umap multi-read mappability These tracks represent the probability that a randomly selected k-mer which overlaps with a given position is uniquely mappable. For greater detail and explanatory diagrams, see the preprint, the Umap and Bismap project website, or the Umap and Bismap software documentation. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, genome annotation is stored in a bigBed or bigWig file that can be downloaded from the download server. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed or bigWigToWig, which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed -chrom=chr6 -start=0 -end=1000000 http://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k24.Unique.Mappability.bb stdout bigWigToWig -chrom=chr6 -start=0 -end=1000000 http://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k24.Umap.MultiTrackMappability.bw stdout Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits Anshul Kundaje (Stanford University) created the original Umap software in MATLAB. The original Umap repository is available here. Mehran Karimzadeh (Michael Hoffman lab, Princess Margaret Cancer Centre) implemented the Python version of Umap and added features, including Bismap. References Karimzadeh M, Ernst C, Kundaje A, Hoffman MM., Umap and Bismap: quantifying genome and methylome mappability bioRxiv bioRxiv, p. 095463, 2016.; doi: https://doi.org/10.1101/095463. 
mappability Mappability Hoffman Lab Umap and Bismap Mappability Mapping and Sequencing Description These tracks indicate regions with uniquely mappable reads of particular lengths before and after bisulfite conversion. Both Umap and Bismap tracks contain single-read mappability and multi-read mappability tracks for four different read lengths: 24 bp, 36 bp, 50 bp, and 100 bp. You can use these tracks for many purposes, including filtering unreliable signal from sequencing assays. The Bismap track can help filter unreliable signal from sequencing assays involving bisulfite conversion, such as whole-genome bisulfite sequencing or reduced representation bisulfite sequencing. Bismap single-read and multi-read mappability Bismap single-read mappability These tracks mark any region of the bisulfite-converted genome that is uniquely mappable by at least one k-mer on the specified strand. Mappability of the forward strand was generated by converting all instances of cytosine to thymine. Similarly, mappability of the reverse strand was generated by converting all instances of guanine to adenine. To calculate the single-read mappability, you must find the overlap of a given region with the region that is uniquely mappable on both strands. Regions not uniquely mappable on both strands or have a low multi-read mappability might bias the downstream analysis. Bismap multi-read mappability These tracks represent the probability that a randomly selected k-mer which overlaps with a given position is uniquely mappable. Multi-read mappability track is calculated for k-mers that are uniquely mappable on both strands, and thus there is no strand specification. Umap single-read and multi-read mappability Umap single-read mappability These tracks mark any region of the genome that is uniquely mappable by at least one k-mer. To calculate the single-read mappability, you must find the overlap of a given region with this track. Umap multi-read mappability These tracks represent the probability that a randomly selected k-mer which overlaps with a given position is uniquely mappable. For greater detail and explanatory diagrams, see the preprint, the Umap and Bismap project website, or the Umap and Bismap software documentation. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, genome annotation is stored in a bigBed or bigWig file that can be downloaded from the download server. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed or bigWigToWig, which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed -chrom=chr6 -start=0 -end=1000000 http://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k24.Unique.Mappability.bb stdout bigWigToWig -chrom=chr6 -start=0 -end=1000000 http://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k24.Umap.MultiTrackMappability.bw stdout Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits Anshul Kundaje (Stanford University) created the original Umap software in MATLAB. The original Umap repository is available here. Mehran Karimzadeh (Michael Hoffman lab, Princess Margaret Cancer Centre) implemented the Python version of Umap and added features, including Bismap. References Karimzadeh M, Ernst C, Kundaje A, Hoffman MM., Umap and Bismap: quantifying genome and methylome mappability bioRxiv bioRxiv, p. 095463, 2016.; doi: https://doi.org/10.1101/095463. 
bismapBigBed Single-read mappability Single-read and multi-read mappability after bisulfite conversion Mapping and Sequencing 
bismap50Neg Bismap S50 - Single-read mappability with 50-mers after bisulfite conversion (reverse strand) Mapping and Sequencing 
bismap100Neg Bismap S100 - Single-read mappability with 100-mers after bisulfite conversion (reverse strand) Mapping and Sequencing 
bismap36Neg Bismap S36 - Single-read mappability with 36-mers after bisulfite conversion (reverse strand) Mapping and Sequencing 
bismap24Neg Bismap S24 - Single-read mappability with 24-mers after bisulfite conversion (reverse strand) Mapping and Sequencing 
bismap100Pos Bismap S100 + Single-read mappability with 100-mers after bisulfite conversion (forward strand) Mapping and Sequencing 
bismap50Pos Bismap S50 + Single-read mappability with 50-mers after bisulfite conversion (forward strand) Mapping and Sequencing 
bismap36Pos Bismap S36 + Single-read mappability with 36-mers after bisulfite conversion (forward strand) Mapping and Sequencing 
bismap24Pos Bismap S24 + Single-read mappability with 24-mers after bisulfite conversion (forward strand) Mapping and Sequencing 
bismapBigWig Multi-read mappability Single-read and multi-read mappability after bisulfite conversion Mapping and Sequencing 
bismap100Quantitative Bismap M100 Multi-read mappability with 100-mers after bisulfite conversion Mapping and Sequencing 
bismap50Quantitative Bismap M50 Multi-read mappability with 50-mers after bisulfite conversion Mapping and Sequencing 
bismap36Quantitative Bismap M36 Multi-read mappability with 36-mers after bisulfite conversion Mapping and Sequencing 
bismap24Quantitative Bismap M24 Multi-read mappability with 24-mers after bisulfite conversion Mapping and Sequencing 
bloodHaoCellType Blood PBMC Cells Blood (PBMCs) binned by cell type (level 1) from Hao et al 2020 Single Cell RNA-seq Description This track displays data from Integrated analysis of multimodal single-cell data. Human peripheral blood mononuclear cells (PBMCs) taken from pre-vaccinated and post-vaccinated individuals were profiled using both CITE-seq and ECCITE-seq. A total of 57 cell type clusters were identified and each cluster included cells from all 24 samples with rare exceptions. This dataset contains three annotations for cell clustering: Level 1 (8 cell types), Level 2 (30 cell types), Level 3 (57 cell types). This track collection contains six bar chart tracks of RNA expression in PBMCs where cells are grouped by cell type level 1 (Blood PBMC Cells), cell type level 2 (Blood PBMC Cells 2), cell type level 3 (Blood PBMC Cells 3), donor (Blood PBMC Donor), phase of cell cycle (Blood PBMC Phase), or time into experiment (Blood PBMC Time). The default track displayed is Blood PBMC Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification immune Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Method PBMC samples were taken from 8 volunteers ages 20-49 enrolled in an HIV vaccine trial (NCT01578889). A total of 24 blood samples were collected at 3 time points: day 0 (the day before), day 3, and day 7 after the administration of a VSV-vectored HIV vaccine. Samples were collected at these different time points to minimize batch effects. Cells were then divided into separate aliquots for modified versions of the 3' CITE-seq and 5' ECCITE-seq staining protocols. In the 3' CITE-seq staining protocol, the samples are simultaneously stained with the antibody and unique hashtag. Whereas, 5' ECCITE-seq samples are stained first with a unique hashtag. 3' libraries were loaded into 8 lanes of a 10x Genomics Chip B using the 10x Genomics 3' v3 kit. 5' libraries were loaded into 2 lanes of a 10x Genomics Chip A using the 10x Genomics V(D)J kit (v1). Both 3' and 5' libraries were pooled together and sequenced on an Illumina Novaseq S4 flowcell. In total, 210,911 cells were profiled after quality control and doublet filtration. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Yuhan Hao, Stephanie Hao, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M et al. Integrated analysis of multimodal single-cell data. Cell. 2021 Jun 24;184(13):3573-3587.e29. PMID: 34062119; PMC: PMC8238499 
bloodHao Blood (PBMC) Hao Peripheral blood mononuclear cells (PBMC) from Hao et al 2020 Single Cell RNA-seq Description This track displays data from Integrated analysis of multimodal single-cell data. Human peripheral blood mononuclear cells (PBMCs) taken from pre-vaccinated and post-vaccinated individuals were profiled using both CITE-seq and ECCITE-seq. A total of 57 cell type clusters were identified and each cluster included cells from all 24 samples with rare exceptions. This dataset contains three annotations for cell clustering: Level 1 (8 cell types), Level 2 (30 cell types), Level 3 (57 cell types). This track collection contains six bar chart tracks of RNA expression in PBMCs where cells are grouped by cell type level 1 (Blood PBMC Cells), cell type level 2 (Blood PBMC Cells 2), cell type level 3 (Blood PBMC Cells 3), donor (Blood PBMC Donor), phase of cell cycle (Blood PBMC Phase), or time into experiment (Blood PBMC Time). The default track displayed is Blood PBMC Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification immune Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Method PBMC samples were taken from 8 volunteers ages 20-49 enrolled in an HIV vaccine trial (NCT01578889). A total of 24 blood samples were collected at 3 time points: day 0 (the day before), day 3, and day 7 after the administration of a VSV-vectored HIV vaccine. Samples were collected at these different time points to minimize batch effects. Cells were then divided into separate aliquots for modified versions of the 3' CITE-seq and 5' ECCITE-seq staining protocols. In the 3' CITE-seq staining protocol, the samples are simultaneously stained with the antibody and unique hashtag. Whereas, 5' ECCITE-seq samples are stained first with a unique hashtag. 3' libraries were loaded into 8 lanes of a 10x Genomics Chip B using the 10x Genomics 3' v3 kit. 5' libraries were loaded into 2 lanes of a 10x Genomics Chip A using the 10x Genomics V(D)J kit (v1). Both 3' and 5' libraries were pooled together and sequenced on an Illumina Novaseq S4 flowcell. In total, 210,911 cells were profiled after quality control and doublet filtration. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Yuhan Hao, Stephanie Hao, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M et al. Integrated analysis of multimodal single-cell data. Cell. 2021 Jun 24;184(13):3573-3587.e29. PMID: 34062119; PMC: PMC8238499 
bloodHaoL2 Blood PBMC Cells 2 Blood PBMCs binned by cell type (level 2) from Hao et al 2020 Single Cell RNA-seq Description This track displays data from Integrated analysis of multimodal single-cell data. Human peripheral blood mononuclear cells (PBMCs) taken from pre-vaccinated and post-vaccinated individuals were profiled using both CITE-seq and ECCITE-seq. A total of 57 cell type clusters were identified and each cluster included cells from all 24 samples with rare exceptions. This dataset contains three annotations for cell clustering: Level 1 (8 cell types), Level 2 (30 cell types), Level 3 (57 cell types). This track collection contains six bar chart tracks of RNA expression in PBMCs where cells are grouped by cell type level 1 (Blood PBMC Cells), cell type level 2 (Blood PBMC Cells 2), cell type level 3 (Blood PBMC Cells 3), donor (Blood PBMC Donor), phase of cell cycle (Blood PBMC Phase), or time into experiment (Blood PBMC Time). The default track displayed is Blood PBMC Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification immune Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Method PBMC samples were taken from 8 volunteers ages 20-49 enrolled in an HIV vaccine trial (NCT01578889). A total of 24 blood samples were collected at 3 time points: day 0 (the day before), day 3, and day 7 after the administration of a VSV-vectored HIV vaccine. Samples were collected at these different time points to minimize batch effects. Cells were then divided into separate aliquots for modified versions of the 3' CITE-seq and 5' ECCITE-seq staining protocols. In the 3' CITE-seq staining protocol, the samples are simultaneously stained with the antibody and unique hashtag. Whereas, 5' ECCITE-seq samples are stained first with a unique hashtag. 3' libraries were loaded into 8 lanes of a 10x Genomics Chip B using the 10x Genomics 3' v3 kit. 5' libraries were loaded into 2 lanes of a 10x Genomics Chip A using the 10x Genomics V(D)J kit (v1). Both 3' and 5' libraries were pooled together and sequenced on an Illumina Novaseq S4 flowcell. In total, 210,911 cells were profiled after quality control and doublet filtration. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Yuhan Hao, Stephanie Hao, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M et al. Integrated analysis of multimodal single-cell data. Cell. 2021 Jun 24;184(13):3573-3587.e29. PMID: 34062119; PMC: PMC8238499 
bloodHaoL3 Blood PBMC Cells 3 Blood PBMCs binned by cell type (level 3) from Hao et al 2020 Single Cell RNA-seq Description This track displays data from Integrated analysis of multimodal single-cell data. Human peripheral blood mononuclear cells (PBMCs) taken from pre-vaccinated and post-vaccinated individuals were profiled using both CITE-seq and ECCITE-seq. A total of 57 cell type clusters were identified and each cluster included cells from all 24 samples with rare exceptions. This dataset contains three annotations for cell clustering: Level 1 (8 cell types), Level 2 (30 cell types), Level 3 (57 cell types). This track collection contains six bar chart tracks of RNA expression in PBMCs where cells are grouped by cell type level 1 (Blood PBMC Cells), cell type level 2 (Blood PBMC Cells 2), cell type level 3 (Blood PBMC Cells 3), donor (Blood PBMC Donor), phase of cell cycle (Blood PBMC Phase), or time into experiment (Blood PBMC Time). The default track displayed is Blood PBMC Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification immune Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Method PBMC samples were taken from 8 volunteers ages 20-49 enrolled in an HIV vaccine trial (NCT01578889). A total of 24 blood samples were collected at 3 time points: day 0 (the day before), day 3, and day 7 after the administration of a VSV-vectored HIV vaccine. Samples were collected at these different time points to minimize batch effects. Cells were then divided into separate aliquots for modified versions of the 3' CITE-seq and 5' ECCITE-seq staining protocols. In the 3' CITE-seq staining protocol, the samples are simultaneously stained with the antibody and unique hashtag. Whereas, 5' ECCITE-seq samples are stained first with a unique hashtag. 3' libraries were loaded into 8 lanes of a 10x Genomics Chip B using the 10x Genomics 3' v3 kit. 5' libraries were loaded into 2 lanes of a 10x Genomics Chip A using the 10x Genomics V(D)J kit (v1). Both 3' and 5' libraries were pooled together and sequenced on an Illumina Novaseq S4 flowcell. In total, 210,911 cells were profiled after quality control and doublet filtration. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Yuhan Hao, Stephanie Hao, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M et al. Integrated analysis of multimodal single-cell data. Cell. 2021 Jun 24;184(13):3573-3587.e29. PMID: 34062119; PMC: PMC8238499 
bloodHaoDonor Blood PBMC Donor Blood PBMCs binned by blood donor from Hao et al 2020 Single Cell RNA-seq Description This track displays data from Integrated analysis of multimodal single-cell data. Human peripheral blood mononuclear cells (PBMCs) taken from pre-vaccinated and post-vaccinated individuals were profiled using both CITE-seq and ECCITE-seq. A total of 57 cell type clusters were identified and each cluster included cells from all 24 samples with rare exceptions. This dataset contains three annotations for cell clustering: Level 1 (8 cell types), Level 2 (30 cell types), Level 3 (57 cell types). This track collection contains six bar chart tracks of RNA expression in PBMCs where cells are grouped by cell type level 1 (Blood PBMC Cells), cell type level 2 (Blood PBMC Cells 2), cell type level 3 (Blood PBMC Cells 3), donor (Blood PBMC Donor), phase of cell cycle (Blood PBMC Phase), or time into experiment (Blood PBMC Time). The default track displayed is Blood PBMC Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification immune Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Method PBMC samples were taken from 8 volunteers ages 20-49 enrolled in an HIV vaccine trial (NCT01578889). A total of 24 blood samples were collected at 3 time points: day 0 (the day before), day 3, and day 7 after the administration of a VSV-vectored HIV vaccine. Samples were collected at these different time points to minimize batch effects. Cells were then divided into separate aliquots for modified versions of the 3' CITE-seq and 5' ECCITE-seq staining protocols. In the 3' CITE-seq staining protocol, the samples are simultaneously stained with the antibody and unique hashtag. Whereas, 5' ECCITE-seq samples are stained first with a unique hashtag. 3' libraries were loaded into 8 lanes of a 10x Genomics Chip B using the 10x Genomics 3' v3 kit. 5' libraries were loaded into 2 lanes of a 10x Genomics Chip A using the 10x Genomics V(D)J kit (v1). Both 3' and 5' libraries were pooled together and sequenced on an Illumina Novaseq S4 flowcell. In total, 210,911 cells were profiled after quality control and doublet filtration. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Yuhan Hao, Stephanie Hao, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M et al. Integrated analysis of multimodal single-cell data. Cell. 2021 Jun 24;184(13):3573-3587.e29. PMID: 34062119; PMC: PMC8238499 
bloodHaoPhase Blood PBMC Phase Blood PBMCs binned by phase of cell cycle from Hao et al 2020 Single Cell RNA-seq Description This track displays data from Integrated analysis of multimodal single-cell data. Human peripheral blood mononuclear cells (PBMCs) taken from pre-vaccinated and post-vaccinated individuals were profiled using both CITE-seq and ECCITE-seq. A total of 57 cell type clusters were identified and each cluster included cells from all 24 samples with rare exceptions. This dataset contains three annotations for cell clustering: Level 1 (8 cell types), Level 2 (30 cell types), Level 3 (57 cell types). This track collection contains six bar chart tracks of RNA expression in PBMCs where cells are grouped by cell type level 1 (Blood PBMC Cells), cell type level 2 (Blood PBMC Cells 2), cell type level 3 (Blood PBMC Cells 3), donor (Blood PBMC Donor), phase of cell cycle (Blood PBMC Phase), or time into experiment (Blood PBMC Time). The default track displayed is Blood PBMC Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification immune Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Method PBMC samples were taken from 8 volunteers ages 20-49 enrolled in an HIV vaccine trial (NCT01578889). A total of 24 blood samples were collected at 3 time points: day 0 (the day before), day 3, and day 7 after the administration of a VSV-vectored HIV vaccine. Samples were collected at these different time points to minimize batch effects. Cells were then divided into separate aliquots for modified versions of the 3' CITE-seq and 5' ECCITE-seq staining protocols. In the 3' CITE-seq staining protocol, the samples are simultaneously stained with the antibody and unique hashtag. Whereas, 5' ECCITE-seq samples are stained first with a unique hashtag. 3' libraries were loaded into 8 lanes of a 10x Genomics Chip B using the 10x Genomics 3' v3 kit. 5' libraries were loaded into 2 lanes of a 10x Genomics Chip A using the 10x Genomics V(D)J kit (v1). Both 3' and 5' libraries were pooled together and sequenced on an Illumina Novaseq S4 flowcell. In total, 210,911 cells were profiled after quality control and doublet filtration. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Yuhan Hao, Stephanie Hao, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M et al. Integrated analysis of multimodal single-cell data. Cell. 2021 Jun 24;184(13):3573-3587.e29. PMID: 34062119; PMC: PMC8238499 
bloodHaoTime Blood PBMC Time Blood PBMCs binned by time into experiment from Hao et al 2020 Single Cell RNA-seq Description This track displays data from Integrated analysis of multimodal single-cell data. Human peripheral blood mononuclear cells (PBMCs) taken from pre-vaccinated and post-vaccinated individuals were profiled using both CITE-seq and ECCITE-seq. A total of 57 cell type clusters were identified and each cluster included cells from all 24 samples with rare exceptions. This dataset contains three annotations for cell clustering: Level 1 (8 cell types), Level 2 (30 cell types), Level 3 (57 cell types). This track collection contains six bar chart tracks of RNA expression in PBMCs where cells are grouped by cell type level 1 (Blood PBMC Cells), cell type level 2 (Blood PBMC Cells 2), cell type level 3 (Blood PBMC Cells 3), donor (Blood PBMC Donor), phase of cell cycle (Blood PBMC Phase), or time into experiment (Blood PBMC Time). The default track displayed is Blood PBMC Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification immune Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Method PBMC samples were taken from 8 volunteers ages 20-49 enrolled in an HIV vaccine trial (NCT01578889). A total of 24 blood samples were collected at 3 time points: day 0 (the day before), day 3, and day 7 after the administration of a VSV-vectored HIV vaccine. Samples were collected at these different time points to minimize batch effects. Cells were then divided into separate aliquots for modified versions of the 3' CITE-seq and 5' ECCITE-seq staining protocols. In the 3' CITE-seq staining protocol, the samples are simultaneously stained with the antibody and unique hashtag. Whereas, 5' ECCITE-seq samples are stained first with a unique hashtag. 3' libraries were loaded into 8 lanes of a 10x Genomics Chip B using the 10x Genomics 3' v3 kit. 5' libraries were loaded into 2 lanes of a 10x Genomics Chip A using the 10x Genomics V(D)J kit (v1). Both 3' and 5' libraries were pooled together and sequenced on an Illumina Novaseq S4 flowcell. In total, 210,911 cells were profiled after quality control and doublet filtration. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Yuhan Hao, Stephanie Hao, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M et al. Integrated analysis of multimodal single-cell data. Cell. 2021 Jun 24;184(13):3573-3587.e29. PMID: 34062119; PMC: PMC8238499 
cons447way Cactus 447-way Cactus Alignment & Conservation on 447 mammal species, including Zoonomia genomes Comparative Genomics Data Access Downloads for data in this track are available from the directory: Cactus 447-way alignments (MAF format), and phylogenetic trees PhyloP conservation (WIG format) Display Conventions and Configuration In full and pack display modes, conservation scores are displayed as a wiggle track (histogram) in which the height reflects the size of the score. The conservation wiggles can be configured in a variety of ways to highlight different aspects of the displayed information. Click the Graph configuration help link for an explanation of the configuration options. Pairwise alignments of each species to the human genome are displayed below the conservation histogram as a grayscale density plot (in pack mode) or as a wiggle (in full mode) that indicates alignment quality. In dense display mode, conservation is shown in grayscale using darker values to indicate higher levels of overall conservation as scored by phastCons. Checkboxes on the track configuration page allow selection of the species to include in the pairwise display. Note that excluding species from the pairwise display does not alter the the conservation score display. To view detailed information about the alignments at a specific position, zoom the display in to 30,000 or fewer bases, then click on the alignment. Gap Annotation The Display chains between alignments configuration option enables display of gaps between alignment blocks in the pairwise alignments in a manner similar to the Chain track display. Missing sequence in any assembly is highlighted in the track display by regions of yellow when zoomed out and by Ns when displayed at base level. The following conventions are used: Single line: No bases in the aligned species. Possibly due to a lineage-specific insertion between the aligned blocks in the human genome or a lineage-specific deletion between the aligned blocks in the aligning species. Double line: Aligning species has one or more unalignable bases in the gap region. Possibly due to excessive evolutionary distance between species or independent indels in the region between the aligned blocks in both species. Pale yellow coloring: Aligning species has Ns in the gap region. Reflects uncertainty in the relationship between the DNA of both species, due to lack of sequence in relevant portions of the aligning species. Genomic Breaks Discontinuities in the genomic context (chromosome, scaffold or region) of the aligned DNA in the aligning species are shown as follows: Vertical blue bar: Represents a discontinuity that persists indefinitely on either side, e.g. a large region of DNA on either side of the bar comes from a different chromosome in the aligned species due to a large scale rearrangement. Green square brackets: Enclose shorter alignments consisting of DNA from one genomic context in the aligned species nested inside a larger chain of alignments from a different genomic context. The alignment within the brackets may represent a short misalignment, a lineage-specific insertion of a transposon in the human genome that aligns to a paralogous copy somewhere else in the aligned species, or other similar occurrence. Base Level When zoomed-in to the base-level display, the track shows the base composition of each alignment. The numbers and symbols on the Gaps line indicate the lengths of gaps in the human sequence at those alignment positions relative to the longest non-human sequence. If there is sufficient space in the display, the size of the gap is shown. If the space is insufficient and the gap size is a multiple of 3, a &quot;*&quot; is displayed; other gap sizes are indicated by &quot;+&quot;. Codon translation is available in base-level display mode if the displayed region is identified as a coding segment. To display this annotation, select the species for translation from the pull-down menu in the Codon Translation configuration section at the top of the page. Then, select one of the following modes: No codon translation: The gene annotation is not used; the bases are displayed without translation. Use default species reading frames for translation: The annotations from the genome displayed in the Default species to establish reading frame pull-down menu are used to translate all the aligned species present in the alignment. Use reading frames for species if available, otherwise no translation: Codon translation is performed only for those species where the region is annotated as protein coding. Use reading frames for species if available, otherwise use default species: Codon translation is done on those species that are annotated as being protein coding over the aligned region using species-specific annotation; the remaining species are translated using the default species annotation. Codon translation uses the following gene tracks as the basis for translation: Gene TrackSpecies RefSeq GenesBos mutus, Canis lupus familiaris, Carlito syrichta, Cercocebus atys, Chinchilla lanigera, Colobus angolensis, Condylura cristata, Dipodomys ordii, Elephantulus edwardii, Eptesicus fuscus, Felis catus, Felis catus fca126, Fukomys damarensis, Homo sapiesn, Ictidomys tridecemlineatus, Macaca mulatta, Macaca nemestrina, Marmota marmota, Microtus ochrogaster, Miniopterus natalensis, Mus musculus, Mus pahari, Myotis brandtii, Myotis davidii, Myotis lucifugus, Odobenus rosmarus, Orcinus orca, Otolemur garnettii, Peromyscus maniculatus, Piliocolobus tephrosceles, Propithecus coquerelli, Pteropus alecto, Pteropus vampyrus, Rattus norvegicus, Rhinopithecus roxellana, Saimiri boliviensis, Sorex araneus, Sus scrofa, Theropithecus gelada, Tupaia chinensis Ensembl GenesCavia aperea Augustus GenesEidolon helvum, Pteronotus parnellii no annotationAcinonyx jubatus, Acomys cahirinus, Ailuropoda melanoleuca, Ailurus fulgens, Allactaga bullata, Allenopithecus nigroviridis, Allochrocebus lhoesti, Allochrocebus preussi, Allochrocebus solatus, Alouatta belzebul, Alouatta caraya, Alouatta discolor, Alouatta juara, Alouatta macconnelli, Alouatta nigerrima, Alouatta palliata, Alouatta puruensis, Alouatta seniculus, Ammotragus lervia, Anoura caudifer, Antilocapra americana, Aotus azarae, Aotus griseimembra, Aotus nancymaae, Aotus trivirgatus, Aotus vociferans, Aplodontia rufa, Arctocebus calabarensis, Artibeus jamaicensis, Ateles geoffroyi_a, Ateles geoffroyi_b, Ateles belzebuth, Ateles chamek, Ateles marginatus, Ateles paniscus, Avahi laniger, Avahi peyrierasi, Balaenoptera acutorostrata, Balaenoptera bonaerensis, Beatragus hunteri, Bison bison, Bos indicus, Bos taurus, Bubalus bubalis, Cacajao ayresi, Cacajao calvus, Cacajao hosomi, Cacajao melanocephalus, Callibella humilis, Callimico goeldii, Callithrix geoffroyi, Callithrix jacchus, Callithrix kuhlii, Camelus bactrianus, Camelus dromedarius, Camelus ferus, Canis lupus VD, Canis lupus dingo, Canis lupus orion, Capra aegagrus, Capra hircus, Capromys pilorides, Carollia perspicillata, Castor canadensis, Catagonus wagneri, Cavia porcellus, Cavia tschudii, Cebuella niveiventris, Cebuella pygmaea, Cebus albifrons, Cebus olivaceus, Cebus unicolor, Cephalopachus bancanus, Ceratotherium simum, Ceratotherium simum cottoni, Cercocebus chrysogaster, Cercocebus lunulatus, Cercocebus torquatus, Cercopithecus ascanius, Cercopithecus cephus, Cercopithecus diana, Cercopithecus hamlyni, Cercopithecus lowei, Cercopithecus albogularis, Cercopithecus mona, Cercopithecus neglectus, Cercopithecus nictitans, Cercopithecus petaurista, Cercopithecus pogonias, Cercopithecus roloway, Chaetophractus vellerosus, Cheirogaleus major, Cheirogaleus medius, Cheracebus lucifer, Cheracebus lugens, Cheracebus regulus, Cheracebus torquatus, Chiropotes albinasus, Chiropotes israelita, Chiropotes sagulatus, Chlorocebus aethiops, Chlorocebus pygerythrus, Chlorocebus sabaeus, Choloepus didactylus, Choloepus hoffmanni, Chrysochloris asiatica, Colobus guereza, Colobus polykomos, Craseonycteris thonglongyai, Cricetomys gambianus, Cricetulus griseus, Crocidura indochinensis, Cryptoprocta ferox, Ctenodactylus gundi, Ctenomys sociabilis, Cuniculus paca, Dasyprocta punctata, Dasypus novemcinctus, Daubentonia madagascariensis, Delphinapterus leucas, Desmodus rotundus, Dicerorhinus sumatrensis, Diceros bicornis, Dinomys branickii, Dipodomys stephensi, Dolichotis patagonum, Echinops telfairi, Elaphurus davidianus, Ellobius lutescens, Ellobius talpinus, Enhydra lutris, Equus asinus, Equus caballus, Equus przewalskii, Erinaceus europaeus, Erythrocebus patas, Eschrichtius robustus, Eubalaena japonica, Eulemur albifrons, Eulemur collaris, Eulemur coronatus, Eulemur flavifrons, Eulemur fulvus, Eulemur macaco, Eulemur mongoz, Eulemur rubriventer, Eulemur rufus, Eulemur sanfordi, Felis nigripes, Galago moholi, Galago senegalensis, Galagoides demidoff, Galeopterus variegatus, Giraffa tippelskirchi, Glis glis, Gorilla beringei, Gorilla gorilla, Graphiurus murinus, Hapalemur alaotrensis, Hapalemur gilberti, Hapalemur griseus, Hapalemur meridionalis, Hapalemur occidentalis, Helogale parvula, Hemitragus hylocrius, Heterocephalus glaber, Heterohyrax brucei, Hippopotamus amphibius, Hipposideros armiger, Hipposideros galeritus, Hoolock leuconedys, Hyaena hyaena, Hydrochoerus hydrochaeris, Hylobates abbotti, Hylobates agilis, Hylobates klossii, Hylobates pileatus, Hylobates muelleri, Hylobates pileatus, Hystrix cristata, Indri indri, Inia geoffrensis, Jaculus jaculus, Kogia breviceps, Lagothrix lagothricha, Lasiurus borealis, Lemur catta, Leontocebus fuscicollis, Leontocebus illigeri, Leontocebus nigricollis, Leontopithecus chrysomelas, Leontopithecus rosalia, Lepilemur ankaranensis, Lepilemur dorsalis, Lepilemur ruficaudatus, Lepilemur septentrionalis, Leptonychotes weddellii, Lepus americanus, Lipotes vexillifer, Lophocebus aterrimus, Loris lydekkerianus, Loris tardigradus, Loxodonta africana, Lycaon pictus, Macaca arctoides, Macaca assamensis, Macaca cyclopis, Macaca fascicularis, Macaca fuscata, Macaca leonina, Macaca maura, Macaca nigra, Macaca radiata, Macaca siberu, Macaca silenus, Macaca thibetana, Macaca tonkeana, Macroglossus sobrinus, Mandrillus leucophaeus, Mandrillus sphinx, Manis javanica, Manis pentadactyla, Megaderma lyra, Mellivora capensis, Meriones unguiculatus, Mesocricetus auratus, Mesoplodon bidens, Mico argentatus, Mico humeralifer, Mico schneideri, Microcebus murinus, Microgale talazaci, Micronycteris hirsuta, Miniopterus schreibersii, Miopithecus ogouensis, Mirounga angustirostris, Mirza zaza, Monodon monoceros, Mormoops blainvillei, Moschus moschiferus, Mungos mungo, Murina feae, Mus caroli, Mus spretus, Muscardinus avellanarius, Mustela putorius, Myocastor coypus, Myotis myotis, Myrmecophaga tridactyla, Nannospalax galili, Nasalis larvatus, Neomonachus schauinslandi, Neophocaena asiaeorientalis, Noctilio leporinus, Nomascus annamensis, Nomascus concolor, Nomascus gabriellae, Nomascus siki_a, Nomascus siki_b, Nyctereutes procyonoides, Nycticebus bengalensis, Nycticebus coucang, Nycticebus pygmaeus, Ochotona princeps, Octodon degus, Odocoileus virginianus, Okapia johnstoni, Ondatra zibethicus, Onychomys torridus, Orycteropus afer, Oryctolagus cuniculus, Otocyon megalotis, Otolemur crassicaudatus, Ovis aries, Ovis canadensis, Pan paniscus, Pan troglodytes, Panthera onca, Panthera pardus, Panthera tigris, Pantholops hodgsonii, Papio anubis, Papio cynocephalus, Papio hamadryas, Papio kindae, Papio papio, Papio ursinus, Paradoxurus hermaphroditus, Perodicticus ibeanus, Perodicticus potto, Perognathus longimembris, Petromus typicus, Phocoena phocoena, Piliocolobus badius, Piliocolobus gordonorum, Piliocolobus kirkii, Pipistrellus pipistrellus, Pithecia albicans, Pithecia chrysocephala, Pithecia hirsuta, Pithecia mittermeieri, Pithecia pissinattii, Pithecia pithecia, Pithecia vanzolinii, Platanista gangetica, Plecturocebus bernhardi, Plecturocebus brunneus, Plecturocebus caligatus, Plecturocebus cinerascens, Plecturocebus cupreus, Plecturocebus dubius, Plecturocebus grovesi, Plecturocebus hoffmannsi, Plecturocebus miltoni, Plecturocebus moloch, Pongo abelii, Pongo pygmaeus, Presbytis comata, Presbytis mitrata, Procavia capensis, Prolemur simus, Propithecus coronatus, Propithecus diadema, Propithecus edwardsi, Propithecus perrieri, Propithecus tattersalli, Propithecus verreauxi, Psammomys obesus, Pteronura brasiliensis, Puma concolor, Pygathrix cinerea, Pygathrix nigripes, Pygathrix nigripes, Rangifer tarandus, Rhinolophus sinicus, Rhinopithecus bieti, Rhinopithecus strykeri, Rousettus aegyptiacus, Saguinus bicolor, Saguinus geoffroyi, Saguinus imperator, Saguinus inustus, Saguinus labiatus, Saguinus midas, Saguinus mystax, Saguinus oedipus, Saiga tatarica, Saimiri cassiquiarensis, Saimiri macrodon, Saimiri oerstedii, Saimiri sciureus, Saimiri ustus, Sapajus apella, Sapajus macrocephalus, Scalopus aquaticus, Semnopithecus entellus, Semnopithecus hypoleucos, Semnopithecus johnii, Semnopithecus priam, Semnopithecus schistaceus, Semnopithecus vetulus, Sigmodon hispidus, Solenodon paradoxus, Spermophilus dauricus, Spilogale gracilis, Suricata suricatta, Symphalangus syndactylus, Tadarida brasiliensis, Tamandua tetradactyla, Tapirus indicus, Tapirus terrestris, Tarsius lariang, Tarsius wallacei, Thryonomys swinderianus, Tolypeutes matacus, Tonatia saurophila, Trachypithecus auratus, Trachypithecus crepusculus, Trachypithecus cristatus, Trachypithecus francoisi, Trachypithecus geei, Trachypithecus germaini, Trachypithecus hatinhensis, Trachypithecus laotum, Trachypithecus leucocephalus, Trachypithecus melamera, Trachypithecus obscurus, Trachypithecus phayrei, Trachypithecus pileatus, Tragulus javanicus, Trichechus manatus, Tupaia tana, Tursiops truncatus, Uropsilus gracilis, Ursus maritimus, Varecia rubra, Varecia variegata, Vicugna pacos, Vulpes lagopus, Xerus inauris, Zalophus californianus, Zapus hudsonius, Ziphius cavirostris Table 2. Gene tracks used for codon translation. Methods This alignment was created by making three edits (using Cactus) to the 241-way mammalian Zoonomia Cactus alignment ( https://cglgenomics.ucsc.edu/data/cactus/). One additional cat genome, &quot;Felis_catus_fca126&quot; (GCA_018350175.1) was added as a sister taxa to the existing &quot;Felis_catus&quot; species Five additional canine genomes were also added: canFam4, &quot;Canis_lupus_dingo&quot; (GCA_003254725.1), &quot;Canis_lupus_orion&quot; (GCA_905319855.2), &quot;Nyctereutes_procyonoides&quot; (GCA_905146905.1) and &quot;Otocyon_megalotis&quot; (GCA_017311455.1). &quot;Canis_lupus&quot; from the Zoonomia alignment was also renamed &quot;Canis_lupus_VD&quot; to reflect the fact that it corresponds to a &quot;village dog&quot; and not &quot;wolf&quot; sample. The 43-species primates clade from the Zoonomia alignment was removed and replaced with the 243-way primates alignment from Identification of constrained sequence elements across 239 primate genomes, increasing the alignment by 200 additional primate species. Phylogenic tree The phylogenic tree was established by the research described in A global catalog of whole-genome diversity from 233 primate species. Sequences count commonname clade scientific&nbsp;name(link&nbsp;to&nbsp;browser&nbsp;when&nbsp;existing) taxon&nbsp;idlink to NCBI 001humanprimates catarrhiniHomo sapiens/hg38reference species9606 002western gorillaprimates catarrhiniGorilla gorillaGCA_900006655.3_Susie39593 003Sumatran orangutanprimates catarrhiniPongo abeliiGCA_002880775.3_Susie_PABv29601 004Eastern Gorillaprimates catarrhiniGorilla beringei499232 005chimpanzeeprimates catarrhiniPan troglodytesGCA_002880755.3_Clint_PTRv29598 006Bornean orangutanprimates catarrhiniPongo pygmaeus9600 007Rhesus monkeyprimates catarrhiniMacaca mulattarheMac109544 008geladaprimates catarrhiniTheropithecus geladaGCF_003255815.1_Tgel_1.09565 009stump-tailed macaqueprimates catarrhiniMacaca arctoides9540 010Northern Talapoin Monkeyprimates catarrhiniMiopithecus ogouensis100488 011crab-eating macaqueprimates catarrhiniMacaca fascicularis9541 012Allen's swamp monkeyprimates catarrhiniAllenopithecus nigroviridis54135 013siamangprimates catarrhiniSymphalangus syndactylus9590 014black crested mangabeyprimates catarrhiniLophocebus aterrimus75566 015drillprimates catarrhiniMandrillus leucophaeus9568 016Bonnet Macaqueprimates catarrhiniMacaca radiata9548 017Red-capped Mangabeyprimates catarrhiniCercocebus torquatus9530 018Golden-bellied Mangabeyprimates catarrhiniCercocebus chrysogaster75569 019Owl-faced Monkeyprimates catarrhiniCercopithecus hamlyni9536 020Siberut Macaqueprimates catarrhiniMacaca siberu244255 021pig-tailed macaqueprimates catarrhiniMacaca nemestrinaGCF_000956065.1_Mnem_1.09545 022White-naped Mangabeyprimates catarrhiniCercocebus lunulatus (Cercocebus atys lunulatus)75570 023Tonkean Macaqueprimates catarrhiniMacaca tonkeana40843 024Diana Monkeyprimates catarrhiniCercopithecus diana36224 025red guenonprimates catarrhiniErythrocebus patas9538 026Northern Pig-tailed Macaqueprimates catarrhiniMacaca leonina90387 027Moor Macaqueprimates catarrhiniMacaca maura90383 028Guinea Baboonprimates catarrhiniPapio papio100937 029hamadryas baboonprimates catarrhiniPapio hamadryas9557 030liontail macaqueprimates catarrhiniMacaca silenus54601 031olive baboonprimates catarrhiniPapio anubisGCA_000264685.2_Panu_3.09555 032Roloway Monkeyprimates catarrhiniCercopithecus roloway1137049 033Kinda Baboonprimates catarrhiniPapio kindae208091 034Chacma Baboonprimates catarrhiniPapio ursinus36229 035Sun-tailed Monkeyprimates catarrhiniAllochrocebus solatus147650 036golden snub-nosed monkeyprimates catarrhiniRhinopithecus roxellanaGCF_007565055.1_ASM756505v161622 037Vervet Monkeyprimates catarrhiniChlorocebus pygerythrus60710 038sooty mangabeyprimates catarrhiniCercocebus atysGCF_000955945.1_Caty_1.09531 039green monkeyprimates catarrhiniChlorocebus sabaeusGCA_000409795.2_Chlorocebus_sabeus_1.160711 040De Brazza's monkeyprimates catarrhiniCercopithecus neglectus36227 041Yellow Baboonprimates catarrhiniPapio cynocephalus9556 042Celebes crested macaqueprimates catarrhiniMacaca nigra54600 043proboscis monkeyprimates catarrhiniNasalis larvatus43780 044Preuss's Monkeyprimates catarrhiniAllochrocebus preussi147649 045Putty-nosed Monkeyprimates catarrhiniCercopithecus nictitans36228 046Javan Suriliprimates catarrhiniPresbytis comata78452 047Sykes' Monkeyprimates catarrhiniCercopithecus albogularis36225 048LHoests Monkeyprimates catarrhiniAllochrocebus lhoesti100224 049Crowned Monkeyprimates catarrhiniCercopithecus pogonias102108 050Southern Mitered Langurprimates catarrhiniPresbytis mitrata (Presbytis melalophos mitrata)272115 051Grey-shanked Douc Langurprimates catarrhiniPygathrix cinerea693712 052Mona monkeyprimates catarrhiniCercopithecus mona36226 053Spot-nosed Monkeyprimates catarrhiniCercopithecus petaurista100487 054grivetprimates catarrhiniChlorocebus aethiops9534 055Lowes Monkeyprimates catarrhiniCercopithecus lowei304410 056Northern Yellow-cheeked Crested Gibbonprimates catarrhiniNomascus annamensis1616038 057Red-cheeked Gibbonprimates catarrhiniNomascus gabriellae61852 058Japanese macaqueprimates catarrhiniMacaca fuscata9542 059Western Red Colobusprimates catarrhiniPiliocolobus badius164648 060southern white-cheeked gibbonprimates catarrhiniNomascus siki_a9586 061Taiwan macaqueprimates catarrhiniMacaca cyclopis78449 062black-shanked douc langurprimates catarrhiniPygathrix nigripes310352 063King Colobusprimates catarrhiniColobus polykomos9572 064Black Crested Gibbonprimates catarrhiniNomascus concolor29089 065Udzungwa Red Colobusprimates catarrhiniPiliocolobus gordonorum591933 066Gee's Golden Langurprimates catarrhiniTrachypithecus geei164650 067Kloss's Gibbonprimates catarrhiniHylobates klossii9587 068Spectacled Leaf Monkeyprimates catarrhiniTrachypithecus obscurus54181 069Zanzibar Red Colobusprimates catarrhiniPiliocolobus kirkii591937 070Indochinese Silvered Langurprimates catarrhiniTrachypithecus germaini271260 071Hatinh Langurprimates catarrhiniTrachypithecus hatinhensis867383 072Moustached Monkeyprimates catarrhiniCercopithecus cephus9535 073Laotian Langurprimates catarrhiniTrachypithecus laotum465718 074Francois's langurprimates catarrhiniTrachypithecus francoisi54180 075Purple-faced Langurprimates catarrhiniSemnopithecus vetulus (Trachypithecus vetulus)54137 076Capped Langurprimates catarrhiniTrachypithecus pileatus164651 077Ugandan red Colobusprimates catarrhiniPiliocolobus tephroscelesGCF_002776525.2_ASM277652v2591936 078Spangled Ebony Langurprimates catarrhiniTrachypithecus auratus222416 079Red-tailed Monkeyprimates catarrhiniCercopithecus ascanius36223 080Silvery Lutungprimates catarrhiniTrachypithecus cristatus122765 081Nilgiri Langurprimates catarrhiniSemnopithecus johnii (Trachypithecus johnii)66063 082Indochinese grey langurprimates catarrhiniTrachypithecus crepusculus (Trachypithecus phayrei crepuscula)272121 083White-headed langurprimates catarrhiniTrachypithecus leucocephalus (Trachypithecus poliocephalus)465719 084pygmy chimpanzeeprimates catarrhiniPan paniscusGCA_000258655.2_panpan1.19597 085northern white-cheeked gibbonprimates catarrhiniNomascus siki_b9586 086Agile Gibbonprimates catarrhiniHylobates agilis9579 087Phayre's Leaf-monkeyprimates catarrhiniTrachypithecus melameran/a 088Nepal Gray Langurprimates catarrhiniSemnopithecus schistaceus2804203 089Abbott's Gray Gibbonprimates catarrhiniHylobates abbotti (Hylobates muelleri abbotti)716694 090Bornean Gibbonprimates catarrhiniHylobates muelleri9588 091Tufted Gray Langurprimates catarrhiniSemnopithecus priam1208733 092Black-footed Gray Langurprimates catarrhiniSemnopithecus hypoleucos1208734 093mantled guerezaprimates catarrhiniColobus guereza33548 094Hanuman langurprimates catarrhiniSemnopithecus entellus88029 095pileated gibbonprimates catarrhiniHylobates pileatus9589 096black snub-nosed monkeyprimates catarrhiniRhinopithecus bieti61621 097Burmese snub-nosed monkeyprimates catarrhiniRhinopithecus strykeri1194336 098Angolan colobusprimates catarrhiniColobus angolensiscolAng154131 099Pileated Gibbonprimates catarrhiniHylobates pileatus9589 100black-shanked douc langurprimates catarrhiniPygathrix nigripes310352 101Milne-edwards' Macaqueprimates catarrhiniMacaca thibetana54602 102Phayre's Leaf-monkeyprimates catarrhiniTrachypithecus phayrei61618 103Assam macaqueprimates catarrhiniMacaca assamensis9551 104Eastern hoolock gibbonprimates catarrhiniHoolock leuconedys61851 105mandrillprimates catarrhiniMandrillus sphinx9561 106White-faced Sakiprimates platyrrhiniPithecia chrysocephala2946515 107Monk Sakiprimates platyrrhiniPithecia hirsuta2946516 108white-faced sakiprimates platyrrhiniPithecia pithecia43777 109Mittermeier's Tapajós sakiprimates platyrrhiniPithecia mittermeieri2946517 110Buffy Sakiprimates platyrrhiniPithecia albicans2946514 111Pissinatti's sakiprimates platyrrhiniPithecia pissinattii (Pithecia pissinatti)2946518 112Vanzolini's Bald-faced Sakiprimates platyrrhiniPithecia vanzolinii2946519 113Bald-headed Uacariprimates platyrrhiniCacajao calvus30596 114Ayres Black Uakariprimates platyrrhiniCacajao ayresi535896 115Black-headed Uacariprimates platyrrhiniCacajao melanocephalus70825 116Black-headed Uacariprimates platyrrhiniCacajao hosomi535897 117Reddish-brown bearded sakiprimates platyrrhiniChiropotes sagulatus (Chiropotes chiropotes)658221 118brown-backed bearded sakiprimates platyrrhiniChiropotes israelita280163 119Collared Titi Monkeyprimates platyrrhiniCheracebus lugens210166 120Brown Titi Monkeyprimates platyrrhiniPlecturocebus brunneus1812042 121Hoffmanns's titi monkeyprimates platyrrhiniPlecturocebus hoffmannsi78255 122Milton's Titi Monkeyprimates platyrrhiniPlecturocebus miltoni1812038 123Widow Monkeyprimates platyrrhiniCheracebus torquatus30592 124Ashy Black Titi Monkeyprimates platyrrhiniPlecturocebus cinerascens1812037 125Prince Bernhard's Titi Monkeyprimates platyrrhiniPlecturocebus bernhardi1812036 126Yellow-handed Titi Monkeyprimates platyrrhiniCheracebus lucifer2487712 127Coppery Titi Monkeyprimates platyrrhiniPlecturocebus cupreus202457 128Chestnut-bellied Titiprimates platyrrhiniPlecturocebus caligatus867332 129Hershkovitzs Titiprimates platyrrhiniPlecturocebus dubius2946520 130Red-bellied Titi Monkeyprimates platyrrhiniPlecturocebus moloch9523 131Groves' Titiprimates platyrrhiniPlecturocebus grovesi2488670 132black-handed spider monkeyprimates platyrrhiniAteles geoffroyi_a9509 133Widow Monkeyprimates platyrrhiniCheracebus regulus1812110 134Guiana Spider Monkeyprimates platyrrhiniAteles paniscus9510 135Black-faced Black Spider Monkeyprimates platyrrhiniAteles chamek118643 136White-cheeked Spider Monkeyprimates platyrrhiniAteles marginatus1529884 137White-bellied Spider Monkeyprimates platyrrhiniAteles belzebuth9507 138Common Woolly Monkeyprimates platyrrhiniLagothrix lagothricha (Lagothrix lagotricha)9519 139large-headed capuchinprimates platyrrhiniSapajus macrocephalus (Sapajus apella macrocephalus)1547595 140Spixs White-fronted Capuchinprimates platyrrhiniCebus unicolor1985288 141Central American spider monkeyprimates platyrrhiniAteles geoffroyi_b9509 142Guinan Weeper Capuchinprimates platyrrhiniCebus olivaceus37295 143mantled howler monkeyprimates platyrrhiniAlouatta palliata30589 144white-fronted capuchinprimates platyrrhiniCebus albifrons9514 145Northern Night Monkeyprimates platyrrhiniAotus trivirgatus9505 146Grey-handed Night Monkeyprimates platyrrhiniAotus griseimembra292213 147Black-and-gold Howler Monkeyprimates platyrrhiniAlouatta caraya9502 148Spixs Night Monkeyprimates platyrrhiniAotus vociferans57176 149Red-handed Howler Monkeyprimates platyrrhiniAlouatta belzebul30590 150Red-handed Howler Monkeyprimates platyrrhiniAlouatta discolor2905217 151Azara's Night Monkeyprimates platyrrhiniAotus azarae (Aotus azarai)30591 152Purús Red Howler Monkeyprimates platyrrhiniAlouatta puruensis (Alouatta seniculus puruensis)1347729 153Black Howler Monkeyprimates platyrrhiniAlouatta nigerrima (Alouatta belzebul)30590 154Guianan Red Howler Monkeyprimates platyrrhiniAlouatta macconnelli198115 155Colombian Red Howler Monkeyprimates platyrrhiniAlouatta juara2946512 156Colombian Red Howler Monkeyprimates platyrrhiniAlouatta seniculus9503 157tufted capuchinprimates platyrrhiniSapajus apella9515 158Ma's night monkeyprimates platyrrhiniAotus nancymaaeGCA_000952055.2_Anan_2.037293 159Bolivian squirrel monkeyprimates platyrrhiniSaimiri boliviensisGCF_016699345.1_BCM_Sbol_2.027679 160White-nosed Sakiprimates platyrrhiniChiropotes albinasus198627 161Black Mantle Tamarinprimates platyrrhiniLeontocebus nigricollis9489 162brown-mantled tamarinprimates platyrrhiniLeontocebus fuscicollis9487 163Illiger's saddle-back tamarinprimates platyrrhiniLeontocebus illigeri (Leontocebus fuscicollis illigeri)881947 164Cotton-headed Tamarinprimates platyrrhiniSaguinus oedipus9490 165Pied Tamarinprimates platyrrhiniSaguinus bicolor37588 166Geoffroy's Tamarinprimates platyrrhiniSaguinus geoffroyi43778 167White-fronted Titi Monkeyprimates platyrrhiniSaguinus inustus1079039 168Moustached Tamarinprimates platyrrhiniSaguinus mystax9488 169tamarinprimates platyrrhiniSaguinus imperator9491 170Guianan Squirrel Monkeyprimates platyrrhiniSaimiri sciureus9521 171Red-chested Mustached Tamarinprimates platyrrhiniSaguinus labiatus78454 172Goeldi's Monkeyprimates platyrrhiniCallimico goeldii9495 173Black-crowned Central American Squirrel Monkeyprimates platyrrhiniSaimiri oerstedii70928 174Golden-headed Lion Tamarinprimates platyrrhiniLeontopithecus chrysomelas57374 175golden lion tamarinprimates platyrrhiniLeontopithecus rosalia30588 176Humboldt's Squirrel Monkeyprimates platyrrhiniSaimiri cassiquiarensis2946521 177bare-eared squirrel monkeyprimates platyrrhiniSaimiri ustus66265 178Ecuadorian squirrel monkeyprimates platyrrhiniSaimiri macrodon2946522 179white-tufted-ear marmosetprimates platyrrhiniCallithrix jacchus9483 180Eastern Pygmy Marmosetprimates platyrrhiniCebuella niveiventris2826950 181Western Pygmy Marmosetprimates platyrrhiniCebuella pygmaea9493 182Black And White Tassel-ear Marmosetprimates platyrrhiniMico humeralifer52232 183Black-crowned Dwarf Marmosetprimates platyrrhiniCallibella humilis (Mico humilis)666519 184Mico schneideriprimates platyrrhiniMico schneiderin/a 185Silvery Marmosetprimates platyrrhiniMico argentatus9482 186Midas tamarinprimates platyrrhiniSaguinus midas30586 187Wieds Marmosetprimates platyrrhiniCallithrix kuhlii867363 188Geoffroy's Tufted-ear Marmosetprimates platyrrhiniCallithrix geoffroyi52231 189Horsfield's tarsierprimates tarsiidaeCephalopachus bancanus9477 190Philippine tarsierprimates tarsiidaeCarlito syrichtatarSyr21868482 191Lariang Tarsierprimates tarsiidaeTarsius lariang630277 192Wallace's Tarsierprimates tarsiidaeTarsius wallacei981131 193aye-ayeprimates strepsirrhiniDaubentonia madagascariensis31869 194Crowned Sifakaprimates strepsirrhiniPropithecus coronatus (Propithecus deckenii coronatus)475619 195Perrier's Sifakaprimates strepsirrhiniPropithecus perrieri989338 196ruffed lemurprimates strepsirrhiniVarecia variegata9455 197Diademed Sifakaprimates strepsirrhiniPropithecus diadema83281 198Milne-Edwards Sifakaprimates strepsirrhiniPropithecus edwardsi543559 199babakotoprimates strepsirrhiniIndri indri34827 200Golden-crowned Sifakaprimates strepsirrhiniPropithecus tattersalli30601 201Eastern Woolly Lemurprimates strepsirrhiniAvahi laniger122246 202Verreauxs Sifakaprimates strepsirrhiniPropithecus verreauxi34825 203Peyrieras Woolly Lemurprimates strepsirrhiniAvahi peyrierasi1313323 204Red Ruffed Lemurprimates strepsirrhiniVarecia rubra554167 205greater bamboo lemurprimates strepsirrhiniProlemur simus1328070 206Red-bellied Lemurprimates strepsirrhiniEulemur rubriventer34829 207mongoose lemurprimates strepsirrhiniEulemur mongoz34828 208Geoffroys Dwarf Lemurprimates strepsirrhiniCheirogaleus major47177 209Crowned Lemurprimates strepsirrhiniEulemur coronatus13514 210black lemurprimates strepsirrhiniEulemur macaco30602 211lesser dwarf lemurprimates strepsirrhiniCheirogaleus medius9460 212Sclater's lemurprimates strepsirrhiniEulemur flavifrons87288 213Coquerel's sifakaprimates strepsirrhiniPropithecus coquerelli (Propithecus coquereli)proCoq1379532 214Collared Brown Lemurprimates strepsirrhiniEulemur collaris (Eulemur fulvus collaris)47178 215Red-tailed Sportive Lemurprimates strepsirrhiniLepilemur ruficaudatus78866 216Red Brown Lemurprimates strepsirrhiniEulemur rufus859983 217Sanfords Brown Lemurprimates strepsirrhiniEulemur sanfordi122225 218White-fronted Lemurprimates strepsirrhiniEulemur albifrons1215604 219Gray's Sportive Lemurprimates strepsirrhiniLepilemur dorsalis78583 220brown lemurprimates strepsirrhiniEulemur fulvus13515 221Sahafary Sportive Lemurprimates strepsirrhiniLepilemur septentrionalis78584 222Sambirano Lesser Bamboo Lemurprimates strepsirrhiniHapalemur occidentalis867377 223Alaotra Reed Lemurprimates strepsirrhiniHapalemur alaotrensis (Hapalemur griseus alaotrensis)122220 224Eastern Lesser Bamboo Lemurprimates strepsirrhiniHapalemur griseus13557 225Ankarana Sportive Lemurprimates strepsirrhiniLepilemur ankaranensis342401 226ring-tailed lemurprimates strepsirrhiniLemur catta9447 227gray bamboo lemurprimates strepsirrhiniHapalemur gilberti3043110 228Rusty-gray Lesser Bamboo Lemurprimates strepsirrhiniHapalemur meridionalis3043112 229Demidoffs Dwarf Galagoprimates strepsirrhiniGalagoides demidoff89672 230northern giant mouse lemurprimates strepsirrhiniMirza zaza339999 231gray mouse lemurprimates strepsirrhiniMicrocebus murinusGCA_000165445.3_Mmur_3.030608 232small-eared galagoprimates strepsirrhiniOtolemur garnettiiotoGar330611 233Northern Lesser Galagoprimates strepsirrhiniGalago senegalensis9465 234Thick-tailed Greater Galagoprimates strepsirrhiniOtolemur crassicaudatus9463 235Grey Slender Lorisprimates strepsirrhiniLoris lydekkerianus300163 236slender lorisprimates strepsirrhiniLoris tardigradus9468 237West African Pottoprimates strepsirrhiniPerodicticus potto9472 238East African Pottoprimates strepsirrhiniPerodicticus ibeanus (Perodicticus potto ibeanus)261737 239Moholi bushbabyprimates strepsirrhiniGalago moholi30609 240Pygmy Slow Lorisprimates strepsirrhiniNycticebus pygmaeus (Xanthonycticebus pygmaeus)101278 241Bengal slow lorisprimates strepsirrhiniNycticebus bengalensis261741 242Calabar Angwantiboprimates strepsirrhiniArctocebus calabarensis261739 243slow lorisprimates strepsirrhiniNycticebus coucang9470 244jaguarcarnivoraPanthera oncaGCA_004023805.1_PanOnc_v1_BIUU9690 245leopardcarnivoraPanthera pardusGCA_001857705.1_PanPar1.09691 246giant pandacarnivoraAiluropoda melanoleucaGCA_002007445.1_ASM200744v19646 247Hawaiian monk sealcarnivoraNeomonachus schauinslandiGCA_002201575.1_ASM220157v129088 248California sea lioncarnivoraZalophus californianusGCA_004024565.1_ZalCal_v1_BIUU9704 249Greenland wolfcarnivoraCanis lupus orionGCA_905319855.2_mCanLor1.22605939 250Pacific walruscarnivoraOdobenus rosmarusodoRosDiv19707 251domestic cat (Fca126)carnivoraFelis catus fca126 (Felis catus)GCF_018350175.1_F.catus_Fca126_mat1.09685 252northern elephant sealcarnivoraMirounga angustirostrisGCA_004023865.1_MirAng_v1_BIUU9716 253domestic catcarnivoraFelis catusfelCat89685 254domestic dog (BS72/Village Dog)carnivoraCanis lupus familiarisGCA_004027395.1_CanFam_VD_v1_BIUU 255German Shepherd dog (Mischka)carnivoraCanis lupus familiaris (CanFam4) (Canis lupus familiaris)canFam4 256dingocarnivoraCanis lupus dingo286419 257raccoon dogcarnivoraNyctereutes procyonoides34880 258fossacarnivoraCryptoprocta ferox94188 259polar bearcarnivoraUrsus maritimusGCA_000687225.1_UrsMar_1.029073 260Asian palm civetcarnivoraParadoxurus hermaphroditusGCA_004024585.1_ParHer_v1_BIUU71117 261African hunting dogcarnivoraLycaon pictusGCA_001887905.1_LycPicSAfr1.09622 262Arctic foxcarnivoraVulpes lagopusGCA_004023825.1_VulLag_v1_BIUU494514 263dogcarnivoraCanis lupus familiarisGCF_000002285.3_CanFam3.19615 264striped hyenacarnivoraHyaena hyaenaGCA_004023945.1_HyaHya_v1_BIUU95912 265n/acarnivoraAcinonyx jubatusGCA_001443585.1_aciJub132536 266tigercarnivoraPanthera tigrisGCA_000464555.1_PanTig1.09694 267Sea ottercarnivoraEnhydra lutrisGCA_002288905.2_ASM228890v234882 268giant ottercarnivoraPteronura brasiliensis9672 269bat-eared foxcarnivoraOtocyon megalotis9624 270Weddell sealcarnivoraLeptonychotes weddelliiGCA_000349705.1_LepWed1.09713 271Lesser pandacarnivoraAilurus fulgensGCA_002007465.1_ASM200746v19649 272ratelcarnivoraMellivora capensisGCA_004024625.1_MelCap_v1_BIUU9664 273banded mongoosecarnivoraMungos mungoGCA_004023785.1_MunMun_v1_BIUU210652 274dwarf mongoosecarnivoraHelogale parvulaGCA_004023845.1_HelPar_v1_BIUU210647 275meerkatcarnivoraSuricata suricattaGCA_004023905.1_SurSur_v1_BIUU37032 276pumacarnivoraPuma concolorGCA_003327715.1_PumCon1.09696 277black-footed catcarnivoraFelis nigripesGCA_004023925.1_FelNig_v1_BIUU61379 278European polecatcarnivoraMustela putoriusGCA_000239315.1_MusPutFurMale1.09668 279western spotted skunkcarnivoraSpilogale gracilisGCA_004023965.1_SpiGra_v1_BIUU30551 280Sumatran rhinoceroslaurasiatheriaDicerorhinus sumatrensisGCA_002844835.1_ASM284483v189632 281black rhinoceroslaurasiatheriaDiceros bicornisGCA_004027315.1_DicBicMic_v1_BIUU9805 282Asiatic tapirlaurasiatheriaTapirus indicusGCA_004024905.1_TapInd_v1_BIUU9802 283Brazilian tapirlaurasiatheriaTapirus terrestrisGCA_004025025.1_TapTer_v1_BIUU9801 284northern white rhinoceroslaurasiatheriaCeratotherium simum cottoni310713 285asslaurasiatheriaEquus asinusGCA_001305755.1_ASM130575v19793 286Southern white rhinoceroslaurasiatheriaCeratotherium simumGCA_000283155.1_CerSimSim1.09807 287Przewalski's horselaurasiatheriaEquus przewalskiiGCA_000696695.1_Burgud9798 288horselaurasiatheriaEquus caballusGCA_000002305.1_EquCab2.09796 289Malayan pangolinlaurasiatheriaManis javanicaGCA_001685135.1_ManJav1.09974 290Chinese pangolinlaurasiatheriaManis pentadactylaGCA_000738955.1_M_pentadactyla-1.1.1143292 291Hispaniolan solenodonlaurasiatheriaSolenodon paradoxus79805 292eastern molelaurasiatheriaScalopus aquaticusGCA_004024925.1_ScaAqu_v1_BIUU71119 293gracile shrew molelaurasiatheriaUropsilus gracilisGCA_004024945.1_UroGra_v1_BIUU182669 294star-nosed molelaurasiatheriaCondylura cristataGCF_000260355.1_ConCri1.0143302 295western European hedgehoglaurasiatheriaErinaceus europaeusGCA_000296755.1_EriEur2.09365 296European shrewlaurasiatheriaSorex araneussorAra242254 297Indochinese shrewlaurasiatheriaCrocidura indochinensisGCA_004027635.1_CroInd_v1_BIUU876679 298Hoffmann's two-fingered slothxenarthraCholoepus hoffmanniGCA_000164785.2_C_hoffmanni-2.0.19358 299nine-banded armadilloxenarthraDasypus novemcinctusGCA_000208655.2_Dasnov3.09361 300giant anteaterxenarthraMyrmecophaga tridactylaGCA_004026745.1_MyrTri_v1_BIUU71006 301southern tamanduaxenarthraTamandua tetradactylaGCA_004025105.1_TamTet_v1_BIUU48850 302placentalsxenarthraTolypeutes matacus183749 303southern two-toed slothxenarthraCholoepus didactylusGCA_004027855.1_ChoDid_v1_BIUU27675 304screaming hairy armadilloxenarthraChaetophractus vellerosusGCA_004027955.1_ChaVel_v1_BIUU340076 305North Pacific right whaleartiodactylaEubalaena japonica302098 306grey whaleartiodactylaEschrichtius robustus9764 307hippopotamusartiodactylaHippopotamus amphibiusGCA_004027065.1_HipAmp_v1_BIUU9833 308Minke whaleartiodactylaBalaenoptera acutorostrataGCA_000493695.1_BalAcu1.09767 309beluga whaleartiodactylaDelphinapterus leucasGCA_002288925.2_ASM228892v29749 310Antarctic minke whaleartiodactylaBalaenoptera bonaerensisGCA_000978805.1_ASM97880v133556 311boutuartiodactylaInia geoffrensis9725 312harbor porpoiseartiodactylaPhocoena phocoena9742 313narwhalartiodactylaMonodon monocerosGCA_004026685.1_MonMon_M_v1_BIUU40151 314Yangtze River dolphinartiodactylaLipotes vexilliferGCA_000442215.1_Lipotes_vexillifer_v1118797 315killer whaleartiodactylaOrcinus orcaorcOrc19733 316Ganges River dolphinartiodactylaPlatanista gangetica118798 317Yangtze finless porpoiseartiodactylaNeophocaena asiaeorientalisGCA_003031525.1_Neophocaena_asiaeorientalis_V1189058 318Sowerby's beaked whaleartiodactylaMesoplodon bidens48745 319alpacaartiodactylaVicugna pacosGCA_000767525.1_Vi_pacos_V1.030538 320Cuvier's beaked whale"artiodactylaZiphius cavirostris9760 321Bactrian camelartiodactylaCamelus bactrianusGCA_000767855.1_Ca_bactrianus_MBC_1.09837 322Arabian camelartiodactylaCamelus dromedariusGCA_000767585.1_PRJNA234474_Ca_dromedarius_V1.09838 323wild Bactrian camelartiodactylaCamelus ferusGCA_000311805.2_CB1419612 324pygmy sperm whaleartiodactylaKogia breviceps27615 325Chacoan peccaryartiodactylaCatagonus wagneriGCA_004024745.1_CatWag_v1_BIUU51154 326reindeerartiodactylaRangifer tarandusGCA_004026565.1_RanTarSib_v1_BIUU9870 327Pere David's deerartiodactylaElaphurus davidianusGCA_002443075.1_Milu1.043332 328okapiartiodactylaOkapia johnstoniGCA_001660835.1_ASM166083v186973 329Masai giraffeartiodactylaGiraffa tippelskirchiGCA_001651235.1_ASM165123v1439328 330Siberian musk deerartiodactylaMoschus moschiferusGCA_004024705.1_MosMos_v1_BIUU68415 331water buffaloartiodactylaBubalus bubalisGCA_000471725.1_UMD_CASPUR_WB_2.089462 332cowartiodactylaBos taurusGCA_000003205.6_Btau_5.0.19913 333pronghornartiodactylaAntilocapra americanaGCA_004027515.1_AntAmePen_v1_BIUU9891 334white-tailed deerartiodactylaOdocoileus virginianusGCA_002102435.1_Ovir.te_1.09874 335aoudadartiodactylaAmmotragus lerviaGCA_002201775.1_ALER1.09899 336bighorn sheepartiodactylaOvis canadensisGCA_004026945.1_OviCan_v1_BIUU37174 337goatartiodactylaCapra hircusGCA_001704415.1_ARS19925 338Nilgiri tahrartiodactylaHemitragus hylocriusGCA_004026825.1_HemHyl_v1_BIUU330464 339hirolaartiodactylaBeatragus hunteriGCA_004027495.1_BeaHun_v1_BIUU59527 340wild yakartiodactylaBos mutusbosMut172004 341American bisonartiodactylaBison bisonGCA_000754665.1_Bison_UMD1.09901 342sheepartiodactylaOvis ariesGCA_000298735.2_Oar_v4.09940 343chiruartiodactylaPantholops hodgsoniiGCA_000400835.1_PHO1.059538 344wild goatartiodactylaCapra aegagrusGCA_000978405.1_CapAeg_1.09923 345Java mouse-deerartiodactylaTragulus javanicusGCA_004024965.1_TraJav_v1_BIUU9849 346pigartiodactylaSus scrofasusScr39823 347zebu cattleartiodactylaBos indicusGCA_000247795.2_Bos_indicus_1.09915 348common bottlenose dolphinartiodactylaTursiops truncatusGCA_001922835.1_NIST_Tur_tru_v19739 349Saiga antelopeartiodactylaSaiga tataricaGCA_004024985.1_SaiTat_v1_BIUU34875 350Chinese rufous horseshoe batchiropteraRhinolophus sinicusGCA_001888835.1_ASM188883v189399 351black flying foxchiropteraPteropus alectopteAle19402 352Cantor's roundleaf batchiropteraHipposideros galeritus58069 353Egyptian rousettechiropteraRousettus aegyptiacusGCA_004024865.1_RouAeg_v1_BIUU9407 354long-tongued fruit batchiropteraMacroglossus sobrinus326083 355large flying foxchiropteraPteropus vampyrusGCF_000151845.1_Pvam_2.0132908 356Brazilian free-tailed batchiropteraTadarida brasiliensisGCA_004025005.1_TadBra_v1_BIUU9438 357great roundleaf batchiropteraHipposideros armigerGCA_001890085.1_ASM189008v1186990 358straw-colored fruit batchiropteraEidolon helvumeidHel177214 359Antillean ghost-faced batchiropteraMormoops blainvilleiGCA_004026545.1_MorMeg_v1_BIUU118852 360tailed tailless batchiropteraAnoura caudiferGCA_004027475.1_AnoCau_v1_BIUU27642 361common vampire batchiropteraDesmodus rotundusGCA_002940915.2_ASM294091v29430 362hairy big-eared batchiropteraMicronycteris hirsutaGCA_004026765.1_MicHir_v1_BIUU148065 363stripe-headed round-eared batchiropteraTonatia saurophilaGCA_004024845.1_TonSau_v1_BIUU171122 364Seba's short-tailed batchiropteraCarollia perspicillataGCA_004027735.1_CarPer_v1_BIUU40233 365Jamaican fruit-eating batchiropteraArtibeus jamaicensisGCA_004027435.1_ArtJam_v1_BIUU9417 366Indian false vampirechiropteraMegaderma lyraGCA_004026885.1_MegLyr_v1_BIUU9413 367Schreibers' long-fingered batchiropteraMiniopterus schreibersiiGCA_004026525.1_MinSch_v1_BIUU9433 368greater bulldog batchiropteraNoctilio leporinusGCA_004026585.1_NocLep_v1_BIUU94963 369Natal long-fingered batchiropteraMiniopterus natalensisGCF_001595765.1_Mnat.v1291302 370hog-nosed batchiropteraCraseonycteris thonglongyaiGCA_004027555.1_CraTho_v1_BIUU208972 371Parnell's mustached batchiropteraPteronotus parnelliiptePar159476 372greater mouse-eared batchiropteraMyotis myotisGCA_004026985.1_MyoMyo_v1_BIUU51298 373Ashy-gray tube-nosed batchiropteraMurina feae (Murina aurata feae)GCA_004026665.1_MurFea_v1_BIUU1453894 374David's myotischiropteraMyotis davidiimyoDav1225400 375Brandt's batchiropteraMyotis brandtiimyoBra1109478 376big brown batchiropteraEptesicus fuscusGCF_000308155.1_EptFus1.029078 377red batchiropteraLasiurus borealisGCA_004026805.1_LasBor_v1_BIUU258930 378little brown batchiropteraMyotis lucifugusmyoLuc259463 379common pipistrellechiropteraPipistrellus pipistrellusGCA_004026625.1_PipPip_v1_BIUU59474 380African savanna elephantafrotheriaLoxodonta africanaGCA_000001905.1_Loxafr3.09785 381Florida manateeafrotheriaTrichechus manatusGCA_000243295.1_TriManLat1.09778 382yellow-spotted hyraxafrotheriaHeterohyrax bruceiGCA_004026845.1_HetBruBak_v1_BIUU77598 383Cape rock hyraxafrotheriaProcavia capensisGCA_004026925.1_ProCapCap_v1_BIUU9813 384aardvarkafrotheriaOrycteropus afer9818 385Cape golden moleafrotheriaChrysochloris asiaticaGCA_004027935.1_ChrAsi_v1_BIUU185453 386Cape elephant shrewafrotheriaElephantulus edwardiieleEdw128737 387Talazac's shrew tenrecafrotheriaMicrogale talazaci (Nesogale talazaci)GCA_004026705.1_MicTal_v1_BIUU2583312 388small Madagascar hedgehogafrotheriaEchinops telfairiGCA_000313985.1_EchTel2.09371 389Sunda flying lemureuarchontogliresGaleopterus variegatusGCA_004027255.1_GalVar_v1_BIUU482537 390Chinese tree shreweuarchontogliresTupaia chinensistupChi1246437 391South African ground squirreleuarchontogliresXerus inaurisGCA_004024805.1_XerIna_v1_BIUU234690 392large tree shreweuarchontogliresTupaia tana70687 393mountain beavereuarchontogliresAplodontia rufaGCA_004027875.1_AplRuf_v1_BIUU51342 394Alpine marmoteuarchontogliresMarmota marmotaGCF_001458135.1_marMar2.19993 395Daurian ground squirreleuarchontogliresSpermophilus dauricusGCA_002406435.1_ASM240643v199837 396crested porcupineeuarchontogliresHystrix cristataGCA_004026905.1_HysCri_v1_BIUU10137 397thirteen-lined ground squirreleuarchontogliresIctidomys tridecemlineatusspeTri243179 398American beavereuarchontogliresCastor canadensisGCA_004027675.1_CasCan_v1_BIUU51338 399long-tailed chinchillaeuarchontogliresChinchilla lanigerachiLan134839 400punctate agoutieuarchontogliresDasyprocta punctata34846 401pacaranaeuarchontogliresDinomys branickiiGCA_004027595.1_DinBra_v1_BIUU108858 402fat dormouseeuarchontogliresGlis glisGCA_004027185.1_GliGli_v1_BIUU41261 403northern gundieuarchontogliresCtenodactylus gundiGCA_004027205.1_CteGun_v1_BIUU10166 404naked mole-rateuarchontogliresHeterocephalus glaberGCA_000247695.1_HetGla_female_1.010181 405Patagonian cavyeuarchontogliresDolichotis patagonumGCA_004027295.1_DolPat_v1_BIUU29091 406capybaraeuarchontogliresHydrochoerus hydrochaerisGCA_004027455.1_HydHyd_v1_BIUU10149 407Montane guinea pigeuarchontogliresCavia tschudiiGCA_004027695.1_CavTsc_v1_BIUU143287 408domestic guinea pigeuarchontogliresCavia porcellusGCA_000151735.1_Cavpor3.010141 409degueuarchontogliresOctodon degusGCA_000260255.1_OctDeg1.010160 410lowland pacaeuarchontogliresCuniculus paca108852 411social tuco-tucoeuarchontogliresCtenomys sociabilisGCA_004027165.1_CteSoc_v1_BIUU43321 412Damara mole-rateuarchontogliresFukomys damarensisfukDam1885580 413woodland dormouseeuarchontogliresGraphiurus murinus51346 414Desmarest's hutiaeuarchontogliresCapromys piloridesGCA_004027915.1_CapPil_v1_BIUU34842 415Upper Galilee mountains blind mole rateuarchontogliresNannospalax galiliGCA_000622305.1_S.galili_v1.01026970 416nutriaeuarchontogliresMyocastor coypusGCA_004027025.1_MyoCoy_v1_BIUU10157 417hazel dormouseeuarchontogliresMuscardinus avellanariusGCA_004027005.1_MusAve_v1_BIUU39082 418dassie-rateuarchontogliresPetromus typicusGCA_004026965.1_PetTyp_v1_BIUU10183 419greater cane rateuarchontogliresThryonomys swinderianusGCA_004025085.1_ThrSwi_v1_BIUU10169 420snowshoe hareeuarchontogliresLepus americanusGCA_004026855.1_LepAme_v1_BIUU48086 421Gambian giant pouched rateuarchontogliresCricetomys gambianusGCA_004027575.1_CriGam_v1_BIUU10085 422Prairie deer mouseeuarchontogliresPeromyscus maniculatusGCF_000500345.1_Pman_1.010042 423southern grasshopper mouseeuarchontogliresOnychomys torridusGCA_004026725.1_OnyTor_v1_BIUU38674 424rabbiteuarchontogliresOryctolagus cuniculusGCA_000003625.1_OryCun2.09986 425muskrateuarchontogliresOndatra zibethicusGCA_004026605.1_OndZib_v1_BIUU10060 426northern mole voleeuarchontogliresEllobius talpinusGCA_001685095.1_ETalpinus_0.1329620 427Mongolian gerbileuarchontogliresMeriones unguiculatusGCA_004026785.1_MerUng_v1_BIUU10047 428fat sand rateuarchontogliresPsammomys obesusGCA_002215935.1_ASM221593v148139 429house mouseeuarchontogliresMus musculusmm1010090 430Chinese hamstereuarchontogliresCricetulus griseusGCA_900186095.1_CHOK1S_HZDv110029 431Norway rateuarchontogliresRattus norvegicusGCF_000001895.5_Rnor_6.010116 432western wild mouseeuarchontogliresMus spretusGCA_001624865.1_SPRET_EiJ_v110096 433meadow jumping mouseeuarchontogliresZapus hudsoniusGCA_004024765.1_ZapHud_v1_BIUU160400 434prairie voleeuarchontogliresMicrotus ochrogastermicOch179684 435Ryukyu mouseeuarchontogliresMus caroliGCA_900094665.2_CAROLI_EIJ_v1.110089 436Egyptian spiny mouseeuarchontogliresAcomys cahirinusGCA_004027535.1_AcoCah_v1_BIUU10068 437Gobi jerboaeuarchontogliresAllactaga bullata (Orientallactaga bullata)GCA_004027895.1_AllBul_v1_BIUU1041416 438shrew mouseeuarchontogliresMus pahariGCF_900095145.1_PAHARI_EIJ_v1.110093 439Transcaucasian mole voleeuarchontogliresEllobius lutescensGCA_001685075.1_ASM168507v139086 440hispid cotton rateuarchontogliresSigmodon hispidusGCA_004025045.1_SigHis_v1_BIUU42415 441lesser Egyptian jerboaeuarchontogliresJaculus jaculusGCA_000280705.1_JacJac1.051337 442Brazilian guinea pigeuarchontogliresCavia apereacavApe137548 443golden hamstereuarchontogliresMesocricetus auratusGCA_000349665.1_MesAur1.010036 444Stephens's kangaroo rateuarchontogliresDipodomys stephensiGCA_004024685.1_DipSte_v1_BIUU323379 445American pikaeuarchontogliresOchotona princepsGCA_000292845.1_OchPri3.09978 446Ord's kangaroo rateuarchontogliresDipodomys ordiidipOrd210020 447little pocket mouseeuarchontogliresPerognathus longimembris38669 Table 1. Genome assemblies included in the 447-way Conservation track. References Kuderna LFK, Ulirsch JC, Rashid S, Ameen M, Sundaram L, Hickey G, Cox AJ, Gao H, Kumar A, Aguet F et al. Identification of constrained sequence elements across 239 primate genomes. Nature. 2023 Nov 29;. DOI: 10.1038/s41586-023-06798-8; PMID: 38030727 Kuderna LFK, Gao H, Janiak MC, Kuhlwilm M, Orkin JD, Bataillon T, Manu S, Valenzuela A, Bergman J, Rousselle M et al. A global catalog of whole-genome diversity from 233 primate species. Science. 2023 Jun 2;380(6648):906-913. DOI: 10.1126/science.abn7829; PMID: 37262161 Zoonomia Consortium. A comparative genomics multitool for scientific discovery and conservation. Nature. 2020 Nov;587(7833):240-245. DOI: 10.1038/s41586-020-2876-6; PMID: 33177664; PMC: PMC7759459 Feng S, Stiller J, Deng Y, Armstrong J, Fang Q, Reeve AH, Xie D, Chen G, Guo C, Faircloth BC et al. Dense sampling of bird diversity increases power of comparative genomics. Nature. 2020 Nov;587(7833):252-257. DOI: 10.1038/s41586-020-2873-9; PMID: 33177665; PMC: PMC7759463 Armstrong J, Hickey G, Diekhans M, Fiddes IT, Novak AM, Deran A, Fang Q, Xie D, Feng S, Stiller J et al. Progressive Cactus is a multiple-genome aligner for the thousand-genome era. Nature. 2020 Nov;587(7833):246-251. DOI: 10.1038/s41586-020-2871-y; PMID: 33177663; PMC: PMC7673649 
cons447wayViewalign Multiz 447-way Cactus Alignment & Conservation on 447 mammal species, including Zoonomia genomes Comparative Genomics 
cactus447way Cactus 447-way Cactus alignment on 447 mammal species, including Zoonomia genomes Comparative Genomics Data Access Downloads for data in this track are available from the directory: Cactus 447-way alignments (MAF format), and phylogenetic trees PhyloP conservation (WIG format) Display Conventions and Configuration In full and pack display modes, conservation scores are displayed as a wiggle track (histogram) in which the height reflects the size of the score. The conservation wiggles can be configured in a variety of ways to highlight different aspects of the displayed information. Click the Graph configuration help link for an explanation of the configuration options. Pairwise alignments of each species to the human genome are displayed below the conservation histogram as a grayscale density plot (in pack mode) or as a wiggle (in full mode) that indicates alignment quality. In dense display mode, conservation is shown in grayscale using darker values to indicate higher levels of overall conservation as scored by phastCons. Checkboxes on the track configuration page allow selection of the species to include in the pairwise display. Note that excluding species from the pairwise display does not alter the the conservation score display. To view detailed information about the alignments at a specific position, zoom the display in to 30,000 or fewer bases, then click on the alignment. Gap Annotation The Display chains between alignments configuration option enables display of gaps between alignment blocks in the pairwise alignments in a manner similar to the Chain track display. Missing sequence in any assembly is highlighted in the track display by regions of yellow when zoomed out and by Ns when displayed at base level. The following conventions are used: Single line: No bases in the aligned species. Possibly due to a lineage-specific insertion between the aligned blocks in the human genome or a lineage-specific deletion between the aligned blocks in the aligning species. Double line: Aligning species has one or more unalignable bases in the gap region. Possibly due to excessive evolutionary distance between species or independent indels in the region between the aligned blocks in both species. Pale yellow coloring: Aligning species has Ns in the gap region. Reflects uncertainty in the relationship between the DNA of both species, due to lack of sequence in relevant portions of the aligning species. Genomic Breaks Discontinuities in the genomic context (chromosome, scaffold or region) of the aligned DNA in the aligning species are shown as follows: Vertical blue bar: Represents a discontinuity that persists indefinitely on either side, e.g. a large region of DNA on either side of the bar comes from a different chromosome in the aligned species due to a large scale rearrangement. Green square brackets: Enclose shorter alignments consisting of DNA from one genomic context in the aligned species nested inside a larger chain of alignments from a different genomic context. The alignment within the brackets may represent a short misalignment, a lineage-specific insertion of a transposon in the human genome that aligns to a paralogous copy somewhere else in the aligned species, or other similar occurrence. Base Level When zoomed-in to the base-level display, the track shows the base composition of each alignment. The numbers and symbols on the Gaps line indicate the lengths of gaps in the human sequence at those alignment positions relative to the longest non-human sequence. If there is sufficient space in the display, the size of the gap is shown. If the space is insufficient and the gap size is a multiple of 3, a &quot;*&quot; is displayed; other gap sizes are indicated by &quot;+&quot;. Codon translation is available in base-level display mode if the displayed region is identified as a coding segment. To display this annotation, select the species for translation from the pull-down menu in the Codon Translation configuration section at the top of the page. Then, select one of the following modes: No codon translation: The gene annotation is not used; the bases are displayed without translation. Use default species reading frames for translation: The annotations from the genome displayed in the Default species to establish reading frame pull-down menu are used to translate all the aligned species present in the alignment. Use reading frames for species if available, otherwise no translation: Codon translation is performed only for those species where the region is annotated as protein coding. Use reading frames for species if available, otherwise use default species: Codon translation is done on those species that are annotated as being protein coding over the aligned region using species-specific annotation; the remaining species are translated using the default species annotation. Codon translation uses the following gene tracks as the basis for translation: Gene TrackSpecies RefSeq GenesBos mutus, Canis lupus familiaris, Carlito syrichta, Cercocebus atys, Chinchilla lanigera, Colobus angolensis, Condylura cristata, Dipodomys ordii, Elephantulus edwardii, Eptesicus fuscus, Felis catus, Felis catus fca126, Fukomys damarensis, Homo sapiesn, Ictidomys tridecemlineatus, Macaca mulatta, Macaca nemestrina, Marmota marmota, Microtus ochrogaster, Miniopterus natalensis, Mus musculus, Mus pahari, Myotis brandtii, Myotis davidii, Myotis lucifugus, Odobenus rosmarus, Orcinus orca, Otolemur garnettii, Peromyscus maniculatus, Piliocolobus tephrosceles, Propithecus coquerelli, Pteropus alecto, Pteropus vampyrus, Rattus norvegicus, Rhinopithecus roxellana, Saimiri boliviensis, Sorex araneus, Sus scrofa, Theropithecus gelada, Tupaia chinensis Ensembl GenesCavia aperea Augustus GenesEidolon helvum, Pteronotus parnellii no annotationAcinonyx jubatus, Acomys cahirinus, Ailuropoda melanoleuca, Ailurus fulgens, Allactaga bullata, Allenopithecus nigroviridis, Allochrocebus lhoesti, Allochrocebus preussi, Allochrocebus solatus, Alouatta belzebul, Alouatta caraya, Alouatta discolor, Alouatta juara, Alouatta macconnelli, Alouatta nigerrima, Alouatta palliata, Alouatta puruensis, Alouatta seniculus, Ammotragus lervia, Anoura caudifer, Antilocapra americana, Aotus azarae, Aotus griseimembra, Aotus nancymaae, Aotus trivirgatus, Aotus vociferans, Aplodontia rufa, Arctocebus calabarensis, Artibeus jamaicensis, Ateles geoffroyi_a, Ateles geoffroyi_b, Ateles belzebuth, Ateles chamek, Ateles marginatus, Ateles paniscus, Avahi laniger, Avahi peyrierasi, Balaenoptera acutorostrata, Balaenoptera bonaerensis, Beatragus hunteri, Bison bison, Bos indicus, Bos taurus, Bubalus bubalis, Cacajao ayresi, Cacajao calvus, Cacajao hosomi, Cacajao melanocephalus, Callibella humilis, Callimico goeldii, Callithrix geoffroyi, Callithrix jacchus, Callithrix kuhlii, Camelus bactrianus, Camelus dromedarius, Camelus ferus, Canis lupus VD, Canis lupus dingo, Canis lupus orion, Capra aegagrus, Capra hircus, Capromys pilorides, Carollia perspicillata, Castor canadensis, Catagonus wagneri, Cavia porcellus, Cavia tschudii, Cebuella niveiventris, Cebuella pygmaea, Cebus albifrons, Cebus olivaceus, Cebus unicolor, Cephalopachus bancanus, Ceratotherium simum, Ceratotherium simum cottoni, Cercocebus chrysogaster, Cercocebus lunulatus, Cercocebus torquatus, Cercopithecus ascanius, Cercopithecus cephus, Cercopithecus diana, Cercopithecus hamlyni, Cercopithecus lowei, Cercopithecus albogularis, Cercopithecus mona, Cercopithecus neglectus, Cercopithecus nictitans, Cercopithecus petaurista, Cercopithecus pogonias, Cercopithecus roloway, Chaetophractus vellerosus, Cheirogaleus major, Cheirogaleus medius, Cheracebus lucifer, Cheracebus lugens, Cheracebus regulus, Cheracebus torquatus, Chiropotes albinasus, Chiropotes israelita, Chiropotes sagulatus, Chlorocebus aethiops, Chlorocebus pygerythrus, Chlorocebus sabaeus, Choloepus didactylus, Choloepus hoffmanni, Chrysochloris asiatica, Colobus guereza, Colobus polykomos, Craseonycteris thonglongyai, Cricetomys gambianus, Cricetulus griseus, Crocidura indochinensis, Cryptoprocta ferox, Ctenodactylus gundi, Ctenomys sociabilis, Cuniculus paca, Dasyprocta punctata, Dasypus novemcinctus, Daubentonia madagascariensis, Delphinapterus leucas, Desmodus rotundus, Dicerorhinus sumatrensis, Diceros bicornis, Dinomys branickii, Dipodomys stephensi, Dolichotis patagonum, Echinops telfairi, Elaphurus davidianus, Ellobius lutescens, Ellobius talpinus, Enhydra lutris, Equus asinus, Equus caballus, Equus przewalskii, Erinaceus europaeus, Erythrocebus patas, Eschrichtius robustus, Eubalaena japonica, Eulemur albifrons, Eulemur collaris, Eulemur coronatus, Eulemur flavifrons, Eulemur fulvus, Eulemur macaco, Eulemur mongoz, Eulemur rubriventer, Eulemur rufus, Eulemur sanfordi, Felis nigripes, Galago moholi, Galago senegalensis, Galagoides demidoff, Galeopterus variegatus, Giraffa tippelskirchi, Glis glis, Gorilla beringei, Gorilla gorilla, Graphiurus murinus, Hapalemur alaotrensis, Hapalemur gilberti, Hapalemur griseus, Hapalemur meridionalis, Hapalemur occidentalis, Helogale parvula, Hemitragus hylocrius, Heterocephalus glaber, Heterohyrax brucei, Hippopotamus amphibius, Hipposideros armiger, Hipposideros galeritus, Hoolock leuconedys, Hyaena hyaena, Hydrochoerus hydrochaeris, Hylobates abbotti, Hylobates agilis, Hylobates klossii, Hylobates pileatus, Hylobates muelleri, Hylobates pileatus, Hystrix cristata, Indri indri, Inia geoffrensis, Jaculus jaculus, Kogia breviceps, Lagothrix lagothricha, Lasiurus borealis, Lemur catta, Leontocebus fuscicollis, Leontocebus illigeri, Leontocebus nigricollis, Leontopithecus chrysomelas, Leontopithecus rosalia, Lepilemur ankaranensis, Lepilemur dorsalis, Lepilemur ruficaudatus, Lepilemur septentrionalis, Leptonychotes weddellii, Lepus americanus, Lipotes vexillifer, Lophocebus aterrimus, Loris lydekkerianus, Loris tardigradus, Loxodonta africana, Lycaon pictus, Macaca arctoides, Macaca assamensis, Macaca cyclopis, Macaca fascicularis, Macaca fuscata, Macaca leonina, Macaca maura, Macaca nigra, Macaca radiata, Macaca siberu, Macaca silenus, Macaca thibetana, Macaca tonkeana, Macroglossus sobrinus, Mandrillus leucophaeus, Mandrillus sphinx, Manis javanica, Manis pentadactyla, Megaderma lyra, Mellivora capensis, Meriones unguiculatus, Mesocricetus auratus, Mesoplodon bidens, Mico argentatus, Mico humeralifer, Mico schneideri, Microcebus murinus, Microgale talazaci, Micronycteris hirsuta, Miniopterus schreibersii, Miopithecus ogouensis, Mirounga angustirostris, Mirza zaza, Monodon monoceros, Mormoops blainvillei, Moschus moschiferus, Mungos mungo, Murina feae, Mus caroli, Mus spretus, Muscardinus avellanarius, Mustela putorius, Myocastor coypus, Myotis myotis, Myrmecophaga tridactyla, Nannospalax galili, Nasalis larvatus, Neomonachus schauinslandi, Neophocaena asiaeorientalis, Noctilio leporinus, Nomascus annamensis, Nomascus concolor, Nomascus gabriellae, Nomascus siki_a, Nomascus siki_b, Nyctereutes procyonoides, Nycticebus bengalensis, Nycticebus coucang, Nycticebus pygmaeus, Ochotona princeps, Octodon degus, Odocoileus virginianus, Okapia johnstoni, Ondatra zibethicus, Onychomys torridus, Orycteropus afer, Oryctolagus cuniculus, Otocyon megalotis, Otolemur crassicaudatus, Ovis aries, Ovis canadensis, Pan paniscus, Pan troglodytes, Panthera onca, Panthera pardus, Panthera tigris, Pantholops hodgsonii, Papio anubis, Papio cynocephalus, Papio hamadryas, Papio kindae, Papio papio, Papio ursinus, Paradoxurus hermaphroditus, Perodicticus ibeanus, Perodicticus potto, Perognathus longimembris, Petromus typicus, Phocoena phocoena, Piliocolobus badius, Piliocolobus gordonorum, Piliocolobus kirkii, Pipistrellus pipistrellus, Pithecia albicans, Pithecia chrysocephala, Pithecia hirsuta, Pithecia mittermeieri, Pithecia pissinattii, Pithecia pithecia, Pithecia vanzolinii, Platanista gangetica, Plecturocebus bernhardi, Plecturocebus brunneus, Plecturocebus caligatus, Plecturocebus cinerascens, Plecturocebus cupreus, Plecturocebus dubius, Plecturocebus grovesi, Plecturocebus hoffmannsi, Plecturocebus miltoni, Plecturocebus moloch, Pongo abelii, Pongo pygmaeus, Presbytis comata, Presbytis mitrata, Procavia capensis, Prolemur simus, Propithecus coronatus, Propithecus diadema, Propithecus edwardsi, Propithecus perrieri, Propithecus tattersalli, Propithecus verreauxi, Psammomys obesus, Pteronura brasiliensis, Puma concolor, Pygathrix cinerea, Pygathrix nigripes, Pygathrix nigripes, Rangifer tarandus, Rhinolophus sinicus, Rhinopithecus bieti, Rhinopithecus strykeri, Rousettus aegyptiacus, Saguinus bicolor, Saguinus geoffroyi, Saguinus imperator, Saguinus inustus, Saguinus labiatus, Saguinus midas, Saguinus mystax, Saguinus oedipus, Saiga tatarica, Saimiri cassiquiarensis, Saimiri macrodon, Saimiri oerstedii, Saimiri sciureus, Saimiri ustus, Sapajus apella, Sapajus macrocephalus, Scalopus aquaticus, Semnopithecus entellus, Semnopithecus hypoleucos, Semnopithecus johnii, Semnopithecus priam, Semnopithecus schistaceus, Semnopithecus vetulus, Sigmodon hispidus, Solenodon paradoxus, Spermophilus dauricus, Spilogale gracilis, Suricata suricatta, Symphalangus syndactylus, Tadarida brasiliensis, Tamandua tetradactyla, Tapirus indicus, Tapirus terrestris, Tarsius lariang, Tarsius wallacei, Thryonomys swinderianus, Tolypeutes matacus, Tonatia saurophila, Trachypithecus auratus, Trachypithecus crepusculus, Trachypithecus cristatus, Trachypithecus francoisi, Trachypithecus geei, Trachypithecus germaini, Trachypithecus hatinhensis, Trachypithecus laotum, Trachypithecus leucocephalus, Trachypithecus melamera, Trachypithecus obscurus, Trachypithecus phayrei, Trachypithecus pileatus, Tragulus javanicus, Trichechus manatus, Tupaia tana, Tursiops truncatus, Uropsilus gracilis, Ursus maritimus, Varecia rubra, Varecia variegata, Vicugna pacos, Vulpes lagopus, Xerus inauris, Zalophus californianus, Zapus hudsonius, Ziphius cavirostris Table 2. Gene tracks used for codon translation. Methods This alignment was created by making three edits (using Cactus) to the 241-way mammalian Zoonomia Cactus alignment ( https://cglgenomics.ucsc.edu/data/cactus/). One additional cat genome, &quot;Felis_catus_fca126&quot; (GCA_018350175.1) was added as a sister taxa to the existing &quot;Felis_catus&quot; species Five additional canine genomes were also added: canFam4, &quot;Canis_lupus_dingo&quot; (GCA_003254725.1), &quot;Canis_lupus_orion&quot; (GCA_905319855.2), &quot;Nyctereutes_procyonoides&quot; (GCA_905146905.1) and &quot;Otocyon_megalotis&quot; (GCA_017311455.1). &quot;Canis_lupus&quot; from the Zoonomia alignment was also renamed &quot;Canis_lupus_VD&quot; to reflect the fact that it corresponds to a &quot;village dog&quot; and not &quot;wolf&quot; sample. The 43-species primates clade from the Zoonomia alignment was removed and replaced with the 243-way primates alignment from Identification of constrained sequence elements across 239 primate genomes, increasing the alignment by 200 additional primate species. Phylogenic tree The phylogenic tree was established by the research described in A global catalog of whole-genome diversity from 233 primate species. Sequences count commonname clade scientific&nbsp;name(link&nbsp;to&nbsp;browser&nbsp;when&nbsp;existing) taxon&nbsp;idlink to NCBI 001humanprimates catarrhiniHomo sapiens/hg38reference species9606 002western gorillaprimates catarrhiniGorilla gorillaGCA_900006655.3_Susie39593 003Sumatran orangutanprimates catarrhiniPongo abeliiGCA_002880775.3_Susie_PABv29601 004Eastern Gorillaprimates catarrhiniGorilla beringei499232 005chimpanzeeprimates catarrhiniPan troglodytesGCA_002880755.3_Clint_PTRv29598 006Bornean orangutanprimates catarrhiniPongo pygmaeus9600 007Rhesus monkeyprimates catarrhiniMacaca mulattarheMac109544 008geladaprimates catarrhiniTheropithecus geladaGCF_003255815.1_Tgel_1.09565 009stump-tailed macaqueprimates catarrhiniMacaca arctoides9540 010Northern Talapoin Monkeyprimates catarrhiniMiopithecus ogouensis100488 011crab-eating macaqueprimates catarrhiniMacaca fascicularis9541 012Allen's swamp monkeyprimates catarrhiniAllenopithecus nigroviridis54135 013siamangprimates catarrhiniSymphalangus syndactylus9590 014black crested mangabeyprimates catarrhiniLophocebus aterrimus75566 015drillprimates catarrhiniMandrillus leucophaeus9568 016Bonnet Macaqueprimates catarrhiniMacaca radiata9548 017Red-capped Mangabeyprimates catarrhiniCercocebus torquatus9530 018Golden-bellied Mangabeyprimates catarrhiniCercocebus chrysogaster75569 019Owl-faced Monkeyprimates catarrhiniCercopithecus hamlyni9536 020Siberut Macaqueprimates catarrhiniMacaca siberu244255 021pig-tailed macaqueprimates catarrhiniMacaca nemestrinaGCF_000956065.1_Mnem_1.09545 022White-naped Mangabeyprimates catarrhiniCercocebus lunulatus (Cercocebus atys lunulatus)75570 023Tonkean Macaqueprimates catarrhiniMacaca tonkeana40843 024Diana Monkeyprimates catarrhiniCercopithecus diana36224 025red guenonprimates catarrhiniErythrocebus patas9538 026Northern Pig-tailed Macaqueprimates catarrhiniMacaca leonina90387 027Moor Macaqueprimates catarrhiniMacaca maura90383 028Guinea Baboonprimates catarrhiniPapio papio100937 029hamadryas baboonprimates catarrhiniPapio hamadryas9557 030liontail macaqueprimates catarrhiniMacaca silenus54601 031olive baboonprimates catarrhiniPapio anubisGCA_000264685.2_Panu_3.09555 032Roloway Monkeyprimates catarrhiniCercopithecus roloway1137049 033Kinda Baboonprimates catarrhiniPapio kindae208091 034Chacma Baboonprimates catarrhiniPapio ursinus36229 035Sun-tailed Monkeyprimates catarrhiniAllochrocebus solatus147650 036golden snub-nosed monkeyprimates catarrhiniRhinopithecus roxellanaGCF_007565055.1_ASM756505v161622 037Vervet Monkeyprimates catarrhiniChlorocebus pygerythrus60710 038sooty mangabeyprimates catarrhiniCercocebus atysGCF_000955945.1_Caty_1.09531 039green monkeyprimates catarrhiniChlorocebus sabaeusGCA_000409795.2_Chlorocebus_sabeus_1.160711 040De Brazza's monkeyprimates catarrhiniCercopithecus neglectus36227 041Yellow Baboonprimates catarrhiniPapio cynocephalus9556 042Celebes crested macaqueprimates catarrhiniMacaca nigra54600 043proboscis monkeyprimates catarrhiniNasalis larvatus43780 044Preuss's Monkeyprimates catarrhiniAllochrocebus preussi147649 045Putty-nosed Monkeyprimates catarrhiniCercopithecus nictitans36228 046Javan Suriliprimates catarrhiniPresbytis comata78452 047Sykes' Monkeyprimates catarrhiniCercopithecus albogularis36225 048LHoests Monkeyprimates catarrhiniAllochrocebus lhoesti100224 049Crowned Monkeyprimates catarrhiniCercopithecus pogonias102108 050Southern Mitered Langurprimates catarrhiniPresbytis mitrata (Presbytis melalophos mitrata)272115 051Grey-shanked Douc Langurprimates catarrhiniPygathrix cinerea693712 052Mona monkeyprimates catarrhiniCercopithecus mona36226 053Spot-nosed Monkeyprimates catarrhiniCercopithecus petaurista100487 054grivetprimates catarrhiniChlorocebus aethiops9534 055Lowes Monkeyprimates catarrhiniCercopithecus lowei304410 056Northern Yellow-cheeked Crested Gibbonprimates catarrhiniNomascus annamensis1616038 057Red-cheeked Gibbonprimates catarrhiniNomascus gabriellae61852 058Japanese macaqueprimates catarrhiniMacaca fuscata9542 059Western Red Colobusprimates catarrhiniPiliocolobus badius164648 060southern white-cheeked gibbonprimates catarrhiniNomascus siki_a9586 061Taiwan macaqueprimates catarrhiniMacaca cyclopis78449 062black-shanked douc langurprimates catarrhiniPygathrix nigripes310352 063King Colobusprimates catarrhiniColobus polykomos9572 064Black Crested Gibbonprimates catarrhiniNomascus concolor29089 065Udzungwa Red Colobusprimates catarrhiniPiliocolobus gordonorum591933 066Gee's Golden Langurprimates catarrhiniTrachypithecus geei164650 067Kloss's Gibbonprimates catarrhiniHylobates klossii9587 068Spectacled Leaf Monkeyprimates catarrhiniTrachypithecus obscurus54181 069Zanzibar Red Colobusprimates catarrhiniPiliocolobus kirkii591937 070Indochinese Silvered Langurprimates catarrhiniTrachypithecus germaini271260 071Hatinh Langurprimates catarrhiniTrachypithecus hatinhensis867383 072Moustached Monkeyprimates catarrhiniCercopithecus cephus9535 073Laotian Langurprimates catarrhiniTrachypithecus laotum465718 074Francois's langurprimates catarrhiniTrachypithecus francoisi54180 075Purple-faced Langurprimates catarrhiniSemnopithecus vetulus (Trachypithecus vetulus)54137 076Capped Langurprimates catarrhiniTrachypithecus pileatus164651 077Ugandan red Colobusprimates catarrhiniPiliocolobus tephroscelesGCF_002776525.2_ASM277652v2591936 078Spangled Ebony Langurprimates catarrhiniTrachypithecus auratus222416 079Red-tailed Monkeyprimates catarrhiniCercopithecus ascanius36223 080Silvery Lutungprimates catarrhiniTrachypithecus cristatus122765 081Nilgiri Langurprimates catarrhiniSemnopithecus johnii (Trachypithecus johnii)66063 082Indochinese grey langurprimates catarrhiniTrachypithecus crepusculus (Trachypithecus phayrei crepuscula)272121 083White-headed langurprimates catarrhiniTrachypithecus leucocephalus (Trachypithecus poliocephalus)465719 084pygmy chimpanzeeprimates catarrhiniPan paniscusGCA_000258655.2_panpan1.19597 085northern white-cheeked gibbonprimates catarrhiniNomascus siki_b9586 086Agile Gibbonprimates catarrhiniHylobates agilis9579 087Phayre's Leaf-monkeyprimates catarrhiniTrachypithecus melameran/a 088Nepal Gray Langurprimates catarrhiniSemnopithecus schistaceus2804203 089Abbott's Gray Gibbonprimates catarrhiniHylobates abbotti (Hylobates muelleri abbotti)716694 090Bornean Gibbonprimates catarrhiniHylobates muelleri9588 091Tufted Gray Langurprimates catarrhiniSemnopithecus priam1208733 092Black-footed Gray Langurprimates catarrhiniSemnopithecus hypoleucos1208734 093mantled guerezaprimates catarrhiniColobus guereza33548 094Hanuman langurprimates catarrhiniSemnopithecus entellus88029 095pileated gibbonprimates catarrhiniHylobates pileatus9589 096black snub-nosed monkeyprimates catarrhiniRhinopithecus bieti61621 097Burmese snub-nosed monkeyprimates catarrhiniRhinopithecus strykeri1194336 098Angolan colobusprimates catarrhiniColobus angolensiscolAng154131 099Pileated Gibbonprimates catarrhiniHylobates pileatus9589 100black-shanked douc langurprimates catarrhiniPygathrix nigripes310352 101Milne-edwards' Macaqueprimates catarrhiniMacaca thibetana54602 102Phayre's Leaf-monkeyprimates catarrhiniTrachypithecus phayrei61618 103Assam macaqueprimates catarrhiniMacaca assamensis9551 104Eastern hoolock gibbonprimates catarrhiniHoolock leuconedys61851 105mandrillprimates catarrhiniMandrillus sphinx9561 106White-faced Sakiprimates platyrrhiniPithecia chrysocephala2946515 107Monk Sakiprimates platyrrhiniPithecia hirsuta2946516 108white-faced sakiprimates platyrrhiniPithecia pithecia43777 109Mittermeier's Tapajós sakiprimates platyrrhiniPithecia mittermeieri2946517 110Buffy Sakiprimates platyrrhiniPithecia albicans2946514 111Pissinatti's sakiprimates platyrrhiniPithecia pissinattii (Pithecia pissinatti)2946518 112Vanzolini's Bald-faced Sakiprimates platyrrhiniPithecia vanzolinii2946519 113Bald-headed Uacariprimates platyrrhiniCacajao calvus30596 114Ayres Black Uakariprimates platyrrhiniCacajao ayresi535896 115Black-headed Uacariprimates platyrrhiniCacajao melanocephalus70825 116Black-headed Uacariprimates platyrrhiniCacajao hosomi535897 117Reddish-brown bearded sakiprimates platyrrhiniChiropotes sagulatus (Chiropotes chiropotes)658221 118brown-backed bearded sakiprimates platyrrhiniChiropotes israelita280163 119Collared Titi Monkeyprimates platyrrhiniCheracebus lugens210166 120Brown Titi Monkeyprimates platyrrhiniPlecturocebus brunneus1812042 121Hoffmanns's titi monkeyprimates platyrrhiniPlecturocebus hoffmannsi78255 122Milton's Titi Monkeyprimates platyrrhiniPlecturocebus miltoni1812038 123Widow Monkeyprimates platyrrhiniCheracebus torquatus30592 124Ashy Black Titi Monkeyprimates platyrrhiniPlecturocebus cinerascens1812037 125Prince Bernhard's Titi Monkeyprimates platyrrhiniPlecturocebus bernhardi1812036 126Yellow-handed Titi Monkeyprimates platyrrhiniCheracebus lucifer2487712 127Coppery Titi Monkeyprimates platyrrhiniPlecturocebus cupreus202457 128Chestnut-bellied Titiprimates platyrrhiniPlecturocebus caligatus867332 129Hershkovitzs Titiprimates platyrrhiniPlecturocebus dubius2946520 130Red-bellied Titi Monkeyprimates platyrrhiniPlecturocebus moloch9523 131Groves' Titiprimates platyrrhiniPlecturocebus grovesi2488670 132black-handed spider monkeyprimates platyrrhiniAteles geoffroyi_a9509 133Widow Monkeyprimates platyrrhiniCheracebus regulus1812110 134Guiana Spider Monkeyprimates platyrrhiniAteles paniscus9510 135Black-faced Black Spider Monkeyprimates platyrrhiniAteles chamek118643 136White-cheeked Spider Monkeyprimates platyrrhiniAteles marginatus1529884 137White-bellied Spider Monkeyprimates platyrrhiniAteles belzebuth9507 138Common Woolly Monkeyprimates platyrrhiniLagothrix lagothricha (Lagothrix lagotricha)9519 139large-headed capuchinprimates platyrrhiniSapajus macrocephalus (Sapajus apella macrocephalus)1547595 140Spixs White-fronted Capuchinprimates platyrrhiniCebus unicolor1985288 141Central American spider monkeyprimates platyrrhiniAteles geoffroyi_b9509 142Guinan Weeper Capuchinprimates platyrrhiniCebus olivaceus37295 143mantled howler monkeyprimates platyrrhiniAlouatta palliata30589 144white-fronted capuchinprimates platyrrhiniCebus albifrons9514 145Northern Night Monkeyprimates platyrrhiniAotus trivirgatus9505 146Grey-handed Night Monkeyprimates platyrrhiniAotus griseimembra292213 147Black-and-gold Howler Monkeyprimates platyrrhiniAlouatta caraya9502 148Spixs Night Monkeyprimates platyrrhiniAotus vociferans57176 149Red-handed Howler Monkeyprimates platyrrhiniAlouatta belzebul30590 150Red-handed Howler Monkeyprimates platyrrhiniAlouatta discolor2905217 151Azara's Night Monkeyprimates platyrrhiniAotus azarae (Aotus azarai)30591 152Purús Red Howler Monkeyprimates platyrrhiniAlouatta puruensis (Alouatta seniculus puruensis)1347729 153Black Howler Monkeyprimates platyrrhiniAlouatta nigerrima (Alouatta belzebul)30590 154Guianan Red Howler Monkeyprimates platyrrhiniAlouatta macconnelli198115 155Colombian Red Howler Monkeyprimates platyrrhiniAlouatta juara2946512 156Colombian Red Howler Monkeyprimates platyrrhiniAlouatta seniculus9503 157tufted capuchinprimates platyrrhiniSapajus apella9515 158Ma's night monkeyprimates platyrrhiniAotus nancymaaeGCA_000952055.2_Anan_2.037293 159Bolivian squirrel monkeyprimates platyrrhiniSaimiri boliviensisGCF_016699345.1_BCM_Sbol_2.027679 160White-nosed Sakiprimates platyrrhiniChiropotes albinasus198627 161Black Mantle Tamarinprimates platyrrhiniLeontocebus nigricollis9489 162brown-mantled tamarinprimates platyrrhiniLeontocebus fuscicollis9487 163Illiger's saddle-back tamarinprimates platyrrhiniLeontocebus illigeri (Leontocebus fuscicollis illigeri)881947 164Cotton-headed Tamarinprimates platyrrhiniSaguinus oedipus9490 165Pied Tamarinprimates platyrrhiniSaguinus bicolor37588 166Geoffroy's Tamarinprimates platyrrhiniSaguinus geoffroyi43778 167White-fronted Titi Monkeyprimates platyrrhiniSaguinus inustus1079039 168Moustached Tamarinprimates platyrrhiniSaguinus mystax9488 169tamarinprimates platyrrhiniSaguinus imperator9491 170Guianan Squirrel Monkeyprimates platyrrhiniSaimiri sciureus9521 171Red-chested Mustached Tamarinprimates platyrrhiniSaguinus labiatus78454 172Goeldi's Monkeyprimates platyrrhiniCallimico goeldii9495 173Black-crowned Central American Squirrel Monkeyprimates platyrrhiniSaimiri oerstedii70928 174Golden-headed Lion Tamarinprimates platyrrhiniLeontopithecus chrysomelas57374 175golden lion tamarinprimates platyrrhiniLeontopithecus rosalia30588 176Humboldt's Squirrel Monkeyprimates platyrrhiniSaimiri cassiquiarensis2946521 177bare-eared squirrel monkeyprimates platyrrhiniSaimiri ustus66265 178Ecuadorian squirrel monkeyprimates platyrrhiniSaimiri macrodon2946522 179white-tufted-ear marmosetprimates platyrrhiniCallithrix jacchus9483 180Eastern Pygmy Marmosetprimates platyrrhiniCebuella niveiventris2826950 181Western Pygmy Marmosetprimates platyrrhiniCebuella pygmaea9493 182Black And White Tassel-ear Marmosetprimates platyrrhiniMico humeralifer52232 183Black-crowned Dwarf Marmosetprimates platyrrhiniCallibella humilis (Mico humilis)666519 184Mico schneideriprimates platyrrhiniMico schneiderin/a 185Silvery Marmosetprimates platyrrhiniMico argentatus9482 186Midas tamarinprimates platyrrhiniSaguinus midas30586 187Wieds Marmosetprimates platyrrhiniCallithrix kuhlii867363 188Geoffroy's Tufted-ear Marmosetprimates platyrrhiniCallithrix geoffroyi52231 189Horsfield's tarsierprimates tarsiidaeCephalopachus bancanus9477 190Philippine tarsierprimates tarsiidaeCarlito syrichtatarSyr21868482 191Lariang Tarsierprimates tarsiidaeTarsius lariang630277 192Wallace's Tarsierprimates tarsiidaeTarsius wallacei981131 193aye-ayeprimates strepsirrhiniDaubentonia madagascariensis31869 194Crowned Sifakaprimates strepsirrhiniPropithecus coronatus (Propithecus deckenii coronatus)475619 195Perrier's Sifakaprimates strepsirrhiniPropithecus perrieri989338 196ruffed lemurprimates strepsirrhiniVarecia variegata9455 197Diademed Sifakaprimates strepsirrhiniPropithecus diadema83281 198Milne-Edwards Sifakaprimates strepsirrhiniPropithecus edwardsi543559 199babakotoprimates strepsirrhiniIndri indri34827 200Golden-crowned Sifakaprimates strepsirrhiniPropithecus tattersalli30601 201Eastern Woolly Lemurprimates strepsirrhiniAvahi laniger122246 202Verreauxs Sifakaprimates strepsirrhiniPropithecus verreauxi34825 203Peyrieras Woolly Lemurprimates strepsirrhiniAvahi peyrierasi1313323 204Red Ruffed Lemurprimates strepsirrhiniVarecia rubra554167 205greater bamboo lemurprimates strepsirrhiniProlemur simus1328070 206Red-bellied Lemurprimates strepsirrhiniEulemur rubriventer34829 207mongoose lemurprimates strepsirrhiniEulemur mongoz34828 208Geoffroys Dwarf Lemurprimates strepsirrhiniCheirogaleus major47177 209Crowned Lemurprimates strepsirrhiniEulemur coronatus13514 210black lemurprimates strepsirrhiniEulemur macaco30602 211lesser dwarf lemurprimates strepsirrhiniCheirogaleus medius9460 212Sclater's lemurprimates strepsirrhiniEulemur flavifrons87288 213Coquerel's sifakaprimates strepsirrhiniPropithecus coquerelli (Propithecus coquereli)proCoq1379532 214Collared Brown Lemurprimates strepsirrhiniEulemur collaris (Eulemur fulvus collaris)47178 215Red-tailed Sportive Lemurprimates strepsirrhiniLepilemur ruficaudatus78866 216Red Brown Lemurprimates strepsirrhiniEulemur rufus859983 217Sanfords Brown Lemurprimates strepsirrhiniEulemur sanfordi122225 218White-fronted Lemurprimates strepsirrhiniEulemur albifrons1215604 219Gray's Sportive Lemurprimates strepsirrhiniLepilemur dorsalis78583 220brown lemurprimates strepsirrhiniEulemur fulvus13515 221Sahafary Sportive Lemurprimates strepsirrhiniLepilemur septentrionalis78584 222Sambirano Lesser Bamboo Lemurprimates strepsirrhiniHapalemur occidentalis867377 223Alaotra Reed Lemurprimates strepsirrhiniHapalemur alaotrensis (Hapalemur griseus alaotrensis)122220 224Eastern Lesser Bamboo Lemurprimates strepsirrhiniHapalemur griseus13557 225Ankarana Sportive Lemurprimates strepsirrhiniLepilemur ankaranensis342401 226ring-tailed lemurprimates strepsirrhiniLemur catta9447 227gray bamboo lemurprimates strepsirrhiniHapalemur gilberti3043110 228Rusty-gray Lesser Bamboo Lemurprimates strepsirrhiniHapalemur meridionalis3043112 229Demidoffs Dwarf Galagoprimates strepsirrhiniGalagoides demidoff89672 230northern giant mouse lemurprimates strepsirrhiniMirza zaza339999 231gray mouse lemurprimates strepsirrhiniMicrocebus murinusGCA_000165445.3_Mmur_3.030608 232small-eared galagoprimates strepsirrhiniOtolemur garnettiiotoGar330611 233Northern Lesser Galagoprimates strepsirrhiniGalago senegalensis9465 234Thick-tailed Greater Galagoprimates strepsirrhiniOtolemur crassicaudatus9463 235Grey Slender Lorisprimates strepsirrhiniLoris lydekkerianus300163 236slender lorisprimates strepsirrhiniLoris tardigradus9468 237West African Pottoprimates strepsirrhiniPerodicticus potto9472 238East African Pottoprimates strepsirrhiniPerodicticus ibeanus (Perodicticus potto ibeanus)261737 239Moholi bushbabyprimates strepsirrhiniGalago moholi30609 240Pygmy Slow Lorisprimates strepsirrhiniNycticebus pygmaeus (Xanthonycticebus pygmaeus)101278 241Bengal slow lorisprimates strepsirrhiniNycticebus bengalensis261741 242Calabar Angwantiboprimates strepsirrhiniArctocebus calabarensis261739 243slow lorisprimates strepsirrhiniNycticebus coucang9470 244jaguarcarnivoraPanthera oncaGCA_004023805.1_PanOnc_v1_BIUU9690 245leopardcarnivoraPanthera pardusGCA_001857705.1_PanPar1.09691 246giant pandacarnivoraAiluropoda melanoleucaGCA_002007445.1_ASM200744v19646 247Hawaiian monk sealcarnivoraNeomonachus schauinslandiGCA_002201575.1_ASM220157v129088 248California sea lioncarnivoraZalophus californianusGCA_004024565.1_ZalCal_v1_BIUU9704 249Greenland wolfcarnivoraCanis lupus orionGCA_905319855.2_mCanLor1.22605939 250Pacific walruscarnivoraOdobenus rosmarusodoRosDiv19707 251domestic cat (Fca126)carnivoraFelis catus fca126 (Felis catus)GCF_018350175.1_F.catus_Fca126_mat1.09685 252northern elephant sealcarnivoraMirounga angustirostrisGCA_004023865.1_MirAng_v1_BIUU9716 253domestic catcarnivoraFelis catusfelCat89685 254domestic dog (BS72/Village Dog)carnivoraCanis lupus familiarisGCA_004027395.1_CanFam_VD_v1_BIUU 255German Shepherd dog (Mischka)carnivoraCanis lupus familiaris (CanFam4) (Canis lupus familiaris)canFam4 256dingocarnivoraCanis lupus dingo286419 257raccoon dogcarnivoraNyctereutes procyonoides34880 258fossacarnivoraCryptoprocta ferox94188 259polar bearcarnivoraUrsus maritimusGCA_000687225.1_UrsMar_1.029073 260Asian palm civetcarnivoraParadoxurus hermaphroditusGCA_004024585.1_ParHer_v1_BIUU71117 261African hunting dogcarnivoraLycaon pictusGCA_001887905.1_LycPicSAfr1.09622 262Arctic foxcarnivoraVulpes lagopusGCA_004023825.1_VulLag_v1_BIUU494514 263dogcarnivoraCanis lupus familiarisGCF_000002285.3_CanFam3.19615 264striped hyenacarnivoraHyaena hyaenaGCA_004023945.1_HyaHya_v1_BIUU95912 265n/acarnivoraAcinonyx jubatusGCA_001443585.1_aciJub132536 266tigercarnivoraPanthera tigrisGCA_000464555.1_PanTig1.09694 267Sea ottercarnivoraEnhydra lutrisGCA_002288905.2_ASM228890v234882 268giant ottercarnivoraPteronura brasiliensis9672 269bat-eared foxcarnivoraOtocyon megalotis9624 270Weddell sealcarnivoraLeptonychotes weddelliiGCA_000349705.1_LepWed1.09713 271Lesser pandacarnivoraAilurus fulgensGCA_002007465.1_ASM200746v19649 272ratelcarnivoraMellivora capensisGCA_004024625.1_MelCap_v1_BIUU9664 273banded mongoosecarnivoraMungos mungoGCA_004023785.1_MunMun_v1_BIUU210652 274dwarf mongoosecarnivoraHelogale parvulaGCA_004023845.1_HelPar_v1_BIUU210647 275meerkatcarnivoraSuricata suricattaGCA_004023905.1_SurSur_v1_BIUU37032 276pumacarnivoraPuma concolorGCA_003327715.1_PumCon1.09696 277black-footed catcarnivoraFelis nigripesGCA_004023925.1_FelNig_v1_BIUU61379 278European polecatcarnivoraMustela putoriusGCA_000239315.1_MusPutFurMale1.09668 279western spotted skunkcarnivoraSpilogale gracilisGCA_004023965.1_SpiGra_v1_BIUU30551 280Sumatran rhinoceroslaurasiatheriaDicerorhinus sumatrensisGCA_002844835.1_ASM284483v189632 281black rhinoceroslaurasiatheriaDiceros bicornisGCA_004027315.1_DicBicMic_v1_BIUU9805 282Asiatic tapirlaurasiatheriaTapirus indicusGCA_004024905.1_TapInd_v1_BIUU9802 283Brazilian tapirlaurasiatheriaTapirus terrestrisGCA_004025025.1_TapTer_v1_BIUU9801 284northern white rhinoceroslaurasiatheriaCeratotherium simum cottoni310713 285asslaurasiatheriaEquus asinusGCA_001305755.1_ASM130575v19793 286Southern white rhinoceroslaurasiatheriaCeratotherium simumGCA_000283155.1_CerSimSim1.09807 287Przewalski's horselaurasiatheriaEquus przewalskiiGCA_000696695.1_Burgud9798 288horselaurasiatheriaEquus caballusGCA_000002305.1_EquCab2.09796 289Malayan pangolinlaurasiatheriaManis javanicaGCA_001685135.1_ManJav1.09974 290Chinese pangolinlaurasiatheriaManis pentadactylaGCA_000738955.1_M_pentadactyla-1.1.1143292 291Hispaniolan solenodonlaurasiatheriaSolenodon paradoxus79805 292eastern molelaurasiatheriaScalopus aquaticusGCA_004024925.1_ScaAqu_v1_BIUU71119 293gracile shrew molelaurasiatheriaUropsilus gracilisGCA_004024945.1_UroGra_v1_BIUU182669 294star-nosed molelaurasiatheriaCondylura cristataGCF_000260355.1_ConCri1.0143302 295western European hedgehoglaurasiatheriaErinaceus europaeusGCA_000296755.1_EriEur2.09365 296European shrewlaurasiatheriaSorex araneussorAra242254 297Indochinese shrewlaurasiatheriaCrocidura indochinensisGCA_004027635.1_CroInd_v1_BIUU876679 298Hoffmann's two-fingered slothxenarthraCholoepus hoffmanniGCA_000164785.2_C_hoffmanni-2.0.19358 299nine-banded armadilloxenarthraDasypus novemcinctusGCA_000208655.2_Dasnov3.09361 300giant anteaterxenarthraMyrmecophaga tridactylaGCA_004026745.1_MyrTri_v1_BIUU71006 301southern tamanduaxenarthraTamandua tetradactylaGCA_004025105.1_TamTet_v1_BIUU48850 302placentalsxenarthraTolypeutes matacus183749 303southern two-toed slothxenarthraCholoepus didactylusGCA_004027855.1_ChoDid_v1_BIUU27675 304screaming hairy armadilloxenarthraChaetophractus vellerosusGCA_004027955.1_ChaVel_v1_BIUU340076 305North Pacific right whaleartiodactylaEubalaena japonica302098 306grey whaleartiodactylaEschrichtius robustus9764 307hippopotamusartiodactylaHippopotamus amphibiusGCA_004027065.1_HipAmp_v1_BIUU9833 308Minke whaleartiodactylaBalaenoptera acutorostrataGCA_000493695.1_BalAcu1.09767 309beluga whaleartiodactylaDelphinapterus leucasGCA_002288925.2_ASM228892v29749 310Antarctic minke whaleartiodactylaBalaenoptera bonaerensisGCA_000978805.1_ASM97880v133556 311boutuartiodactylaInia geoffrensis9725 312harbor porpoiseartiodactylaPhocoena phocoena9742 313narwhalartiodactylaMonodon monocerosGCA_004026685.1_MonMon_M_v1_BIUU40151 314Yangtze River dolphinartiodactylaLipotes vexilliferGCA_000442215.1_Lipotes_vexillifer_v1118797 315killer whaleartiodactylaOrcinus orcaorcOrc19733 316Ganges River dolphinartiodactylaPlatanista gangetica118798 317Yangtze finless porpoiseartiodactylaNeophocaena asiaeorientalisGCA_003031525.1_Neophocaena_asiaeorientalis_V1189058 318Sowerby's beaked whaleartiodactylaMesoplodon bidens48745 319alpacaartiodactylaVicugna pacosGCA_000767525.1_Vi_pacos_V1.030538 320Cuvier's beaked whale"artiodactylaZiphius cavirostris9760 321Bactrian camelartiodactylaCamelus bactrianusGCA_000767855.1_Ca_bactrianus_MBC_1.09837 322Arabian camelartiodactylaCamelus dromedariusGCA_000767585.1_PRJNA234474_Ca_dromedarius_V1.09838 323wild Bactrian camelartiodactylaCamelus ferusGCA_000311805.2_CB1419612 324pygmy sperm whaleartiodactylaKogia breviceps27615 325Chacoan peccaryartiodactylaCatagonus wagneriGCA_004024745.1_CatWag_v1_BIUU51154 326reindeerartiodactylaRangifer tarandusGCA_004026565.1_RanTarSib_v1_BIUU9870 327Pere David's deerartiodactylaElaphurus davidianusGCA_002443075.1_Milu1.043332 328okapiartiodactylaOkapia johnstoniGCA_001660835.1_ASM166083v186973 329Masai giraffeartiodactylaGiraffa tippelskirchiGCA_001651235.1_ASM165123v1439328 330Siberian musk deerartiodactylaMoschus moschiferusGCA_004024705.1_MosMos_v1_BIUU68415 331water buffaloartiodactylaBubalus bubalisGCA_000471725.1_UMD_CASPUR_WB_2.089462 332cowartiodactylaBos taurusGCA_000003205.6_Btau_5.0.19913 333pronghornartiodactylaAntilocapra americanaGCA_004027515.1_AntAmePen_v1_BIUU9891 334white-tailed deerartiodactylaOdocoileus virginianusGCA_002102435.1_Ovir.te_1.09874 335aoudadartiodactylaAmmotragus lerviaGCA_002201775.1_ALER1.09899 336bighorn sheepartiodactylaOvis canadensisGCA_004026945.1_OviCan_v1_BIUU37174 337goatartiodactylaCapra hircusGCA_001704415.1_ARS19925 338Nilgiri tahrartiodactylaHemitragus hylocriusGCA_004026825.1_HemHyl_v1_BIUU330464 339hirolaartiodactylaBeatragus hunteriGCA_004027495.1_BeaHun_v1_BIUU59527 340wild yakartiodactylaBos mutusbosMut172004 341American bisonartiodactylaBison bisonGCA_000754665.1_Bison_UMD1.09901 342sheepartiodactylaOvis ariesGCA_000298735.2_Oar_v4.09940 343chiruartiodactylaPantholops hodgsoniiGCA_000400835.1_PHO1.059538 344wild goatartiodactylaCapra aegagrusGCA_000978405.1_CapAeg_1.09923 345Java mouse-deerartiodactylaTragulus javanicusGCA_004024965.1_TraJav_v1_BIUU9849 346pigartiodactylaSus scrofasusScr39823 347zebu cattleartiodactylaBos indicusGCA_000247795.2_Bos_indicus_1.09915 348common bottlenose dolphinartiodactylaTursiops truncatusGCA_001922835.1_NIST_Tur_tru_v19739 349Saiga antelopeartiodactylaSaiga tataricaGCA_004024985.1_SaiTat_v1_BIUU34875 350Chinese rufous horseshoe batchiropteraRhinolophus sinicusGCA_001888835.1_ASM188883v189399 351black flying foxchiropteraPteropus alectopteAle19402 352Cantor's roundleaf batchiropteraHipposideros galeritus58069 353Egyptian rousettechiropteraRousettus aegyptiacusGCA_004024865.1_RouAeg_v1_BIUU9407 354long-tongued fruit batchiropteraMacroglossus sobrinus326083 355large flying foxchiropteraPteropus vampyrusGCF_000151845.1_Pvam_2.0132908 356Brazilian free-tailed batchiropteraTadarida brasiliensisGCA_004025005.1_TadBra_v1_BIUU9438 357great roundleaf batchiropteraHipposideros armigerGCA_001890085.1_ASM189008v1186990 358straw-colored fruit batchiropteraEidolon helvumeidHel177214 359Antillean ghost-faced batchiropteraMormoops blainvilleiGCA_004026545.1_MorMeg_v1_BIUU118852 360tailed tailless batchiropteraAnoura caudiferGCA_004027475.1_AnoCau_v1_BIUU27642 361common vampire batchiropteraDesmodus rotundusGCA_002940915.2_ASM294091v29430 362hairy big-eared batchiropteraMicronycteris hirsutaGCA_004026765.1_MicHir_v1_BIUU148065 363stripe-headed round-eared batchiropteraTonatia saurophilaGCA_004024845.1_TonSau_v1_BIUU171122 364Seba's short-tailed batchiropteraCarollia perspicillataGCA_004027735.1_CarPer_v1_BIUU40233 365Jamaican fruit-eating batchiropteraArtibeus jamaicensisGCA_004027435.1_ArtJam_v1_BIUU9417 366Indian false vampirechiropteraMegaderma lyraGCA_004026885.1_MegLyr_v1_BIUU9413 367Schreibers' long-fingered batchiropteraMiniopterus schreibersiiGCA_004026525.1_MinSch_v1_BIUU9433 368greater bulldog batchiropteraNoctilio leporinusGCA_004026585.1_NocLep_v1_BIUU94963 369Natal long-fingered batchiropteraMiniopterus natalensisGCF_001595765.1_Mnat.v1291302 370hog-nosed batchiropteraCraseonycteris thonglongyaiGCA_004027555.1_CraTho_v1_BIUU208972 371Parnell's mustached batchiropteraPteronotus parnelliiptePar159476 372greater mouse-eared batchiropteraMyotis myotisGCA_004026985.1_MyoMyo_v1_BIUU51298 373Ashy-gray tube-nosed batchiropteraMurina feae (Murina aurata feae)GCA_004026665.1_MurFea_v1_BIUU1453894 374David's myotischiropteraMyotis davidiimyoDav1225400 375Brandt's batchiropteraMyotis brandtiimyoBra1109478 376big brown batchiropteraEptesicus fuscusGCF_000308155.1_EptFus1.029078 377red batchiropteraLasiurus borealisGCA_004026805.1_LasBor_v1_BIUU258930 378little brown batchiropteraMyotis lucifugusmyoLuc259463 379common pipistrellechiropteraPipistrellus pipistrellusGCA_004026625.1_PipPip_v1_BIUU59474 380African savanna elephantafrotheriaLoxodonta africanaGCA_000001905.1_Loxafr3.09785 381Florida manateeafrotheriaTrichechus manatusGCA_000243295.1_TriManLat1.09778 382yellow-spotted hyraxafrotheriaHeterohyrax bruceiGCA_004026845.1_HetBruBak_v1_BIUU77598 383Cape rock hyraxafrotheriaProcavia capensisGCA_004026925.1_ProCapCap_v1_BIUU9813 384aardvarkafrotheriaOrycteropus afer9818 385Cape golden moleafrotheriaChrysochloris asiaticaGCA_004027935.1_ChrAsi_v1_BIUU185453 386Cape elephant shrewafrotheriaElephantulus edwardiieleEdw128737 387Talazac's shrew tenrecafrotheriaMicrogale talazaci (Nesogale talazaci)GCA_004026705.1_MicTal_v1_BIUU2583312 388small Madagascar hedgehogafrotheriaEchinops telfairiGCA_000313985.1_EchTel2.09371 389Sunda flying lemureuarchontogliresGaleopterus variegatusGCA_004027255.1_GalVar_v1_BIUU482537 390Chinese tree shreweuarchontogliresTupaia chinensistupChi1246437 391South African ground squirreleuarchontogliresXerus inaurisGCA_004024805.1_XerIna_v1_BIUU234690 392large tree shreweuarchontogliresTupaia tana70687 393mountain beavereuarchontogliresAplodontia rufaGCA_004027875.1_AplRuf_v1_BIUU51342 394Alpine marmoteuarchontogliresMarmota marmotaGCF_001458135.1_marMar2.19993 395Daurian ground squirreleuarchontogliresSpermophilus dauricusGCA_002406435.1_ASM240643v199837 396crested porcupineeuarchontogliresHystrix cristataGCA_004026905.1_HysCri_v1_BIUU10137 397thirteen-lined ground squirreleuarchontogliresIctidomys tridecemlineatusspeTri243179 398American beavereuarchontogliresCastor canadensisGCA_004027675.1_CasCan_v1_BIUU51338 399long-tailed chinchillaeuarchontogliresChinchilla lanigerachiLan134839 400punctate agoutieuarchontogliresDasyprocta punctata34846 401pacaranaeuarchontogliresDinomys branickiiGCA_004027595.1_DinBra_v1_BIUU108858 402fat dormouseeuarchontogliresGlis glisGCA_004027185.1_GliGli_v1_BIUU41261 403northern gundieuarchontogliresCtenodactylus gundiGCA_004027205.1_CteGun_v1_BIUU10166 404naked mole-rateuarchontogliresHeterocephalus glaberGCA_000247695.1_HetGla_female_1.010181 405Patagonian cavyeuarchontogliresDolichotis patagonumGCA_004027295.1_DolPat_v1_BIUU29091 406capybaraeuarchontogliresHydrochoerus hydrochaerisGCA_004027455.1_HydHyd_v1_BIUU10149 407Montane guinea pigeuarchontogliresCavia tschudiiGCA_004027695.1_CavTsc_v1_BIUU143287 408domestic guinea pigeuarchontogliresCavia porcellusGCA_000151735.1_Cavpor3.010141 409degueuarchontogliresOctodon degusGCA_000260255.1_OctDeg1.010160 410lowland pacaeuarchontogliresCuniculus paca108852 411social tuco-tucoeuarchontogliresCtenomys sociabilisGCA_004027165.1_CteSoc_v1_BIUU43321 412Damara mole-rateuarchontogliresFukomys damarensisfukDam1885580 413woodland dormouseeuarchontogliresGraphiurus murinus51346 414Desmarest's hutiaeuarchontogliresCapromys piloridesGCA_004027915.1_CapPil_v1_BIUU34842 415Upper Galilee mountains blind mole rateuarchontogliresNannospalax galiliGCA_000622305.1_S.galili_v1.01026970 416nutriaeuarchontogliresMyocastor coypusGCA_004027025.1_MyoCoy_v1_BIUU10157 417hazel dormouseeuarchontogliresMuscardinus avellanariusGCA_004027005.1_MusAve_v1_BIUU39082 418dassie-rateuarchontogliresPetromus typicusGCA_004026965.1_PetTyp_v1_BIUU10183 419greater cane rateuarchontogliresThryonomys swinderianusGCA_004025085.1_ThrSwi_v1_BIUU10169 420snowshoe hareeuarchontogliresLepus americanusGCA_004026855.1_LepAme_v1_BIUU48086 421Gambian giant pouched rateuarchontogliresCricetomys gambianusGCA_004027575.1_CriGam_v1_BIUU10085 422Prairie deer mouseeuarchontogliresPeromyscus maniculatusGCF_000500345.1_Pman_1.010042 423southern grasshopper mouseeuarchontogliresOnychomys torridusGCA_004026725.1_OnyTor_v1_BIUU38674 424rabbiteuarchontogliresOryctolagus cuniculusGCA_000003625.1_OryCun2.09986 425muskrateuarchontogliresOndatra zibethicusGCA_004026605.1_OndZib_v1_BIUU10060 426northern mole voleeuarchontogliresEllobius talpinusGCA_001685095.1_ETalpinus_0.1329620 427Mongolian gerbileuarchontogliresMeriones unguiculatusGCA_004026785.1_MerUng_v1_BIUU10047 428fat sand rateuarchontogliresPsammomys obesusGCA_002215935.1_ASM221593v148139 429house mouseeuarchontogliresMus musculusmm1010090 430Chinese hamstereuarchontogliresCricetulus griseusGCA_900186095.1_CHOK1S_HZDv110029 431Norway rateuarchontogliresRattus norvegicusGCF_000001895.5_Rnor_6.010116 432western wild mouseeuarchontogliresMus spretusGCA_001624865.1_SPRET_EiJ_v110096 433meadow jumping mouseeuarchontogliresZapus hudsoniusGCA_004024765.1_ZapHud_v1_BIUU160400 434prairie voleeuarchontogliresMicrotus ochrogastermicOch179684 435Ryukyu mouseeuarchontogliresMus caroliGCA_900094665.2_CAROLI_EIJ_v1.110089 436Egyptian spiny mouseeuarchontogliresAcomys cahirinusGCA_004027535.1_AcoCah_v1_BIUU10068 437Gobi jerboaeuarchontogliresAllactaga bullata (Orientallactaga bullata)GCA_004027895.1_AllBul_v1_BIUU1041416 438shrew mouseeuarchontogliresMus pahariGCF_900095145.1_PAHARI_EIJ_v1.110093 439Transcaucasian mole voleeuarchontogliresEllobius lutescensGCA_001685075.1_ASM168507v139086 440hispid cotton rateuarchontogliresSigmodon hispidusGCA_004025045.1_SigHis_v1_BIUU42415 441lesser Egyptian jerboaeuarchontogliresJaculus jaculusGCA_000280705.1_JacJac1.051337 442Brazilian guinea pigeuarchontogliresCavia apereacavApe137548 443golden hamstereuarchontogliresMesocricetus auratusGCA_000349665.1_MesAur1.010036 444Stephens's kangaroo rateuarchontogliresDipodomys stephensiGCA_004024685.1_DipSte_v1_BIUU323379 445American pikaeuarchontogliresOchotona princepsGCA_000292845.1_OchPri3.09978 446Ord's kangaroo rateuarchontogliresDipodomys ordiidipOrd210020 447little pocket mouseeuarchontogliresPerognathus longimembris38669 Table 1. Genome assemblies included in the 447-way Conservation track. References Kuderna LFK, Ulirsch JC, Rashid S, Ameen M, Sundaram L, Hickey G, Cox AJ, Gao H, Kumar A, Aguet F et al. Identification of constrained sequence elements across 239 primate genomes. Nature. 2023 Nov 29;. DOI: 10.1038/s41586-023-06798-8; PMID: 38030727 Kuderna LFK, Gao H, Janiak MC, Kuhlwilm M, Orkin JD, Bataillon T, Manu S, Valenzuela A, Bergman J, Rousselle M et al. A global catalog of whole-genome diversity from 233 primate species. Science. 2023 Jun 2;380(6648):906-913. DOI: 10.1126/science.abn7829; PMID: 37262161 Zoonomia Consortium. A comparative genomics multitool for scientific discovery and conservation. Nature. 2020 Nov;587(7833):240-245. DOI: 10.1038/s41586-020-2876-6; PMID: 33177664; PMC: PMC7759459 Feng S, Stiller J, Deng Y, Armstrong J, Fang Q, Reeve AH, Xie D, Chen G, Guo C, Faircloth BC et al. Dense sampling of bird diversity increases power of comparative genomics. Nature. 2020 Nov;587(7833):252-257. DOI: 10.1038/s41586-020-2873-9; PMID: 33177665; PMC: PMC7759463 Armstrong J, Hickey G, Diekhans M, Fiddes IT, Novak AM, Deran A, Fang Q, Xie D, Feng S, Stiller J et al. Progressive Cactus is a multiple-genome aligner for the thousand-genome era. Nature. 2020 Nov;587(7833):246-251. DOI: 10.1038/s41586-020-2871-y; PMID: 33177663; PMC: PMC7673649 
cons447wayViewphyloP Basewise Conservation (phyloP) Cactus Alignment & Conservation on 447 mammal species, including Zoonomia genomes Comparative Genomics 
phyloP447wayLRT 447 phyloP primates LRT 447 mammals Basewise Conservation by PhyloP, primates subset LRT Comparative Genomics 
phyloP447wayBW 447 phyloP REV 447 mammals Basewise Conservation by PhyloP phyloFit REV model Comparative Genomics 
cadd CADD 1.6 CADD 1.6 Score for all possible single-basepair mutations (zoom in for scores) Phenotype and Literature Description This track collection shows Combined Annotation Dependent Depletion scores. CADD is a tool for scoring the deleteriousness of single nucleotide variants as well as insertion/deletion variants in the human genome. Some mutation annotations tend to exploit a single information type (e.g., phastCons or phyloP for conservation) and/or are restricted in scope (e.g., to missense changes). Thus, a broadly applicable metric that objectively weights and integrates diverse information is needed. Combined Annotation Dependent Depletion (CADD) is a framework that integrates multiple annotations into one metric by contrasting variants that survived natural selection with simulated mutations. CADD scores strongly correlate with allelic diversity, pathogenicity of both coding and non-coding variants, experimentally measured regulatory effects, and also rank causal variants within individual genome sequences with a higher value than non-causal variants. Finally, CADD scores of complex trait-associated variants from genome-wide association studies (GWAS) are significantly higher than matched controls and correlate with study sample size, likely reflecting the increased accuracy of larger GWAS. A CADD score represents a ranking not a prediction, and no threshold is defined for a specific purpose. Higher scores are more likely to be deleterious: Scores are 10 * -log of the rank so that variants with scores above 20 are predicted to be among the 1.0% most deleterious possible substitutions in the human genome. We recommend thinking carefully about what threshold is appropriate for your application. Display Conventions and Configuration There are six subtracks of this track: four for single-nucleotide mutations, one for each base, showing all possible substitutions, one for insertions and one for deletions. All subtracks show the CADD Phred score on mouseover. Zooming in shows the exact score on mouseover, same basepair = score 0.0. PHRED-scaled scores are normalized to all potential &#126;9 billion SNVs, and thereby provide an externally comparable unit for analysis. For example, a scaled score of 10 or greater indicates a raw score in the top 10% of all possible reference genome SNVs, and a score of 20 or greater indicates a raw score in the top 1%, regardless of the details of the annotation set, model parameters, etc. The four single-nucleotide mutation tracks have a default viewing range of score 10 to 50. As explained in the paragraph above, that results in slightly less than 10% of the data displayed. The deletion and insertion tracks have a default filter of 10-100, because they display discrete items and not graphical data. Single nucleotide variants (SNV): For SNVs, at every genome position, there are three values per position, one for every possible nucleotide mutation. The fourth value, &quot;no mutation&quot;, representing the reference allele, e.g., A to A, is always set to zero. When using this track, zoom in until you can see every basepair at the top of the display. Otherwise, there are several nucleotides per pixel under your mouse cursor and instead of an actual score, the tooltip text will show the average score of all nucleotides under the cursor. This is indicated by the prefix &quot;~&quot; in the mouseover. Averages of scores are not useful for any application of CADD. Insertions and deletions: Scores are also shown on mouseover for a set of insertions and deletions. On hg38, the set has been obtained from gnomAD3. On hg19, the set of indels has been obtained from various sources (gnomAD2, ExAC, 1000 Genomes, ESP). If your insertion or deleletion of interest is not in the track, you will need to use CADD's online scoring tool to obtain them. Data access CADD scores are freely available for all non-commercial applications from the CADD website. For commercial applications, see the license instructions there. The CADD data on the UCSC Genome Browser can be explored interactively with the Table Browser or the Data Integrator. For automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed files that can be downloaded from our download server. The files for this track are called a.bw, c.bw, g.bw, t.bw, ins.bb and del.bb. Individual regions or the whole genome annotation can be obtained using our tools bigWigToWig or bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tools can also be used to obtain features confined to a given range, e.g., bigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd/a.bw stdout or bigBedToBed -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd/ins.bb stdout Methods Data were converted from the files provided on the CADD Downloads website, provided by the Kircher lab, using custom Python scripts, documented in our makeDoc files. Credits Thanks to the CADD development team for providing precomputed data as simple tab-separated files. References Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014 Mar;46(3):310-5. PMID: 24487276; PMC: PMC3992975 Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 2019 Jan 8;47(D1):D886-D894. PMID: 30371827; PMC: PMC6323892 
caddSuper CADD 1.6 CADD 1.6 Score for all single-basepair mutations and selected insertions/deletions Phenotype and Literature Description This track collection shows Combined Annotation Dependent Depletion scores. CADD is a tool for scoring the deleteriousness of single nucleotide variants as well as insertion/deletion variants in the human genome. Some mutation annotations tend to exploit a single information type (e.g., phastCons or phyloP for conservation) and/or are restricted in scope (e.g., to missense changes). Thus, a broadly applicable metric that objectively weights and integrates diverse information is needed. Combined Annotation Dependent Depletion (CADD) is a framework that integrates multiple annotations into one metric by contrasting variants that survived natural selection with simulated mutations. CADD scores strongly correlate with allelic diversity, pathogenicity of both coding and non-coding variants, experimentally measured regulatory effects, and also rank causal variants within individual genome sequences with a higher value than non-causal variants. Finally, CADD scores of complex trait-associated variants from genome-wide association studies (GWAS) are significantly higher than matched controls and correlate with study sample size, likely reflecting the increased accuracy of larger GWAS. A CADD score represents a ranking not a prediction, and no threshold is defined for a specific purpose. Higher scores are more likely to be deleterious: Scores are 10 * -log of the rank so that variants with scores above 20 are predicted to be among the 1.0% most deleterious possible substitutions in the human genome. We recommend thinking carefully about what threshold is appropriate for your application. Display Conventions and Configuration There are six subtracks of this track: four for single-nucleotide mutations, one for each base, showing all possible substitutions, one for insertions and one for deletions. All subtracks show the CADD Phred score on mouseover. Zooming in shows the exact score on mouseover, same basepair = score 0.0. PHRED-scaled scores are normalized to all potential &#126;9 billion SNVs, and thereby provide an externally comparable unit for analysis. For example, a scaled score of 10 or greater indicates a raw score in the top 10% of all possible reference genome SNVs, and a score of 20 or greater indicates a raw score in the top 1%, regardless of the details of the annotation set, model parameters, etc. The four single-nucleotide mutation tracks have a default viewing range of score 10 to 50. As explained in the paragraph above, that results in slightly less than 10% of the data displayed. The deletion and insertion tracks have a default filter of 10-100, because they display discrete items and not graphical data. Single nucleotide variants (SNV): For SNVs, at every genome position, there are three values per position, one for every possible nucleotide mutation. The fourth value, &quot;no mutation&quot;, representing the reference allele, e.g., A to A, is always set to zero. When using this track, zoom in until you can see every basepair at the top of the display. Otherwise, there are several nucleotides per pixel under your mouse cursor and instead of an actual score, the tooltip text will show the average score of all nucleotides under the cursor. This is indicated by the prefix &quot;~&quot; in the mouseover. Averages of scores are not useful for any application of CADD. Insertions and deletions: Scores are also shown on mouseover for a set of insertions and deletions. On hg38, the set has been obtained from gnomAD3. On hg19, the set of indels has been obtained from various sources (gnomAD2, ExAC, 1000 Genomes, ESP). If your insertion or deleletion of interest is not in the track, you will need to use CADD's online scoring tool to obtain them. Data access CADD scores are freely available for all non-commercial applications from the CADD website. For commercial applications, see the license instructions there. The CADD data on the UCSC Genome Browser can be explored interactively with the Table Browser or the Data Integrator. For automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed files that can be downloaded from our download server. The files for this track are called a.bw, c.bw, g.bw, t.bw, ins.bb and del.bb. Individual regions or the whole genome annotation can be obtained using our tools bigWigToWig or bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tools can also be used to obtain features confined to a given range, e.g., bigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd/a.bw stdout or bigBedToBed -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd/ins.bb stdout Methods Data were converted from the files provided on the CADD Downloads website, provided by the Kircher lab, using custom Python scripts, documented in our makeDoc files. Credits Thanks to the CADD development team for providing precomputed data as simple tab-separated files. References Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014 Mar;46(3):310-5. PMID: 24487276; PMC: PMC3992975 Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 2019 Jan 8;47(D1):D886-D894. PMID: 30371827; PMC: PMC6323892 
caddT Mutation: T CADD 1.6 Score: Mutation is T Phenotype and Literature 
caddG Mutation: G CADD 1.6 Score: Mutation is G Phenotype and Literature 
caddC Mutation: C CADD 1.6 Score: Mutation is C Phenotype and Literature 
caddA Mutation: A CADD 1.6 Score: Mutation is A Phenotype and Literature 
caddDel CADD 1.6 Del CADD 1.6 Score: Deletions - label is length of deletion Phenotype and Literature Description This track collection shows Combined Annotation Dependent Depletion scores. CADD is a tool for scoring the deleteriousness of single nucleotide variants as well as insertion/deletion variants in the human genome. Some mutation annotations tend to exploit a single information type (e.g., phastCons or phyloP for conservation) and/or are restricted in scope (e.g., to missense changes). Thus, a broadly applicable metric that objectively weights and integrates diverse information is needed. Combined Annotation Dependent Depletion (CADD) is a framework that integrates multiple annotations into one metric by contrasting variants that survived natural selection with simulated mutations. CADD scores strongly correlate with allelic diversity, pathogenicity of both coding and non-coding variants, experimentally measured regulatory effects, and also rank causal variants within individual genome sequences with a higher value than non-causal variants. Finally, CADD scores of complex trait-associated variants from genome-wide association studies (GWAS) are significantly higher than matched controls and correlate with study sample size, likely reflecting the increased accuracy of larger GWAS. A CADD score represents a ranking not a prediction, and no threshold is defined for a specific purpose. Higher scores are more likely to be deleterious: Scores are 10 * -log of the rank so that variants with scores above 20 are predicted to be among the 1.0% most deleterious possible substitutions in the human genome. We recommend thinking carefully about what threshold is appropriate for your application. Display Conventions and Configuration There are six subtracks of this track: four for single-nucleotide mutations, one for each base, showing all possible substitutions, one for insertions and one for deletions. All subtracks show the CADD Phred score on mouseover. Zooming in shows the exact score on mouseover, same basepair = score 0.0. PHRED-scaled scores are normalized to all potential &#126;9 billion SNVs, and thereby provide an externally comparable unit for analysis. For example, a scaled score of 10 or greater indicates a raw score in the top 10% of all possible reference genome SNVs, and a score of 20 or greater indicates a raw score in the top 1%, regardless of the details of the annotation set, model parameters, etc. The four single-nucleotide mutation tracks have a default viewing range of score 10 to 50. As explained in the paragraph above, that results in slightly less than 10% of the data displayed. The deletion and insertion tracks have a default filter of 10-100, because they display discrete items and not graphical data. Single nucleotide variants (SNV): For SNVs, at every genome position, there are three values per position, one for every possible nucleotide mutation. The fourth value, &quot;no mutation&quot;, representing the reference allele, e.g., A to A, is always set to zero. When using this track, zoom in until you can see every basepair at the top of the display. Otherwise, there are several nucleotides per pixel under your mouse cursor and instead of an actual score, the tooltip text will show the average score of all nucleotides under the cursor. This is indicated by the prefix &quot;~&quot; in the mouseover. Averages of scores are not useful for any application of CADD. Insertions and deletions: Scores are also shown on mouseover for a set of insertions and deletions. On hg38, the set has been obtained from gnomAD3. On hg19, the set of indels has been obtained from various sources (gnomAD2, ExAC, 1000 Genomes, ESP). If your insertion or deleletion of interest is not in the track, you will need to use CADD's online scoring tool to obtain them. Data access CADD scores are freely available for all non-commercial applications from the CADD website. For commercial applications, see the license instructions there. The CADD data on the UCSC Genome Browser can be explored interactively with the Table Browser or the Data Integrator. For automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed files that can be downloaded from our download server. The files for this track are called a.bw, c.bw, g.bw, t.bw, ins.bb and del.bb. Individual regions or the whole genome annotation can be obtained using our tools bigWigToWig or bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tools can also be used to obtain features confined to a given range, e.g., bigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd/a.bw stdout or bigBedToBed -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd/ins.bb stdout Methods Data were converted from the files provided on the CADD Downloads website, provided by the Kircher lab, using custom Python scripts, documented in our makeDoc files. Credits Thanks to the CADD development team for providing precomputed data as simple tab-separated files. References Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014 Mar;46(3):310-5. PMID: 24487276; PMC: PMC3992975 Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 2019 Jan 8;47(D1):D886-D894. PMID: 30371827; PMC: PMC6323892 
caddIns CADD 1.6 Ins CADD 1.6 Score: Insertions - label is length of insertion Phenotype and Literature Description This track collection shows Combined Annotation Dependent Depletion scores. CADD is a tool for scoring the deleteriousness of single nucleotide variants as well as insertion/deletion variants in the human genome. Some mutation annotations tend to exploit a single information type (e.g., phastCons or phyloP for conservation) and/or are restricted in scope (e.g., to missense changes). Thus, a broadly applicable metric that objectively weights and integrates diverse information is needed. Combined Annotation Dependent Depletion (CADD) is a framework that integrates multiple annotations into one metric by contrasting variants that survived natural selection with simulated mutations. CADD scores strongly correlate with allelic diversity, pathogenicity of both coding and non-coding variants, experimentally measured regulatory effects, and also rank causal variants within individual genome sequences with a higher value than non-causal variants. Finally, CADD scores of complex trait-associated variants from genome-wide association studies (GWAS) are significantly higher than matched controls and correlate with study sample size, likely reflecting the increased accuracy of larger GWAS. A CADD score represents a ranking not a prediction, and no threshold is defined for a specific purpose. Higher scores are more likely to be deleterious: Scores are 10 * -log of the rank so that variants with scores above 20 are predicted to be among the 1.0% most deleterious possible substitutions in the human genome. We recommend thinking carefully about what threshold is appropriate for your application. Display Conventions and Configuration There are six subtracks of this track: four for single-nucleotide mutations, one for each base, showing all possible substitutions, one for insertions and one for deletions. All subtracks show the CADD Phred score on mouseover. Zooming in shows the exact score on mouseover, same basepair = score 0.0. PHRED-scaled scores are normalized to all potential &#126;9 billion SNVs, and thereby provide an externally comparable unit for analysis. For example, a scaled score of 10 or greater indicates a raw score in the top 10% of all possible reference genome SNVs, and a score of 20 or greater indicates a raw score in the top 1%, regardless of the details of the annotation set, model parameters, etc. The four single-nucleotide mutation tracks have a default viewing range of score 10 to 50. As explained in the paragraph above, that results in slightly less than 10% of the data displayed. The deletion and insertion tracks have a default filter of 10-100, because they display discrete items and not graphical data. Single nucleotide variants (SNV): For SNVs, at every genome position, there are three values per position, one for every possible nucleotide mutation. The fourth value, &quot;no mutation&quot;, representing the reference allele, e.g., A to A, is always set to zero. When using this track, zoom in until you can see every basepair at the top of the display. Otherwise, there are several nucleotides per pixel under your mouse cursor and instead of an actual score, the tooltip text will show the average score of all nucleotides under the cursor. This is indicated by the prefix &quot;~&quot; in the mouseover. Averages of scores are not useful for any application of CADD. Insertions and deletions: Scores are also shown on mouseover for a set of insertions and deletions. On hg38, the set has been obtained from gnomAD3. On hg19, the set of indels has been obtained from various sources (gnomAD2, ExAC, 1000 Genomes, ESP). If your insertion or deleletion of interest is not in the track, you will need to use CADD's online scoring tool to obtain them. Data access CADD scores are freely available for all non-commercial applications from the CADD website. For commercial applications, see the license instructions there. The CADD data on the UCSC Genome Browser can be explored interactively with the Table Browser or the Data Integrator. For automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed files that can be downloaded from our download server. The files for this track are called a.bw, c.bw, g.bw, t.bw, ins.bb and del.bb. Individual regions or the whole genome annotation can be obtained using our tools bigWigToWig or bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tools can also be used to obtain features confined to a given range, e.g., bigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd/a.bw stdout or bigBedToBed -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd/ins.bb stdout Methods Data were converted from the files provided on the CADD Downloads website, provided by the Kircher lab, using custom Python scripts, documented in our makeDoc files. Credits Thanks to the CADD development team for providing precomputed data as simple tab-separated files. References Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014 Mar;46(3):310-5. PMID: 24487276; PMC: PMC3992975 Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 2019 Jan 8;47(D1):D886-D894. PMID: 30371827; PMC: PMC6323892 
cadd1_7 CADD 1.7 CADD 1.7 Score for all possible single-basepair mutations (zoom in for scores) Phenotype and Literature Description This track collection shows Combined Annotation Dependent Depletion scores. CADD is a tool for scoring the deleteriousness of single nucleotide variants as well as insertion/deletion variants in the human genome. Some mutation annotations tend to exploit a single information type (e.g., phastCons or phyloP for conservation) and/or are restricted in scope (e.g., to missense changes). Thus, a broadly applicable metric that objectively weights and integrates diverse information is needed. Combined Annotation Dependent Depletion (CADD) is a framework that integrates multiple annotations into one metric by contrasting variants that survived natural selection with simulated mutations. CADD scores strongly correlate with allelic diversity, pathogenicity of both coding and non-coding variants, experimentally measured regulatory effects, and also rank causal variants within individual genome sequences with a higher value than non-causal variants. Finally, CADD scores of complex trait-associated variants from genome-wide association studies (GWAS) are significantly higher than matched controls and correlate with study sample size, likely reflecting the increased accuracy of larger GWAS. A CADD score represents a ranking not a prediction, and no threshold is defined for a specific purpose. Higher scores are more likely to be deleterious: Scores are 10 * -log of the rank so that variants with scores above 20 are predicted to be among the 1.0% most deleterious possible substitutions in the human genome. We recommend thinking carefully about what threshold is appropriate for your application. Display Conventions and Configuration There are six subtracks of this track: four for single-nucleotide mutations, one for each base, showing all possible substitutions, one for insertions and one for deletions. All subtracks show the CADD Phred score on mouseover. Zooming in shows the exact score on mouseover, same basepair = score 0.0. PHRED-scaled scores are normalized to all potential &#126;9 billion SNVs, and thereby provide an externally comparable unit for analysis. For example, a scaled score of 10 or greater indicates a raw score in the top 10% of all possible reference genome SNVs, and a score of 20 or greater indicates a raw score in the top 1%, regardless of the details of the annotation set, model parameters, etc. The four single-nucleotide mutation tracks have a default viewing range of score 10 to 50. As explained in the paragraph above, that results in slightly less than 10% of the data displayed. The deletion and insertion tracks have a default filter of 10-100, because they display discrete items and not graphical data. Single nucleotide variants (SNV): For SNVs, at every genome position, there are three values per position, one for every possible nucleotide mutation. The fourth value, &quot;no mutation&quot;, representing the reference allele, e.g., A to A, is always set to zero. When using this track, zoom in until you can see every basepair at the top of the display. Otherwise, there are several nucleotides per pixel under your mouse cursor and instead of an actual score, the tooltip text will show the average score of all nucleotides under the cursor. This is indicated by the prefix &quot;~&quot; in the mouseover. Averages of scores are not useful for any application of CADD. Insertions and deletions: Scores are also shown on mouseover for a set of insertions and deletions. On hg38, the set has been obtained from gnomAD3. On hg19, the set of indels has been obtained from various sources (gnomAD2, ExAC, 1000 Genomes, ESP). If your insertion or deleletion of interest is not in the track, you will need to use CADD's online scoring tool to obtain them. Methods In CADD version 1.7, new features have been added to improve CADD scores for certain variant effects, boosting the overall performance of CADD and bringing new developments to the community. CADD v1.7 integrates annotations from recent efforts to assess variant effects, along with new conservation and mutation scores. CADD v1.7 supports only the major chromosomes of the hg38/GRCh38 reference genome (chromosomes 1-22, X, and Y) and may be the last version to support the hg19/GRCh37 human reference genome. This version includes scores derived from Evolutionary Scale Modeling (ESM) for assessing variants in protein-coding regions, along with scores from a convolutional neural network (CNN) trained on open chromatin sequences, used as a proxy for regulatory regions in the genome. The previously included conservation scores have been updated with data from the Zoonomia project. New annotations have also been added for 3' Untranslated Regions (3' UTRs), along with models of genome-wide mutational rates. The gene and transcript models have been updated by advancing from Ensembl version 95 to version 110, and the Ensembl Variant Effect Predictor (VEP) has been upgraded accordingly. The models in CADD v1.7 have been trained similarly to the version 1.6 release. The logistic regression uses an L2 penalty with C = 1, and training was completed after thirteen L-BFGS iterations using the sklearn library The new models exhibit a high degree of similarity to the previous release, with a Spearman correlation of 0.946 for CADD scores calculated for 100,000 randomly selected variants between CADD GRCh38-v1.6 and CADD GRCh38-v1.7. The v1.7 models perform comparably to earlier versions in distinguishing known pathogenic variants (ClinVar) from common variants (gnomAD) across the genome. Improvements in CADD v1.7 are particularly evident when focusing on specific variant categories, such as missense or 3' UTR variants, where the latest release includes updated annotations. More information can be found at the CADD site and the Schubach et al., Nucleic Acids Res, 2024 publication. Data were converted from the files provided on the CADD Downloads website, provided by the Kircher lab, using custom Python scripts, documented in our makeDoc files. Data access CADD scores are freely available for all non-commercial applications from the CADD website. For commercial applications, see the license instructions there. The CADD data on the UCSC Genome Browser can be explored interactively with the Table Browser or the Data Integrator. For automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed files that can be downloaded from our download server. The files for this track are called a.bw, c.bw, g.bw, t.bw, ins.bb and del.bb. Individual regions or the whole genome annotation can be obtained using our tools bigWigToWig or bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tools can also be used to obtain features confined to a given range, e.g., bigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd1.7/a.bw stdout or bigBedToBed -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd1.7/ins.bb stdout Credits Thanks to the CADD development team for providing precomputed data as simple tab-separated files. References Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014 Mar;46(3):310-5. PMID: 24487276; PMC: PMC3992975 Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 2019 Jan 8;47(D1):D886-D894. PMID: 30371827; PMC: PMC6323892 Schubach M, Maass T, Nazaretyan L, R&#246;ner S, Kircher M. CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions. Nucleic Acids Res. 2024 Jan 5;52(D1):D1143-D1154. PMID: 38183205; PMC: PMC10767851 
caddSuper1_7 CADD 1.7 CADD 1.7 Score for all single-basepair mutations and selected insertions/deletions Phenotype and Literature Description This track collection shows Combined Annotation Dependent Depletion scores. CADD is a tool for scoring the deleteriousness of single nucleotide variants as well as insertion/deletion variants in the human genome. Some mutation annotations tend to exploit a single information type (e.g., phastCons or phyloP for conservation) and/or are restricted in scope (e.g., to missense changes). Thus, a broadly applicable metric that objectively weights and integrates diverse information is needed. Combined Annotation Dependent Depletion (CADD) is a framework that integrates multiple annotations into one metric by contrasting variants that survived natural selection with simulated mutations. CADD scores strongly correlate with allelic diversity, pathogenicity of both coding and non-coding variants, experimentally measured regulatory effects, and also rank causal variants within individual genome sequences with a higher value than non-causal variants. Finally, CADD scores of complex trait-associated variants from genome-wide association studies (GWAS) are significantly higher than matched controls and correlate with study sample size, likely reflecting the increased accuracy of larger GWAS. A CADD score represents a ranking not a prediction, and no threshold is defined for a specific purpose. Higher scores are more likely to be deleterious: Scores are 10 * -log of the rank so that variants with scores above 20 are predicted to be among the 1.0% most deleterious possible substitutions in the human genome. We recommend thinking carefully about what threshold is appropriate for your application. Display Conventions and Configuration There are six subtracks of this track: four for single-nucleotide mutations, one for each base, showing all possible substitutions, one for insertions and one for deletions. All subtracks show the CADD Phred score on mouseover. Zooming in shows the exact score on mouseover, same basepair = score 0.0. PHRED-scaled scores are normalized to all potential &#126;9 billion SNVs, and thereby provide an externally comparable unit for analysis. For example, a scaled score of 10 or greater indicates a raw score in the top 10% of all possible reference genome SNVs, and a score of 20 or greater indicates a raw score in the top 1%, regardless of the details of the annotation set, model parameters, etc. The four single-nucleotide mutation tracks have a default viewing range of score 10 to 50. As explained in the paragraph above, that results in slightly less than 10% of the data displayed. The deletion and insertion tracks have a default filter of 10-100, because they display discrete items and not graphical data. Single nucleotide variants (SNV): For SNVs, at every genome position, there are three values per position, one for every possible nucleotide mutation. The fourth value, &quot;no mutation&quot;, representing the reference allele, e.g., A to A, is always set to zero. When using this track, zoom in until you can see every basepair at the top of the display. Otherwise, there are several nucleotides per pixel under your mouse cursor and instead of an actual score, the tooltip text will show the average score of all nucleotides under the cursor. This is indicated by the prefix &quot;~&quot; in the mouseover. Averages of scores are not useful for any application of CADD. Insertions and deletions: Scores are also shown on mouseover for a set of insertions and deletions. On hg38, the set has been obtained from gnomAD3. On hg19, the set of indels has been obtained from various sources (gnomAD2, ExAC, 1000 Genomes, ESP). If your insertion or deleletion of interest is not in the track, you will need to use CADD's online scoring tool to obtain them. Methods In CADD version 1.7, new features have been added to improve CADD scores for certain variant effects, boosting the overall performance of CADD and bringing new developments to the community. CADD v1.7 integrates annotations from recent efforts to assess variant effects, along with new conservation and mutation scores. CADD v1.7 supports only the major chromosomes of the hg38/GRCh38 reference genome (chromosomes 1-22, X, and Y) and may be the last version to support the hg19/GRCh37 human reference genome. This version includes scores derived from Evolutionary Scale Modeling (ESM) for assessing variants in protein-coding regions, along with scores from a convolutional neural network (CNN) trained on open chromatin sequences, used as a proxy for regulatory regions in the genome. The previously included conservation scores have been updated with data from the Zoonomia project. New annotations have also been added for 3' Untranslated Regions (3' UTRs), along with models of genome-wide mutational rates. The gene and transcript models have been updated by advancing from Ensembl version 95 to version 110, and the Ensembl Variant Effect Predictor (VEP) has been upgraded accordingly. The models in CADD v1.7 have been trained similarly to the version 1.6 release. The logistic regression uses an L2 penalty with C = 1, and training was completed after thirteen L-BFGS iterations using the sklearn library The new models exhibit a high degree of similarity to the previous release, with a Spearman correlation of 0.946 for CADD scores calculated for 100,000 randomly selected variants between CADD GRCh38-v1.6 and CADD GRCh38-v1.7. The v1.7 models perform comparably to earlier versions in distinguishing known pathogenic variants (ClinVar) from common variants (gnomAD) across the genome. Improvements in CADD v1.7 are particularly evident when focusing on specific variant categories, such as missense or 3' UTR variants, where the latest release includes updated annotations. More information can be found at the CADD site and the Schubach et al., Nucleic Acids Res, 2024 publication. Data were converted from the files provided on the CADD Downloads website, provided by the Kircher lab, using custom Python scripts, documented in our makeDoc files. Data access CADD scores are freely available for all non-commercial applications from the CADD website. For commercial applications, see the license instructions there. The CADD data on the UCSC Genome Browser can be explored interactively with the Table Browser or the Data Integrator. For automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed files that can be downloaded from our download server. The files for this track are called a.bw, c.bw, g.bw, t.bw, ins.bb and del.bb. Individual regions or the whole genome annotation can be obtained using our tools bigWigToWig or bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tools can also be used to obtain features confined to a given range, e.g., bigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd1.7/a.bw stdout or bigBedToBed -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd1.7/ins.bb stdout Credits Thanks to the CADD development team for providing precomputed data as simple tab-separated files. References Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014 Mar;46(3):310-5. PMID: 24487276; PMC: PMC3992975 Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 2019 Jan 8;47(D1):D886-D894. PMID: 30371827; PMC: PMC6323892 Schubach M, Maass T, Nazaretyan L, R&#246;ner S, Kircher M. CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions. Nucleic Acids Res. 2024 Jan 5;52(D1):D1143-D1154. PMID: 38183205; PMC: PMC10767851 
cadd1_7_T Mutation: T CADD 1.7 Score: Mutation is T Phenotype and Literature 
cadd1_7_G Mutation: G CADD 1.7 Score: Mutation is G Phenotype and Literature 
cadd1_7_C Mutation: C CADD 1.7 Score: Mutation is C Phenotype and Literature 
cadd1_7_A Mutation: A CADD 1.7 Score: Mutation is A Phenotype and Literature 
cadd1_7_Del CADD 1.7 Del CADD 1.7 Score: Deletions - label is length of deletion Phenotype and Literature Description This track collection shows Combined Annotation Dependent Depletion scores. CADD is a tool for scoring the deleteriousness of single nucleotide variants as well as insertion/deletion variants in the human genome. Some mutation annotations tend to exploit a single information type (e.g., phastCons or phyloP for conservation) and/or are restricted in scope (e.g., to missense changes). Thus, a broadly applicable metric that objectively weights and integrates diverse information is needed. Combined Annotation Dependent Depletion (CADD) is a framework that integrates multiple annotations into one metric by contrasting variants that survived natural selection with simulated mutations. CADD scores strongly correlate with allelic diversity, pathogenicity of both coding and non-coding variants, experimentally measured regulatory effects, and also rank causal variants within individual genome sequences with a higher value than non-causal variants. Finally, CADD scores of complex trait-associated variants from genome-wide association studies (GWAS) are significantly higher than matched controls and correlate with study sample size, likely reflecting the increased accuracy of larger GWAS. A CADD score represents a ranking not a prediction, and no threshold is defined for a specific purpose. Higher scores are more likely to be deleterious: Scores are 10 * -log of the rank so that variants with scores above 20 are predicted to be among the 1.0% most deleterious possible substitutions in the human genome. We recommend thinking carefully about what threshold is appropriate for your application. Display Conventions and Configuration There are six subtracks of this track: four for single-nucleotide mutations, one for each base, showing all possible substitutions, one for insertions and one for deletions. All subtracks show the CADD Phred score on mouseover. Zooming in shows the exact score on mouseover, same basepair = score 0.0. PHRED-scaled scores are normalized to all potential &#126;9 billion SNVs, and thereby provide an externally comparable unit for analysis. For example, a scaled score of 10 or greater indicates a raw score in the top 10% of all possible reference genome SNVs, and a score of 20 or greater indicates a raw score in the top 1%, regardless of the details of the annotation set, model parameters, etc. The four single-nucleotide mutation tracks have a default viewing range of score 10 to 50. As explained in the paragraph above, that results in slightly less than 10% of the data displayed. The deletion and insertion tracks have a default filter of 10-100, because they display discrete items and not graphical data. Single nucleotide variants (SNV): For SNVs, at every genome position, there are three values per position, one for every possible nucleotide mutation. The fourth value, &quot;no mutation&quot;, representing the reference allele, e.g., A to A, is always set to zero. When using this track, zoom in until you can see every basepair at the top of the display. Otherwise, there are several nucleotides per pixel under your mouse cursor and instead of an actual score, the tooltip text will show the average score of all nucleotides under the cursor. This is indicated by the prefix &quot;~&quot; in the mouseover. Averages of scores are not useful for any application of CADD. Insertions and deletions: Scores are also shown on mouseover for a set of insertions and deletions. On hg38, the set has been obtained from gnomAD3. On hg19, the set of indels has been obtained from various sources (gnomAD2, ExAC, 1000 Genomes, ESP). If your insertion or deleletion of interest is not in the track, you will need to use CADD's online scoring tool to obtain them. Methods In CADD version 1.7, new features have been added to improve CADD scores for certain variant effects, boosting the overall performance of CADD and bringing new developments to the community. CADD v1.7 integrates annotations from recent efforts to assess variant effects, along with new conservation and mutation scores. CADD v1.7 supports only the major chromosomes of the hg38/GRCh38 reference genome (chromosomes 1-22, X, and Y) and may be the last version to support the hg19/GRCh37 human reference genome. This version includes scores derived from Evolutionary Scale Modeling (ESM) for assessing variants in protein-coding regions, along with scores from a convolutional neural network (CNN) trained on open chromatin sequences, used as a proxy for regulatory regions in the genome. The previously included conservation scores have been updated with data from the Zoonomia project. New annotations have also been added for 3' Untranslated Regions (3' UTRs), along with models of genome-wide mutational rates. The gene and transcript models have been updated by advancing from Ensembl version 95 to version 110, and the Ensembl Variant Effect Predictor (VEP) has been upgraded accordingly. The models in CADD v1.7 have been trained similarly to the version 1.6 release. The logistic regression uses an L2 penalty with C = 1, and training was completed after thirteen L-BFGS iterations using the sklearn library The new models exhibit a high degree of similarity to the previous release, with a Spearman correlation of 0.946 for CADD scores calculated for 100,000 randomly selected variants between CADD GRCh38-v1.6 and CADD GRCh38-v1.7. The v1.7 models perform comparably to earlier versions in distinguishing known pathogenic variants (ClinVar) from common variants (gnomAD) across the genome. Improvements in CADD v1.7 are particularly evident when focusing on specific variant categories, such as missense or 3' UTR variants, where the latest release includes updated annotations. More information can be found at the CADD site and the Schubach et al., Nucleic Acids Res, 2024 publication. Data were converted from the files provided on the CADD Downloads website, provided by the Kircher lab, using custom Python scripts, documented in our makeDoc files. Data access CADD scores are freely available for all non-commercial applications from the CADD website. For commercial applications, see the license instructions there. The CADD data on the UCSC Genome Browser can be explored interactively with the Table Browser or the Data Integrator. For automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed files that can be downloaded from our download server. The files for this track are called a.bw, c.bw, g.bw, t.bw, ins.bb and del.bb. Individual regions or the whole genome annotation can be obtained using our tools bigWigToWig or bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tools can also be used to obtain features confined to a given range, e.g., bigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd1.7/a.bw stdout or bigBedToBed -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd1.7/ins.bb stdout Credits Thanks to the CADD development team for providing precomputed data as simple tab-separated files. References Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014 Mar;46(3):310-5. PMID: 24487276; PMC: PMC3992975 Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 2019 Jan 8;47(D1):D886-D894. PMID: 30371827; PMC: PMC6323892 Schubach M, Maass T, Nazaretyan L, R&#246;ner S, Kircher M. CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions. Nucleic Acids Res. 2024 Jan 5;52(D1):D1143-D1154. PMID: 38183205; PMC: PMC10767851 
cadd1_7_Ins CADD 1.7 Ins CADD 1.7 Score: Insertions - label is length of insertion Phenotype and Literature Description This track collection shows Combined Annotation Dependent Depletion scores. CADD is a tool for scoring the deleteriousness of single nucleotide variants as well as insertion/deletion variants in the human genome. Some mutation annotations tend to exploit a single information type (e.g., phastCons or phyloP for conservation) and/or are restricted in scope (e.g., to missense changes). Thus, a broadly applicable metric that objectively weights and integrates diverse information is needed. Combined Annotation Dependent Depletion (CADD) is a framework that integrates multiple annotations into one metric by contrasting variants that survived natural selection with simulated mutations. CADD scores strongly correlate with allelic diversity, pathogenicity of both coding and non-coding variants, experimentally measured regulatory effects, and also rank causal variants within individual genome sequences with a higher value than non-causal variants. Finally, CADD scores of complex trait-associated variants from genome-wide association studies (GWAS) are significantly higher than matched controls and correlate with study sample size, likely reflecting the increased accuracy of larger GWAS. A CADD score represents a ranking not a prediction, and no threshold is defined for a specific purpose. Higher scores are more likely to be deleterious: Scores are 10 * -log of the rank so that variants with scores above 20 are predicted to be among the 1.0% most deleterious possible substitutions in the human genome. We recommend thinking carefully about what threshold is appropriate for your application. Display Conventions and Configuration There are six subtracks of this track: four for single-nucleotide mutations, one for each base, showing all possible substitutions, one for insertions and one for deletions. All subtracks show the CADD Phred score on mouseover. Zooming in shows the exact score on mouseover, same basepair = score 0.0. PHRED-scaled scores are normalized to all potential &#126;9 billion SNVs, and thereby provide an externally comparable unit for analysis. For example, a scaled score of 10 or greater indicates a raw score in the top 10% of all possible reference genome SNVs, and a score of 20 or greater indicates a raw score in the top 1%, regardless of the details of the annotation set, model parameters, etc. The four single-nucleotide mutation tracks have a default viewing range of score 10 to 50. As explained in the paragraph above, that results in slightly less than 10% of the data displayed. The deletion and insertion tracks have a default filter of 10-100, because they display discrete items and not graphical data. Single nucleotide variants (SNV): For SNVs, at every genome position, there are three values per position, one for every possible nucleotide mutation. The fourth value, &quot;no mutation&quot;, representing the reference allele, e.g., A to A, is always set to zero. When using this track, zoom in until you can see every basepair at the top of the display. Otherwise, there are several nucleotides per pixel under your mouse cursor and instead of an actual score, the tooltip text will show the average score of all nucleotides under the cursor. This is indicated by the prefix &quot;~&quot; in the mouseover. Averages of scores are not useful for any application of CADD. Insertions and deletions: Scores are also shown on mouseover for a set of insertions and deletions. On hg38, the set has been obtained from gnomAD3. On hg19, the set of indels has been obtained from various sources (gnomAD2, ExAC, 1000 Genomes, ESP). If your insertion or deleletion of interest is not in the track, you will need to use CADD's online scoring tool to obtain them. Methods In CADD version 1.7, new features have been added to improve CADD scores for certain variant effects, boosting the overall performance of CADD and bringing new developments to the community. CADD v1.7 integrates annotations from recent efforts to assess variant effects, along with new conservation and mutation scores. CADD v1.7 supports only the major chromosomes of the hg38/GRCh38 reference genome (chromosomes 1-22, X, and Y) and may be the last version to support the hg19/GRCh37 human reference genome. This version includes scores derived from Evolutionary Scale Modeling (ESM) for assessing variants in protein-coding regions, along with scores from a convolutional neural network (CNN) trained on open chromatin sequences, used as a proxy for regulatory regions in the genome. The previously included conservation scores have been updated with data from the Zoonomia project. New annotations have also been added for 3' Untranslated Regions (3' UTRs), along with models of genome-wide mutational rates. The gene and transcript models have been updated by advancing from Ensembl version 95 to version 110, and the Ensembl Variant Effect Predictor (VEP) has been upgraded accordingly. The models in CADD v1.7 have been trained similarly to the version 1.6 release. The logistic regression uses an L2 penalty with C = 1, and training was completed after thirteen L-BFGS iterations using the sklearn library The new models exhibit a high degree of similarity to the previous release, with a Spearman correlation of 0.946 for CADD scores calculated for 100,000 randomly selected variants between CADD GRCh38-v1.6 and CADD GRCh38-v1.7. The v1.7 models perform comparably to earlier versions in distinguishing known pathogenic variants (ClinVar) from common variants (gnomAD) across the genome. Improvements in CADD v1.7 are particularly evident when focusing on specific variant categories, such as missense or 3' UTR variants, where the latest release includes updated annotations. More information can be found at the CADD site and the Schubach et al., Nucleic Acids Res, 2024 publication. Data were converted from the files provided on the CADD Downloads website, provided by the Kircher lab, using custom Python scripts, documented in our makeDoc files. Data access CADD scores are freely available for all non-commercial applications from the CADD website. For commercial applications, see the license instructions there. The CADD data on the UCSC Genome Browser can be explored interactively with the Table Browser or the Data Integrator. For automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed files that can be downloaded from our download server. The files for this track are called a.bw, c.bw, g.bw, t.bw, ins.bb and del.bb. Individual regions or the whole genome annotation can be obtained using our tools bigWigToWig or bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tools can also be used to obtain features confined to a given range, e.g., bigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd1.7/a.bw stdout or bigBedToBed -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd1.7/ins.bb stdout Credits Thanks to the CADD development team for providing precomputed data as simple tab-separated files. References Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014 Mar;46(3):310-5. PMID: 24487276; PMC: PMC3992975 Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 2019 Jan 8;47(D1):D886-D894. PMID: 30371827; PMC: PMC6323892 Schubach M, Maass T, Nazaretyan L, R&#246;ner S, Kircher M. CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions. Nucleic Acids Res. 2024 Jan 5;52(D1):D1143-D1154. PMID: 38183205; PMC: PMC10767851 
tcgaGeneExpr Cancer Gene Expr Gene Expression in 33 TCGA Cancer Tissues (GENCODE v23) Phenotype and Literature Description The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), has generated comprehensive, multi-dimensional maps of the key genomic changes in 33 types of cancer. The TCGA dataset, 2.5 petabytes of data describing tumor tissue and matched normal tissues from more than 11,000 patients, is publically available and has been used widely by the research community. The Cancer Genome Atlas is a NIH-funded project to catalog genetic mutations responsible for cancer. The data shown here is RNA-seq expression data produced by the consortium. For questions or feedback on the data, please contact TCGA. TCGA Gene Expression The gene track shows RNA expression level for each TCGA tissue in GENCODE canonical genes. The gene scores are a total of all transcripts in that gene. TCGA Transcript Expression The transcript track shows RNA expression levels for each TCGA tissue using GENCODE v23 transcripts. Display Conventions In Full and Pack display modes, expression for each genomic item (gene/transcript) is represented by a colored bar chart, where the height of each bar represents the median expression level across all samples for a tissue, and the bar color indicates the tissue. The bar chart display has the same width and tissue order for all genomic items. Mouse hover over a bar will show the tissue and median expression levels. The Squish display mode draws a rectangle for each gene, colored to indicate the tissue with highest expression level if it contributes more than 10% to the overall expression (and colored black if no tissue predominates). In Dense mode, the darkness of the grayscale rectangle displayed for the gene reflects the total median expression level across all tissues. This track was designed to be used in conjunction with the GTEx expression tracks that can act as a control. The color of each cancer was derived by mapping the tissue of origin to the closest GTEx tissue, then taking the GTEx tissue's color. Five cancers did not have a matching GTEx tissue and were assigned a rainbow color scheme; these cancers are Cholangiocarcinoma, Esophageal carcinoma, Head and Neck squamous cell carcinoma, Sarcoma and Uveal Melanoma. The ordering of the cancers is based on the alphabetical ordering of their GTEx tissues. The five cancers that did not match were ordered alphabetically. Methods TCGA chose cancers for study based on two broad criteria; poor prognosis/overall public health impact and availability of human tumor and matched normal tissue samples that meet TCGA standards. RNA sequencing was performed using a polyA library and the Illumina HiSeq 2000 platform. All RNA sequencing was performed by UNC. Sequence reads for this track were quantified to the hg38/GRCh38 human genome using kallisto assisted by the GENCODE v23 transcriptome definition. Read quantification was performed at UCSC by the Computational Genomics lab, using the Toil pipeline. The resulting kallisto files were combined to generate a transcript per million (tpm) expression matrix using the UCSC tool, kallistoToMatrix. By totaling the TPM values for all transcripts associated to the canonical transcript/gene, a condensed gene per million (gpm) matrix was made. For both matrices average expression values for each tissue were calculated and used to generate a bed6+5 file that is the base of each track. This was done using the UCSC tool, expMatrixToBarchartBed. The bed track was then converted to a bigBed file using the UCSC tool, bedToBigBed. Credits Data shown here are in whole based upon data generated by the TCGA Research Network. John Vivian, Melissa Cline, and Benedict Paten of the UCSC Computational Genomics lab were responsible for the sequence read quantification used to produce this track. Chris Eisenhart and Kate Rosenbloom of the UCSC Genome Browser group were responsible for data file post-processing, track configuration and display type. References J. Vivian et al., Rapid and efficient analysis of 20,000 RNA-seq samples with Toil bioRxiv bioRxiv, vol. 2, p. 62497, 2016. 
cancerExpr Cancer Gene Expr Gene Expression in 33 TCGA Cancer Tissues (GENCODE v23) Phenotype and Literature Description The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), has generated comprehensive, multi-dimensional maps of the key genomic changes in 33 types of cancer. The TCGA dataset, 2.5 petabytes of data describing tumor tissue and matched normal tissues from more than 11,000 patients, is publically available and has been used widely by the research community. The Cancer Genome Atlas is a NIH-funded project to catalog genetic mutations responsible for cancer. The data shown here is RNA-seq expression data produced by the consortium. For questions or feedback on the data, please contact TCGA. TCGA Gene Expression The gene track shows RNA expression level for each TCGA tissue in GENCODE canonical genes. The gene scores are a total of all transcripts in that gene. TCGA Transcript Expression The transcript track shows RNA expression levels for each TCGA tissue using GENCODE v23 transcripts. Display Conventions In Full and Pack display modes, expression for each genomic item (gene/transcript) is represented by a colored bar chart, where the height of each bar represents the median expression level across all samples for a tissue, and the bar color indicates the tissue. The bar chart display has the same width and tissue order for all genomic items. Mouse hover over a bar will show the tissue and median expression levels. The Squish display mode draws a rectangle for each gene, colored to indicate the tissue with highest expression level if it contributes more than 10% to the overall expression (and colored black if no tissue predominates). In Dense mode, the darkness of the grayscale rectangle displayed for the gene reflects the total median expression level across all tissues. This track was designed to be used in conjunction with the GTEx expression tracks that can act as a control. The color of each cancer was derived by mapping the tissue of origin to the closest GTEx tissue, then taking the GTEx tissue's color. Five cancers did not have a matching GTEx tissue and were assigned a rainbow color scheme; these cancers are Cholangiocarcinoma, Esophageal carcinoma, Head and Neck squamous cell carcinoma, Sarcoma and Uveal Melanoma. The ordering of the cancers is based on the alphabetical ordering of their GTEx tissues. The five cancers that did not match were ordered alphabetically. Methods TCGA chose cancers for study based on two broad criteria; poor prognosis/overall public health impact and availability of human tumor and matched normal tissue samples that meet TCGA standards. RNA sequencing was performed using a polyA library and the Illumina HiSeq 2000 platform. All RNA sequencing was performed by UNC. Sequence reads for this track were quantified to the hg38/GRCh38 human genome using kallisto assisted by the GENCODE v23 transcriptome definition. Read quantification was performed at UCSC by the Computational Genomics lab, using the Toil pipeline. The resulting kallisto files were combined to generate a transcript per million (tpm) expression matrix using the UCSC tool, kallistoToMatrix. By totaling the TPM values for all transcripts associated to the canonical transcript/gene, a condensed gene per million (gpm) matrix was made. For both matrices average expression values for each tissue were calculated and used to generate a bed6+5 file that is the base of each track. This was done using the UCSC tool, expMatrixToBarchartBed. The bed track was then converted to a bigBed file using the UCSC tool, bedToBigBed. Credits Data shown here are in whole based upon data generated by the TCGA Research Network. John Vivian, Melissa Cline, and Benedict Paten of the UCSC Computational Genomics lab were responsible for the sequence read quantification used to produce this track. Chris Eisenhart and Kate Rosenbloom of the UCSC Genome Browser group were responsible for data file post-processing, track configuration and display type. References J. Vivian et al., Rapid and efficient analysis of 20,000 RNA-seq samples with Toil bioRxiv bioRxiv, vol. 2, p. 62497, 2016. 
tcgaTranscExpr Cancer Transc Expr Transcript-level Expression in 33 TCGA Cancer Tissues (GENCODE v23) Phenotype and Literature Description The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), has generated comprehensive, multi-dimensional maps of the key genomic changes in 33 types of cancer. The TCGA dataset, 2.5 petabytes of data describing tumor tissue and matched normal tissues from more than 11,000 patients, is publically available and has been used widely by the research community. The Cancer Genome Atlas is a NIH-funded project to catalog genetic mutations responsible for cancer. The data shown here is RNA-seq expression data produced by the consortium. For questions or feedback on the data, please contact TCGA. TCGA Gene Expression The gene track shows RNA expression level for each TCGA tissue in GENCODE canonical genes. The gene scores are a total of all transcripts in that gene. TCGA Transcript Expression The transcript track shows RNA expression levels for each TCGA tissue using GENCODE v23 transcripts. Display Conventions In Full and Pack display modes, expression for each genomic item (gene/transcript) is represented by a colored bar chart, where the height of each bar represents the median expression level across all samples for a tissue, and the bar color indicates the tissue. The bar chart display has the same width and tissue order for all genomic items. Mouse hover over a bar will show the tissue and median expression levels. The Squish display mode draws a rectangle for each gene, colored to indicate the tissue with highest expression level if it contributes more than 10% to the overall expression (and colored black if no tissue predominates). In Dense mode, the darkness of the grayscale rectangle displayed for the gene reflects the total median expression level across all tissues. This track was designed to be used in conjunction with the GTEx expression tracks that can act as a control. The color of each cancer was derived by mapping the tissue of origin to the closest GTEx tissue, then taking the GTEx tissue's color. Five cancers did not have a matching GTEx tissue and were assigned a rainbow color scheme; these cancers are Cholangiocarcinoma, Esophageal carcinoma, Head and Neck squamous cell carcinoma, Sarcoma and Uveal Melanoma. The ordering of the cancers is based on the alphabetical ordering of their GTEx tissues. The five cancers that did not match were ordered alphabetically. Methods TCGA chose cancers for study based on two broad criteria; poor prognosis/overall public health impact and availability of human tumor and matched normal tissue samples that meet TCGA standards. RNA sequencing was performed using a polyA library and the Illumina HiSeq 2000 platform. All RNA sequencing was performed by UNC. Sequence reads for this track were quantified to the hg38/GRCh38 human genome using kallisto assisted by the GENCODE v23 transcriptome definition. Read quantification was performed at UCSC by the Computational Genomics lab, using the Toil pipeline. The resulting kallisto files were combined to generate a transcript per million (tpm) expression matrix using the UCSC tool, kallistoToMatrix. By totaling the TPM values for all transcripts associated to the canonical transcript/gene, a condensed gene per million (gpm) matrix was made. For both matrices average expression values for each tissue were calculated and used to generate a bed6+5 file that is the base of each track. This was done using the UCSC tool, expMatrixToBarchartBed. The bed track was then converted to a bigBed file using the UCSC tool, bedToBigBed. Credits Data shown here are in whole based upon data generated by the TCGA Research Network. John Vivian, Melissa Cline, and Benedict Paten of the UCSC Computational Genomics lab were responsible for the sequence read quantification used to produce this track. Chris Eisenhart and Kate Rosenbloom of the UCSC Genome Browser group were responsible for data file post-processing, track configuration and display type. References J. Vivian et al., Rapid and efficient analysis of 20,000 RNA-seq samples with Toil bioRxiv bioRxiv, vol. 2, p. 62497, 2016. 
ccdsGene CCDS Consensus CDS Genes and Gene Predictions Description This track shows human genome high-confidence gene annotations from the Consensus Coding Sequence (CCDS) project. This project is a collaborative effort to identify a core set of human protein-coding regions that are consistently annotated and of high quality. The long-term goal is to support convergence towards a standard set of gene annotations on the human genome. Collaborators include: European Bioinformatics Institute (EBI) National Center for Biotechnology Information (NCBI) University of California, Santa Cruz (UCSC) Wellcome Trust Sanger Institute (WTSI) For more information on the different gene tracks, see our Genes FAQ. Methods CDS annotations of the human genome were obtained from two sources: NCBI RefSeq and a union of the gene annotations from Ensembl and Vega, collectively known as Hinxton. Genes with identical CDS genomic coordinates in both sets become CCDS candidates. The genes undergo a quality evaluation, which must be approved by all collaborators. The following criteria are currently used to assess each gene: an initiating ATG (Exception: a non-ATG translation start codon is annotated if it has sufficient experimental support), a valid stop codon, and no in-frame stop codons (Exception: selenoproteins, which contain a TGA codon that is known to be translated to a selenocysteine instead of functioning as a stop codon) ability to be translated from the genome reference sequence without frameshifts recognizable splicing sites no intersection with putative pseudogene predictions supporting transcripts and protein homology conservation evidence with other species A unique CCDS ID is assigned to the CCDS, which links together all gene annotations with the same CDS. CCDS gene annotations are under continuous review, with periodic updates to this track. Credits This track was produced at UCSC from data downloaded from the CCDS project web site. References Hubbard T, Barker D, Birney E, Cameron G, Chen Y, Clark L, Cox T, Cuff J, Curwen V, Down T et al. The Ensembl genome database project. Nucleic Acids Res. 2002 Jan 1;30(1):38-41. PMID: 11752248; PMC: PMC99161 Pruitt KD, Harrow J, Harte RA, Wallin C, Diekhans M, Maglott DR, Searle S, Farrell CM, Loveland JE, Ruef BJ et al. The consensus coding sequence (CCDS) project: Identifying a common protein-coding gene set for the human and mouse genomes. Genome Res. 2009 Jul;19(7):1316-23. PMID: 19498102; PMC: PMC2704439 Pruitt KD, Tatusova T, Maglott DR. NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2005 Jan 1;33(Database issue):D501-4. PMID: 15608248; PMC: PMC539979 
centromeres Centromeres Centromere Locations Mapping and Sequencing Description Track indicating the location of the centromere sequences. Centromeres are specialized chromatin structures that are required for cell division. These genomic regions are normally defined by long tracts of tandem repeats, or satellite DNA, that contain a limited number of sequence differences to distinguish the linear order of repeat copies. The size and repetitive nature of these regions mean they are typically not represented in reference assemblies. Unlike all previous versions of the human reference assembly, where the centromere regions have been represented by a multi-megabase gap, GRCh38 incorporates centromere reference models that provide an initial genomic description derived from chromosome-assigned whole genome shotgun (WGS) read libraries of alpha satellite. Each reference model provides an approximation of the true array sequence organization. Although the long-range repeat ordering is not expected to represent the true organization, the submissions are expected to provide a biologically rich description of array variants and local-monomer organization as observed in the initial WGS read dataset. As a result, these sequences serve as a useful mapping target to extend sequence-based studies to sites previously omitted from the human reference genome. Methods The sequences are generated based on second-order Markov models of monomer variants, and graphical models of larger scale higher order repeats. The graphical models are based on an analysis of Sanger reads from the HuRef sequencing project (Assembly GCA_000002125.1; BioProject PRJNA19621), and their local-ordering is supported by observed same-read monomer adjacencies. The Markov models are generated by the program linearSat, which was written for this project and that also generates a linear representation of monomer order. The software linearSat generates a second-order Markov chain to the size of a given array provided by sequence coverage normalization estimates. The sequence definitions of transposable element insertions are limited to the sequences directly adjacent to alpha satellite within the read database, and incomplete representations are noted with an adjacent 100 bp gap. In total, these sequences provide a more complete reference of sequence composition and higher order repeat variation inherent to a given alpha satellite array, used to assemble centromeric regions of the human chromosomes. Credits The data for this track was supplied by Karen Miga. References Miga KH, Newton Y, Jain M, Altemose N, Willard HF, Kent WJ. Centromere reference models for human chromosomes X and Y satellite arrays. Genome Res. 2014 Apr;24(4):697-707. PMID: 24501022; PMC: PMC3975068 
chm13LiftOver CHM13 alignments CHM13 (GCA_009914755.4) v1_nfLO liftOver alignments Comparative Genomics Description These tracks show the one-to-one v1_nfLO alignments of the GRCh38/hg38 to the T2T-CHM13 v2.0 assembly. Display Conventions The track displays boxes joined together by either single or double lines, with the boxes represent aligning regions, single lines indicating gaps that are largely due to a deletion in the CHM13 v2.0 assembly or an insertion in the GRCh38/hg38, and double lines representing more complex gaps that involve substantial sequence in both assembly. Methods GRCh38/hg38 pre-processing To prevent ambiguous alignments, all false duplications, as determined by the Genome in a Bottle Consortium (GCA_000001405.15_GRCh38_GRC_exclusions_T2Tv2.bed), as well as the GRCh38 modeled centromeres, were masked from the GRCh38/hg38 primary assembly. In addition, unlocalized and unplaced (random) contigs were removed. Alignment and Chain Creation For the minimap2-based pipeline, the initial chain file was generated using nf-LO v1.5.1 with minimap2 v2.24 alignments. These chains were then split at all locations that contained unaligned segments greater than 1kbp or gaps greater than 10kbp. Split chain files were then converted to PAF format with extended CIGAR strings using chaintools (v0.1), and alignments between nonhomologous chromosomes were removed. The trim-paf operation of rustybam (v0.1.29) was next used to remove overlapping alignments in the query sequence, and then the target sequence, to create 1:1 alignments. PAF alignments were converted back to the chain format with paf2chain commit f68eeca, and finally, chaintools was used to generate the inverted chain file. Full commands with parameters used were: nextflow run main.nf --source GRCh38.fa --target chm13v2.0.fasta --outdir dir -profile local --aligner minimap2 python chaintools/src/split.py -c input.chain -o input-split.chain python chaintools/src/to_paf.py -c input-split.chain -t target.fa -q query.fa -o input-split.paf awk '$1==$6' input-split.paf | rb break-paf --max-size 10000 | rb trim-paf -r | rb invert | rb trim-paf -r | rb invert > out.paf paf2chain -i out.paf > out.chain python chaintools/src/invert.py -c out.chain -o out_inverted.chain The above process does not add chain ids or scores. The UCSC utilities chainMergeSort and chainScore are used to update the chains: chainMergeSort out.chain | chainScore stdin chm13v2.0.2bit hg38.2bit chm13v2.0-hg38.chain chainMergeSort out_inverted.chain | chainScore stdin hg38.2bit chm13v2.0.2bit hg38-chm13v2.0.chain Rustybam trim-paf uses dynamic programming and the CIGAR string to find an optimal splitting point between overlapping alignments in the query sequence. It starts its trimming with the largest overlap and then recursively trims smaller overlaps. Results were validated by using chaintools to confirm that there were no overlapping sequences with respect to both CHM13v2.0 and GRCh38 in the released chain file. In addition, trimmed alignments were visually inspected with SafFire to confirm their quality. Chains were swapped to make GRCh38/hg38 the target. Credits The v1_nflo chains were generated by Nae-Chyun Chen&lt;naechyun.chen@gmail.com&gt; and Mitchell Vollger&lt;mvollger@uw.edu&gt; References Nurk S, Koren S, Rhie A, Rautiainen M, et al. The complete sequence of a human genome. bioRxiv, 2021. 
cytoBand Chromosome Band Chromosome Bands Localized by FISH Mapping Clones Mapping and Sequencing Description The chromosome band track represents the approximate location of bands seen on Giemsa-stained chromosomes. Chromosomes are displayed in the browser with the short arm first. Cytologically identified bands on the chromosome are numbered outward from the centromere on the short (p) and long (q) arms. At low resolution, bands are classified using the nomenclature [chromosome][arm][band], where band is a single digit. Examples of bands on chromosome 3 include 3p2, 3p1, cen, 3q1, and 3q2. At a finer resolution, some of the bands are subdivided into sub-bands, adding a second digit to the band number, e.g. 3p26. This resolution produces about 500 bands. A final subdivision into a total of 862 sub-bands is made by adding a period and another digit to the band, resulting in 3p26.3, 3p26.2, etc. Methods Chromosome band information was downloaded from NCBI using the ideogram.gz file for the respective assembly. These data were then transformed into our visualization format. See our assembly creation documentation for the organism of interest to see the specific steps taken to transform these data. Band lengths are typically estimated based on FISH or other molecular markers interpreted via microscopy. For some of our older assemblies, greater than 10 years old, the tracks were created as detailed below and in Furey and Haussler, 2003. Barbara Trask, Vivian Cheung, Norma Nowak and others in the BAC Resource Consortium used fluorescent in-situ hybridization (FISH) to determine a cytogenetic location for large genomic clones on the chromosomes. The results from these experiments are the primary source of information used in estimating the chromosome band locations. For more information about the process, see the paper, Cheung, et al., 2001. and the accompanying web site, Human BAC Resource. BAC clone placements in the human sequence are determined at UCSC using a combination of full BAC clone sequence, BAC end sequence, and STS marker information. Credits We would like to thank all the labs that have contributed to this resource: Fred Hutchinson Cancer Research Center (FHCRC) National Cancer Institute (NCI) Roswell Park Cancer Institute (RPCI) The Wellcome Trust Sanger Institute (SC) Cedars-Sinai Medical Center (CSMC) Los Alamos National Laboratory (LANL) UC San Francisco Cancer Center (UCSF) References Cheung VG, Nowak N, Jang W, Kirsch IR, Zhao S, Chen XN, Furey TS, Kim UJ, Kuo WL, Olivier M et al. Integration of cytogenetic landmarks into the draft sequence of the human genome. Nature. 2001 Feb 15;409(6822):953-8. PMID: 11237021 Furey TS, Haussler D. Integration of the cytogenetic map with the draft human genome sequence. Hum Mol Genet. 2003 May 1;12(9):1037-44. PMID: 12700172 
cytoBandIdeo Chromosome Band (Ideogram) Chromosome Bands Localized by FISH Mapping Clones (for Ideogram) Mapping and Sequencing 
clinGenComp ClinGen ClinGen curation activities (Dosage Sensitivity and Gene-Disease Validity) Phenotype and Literature Description NOTE: These data are for research purposes only. While the ClinGen data are open to the public, users seeking information about a personal medical or genetic condition are urged to consult with a qualified physician for diagnosis and for answers to personal medical questions. UCSC presents these data for use by qualified professionals, and even such professionals should use caution in interpreting the significance of information found here. No single data point should be taken at face value and such data should always be used in conjunction with as much corroborating data as possible. No treatment protocols should be developed or patient advice given on the basis of these data without careful consideration of all possible sources of information. No attempt to identify individual patients should be undertaken. No one is authorized to attempt to identify patients by any means. The Clinical Genome Resource (ClinGen) tracks display data generated from several key curation activities related to gene-disease validity, dosage sensitivity, and variant pathogenicity. ClinGen is a National Institute of Health (NIH)-funded initiative dedicated to identifying clinically relevant genes and variants for use in precision medicine and research. This is accomplished by harnessing the data from both research efforts and clinical genetic testing and using it to propel expert and machine-driven curation activities. ClinGen works closely with the National Center for Biotechnology Information (NCBI) of the National Library of Medicine (NLM) which distributes part of this information through its ClinVar database. The available data tracks are: ClinGen Dosage Sensitivity Map -Haploinsufficiency (ClinGen Haploinsufficiency) and -Triplosensitivity (ClinGen Triplosensitivity) - Shows evidence supporting or refuting haploinsufficiency (loss) and triplosensitivity (gain) as mechanisms for disease at gene-level and larger genomic regions. ClinGen Gene-Disease Validity Classification (ClinGen Validity) - Provides a semi-qualitative measurement for the strength of evidence of a gene-disease relationship. Clingen CSPEC variant interpretation VCEP specifications - Identifies loci that have ClinGen criteria Specification (CSpec) information. This is used and applied by ClinGen Variant Curation Expert Panels (VCEPs) and biocurators in the classification of variants. A rating system is used to classify the evidence supporting or refuting dosage sensitivity for individual genes and regions, which takes in consideration the following criteria: number of causative variants reported, patterns of inheritance, consistency of phenotype, evidence from large-scale case-control studies, mutational mechanisms, data from public genome variation databases, and expert consensus opinion. The system is intended to be of a &quot;dynamic nature&quot;, with regions being reevaluated periodically to incorporate emerging evidence. The evidence collected is displayed within a publicly available database. Evidence that haploinsufficiency or triplosensitivity of a gene is associated with a specific phenotype will aid in the interpretive assessment of CNVs including that gene or genomic region. Similarly, a qualitative classification system is used to correlate the evidence of a gene-disease relationship: &quot;Definitive&quot;, &quot;Strong&quot;, &quot;Moderate&quot;, &quot;Limited&quot;, &quot;Animal Model Only&quot;, &quot;No Known Disease Relationship&quot;, &quot;Disputed&quot;, or &quot;Refuted&quot;. Display Conventions Haploinsufficiency/Triplosensitivity tracks Items are shaded according to dosage sensitivity type, red for haploinsufficiency score 3, blue for triplosensitivity score 3, and grey for other evidence scores or not yet evaluated). Mouseover on items shows the supporting evidence of dosage sensitivity. Tracks can be filtered according to the supporting evidence of dosage sensitivity. Dosage Scores are used to classify the evidence of the supporting dosage sensitivity map: 0 - no evidence available 1 - little evidence for dosage pathogenicity 2 - some evidence for dosage pathogenicity 3 - sufficient evidence for dosage pathogenicity 30 - gene associated with autosomal recessive phenotype 40 - dosage sensitivity unlikely For more information on the use of the scores see the ClinGen FAQs. Gene-Disease Validity track The gene-disease validity classifications are labeled with the disease entity and hovering over the features shows the associated gene. Items are color coded based on the strength of their classification as provided below: Color Classifications Definitive: The role of this gene in this particular disease has been repeatedly demonstrated and has been upheld over time Strong: The role of this gene in disease has been independently demonstrated typically in at least two separate studies, including both strong variant-level evidence in unrelated probands and compelling gene-level evidence from experimental data Moderate: There is moderate evidence to support a causal role for this gene in this disease, typically including both several probands with variants and moderate experimental data supporting the gene-disease assertion Limited: There is limited evidence to support a causal role for this gene in this disease, such as few probands with variants and limited experimental data supporting the gene-disease assertion Animal Model Only: There are no published human probands with variants but there is animal model data supporting the gene-disease assertion No Known Disease Relationship: Evidence for a causal role in disease has not been reported Disputed: Conflicting evidence disputing a role for this gene in this disease has arisen since the initial report identifying an association between the gene and disease Refuted: Evidence refuting the role of the gene in the specified disease has been reported and significantly outweighs any evidence supporting the role The version of the ClinGen Standard Operating Procedures (SOPs) that each gene-disease classification was performed with is provided as well. An older or newer SOP version does not necessarily mean the classification is any more or less valid but is provided for clarity. Each details page also contains a direct link to an evidence summary detailing the rationale behind the specific classification and information such as a breakdown of the semi-qualitative framework, relevant PubMed IDs, the type of data (Genetic vs Experimental Evidence), and a detailed summary. These tracks are multi-view composite tracks that contain multiple data types (views). Each view within a track has separate display controls, as described here. ClinGen VCEP Specifications track Item names correspond to the VCEP loci, usually the gene symbol. Mouseovers display the disease with a link to the CSpec, the VCEP panel with a link to the ClinGen VCEP page, and the current expert panel status. Data Updates Our programs check every day if ClinGen has an updated data file, and if so, update the track with the latest file. Click the "Data Format" on this track documentation page to see when the track was last updated. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Data is also freely available on the ClinGen website (gene-disease curation methods) and FTP (dosage curations). Credits Thank you to ClinGen and NCBI, especially Erin Rooney Riggs, Christa Lese Martin, Tristan Nelson, May Flowers, Scott Goehringer, and Phillip Weller for technical coordination and consultation, and to Christopher Lee, Luis Nassar, and Anna Benet-Pages of the Genome Browser team. References Rehm HL, Berg JS, Brooks LD, Bustamante CD, Evans JP, Landrum MJ, Ledbetter DH, Maglott DR, Martin CL, Nussbaum RL et al. ClinGen--the Clinical Genome Resource. N Engl J Med. 2015 Jun 4;372(23):2235-42. PMID: 26014595; PMC: PMC4474187 Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW, Hegde M, Lyon E, Spector E et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015 May;17(5):405-24. PMID: 25741868; PMC: PMC4544753 Riggs ER, Church DM, Hanson K, Horner VL, Kaminsky EB, Kuhn RM, Wain KE, Williams ES, Aradhya S, Kearney HM et al. Towards an evidence-based process for the clinical interpretation of copy number variation. Clin Genet. 2012 May;81(5):403-12. PMID: 22097934; PMC: PMC5008023 Strande NT, Riggs ER, Buchanan AH, Ceyhan-Birsoy O, DiStefano M, Dwight SS, Goldstein J, Ghosh R, Seifert BA, Sneddon TP et al. Evaluating the Clinical Validity of Gene-Disease Associations: An Evidence-Based Framework Developed by the Clinical Genome Resource. Am J Hum Genet. 2017 Jun 1;100(6):895-906. PMID: 28552198; PMC: PMC5473734 
clinGenCspec ClinGen VCEP Specifications Clingen CSpec Variant Interpretation VCEP Specifications Phenotype and Literature 
clinGenGeneDisease ClinGen Validity ClinGen Gene-Disease Validity Classification Phenotype and Literature 
clinGenTriplo ClinGen Triplosensitivity ClinGen Dosage Sensitivity Map - Triplosensitivity Phenotype and Literature 
clinGenHaplo ClinGen Haploinsufficiency ClinGen Dosage Sensitivity Map - Haploinsufficiency Phenotype and Literature 
iscaComposite ClinGen CNVs Clinical Genome Resource (ClinGen) CNVs Phenotype and Literature The ClinGen CNVs track is no longer being updated. These data, along with updates, can be found in the ClinVar Copy Number Variants (ClinVar CNVs) track. See our news archive for more information. Description NOTE: These data are for research purposes only. While the ClinGen data are open to the public, users seeking information about a personal medical or genetic condition are urged to consult with a qualified physician for diagnosis and for answers to personal medical questions. UCSC presents these data for use by qualified professionals, and even such professionals should use caution in interpreting the significance of information found here. No single data point should be taken at face value and such data should always be used in conjunction with as much corroborating data as possible. No treatment protocols should be developed or patient advice given on the basis of these data without careful consideration of all possible sources of information. No attempt to identify individual patients should be undertaken. No one is authorized to attempt to identify patients by any means. The Clinical Genome Resource (ClinGen) is a National Institutes of Health (NIH)-funded program dedicated to building a genomic knowledge base to improve patient care. This will be accomplished by harnessing the data from both research efforts and clinical genetic testing, and using it to propel expert and machine-driven curation activities. By facilitating collaboration within the genomics community, we will all better understand the relationship between genomic variation and human health. ClinGen will work closely with the National Center for Biotechnology Information (NCBI) of the National Library of Medicine (NLM), which will distribute this information through its ClinVar database. The ClinGen dataset displays clinical microarray data submitted to dbGaP/dbVar at NCBI by ClinGen member laboratories (dbVar study nstd37), as well as clinical data reported in Kaminsky et al., 2011 (dbVar study ntsd101) (see reference below). This track shows copy number variants (CNVs) found in patients referred for genetic testing for indications such as intellectual disability, developmental delay, autism and congenital anomalies. Additionally, the ClinGen &quot;Curated Pathogenic&quot; and &quot;Curated Benign&quot; tracks represent genes/genomic regions reviewed for dosage sensitivity in an evidence-based manner by the ClinGen Structural Variation Working Group (dbVar study nstd45). The CNVs in this study have been reviewed for their clinical significance by the submitting ClinGen laboratory. Some of the deletions and duplications in the track have been reported as causative for a phenotype by the submitting clinical laboratory; this information was based on current knowledge at the time of submission. However, it should be noted that phenotype information is often vague and imprecise and should be used with caution. While all samples were submitted because of a phenotype in a patient, only 15% of patients had variants determined to be causal, and most patients will have additional variants that are not causal. CNVs are separated into subtracks and are labeled as: Pathogenic Uncertain: Likely Pathogenic Uncertain Uncertain: Likely Benign Benign The user should be aware that some of the data were submitted using a 3-class system, with the two &quot;Likely&quot; categories omitted. Two subtracks, &quot;Path Gain&quot; and &quot;Path Loss&quot;, are aggregate tracks showing graphically the accumulated level of gains and losses in the Pathogenic subtrack across the genome. Similarly, &quot;Benign Gain&quot; and &quot;Benign Loss&quot; show the accumulated level of gains and losses in the Benign subtrack. These tracks are collectively called &quot;Coverage&quot; tracks. Many samples have multiple variants, not all of which are causative of the phenotype. The CNVs in these samples have been decoupled, so it is not possible to connect multiple imbalances as coming from a single patient. It is therefore not possible to identify individuals via their genotype. Methods and Color Convention The samples were analyzed by arrays from patients referred for cytogenetic testing due to clinical phenotypes. Samples were analyzed with a probe spacing of 20-75 kb. The minimum CNV breakpoints are shown; if available, the maximum CNV breakpoints are provided in the details page, but are not shown graphically on the Browser image. Data were submitted to dbGaP at NCBI and thence decoupled as described into dbVar for unrestricted release. The entries are colored red for loss and blue for gain. The names of items use the ClinVar convention of appending "_inheritance" indicating the mechanism of inheritance, if known: "_pat, _mat, _dnovo, _unk" as paternal, maternal, de novo and unknown, respectively. Verification Most data were validated by the submitting laboratory using various methods, including FISH, G-banded karyotype, MLPA and qPCR. Credits Thank you to ClinGen and NCBI for technical coordination and consultation, and to the UCSC Genome Browser staff for engineering the track display. References Miller DT, Adam MP, Aradhya S, Biesecker LG, Brothman AR, Carter NP, Church DM, Crolla JA, Eichler EE, Epstein CJ et al. Consensus statement: chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies. Am J Hum Genet. 2010 May 14;86(5):749-64. PMID: 20466091; PMC: PMC2869000 Kaminsky EB, Kaul V, Paschall J, Church DM, Bunke B, Kunig D, Moreno-De-Luca D, Moreno-De-Luca A, Mulle JG, Warren ST et al. An evidence-based approach to establish the functional and clinical significance of copy number variants in intellectual and developmental disabilities. Genet Med. 2011 Sep;13(9):777-84. PMID: 21844811; PMC: PMC3661946 
iscaViewTotal Coverage (Graphical) Clinical Genome Resource (ClinGen) CNVs Phenotype and Literature 
iscaPathLossCum Path Loss ClinGen CNVs: Pathogenic Loss Coverage Phenotype and Literature 
iscaPathGainCum Path Gain ClinGen CNVs: Pathogenic Gain Coverage Phenotype and Literature 
iscaBenignLossCum Benign Loss ClinGen CNVs: Benign Loss Coverage Phenotype and Literature 
iscaBenignGainCum Benign Gain ClinGen CNVs: Benign Gain Coverage Phenotype and Literature 
iscaViewDetail CNVs Clinical Genome Resource (ClinGen) CNVs Phenotype and Literature 
iscaUncertain Uncertain ClinGen CNVs: Uncertain Phenotype and Literature 
iscaPathogenic Pathogenic ClinGen CNVs: Pathogenic Phenotype and Literature 
iscaCuratedPathogenic Curated Path ClinGen CNVs: Curated Pathogenic Phenotype and Literature 
iscaLikelyPathogenic Uncert Path ClinGen CNVs: Uncertain: Likely Pathogenic Phenotype and Literature 
iscaLikelyBenign Uncert Ben ClinGen CNVs: Uncertain: Likely Benign Phenotype and Literature 
iscaBenign Benign ClinGen CNVs: Benign Phenotype and Literature 
iscaCuratedBenign Curated Ben ClinGen CNVs: Curated Benign Phenotype and Literature 
clinvar ClinVar Variants ClinVar Variants Phenotype and Literature Description NOTE: ClinVar is intended for use primarily by physicians and other professionals concerned with genetic disorders, by genetics researchers, and by advanced students in science and medicine. While the ClinVar database is open to all academic users, users seeking information about a personal medical or genetic condition are urged to consult with a qualified physician for diagnosis and for answers to personal questions. These tracks show the genomic positions of variants in the ClinVar database. ClinVar is a free, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence. The ClinVar SNVs track displays substitutions and indels shorter than 50 bp and the ClinVar CNVs track displays copy number variants (CNVs) equal or larger than 50 bp. Until October 2017, all variants with the ClinVar types copy number gain/loss and DbVar "nsv" accessions were assigned in the CNV category. Because the ClinVar type no longer captures this information, any variation equal to or larger than 50 bp is now considered a CNV. The ClinVar Interpretations track displays the genomic positions of individual variant submissions and interpretations of the clinical significance and their relationship to disease in the ClinVar database. Note: The data in the track are obtained directly from ClinVar's FTP site. We display the data obtained from ClinVar as-is to avoid discrepancies between UCSC and NCBI. However, be aware that the ClinVar conventions are different from the VCF standard. Variants may be right-aligned or may contain additional context, e.g. for inserts. ExAC/gnomAD make available a converter to make ClinVar more comparable to VCF files. Display Conventions and Configuration Items can be filtered according to the size of the variant, variant type, clinical significance, allele origin, and molecular consequence, using the track Configure options. Each subtrack has separate display controls, as described here. Mouseover on the genomic locations of ClinVar variants shows variant details, clinical interpretation, and associated conditions. Further information on each variant is displayed on the details page by a click onto any variant. ClinVar is an archive for assertions of clinical significance made by the submitters. The level of review supporting the assertion of clinical significance for the variation is reported as the review status. Stars (0 to 4) provide a graphical representation of the aggregate review status. Entries in the ClinVar CNVs track are colored by type of variant, among others: red for loss blue for gain purple for inversion orange for insertion A light-to-dark color gradient indicates the clinical significance of each variant, with the lightest shade being benign, to the darkest shade being pathogenic. Detailed information on the CNV color code is described here. Entries in the ClinVar SNVs and ClinVar Interpretations tracks are colored by clinical significance: red for pathogenic dark blue for variant of uncertain significance green for benign dark grey for not provided light blue for conflicting The variants in the ClinVar Interpretations track are sorted by the variant classification of each submission: P: Pathogenic LP: Likely Pathogenic VUS: Variant of Unknown Significance LB: Likely Benign B: Benign OTH: Others The size of the bead represents the number of submissions at that genomic position. For track display clarity, these submission numbers are binned into three categories: Small-sized beads: 1-2 submissions Medium-sized beads: 3-7 submissions Large-sized beads: 8 or more submissions Hovering on the track items shows the genomic variations which start at that position and the number of individual submissions with that classification. The details page lists all rated submissions from ClinVar, with specific details to the interpretation of the clinical or functional significance of each variant in relation to a condition. Interpretation is at variant-level, not at case (or patient-specific) level. More information about using and understanding the ClinVar data can be found here. For the human genome version hg19: the hg19 genome released by UCSC in 2009 had a mitochondrial genome "chrM" that was not the same as the one later used for most databases like ClinVar. As a result, we added the official mitochondrial genome in 2020 as "chrMT" and all mitochondrial annotations of ClinVar and most other databases are shown on the mitochondrial genome called "chrMT". For full description of the issue of the mitochondrial genome in hg19, please see the README file on our download site. Data updates ClinVar publishes a new release on the first Thursday every month. This track is then updated automatically at most six days later. The exact date of our last update is shown when you click onto any variant. You can find the previous versions of the track organized by month on our downloads server in the archive directory. To display one of these previous versions, paste the URL to one of the older files into the custom track text input field under "My Data &gt; Custom Tracks". Data access The raw data can be explored interactively with the Table Browser or the Data Integrator. The data can be accessed from scripts through our API, the track names are "clinVarMain and "clinVarCnv". For automated download and analysis, the genome annotation is stored in a bigBed file that can be downloaded from our download server. The files for this track are called clinVarMain.bb and clinVarCnv.bb. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg19/bbi/clinvar/clinvarMain.bb -chrom=chr21 -start=0 -end=100000000 stdout Methods ClinVar files were reformatted at UCSC to the bigBed format. The data is updated every month, one week after the ClinVar release date. The program that performs the update is available on Github. Credits Thanks to NCBI for making the ClinVar data available on their FTP site as a tab-separated file. References Landrum MJ, Lee JM, Benson M, Brown G, Chao C, Chitipiralla S, Gu B, Hart J, Hoffman D, Hoover J et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 2016 Jan 4;44(D1):D862-8. PMID: 26582918; PMC: PMC4702865 Azzariti DR, Riggs ER, Niehaus A, Rodriguez LL, Ramos EM, Kattman B, Landrum MJ, Martin CL, Rehm HL. Points to consider for sharing variant-level information from clinical genetic testing with ClinVar. Cold Spring Harb Mol Case Stud. 2018 Feb;4(1). PMID: 29437798; PMC: PMC5793773 
clinvarSubLolly ClinVar interp ClinVar SNVs submitted interpretations and evidence Phenotype and Literature 
clinvarCnv ClinVar CNVs ClinVar Copy Number Variants >= 50bp Phenotype and Literature 
clinvarMain ClinVar SNVs ClinVar Short Variants < 50bp Phenotype and Literature 
cloneEndSuper Clone Ends Mapping of clone libraries end placements Mapping and Sequencing Description This track shows the NCBI clone end mappings from the NCBI Clone DB database. Libraries with more than 30,000 clones are included in this track display. Bacterial artificial chromosomes (BACs) are a key part of many large-scale sequencing projects. A BAC typically consists of 50 - 300 kb of DNA. During the early phase of a sequencing project, it is common to sequence a single read (approximately 500 bases) off each end of a large number of BACs. Later on in the project, these BAC end reads can be mapped to the genome sequence. These BAC end pairs can be useful for validating the assembly over relatively long ranges. In some cases, the BACs are useful biological reagents. This track can also be used for determining which BAC contains a given gene, useful information for certain wet lab experiments. The scoring scheme used for this annotation assigns 1000 to an alignment when the BAC end pair aligns to only one location in the genome (after filtering). When a BAC end pair or clone aligns to multiple locations, the score is calculated as 1500/(number of alignments). Display Conventions and Configuration Items in this track are colored according to their strand orientation. Blue indicates alignment to the forward strand, and green indicates alignment to the negative strand. Methods The mappings of these BAC end sequences are taken directly from the NCBI Clone DB FTP site ftp.ncbi.nih.gov/repository/clone/reports/Homo_sapiens/ *.GCF_000001405.26.106.*.gff files. UCSC filtered the NCBI Clone DB mapped ends to drop ends that mapped to a region that was three times longer than the median size of the clones in the library. Only libraries with more than 30,000 clones are included in this track display. Click through on displayed items to the Clone DB database information, including Clone DB distributor references. clone information from NCBI Clone DB and UCSC mapping statistics libraryname totalclones total&nbsp;endsequences NCBI&nbsp;mappedends UCSC&nbsp;filteredends UCSCdropped per-centdropped ABC82,007,0473,888,4761,205,4661,192,78412,682% 1.05 WI21,122,5642,298,885589,547582,8436,704% 1.14 ABC121,120,9392,169,280778,216771,8276,3890.82 ABC71,116,9662,152,975650,329644,0716,2580.96 ABC91,065,5032,084,892757,644750,6486,9960.92 ABC101,062,0822,121,489788,344781,3317,0130.89 ABC141,042,9292,089,193846,055839,1266,9290.82 ABC131,009,6432,057,345811,829803,5898,2401.01 ABC11998,8801,966,644730,565724,8645,7010.78 ABC23942,1331,535,766437,098433,8963,2020.73 ABC16907,9481,534,288452,316449,1013,2150.71 ABC24835,6001,383,475399,056395,7763,2800.82 ABC27768,3361,229,804334,232331,8222,4100.72 ABC18743,6401,204,811325,150322,9042,2460.69 COR2A723,5691,441,881583,327578,5784,7490.81 ABC22519,274780,151189,988188,7431,2450.66 ABC21436,930680,160182,214180,9731,2410.68 RP11292,975394,81386,87585,9039721.12 COR02272,396546,984208,377206,7821,5950.77 CTD226,848403,68896,59494,9411,6531.71 CH17176,209325,659105,805105,0607450.70 ABC2049,13280,35024,72024,4742461.00 UCSCdropped152,979n/an/an/an/an/a multiplemappings775,629n/an/an/an/an/a Credits Additional information about the clone, including how it can be obtained, may be found at the NCBI Clone Registry. To view the registry entry for a specific clone, open the details page for the clone and click on its name at the top of the page. 
cloneEndWI2 WI2 WIBR-2 Fosmid library Mapping and Sequencing 
cloneEndRP11 RP11 RPCI BAC library 11 Mapping and Sequencing 
cloneEndmultipleMaps Multiple mappings Clone end placements that map to multiple locations in the genome Mapping and Sequencing 
cloneEndcoverageReverse Coverage reverse Clone end placements overlap coverage on the reverse strand Mapping and Sequencing 
cloneEndcoverageForward Coverage forward Clone end placements overlap coverage on the forward strand Mapping and Sequencing 
cloneEndbadEnds Bad end mappings Clone end placements dropped at UCSC, map distance 3X median library size Mapping and Sequencing 
cloneEndCOR2A COR2A NHGRI-CORIELLE CORIELL-02A-F-39-40KB Mapping and Sequencing 
cloneEndCOR02 COR02 NHGRI-CORIELLE CORIELL-02-F-39-40KB Mapping and Sequencing 
cloneEndCH17 CH17 CHORI BAC hydatidiform mole Mapping and Sequencing 
cloneEndCTD CTD CalTech BAC library D Mapping and Sequencing 
cloneEndABC9 ABC9 Agencourt fosmid library 9 Mapping and Sequencing 
cloneEndABC8 ABC8 Agencourt fosmid library 8 Mapping and Sequencing 
cloneEndABC7 ABC7 Agencourt fosmid library 7 Mapping and Sequencing 
cloneEndABC27 ABC27 Agencourt fosmid library 27 Mapping and Sequencing 
cloneEndABC24 ABC24 Agencourt fosmid library 24 Mapping and Sequencing 
cloneEndABC23 ABC23 Agencourt fosmid library 23 Mapping and Sequencing 
cloneEndABC22 ABC22 Agencourt fosmid library 22 Mapping and Sequencing 
cloneEndABC21 ABC21 Agencourt fosmid library 21 Mapping and Sequencing 
cloneEndABC20 ABC20 Agencourt fosmid library 20 Mapping and Sequencing 
cloneEndABC18 ABC18 Agencourt fosmid library 18 Mapping and Sequencing 
cloneEndABC16 ABC16 Agencourt fosmid library 16 Mapping and Sequencing 
cloneEndABC14 ABC14 Agencourt fosmid library 14 Mapping and Sequencing 
cloneEndABC13 ABC13 Agencourt fosmid library 13 Mapping and Sequencing 
cloneEndABC12 ABC12 Agencourt fosmid library 12 Mapping and Sequencing 
cloneEndABC11 ABC11 Agencourt fosmid library 11 Mapping and Sequencing 
cloneEndABC10 ABC10 Agencourt fosmid library 10 Mapping and Sequencing 
colonWangCellType Colon Cells Colon cells binned by cell type from Wang et al 2020 Single Cell RNA-seq Description This track shows data from Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. Droplet-based single-cell RNA sequencing (scRNA-seq) was used to survey gene expression profiles of the epithelium in the human ileum, colon, and rectum. A total of 7 cell clusters were identified: enterocytes (EC), goblet cells (G), paneth-like cells (PLC), enteroendocrine cells (EEC), progenitor cells (PRO), transient-amplifying cells (TA) and stem cells (SC). This track collection contains two bar chart tracks of RNA expression in colon cells where cells are grouped by cell type (Colon Cells) or donor (Colon Donor). The default track displayed is Colon Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification epithelial secretory stem cell Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Note that the Colon Donor track is colored by donor for improved clarity. Method Using scRNA-seq, RNA profiles of intestinal epithelial cells were obtained for 4,472 cells from two human colon samples. Tissue samples belonged to a male donor age 54 (Colon-1) and a female donor age 67 (Colon-2) both diagnosed with Adenocarcinoma. The healthy intestinal mucous membranes used for each sample were cut away from the tumor border in surgically removed ascending colon tissue. Additionally, the intestinal tissues were washed in Hank's balanced salt solution (HBSS) to remove mucus, blood cells, and muscle tissue. The sample was enriched for epithelial cells through centrifugation before being dissociated with Tryple to obtain single-cell suspensions. RNA-seq libraries were prepared using 10x Genomics 3' v2 kit and sequenced on an Illumina Hiseq X Ten PE150. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Yalong Wang, Wanlu Song, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Luis Nassar. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Wang Y, Song W, Wang J, Wang T, Xiong X, Qi Z, Fu W, Yang X, Chen YG. Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. J Exp Med. 2020 Feb 3;217(2). PMID: 31753849; PMC: PMC7041720 
colonWang Colon Wang Colon single cell sequencing from Wang et al 2020 Single Cell RNA-seq Description This track shows data from Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. Droplet-based single-cell RNA sequencing (scRNA-seq) was used to survey gene expression profiles of the epithelium in the human ileum, colon, and rectum. A total of 7 cell clusters were identified: enterocytes (EC), goblet cells (G), paneth-like cells (PLC), enteroendocrine cells (EEC), progenitor cells (PRO), transient-amplifying cells (TA) and stem cells (SC). This track collection contains two bar chart tracks of RNA expression in colon cells where cells are grouped by cell type (Colon Cells) or donor (Colon Donor). The default track displayed is Colon Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification epithelial secretory stem cell Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Note that the Colon Donor track is colored by donor for improved clarity. Method Using scRNA-seq, RNA profiles of intestinal epithelial cells were obtained for 4,472 cells from two human colon samples. Tissue samples belonged to a male donor age 54 (Colon-1) and a female donor age 67 (Colon-2) both diagnosed with Adenocarcinoma. The healthy intestinal mucous membranes used for each sample were cut away from the tumor border in surgically removed ascending colon tissue. Additionally, the intestinal tissues were washed in Hank's balanced salt solution (HBSS) to remove mucus, blood cells, and muscle tissue. The sample was enriched for epithelial cells through centrifugation before being dissociated with Tryple to obtain single-cell suspensions. RNA-seq libraries were prepared using 10x Genomics 3' v2 kit and sequenced on an Illumina Hiseq X Ten PE150. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Yalong Wang, Wanlu Song, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Luis Nassar. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Wang Y, Song W, Wang J, Wang T, Xiong X, Qi Z, Fu W, Yang X, Chen YG. Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. J Exp Med. 2020 Feb 3;217(2). PMID: 31753849; PMC: PMC7041720 
colonWangDonor Colon Donor Colon cells binned by organ donor from Wang et al 2020 Single Cell RNA-seq Description This track shows data from Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. Droplet-based single-cell RNA sequencing (scRNA-seq) was used to survey gene expression profiles of the epithelium in the human ileum, colon, and rectum. A total of 7 cell clusters were identified: enterocytes (EC), goblet cells (G), paneth-like cells (PLC), enteroendocrine cells (EEC), progenitor cells (PRO), transient-amplifying cells (TA) and stem cells (SC). This track collection contains two bar chart tracks of RNA expression in colon cells where cells are grouped by cell type (Colon Cells) or donor (Colon Donor). The default track displayed is Colon Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification epithelial secretory stem cell Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Note that the Colon Donor track is colored by donor for improved clarity. Method Using scRNA-seq, RNA profiles of intestinal epithelial cells were obtained for 4,472 cells from two human colon samples. Tissue samples belonged to a male donor age 54 (Colon-1) and a female donor age 67 (Colon-2) both diagnosed with Adenocarcinoma. The healthy intestinal mucous membranes used for each sample were cut away from the tumor border in surgically removed ascending colon tissue. Additionally, the intestinal tissues were washed in Hank's balanced salt solution (HBSS) to remove mucus, blood cells, and muscle tissue. The sample was enriched for epithelial cells through centrifugation before being dissociated with Tryple to obtain single-cell suspensions. RNA-seq libraries were prepared using 10x Genomics 3' v2 kit and sequenced on an Illumina Hiseq X Ten PE150. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Yalong Wang, Wanlu Song, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Luis Nassar. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Wang Y, Song W, Wang J, Wang T, Xiong X, Qi Z, Fu W, Yang X, Chen YG. Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. J Exp Med. 2020 Feb 3;217(2). PMID: 31753849; PMC: PMC7041720 
coriellDelDup Coriell CNVs Coriell Cell Line Copy Number Variants Phenotype and Literature Description The Coriell Cell Line Copy Number Variants track displays copy-number variants (CNVs) in chromosomal aberration and inherited disorder cell lines in the NIGMS Human Genetic Cell Repository. The Repository, sponsored by the National Institute of General Medical Sciences, provides scientists around the world with resources for cell and genetic research. The samples include highly characterized cell lines and high quality DNA. NIGMS Repository samples represent a variety of disease states, chromosomal abnormalities, apparently healthy individuals and many distinct human populations. Approximately 1000 samples from the Chromosomal Aberrations and Heritable Diseases collections of the NIGMS Repository were genotyped on the Affymetrix Genome-Wide Human SNP 6.0 Array and analyzed for CNVs at the Coriell Institute for Medical Research. Genotyping data for many of these samples is available through dbGaP. The genotyped samples represent a diverse set of copy-number variants. The selection was weighted to over-sample commonly manifested types of aberrations. Karyotyping was performed on all NIGMS Repository cell lines that were submitted with reported chromosome abnormalities. When available, the ISCN description of the sample, based on G-banding and FISH analysis, is included in the phenotypic data. Karyotypes for these cells can be viewed in the online Repository catalog. Field definitions for an item description: CN State: Copy Number of the imbalance. Note that all CNVs with a copy number of 2 are colored neutral (black) and occur on the sex chromosomes, where a CN State of 2 should not be interpreted as normal, as it would be on an autosome. Cell Type: Type of cell culture; one of the following: B Lymphocyte, Fibroblast, Amniotic fluid-derived cell line or Chorionic villus-derived cell line. Description (Diagnosis): May be a medical diagnosis, such as "albinism" or a chromosomal phenotype, such as "translocation" or other description. ISCN nomenclature: A description of the chromosomal karyotype in formal ISCN nomenclature. CN State item coloring: CN State 0 == score 0 CN State 1 == score 100 CN State 2 == score 200 CN State 3 == score 300 CN State 4 == score 400 Use the score filter limits on the configuration page to select desired CN States. Credits We thank Dorit Berlin and Zhenya Tang of the NIGMS Human Genetic Cell Repository at the Coriell Institute for Medical Research for these data. References NCBI dbGaP: Genotyping NIGMS Chromosomal Aberration and Inherited Disorder Samples. NIGMS Human Genetic Cell Repository online catalog at the Coriell Institute for Medical Research. 
cortexVelmeshevCellType Cortex Cells Cerebral cortex RNA binned by cell type from Velmeshev et al 2019 Single Cell RNA-seq Description This track displays data from Single-cell genomics identifies cell type-specific molecular changes in autism. Single-nucleus RNA sequencing (snRNA-seq) was performed on post-mortem cortical tissue samples from patients with autism spectrum disorder (ASD) as well as control donors. A total of 17 cell clusters were identified using known cell type markers found in Velmeshev et al., 2019. This track collection contains five bar chart tracks of RNA expression in the human cerebral cortex where cells are grouped by cell type (Cortex Cells), diagnosis (Cortex Diagnosis), donor (Cortex Donor), sample (Cortex Sample), and sex (Cortex Sex). The default track displayed is Cortex Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification neural immune endothelial glia Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Cortex Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Healthy cortical samples were taken from 16 controls (ages 4-22) without neurological disorders and 15 ASD patients (ages 7-21). A total of 41 post-mortem tissue samples were obtained from both the prefrontal cortex (PFC) and anterior cingulate cortex (ACC). When present, subcortical white matter was removed prior to collection from cortical samples containing all layers of cortical grey matter. ASD and control samples were matched for sex and age and processed together to minimize batch effects. Nuclei were isolated from brain tissue using a glass dounce homogenizer in lysis buffer and then filtered twice through a 30 &#181;m cell strainer. Next, samples were processed using 10x Genomics 3' library kit and the resulting single-nucleus libraries were pooled together and sequenced on an Illumina NovaSeq 6000. This process generated 104,559 single-nuclei gene expression profiles in total. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Dmitry Velmeshev and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by by Daniel Schmelter. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Velmeshev D, Schirmer L, Jung D, Haeussler M, Perez Y, Mayer S, Bhaduri A, Goyal N, Rowitch DH, Kriegstein AR. Single-cell genomics identifies cell type-specific molecular changes in autism. Science. 2019 May 17;364(6441):685-689. PMID: 31097668; PMC: PMC7678724 
cortexVelmeshev Cortex Velmeshev Cerebral cortex single cell data from  Velmeshev et al 2019 Single Cell RNA-seq Description This track displays data from Single-cell genomics identifies cell type-specific molecular changes in autism. Single-nucleus RNA sequencing (snRNA-seq) was performed on post-mortem cortical tissue samples from patients with autism spectrum disorder (ASD) as well as control donors. A total of 17 cell clusters were identified using known cell type markers found in Velmeshev et al., 2019. This track collection contains five bar chart tracks of RNA expression in the human cerebral cortex where cells are grouped by cell type (Cortex Cells), diagnosis (Cortex Diagnosis), donor (Cortex Donor), sample (Cortex Sample), and sex (Cortex Sex). The default track displayed is Cortex Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification neural immune endothelial glia Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Cortex Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Healthy cortical samples were taken from 16 controls (ages 4-22) without neurological disorders and 15 ASD patients (ages 7-21). A total of 41 post-mortem tissue samples were obtained from both the prefrontal cortex (PFC) and anterior cingulate cortex (ACC). When present, subcortical white matter was removed prior to collection from cortical samples containing all layers of cortical grey matter. ASD and control samples were matched for sex and age and processed together to minimize batch effects. Nuclei were isolated from brain tissue using a glass dounce homogenizer in lysis buffer and then filtered twice through a 30 &#181;m cell strainer. Next, samples were processed using 10x Genomics 3' library kit and the resulting single-nucleus libraries were pooled together and sequenced on an Illumina NovaSeq 6000. This process generated 104,559 single-nuclei gene expression profiles in total. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Dmitry Velmeshev and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by by Daniel Schmelter. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Velmeshev D, Schirmer L, Jung D, Haeussler M, Perez Y, Mayer S, Bhaduri A, Goyal N, Rowitch DH, Kriegstein AR. Single-cell genomics identifies cell type-specific molecular changes in autism. Science. 2019 May 17;364(6441):685-689. PMID: 31097668; PMC: PMC7678724 
cortexVelmeshevDiagnosis Cortex Diagnosis Cerebral cortex RNA binned by ASD/control diagnosis from Velmeshev et al 2019 Single Cell RNA-seq Description This track displays data from Single-cell genomics identifies cell type-specific molecular changes in autism. Single-nucleus RNA sequencing (snRNA-seq) was performed on post-mortem cortical tissue samples from patients with autism spectrum disorder (ASD) as well as control donors. A total of 17 cell clusters were identified using known cell type markers found in Velmeshev et al., 2019. This track collection contains five bar chart tracks of RNA expression in the human cerebral cortex where cells are grouped by cell type (Cortex Cells), diagnosis (Cortex Diagnosis), donor (Cortex Donor), sample (Cortex Sample), and sex (Cortex Sex). The default track displayed is Cortex Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification neural immune endothelial glia Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Cortex Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Healthy cortical samples were taken from 16 controls (ages 4-22) without neurological disorders and 15 ASD patients (ages 7-21). A total of 41 post-mortem tissue samples were obtained from both the prefrontal cortex (PFC) and anterior cingulate cortex (ACC). When present, subcortical white matter was removed prior to collection from cortical samples containing all layers of cortical grey matter. ASD and control samples were matched for sex and age and processed together to minimize batch effects. Nuclei were isolated from brain tissue using a glass dounce homogenizer in lysis buffer and then filtered twice through a 30 &#181;m cell strainer. Next, samples were processed using 10x Genomics 3' library kit and the resulting single-nucleus libraries were pooled together and sequenced on an Illumina NovaSeq 6000. This process generated 104,559 single-nuclei gene expression profiles in total. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Dmitry Velmeshev and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by by Daniel Schmelter. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Velmeshev D, Schirmer L, Jung D, Haeussler M, Perez Y, Mayer S, Bhaduri A, Goyal N, Rowitch DH, Kriegstein AR. Single-cell genomics identifies cell type-specific molecular changes in autism. Science. 2019 May 17;364(6441):685-689. PMID: 31097668; PMC: PMC7678724 
cortexVelmeshevDonor Cortex Donor Cerebral cortex RNA binned by organ donor from Velmeshev et al 2019 Single Cell RNA-seq Description This track displays data from Single-cell genomics identifies cell type-specific molecular changes in autism. Single-nucleus RNA sequencing (snRNA-seq) was performed on post-mortem cortical tissue samples from patients with autism spectrum disorder (ASD) as well as control donors. A total of 17 cell clusters were identified using known cell type markers found in Velmeshev et al., 2019. This track collection contains five bar chart tracks of RNA expression in the human cerebral cortex where cells are grouped by cell type (Cortex Cells), diagnosis (Cortex Diagnosis), donor (Cortex Donor), sample (Cortex Sample), and sex (Cortex Sex). The default track displayed is Cortex Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification neural immune endothelial glia Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Cortex Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Healthy cortical samples were taken from 16 controls (ages 4-22) without neurological disorders and 15 ASD patients (ages 7-21). A total of 41 post-mortem tissue samples were obtained from both the prefrontal cortex (PFC) and anterior cingulate cortex (ACC). When present, subcortical white matter was removed prior to collection from cortical samples containing all layers of cortical grey matter. ASD and control samples were matched for sex and age and processed together to minimize batch effects. Nuclei were isolated from brain tissue using a glass dounce homogenizer in lysis buffer and then filtered twice through a 30 &#181;m cell strainer. Next, samples were processed using 10x Genomics 3' library kit and the resulting single-nucleus libraries were pooled together and sequenced on an Illumina NovaSeq 6000. This process generated 104,559 single-nuclei gene expression profiles in total. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Dmitry Velmeshev and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by by Daniel Schmelter. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Velmeshev D, Schirmer L, Jung D, Haeussler M, Perez Y, Mayer S, Bhaduri A, Goyal N, Rowitch DH, Kriegstein AR. Single-cell genomics identifies cell type-specific molecular changes in autism. Science. 2019 May 17;364(6441):685-689. PMID: 31097668; PMC: PMC7678724 
cortexVelmeshevSample Cortex Sample Cerebral cortex RNA binned by biosample from Velmeshev et al 2019 Single Cell RNA-seq Description This track displays data from Single-cell genomics identifies cell type-specific molecular changes in autism. Single-nucleus RNA sequencing (snRNA-seq) was performed on post-mortem cortical tissue samples from patients with autism spectrum disorder (ASD) as well as control donors. A total of 17 cell clusters were identified using known cell type markers found in Velmeshev et al., 2019. This track collection contains five bar chart tracks of RNA expression in the human cerebral cortex where cells are grouped by cell type (Cortex Cells), diagnosis (Cortex Diagnosis), donor (Cortex Donor), sample (Cortex Sample), and sex (Cortex Sex). The default track displayed is Cortex Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification neural immune endothelial glia Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Cortex Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Healthy cortical samples were taken from 16 controls (ages 4-22) without neurological disorders and 15 ASD patients (ages 7-21). A total of 41 post-mortem tissue samples were obtained from both the prefrontal cortex (PFC) and anterior cingulate cortex (ACC). When present, subcortical white matter was removed prior to collection from cortical samples containing all layers of cortical grey matter. ASD and control samples were matched for sex and age and processed together to minimize batch effects. Nuclei were isolated from brain tissue using a glass dounce homogenizer in lysis buffer and then filtered twice through a 30 &#181;m cell strainer. Next, samples were processed using 10x Genomics 3' library kit and the resulting single-nucleus libraries were pooled together and sequenced on an Illumina NovaSeq 6000. This process generated 104,559 single-nuclei gene expression profiles in total. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Dmitry Velmeshev and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by by Daniel Schmelter. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Velmeshev D, Schirmer L, Jung D, Haeussler M, Perez Y, Mayer S, Bhaduri A, Goyal N, Rowitch DH, Kriegstein AR. Single-cell genomics identifies cell type-specific molecular changes in autism. Science. 2019 May 17;364(6441):685-689. PMID: 31097668; PMC: PMC7678724 
cortexVelmeshevSex Cortex Sex Cerebral cortex RNA binned by sex of donor from Velmeshev et al 2019 Single Cell RNA-seq Description This track displays data from Single-cell genomics identifies cell type-specific molecular changes in autism. Single-nucleus RNA sequencing (snRNA-seq) was performed on post-mortem cortical tissue samples from patients with autism spectrum disorder (ASD) as well as control donors. A total of 17 cell clusters were identified using known cell type markers found in Velmeshev et al., 2019. This track collection contains five bar chart tracks of RNA expression in the human cerebral cortex where cells are grouped by cell type (Cortex Cells), diagnosis (Cortex Diagnosis), donor (Cortex Donor), sample (Cortex Sample), and sex (Cortex Sex). The default track displayed is Cortex Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification neural immune endothelial glia Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Cortex Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Healthy cortical samples were taken from 16 controls (ages 4-22) without neurological disorders and 15 ASD patients (ages 7-21). A total of 41 post-mortem tissue samples were obtained from both the prefrontal cortex (PFC) and anterior cingulate cortex (ACC). When present, subcortical white matter was removed prior to collection from cortical samples containing all layers of cortical grey matter. ASD and control samples were matched for sex and age and processed together to minimize batch effects. Nuclei were isolated from brain tissue using a glass dounce homogenizer in lysis buffer and then filtered twice through a 30 &#181;m cell strainer. Next, samples were processed using 10x Genomics 3' library kit and the resulting single-nucleus libraries were pooled together and sequenced on an Illumina NovaSeq 6000. This process generated 104,559 single-nuclei gene expression profiles in total. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Dmitry Velmeshev and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by by Daniel Schmelter. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Velmeshev D, Schirmer L, Jung D, Haeussler M, Perez Y, Mayer S, Bhaduri A, Goyal N, Rowitch DH, Kriegstein AR. Single-cell genomics identifies cell type-specific molecular changes in autism. Science. 2019 May 17;364(6441):685-689. PMID: 31097668; PMC: PMC7678724 
cosmicMuts COSMIC Catalogue of Somatic Mutations in Cancer V98 Phenotype and Literature Description COSMIC, the "Catalogue Of Somatic Mutations In Cancer," is an online database of somatic mutations found in human cancer. Focused exclusively on non-inherited acquired mutations, COSMIC combines information from a range of sources, curating the described relationships between cancer phenotypes and gene (and genomic) mutations. These data are then made available in a number of ways including here in the UCSC genome browser, on the COSMIC website with custom analytical tools, or via the COSMIC sftp server. Publications using COSMIC as a data source may cite our reference below. Methods The data in COSMIC are curated from a number of high-quality sources and combined into a single resource. The sources include: Peer-reviewed journal articles CGP laboratories at the Sanger Institute, UK TCGA data portal The ICGC data portal IARC p53 database Information on known cancer genes, selected from the Cancer Gene Census is curated manually to maximize its descriptive content. UCSC was provided with the COSMIC annotations directly. The columns were reconfigured to match our BED format, and 35 mutations were removed as they had illegal coordinates (start&gt;stop). The resulting file was converted to a bigBed for display using the bedToBigBed utility. Display Dense - Indicate the positions where COSMIC mutations have been annotated in a single horizontal track. Squish - Indicate each mutation, in vertical pileups where appropriate, while minimizing screen space used. Pack - Indicate each mutation with Genomic Mutation ID (COSVnnnnn). Full - Show each mutation in detail, one per line, with Genomic Mutation ID (COSVnnnnn). Clicking into any item also displays the reference allele, alternate allele, and the Cosmic legacy mutation identifier (COSNnnnnn). Outlinks can also be found directly to COSMIC for additional information. Data Access The limited data available to UCSC can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. The complete data can be explored and downloaded via the COSMIC website. Contacts For further information on COSMIC, or for help with the information provided, please contact &#99;&#111;sm&#105;&#99;&#64;&#115;a&#110;g&#101;&#114;. &#97;&#99;. &#117;&#107;. References Forbes SA, Beare D, Boutselakis H, Bamford S, Bindal N, Tate J, Cole CG, Ward S, Dawson E, Ponting L et al. COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res. 2017 Jan 4;45(D1):D777-D783. PMID: 27899578; PMC: PMC5210583 
cosmicRegions COSMIC Regions Catalogue of Somatic Mutations in Cancer V82 Phenotype and Literature Description COSMIC, the "Catalogue Of Somatic Mutations In Cancer," is an online database of somatic mutations found in human cancer. Focused exclusively on non-inherited acquired mutations, COSMIC combines information from a range of sources, curating the described relationships between cancer phenotypes and gene (and genomic) mutations. These data are then made available in a number of ways including here in the UCSC genome browser, on the COSMIC website with custom analytical tools, or via the COSMIC sftp server. Publications using COSMIC as a data source may cite our reference below. Methods The data in COSMIC are curated from a number of high-quality sources and combined into a single resource. The sources include: Peer-reviewed journal articles CGP laboratories at the Sanger Institute, UK TCGA data portal The ICGC data portal IARC p53 database Information on known cancer genes, selected from the Cancer Gene Census is curated manually to maximize its descriptive content. The data was downloaded from the COSMIC sftp server. It was first converted to a bed file using the UCSC utility cosmicToBed, then converted into a bigBed file using the UCSC utility bedToBigBed. The bigBed file is used to generate the track. Display Dense - Indicate the positions where COSMIC mutations have been annotated in a single horizontal track. Squish - Indicate each mutation, in vertical pileups where appropriate, while minimizing screen space used. Pack - Indicate each mutation with COSMIC identifier (COSMnnnnn). Full - Show each mutation in detail, one per line, with COSM identifier (COSMnnnnn). Data Access Due to licensed material, we do not allow downloads or Table Browser access for the bigBed data. The raw data underlying this track can be explored and downloaded via the COSMIC website. The CosmicMutantExport.tsv.gz file was converted to a BED file using the cosmicToBed utility, and then converted into a bigBed file using the bedToBigBed utility. You can download these tools from the utilities directory. Contacts For further information on COSMIC, or for help with the information provided, please contact &#99;&#111;sm&#105;&#99;&#64;&#115;a&#110;g&#101;&#114;. &#97;&#99;. &#117;&#107;. References Forbes SA, Beare D, Boutselakis H, Bamford S, Bindal N, Tate J, Cole CG, Ward S, Dawson E, Ponting L et al. COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res. 2017 Jan 4;45(D1):D777-D783. PMID: 27899578; PMC: PMC5210583 
crisprAllTargets CRISPR Targets CRISPR/Cas9 -NGG Targets, whole genome Genes and Gene Predictions Description This track shows the DNA sequences targetable by CRISPR RNA guides using the Cas9 enzyme from S. pyogenes (PAM: NGG) over the entire human (hg38) genome. CRISPR target sites were annotated with predicted specificity (off-target effects) and predicted efficiency (on-target cleavage) by various algorithms through the tool CRISPOR. Sp-Cas9 usually cuts double-stranded DNA three or four base pairs 5' of the PAM site. Display Conventions and Configuration The track &quot;CRISPR Targets&quot; shows all potential -NGG target sites across the genome. The target sequence of the guide is shown with a thick (exon) bar. The PAM motif match (NGG) is shown with a thinner bar. Guides are colored to reflect both predicted specificity and efficiency. Specificity reflects the &quot;uniqueness&quot; of a 20mer sequence in the genome; the less unique a sequence is, the more likely it is to cleave other locations of the genome (off-target effects). Efficiency is the frequency of cleavage at the target site (on-target efficiency). Shades of gray stand for sites that are hard to target specifically, as the 20mer is not very unique in the genome: impossible to target: target site has at least one identical copy in the genome and was not scored hard to target: many similar sequences in the genome that alignment stopped, repeat? hard to target: target site was aligned but results in a low specificity score &lt;= 50 (see below) Colors highlight targets that are specific in the genome (MIT specificity &gt; 50) but have different predicted efficiencies: unable to calculate Doench/Fusi 2016 efficiency score low predicted cleavage: Doench/Fusi 2016 Efficiency percentile &lt;= 30 medium predicted cleavage: Doench/Fusi 2016 Efficiency percentile &gt; 30 and &lt; 55 high predicted cleavage: Doench/Fusi 2016 Efficiency &gt; 55 Mouse-over a target site to show predicted specificity and efficiency scores: The MIT Specificity score summarizes all off-targets into a single number from 0-100. The higher the number, the fewer off-target effects are expected. We recommend guides with an MIT specificity &gt; 50. The efficiency score tries to predict if a guide leads to rather strong or weak cleavage. According to (Haeussler et al. 2016), the Doench 2016 Efficiency score should be used to select the guide with the highest cleavage efficiency when expressing guides from RNA PolIII Promoters such as U6. Scores are given as percentiles, e.g. &quot;70%&quot; means that 70% of mammalian guides have a score equal or lower than this guide. The raw score number is also shown in parentheses after the percentile. The Moreno-Mateos 2015 Efficiency score should be used instead of the Doench 2016 score when transcribing the guide in vitro with a T7 promoter, e.g. for injections in mouse, zebrafish or Xenopus embryos. The Moreno-Mateos score is given in percentiles and the raw value in parentheses, see the note above. Click onto features to show all scores and predicted off-targets with up to four mismatches. The Out-of-Frame score by Bae et al. 2014 is correlated with the probability that mutations induced by the guide RNA will disrupt the open reading frame. The authors recommend out-of-frame scores &gt; 66 to create knock-outs with a single guide efficiently. Off-target sites are sorted by the CFD (Cutting Frequency Determination) score (Doench et al. 2016). The higher the CFD score, the more likely there is off-target cleavage at that site. Off-targets with a CFD score &lt; 0.023 are not shown on this page, but are available when following the link to the external CRISPOR tool. When compared against experimentally validated off-targets by Haeussler et al. 2016, the large majority of predicted off-targets with CFD scores &lt; 0.023 were false-positives. For storage and performance reasons, on the level of individual off-targets, only CFD scores are available. Methods Relationship between predictions and experimental data Like most algorithms, the MIT specificity score is not always a perfect predictor of off-target effects. Despite low scores, many tested guides caused few and/or weak off-target cleavage when tested with whole-genome assays (Figure 2 from Haeussler et al. 2016), as shown below, and the published data contains few data points with high specificity scores. Overall though, the assays showed that the higher the specificity score, the lower the off-target effects. Similarly, efficiency scoring is not very accurate: guides with low scores can be efficient and vice versa. As a general rule, however, the higher the score, the less likely that a guide is very inefficient. The following histograms illustrate, for each type of score, how the share of inefficient guides drops with increasing efficiency scores: When reading this plot, keep in mind that both scores were evaluated on their own training data. Especially for the Moreno-Mateos score, the results are too optimistic, due to overfitting. When evaluated on independent datasets, the correlation of the prediction with other assays was around 25% lower, see Haeussler et al. 2016. At the time of writing, there is no independent dataset available yet to determine the Moreno-Mateos accuracy for each score percentile range. Track methods The entire human (hg38) genome was scanned for the -NGG motif. Flanking 20mer guide sequences were aligned to the genome with BWA and scored with MIT Specificity scores using the command-line version of crispor.org. Non-unique guide sequences were skipped. Flanking sequences were extracted from the genome and input for Crispor efficiency scoring, available from the Crispor downloads page, which includes the Doench 2016, Moreno-Mateos 2015 and Bae 2014 algorithms, among others. Note that the Doench 2016 scores were updated by the Broad institute in 2017 (&quot;Azimuth&quot; update). As a result, earlier versions of the track show the old Doench 2016 scores and this version of the track shows new Doench 2016 scores. Old and new scores are almost identical, they are correlated to 0.99 and for more than 80% of the guides the difference is below 0.02. However, for very few guides, the difference can be bigger. In case of doubt, we recommend the new scores. Crispor.org can display both scores and many more with the &quot;Show all scores&quot; link. Data Access Positional data can be explored interactively with the Table Browser or the Data Integrator. For small programmatic positional queries, the track can be accessed using our REST API. For genome-wide data or automated analysis, CRISPR genome annotations can be downloaded from our download server as a bigBedFile. The files for this track are called crispr.bb, which lists positions and scores, and crisprDetails.tab, which has information about off-target matches. Individual regions or whole genome annotations can be obtained using our tool bigBedToBed, which can be compiled from the source code or downloaded as a pre-compiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/crisprAllTargets/crispr.bb -chrom=chr21 -start=0 -end=1000000 stdout Credits Track created by Maximilian Haeussler, with helpful input from Jean-Paul Concordet (MNHN Paris) and Alberto Stolfi (NYU). References Haeussler M, Sch&#246;nig K, Eckert H, Eschstruth A, Miann&#233; J, Renaud JB, Schneider-Maunoury S, Shkumatava A, Teboul L, Kent J et al. Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol. 2016 Jul 5;17(1):148. PMID: 27380939; PMC: PMC4934014 Bae S, Kweon J, Kim HS, Kim JS. Microhomology-based choice of Cas9 nuclease target sites. Nat Methods. 2014 Jul;11(7):705-6. PMID: 24972169 Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, Smith I, Tothova Z, Wilen C, Orchard R et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat Biotechnol. 2016 Feb;34(2):184-91. PMID: 26780180; PMC: PMC4744125 Hsu PD, Scott DA, Weinstein JA, Ran FA, Konermann S, Agarwala V, Li Y, Fine EJ, Wu X, Shalem O et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat Biotechnol. 2013 Sep;31(9):827-32. PMID: 23873081; PMC: PMC3969858 Moreno-Mateos MA, Vejnar CE, Beaudoin JD, Fernandez JP, Mis EK, Khokha MK, Giraldez AJ. CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. Nat Methods. 2015 Oct;12(10):982-8. PMID: 26322839; PMC: PMC4589495 
dbVar_common dbVar Common SV NCBI dbVar Curated Common Structural Variants Variation Description This track displays common copy number genomic variations from nstd186 (NCBI Curated Common Structural Variants), divided into subtracks according to population and source of original submission. This curated dataset of all structural variants in dbVar includes variants from gnomAD, 1000 Genomes Phase 3, and DECIPHER (dbVar studies nstd166, estd219, and nstd183, respectively). It only includes copy number gain, copy number loss, copy number variation, duplications, and deletions (including mobile element deletions), that are part of a study with at least 100 samples, include allele frequency data, and have an allele frequency of &gt;=0.01 in at least one population. For more information on the number of variant calls and latest statistics for nstd186 see Summary of nstd186 (NCBI Curated Common Structural Variants). There are six subtracks in this track set: NCBI Curated Common SVs: African - Variants with AF &gt;= 0.01 for African Population. NCBI Curated Common SVs: European - Variants with AF &gt;= 0.01 for European Population. NCBI Curated Common SVs: all populations - Variants with AF &gt;= 0.01 for Global Population. NCBI Curated Common SVs: all populations from gnomAD - Variants with AF &gt;= 0.01 from gnomAD Structural Variants. NCBI Curated Common SVs: all populations from 1000 Genomes - Variants with AF &gt;= 0.01 from 1000 Genomes Consortium Phase 3 Integrated SV. NCBI Curated Common SVs: all populations from DECIPHER - Variants with AF &gt;= 0.01 from DECIPHER Consensus CNVs. Display Conventions and Configuration Items in all subtracks follow the same conventions: items are colored by variant type, and are based on the dbVar colors described in the dbVar Overview page. Red for copy number loss or deletion, blue for copy number gain or duplication, and violet for copy number variation. Mouseover on items indicates genes affected, size, variant type, and allele frequencies (AF). All tracks can be filtered according to the Variant Size and Variant Type. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. The data can also be found directly from the dbVar nstd186 data access, as well as in the dbVar Track Hub, where additional subtracks are included. For questions about dbVar track data, please contact &#100;&#98;&#118;&#97;r&#64;&#110;&#99;&#98;&#105;.n&#108;&#109;.&#110;&#105;&#104;.&#103;&#111;v . Credits Thanks to the dbVAR team at NCBI, especially John Lopez and Timothy Hefferon for technical coordination and consultation, and to Christopher Lee, Anna Benet-Pages, and Daniel Schmelter, of the Genome Browser team for engineering the track display. References Lappalainen I, Lopez J, Skipper L, Hefferon T, Spalding JD, Garner J, Chen C, Maguire M, Corbett M, Zhou G et al. DbVar and DGVa: public archives for genomic structural variation. Nucleic Acids Res. 2013 Jan;41(Database issue):D936-41. PMID: 23193291; PMC: PMC3531204 
dbVarSv dbVar Common Struct Var NCBI Curated Common Structural Variants from dbVar Variation Description The tracks listed here contain data from the nstd186 (NCBI Curated Common Structural Variants) study. This is a collection of structural variants (SV) originally submitted to dbVar which are part of a study with at least 100 samples and have an allele frequency of &gt;=0.01 in at least one population. The complete dataset is imported from these common-population studies: gnomAD Structural Variants (nstd166): Catalog of SVs detected from the sequencing of the complete genome of 10,847 unrelated individuals from the GnomAD v2.1 release. 1000 Genomes Consortium Phase 3 Integrated SV (estd219): Structural variants of the 1000 Genomes project Phase 3 as reported in a separate article specifically dedicated to the analysis of SVs. Many of these data are identical to those reported in the estd214 study. DECIPHER Common CNVs (nstd183): Consensus set of common population CNVs selected from high-resolution controls sets where frequency information is available. There are two tracks in this collection: NCBI dbVar Curated Common Structural Variants (dbVar Common SV): Shows copy number variants calls (variants &gt;=50 nucleotides) from the nstd186 study. NCBI dbVar Curated Conflict Variants (dbVar Conflict SV): Shows copy number variants from nstd186 (NCBI Curated Common Structural Variants) that overlap with nstd102 (Clinical Structural Variants). Display Conventions These tracks are multi-view composite tracks that contain multiple data types (views). Each view within a track has separate display controls, as described here. Some dbVar tracks contain multiple subtracks, corresponding to subsets of data. If a track contains many subtracks, only some subtracks will be displayed by default. The user can select which subtracks are displayed via the display controls on the track details page. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. The data can also be found directly from the dbVar nstd186 data access, as well as in the dbVar Track Hub, where additional subtracks are included. For questions about dbVar track data, please contact &#100;&#98;&#118;&#97;r&#64;&#110;&#99;&#98;&#105;. n&#108;&#109;. &#110;&#105;&#104;. &#103;&#111;v. Credits Thanks to the dbVAR team at NCBI, especially John Lopez and Timothy Hefferon for technical coordination and consultation, and to Christopher Lee, Anna Benet-Pages, and Daniel Schmelter of the Genome Browser team for engineering the track display. References Lappalainen I, Lopez J, Skipper L, Hefferon T, Spalding JD, Garner J, Chen C, Maguire M, Corbett M, Zhou G et al. DbVar and DGVa: public archives for genomic structural variation. Nucleic Acids Res. 2013 Jan;41(Database issue):D936-41. PMID: 23193291; PMC: PMC3531204 
dbVar_common_gnomad dbVar Curated gnomAD SVs NCBI dbVar Curated Common SVs: all populations from gnomAD Variation 
dbVar_common_european dbVar Curated European SVs NCBI dbVar Curated Common SVs: European Variation 
dbVar_common_decipher dbVar Curated DECIPHER SVs NCBI dbVar Curated Common SVs: all populations from DECIPHER Variation 
dbVar_common_global dbVar Curated All Populations NCBI dbVar Curated Common SVs: all populations Variation 
dbVar_common_african dbVar Curated African SVs NCBI dbVar Curated Common SVs: African Variation 
dbVar_common_1000g dbVar Curated 1000 Genomes SVs NCBI dbVar Curated Common SVs: all populations from 1000 Genomes Variation 
dbVar_conflict dbVar Conflict SV NCBI dbVar Curated Conflict Variants Variation Description The track NCBI dbVar Curated Common SVs: Conflicts with Pathogenic highlights loci where common copy number variants from nstd186 (NCBI Curated Common Structural Variants) overlap with structural Variants with clinical assertions, submitted to ClinVar by external labs (Clinical Structural Variants - nstd102). Overlap in the track refers to reciprocal overlap between variants in the common (NCBI Curated Common Structural Variants) versus clinical (ClinVar Long Variants) tracks. Reciprocal overlap values can be anywhere from 10% to 100%. For more information on the number of variant calls and latest statistics for nstd186 see Summary of nstd186 (NCBI Curated Common Structural Variants). Display Conventions and Configuration Items in all subtracks follow the same conventions: items are colored by variant type, and are based on the dbVar colors described in the dbVar Overview page. Red for copy number loss or deletion, blue for copy number gain or duplication, and violet for copy number variation. Mouseover on items indicates genes affected, size, variant type, and allele frequencies (AF). All tracks can be filtered according to the variant length, variant type and variant overlap. This last filter defines four bins within that range from which the user can choose. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. The data can also be found directly from the dbVar nstd186 data access, as well as in the dbVar Track Hub, where additional subtracks are included. For questions about dbVar track data, please contact &#100;&#98;&#118;&#97;r&#64;&#110;&#99;&#98;&#105;. n&#108;&#109;. &#110;&#105;&#104;. &#103;&#111;v. Thanks to the dbVAR team at NCBI, especially John Lopez and Timothy Hefferon for technical coordination and consultation, and to Christopher Lee, Anna Benet-Pages, and Daniel Schmelter of the Genome Browser team for engineering the track display. References Lappalainen I, Lopez J, Skipper L, Hefferon T, Spalding JD, Garner J, Chen C, Maguire M, Corbett M, Zhou G et al. DbVar and DGVa: public archives for genomic structural variation. Nucleic Acids Res. 2013 Jan;41(Database issue):D936-41. PMID: 23193291; PMC: PMC3531204 
dbVar_conflict_pathogenic dbVar Curated Conflict SVs NCBI dbVar Curated Common SVs: Conflicts with Pathogenic Variation 
decipher DECIPHER CNVs DECIPHER CNVs Phenotype and Literature Description NOTE: While the DECIPHER database is open to the public, users seeking information about a personal medical or genetic condition are urged to consult with a qualified physician for diagnosis and for answers to personal questions. Because the UCSC Genes mappings for CNVs are based on associations from RefSeq and UniProt, they are dependent on any interpretations from those sources. Furthermore, because many DECIPHER records refer to multiple gene names, or syndromes not tightly mapped to individual genes, the associations in this track should be treated with skepticism and any conclusions based on them should be carefully scrutinized using independent resources. Data Display Agreement Notice These data are only available for display in the Browser, and not for bulk download. Access to bulk data may be obtained directly from DECIPHER (https://www.deciphergenomics.org/about/data-sharing) and is subject to a Data Access Agreement, in which the user certifies that no attempt to identify individual patients will be undertaken. The same restrictions apply to the public data displayed at UCSC in the UCSC Genome Browser; no one is authorized to attempt to identify patients by any means. These data are made available as soon as possible and may be a pre-publication release. For information on the proper use of DECIPHER data, please see https://www.deciphergenomics.org/about/data-sharing. The DECIPHER consortium provides these data in good faith as a research tool, but without verifying the accuracy, clinical validity, or utility of the data. The DECIPHER consortium makes no warranty, express or implied, nor assumes any legal liability or responsibility for any purpose for which the data are used. The DECIPHER database of submicroscopic chromosomal imbalance collects clinical information about chromosomal microdeletions/duplications/insertions, translocations and inversions, and displays this information on the human genome map. This track shows genomic regions of reported cases and their associated phenotype information. All data have passed the strict consent requirements of the DECIPHER project and are approved for unrestricted public release. Clicking the Patient View ID link brings up a more detailed informational page on the patient at the DECIPHER web site. Display Conventions and Configuration The genomic locations of DECIPHER variants are labeled with the DECIPHER variant descriptions. Mouseover on items shows variant details, clinical interpretation, and associated conditions. Further information on each variant is displayed on the details page by a click onto any variant. For the CNVs track, the entries are colored by the type of variant: red for loss blue for gain grey for amplification A light-to-dark color gradient indicates the clinical significance of each variant, with the lightest shade being benign, to the darkest shade being pathogenic. Detailed information on the CNV color code is described here. Items can be filtered according to the size of the variant, variant type, and clinical significance using the track Configure options. For the SNVs track, the entries are colored according to the estimated clinical significance of the variant: black for likely or definitely pathogenic dark grey for uncertain or unknown light grey for likely or definitely benign Method Data provided by the DECIPHER project group are imported and processed to create a simple BED track to annotate the genomic regions associated with individual patients. Contact For more information on DECIPHER, please contact &#99;&#111;n&#116;&#97;c&#116;&#64;&#100;&#101;&#99;&#105;p&#104;&#101;&#114;&#103;&#101;&#110;&#111;&#109;&#105;c&#115;. &#111;&#114;g References Firth HV, Richards SM, Bevan AP, Clayton S, Corpas M, Rajan D, Van Vooren S, Moreau Y, Pettett RM, Carter NP. DECIPHER: Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources. Am J Hum Genet. 2009 Apr;84(4):524-33. PMID: 19344873; PMC: PMC2667985 
decipherSnvs DECIPHER SNVs DECIPHER: Chromosomal Imbalance and Phenotype in Humans (SNVs) Phenotype and Literature Description NOTE: While the DECIPHER database is open to the public, users seeking information about a personal medical or genetic condition are urged to consult with a qualified physician for diagnosis and for answers to personal questions. Because the UCSC Genes mappings for CNVs are based on associations from RefSeq and UniProt, they are dependent on any interpretations from those sources. Furthermore, because many DECIPHER records refer to multiple gene names, or syndromes not tightly mapped to individual genes, the associations in this track should be treated with skepticism and any conclusions based on them should be carefully scrutinized using independent resources. Data Display Agreement Notice These data are only available for display in the Browser, and not for bulk download. Access to bulk data may be obtained directly from DECIPHER (https://www.deciphergenomics.org/about/data-sharing) and is subject to a Data Access Agreement, in which the user certifies that no attempt to identify individual patients will be undertaken. The same restrictions apply to the public data displayed at UCSC in the UCSC Genome Browser; no one is authorized to attempt to identify patients by any means. These data are made available as soon as possible and may be a pre-publication release. For information on the proper use of DECIPHER data, please see https://www.deciphergenomics.org/about/data-sharing. The DECIPHER consortium provides these data in good faith as a research tool, but without verifying the accuracy, clinical validity, or utility of the data. The DECIPHER consortium makes no warranty, express or implied, nor assumes any legal liability or responsibility for any purpose for which the data are used. The DECIPHER database of submicroscopic chromosomal imbalance collects clinical information about chromosomal microdeletions/duplications/insertions, translocations and inversions, and displays this information on the human genome map. This track shows genomic regions of reported cases and their associated phenotype information. All data have passed the strict consent requirements of the DECIPHER project and are approved for unrestricted public release. Clicking the Patient View ID link brings up a more detailed informational page on the patient at the DECIPHER web site. Display Conventions and Configuration The genomic locations of DECIPHER variants are labeled with the DECIPHER variant descriptions. Mouseover on items shows variant details, clinical interpretation, and associated conditions. Further information on each variant is displayed on the details page by a click onto any variant. For the CNVs track, the entries are colored by the type of variant: red for loss blue for gain grey for amplification A light-to-dark color gradient indicates the clinical significance of each variant, with the lightest shade being benign, to the darkest shade being pathogenic. Detailed information on the CNV color code is described here. Items can be filtered according to the size of the variant, variant type, and clinical significance using the track Configure options. For the SNVs track, the entries are colored according to the estimated clinical significance of the variant: black for likely or definitely pathogenic dark grey for uncertain or unknown light grey for likely or definitely benign Method Data provided by the DECIPHER project group are imported and processed to create a simple BED track to annotate the genomic regions associated with individual patients. Contact For more information on DECIPHER, please contact &#99;&#111;n&#116;&#97;c&#116;&#64;&#100;&#101;&#99;&#105;p&#104;&#101;&#114;&#103;&#101;&#110;&#111;&#109;&#105;c&#115;. &#111;&#114;g References Firth HV, Richards SM, Bevan AP, Clayton S, Corpas M, Rajan D, Van Vooren S, Moreau Y, Pettett RM, Carter NP. DECIPHER: Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources. Am J Hum Genet. 2009 Apr;84(4):524-33. PMID: 19344873; PMC: PMC2667985 
cnvDevDelay Development Delay Copy Number Variation Morbidity Map of Developmental Delay Phenotype and Literature Description Enrichment of large copy number variants (CNVs) has been linked to severe pediatric disease including developmental delay, intellectual disability and autism spectrum disorder. The association of individual loci with specific disorders, however, has still been problematic. This track shows CNVs from cases of developmental delay along with healthy control sets from two separate studies. The study by Cooper et al. (2011) analyzed samples from 15,767 children with various developmental disabilities and compared them with samples from 8,329 adult controls to produce a detailed genome-wide morbidity map of developmental delay and congenital birth defects. The study by Coe et al. (2014) further expanded the morbidity map by analyzing 13,318 new case samples along with 11,255 new controls. Display Conventions and Configuration This is a composite track consisting of a Case subtrack and a Control subtrack. To turn a subtrack on or off, toggle the checkbox to the left of the subtrack name in the track controls at the top of the track description page. Items in this track are colored red for copy number loss and blue for copy number gain. Methods The samples were analyzed using nine different CGH platforms with initial CNV calls filtered as described in Coe et al. (2014). Final CNV calls were decoupled from identifying information and submitted to dbVar as nstd54 and nstd100 for unrestricted release. The 15,767 case individuals from the Cooper study comprise nstd54 sampleset 1, while the 8,329 control individuals from the Cooper study comprise nstd54 samplesets 2-12. The 13,318 case individuals from the Coe study were combined with the Cooper case individuals to comprise nstd100 sampleset 1. The 11,255 control individuals from the Coe study comprise nsdt100 samplesets 2 and 3. The Case subtrack was constructed using nstd100 sampleset 1. The Control subtrack was constructed by combining nstd100 samplesets 2 and 3 with nstd54 samplesets 2-12. Credits We would like to thank Gregory Cooper, Brad Coe and the Eichler Lab at the University of Washington for providing the data for this track. References Coe BP, Witherspoon K, Rosenfeld JA, van Bon BW, Vulto-van Silfhout AT, Bosco P, Friend KL, Baker C, Buono S, Vissers LE et al. Refining analyses of copy number variation identifies specific genes associated with developmental delay. Nat Genet. 2014 Oct;46(10):1063-71. PMID: 25217958; PMC: PMC4177294 Cooper GM, Coe BP, Girirajan S, Rosenfeld JA, Vu TH, Baker C, Williams C, Stalker H, Hamid R, Hannig V et al. A copy number variation morbidity map of developmental delay. Nat Genet. 2011 Aug 14;43(9):838-46. PMID: 21841781; PMC: PMC3171215 
cnvDevDelayControl Control Copy Number Variation Morbidity Map of Developmental Delay - Control Phenotype and Literature 
cnvDevDelayCase Case Copy Number Variation Morbidity Map of Developmental Delay - Case Phenotype and Literature 
dgvPlus DGV Struct Var Database of Genomic Variants: Structural Variation (CNV, Inversion, In/del) Variation Description This track displays copy number variants (CNVs), insertions/deletions (InDels), inversions and inversion breakpoints annotated by the Database of Genomic Variants (DGV), which contains genomic variations observed in healthy individuals. DGV focuses on structural variation, defined as genomic alterations that involve segments of DNA that are larger than 1000 bp. Insertions/deletions of 50 bp or larger are also included. Display Conventions This track contains three subtracks: Structural Variant Regions: annotations that have been generated from one or more reported structural variants at the same location. Supporting Structural Variants: the sample-level reported structural variants. Gold Standard Variants: curated variants from a selected number of studies in DGV. Color is used in both subtracks to indicate the type of variation: Inversions and inversion breakpoints are purple. CNVs and InDels are blue if there is a gain in size relative to the reference. CNVs and InDels are red if there is a loss in size relative to the reference. CNVs and InDels are brown if there are reports of both a loss and a gain in size relative to the reference. The DGV Gold Standard subtrack utilizes a boxplot-like display to represent the merging of records as explained in the Methods section below. In this track, the middle box (where applicable), represents the high confidence location of the CNV, while the thin lines and end boxes represent the possible range of the CNV. Clicking on a variant leads to a page with detailed information about the variant, such as the study reference and PubMed abstract link, the study's method and any genes overlapping the variant. Also listed, if available, are the sequencing or array platform used for the study, a sample cohort description, sample size, sample ID(s) in which the variant was observed, observed gains and observed losses. If the particular variant is a merged variant, links to genome browser views of the supporting variants are listed. If the particular variant is a supporting variant, a link to the genome browser view of its merged variant is displayed. A link to DGV's Variant Details page for each variant is also provided. For most variants, DGV uses accessions from peer archives of structural variation (dbVar at NCBI or DGVa at EBI). These accessions begin with either &quot;essv&quot;, &quot;esv&quot;, &quot;nssv&quot;, or &quot;nsv&quot;, followed by a number. Variant submissions processed by EBI begin with &quot;e&quot; and those processed by NCBI begin with &quot;n&quot;. Accessions with ssv are for variant calls on a particular sample, and if they are copy number variants, they generally indicate whether the change is a gain or loss. In a few studies the ssv represents the variant called by a single algorithm. If multiple algorithms were used, overlapping ssv's from the same individual would be combined to generate a sample level sv. If there are many samples analyzed in a study, and if there are many samples which have the same variant, there will be multiple ssv's with the same start and end coordinates. These sample level variants are then merged and combined to form a representative variant that highlights the common variant found in that study. The result is called a structural variant (sv) record. Accessions with sv are for regions asserted by submitters to contain structural variants, and often span ssv elements for both losses and gains. dbVar and DGVa do not record numbers of losses and gains encompassed within sv regions. DGV merges clusters of variants that share at least 70% reciprocal overlap in size/location, and assigns an accession beginning with &quot;dgv&quot;, followed by an internal variant serial number, followed by an abbreviated study id. For example, the first merged variant from the Shaikh et al. 2009 study (study accession=nstd21) would be dgv1n21. The second merged variant would be dgv2n21 and so forth. Since in this case there is an additional level of clustering, it is possible for an &quot;sv&quot; variant to be both a merged variant and a supporting variant. For most sv and dgv variants, DGV displays the total number of sample-level gains and/or losses at the bottom of their variant detail page. Since each ssv variant is for one sample, its total is 1. Methods Published structural variants are imported from peer archives dbVar and DGVa. DGV then applies quality filters and merges overlapping variants. For data sets where the variation calls are reported at a sample-by-sample level, DGV merges calls with similar boundaries across the sample set. Only variants of the same type (i.e. CNVs, Indels, inversions) are merged, and gains and losses are merged separately. Sample level calls that overlap by &ge; 70% are merged in this process. The initial criteria for the Gold Standard set require that a variant is found in at least two different studies and found in at least two different samples. After filtering out low-quality variants, the remaining variants are clustered according to 50% minimum overlap, and then merged into a single record. Gains and losses are merged separately. The highest ranking variant in the cluster defines the inner box, while the outer lines define the maximum possible start and stop coordinates of the CNV. In this way, the inner box forms a high-confidence CNV location and the thin connecting lines indicate confidence intervals for the location of CNV. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated access, this track, like all others, is available via our API. However, for bulk processing, it is recommended to download the dataset. The genome annotation is stored in a bigBed file that can be downloaded from the download server. The exact filenames can be found in the track configuration file. Annotations can be converted to ASCII text by our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed https://hgdownload.soe.ucsc.edu/gbdb/hg38/dgv/dgvMerged.bb -chrom=chr6 -start=0 -end=1000000 stdout Credits Thanks to the Database of Genomic Variants for providing these data. In citing the Database of Genomic Variants please refer to MacDonald et al. References Iafrate AJ, Feuk L, Rivera MN, Listewnik ML, Donahoe PK, Qi Y, Scherer SW, Lee C. Detection of large-scale variation in the human genome. Nat Genet. 2004 Sep;36(9):949-51. PMID: 15286789 MacDonald JR, Ziman R, Yuen RK, Feuk L, Scherer SW. The Database of Genomic Variants: a curated collection of structural variation in the human genome. Nucleic Acids Res. 2014 Jan;42(Database issue):D986-92. PMID: 24174537; PMC: PMC3965079 Zhang J, Feuk L, Duggan GE, Khaja R, Scherer SW. Development of bioinformatics resources for display and analysis of copy number and other structural variants in the human genome. Cytogenet Genome Res. 2006;115(3-4):205-14. PMID: 17124402 
dgvGold DGV Gold Standard Database of Genomic Variants: Gold Standard Variants Variation 
dgvSupporting DGV Supp Var Database of Genomic Variants: Supporting Structural Var (CNV, Inversion, In/del) Variation 
dgvMerged DGV Struct Var Database of Genomic Variants: Structural Var Regions (CNV, Inversion, In/del) Variation 
dosageSensitivity Dosage Sensitivity pHaplo and pTriplo dosage sensitivity map from Collins et al 2022 Phenotype and Literature Description This container track represents dosage sensitivity map data from Collins et al 2022. There are two tracks, one corresponding to the probability of haploinsufficiency (pHaplo) and one to the probability of triplosensitivity (pTriplo). Rare copy-number variants (rCNVs) include deletions and duplications that occur infrequently in the global human population and can confer substantial risk for disease. Collins et al aimed to quantify the properties of haploinsufficiency (i.e., deletion intolerance) and triplosensitivity (i.e., duplication intolerance) throughout the human genome by analyzing rCNVs from nearly one million individuals to construct a genome-wide catalog of dosage sensitivity across 54 disorders, which defined 163 dosage sensitive segments associated with at least one disorder. These segments were typically gene-dense and often harbored dominant dosage sensitive driver genes. An ensemble machine learning model was built to predict dosage sensitivity probabilities (pHaplo &amp; pTriplo) for all autosomal genes, which identified 2,987 haploinsufficient and 1,559 triplosensitive genes, including 648 that were uniquely triplosensitive. Display Conventions and Configuration Each of the tracks is displayed with a distinct item (bed track) covering the entire gene locus wherever a score was available. Clicking on an item provides a link to DECIPHER which contains the sensitivity scores as well as additional information. Mousing over the items will display the gene symbol, the ESNG ID for that gene, and the respective sensitivity score for the track rounded to two decimal places. Filters are also available to specify specific score thresholds to display for each of the tracks. Coloring and Interpretation Each of the tracks is colored based on standardized cutoffs for pHaplo and pTriplo as described by the authors: pHaplo scores &ge;0.86 indicate that the average effect sizes of deletions are as strong as the loss-of-function of genes known to be constrained against protein truncating variants (average OR&ge;2.7) (Karczewski et al., 2020). pHaplo scores &ge;0.55 indicate an odds ratio &ge;2. pTriplo scores &ge;0.94 indicate that the average effect sizes of deletions are as strong as the loss-of-function of genes known to be constrained against protein truncating variants (average OR&ge;2.7) (Karczewski et al., 2020). pHaplo scores &ge;0.68 indicate an odds ratio &ge;2. Applying these cutoffs defined 2,987 haploinsufficient (pHaplo&ge;0.86) and 1,559 triplosensitive (pTriplo&ge;0.94) genes with rCNV effect sizes comparable to loss-of-function of gold-standard PTV-constrained genes. See below for a summary of the color scheme: Dark red items - pHaplo &ge; 0.86 Bright red items - pHaplo &lt; 0.86 Dark blue items - pTriplo &ge; 0.94 Bright blue items - pTriplo &lt; 0.94 Methods The data were downloaded from Zenodo which consisted of a 3-column file with gene symbols, pHaplo, and pTriplo scores. Since the data were created using GENCODEv19 models, the hg19 data was mapped using those coordinates by picking the earliest transcription start site of all of the respective gene transcripts and the furthest transcription end site. This leads to some gene boundaries that are not representative of a real transcript, but since the data are for gene loci annotations this maximum coverage was used. Finally, both scores were rounded to two decimal points for easier interpretation. For hg38, we attempted to use updated gene positions using a few different datasets since gene symbols have been updated many times since GENCODEv19. A summary of the workflow can be seen below, with each subsequent step being used only for genes where mapping failed: Gene symbols were mapped using MANE1.0. &lt; 2000 items failed mapping here. Mapping with GENCODEv45 was attempted. Mapping with GENCODEv20 was attempted. At this point, 448 items were not mapped. Finally, any missing items were lifted using the hg19 track. 19/448 items failed mapping due to their regions having been split from hg19 to hg38. In summary, the hg19 track was mapped using the original GENCODEv19 mappings, and a series of steps were taken to map the hg38 gene symbols with updated coordinates. 19/18641 items could not be mapped and are missing from the hg38 tracks. The complete makeDoc can be found online. This includes all of the track creation steps. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated access, this track, like all others, is available via our API. However, for bulk processing, it is recommended to download the dataset. For automated download and analysis, the genome annotation is stored at UCSC in bigBed files that can be downloaded from our download server. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tools can also be used to obtain features confined to a given range, e.g., bigBedToBed -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/dosageSensitivityCollins2022/pHaploDosageSensitivity.bb stdout Please refer to our Data Access FAQ for more information. Credits Thanks to DECIPHER for their support and assistance with the data. We would also like to thank Anna Benet-Pagès for suggesting and assisting in track development and interpretation. References Collins RL, Glessner JT, Porcu E, Lepamets M, Brandon R, Lauricella C, Han L, Morley T, Niestroj LM, Ulirsch J et al. A cross-disorder dosage sensitivity map of the human genome. Cell. 2022 Aug 4;185(16):3041-3055.e25. PMID: 35917817; PMC: PMC9742861 
pTriplo pTriplosensitivity Probability of triplosensitivity Phenotype and Literature 
pHaplo pHaploinsufficiency Probability of haploinsufficiency Phenotype and Literature 
epdNew EPDnew Promoters Promoters from EPDnew Expression Description These tracks represent the experimentally validated promoters generated by the Eukaryotic Promoter Database. Display Conventions and Configuration Each item in the track is a representation of the promoter sequence identified by EPD. The &quot;thin&quot; part of the element represents the 49 bp upstream of the annotated transcription start site (TSS) whereas the &quot;thick&quot; part represents the TSS plus 10 bp downstream. The relative position of the thick and thin parts define the orientation of the promoter. Note that the EPD team has created a public track hub containing promoter and supporting annotations for human, mouse, and other vertebrate and model organism genomes. Methods Briefly, gene transcript coordinates were obtained from multiple sources (HGNC, GENCODE, Ensembl, RefSeq) and validated using data from CAGE and RAMPAGE experimental studies obtained from FANTOM 5, UCSC, and ENCODE. Peak calling, clustering and filtering based on relative expression were applied to identify the most expressed promoters and those present in the largest number of samples. For the methodology and principles used by EPD to predict TSSs, refer to Dreos et al. (2013) in the References section below. A more detailed description of how this data was generated can be found at the following links: Human promoter pipelines: coding, non-coding Mouse promoter pipelines: coding, non-coding Credits Data was generated by the EPD team at the Swiss Institute of Bioinformatics. For inquiries, contact the EPD team using this on-line form or email &#112;&#104;&#105;&#108;&#105;&#112;&#112;. &#98;u&#99;h&#101;r&#64;&#101;&#112;fl. &#99;&#104; . References Dreos R, Ambrosini G, Perier RC, Bucher P. EPD and EPDnew, high-quality promoter resources in the next-generation sequencing era. Nucleic Acids Res. 2013 Jan 1;41(D1):D157-64. PMID: 23193273. 
epdNewPromoterNonCoding EPDnew NC v1 ncRNA promoters from EPDnewNC human version 001 Expression 
epdNewPromoter EPDnew v6 Promoters from EPDnew human version 006 Expression Description These tracks represent the experimentally validated promoters generated by the Eukaryotic Promoter Database. Display Conventions and Configuration Each item in the track is a representation of the promoter sequence identified by EPD. The &quot;thin&quot; part of the element represents the 49 bp upstream of the annotated transcription start site (TSS) whereas the &quot;thick&quot; part represents the TSS plus 10 bp downstream. The relative position of the thick and thin parts define the orientation of the promoter. Note that the EPD team has created a public track hub containing promoter and supporting annotations for human, mouse, and other vertebrate and model organism genomes. Methods Briefly, gene transcript coordinates were obtained from multiple sources (HGNC, GENCODE, Ensembl, RefSeq) and validated using data from CAGE and RAMPAGE experimental studies obtained from FANTOM 5, UCSC, and ENCODE. Peak calling, clustering and filtering based on relative expression were applied to identify the most expressed promoters and those present in the largest number of samples. For the methodology and principles used by EPD to predict TSSs, refer to Dreos et al. (2013) in the References section below. A more detailed description of how this data was generated can be found at the following links: Human promoter pipelines: coding, non-coding Mouse promoter pipelines: coding, non-coding Credits Data was generated by the EPD team at the Swiss Institute of Bioinformatics. For inquiries, contact the EPD team using this on-line form or email &#112;&#104;&#105;&#108;&#105;&#112;&#112;. &#98;u&#99;h&#101;r&#64;&#101;&#112;fl. &#99;&#104; . References Dreos R, Ambrosini G, Perier RC, Bucher P. EPD and EPDnew, high-quality promoter resources in the next-generation sequencing era. Nucleic Acids Res. 2013 Jan 1;41(D1):D157-64. PMID: 23193273. 
exomeProbesets Exome Probesets Exome Capture Probesets and Targeted Region Mapping and Sequencing Description This set of tracks shows the genomic positions of probes and targets from a full suite of in-solution-capture target enrichment exome kits for Next Generation Sequencing (NGS) applications. Also known as exome sequencing or whole exome sequencing (WES), this technique allows high-throughput parallel sequencing of all exons (e.g., coding regions of genes which affect protein function), constituting about 1% of the human genome, or approximately 30 million base pairs. The tracks are intended to show the major differences in target genomic regions between the different exome capture kits from the major players in the NGS sequencing market: Illumina Inc., Roche NimbleGen Inc., Agilent Technologies Inc., MGI Tech, Twist Bioscience, and Integrated DNA Technologies Inc.. Display Conventions and Configuration Items are shaded according to manufacturing company: IDT (Integrated DNA Technologies) Twist Biosciences MGI Tech (Beijing Genomics Institute) Roche NimbleGen Agilent Technologies Illumina Tracks labeled as Probes (P) indicate the footprint of the oligonucleotide probes mapped to the human genome. This is the technically relevant targeted region by the assay. However, the sequenced region will be bigger than this since flanking sequences are sequenced as well. Tracks labeled as Target Regions (T) indicate the genomic regions targeted by the assay. This is the biologically relevant target region. Not all targeted regions will necessarily be sequenced perfectly; there might be some capture bias at certain locations. The Target Regions are those normally used for coverage analysis. Note that most exome probesets are available on hg19 only. If you are working with hg38 and cannot find a particular probeset there, try to go to hg19, configure the same track, and see if it exists there. If you cannot find an array, do not hesitate to send us an email with the name of the manufacturer website with the probe file. If an array is available on hg19 but not on hg38 and you need it for your work, we can lift the locations. Our mailing list can be reached at genome@soe.ucsc.edu. Methods The capture of the genomic regions of interest using in-solution capture, is achieved through the hybridization of a set of probes (oligonucleotides) with a sample of fragmented genomic DNA in a solution environment. The probes hybridize selectively to the genomic regions of interest which, after a process of exclusion of the non-selective DNA material, can be pulled down and sequenced, enabling selective DNA sequencing of the genomic regions of interest (e.g., exons). In-solution capture sequencing is a sensitive method to detect single nucleotide variants, insertions and deletions, and copy number variations. #kit, #kit table, #kit th, #kit td { border: 1px solid black; border-collapse: collapse; padding: 2px; } Kit Targeted Region Databases Used for Design Year of Release IDT - xGen Exome Research Panel V1.0 39 Mb Coding sequences from RefSeq (19,396 genes) 2015 IDT - xGen Exome Research Panel V2.0 34 Mb Coding sequences from RefSeq 109 (19,433 genes) 2020 Twist - RefSeq Exome Panel 3.6 Mb Curated subset of protein coding genes from CCDS N/A Twist - Core Exome Panel 33 Mb Protein coding genes from CCDS N/A Twist - Comprehensive Exome Panel 36.8 Mb Protein coding genes from RefSeq, CCDS, and GENCODE 2020 Twist - Exome Panel 2.0 36.4 Mb Protein coding genes from RefSeq, CCDS, and GENCODE 2021 MGI - Easy Exome Capture V4 59 Mb CCDS, GENCODE, RefSeq, and miRBase N/A MGI - Easy Exome Capture V5 69 Mb CCDS, GENCODE, RefSeq, miRBase, and MGI Clinical Database N/A Agilent - SureSelect Clinical Research Exome 54 Mb Disease-associated regions from OMIM, HGMD, and ClinVar 2014 Agilent - SureSelect Clinical Research Exome V2 63.7 Mb Disease-associated regions from OMIM, HGMD, ClinVar, and ACMG 2017 Agilent - SureSelect Focused Exome 12 Mb Disease-associated regions from HGMD, OMIM and ClinVar 2016 Agilent - SureSelect All Exon V4 51 Mb Coding regions from CCDS, RefSeq, and GENCODE v6, miRBase v17, TCGA v6, and UCSC known genes 2011 Agilent - SureSelect All Exon V4 + UTRs 71 Mb Coding regions and 5' and 3' UTR sequences from CCDS, RefSeq, and GENCODE v6, regions from miRBase v17, TCGA v6, and UCSC known genes 2011 Agilent - SureSelect All Exon V5 50 Mb Coding regions from Refseq, GENCODE, UCSC, TCGA, CCDS, and miRBase (21.522 genes) 2012 Agilent - SureSelect All Exon V5 + UTRs 74 Mb Coding regions and 5' and 3' UTR sequences from Refseq, GENCODE, UCSC, TCGA, CCDS, and miRBase (21.522 genes) 2012 Agilent - SureSelect All Exon V6 r2 60 Mb Coding regions from RefSeq, CCDS, GENCODE, HGMD, and OMIM 2016 Agilent - SureSelect All Exon V6 + COSMIC r2 66 Mb Coding regions from RefSeq, CCDS, GENCODE, HGMD, and OMIM, and targets from both TCGA and COSMIC 2016 Agilent - SureSelect All Exon V6 + UTR r2 75 Mb Coding regions and 5' and 3' UTR sequences from RefSeq, GENCODE, CCDS, and UCSC known genes,and miRNAs and lncRNA sequences 2016 Agilent - SureSelect All Exon V7 35.7 Mb Coding regions from RefSeq, CCDS, GENCODE, and UCSC known genes 2018 Roche - KAPA HyperExome 43Mb Coding regions from CCDS, RefSeq, Ensembl, GENCODE,and variants from ClinVar 2020 Roche - SeqCap EZ Exome V3 64 Mb Coding regions from RefSeq RefGene CDS, CCDS, and miRBase v14 databases, plus coverage of 97% Vega, 97% Gencode, and 99% Ensembl 2018 Roche - SeqCap EZ Exome V3 + UTR 92 Mb Coding sequences from RefSeq RefGene, CCDS, and miRBase v14, plus coverage of 97% Vega, 97% Gencode, and 99% Ensembl and UTRs from RefSeq RefGene table from UCSC GRCh37/hg19 March 2012 and Ensembl (GRCh37 v64) 2018 Roche - SeqCap EZ MedExome 47 Mb Coding sequences from CCDS 17, RefSeq, Ensembl 76, VEGA 56, GENCODE 20, miRBase 21, and disease-associated regions from GeneTests, ClinVar, and based on customer input 2014 Roche - SeqCap EZ MedExome + Mito 47 Mb Coding sequences and mitochondrial genes from CCDS 17, RefSeq, Ensembl 76, VEGA 56, GENCODE 20 and miRBase 21, disease-associated regions from GeneTests, ClinVar, and based on customer input 2014 Illumina - Nextera DNA Exome V1.2 45 Mb Coding regions from RefSeq, CCDS, Ensembl, and GENCODE v19 2015 Illumina - Nextera Rapid Capture Exome 37 Mb 212,158 targeted exonic regions with start and stop chromosome locations in GRCh37/hg19 2013 Illumina - Nextera Rapid Capture Exome V1.2 37 Mb Coding regions from RefSeq, CCDS, Ensembl, and GENCODE v12 2014 Illumina - Nextera Rapid Capture Expanded Exome 66 Mb Coding regions from RefSeq, CCDS, Ensembl, and GENCODE v12 2013 Illumina - TruSeq DNA Exome V1.2 45 Mb Coding regions from RefSeq, CCDS, and Ensembl 2017 Illumina - TruSeq Rapid Exome V1.2 45 Mb Coding regions from RefSeq, CCDS, Ensembl, and GENECODE v19 2015 Illumina - TruSight ONE V1.1 12 Mb Coding regions of 6700 genes from HGMD, OMIM, and GeneTest 2017 Illumina - TruSight Exome 7 Mb Disease-causing mutations as curated by HGMD 2017 Illumina - AmpliSeq Exome Panel N/A CCDS coding regions 2019 Data Access The raw data can be explored interactively with the Table Browser or cross-referenced with Data Integrator. The data can be accessed from scripts through our API, with track names found in the Table Schema page for each subtrack after "Primary Table:". For downloading the data, the annotations are stored in bigBed files that can be accessed at our download directory. Regional or the whole genome text annotations can be obtained using our utility bigBedToBed. Instructions for downloading utilities can be found here. Credits Thanks to Illumina (U.S.), Roche NimbleGen, Inc. (U.S.), Agilent Technologies (U.S.), MGI Tech (Beijing Genomics Institute, China), Twist Bioscience (U.S.), and Integrated DNA Technologies (IDT), Inc. (U.S.) for making these data available and to Tiana Pereira, Pranav Muthuraman, Began Nguy and Anna Benet-Pages for enginering these tracks. 
Twist_Exome_RefSeq_Targets Twist RefSeq T Twist - RefSeq Exome Panel Target Regions Mapping and Sequencing 
Twist_Exome_Target2 Twist Exome 2.0 Twist - Exome 2.0 Panel Target Regions Mapping and Sequencing 
Twist_Exome_Target Twist Core T Twist - Bioscience - Core Exome Panel Target Regions Mapping and Sequencing 
Twist_Comp_Exome_Target Twist Compr. T Twist - Comprehensive Exome Panel Target Regions Mapping and Sequencing 
Agilent_Human_Exon_V7_Regions SureSel. V7 T Agilent - SureSelect All Exon V7 Target Regions Mapping and Sequencing 
Agilent_Human_Exon_V7_Covered SureSel. V7 P Agilent - SureSelect All Exon V7 Covered by Probes Mapping and Sequencing 
Agilent_Human_Exon_V6_UTRs_Covered SureSel. V6+UTR P Agilent - SureSelect All Exon V6 + UTR r2 Covered by Probes Mapping and Sequencing 
Agilent_Human_Exon_V6_COSMIC_Regions SureSel. V6+COSMIC T Agilent - SureSelect All Exon V6 + COSMIC r2 Target Regions Mapping and Sequencing 
Agilent_Human_Exon_V6_COSMIC_Covered SureSel. V6+COSMIC P Agilent - SureSelect All Exon V6 + COSMIC r2 Covered by Probes Mapping and Sequencing 
Agilent_Human_Exon_V6_Regions SureSel. V6 T Agilent - SureSelect All Exon V6 r2 Target Regions Mapping and Sequencing 
Agilent_Human_Exon_V6_Covered SureSel. V6 P Agilent - SureSelect All Exon V6 r2 Covered by Probes Mapping and Sequencing 
Agilent_Human_Exon_V6_UTRs_Regions SureSel. V6 +UTR T Agilent - SureSelect All Exon V6 + UTR r2 Target Regions Mapping and Sequencing 
Agilent_Human_Exon_V5_UTRs_Regions SureSel. V5+UTR T Agilent - SureSelect All Exon V5 + UTRs Target Regions Mapping and Sequencing 
Agilent_Human_Exon_V5_UTRs_Covered SureSel. V5+UTR P Agilent - SureSelect All Exon V5 + UTRs Covered by Probes Mapping and Sequencing 
Agilent_Human_Exon_V4_Regions SureSel. V4+UTR T Agilent - SureSelect All Exon V4 + UTRs Target Regions Mapping and Sequencing 
Agilent_Human_Exon_V4_Covered SureSel. V4+UTR P Agilent - SureSelect All Exon V4 + UTRs Covered by Probes Mapping and Sequencing 
Agilent_Human_Exon_Focused_Regions SureSel. Focused T Agilent - SureSelect Focused Exome Target Regions Mapping and Sequencing 
Agilent_Human_Exon_Focused_Covered SureSel. Focused P Agilent - SureSelect Focused Exome Covered by Probes Mapping and Sequencing 
Agilent_Human_Exon_Clinical_Research_V2_Regions SureSel. Clinical V2 T Agilent - SureSelect Clinical Research Exome V2 Target Regions Mapping and Sequencing 
Agilent_Human_Exon_Clinical_Research_V2_Covered SureSel. Clinical V2 P Agilent - SureSelect Clinical Research Exome V2 Covered by Probes Mapping and Sequencing 
SeqCap-EZ_MedExomePlusMito_hg19_empirical_targets SeqCap EZ Med+Mito T Roche - SeqCap EZ MedExome + Mito Empirical Target Regions Mapping and Sequencing 
SeqCap-EZ_MedExomePlusMito_hg19_capture_targets SeqCap EZ Med+Mito P Roche - SeqCap EZ MedExome + Mito Capture Probe Footprint Mapping and Sequencing 
SeqCap-EZ_MedExome_hg19_empirical_targets SeqCap EZ Med T Roche - SeqCap EZ MedExome Empirical Target Regions Mapping and Sequencing 
SeqCap-EZ_MedExome_hg38_capture_targets SeqCap EZ Med P Roche - SeqCap EZ MedExome Capture Probe Footprint Mapping and Sequencing 
KAPA_HyperExome_hg38_primary_targets KAPA Hyper T Roche - KAPA HyperExome Primary Target Regions Mapping and Sequencing 
KAPA_HyperExome_hg38_capture_targets KAPA Hyper P Roche - KAPA HyperExome Capture Probe Footprint Mapping and Sequencing 
xGen_Research_Targets_V2 IDT xGen V2 T IDT - xGen Exome Research Panel V2 Target Regions Mapping and Sequencing 
xGen_Research_Probes_V2 IDT xGen V2 P IDT - xGen Exome Research Panel V2 Probes Mapping and Sequencing 
xGen_Research_Targets_V1 IDT xGen V1 T IDT - xGen Exome Research Panel V1 Target Regions Mapping and Sequencing 
xGen_Research_Probes_V1 IDT xGen V1 P IDT - xGen Exome Research Panel V1 Probes Mapping and Sequencing 
fetalGeneAtlasAssay Fetal Assay Fetal Gene Atlas binned by assay (cell/nucleus) from Cao et al 2020 Single Cell RNA-seq Description This group of tracks shows data from A human cell atlas of fetal gene expression. This is a collection of single cell and single nucleus combinatorial indexing-based RNA-seq data covering 4 million cells from 15 organs obtained during mid-gestation. The cells were sequenced in a highly multiplexed fashion and then clustered with annotations as described in Cao et al., 2020. The Fetal Cells subtrack contains the data organized by cell type, with RNA signals from all cells of a given type pooled and averaged into one bar for each cell type. The Fetal Lineage subtrack shows similar data, but with the cell types subdivided more finely and by organ. Additional bar chart subtracks pool the cell by other characteristics such as by sex (Fetal Sex), assay (FetalAssay), donor (Fetal Donor ID), experiment (Fetal Exp), organ (Fetal Organ), and reverse transcription group (Fetal RT Group). Please see descartes.brotmanbaty.org for further interactive displays and additional data. Display Conventions and Configuration The cell types are colored by which class they belong to according to the following table. The coloring algorithm allows cells that show some blended characteristics to show blended colors so there will be some color variation within a class. The colors will be purest in the Fetal Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Color Cell classification neural adipose fibroblast immune muscle hepatocyte trophoblast secretory ciliated epithelial endothelial glia Methods Three-level single-cell combinatorial indexing (sci-RNAseq3) as described in Cao et al., 2020 was used on 121 samples from 28 fetuses estimated 72 to 129 days post-conception. This included samples from 15 organs. and resulted in RNA profiles for 4 million cells. The samples were flash-frozen for majority of the experiments and then nuclei extracted for sequencing. Samples from tissues from the kidney and digestive system were fixed after disassociation to deactivate endogenous RNases and proteases. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits Thanks to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Cao J, O'Day DR, Pliner HA, Kingsley PD, Deng M, Daza RM, Zager MA, Aldinger KA, Blecher-Gonen R, Zhang F et al. A human cell atlas of fetal gene expression. Science. 2020 Nov 13;370(6518). PMID: 33184181; PMC: PMC7780123 Cao J, Spielmann M, Qiu X, Huang X, Ibrahim DM, Hill AJ, Zhang F, Mundlos S, Christiansen L, Steemers FJ et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019 Feb;566(7745):496-502. PMID: 30787437; PMC: PMC6434952 body.cgi { background: #F0F0F0; } table.hgInside { background: #FFFFFF; } 
fetalGeneAtlas Fetal Gene Atlas Fetal Gene Atlas from Cao et al 2020 Single Cell RNA-seq Description This group of tracks shows data from A human cell atlas of fetal gene expression. This is a collection of single cell and single nucleus combinatorial indexing-based RNA-seq data covering 4 million cells from 15 organs obtained during mid-gestation. The cells were sequenced in a highly multiplexed fashion and then clustered with annotations as described in Cao et al., 2020. The Fetal Cells subtrack contains the data organized by cell type, with RNA signals from all cells of a given type pooled and averaged into one bar for each cell type. The Fetal Lineage subtrack shows similar data, but with the cell types subdivided more finely and by organ. Additional bar chart subtracks pool the cell by other characteristics such as by sex (Fetal Sex), assay (FetalAssay), donor (Fetal Donor ID), experiment (Fetal Exp), organ (Fetal Organ), and reverse transcription group (Fetal RT Group). Please see descartes.brotmanbaty.org for further interactive displays and additional data. Display Conventions and Configuration The cell types are colored by which class they belong to according to the following table. The coloring algorithm allows cells that show some blended characteristics to show blended colors so there will be some color variation within a class. The colors will be purest in the Fetal Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Color Cell classification neural adipose fibroblast immune muscle hepatocyte trophoblast secretory ciliated epithelial endothelial glia Methods Three-level single-cell combinatorial indexing (sci-RNAseq3) as described in Cao et al., 2020 was used on 121 samples from 28 fetuses estimated 72 to 129 days post-conception. This included samples from 15 organs. and resulted in RNA profiles for 4 million cells. The samples were flash-frozen for majority of the experiments and then nuclei extracted for sequencing. Samples from tissues from the kidney and digestive system were fixed after disassociation to deactivate endogenous RNases and proteases. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits Thanks to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Cao J, O'Day DR, Pliner HA, Kingsley PD, Deng M, Daza RM, Zager MA, Aldinger KA, Blecher-Gonen R, Zhang F et al. A human cell atlas of fetal gene expression. Science. 2020 Nov 13;370(6518). PMID: 33184181; PMC: PMC7780123 Cao J, Spielmann M, Qiu X, Huang X, Ibrahim DM, Hill AJ, Zhang F, Mundlos S, Christiansen L, Steemers FJ et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019 Feb;566(7745):496-502. PMID: 30787437; PMC: PMC6434952 body.cgi { background: #F0F0F0; } table.hgInside { background: #FFFFFF; } 
fetalGeneAtlasCellType Fetal Cells Fetal Gene Atlas binned by cell type from Cao et al 2020 Single Cell RNA-seq Description This group of tracks shows data from A human cell atlas of fetal gene expression. This is a collection of single cell and single nucleus combinatorial indexing-based RNA-seq data covering 4 million cells from 15 organs obtained during mid-gestation. The cells were sequenced in a highly multiplexed fashion and then clustered with annotations as described in Cao et al., 2020. The Fetal Cells subtrack contains the data organized by cell type, with RNA signals from all cells of a given type pooled and averaged into one bar for each cell type. The Fetal Lineage subtrack shows similar data, but with the cell types subdivided more finely and by organ. Additional bar chart subtracks pool the cell by other characteristics such as by sex (Fetal Sex), assay (FetalAssay), donor (Fetal Donor ID), experiment (Fetal Exp), organ (Fetal Organ), and reverse transcription group (Fetal RT Group). Please see descartes.brotmanbaty.org for further interactive displays and additional data. Display Conventions and Configuration The cell types are colored by which class they belong to according to the following table. The coloring algorithm allows cells that show some blended characteristics to show blended colors so there will be some color variation within a class. The colors will be purest in the Fetal Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Color Cell classification neural adipose fibroblast immune muscle hepatocyte trophoblast secretory ciliated epithelial endothelial glia Methods Three-level single-cell combinatorial indexing (sci-RNAseq3) as described in Cao et al., 2020 was used on 121 samples from 28 fetuses estimated 72 to 129 days post-conception. This included samples from 15 organs. and resulted in RNA profiles for 4 million cells. The samples were flash-frozen for majority of the experiments and then nuclei extracted for sequencing. Samples from tissues from the kidney and digestive system were fixed after disassociation to deactivate endogenous RNases and proteases. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits Thanks to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Cao J, O'Day DR, Pliner HA, Kingsley PD, Deng M, Daza RM, Zager MA, Aldinger KA, Blecher-Gonen R, Zhang F et al. A human cell atlas of fetal gene expression. Science. 2020 Nov 13;370(6518). PMID: 33184181; PMC: PMC7780123 Cao J, Spielmann M, Qiu X, Huang X, Ibrahim DM, Hill AJ, Zhang F, Mundlos S, Christiansen L, Steemers FJ et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019 Feb;566(7745):496-502. PMID: 30787437; PMC: PMC6434952 body.cgi { background: #F0F0F0; } table.hgInside { background: #FFFFFF; } 
fetalGeneAtlasDonor Fetal Donor ID Fetal Gene Atlas binned by donor ID from Cao et al 2020 Single Cell RNA-seq Description This group of tracks shows data from A human cell atlas of fetal gene expression. This is a collection of single cell and single nucleus combinatorial indexing-based RNA-seq data covering 4 million cells from 15 organs obtained during mid-gestation. The cells were sequenced in a highly multiplexed fashion and then clustered with annotations as described in Cao et al., 2020. The Fetal Cells subtrack contains the data organized by cell type, with RNA signals from all cells of a given type pooled and averaged into one bar for each cell type. The Fetal Lineage subtrack shows similar data, but with the cell types subdivided more finely and by organ. Additional bar chart subtracks pool the cell by other characteristics such as by sex (Fetal Sex), assay (FetalAssay), donor (Fetal Donor ID), experiment (Fetal Exp), organ (Fetal Organ), and reverse transcription group (Fetal RT Group). Please see descartes.brotmanbaty.org for further interactive displays and additional data. Display Conventions and Configuration The cell types are colored by which class they belong to according to the following table. The coloring algorithm allows cells that show some blended characteristics to show blended colors so there will be some color variation within a class. The colors will be purest in the Fetal Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Color Cell classification neural adipose fibroblast immune muscle hepatocyte trophoblast secretory ciliated epithelial endothelial glia Methods Three-level single-cell combinatorial indexing (sci-RNAseq3) as described in Cao et al., 2020 was used on 121 samples from 28 fetuses estimated 72 to 129 days post-conception. This included samples from 15 organs. and resulted in RNA profiles for 4 million cells. The samples were flash-frozen for majority of the experiments and then nuclei extracted for sequencing. Samples from tissues from the kidney and digestive system were fixed after disassociation to deactivate endogenous RNases and proteases. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits Thanks to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Cao J, O'Day DR, Pliner HA, Kingsley PD, Deng M, Daza RM, Zager MA, Aldinger KA, Blecher-Gonen R, Zhang F et al. A human cell atlas of fetal gene expression. Science. 2020 Nov 13;370(6518). PMID: 33184181; PMC: PMC7780123 Cao J, Spielmann M, Qiu X, Huang X, Ibrahim DM, Hill AJ, Zhang F, Mundlos S, Christiansen L, Steemers FJ et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019 Feb;566(7745):496-502. PMID: 30787437; PMC: PMC6434952 body.cgi { background: #F0F0F0; } table.hgInside { background: #FFFFFF; } 
fetalGeneAtlasExperiment Fetal Exp Fetal Gene Atlas binned by experiment id from Cao et al 2020 Single Cell RNA-seq Description This group of tracks shows data from A human cell atlas of fetal gene expression. This is a collection of single cell and single nucleus combinatorial indexing-based RNA-seq data covering 4 million cells from 15 organs obtained during mid-gestation. The cells were sequenced in a highly multiplexed fashion and then clustered with annotations as described in Cao et al., 2020. The Fetal Cells subtrack contains the data organized by cell type, with RNA signals from all cells of a given type pooled and averaged into one bar for each cell type. The Fetal Lineage subtrack shows similar data, but with the cell types subdivided more finely and by organ. Additional bar chart subtracks pool the cell by other characteristics such as by sex (Fetal Sex), assay (FetalAssay), donor (Fetal Donor ID), experiment (Fetal Exp), organ (Fetal Organ), and reverse transcription group (Fetal RT Group). Please see descartes.brotmanbaty.org for further interactive displays and additional data. Display Conventions and Configuration The cell types are colored by which class they belong to according to the following table. The coloring algorithm allows cells that show some blended characteristics to show blended colors so there will be some color variation within a class. The colors will be purest in the Fetal Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Color Cell classification neural adipose fibroblast immune muscle hepatocyte trophoblast secretory ciliated epithelial endothelial glia Methods Three-level single-cell combinatorial indexing (sci-RNAseq3) as described in Cao et al., 2020 was used on 121 samples from 28 fetuses estimated 72 to 129 days post-conception. This included samples from 15 organs. and resulted in RNA profiles for 4 million cells. The samples were flash-frozen for majority of the experiments and then nuclei extracted for sequencing. Samples from tissues from the kidney and digestive system were fixed after disassociation to deactivate endogenous RNases and proteases. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits Thanks to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Cao J, O'Day DR, Pliner HA, Kingsley PD, Deng M, Daza RM, Zager MA, Aldinger KA, Blecher-Gonen R, Zhang F et al. A human cell atlas of fetal gene expression. Science. 2020 Nov 13;370(6518). PMID: 33184181; PMC: PMC7780123 Cao J, Spielmann M, Qiu X, Huang X, Ibrahim DM, Hill AJ, Zhang F, Mundlos S, Christiansen L, Steemers FJ et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019 Feb;566(7745):496-502. PMID: 30787437; PMC: PMC6434952 body.cgi { background: #F0F0F0; } table.hgInside { background: #FFFFFF; } 
fetalGeneAtlasOrganCellLineage Fetal Lineage Fetal Gene Atlas binned by cell lineage and organ from Cao et al 2020 Single Cell RNA-seq Description This group of tracks shows data from A human cell atlas of fetal gene expression. This is a collection of single cell and single nucleus combinatorial indexing-based RNA-seq data covering 4 million cells from 15 organs obtained during mid-gestation. The cells were sequenced in a highly multiplexed fashion and then clustered with annotations as described in Cao et al., 2020. The Fetal Cells subtrack contains the data organized by cell type, with RNA signals from all cells of a given type pooled and averaged into one bar for each cell type. The Fetal Lineage subtrack shows similar data, but with the cell types subdivided more finely and by organ. Additional bar chart subtracks pool the cell by other characteristics such as by sex (Fetal Sex), assay (FetalAssay), donor (Fetal Donor ID), experiment (Fetal Exp), organ (Fetal Organ), and reverse transcription group (Fetal RT Group). Please see descartes.brotmanbaty.org for further interactive displays and additional data. Display Conventions and Configuration The cell types are colored by which class they belong to according to the following table. The coloring algorithm allows cells that show some blended characteristics to show blended colors so there will be some color variation within a class. The colors will be purest in the Fetal Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Color Cell classification neural adipose fibroblast immune muscle hepatocyte trophoblast secretory ciliated epithelial endothelial glia Methods Three-level single-cell combinatorial indexing (sci-RNAseq3) as described in Cao et al., 2020 was used on 121 samples from 28 fetuses estimated 72 to 129 days post-conception. This included samples from 15 organs. and resulted in RNA profiles for 4 million cells. The samples were flash-frozen for majority of the experiments and then nuclei extracted for sequencing. Samples from tissues from the kidney and digestive system were fixed after disassociation to deactivate endogenous RNases and proteases. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits Thanks to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Cao J, O'Day DR, Pliner HA, Kingsley PD, Deng M, Daza RM, Zager MA, Aldinger KA, Blecher-Gonen R, Zhang F et al. A human cell atlas of fetal gene expression. Science. 2020 Nov 13;370(6518). PMID: 33184181; PMC: PMC7780123 Cao J, Spielmann M, Qiu X, Huang X, Ibrahim DM, Hill AJ, Zhang F, Mundlos S, Christiansen L, Steemers FJ et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019 Feb;566(7745):496-502. PMID: 30787437; PMC: PMC6434952 body.cgi { background: #F0F0F0; } table.hgInside { background: #FFFFFF; } 
fetalGeneAtlasOrgan Fetal Organ Fetal Gene Atlas binned by organ from Cao et al 2020 Single Cell RNA-seq Description This group of tracks shows data from A human cell atlas of fetal gene expression. This is a collection of single cell and single nucleus combinatorial indexing-based RNA-seq data covering 4 million cells from 15 organs obtained during mid-gestation. The cells were sequenced in a highly multiplexed fashion and then clustered with annotations as described in Cao et al., 2020. The Fetal Cells subtrack contains the data organized by cell type, with RNA signals from all cells of a given type pooled and averaged into one bar for each cell type. The Fetal Lineage subtrack shows similar data, but with the cell types subdivided more finely and by organ. Additional bar chart subtracks pool the cell by other characteristics such as by sex (Fetal Sex), assay (FetalAssay), donor (Fetal Donor ID), experiment (Fetal Exp), organ (Fetal Organ), and reverse transcription group (Fetal RT Group). Please see descartes.brotmanbaty.org for further interactive displays and additional data. Display Conventions and Configuration The cell types are colored by which class they belong to according to the following table. The coloring algorithm allows cells that show some blended characteristics to show blended colors so there will be some color variation within a class. The colors will be purest in the Fetal Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Color Cell classification neural adipose fibroblast immune muscle hepatocyte trophoblast secretory ciliated epithelial endothelial glia Methods Three-level single-cell combinatorial indexing (sci-RNAseq3) as described in Cao et al., 2020 was used on 121 samples from 28 fetuses estimated 72 to 129 days post-conception. This included samples from 15 organs. and resulted in RNA profiles for 4 million cells. The samples were flash-frozen for majority of the experiments and then nuclei extracted for sequencing. Samples from tissues from the kidney and digestive system were fixed after disassociation to deactivate endogenous RNases and proteases. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits Thanks to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Cao J, O'Day DR, Pliner HA, Kingsley PD, Deng M, Daza RM, Zager MA, Aldinger KA, Blecher-Gonen R, Zhang F et al. A human cell atlas of fetal gene expression. Science. 2020 Nov 13;370(6518). PMID: 33184181; PMC: PMC7780123 Cao J, Spielmann M, Qiu X, Huang X, Ibrahim DM, Hill AJ, Zhang F, Mundlos S, Christiansen L, Steemers FJ et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019 Feb;566(7745):496-502. PMID: 30787437; PMC: PMC6434952 body.cgi { background: #F0F0F0; } table.hgInside { background: #FFFFFF; } 
fetalGeneAtlasRtGroup Fetal RT Group Fetal Gene Atlas binned by RT group from Cao et al 2020 Single Cell RNA-seq Description This group of tracks shows data from A human cell atlas of fetal gene expression. This is a collection of single cell and single nucleus combinatorial indexing-based RNA-seq data covering 4 million cells from 15 organs obtained during mid-gestation. The cells were sequenced in a highly multiplexed fashion and then clustered with annotations as described in Cao et al., 2020. The Fetal Cells subtrack contains the data organized by cell type, with RNA signals from all cells of a given type pooled and averaged into one bar for each cell type. The Fetal Lineage subtrack shows similar data, but with the cell types subdivided more finely and by organ. Additional bar chart subtracks pool the cell by other characteristics such as by sex (Fetal Sex), assay (FetalAssay), donor (Fetal Donor ID), experiment (Fetal Exp), organ (Fetal Organ), and reverse transcription group (Fetal RT Group). Please see descartes.brotmanbaty.org for further interactive displays and additional data. Display Conventions and Configuration The cell types are colored by which class they belong to according to the following table. The coloring algorithm allows cells that show some blended characteristics to show blended colors so there will be some color variation within a class. The colors will be purest in the Fetal Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Color Cell classification neural adipose fibroblast immune muscle hepatocyte trophoblast secretory ciliated epithelial endothelial glia Methods Three-level single-cell combinatorial indexing (sci-RNAseq3) as described in Cao et al., 2020 was used on 121 samples from 28 fetuses estimated 72 to 129 days post-conception. This included samples from 15 organs. and resulted in RNA profiles for 4 million cells. The samples were flash-frozen for majority of the experiments and then nuclei extracted for sequencing. Samples from tissues from the kidney and digestive system were fixed after disassociation to deactivate endogenous RNases and proteases. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits Thanks to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Cao J, O'Day DR, Pliner HA, Kingsley PD, Deng M, Daza RM, Zager MA, Aldinger KA, Blecher-Gonen R, Zhang F et al. A human cell atlas of fetal gene expression. Science. 2020 Nov 13;370(6518). PMID: 33184181; PMC: PMC7780123 Cao J, Spielmann M, Qiu X, Huang X, Ibrahim DM, Hill AJ, Zhang F, Mundlos S, Christiansen L, Steemers FJ et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019 Feb;566(7745):496-502. PMID: 30787437; PMC: PMC6434952 body.cgi { background: #F0F0F0; } table.hgInside { background: #FFFFFF; } 
fetalGeneAtlasSex Fetal Sex Fetal Gene Atlas binned by sex from Cao et al 2020 Single Cell RNA-seq Description This group of tracks shows data from A human cell atlas of fetal gene expression. This is a collection of single cell and single nucleus combinatorial indexing-based RNA-seq data covering 4 million cells from 15 organs obtained during mid-gestation. The cells were sequenced in a highly multiplexed fashion and then clustered with annotations as described in Cao et al., 2020. The Fetal Cells subtrack contains the data organized by cell type, with RNA signals from all cells of a given type pooled and averaged into one bar for each cell type. The Fetal Lineage subtrack shows similar data, but with the cell types subdivided more finely and by organ. Additional bar chart subtracks pool the cell by other characteristics such as by sex (Fetal Sex), assay (FetalAssay), donor (Fetal Donor ID), experiment (Fetal Exp), organ (Fetal Organ), and reverse transcription group (Fetal RT Group). Please see descartes.brotmanbaty.org for further interactive displays and additional data. Display Conventions and Configuration The cell types are colored by which class they belong to according to the following table. The coloring algorithm allows cells that show some blended characteristics to show blended colors so there will be some color variation within a class. The colors will be purest in the Fetal Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Color Cell classification neural adipose fibroblast immune muscle hepatocyte trophoblast secretory ciliated epithelial endothelial glia Methods Three-level single-cell combinatorial indexing (sci-RNAseq3) as described in Cao et al., 2020 was used on 121 samples from 28 fetuses estimated 72 to 129 days post-conception. This included samples from 15 organs. and resulted in RNA profiles for 4 million cells. The samples were flash-frozen for majority of the experiments and then nuclei extracted for sequencing. Samples from tissues from the kidney and digestive system were fixed after disassociation to deactivate endogenous RNases and proteases. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits Thanks to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Cao J, O'Day DR, Pliner HA, Kingsley PD, Deng M, Daza RM, Zager MA, Aldinger KA, Blecher-Gonen R, Zhang F et al. A human cell atlas of fetal gene expression. Science. 2020 Nov 13;370(6518). PMID: 33184181; PMC: PMC7780123 Cao J, Spielmann M, Qiu X, Huang X, Ibrahim DM, Hill AJ, Zhang F, Mundlos S, Christiansen L, Steemers FJ et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019 Feb;566(7745):496-502. PMID: 30787437; PMC: PMC6434952 body.cgi { background: #F0F0F0; } table.hgInside { background: #FFFFFF; } 
fishClones FISH Clones Clones Placed on Cytogenetic Map Using FISH Mapping and Sequencing Description This track shows the location of fluorescent in situ hybridization (FISH)-mapped clones along the assembly sequence. The locations of these clones were obtained from the NCBI Human BAC Resource here. Earlier versions of this track obtained this information directly from the paper Cheung, et al. (2001). More information about the BAC clones, including how they may be obtained, can be found at the Human BAC Resource and the Clone Registry web sites hosted by NCBI. To view Clone Registry information for a clone, click on the clone name at the top of the details page for that item. Using the Filter This track has a filter that can be used to change the color or include/exclude the display of a dataset from an individual lab. This is helpful when many items are shown in the track display, especially when only some are relevant to the current task. The filter is located at the top of the track description page, which is accessed via the small button to the left of the track's graphical display or through the link on the track's control menu. To use the filter: In the pulldown menu, select the lab whose data you would like to highlight or exclude in the display. Choose the color or display characteristic that will be used to highlight or include/exclude the filtered items. If &quot;exclude&quot; is chosen, the browser will not display clones from the lab selected in the pulldown list. If &quot;include&quot; is selected, the browser will display clones only from the selected lab. When you have finished configuring the filter, click the Submit button. Credits We would like to thank all of the labs that have contributed to this resource: Fred Hutchinson Cancer Research Center (FHCRC) National Cancer Institute (NCI) Roswell Park Cancer Institute (RPCI) The Wellcome Trust Sanger Institute (SC) Cedars-Sinai Medical Center (CSMC) Los Alamos National Laboratory (LANL) UC San Francisco Cancer Center (UCSF) References Cheung VG, Nowak N, Jang W, Kirsch IR, Zhao S, Chen XN, Furey TS, Kim UJ, Kuo WL, Olivier M et al. Integration of cytogenetic landmarks into the draft sequence of the human genome. Nature. 2001 Feb 15;409(6822):953-8. PMID: 11237021 
assemblyContainer Assembly Tracks Assembly identifiers, clones, and markers Mapping and Sequencing Description This is a container track for data related to the genome assembly. It contains tracks about the assembly identifiers, certain clones, and STS markers. Click into any of the sub-tracks to see information details on the specific annotations. 
gap Gap Gap Locations Mapping and Sequencing Description This track shows the gaps in the GRCh38 (hg38) genome assembly defined in the AGP file delivered with the sequence. These gaps are being closed during the finishing process on the human genome. For information on the AGP file format, see the NCBI AGP Specification. The NCBI website also provides an overview of genome assembly procedures, as well as specific information about the hg38 assembly. Gaps are represented as black boxes in this track. If the relative order and orientation of the contigs on either side of the gap is supported by read pair data, it is a bridged gap and a white line is drawn through the black box representing the gap. This assembly contains the following principal types of gaps: short_arm - short arm gaps (count: 5; size range: 5,000,000 - 16,990,000 bases) heterochromatin - heterochromatin gaps (count: 11; size range: 20,000 - 30,000,000 bases) telomere - telomere gaps (count: 48; all of size 10,000 bases) contig - gaps between contigs in scaffolds (count: 285; size range: 100 - 400,000 bases) scaffold - gaps between scaffolds in chromosome assemblies (count: 470; size range: 10 - 624,000 bases) 
gc5BaseBw GC Percent GC Percent in 5-Base Windows Mapping and Sequencing Description The GC percent track shows the percentage of G (guanine) and C (cytosine) bases in 5-base windows. High GC content is typically associated with gene-rich areas. This track may be configured in a variety of ways to highlight different apsects of the displayed information. Click the &quot;Graph configuration help&quot; link for an explanation of the configuration options. Credits The data and presentation of this graph were prepared by Hiram Clawson. 
genCC GenCC GenCC: The Gene Curation Coalition Annotations Phenotype and Literature Description This track shows annotations from The Gene Curation Coalition (GenCC). The GenCC provides information pertaining to the validity of gene-disease relationships, with a current focus on Mendelian diseases. Curated gene-disease relationships are submitted by GenCC member organizations that currently provide online resources (e.g. ClinGen, DECIPHER, Orphanet, etc.), as well as diagnostic laboratories that have committed to sharing their internal curated gene-level knowledge (e.g. Ambry Genetics, Illumina, Invitae, etc.). The GenCC aims to clarify overlap between gene curation efforts and develop consistent terminology for validity, allelic requirement and mechanism of disease. Each item on this track corresponds with a gene, and contains a large number of information such as associated disease, evidence classification, specific submission notes and identifiers from different databases. In cases where multiple annotations exist for the same gene, multiple items are displayed. Display Conventions and Configuration Each item displayed represents a submission to the GenCC database. The displayed name is a combination of the gene symbol and the disease's original submission ID. This submission ID is either the OMIM#, MONDO# or Orphanet#. Clicking on any item will display the complete meta data for that item, including linkouts to the GenCC, NCBI, Ensembl, HGNC, GeneCards, Pombase (MONDO), and Human Phenotype Ontology (HPO). Mousing over any item will display the associated disease title, the classification title, and the mode of inheritance title. Items are colored based on the GenCC classification, or validation, of the evidence in the color scheme seen in the table below. For more information on this process, see the GenCC validity terms FAQ. A filter for the track is also available to display a subset of the items based on their classification. Color Evidence classification Definitive Strong Moderate Supportive Limited Disputed Evidence Refuted Evidence No Known Disease Relationship Limitations: Most entries include both NM_ accessions as well as ENST and ENSG identifiers. From the original file, which contains no coordinates, two genes were not mapped to the hg38 genome, SLCO1B7 and ATXN8. This means that the hg38 track has 2 fewer items than what can be found in the GenCC download file. For hg19, one additional gene was not mapped, KCNJ18. In addition to this, the GenCC data in the Genome Browser does not include OMIM data due to licensing restrictions. For more information, see the Methods section below. Data Access The source data can be explored in GenCC database. The source files can also be found on the GenCC downloads page. The GenCC data on the UCSC Genome Browser can be explored interactively with the Table Browser or the Data Integrator. For automated download and analysis, the genome annotation is stored at UCSC in bigBed files that can be downloaded from our download server. The data may also be explored interactively using our REST API. The file for this track may also be locally explored using our tools bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tools can also be used to obtain features confined to a given range, e.g., bigBedToBed -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/genCC.bb stdout Methods The data were downloaded from the GenCC downloads page in tsv format. Manual curation was performed on the file to remove newline characters and tab characters present in the submission notes, in total fewer than 20 manual edits were made. The track was first built on hg38 by associating the gene symbols with the NCBI MANE 1.0 release transcripts. These coordinates were added to the items as well as the NM_ accession, ENST ID and ENSG ID. For items where there was no gene symbol match in MANE (~130), the gene symbols were queried against GENCODEv40 comprehensive set release. In places where multiple transcript matches were found, the earliest transcription start and latest end site was used from among the transcripts to encompass the entire gene coordinates. Two genes were not able to be mapped for hg38, SLCO1B7 and ATXN8, resulting in two missing submissions in the Genome Browser when compared to the raw file. Lastly, the items were colored according to their evidence classification as seen on the GenCC database. For hg19, the hg38 NM_ accessions were used to convert the item coordinates according to the latest hg19 refseq release. For items that failed to convert, the gene symbols were queried using the GENCODEv40 hg19 lift comprehensive set. One additional gene symbol failed to map in hg19, KCNJ18, leading to 3 fewer items on this track when compared to the raw file. For both assemblies, GenCC OMIM data is excluded do to data restrictions. For complete documentation of the processing of these tracks, read the GenCC MakeDoc. Credits Thanks to the entire GenCC committee for creating these annotations and making them available. References DiStefano MT, Goehringer S, Babb L, Alkuraya FS, Amberger J, Amin M, Austin-Tse C, Balzotti M, Berg JS, Birney E et al. The Gene Curation Coalition: A global effort to harmonize gene-disease evidence resources. Genet Med. 2022 May 4;. PMID: 35507016 
interactions Gene Interactions Protein Interactions from Curated Databases and Text-Mining Phenotype and Literature Description The Pathways and Gene Interactions track shows a summary of gene interaction and pathway data collected from two sources: curated pathway/protein-interaction databases and interactions found through text mining of PubMed abstracts. Display Conventions and Configuration Track Display The track features a single item for each gene loci in the genome. On the item itself, the gene symbol for the loci is displayed followed by the top gene interactions noted by their gene symbol. Clicking an item will take you a gene interaction graph that includes detailed information on the support for the various interactions. Items are colored based on the number of documents supporting the interactions of a particular gene. Genes with &gt;100 supporting documents are colored black, genes with &gt;10 but &lt;100 supporting documents are colored dark blue, and those with &gt;10 supporting documents are colored light blue. Pathway and Gene Interaction Display See the help documentation accompanying this gene interaction graph for more information on its configuration. Methods The pathways and gene interactions were imported from a number of databases and mined from millions of PubMed abstracts. More information can be found in the &quot;Data Sources and Methods&quot; section of the help page for the gene interaction graph. Data Access The underlying data for this track can be accessed interactively through the Table Browser or Data Integrator. The data for this track is spread across a number of relational tables. The best way to export or analyze the data is using our public MySQL server. The list of tables and how they are linked together are described in the documentation linked at the bottom of the gene interaction viewer. The genome annotation is just a summary of the actual interactions database and therefore often not of interest to most users. It is stored in a bigBed file that can be obtained from the download server. The data underlying the graphical display is in bigBed formatted file named interactions.bb. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed. Instructions for downloading source code and precompiled binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/interactions.bb -chrom=chr6 -start=0 -end=1000000 stdout Credits The text-mined data for the gene interactions and pathways were generated by Chris Quirk and Hoifung Poon as part of Microsoft Research, Project Hanover. Pathway data was provided by the databases listed under &quot;Data Sources and Methods&quot; section of the help page for the gene interaction graph. In particular, thank you to Ian Donaldson from IRef for his unique collection of interaction databases. The short gene descriptions are a merge of the HPRD and PantherDB gene/molecule classifications. Thanks to Arun Patil from HPRD for making them available as a download. The track display and gene interaction graph were developed at the UCSC Genome Browser by Max Haeussler. References Poon H, Quirk C, DeZiel C, Heckerman D. Literome: PubMed-scale genomic knowledge base in the cloud Bioinformatics. 2014 Oct;30(19):2840-2. PMID: 24939151 
geneHancer GeneHancer GeneHancer Regulatory Elements and Gene Interactions Regulation Description GeneHancer is a database of human regulatory elements (enhancers and promoters) and their inferred target genes, which is embedded in GeneCards, a human gene compendium. The GeneHancer database was created by integrating &gt;1 million regulatory elements from multiple genome-wide databases. Associations between the regulatory elements and target genes were based on multiple sources of linking molecular data, along with distance, as described in Methods below. The GeneHancer track set contains tracks representing: Regulatory elements (GeneHancers) Gene transcription start sites Interactions (associations) between regulatory elements and genes Clustered interactions, by gene target or GeneHancer The full set of elements and interactions is included, along with a highly filtered &quot;double elite&quot; subset. Display Conventions Each GeneHancer regulatory element is identified by a GeneHancer id. For example: GH0XJ101383 is located on chromosome X, with starting position of 101,383 kb (GRCh38/hg38 reference). Based on the id, one can obtain full GeneHancer information, as displayed in the Genomics section within the gene-centric web pages of GeneCards. Links to the GeneCards information pages are provided on the track details pages. For the interaction tracks (Clusters and Interactions) a slight offset can be noticed between the line endpoints. This helps to identify the start and end of the feature. In this case, the higher point is the source (enhancers) and the lower point is the target. Regulatory elements Colors are used to distinguish promoters and enhancers and to indicate the GeneHancer element confidence score: Promoters:&nbsp; &emsp;&emsp;&nbsp;High &emsp;&emsp;&nbsp;Medium &emsp;&emsp;&nbsp;Low Enhancers:&nbsp; &emsp;&emsp;&nbsp;High &emsp;&emsp;&nbsp;Medium &emsp;&emsp;&nbsp;Low Gene TSS Colors are used to improve gene and interactions visibility. Successive genes are colored in different colors, and interactions of a gene have the same color. Interactions The Interactions view in Full mode shows GeneHancers and target genes connected by curves or half-rectangles (when one of the connected regions is off-screen). Configuration options are available to change the drawing style, and to limit the view to interactions with one or both connected items in the region. Interactions are identified on mouseover or clicked on for details at the end regions, or at the curve peak, which is marked with a gray ring shape. Interactions in the reverse direction (Gene TSS precedes GeneHancer on the genome) are drawn with a dashed line. Clusters The Clusters view groups interactions by target gene; the target gene and all GeneHancers associated with it are displayed in a single browser item. The gene TSS and associated GeneHancers are shown as blocks linked together, with the TSS drawn as a &quot;tall&quot; item, and the GeneHancers drawn &quot;short&quot;. A user configuration option is provided to change the view to group by GeneHancer (with tall GeneHancer and short TSS's). Clusters composed of interactions with a single gene are colored to correspond to the gene, and those composed of interactions with multiple genes are colored dark gray. Methods GeneHancer identifications were created from &gt;1 million regulatory elements obtained from seven genome-wide databases: ENCODE project Z-Lab Enhancer-like regions (version v3) Ensembl regulatory build (version 92) FANTOM5 atlas of active enhancers VISTA Enhancer Browser dbSUPER super-enhancers EPDnew promoters UCNEbase ultra-conserved noncoding elements Employing an integration algorithm that removes redundancy, the GeneHancer pipeline identified &tilde;250k integrated candidate regulatory elements (GeneHancers). Each GeneHancer is assigned an annotation-derived confidence score. The GeneHancers that are derived from more than one information source are defined as &quot;elite&quot; GeneHancers. Gene-GeneHancer associations, and their likelihood-based scores, were generated using information that helps link regulatory elements to genes: eQTLs (expression quantitative trait loci) from GTEx (version v6p) Capture Hi-C promoter-enhancer long range interactions FANTOM5 eRNA-gene expression correlations Cross-tissue expression correlations between a transcription factor interacting with a GeneHancer and a candidate target gene Distance-based associations, including several approaches: Nearest neighbors, where each GeneHancer is associated with its two proximal genes Overlaps with the gene territory (intragenic) Proximity to the gene TSS (&lt;2kb) Associations that are derived from more than one information source are defined as &quot;elite&quot; associations, which leads to the definition of the &quot;double elite&quot; dataset - elite gene associations of elite GeneHancers. More details are provided at the GeneCards information page. For a full description of the methods used, refer to the GeneHancer manuscript1. Source data for the GeneHancer version 4.8 was downloaded during May 2018. Data Access Due to our agreement with the Weizmann Institute, we cannot allow full genome queries from the Table Browser or share download files. You can still access data for individual chromosomes or positional data from the Table Browser. GeneHancer is the property of the Weizmann Institute of Science and is not available for download or mirroring by any third party without permission. Please contact the Weizmann Institute directly for data inquiries. Credits Thanks to Simon Fishilevich, Marilyn Safran, Naomi Rosen, and Tsippi Iny Stein of the GeneCards group and Shifra Ben-Dor of the Bioinformatics Core group at the Weizmann Institute, for providing this data and documentation, creating track hub versions of these tracks as prototypes, and overall responsiveness during development of these tracks. Contact: &#115;&#105;&#109;&#111;&#110;. &#102;&#105;&#115;&#104;&#105;&#108;ev&#105;&#99;h&#64;&#119;&#101;&#105;&#122;m&#97;&#110;&#110;. a&#99;. &#105;l Supported in part by a grant from LifeMap Sciences Inc. References Fishilevich S., Nudel R., Rappaport N., Hadar R., Plaschkes I., Iny Stein T., Rosen N., Kohn A., Twik M., Safran M., Lancet D. and Cohen D. GeneHancer: genome-wide integration of enhancers and target genes in GeneCards, Database (Oxford) (2017), doi:10.1093/database/bax028. [PDF] PMID 28605766 Stelzer G, Rosen R, Plaschkes I, Zimmerman S, Twik M, Fishilevich S, Iny Stein T, Nudel R, Lieder I, Mazor Y, Kaplan S, Dahary, D, Warshawsky D, Guan- Golan Y, Kohn A, Rappaport N, Safran M, and Lancet D. The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analysis, Current Protocols in Bioinformatics (2016), 54:1.30.1-1.30.33. doi: 10.1002/cpbi.5. PMID 27322403 
ghGeneHancer Reg Elem GeneHancer Regulatory Elements and Gene Interactions Regulation 
geneHancerRegElements GH Reg Elems Enhancers and promoters from GeneHancer Regulation 
geneHancerRegElementsDoubleElite GH Reg Elems (DE) Enhancers and promoters from GeneHancer (Double Elite) Regulation 
ghInteraction Interactions GeneHancer Regulatory Elements and Gene Interactions Regulation 
geneHancerInteractions GH Interactions Interactions between GeneHancer regulatory elements and genes Regulation 
geneHancerInteractionsDoubleElite GH Interactions (DE) Interactions between GeneHancer regulatory elements and genes (Double Elite) Regulation 
ghGeneTss Gene TSS GeneHancer Regulatory Elements and Gene Interactions Regulation 
geneHancerGenes GH genes TSS GH genes TSS Regulation 
geneHancerGenesDoubleElite GH genes TSS (DE) GeneCards genes TSS (Double Elite) Regulation 
ghClusteredInteraction Clustered Interactions GeneHancer Regulatory Elements and Gene Interactions Regulation 
geneHancerClusteredInteractions GH Clusters Clustered interactions of GeneHancer regulatory elements and genes Regulation 
geneHancerClusteredInteractionsDoubleElite GH Clusters (DE) Clustered interactions of GeneHancer regulatory elements and genes (Double Elite) Regulation 
geneid Geneid Genes Geneid Gene Predictions Genes and Gene Predictions Description This track shows gene predictions from the geneid program developed by Roderic Guig&oacute;'s Computational Biology of RNA Processing group which is part of the Centre de Regulaci&oacute; Gen&ograve;mica (CRG) in Barcelona, Catalunya, Spain. Methods Geneid is a program to predict genes in anonymous genomic sequences designed with a hierarchical structure. In the first step, splice sites, start and stop codons are predicted and scored along the sequence using Position Weight Arrays (PWAs). Next, exons are built from the sites. Exons are scored as the sum of the scores of the defining sites, plus the the log-likelihood ratio of a Markov Model for coding DNA. Finally, from the set of predicted exons, the gene structure is assembled, maximizing the sum of the scores of the assembled exons. Credits Thanks to Computational Biology of RNA Processing for providing these data. References Blanco E, Parra G, Guig&#243; R. Using geneid to identify genes. Curr Protoc Bioinformatics. 2007 Jun;Chapter 4:Unit 4.3. PMID: 18428791 Parra G, Blanco E, Guig&#243; R. GeneID in Drosophila. Genome Res. 2000 Apr;10(4):511-5. PMID: 10779490; PMC: PMC310871 
geneReviews GeneReviews GeneReviews Phenotype and Literature Description GeneReviews is an online collection of expert-authored, peer-reviewed articles that describe specific gene-related diseases. GeneReviews articles are searchable by disease name, gene symbol, protein name, author, or title. GeneReviews is supported by the National Institutes of Health, hosted at NCBI as part of the Genetic Testing Registry (GTR). The GeneReviews data underlying this track will be updated frequently. The GeneReviews track allows the user to locate the NCBI GeneReviews resource quickly from the Genome Browser. Hovering the mouse on track items shows the gene symbol and associated diseases. A condensed version of the GeneReviews article name and its related diseases are displayed on the item details page as links. Similar information, when available, is provided in the details page of items from the UCSC Genes, RefSeq Genes, and OMIM Genes tracks. Data Access The raw data for the GeneReviews track can be explored interactively with the Table Browser. Cross-referencing can be done with Data Integrator. The complete source file, in bigBed format, can be downloaded from our downloads directory. For automated analysis, the data may be queried from our REST API. Previous versions of this track can be found on our archive download server. References Pagon RA, Adam MP, Bird TD, et al., editors. GeneReviews&reg; [Internet]. Seattle (WA): University of Washington, Seattle; 1993-2014. Available from: https://www.ncbi.nlm.nih.gov/books/NBK1116. 
giab Genome In a Bottle Genome In a Bottle Structural Variants and Trios Variation Description The tracks listed here contain data from The Genome in a Bottle Consortium (GIAB), an open, public consortium hosted by NIST. The priority of GIAB is to develop reference standards, reference methods, and reference data by authoritative characterization of human genomes for use in benchmarking, including analytical validation and technology development that will support translation of whole human genome sequencing to clinical practice. The sole purpose of this work is to provide validated variants and regions to enable technology and bioinformatics developers to benchmark and optimize their detection methods. The Ashkenazim and the Chinese Trio tracks show benchmark SNV calls from two son/father/mother trios of Ashkenazi Jewish and Han Chinese ancestry from the Personal Genome Project, consented for commercial redistribution. The Genome In a Bottle Structural Variants track shows benchmark SV calls (nssv) and variant regions (nsv) (5,262 insertions and 4,095 deletions, &gt 50 bp, in 2.51 Gb of the genome) from the son (HG002/NA24385) from the Ashkenazi Jewish trio. Samples are disseminated as National Institute of Standards and Technology (NIST) Reference Materials. Display Conventions and Configuration These tracks are multi-view composite tracks that contain multiple data types (views). Each view within a track has separate display controls, as described here. Unlike a regular genome browser track, the Ashkenazim and the Chinese Trio tracks display the genome variants of each individual as two haplotypes; SNPs, small insertions and deletions are mapped to each haplotype based on the phasing information of the VCF file. The haplotype 1 and the haplotype 2 are displayed as two separate black lanes for the browser window region. Each variant is drawn as a vertical dash. Homozygous variants will show two identical dashes on both haplotype lanes. Phased heterozygous variants are placed on one of the haplotype lanes and unphased heterozygous variants are displayed in the area between the two haplotype lanes. Predicted de novo variants and variants that are inconsistent with phasing in the trio son can be colored in red using the track Configuration options. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Benchmark VCF and BED files for small variants are available for GRCh37 and GRCh38 under each genome at NCBI FTP site. Structural variants are available for GRCh37 at dbVAR nst175. References Zook JM, McDaniel J, Olson ND, Wagner J, Parikh H, Heaton H, Irvine SA, Trigg L, Truty R, McLean CY et al. An open resource for accurately benchmarking small variant and reference calls. Nat Biotechnol. 2019 May;37(5):561-566. PMID: 30936564; PMC: PMC6500473 Zook JM, Hansen NF, Olson ND, Chapman L, Mullikin JC, Xiao C, Sherry S, Koren S, Phillippy AM, Boutros PC et al. A robust benchmark for detection of germline large deletions and insertions. Nat Biotechnol. 2020 Jun 15;. PMID: 32541955 
svView Structural Variants Genome In a Bottle Structural Variants (dbVar nstd175) Variation 
giabSv Structural Variants Genome in a Bottle Structural Variants (dbVar nstd175) Variation 
triosView Genome In a Bottle Trios Genome in a Bottle Ashkenazim and Chinese Trios Variation 
chineseTrio Chinese Trio Genome In a Bottle Chinese  Trio Variation 
ashkenazimTrio Ashkenazim Trio Genome In a Bottle Ashkenazim Trio Variation 
genscan Genscan Genes Genscan Gene Predictions Genes and Gene Predictions Description This track shows predictions from the Genscan program written by Chris Burge. The predictions are based on transcriptional, translational and donor/acceptor splicing signals as well as the length and compositional distributions of exons, introns and intergenic regions. For more information on the different gene tracks, see our Genes FAQ. Display Conventions and Configuration This track follows the display conventions for gene prediction tracks. The track description page offers the following filter and configuration options: Color track by codons: Select the genomic codons option to color and label each codon in a zoomed-in display to facilitate validation and comparison of gene predictions. Go to the Coloring Gene Predictions and Annotations by Codon page for more information about this feature. Methods For a description of the Genscan program and the model that underlies it, refer to Burge and Karlin (1997) in the References section below. The splice site models used are described in more detail in Burge (1998) below. Credits Thanks to Chris Burge for providing the Genscan program. References Burge C. Modeling Dependencies in Pre-mRNA Splicing Signals. In: Salzberg S, Searls D, Kasif S, editors. Computational Methods in Molecular Biology. Amsterdam: Elsevier Science; 1998. p. 127-163. Burge C, Karlin S. Prediction of complete gene structures in human genomic DNA. J. Mol. Biol. 1997 Apr 25;268(1):78-94. PMID: 9149143 
gnfAtlas2 GNF Atlas 2 GNF Expression Atlas 2 Expression Description This track shows expression data from the GNF Gene Expression Atlas 2. This contains two replicates each of 79 human tissues run over Affymetrix microarrays. By default, averages of related tissues are shown. Display all tissues by selecting &quot;All Arrays&quot; from the &quot;Combine arrays&quot; menu on the track settings page. As is standard with microarray data red indicates overexpression in the tissue, and green indicates underexpression. You may want to view gene expression with the Gene Sorter as well as the Genome Browser. Credits Thanks to the Genomics Institute of the Novartis Research Foundation (GNF) for the data underlying this track. References Su AI, Wiltshire T, Batalov S, Lapp H, Ching KA, Block D, Zhang J, Soden R, Hayakawa M, Kreiman G et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci U S A. 2004 Apr 20;101(16):6062-7. PMID: 15075390; PMC: PMC395923 
gnomadPLI gnomAD Constraint Metrics Genome Aggregation Database (gnomAD) Predicted Constraint Metrics (LOEUF, pLI, and Z-scores) Variation Description The Genome Aggregation Database (gnomAD) - Predicted Constraint Metrics track set contains metrics of pathogenicity per-gene as predicted for gnomAD v2.1.1, v4.0, or v4.1 and identifies genes subject to strong selection against various classes of mutation. This track includes several subtracks of constraint metrics calculated at gene (canonical transcript) and transcript level. For more information see the following blog post. The metrics include: Observed and expected variant counts per transcript/gene Observed/Expected ratio (O/E) Z-scores of the observed counts compared to expected Probability of loss of function intolerance (pLI), for predicted loss-of-function (pLoF) variation only Display Conventions and Configuration There are two &quot;groups&quot; of tracks in this set, and three gnomAD versions (v2.1.1, v4.0, and v4.1): Gene/Transcript LoF Constraint tracks: Predicted constraint metrics at the whole gene level or whole transcript level for three different types of variation: missense, synonymous, and predicted loss of function. The Gene Constraint track displays metrics for a canonical transcript per gene defined as the longest isoform. The Transcript Constraint track displays metrics for all transcript isoforms. Items on both tracks are shaded according to the pLI score, with outlier items shaded in grey. Please note there is no gene-level track available for v4.0 and v4.1. Gene/Transcript Missense Constraint tracks: The missense constraint tracks are built similarly to the LoF constraint tracks, however the items displayed are based on missense Z scores. All items are colored black, and individual Z scores can be seen on mouseover. All tracks follow the general configuration settings for bigBed tracks. Mouseover on the Gene/Transcript Constraint tracks shows the pLI score and the loss of function observed/expected upper bound fraction (LOEUF), while mouseover on the Regional Constraint track shows only the missense O/E ratio. Clicking on items in any track brings up a table of constraint metrics. Clicking the grey box to the left of the track, or right-clicking and choosing the Configure option, brings up the interface for filtering items based on their pLI score, or labeling the items based on their Ensembl identifier and/or Gene Name. Methods Please see the gnomAD browser help page and FAQ for further explanation of the topics below. Observed and Expected Variant Counts Observed count: The number of unique single-nucleotide variants in each transcript/gene with 123 or fewer alternative alleles (MAF &lt; 0.1%). Expected count: A depth-corrected probability prediction model that takes into account sequence context, coverage, and methylation was used to predict expected variant counts. For more information please see Lek et al., 2016. Variants found in exons with a median depth &lt; 1 were removed from both counts. The O/E constraint score is the ratio of the observed/expected variants in that gene. Each item in this track shows the O/E ratio for three different types of variation: missense, synonymous, and loss-of-function. The O/E ratio is a continuous measurement of how tolerant a gene or transcript is to a certain class of variation. When a gene has a low O/E value, it is under stronger selection for that class of variation than a gene with a higher O/E value. Because Counts depend on gene size and sample size, the precision of the values varies a lot from one gene to the next. Therefore, the 90% confidence interval (CI) is also displayed along with the O/E ratio to better assist interpretation of the scores. When evaluating how constrained a gene is, it is essential to consider the CI when using O/E. In research and clinical interpretation of Mendelian cases, pLI > 0.9 has been widely used for filtering. Accordingly, the Gnomad team suggests using the upper bound of the O/E confidence interval LOEUF &lt; 0.35 as a threshold if needed. Please see the Methods section below for more information about how the scores were calculated. pLI and Z-scores The pLI and Z-scores of the deviation of observed variant counts relative to the expected number are intended to measure how constrained or intolerant a gene or transcript is to a specific type of variation. Genes or transcripts that are particularly depleted of a specific class of variation (as observed in the gnomAD data set) are considered intolerant of that specific type of variation. Z-scores are available for the missense and synonynmous categories and pLI scores are available for the loss-of-function variation. Missense and Synonymous: Positive Z-scores indicate more constraint (fewer observed variants than expected), and negative scores indicate less constraint (more observed variants than expected). A greater Z-score indicates more intolerance to the class of variation. Z-scores were generated by a sequence-context-based mutational model that predicted the number of expected rare (&lt; 1% MAF) variants per transcript. The square root of the chi-squared value of the deviation of observed counts from expected counts was multiplied by -1 if the observed count was greater than the expected and vice versa. For the synonymous score, each Z-score was corrected by dividing by the standard deviation of all synonymous Z-scores between -5 and 5. For the missense scores, a mirrored distribution of all Z-scores between -5 and 0 was created, and then all missense Z-scores were corrected by dividing by the standard deviation of the Z-score of the mirror distribution. Loss-of-function: pLI closer to 1 indicates that the gene or transcript cannot tolerate protein truncating variation (nonsense, splice acceptor and splice donor variation). The gnomAD team recommends transcripts with a pLI &gt;= 0.9 for the set of transcripts extremely intolerant to truncating variants. pLI is based on the idea that transcripts can be classified into three categories: null: heterozygous or homozygous protein truncating variation is completely tolerated recessive: heterozygous variants are tolerated but homozygous variants are not haploinsufficient: heterozygous variants are not tolerated An expectation-maximization algorithm was then used to assign a probability of belonging in each class to each gene or transcript. pLI is the probability of belonging in the haploinsufficient class. Please see Samocha et al., 2014 and Lek et al., 2016 for further discussion of these metrics. Transcripts Included For version 2.1.1 only, the GENCODE transcripts were filtered according to the following criteria: Must have methionine at start of coding sequence Must have stop codon at end of coding sequence Must be divisible by 3 Must have at least one observed variant when removing exons with median depth &lt; 1 Must have reasonable number of missense and synonymous variants as determined by a Z-score cutoff For version v2.1.1, the gnomAD gene/transcript data is based on hg19. In order to map transcripts and genes to the hg38 genome the following steps were taken: Transcript track: The gnomAD ENST identifiers were attempted to be matched to all GENCODE versions between V20 and V44, giving coordinate priorities to the most recent models. In total 74550/80950 transcripts were mapped. Genes track: The gnomAD file ENSG identifiers were attempted to be matched to all GENCODE versions between V20 and V44, giving coordinate priorities to the most recent models. This mapped 19221/19704 genes. The remainder of the genes were attempted to be mapped using the same strategy, but matching on gene symbols instead of ENSG identifiers. In total 19567/19704 genes were mapped. For version v4.0 and v4.1, the gnomAD transcript data is based on hg38. In order to map the transcripts to hg38, the transcript version numbers in the gnomAD download file were joined with GENCODE V39 and NCBI RefSeq coordinates available at UCSC. UCSC Track Methods Version based on gnomAD v2.1.1 Gene and Transcript Constraint tracks Per gene and per transcript data were downloaded from the gnomAD Google Storage bucket: gs://gnomad-public/release/2.1.1/constraint/gnomad.v2.1.1.lof_metrics.by_gene.txt.bgz gs://gnomad-public/release/2.1.1/constraint/gnomad.v2.1.1.lof_metrics.by_transcript.txt.bgz These data were then joined to the Gencode set of genes/transcripts available at the UCSC Genome Browser (see previous section) and then transformed into a bigBed 12+5. For the full list of commands used to make this track please see the makedoc. Version based on gnomAD v4.0 Gene and Transcript Constraint tracks Per gene and per transcript data were downloaded from the gnomAD Google Storage bucket: https://storage.googleapis.com/gcp-public-data--gnomad/release/4.0/constraint/gnomad.v4.0.constraint_metrics.tsv These data were then joined to the Gencode/NCBI set of genes/transcripts available at the UCSC Genome Browser and then transformed into a bigBed 12+5. For the full list of commands used to make this track please see the makedoc. Version based on gnomAD v4.1 Gene and Transcript Constraint tracks Per gene and per transcript data were downloaded from the gnomAD Google Storage bucket: https://storage.googleapis.com/gcp-public-data--gnomad/release/4.1/constraint/gnomad.v4.1.constraint_metrics.tsv These data were then joined to the Gencode/NCBI set of genes/transcripts available at the UCSC Genome Browser and then transformed into a bigBed 12+5. For the full list of commands used to make this track please see the makedoc. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated access, this track, like all others, is available via our API. However, for bulk processing, it is recommended to download the dataset. The genome annotation is stored in a bigBed file that can be downloaded from the download server. The exact filenames can be found in the track configuration file. Annotations can be converted to ASCII text by our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/gnomAD/pLI/pliByTranscript.bb -chrom=chr6 -start=0 -end=1000000 stdout Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. More information about using and understanding the gnomAD data can be found in the gnomAD FAQ site. Credits Thanks to the Genome Aggregation Database Consortium for making these data available. The data are released under the ODC Open Database License (ODbL) as described here. References Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill AJ, Cummings BB et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016 Aug 18;536(7616):285-91. PMID: 27535533; PMC: PMC5018207 Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alf&#246;ldi J, Wang Q, Collins RL, Laricchia KM, Ganna A, Birnbaum DP et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020 May;581(7809):434-443. PMID: 32461654; PMC: PMC7334197 Collins RL, Brand H, Karczewski KJ, Zhao X, Alf&#246;ldi J, Francioli LC, Khera AV, Lowther C, Gauthier LD, Wang H et al. A structural variation reference for medical and population genetics. Nature. 2020 May;581(7809):444-451. PMID: 32461652; PMC: PMC7334194 Cummings BB, Karczewski KJ, Kosmicki JA, Seaby EG, Watts NA, Singer-Berk M, Mudge JM, Karjalainen J, Satterstrom FK, O'Donnell-Luria AH et al. Transcript expression-aware annotation improves rare variant interpretation. Nature. 2020 May;581(7809):452-458. PMID: 32461655; PMC: PMC7334198 
constraintV4_1 Constraint V4.1 gnomAD Constraint Metrics V4.1 Variation 
missenseByTranscriptV4_1 Transcript Missense v4.1 gnomAD Predicted Missense Constraint Metrics By Transcript (Z-scores) v4.1 Variation 
pliByTranscriptV4_1 Transcript LoF v4.1 gnomAD Predicted Loss of Function Constraint Metrics By Transcript (LOEUF and pLI) v4.1 Variation 
constraintV4 Constraint V4 gnomAD Constraint Metrics V4 Variation 
missenseByTranscriptV4 Transcript Missense v4 gnomAD Predicted Missense Constraint Metrics By Transcript (Z-scores) v4 Variation 
pliByTranscriptV4 Transcript LoF v4 gnomAD Predicted Loss of Function Constraint Metrics By Transcript (LOEUF and pLI) v4 Variation 
constraintV2 Constraint V2 gnomAD Constraint Metrics V2 Variation 
missenseByTranscript Transcript Missense v2 gnomAD Predicted Missense Constraint Metrics By Transcript (Z-scores) v2.1.1 Variation 
pliByTranscript Transcript LoF v2 gnomAD Predicted Loss of Function Constraint Metrics By Transcript (LOEUF and pLI) v2.1.1 Variation 
missenseByGene Gene Missense gnomAD Predicted Missense Constraint Metrics By Gene (Z-scores) v2.1.1 Variation 
pliByGene Gene LoF gnomAD Predicted Loss of Function Constraint Metrics By Gene (LOEUF and pLI) v2.1.1 Variation 
gnomadCopyNumberVariants gnomAD Rare CNV Variants Genome Aggregation Database (gnomAD) - Rare CNV variants (<1% overall site frequency) v4.1 Variation Description The Genome Aggregation Database (gnomAD) - Rare CNV variants ( track set shows rare autosomal coding copy number variants (CNVs) with an overall site frequency of less than 1%. These variants were identified from exome sequencing (ES) data of 464,297 individuals. The data can also be explored via the gnomAD browser. Display Conventions and Configuration Items are colored by the type of variant: Variant Type Deletion (DEL) 20989 Duplication (DUP) 25026 . Mouseover on an item will display the position, size of variant, genes impacted by variant (&gt;=10% CDS overlap by deletion or &gt;=75% CDS overlap by duplication), and site frequency of non-neuro control samples. Item description pages include a linkout to the gnomAD browser showing additional genetic ancestry group information. Methods Exome CNV Discovery Method: GATK-gCNV To identify rare coding CNVs from the ES data of 464,297 individuals in gnomAD v4, the GATK-gCNV method was employed, as described in Babadi et al., Nat Genet, 2023. The CNV discovery process started with collecting the number of reads mapped to 363,301 autosomal target intervals derived from protein-coding exons (Fig. 1a, b; Babadi et al.). These read counts were used to capture sample-level technical variability, such as differences in exome capture kits or sequencing centers, and generated 1,045 different batches of samples for parallel processing (Fig. 1c). For each of these batches, 200 random samples were selected for training GATK-gCNV in cohort mode,which can be thought of as the creation of a &quot;panel of normals&quot; (PoN). The resulting PoN models were then used to efficiently delineate CNV events on all of the samples of their respective cohorts using the GATK-gCNV case mode (Fig. 1d,e). The raw, individual-level CNV calls produced by GATK-gCNV for all samples were then collated, and variants observed in multiple individuals were clustered using single-linkage clustering. Quality filtering followed the procedures outlined in Babadi et al., filtering CNVs based on sample-level (number of events per individual) and call-level (frequency, size, quality score) metrics Due to the significant increase in cohort size and heterogeneity compared to the datasets reported in Babadi et al., additional filters were applied. Samples with more than five chromosomes harboring rare CNVs, as well as those containing more than three rare terminal CNVs, were excluded. 1,049 sites producing noisy normalized read-depth signals were masked. The final retained CNVs and sites were subsequently annotated for impacted genes and frequencies. Limitations of ES-based rare coding CNVs in gnomAD v4 This dataset includes only rare coding CNVs, filtered to &lt;1% site frequency in the overall dataset. This dataset only includes variants that span three or more exons that received sufficient coverage. This dataset is limited to autosomal CNVs for now. More information can be found at the gnomAD site. The bed files was obtained from the gnomAD Google Storage bucket: https://storage.googleapis.com/gcp-public-data--gnomad/release/4.1/exome_cnv/gnomad.v4.1.cnv.non_neuro_controls.bed The data was then transformed into a bigBed track. For the full list of commands used to make this track please see the &quot;gnomAD CNVs v4.1&quot; section of the makedoc. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated access, this track, like all others, is available via our API. However, for bulk processing, it is recommended to download the dataset. The genome annotation is stored in a bigBed file that can be downloaded from the download server. The exact filenames can be found in the track configuration file. Annotations can be converted to ASCII text by our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/gnomAD/v4/cnv/gnomad.v4.1.cnv.non_neuro_controls.bb -chrom=chr6 -start=0 -end=1000000 stdout Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. More information about using and understanding the gnomAD data can be found in the gnomAD FAQ site. Credits Thanks to the Genome Aggregation Database Consortium for making these data available. The data are released under the ODC Open Database License (ODbL) as described here. References Babadi M, Fu JM, Lee SK, Smirnov AN, Gauthier LD, Walker M, Benjamin DI, Zhao X, Karczewski KJ, Wong I et al. GATK-gCNV enables the discovery of rare copy number variants from exome sequencing data. Nat Genet. 2023 Sep;55(9):1589-1597. PMID: 37604963; PMC: PMC10904014 Collins RL, Brand H, Karczewski KJ, Zhao X, Alf&#246;ldi J, Francioli LC, Khera AV, Lowther C, Gauthier LD, Wang H et al. A structural variation reference for medical and population genetics. Nature. 2020 May;581(7809):444-451. PMID: 32461652; PMC: PMC7334194 Cummings BB, Karczewski KJ, Kosmicki JA, Seaby EG, Watts NA, Singer-Berk M, Mudge JM, Karjalainen J, Satterstrom FK, O'Donnell-Luria AH et al. Transcript expression-aware annotation improves rare variant interpretation. Nature. 2020 May;581(7809):452-458. PMID: 32461655; PMC: PMC7334198 Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alf&#246;ldi J, Wang Q, Collins RL, Laricchia KM, Ganna A, Birnbaum DP et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020 May;581(7809):434-443. PMID: 32461654; PMC: PMC7334197 Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill AJ, Cummings BB et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016 Aug 18;536(7616):285-91. PMID: 27535533; PMC: PMC5018207 
gnomadStructuralVariants gnomAD Structural Variants Genome Aggregation Database (gnomAD) - Structural Variants v4.1 Variation Description The Genome Aggregation Database (gnomAD) - Structural Variants v4.1 track set shows structural variants calls (&gt;=50 nucleotides) from the gnomAD v4.1 release on 63,046 unrelated genomes. It mostly (but not entirely) overlaps with the genome set used for the gnomAD short variant release. For more information see the following blog post, Structural variants in gnomAD. Display Conventions and Configuration Items are shaded according to variant type, mouseover on items indicates affected protein-coding genes, size of the variant (which may differ from the chromosomal coordinates in cases like insertions), variant type (insertion, duplication, etc), allele count, allele number, and allele frequency. When more than 2 genes are affected by a variant, the full list can be obtained by clicking on the item and reading the details page. A short summary is available in the below table: Variant Type All SV's Breakend (BND) 356035 Complex (CPX) 15189 Translocation (CTX) 99 Deletion (DEL) 1206278 Duplication (DUP) 269326 Insertion (INS) 304645 Inversion (INV) 2193 Copy number variants (CNV) 721 Detailed information on the CNV color code is described here. All tracks can be filtered according to the size of the variant and variant type, using the track Configure options. Filtering Options Three filters are available for this track: Variant Size: Used to exclude/include variants according to the size. Non-neurological allele frequency: Used to exclude/include allele frequency of variants in individuals who do not have a neurological condition, as identified in a case-control study. Common disease control allele frequency: Used to exclude/include allele frequency of variants in individuals not identified as cases in a case-control study of common disease. Methods The bed files was obtained from the gnomAD Google Storage bucket: https://storage.googleapis.com/gcp-public-data--gnomad/release/4.1/genome_sv/gnomad.v4.1.sv.non_neuro_controls.sites.bed.gz The data was then transformed into a bigBed track. For the full list of commands used to make this track please see the &quot;gnomAD Structural Variants v4&quot; section of the makedoc. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated access, this track, like all others, is available via our API. However, for bulk processing, it is recommended to download the dataset. The genome annotation is stored in a bigBed file that can be downloaded from the download server. The exact filenames can be found in the track configuration file. Annotations can be converted to ASCII text by our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/gnomAD/v4/structuralVariants/gnomad.v4.1.sv.non_neuro_controls.sites.bb -chrom=chr6 -start=0 -end=1000000 stdout Please refer to our mailing list archives for questions and example queries, or our Data Access FAQ for more information. More information about using and understanding the gnomAD data can be found in the gnomAD FAQ site. Credits Thanks to the Genome Aggregation Database Consortium for making these data available. The data are released under the ODC Open Database License (ODbL) as described here. References Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill AJ, Cummings BB et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016 Aug 18;536(7616):285-91. PMID: 27535533; PMC: PMC5018207 Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alf&#246;ldi J, Wang Q, Collins RL, Laricchia KM, Ganna A, Birnbaum DP et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020 May;581(7809):434-443. PMID: 32461654; PMC: PMC7334197 Collins RL, Brand H, Karczewski KJ, Zhao X, Alf&#246;ldi J, Francioli LC, Khera AV, Lowther C, Gauthier LD, Wang H et al. A structural variation reference for medical and population genetics. Nature. 2020 May;581(7809):444-451. PMID: 32461652; PMC: PMC7334194 Cummings BB, Karczewski KJ, Kosmicki JA, Seaby EG, Watts NA, Singer-Berk M, Mudge JM, Karjalainen J, Satterstrom FK, O'Donnell-Luria AH et al. Transcript expression-aware annotation improves rare variant interpretation. Nature. 2020 May;581(7809):452-458. PMID: 32461655; PMC: PMC7334198 
ctgPos2 GRC Contigs Genome Reference Consortium Contigs Mapping and Sequencing Description This track shows the names of the assembled supercontigs for the GRCh38 (hg38) assembly determined by the Genome Reference Consortium (GRC). Data for this track were obtained from localId2acc files downloaded from GenBank. 
grcIncidentDb GRC Incident GRC Incident Database Mapping and Sequencing Description This track shows locations in the human assembly where assembly problems have been noted or resolved, as reported by the Genome Reference Consortium (GRC). If you would like to report an assembly problem, please use the GRC issue reporting system. Methods Data for this track are extracted from the GRC incident database from the specific species *_issues.gff3 file. The track is synchronized once daily to incorporate new updates. Credits The data and presentation of this track were prepared by Hiram Clawson. 
gtexEqtlHighConf GTEx cis-eQTLs GTEx fine-mapped cis-eQTLs Regulation Description This track shows genetic variants likely affecting proximal gene expression in 49 human tissues from the Genotype-Tissue Expression (GTEx) V8 data release. The data items displayed are gene expression quantitative trait loci within 1MB of gene transcription start sites (cis-eQTLs), significantly associated with gene expression and in the credible set of variants for the gene at a high confidence level. The data can only be calculated for the autosomes, so no data is shown on chrX. Display Conventions Both the CAVIAR and DAP-G tracks show gene/variant pairs for 49 GTEx tissues. Variants are linked to the genes they interact with by a line. Variants are represented by thicker-width, single-base items. Genes are represented as thinner-width items covering the length of the gene. The direction of the chevrons on the line indicate whether the variant is upstream or downstream of the gene with the chevrons always pointing from the variant to the gene. If a variant is internal to the gene, then the variant is shown as a thicker segment than the gene. Items in the track are colored according to their tissue, with the color matching those in the GTEx Gene V8 Track. Hovering over items in the track display will show the variant ID (often a dbSNP rsID), the target gene, tissue, and posterior probablity (Causal Posterior Probability (CPP) for CAVIAR; SNP Posterior Inclusion Probability (PIP) for DAP-G). Clicking an item will show the details of that interaction with link outs to view more details on the GTEx website. Track configuration supports filtering by tissue, gene, or posterior probability. Methods Details on GTEx v8 analysis, including code, can be found in the GTEx GWAS Analysis Github. Raw data for these analyses are available from the GTEx Portal. CAVIAR The CAVIAR track at UCSC was created using the CAVIAR high-confidence set, which represents the high causal variants that have a causal posterior probability (CPP) of &gt; 0.1. DAP-G The DAP-G track at UCSC was created using the DAP-G 95% credible set, which represents varaints with strong eQTLs signals, which are signal clusters with signal-level posterior inclusion probability (SPIP) &gt; 0.95. Data Access The raw data for this track can be accessed in multiple ways. It can be explored interactively using the Table Browser or Data Integrator. You can also access the data entries in JSON format through our JSON API. The data in this track are organized in bigBed file format. The underlying files can be obtained from our downloads server: GTEx CAVIAR - gtexCaviar.bb GTEx DAP-G - gtexDapg.bb Individual regions or the whole set of genome-wide annotations can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system from the utilities directory linked below. For example, to extract only annotations in a given region, you could use the following command: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/gtex/eQtl/gtexCaviar.bb -chrom=chr16 -start=34990190 -end=36727467 stdout Credits Thanks to GTEx investigators, analysts, and portal team for providing this data. References GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science. 2020 Sep 11;369(6509):1318-1330. PMID: 32913098; PMC: PMC7737656 Lee Y, Luca F, Pique-Regi R, Wen X. Bayesian Multi-SNP Genetic Association Analysis: Control of FDR and Use of Summary Statistics. bioRxiv. 2018 May 8. Wen X, Lee Y, Luca F, Pique-Regi R. Efficient Integrative Multi-SNP Association Analysis via Deterministic Approximation of Posteriors. Am J Hum Genet. 2016 Jun 2;98(6):1114-1129. PMID: 27236919; PMC: PMC4908152 Ongen H, Buil A, Brown AA, Dermitzakis ET, Delaneau O. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics. 2016 May 15;32(10):1479-85. PMID: 26708335; PMC: PMC4866519 Hormozdiari F, Kostem E, Kang EY, Pasaniuc B, Eskin E. Identifying causal variants at loci with multiple signals of association. Genetics. 2014 Oct;198(2):497-508. PMID: 25104515; PMC: PMC4196608 GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013 Jun;45(6):580-5. PMID: 23715323; PMC: PMC4010069 GTEx Portal Documentation 
gtexEqtlDapg GTEx DAP-G eQTLs GTEx High-Confidence cis-eQTLs from DAP-G (no chrX) Regulation 
gtexEqtlCaviar GTEx CAVIAR eQTLs GTEx High-Confidence cis-eQTLs from CAVIAR (no chrX) Regulation 
gtexGene GTEx Gene Gene Expression in 53 tissues from GTEx RNA-seq of 8555 samples (570 donors) Expression Description The NIH Genotype-Tissue Expression (GTEx) project was created to establish a sample and data resource for studies on the relationship between genetic variation and gene expression in multiple human tissues. This track shows median gene expression levels in 51 tissues and 2 cell lines, based on RNA-seq data from the GTEx midpoint milestone data release (V6, October 2015). This release is based on data from 8555 tissue samples obtained from 570 adult post-mortem individuals. Display Conventions In Full and Pack display modes, expression for each gene is represented by a colored bargraph, where the height of each bar represents the median expression level across all samples for a tissue, and the bar color indicates the tissue. Tissue colors were assigned to conform to the GTEx Consortium publication conventions. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The bargraph display has the same width and tissue order for all genes. Mouse hover over a bar will show the tissue and median expression level. The Squish display mode draws a rectangle for each gene, colored to indicate the tissue with highest expression level if it contributes more than 10% to the overall expression (and colored black if no tissue predominates). In Dense mode, the darkness of the grayscale rectangle displayed for the gene reflects the total median expression level across all tissues. The GTEx transcript model used to quantify expression level is displayed below the graph, colored to indicate the transcript class (coding, noncoding, pseudogene, problem), following GENCODE conventions. Click-through on a graph displays a boxplot of expression level quartiles with outliers, per tissue, along with a link to the corresponding gene page on the GTEx Portal. The track configuration page provides controls to limit the genes and tissues displayed, and to select raw or log transformed expression level display. Methods Tissue samples were obtained using the GTEx standard operating procedures for informed consent and tissue collection, in conjunction with the National Cancer Institute Biorepositories and Biospecimen. All tissue specimens were reviewed by pathologists to characterize and verify organ source. Images from stained tissue samples can be viewed via the NCI histopathology viewer. The Qiagen PAXgene non-formalin tissue preservation product was used to stabilize tissue specimens without cross-linking biomolecules. RNA-seq was performed by the GTEx Laboratory, Data Analysis and Coordinating Center (LDACC) at the Broad Institute. The Illumina TruSeq protocol was used to create an unstranded polyA+ library sequenced on the Illumina HiSeq 2000 platform to produce 76-bp paired end reads at a depth averaging 50M aligned reads per sample. Sequence reads were aligned to the hg19/GRCh37 human genome using Tophat v1.4.1 assisted by the GENCODE v19 transcriptome definition. Gene annotations were produced by taking the union of the GENCODE exons for each gene. Gene expression levels in RPKM were called via the RNA-SeQC tool, after filtering for unique mapping, proper pairing, and exon overlap. For further method details, see the GTEx Portal Documentation page. UCSC obtained the gene-level expression files, gene annotations and sample metadata from the GTEx Portal Download page. Median expression level in RPKM was computed per gene/per tissue. Subject and Sample Characteristics The scientific goal of the GTEx project required that the donors and their biospecimen present with no evidence of disease. The tissue types collected were chosen based on their clinical significance, logistical feasibility and their relevance to the scientific goal of the project and the research community. Postmortem samples were collected from non-diseased donors with ages ranging from 20 to 79. 34.4% of donors were female and 65.6% male. Additional summary plots of GTEx sample characteristics are available at the GTEx Portal Tissue Summary page. Data Access The raw data for the GTEx Gene expression track can be accessed interactively through the Table Browser or Data Integrator. Metadata can be found in the connected tables below. gtexGeneModel describes the gene names and coordinates in genePred format. hgFixed.gtexTissue lists each of the 53 tissues in alphabetical order, corresponding to the comma separated expression values in gtexGene. hgFixed.gtexSampleData has RPKM expression scores for each individual gene-sample data point, connected to gtexSample. hgFixed.gtexSample contains metadata about sample time, collection site, and tissue, connected to the donor field in the gtexDonor table. hgFixed.gtexDonor has anonymized information on the tissue donor. For automated analysis and downloads, the track data files can be downloaded from our downloads server or the JSON API. Individual regions or the whole genome annotation can be accessed as text using our utility bigBedToBed. Instructions for downloading the utility can be found here. That utility can also be used to obtain features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg19/gtex/gtexTranscExpr.bb -chrom=chr21 -start=0 -end=100000000 stdout Data can also be obtained directly from GTEx at the following link: https://gtexportal.org/home/datasets Credits Statistical analysis and data interpretation was performed by The GTEx Consortium Analysis Working Group. Data was provided by the GTEx LDACC at The Broad Institute of MIT and Harvard. References GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013 Jun;45(6):580-5. PMID: 23715323; PMC: PMC4010069 Carithers LJ, Ardlie K, Barcus M, Branton PA, Britton A, Buia SA, Compton CC, DeLuca DS, Peter-Demchok J, Gelfand ET et al. A Novel Approach to High-Quality Postmortem Tissue Procurement: The GTEx Project. Biopreserv Biobank. 2015 Oct;13(5):311-9. PMID: 26484571; PMC: PMC4675181 Mel&#233; M, Ferreira PG, Reverter F, DeLuca DS, Monlong J, Sammeth M, Young TR, Goldmann JM, Pervouchine DD, Sullivan TJ et al. Human genomics. The human transcriptome across tissues and individuals. Science. 2015 May 8;348(6235):660-5. PMID: 25954002; PMC: PMC4547472 DeLuca DS, Levin JZ, Sivachenko A, Fennell T, Nazaire MD, Williams C, Reich M, Winckler W, Getz G. RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics. 2012 Jun 1;28(11):1530-2. PMID: 22539670; PMC: PMC3356847 
gtexTranscExpr GTEx Transcript Transcript Expression in 53 tissues from GTEx RNA-seq of 8555 samples/570 donors Expression Description The NIH Genotype-Tissue Expression (GTEx) project was created to establish a sample and data resource for studies on the relationship between genetic variation and gene expression in multiple human tissues. This track displays median transcript expression levels in 53 tissues, based on RNA-seq data from the GTEx midpoint milestone data release (V6, October 2015). To view the GTEx tissues in anatomical context, see the GTEx Body Map. Data for this track were computed at UCSC from GTEx RNA-seq sequence data using the Toil pipeline running the kallisto transcript-level quantification tool. Display Conventions In Full and Pack display modes, expression for each transcript is represented by a colored bar chart, where the height of each bar represents the median expression level across all samples for a tissue, and the bar color indicates the tissue. The bar chart display has the same width and tissue order for all transcripts. Mouse hover over a bar will show the tissue and median expression level. The Squish display mode draws a rectangle for each gene, colored to indicate the tissue with highest expression level if it contributes more than 10% to the overall expression (and colored black if no tissue predominates). In Dense mode, the darkness of the grayscale rectangle displayed for the transcript reflects the total median expression level across all tissues. Click-through on a graph displays a boxplot of expression level quartiles with outliers, per tissue. Methods Tissue samples were obtained using the GTEx standard operating procedures for informed consent and tissue collection, in conjunction with the National Cancer Institute Biorepositories and Biospecimen. All tissue specimens were reviewed by pathologists to characterize and verify organ source. Images from stained tissue samples can be viewed via the NCI histopathology viewer. The Qiagen PAXgene non-formalin tissue preservation product was used to stabilize tissue specimens without cross-linking biomolecules. RNA-seq was performed by the GTEx Laboratory, Data Analysis and Coordinating Center (LDACC) at the Broad Institute. The Illumina TruSeq protocol was used to create an unstranded polyA+ library sequenced on the Illumina HiSeq 2000 platform to produce 76-bp paired end reads at a depth averaging 50M aligned reads per sample. Sequence reads for this track were quantified to the hg38/GRCh38 human genome using kallisto assisted by the GENCODE v23 transcriptome definition. Read quantification was performed at UCSC by the Computational Genomics lab, using the Toil pipeline. The resulting kallisto files were combined to generate a transcript per million (TPM) expression matrix using the UCSC tool, kallistoToMatrix. Average TPM expression values for each tissue were calculated and used to generate a bed6+5 file that is the base of the track. This was done using the UCSC tool, expMatrixToBarchartBed. The bed track was then converted to a bigBed file using the UCSC tool, bedToBigBed. The data in the hg19/GRCh37 version of this track was generated by converting the coordinates from the hg38/GRCh38 track data. Of the 189,615 BED entries from the original hg38 track, 176,220 were mapped over by transcript name to hg19 using wgEncodeGencodeCompV24lift37 (~93% coverage). Subject and Sample Characteristics The scientific goal of the GTEx project required that the donors and their biospecimen present with no evidence of disease. The tissue types collected were chosen based on their clinical significance, logistical feasibility and their relevance to the scientific goal of the project and the research community. Postmortem samples were collected from non-diseased donors with ages ranging from 20 to 79. 34.4% of donors were female and 65.6% male. Additional summary plots of GTEx sample characteristics are available at the GTEx Portal Tissue Summary page. Credits Samples were collected by the GTEx Consortium. RNA-seq was performed by the GTEx Laboratory, Data Analysis and Coordinating Center (LDACC) at the Broad Institute. John Vivian, Melissa Cline, and Benedict Paten of the UCSC Computational Genomics lab were responsible for the sequence read quantification used to produce this track. Kate Rosenbloom and Chris Eisenhart of the UCSC Genome Browser group were responsible for data file post-processing and track configuration. References J. Vivian et al., Rapid and efficient analysis of 20,000 RNA-seq samples with Toil bioRxiv bioRxiv, vol. 2, p. 62497, 2016. GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013 Jun;45(6):580-5. PMID: 23715323; PMC: PMC4010069 Carithers LJ, Ardlie K, Barcus M, Branton PA, Britton A, Buia SA, Compton CC, DeLuca DS, Peter-Demchok J, Gelfand ET et al. A Novel Approach to High-Quality Postmortem Tissue Procurement: The GTEx Project. Biopreserv Biobank. 2015 Oct;13(5):311-9. PMID: 26484571; PMC: PMC4675181 Mel&#233; M, Ferreira PG, Reverter F, DeLuca DS, Monlong J, Sammeth M, Young TR, Goldmann JM, Pervouchine DD, Sullivan TJ et al. Human genomics. The human transcriptome across tissues and individuals. Science. 2015 May 8;348(6235):660-5. PMID: 25954002; PMC: PMC4547472 DeLuca DS, Levin JZ, Sivachenko A, Fennell T, Nazaire MD, Williams C, Reich M, Winckler W, Getz G. RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics. 2012 Jun 1;28(11):1530-2. PMID: 22539670; PMC: PMC3356847 
gwasCatalog GWAS Catalog NHGRI-EBI Catalog of Published Genome-Wide Association Studies Phenotype and Literature Description This track displays single nucleotide polymorphisms (SNPs) identified by published Genome-Wide Association Studies (GWAS), collected in the NHGRI-EBI GWAS Catalog published jointly by the National Human Genome Research Institute (NHGRI) and the European Bioinformatics Institute (EMBL-EBI). Some abbreviations are used above. From http://www.ebi.ac.uk/gwas/docs/about: The Catalog is a quality controlled, manually curated, literature-derived collection of all published genome-wide association studies assaying at least 100,000 SNPs and all SNP-trait associations with p-values &lt; 1.0 x 10-5 (Hindorff et al., 2009). For more details about the Catalog curation process and data extraction procedures, please refer to the Methods page. Methods From http://www.ebi.ac.uk/gwas/docs/methods: The GWAS Catalog data is extracted from the literature. Extracted information includes publication information, study cohort information such as cohort size, country of recruitment and subject ethnicity, and SNP-disease association information including SNP identifier (i.e. RSID), p-value, gene and risk allele. Each study is also assigned a trait that best represents the phenotype under investigation. When multiple traits are analysed in the same study either multiple entries are created, or individual SNPs are annotated with their specific traits. Traits are used both to query and visualise the data in the Catalog's web form and diagram-based query interfaces. Data extraction and curation for the GWAS Catalog is an expert activity; each step is performed by scientists supported by a web-based tracking and data entry system which allows multiple curators to search, annotate, verify and publish the Catalog data. Papers that qualify for inclusion in the Catalog are identified through weekly PubMed searches. They then undergo two levels of curation. First all data, including association information for SNPs, traits and general information about the study, are extracted by one curator. A second curator then performs an additional round of curation to double-check the accuracy and consistency of all the information. Finally, an automated pipeline performs validation of the extracted data, see the Quality control and SNP mapping section below for more details. This information is then used for queries and in the production of the diagram. Data Access The raw data can be explored interactively with the Table Browser, or Data Integrator. For automated analysis, the genome annotation can be downloaded from the downloads server (gwasCatalog*.txt.gz) or the public MySQL server. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Previous versions of this track can be found on our archive download server. References Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A. 2009 Jun 9;106(23):9362-7. PMID: 19474294; PMC: PMC2687147 
gwipsvizRiboseq GWIPS-viz Riboseq Ribosome Profiling from GWIPS-viz Expression Description Ribosome profiling (ribo-seq) is a technique that takes advantage of NGS technology to sequence ribosome-protected mRNA fragments and consequently allows the locations of translating ribosomes to be determined at the entire transcriptome level (Ingolia et al., 2009). For a more detailed description of the protocol, see Ingolia et al. (2012). For reviews on this technique and its applications, please refer to Ingolia (2014) and Michel et al. (2013). This track displays cumulative ribo-seq data obtained from human cells under different conditions and can be used for the exploration of human genomic loci that are being translated. The values on the y-axis represent the number of ribosome footprint sequence reads at a given position. As of February 2016, the track contains data from 9 studies (see References section for details). Further details about the aggregated track and additional ribo-seq data from these and other studies including data obtained from other organisms can be found at the specialized ribo-seq browser GWIPS-viz. Methods For each study used to generate this track, raw fastq files were downloaded from a repository (e.g., NCBI GEO datasets). Cutadapt was used to trim the relevant adapter sequence from the reads, after which reads below 25 nt in length were discarded. The trimmed reads were aligned to ribosomal RNA using Bowtie and aligning reads were discarded. The remaining reads were then aligned to the hg38 (GRCh38) genome assembly using Bowtie. An offset of 15 nt (to infer the position of the A-site) was added to the most 5' nucleotide coordinate of each uniquely-mapped read. The alignment files from each of the included studies were merged to generate this aggregate track. See individual studies at GWIPS-viz for a full description of the methods of data acquisition and processing. Credits Thanks to Audrey Michel, Stephen Kiniry and GWIPS-viz for providing the data for this track. If you wish to cite this track, please reference: Michel AM, Fox G, M Kiran A, De Bo C, O'Connor PB, Heaphy SM, Mullan JP, Donohue CA, Higgins DG, Baranov PV. GWIPS-viz: development of a ribo-seq genome browser. Nucleic Acids Res. 2014 Jan;42(Database issue):D859-64. PMID: 24185699; PMC: PMC3965066 References Data Battle A, Khan Z, Wang SH, Mitrano A, Ford MJ, Pritchard JK, Gilad Y. Impact of regulatory variation from RNA to protein. Science. 2015 Feb 6;347(6222):664-7. PMID: 25657249; PMC: PMC4507520 Cenik C, Cenik ES, Byeon GW, Grubert F, Candille SI, Spacek D, Alsallakh B, Tilgner H, Araya CL, Tang H et al. Integrative analysis of RNA, translation and protein levels reveals distinct regulatory variation across humans. Genome Res. 2015 Nov;25(11):1610-21. PMID: 26297486; PMC: PMC4617958 Elkon R, Loayza-Puch F, Korkmaz G, Lopes R, van Breugel PC, Bleijerveld OB, Altelaar AM, Wolf E, Lorenzin F, Eilers M et al. Myc coordinates transcription and translation to enhance transformation and suppress invasiveness. EMBO Rep. 2015 Dec;16(12):1723-36. PMID: 26538417; PMC: PMC4687422 Jang C, Lahens NF, Hogenesch JB, Sehgal A. Ribosome profiling reveals an important role for translational control in circadian gene expression. Genome Res 2015 Dec;25(12):1836-47. PMID: 26338483; PMC: PMC4665005 Ji Z, Song R, Regev A, Struhl K. Many lncRNAs, 5&#39UTRs, and pseudogenes are translated and some are likely to express functional proteins. Elife. 2015 Dec 19;4. PMID: 26687005; PMC: PMC4739776 Sidrauski C, McGeachy AM, Ingolia NT, Walter P. The small molecule ISRIB reverses the effects of eIF2&#945; phosphorylation on translation and stress granule assembly. Elife. 2015 Feb 26;4. PMID: 25719440; PMC: PMC4341466 Tanenbaum ME, Stern-Ginossar N, Weissman JS, Vale RD. Regulation of mRNA translation during mitosis. Elife. 2015 Aug 25;4. PMID: 26305499; PMC: PMC4548207 Tirosh O, Cohen Y, Shitrit A, Shani O, Le-Trilling VT, Trilling M, Friedlander G, Tanenbaum M, Stern-Ginossar N. The transcription and translation landscapes during human cytomegalovirus infection reveal novel host-pathogen interactions. PLoS Pathog. 2015 Nov 24;11(11):e1005288. PMID: 26599541; PMC: PMC4658056 Werner A, Iwasaki S, McGourty CA, Medina-Ruiz S, Teerikorpi N, Fedrigo I, Ingolia NT, Rape M. Cell fate determination by ubiquitin-dependent regulation of translation. Nature. 2015 Sep 24;525(7570):523-7. PMID: 26399832; PMC: PMC4602398 Protocol/Technique Ingolia NT. Ribosome profiling: new views of translation, from single codons to genome scale. Nat Rev Genet. 2014 Mar;15(3):205-13. PMID: 24468696 Ingolia NT, Brar GA, Rouskin S, McGeachy AM, Weissman JS. The ribosome profiling strategy for monitoring translation in vivo by deep sequencing of ribosome- protected mRNA fragments. Nat Protoc. 2012 Jul 26;7(8):1534-50. PMID: 22836135; PMC: PMC3535016 Ingolia NT, Ghaemmaghami S, Newman JR, Weissman JS. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science. 2009 Apr 10;324(5924):218-23. PMID: 19213877; PMC: PMC2746483 Michel AM, Baranov PV. Ribosome profiling: a Hi-Def monitor for protein synthesis at the genome-wide scale. Wiley Interdiscip Rev RNA. 2013 Sep-Oct;4(5):473-90. PMID: 23696005; PMC: PMC3823065 
heartAtlasAgeGroup Heart HCA Age Heart cell RNA binned by age group of donor from https://heartcellatlas.org Single Cell RNA-seq Description This track displays data from Cells of the adult human heart. Single-cell and single-nucleus RNA sequencing (RNA-seq) was used to profile transcriptomes from six regions of the heart: the interventricular septum (SP), apex (AX), left ventricle (LV), right ventricle (RV), left atrium (LA), and right atrium (RA). A total of 11 cardiac cell types were identified along with their marker genes after uniform manifold approximation and projection (UMAP) embedding of 487,106 cells. Note that the RNA-seq data is generated using Tag-sequencing (Tag-seq) and does not cover all exons. This track collection contains nine bar chart tracks of RNA expression in the human heart where cells are grouped by cell type (Heart HCA Cells), age (Heart HCA Age), donor (Heart HCA Donor), region of the heart (Heart HCA Region), sample (Heart HCA Sample), sex (Heart HCA Sex), source (Heart HCA Source), cell state (Heart HCA State), and 10x chemistry version (Heart HCA Version). The default track displayed is Heart HCA Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification neural adipose fibroblast immune muscle lymphoid epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Heart HCA Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Healthy heart tissues were obtained from 14 UK and North American transplant organ donors ages 40-75. Tissues were taken from deceased donors after circulatory death (DCD) and after brain death (DBD). To minimize transcriptional degradation, heart tissues were stored and transported on ice until freezing or tissue dissociation. Single nuclei were isolated from flash-frozen tissue using mechanical homogenization with a glass Dounce tissue grinder. Fresh heart tissues were enzymatically dissociated and automatically digested using gentleMACS Octo Dissociator. Next, Hoechst-positive single nuclei were FACS sorted prior to library preparation. In parallel, Cell suspensions from fresh heart tissue were enriched for CD45+ cells using MACS LS columns. Libraries of single cell and single nuclei were prepared using 10x Genomics 3' v2 or v3. 3' gene expression libraries were sequenced on an Illumina HiSeq4000 and NextSeq500. In total 45,870 cells, 78,023 CD45+ enriched cells, and 363,213 nuclei were profiled for 11 major cell types of the heart. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Monika Litvi&#328ukov&#225, Carlos Talavera-L&#243, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. References Litvi&#328;ukov&#225; M, Talavera-L&#243;pez C, Maatz H, Reichart D, Worth CL, Lindberg EL, Kanda M, Polanski K, Heinig M, Lee M et al. Cells of the adult human heart. Nature. 2020 Dec;588(7838):466-472. PMID: 32971526; PMC: PMC7681775 
heartCellAtlas Heart Cell Atlas Heart single cell RNA data from https://heartcellatlas.com Single Cell RNA-seq Description This track displays data from Cells of the adult human heart. Single-cell and single-nucleus RNA sequencing (RNA-seq) was used to profile transcriptomes from six regions of the heart: the interventricular septum (SP), apex (AX), left ventricle (LV), right ventricle (RV), left atrium (LA), and right atrium (RA). A total of 11 cardiac cell types were identified along with their marker genes after uniform manifold approximation and projection (UMAP) embedding of 487,106 cells. Note that the RNA-seq data is generated using Tag-sequencing (Tag-seq) and does not cover all exons. This track collection contains nine bar chart tracks of RNA expression in the human heart where cells are grouped by cell type (Heart HCA Cells), age (Heart HCA Age), donor (Heart HCA Donor), region of the heart (Heart HCA Region), sample (Heart HCA Sample), sex (Heart HCA Sex), source (Heart HCA Source), cell state (Heart HCA State), and 10x chemistry version (Heart HCA Version). The default track displayed is Heart HCA Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification neural adipose fibroblast immune muscle lymphoid epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Heart HCA Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Healthy heart tissues were obtained from 14 UK and North American transplant organ donors ages 40-75. Tissues were taken from deceased donors after circulatory death (DCD) and after brain death (DBD). To minimize transcriptional degradation, heart tissues were stored and transported on ice until freezing or tissue dissociation. Single nuclei were isolated from flash-frozen tissue using mechanical homogenization with a glass Dounce tissue grinder. Fresh heart tissues were enzymatically dissociated and automatically digested using gentleMACS Octo Dissociator. Next, Hoechst-positive single nuclei were FACS sorted prior to library preparation. In parallel, Cell suspensions from fresh heart tissue were enriched for CD45+ cells using MACS LS columns. Libraries of single cell and single nuclei were prepared using 10x Genomics 3' v2 or v3. 3' gene expression libraries were sequenced on an Illumina HiSeq4000 and NextSeq500. In total 45,870 cells, 78,023 CD45+ enriched cells, and 363,213 nuclei were profiled for 11 major cell types of the heart. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Monika Litvi&#328ukov&#225, Carlos Talavera-L&#243, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. References Litvi&#328;ukov&#225; M, Talavera-L&#243;pez C, Maatz H, Reichart D, Worth CL, Lindberg EL, Kanda M, Polanski K, Heinig M, Lee M et al. Cells of the adult human heart. Nature. 2020 Dec;588(7838):466-472. PMID: 32971526; PMC: PMC7681775 
heartAtlasCellTypes Heart HCA Cells Heart cell RNA binned by cell type from https://heartcellatlas.org Single Cell RNA-seq Description This track displays data from Cells of the adult human heart. Single-cell and single-nucleus RNA sequencing (RNA-seq) was used to profile transcriptomes from six regions of the heart: the interventricular septum (SP), apex (AX), left ventricle (LV), right ventricle (RV), left atrium (LA), and right atrium (RA). A total of 11 cardiac cell types were identified along with their marker genes after uniform manifold approximation and projection (UMAP) embedding of 487,106 cells. Note that the RNA-seq data is generated using Tag-sequencing (Tag-seq) and does not cover all exons. This track collection contains nine bar chart tracks of RNA expression in the human heart where cells are grouped by cell type (Heart HCA Cells), age (Heart HCA Age), donor (Heart HCA Donor), region of the heart (Heart HCA Region), sample (Heart HCA Sample), sex (Heart HCA Sex), source (Heart HCA Source), cell state (Heart HCA State), and 10x chemistry version (Heart HCA Version). The default track displayed is Heart HCA Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification neural adipose fibroblast immune muscle lymphoid epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Heart HCA Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Healthy heart tissues were obtained from 14 UK and North American transplant organ donors ages 40-75. Tissues were taken from deceased donors after circulatory death (DCD) and after brain death (DBD). To minimize transcriptional degradation, heart tissues were stored and transported on ice until freezing or tissue dissociation. Single nuclei were isolated from flash-frozen tissue using mechanical homogenization with a glass Dounce tissue grinder. Fresh heart tissues were enzymatically dissociated and automatically digested using gentleMACS Octo Dissociator. Next, Hoechst-positive single nuclei were FACS sorted prior to library preparation. In parallel, Cell suspensions from fresh heart tissue were enriched for CD45+ cells using MACS LS columns. Libraries of single cell and single nuclei were prepared using 10x Genomics 3' v2 or v3. 3' gene expression libraries were sequenced on an Illumina HiSeq4000 and NextSeq500. In total 45,870 cells, 78,023 CD45+ enriched cells, and 363,213 nuclei were profiled for 11 major cell types of the heart. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Monika Litvi&#328ukov&#225, Carlos Talavera-L&#243, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. References Litvi&#328;ukov&#225; M, Talavera-L&#243;pez C, Maatz H, Reichart D, Worth CL, Lindberg EL, Kanda M, Polanski K, Heinig M, Lee M et al. Cells of the adult human heart. Nature. 2020 Dec;588(7838):466-472. PMID: 32971526; PMC: PMC7681775 
heartAtlasDonor Heart HCA Donor Heart cell RNA binned by organ donor from https://heartcellatlas.org Single Cell RNA-seq Description This track displays data from Cells of the adult human heart. Single-cell and single-nucleus RNA sequencing (RNA-seq) was used to profile transcriptomes from six regions of the heart: the interventricular septum (SP), apex (AX), left ventricle (LV), right ventricle (RV), left atrium (LA), and right atrium (RA). A total of 11 cardiac cell types were identified along with their marker genes after uniform manifold approximation and projection (UMAP) embedding of 487,106 cells. Note that the RNA-seq data is generated using Tag-sequencing (Tag-seq) and does not cover all exons. This track collection contains nine bar chart tracks of RNA expression in the human heart where cells are grouped by cell type (Heart HCA Cells), age (Heart HCA Age), donor (Heart HCA Donor), region of the heart (Heart HCA Region), sample (Heart HCA Sample), sex (Heart HCA Sex), source (Heart HCA Source), cell state (Heart HCA State), and 10x chemistry version (Heart HCA Version). The default track displayed is Heart HCA Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification neural adipose fibroblast immune muscle lymphoid epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Heart HCA Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Healthy heart tissues were obtained from 14 UK and North American transplant organ donors ages 40-75. Tissues were taken from deceased donors after circulatory death (DCD) and after brain death (DBD). To minimize transcriptional degradation, heart tissues were stored and transported on ice until freezing or tissue dissociation. Single nuclei were isolated from flash-frozen tissue using mechanical homogenization with a glass Dounce tissue grinder. Fresh heart tissues were enzymatically dissociated and automatically digested using gentleMACS Octo Dissociator. Next, Hoechst-positive single nuclei were FACS sorted prior to library preparation. In parallel, Cell suspensions from fresh heart tissue were enriched for CD45+ cells using MACS LS columns. Libraries of single cell and single nuclei were prepared using 10x Genomics 3' v2 or v3. 3' gene expression libraries were sequenced on an Illumina HiSeq4000 and NextSeq500. In total 45,870 cells, 78,023 CD45+ enriched cells, and 363,213 nuclei were profiled for 11 major cell types of the heart. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Monika Litvi&#328ukov&#225, Carlos Talavera-L&#243, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. References Litvi&#328;ukov&#225; M, Talavera-L&#243;pez C, Maatz H, Reichart D, Worth CL, Lindberg EL, Kanda M, Polanski K, Heinig M, Lee M et al. Cells of the adult human heart. Nature. 2020 Dec;588(7838):466-472. PMID: 32971526; PMC: PMC7681775 
heartAtlasRegion Heart HCA Region Heart cell RNA binned by region of collection from https://heartcellatlas.org Single Cell RNA-seq Description This track displays data from Cells of the adult human heart. Single-cell and single-nucleus RNA sequencing (RNA-seq) was used to profile transcriptomes from six regions of the heart: the interventricular septum (SP), apex (AX), left ventricle (LV), right ventricle (RV), left atrium (LA), and right atrium (RA). A total of 11 cardiac cell types were identified along with their marker genes after uniform manifold approximation and projection (UMAP) embedding of 487,106 cells. Note that the RNA-seq data is generated using Tag-sequencing (Tag-seq) and does not cover all exons. This track collection contains nine bar chart tracks of RNA expression in the human heart where cells are grouped by cell type (Heart HCA Cells), age (Heart HCA Age), donor (Heart HCA Donor), region of the heart (Heart HCA Region), sample (Heart HCA Sample), sex (Heart HCA Sex), source (Heart HCA Source), cell state (Heart HCA State), and 10x chemistry version (Heart HCA Version). The default track displayed is Heart HCA Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification neural adipose fibroblast immune muscle lymphoid epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Heart HCA Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Healthy heart tissues were obtained from 14 UK and North American transplant organ donors ages 40-75. Tissues were taken from deceased donors after circulatory death (DCD) and after brain death (DBD). To minimize transcriptional degradation, heart tissues were stored and transported on ice until freezing or tissue dissociation. Single nuclei were isolated from flash-frozen tissue using mechanical homogenization with a glass Dounce tissue grinder. Fresh heart tissues were enzymatically dissociated and automatically digested using gentleMACS Octo Dissociator. Next, Hoechst-positive single nuclei were FACS sorted prior to library preparation. In parallel, Cell suspensions from fresh heart tissue were enriched for CD45+ cells using MACS LS columns. Libraries of single cell and single nuclei were prepared using 10x Genomics 3' v2 or v3. 3' gene expression libraries were sequenced on an Illumina HiSeq4000 and NextSeq500. In total 45,870 cells, 78,023 CD45+ enriched cells, and 363,213 nuclei were profiled for 11 major cell types of the heart. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Monika Litvi&#328ukov&#225, Carlos Talavera-L&#243, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. References Litvi&#328;ukov&#225; M, Talavera-L&#243;pez C, Maatz H, Reichart D, Worth CL, Lindberg EL, Kanda M, Polanski K, Heinig M, Lee M et al. Cells of the adult human heart. Nature. 2020 Dec;588(7838):466-472. PMID: 32971526; PMC: PMC7681775 
heartAtlasSample Heart HCA Sample Heart cell RNA binned by biosample from https://heartcellatlas.org Single Cell RNA-seq Description This track displays data from Cells of the adult human heart. Single-cell and single-nucleus RNA sequencing (RNA-seq) was used to profile transcriptomes from six regions of the heart: the interventricular septum (SP), apex (AX), left ventricle (LV), right ventricle (RV), left atrium (LA), and right atrium (RA). A total of 11 cardiac cell types were identified along with their marker genes after uniform manifold approximation and projection (UMAP) embedding of 487,106 cells. Note that the RNA-seq data is generated using Tag-sequencing (Tag-seq) and does not cover all exons. This track collection contains nine bar chart tracks of RNA expression in the human heart where cells are grouped by cell type (Heart HCA Cells), age (Heart HCA Age), donor (Heart HCA Donor), region of the heart (Heart HCA Region), sample (Heart HCA Sample), sex (Heart HCA Sex), source (Heart HCA Source), cell state (Heart HCA State), and 10x chemistry version (Heart HCA Version). The default track displayed is Heart HCA Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification neural adipose fibroblast immune muscle lymphoid epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Heart HCA Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Healthy heart tissues were obtained from 14 UK and North American transplant organ donors ages 40-75. Tissues were taken from deceased donors after circulatory death (DCD) and after brain death (DBD). To minimize transcriptional degradation, heart tissues were stored and transported on ice until freezing or tissue dissociation. Single nuclei were isolated from flash-frozen tissue using mechanical homogenization with a glass Dounce tissue grinder. Fresh heart tissues were enzymatically dissociated and automatically digested using gentleMACS Octo Dissociator. Next, Hoechst-positive single nuclei were FACS sorted prior to library preparation. In parallel, Cell suspensions from fresh heart tissue were enriched for CD45+ cells using MACS LS columns. Libraries of single cell and single nuclei were prepared using 10x Genomics 3' v2 or v3. 3' gene expression libraries were sequenced on an Illumina HiSeq4000 and NextSeq500. In total 45,870 cells, 78,023 CD45+ enriched cells, and 363,213 nuclei were profiled for 11 major cell types of the heart. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Monika Litvi&#328ukov&#225, Carlos Talavera-L&#243, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. References Litvi&#328;ukov&#225; M, Talavera-L&#243;pez C, Maatz H, Reichart D, Worth CL, Lindberg EL, Kanda M, Polanski K, Heinig M, Lee M et al. Cells of the adult human heart. Nature. 2020 Dec;588(7838):466-472. PMID: 32971526; PMC: PMC7681775 
heartAtlasSex Heart HCA Sex Heart cell RNA binned by sex of donor from https://heartcellatlas.org Single Cell RNA-seq Description This track displays data from Cells of the adult human heart. Single-cell and single-nucleus RNA sequencing (RNA-seq) was used to profile transcriptomes from six regions of the heart: the interventricular septum (SP), apex (AX), left ventricle (LV), right ventricle (RV), left atrium (LA), and right atrium (RA). A total of 11 cardiac cell types were identified along with their marker genes after uniform manifold approximation and projection (UMAP) embedding of 487,106 cells. Note that the RNA-seq data is generated using Tag-sequencing (Tag-seq) and does not cover all exons. This track collection contains nine bar chart tracks of RNA expression in the human heart where cells are grouped by cell type (Heart HCA Cells), age (Heart HCA Age), donor (Heart HCA Donor), region of the heart (Heart HCA Region), sample (Heart HCA Sample), sex (Heart HCA Sex), source (Heart HCA Source), cell state (Heart HCA State), and 10x chemistry version (Heart HCA Version). The default track displayed is Heart HCA Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification neural adipose fibroblast immune muscle lymphoid epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Heart HCA Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Healthy heart tissues were obtained from 14 UK and North American transplant organ donors ages 40-75. Tissues were taken from deceased donors after circulatory death (DCD) and after brain death (DBD). To minimize transcriptional degradation, heart tissues were stored and transported on ice until freezing or tissue dissociation. Single nuclei were isolated from flash-frozen tissue using mechanical homogenization with a glass Dounce tissue grinder. Fresh heart tissues were enzymatically dissociated and automatically digested using gentleMACS Octo Dissociator. Next, Hoechst-positive single nuclei were FACS sorted prior to library preparation. In parallel, Cell suspensions from fresh heart tissue were enriched for CD45+ cells using MACS LS columns. Libraries of single cell and single nuclei were prepared using 10x Genomics 3' v2 or v3. 3' gene expression libraries were sequenced on an Illumina HiSeq4000 and NextSeq500. In total 45,870 cells, 78,023 CD45+ enriched cells, and 363,213 nuclei were profiled for 11 major cell types of the heart. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Monika Litvi&#328ukov&#225, Carlos Talavera-L&#243, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. References Litvi&#328;ukov&#225; M, Talavera-L&#243;pez C, Maatz H, Reichart D, Worth CL, Lindberg EL, Kanda M, Polanski K, Heinig M, Lee M et al. Cells of the adult human heart. Nature. 2020 Dec;588(7838):466-472. PMID: 32971526; PMC: PMC7681775 
heartAtlasSource Heart HCA Source Heart cell RNA binned by source (nucleus vs whole cell) from https://heartcellatlas.org Single Cell RNA-seq Description This track displays data from Cells of the adult human heart. Single-cell and single-nucleus RNA sequencing (RNA-seq) was used to profile transcriptomes from six regions of the heart: the interventricular septum (SP), apex (AX), left ventricle (LV), right ventricle (RV), left atrium (LA), and right atrium (RA). A total of 11 cardiac cell types were identified along with their marker genes after uniform manifold approximation and projection (UMAP) embedding of 487,106 cells. Note that the RNA-seq data is generated using Tag-sequencing (Tag-seq) and does not cover all exons. This track collection contains nine bar chart tracks of RNA expression in the human heart where cells are grouped by cell type (Heart HCA Cells), age (Heart HCA Age), donor (Heart HCA Donor), region of the heart (Heart HCA Region), sample (Heart HCA Sample), sex (Heart HCA Sex), source (Heart HCA Source), cell state (Heart HCA State), and 10x chemistry version (Heart HCA Version). The default track displayed is Heart HCA Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification neural adipose fibroblast immune muscle lymphoid epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Heart HCA Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Healthy heart tissues were obtained from 14 UK and North American transplant organ donors ages 40-75. Tissues were taken from deceased donors after circulatory death (DCD) and after brain death (DBD). To minimize transcriptional degradation, heart tissues were stored and transported on ice until freezing or tissue dissociation. Single nuclei were isolated from flash-frozen tissue using mechanical homogenization with a glass Dounce tissue grinder. Fresh heart tissues were enzymatically dissociated and automatically digested using gentleMACS Octo Dissociator. Next, Hoechst-positive single nuclei were FACS sorted prior to library preparation. In parallel, Cell suspensions from fresh heart tissue were enriched for CD45+ cells using MACS LS columns. Libraries of single cell and single nuclei were prepared using 10x Genomics 3' v2 or v3. 3' gene expression libraries were sequenced on an Illumina HiSeq4000 and NextSeq500. In total 45,870 cells, 78,023 CD45+ enriched cells, and 363,213 nuclei were profiled for 11 major cell types of the heart. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Monika Litvi&#328ukov&#225, Carlos Talavera-L&#243, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. References Litvi&#328;ukov&#225; M, Talavera-L&#243;pez C, Maatz H, Reichart D, Worth CL, Lindberg EL, Kanda M, Polanski K, Heinig M, Lee M et al. Cells of the adult human heart. Nature. 2020 Dec;588(7838):466-472. PMID: 32971526; PMC: PMC7681775 
heartAtlasCellStates Heart HCA State Heart cell RNA binned by cell state from https://heartcellatlas.org Single Cell RNA-seq Description This track displays data from Cells of the adult human heart. Single-cell and single-nucleus RNA sequencing (RNA-seq) was used to profile transcriptomes from six regions of the heart: the interventricular septum (SP), apex (AX), left ventricle (LV), right ventricle (RV), left atrium (LA), and right atrium (RA). A total of 11 cardiac cell types were identified along with their marker genes after uniform manifold approximation and projection (UMAP) embedding of 487,106 cells. Note that the RNA-seq data is generated using Tag-sequencing (Tag-seq) and does not cover all exons. This track collection contains nine bar chart tracks of RNA expression in the human heart where cells are grouped by cell type (Heart HCA Cells), age (Heart HCA Age), donor (Heart HCA Donor), region of the heart (Heart HCA Region), sample (Heart HCA Sample), sex (Heart HCA Sex), source (Heart HCA Source), cell state (Heart HCA State), and 10x chemistry version (Heart HCA Version). The default track displayed is Heart HCA Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification neural adipose fibroblast immune muscle lymphoid epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Heart HCA Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Healthy heart tissues were obtained from 14 UK and North American transplant organ donors ages 40-75. Tissues were taken from deceased donors after circulatory death (DCD) and after brain death (DBD). To minimize transcriptional degradation, heart tissues were stored and transported on ice until freezing or tissue dissociation. Single nuclei were isolated from flash-frozen tissue using mechanical homogenization with a glass Dounce tissue grinder. Fresh heart tissues were enzymatically dissociated and automatically digested using gentleMACS Octo Dissociator. Next, Hoechst-positive single nuclei were FACS sorted prior to library preparation. In parallel, Cell suspensions from fresh heart tissue were enriched for CD45+ cells using MACS LS columns. Libraries of single cell and single nuclei were prepared using 10x Genomics 3' v2 or v3. 3' gene expression libraries were sequenced on an Illumina HiSeq4000 and NextSeq500. In total 45,870 cells, 78,023 CD45+ enriched cells, and 363,213 nuclei were profiled for 11 major cell types of the heart. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Monika Litvi&#328ukov&#225, Carlos Talavera-L&#243, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. References Litvi&#328;ukov&#225; M, Talavera-L&#243;pez C, Maatz H, Reichart D, Worth CL, Lindberg EL, Kanda M, Polanski K, Heinig M, Lee M et al. Cells of the adult human heart. Nature. 2020 Dec;588(7838):466-472. PMID: 32971526; PMC: PMC7681775 
heartAtlasVersion Heart HCA Version Heart cell RNA binned by 10x chemistry version from https://heartcellatlas.org Single Cell RNA-seq Description This track displays data from Cells of the adult human heart. Single-cell and single-nucleus RNA sequencing (RNA-seq) was used to profile transcriptomes from six regions of the heart: the interventricular septum (SP), apex (AX), left ventricle (LV), right ventricle (RV), left atrium (LA), and right atrium (RA). A total of 11 cardiac cell types were identified along with their marker genes after uniform manifold approximation and projection (UMAP) embedding of 487,106 cells. Note that the RNA-seq data is generated using Tag-sequencing (Tag-seq) and does not cover all exons. This track collection contains nine bar chart tracks of RNA expression in the human heart where cells are grouped by cell type (Heart HCA Cells), age (Heart HCA Age), donor (Heart HCA Donor), region of the heart (Heart HCA Region), sample (Heart HCA Sample), sex (Heart HCA Sex), source (Heart HCA Source), cell state (Heart HCA State), and 10x chemistry version (Heart HCA Version). The default track displayed is Heart HCA Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification neural adipose fibroblast immune muscle lymphoid epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Heart HCA Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Healthy heart tissues were obtained from 14 UK and North American transplant organ donors ages 40-75. Tissues were taken from deceased donors after circulatory death (DCD) and after brain death (DBD). To minimize transcriptional degradation, heart tissues were stored and transported on ice until freezing or tissue dissociation. Single nuclei were isolated from flash-frozen tissue using mechanical homogenization with a glass Dounce tissue grinder. Fresh heart tissues were enzymatically dissociated and automatically digested using gentleMACS Octo Dissociator. Next, Hoechst-positive single nuclei were FACS sorted prior to library preparation. In parallel, Cell suspensions from fresh heart tissue were enriched for CD45+ cells using MACS LS columns. Libraries of single cell and single nuclei were prepared using 10x Genomics 3' v2 or v3. 3' gene expression libraries were sequenced on an Illumina HiSeq4000 and NextSeq500. In total 45,870 cells, 78,023 CD45+ enriched cells, and 363,213 nuclei were profiled for 11 major cell types of the heart. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Monika Litvi&#328ukov&#225, Carlos Talavera-L&#243, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. References Litvi&#328;ukov&#225; M, Talavera-L&#243;pez C, Maatz H, Reichart D, Worth CL, Lindberg EL, Kanda M, Polanski K, Heinig M, Lee M et al. Cells of the adult human heart. Nature. 2020 Dec;588(7838):466-472. PMID: 32971526; PMC: PMC7681775 
hg38ContigDiff Hg19 Diff Contigs New to GRCh38/(hg38), Not Carried Forward from GRCh37/(hg19) Mapping and Sequencing Description This track shows the differences between the GRCh38 (hg38) and previous GRCh37 (hg19) human genome assemblies, indicating contigs (or portions of contigs) that are new to the hg38 assembly. The following color/score key is used: colorscorechange from hg19 to hg38 &nbsp;0New contig added to hg38 to update sequence or fill gaps present in hg19 &nbsp;500Different portions of this same contig used in the construction of hg38 and hg19 assemblies &nbsp;1000Updated version of an hg19 contig in which sequence errors have been corrected Use the score filter to select which categories to show in the display. Methods The contig coordinates were extracted from the AGP files for both assemblies. Contigs that matched the same name, same version, and the same specific portion of sequence in both assemblies were considered identical between the two assemblies and were excluded from this data set. The remaining contigs are shown in this track. Credits The data and presentation of this track were prepared by Hiram Clawson, UCSC Genome Browser engineering. 
hgmd HGMD public Human Gene Mutation Database - Public Version Dec 2023 Phenotype and Literature Description NOTE: HGMD public is intended for use primarily by physicians and other professionals concerned with genetic disorders, by genetics researchers, and by advanced students in science and medicine. While the HGMD public database is open to all academic users, users seeking information about a personal medical or genetic condition are urged to consult with a qualified physician for diagnosis and for answers to personal questions. DOWNLOADS: As requested by Qiagen, this track is not available for download or mirroring but only for limited API queries, see below. This track shows the genomic positions of variants in the public version of the Human Gene Mutation Database (HGMD). UCSC does not host any further information and provides only the coordinates of mutations. To get details on a mutation (bibliographic reference, phenotype, disease, nucleotide change, etc.), follow the &quot;Link to HGMD&quot; at the top of the details page. Mouse over to show the type of variant (substitution, insertion, deletion, regulatory or splice variant). For deletions, only start coordinates are shown as the end coordinates have not been provided by HGMD. Insertions are located between the two annotated nucleic acids. The HGMD public database is produced at Cardiff University, but is free only for academic use. Academic users can register for a free account at the HGMD User Registration page. Download and commercial use requires a license for the HGMD Professional database, which also contains many mutations not yet added to the public version of HGMD public. The public version is usually 1-2 years behind the professional version. The HGMD database itself does not come with a mapping to genome coordinates, but there is a related product called "GenomeTrax" which includes HGMD in the UCSC Custom Track format. Contact Qiagen for more information. Batch queries Due to license restrictions, the HGMD data is not available for download or for batch queries in the Table Browser. However, it is available for programmatic access via the Global Alliance Beacon API, a web service that accepts queries in the form (genome, chromosome, position, allele) and returns "true" or "false" depending on whether there is information about this allele in the database. For more details see our Beacon Server. Subscribers of the HGMD database can also download the full database or use the HGMD API to retrieve full details, please contact Qiagen support for further information. Academic or non-profit users may be able to obtain a limited version of HGMD public from Qiagen. Display Conventions and Configuration Genomic locations of HGMD variants are labeled with the gene symbol and the accession of the mutation, separated by a colon. All other information is shown on the respective HGMD variation page, accessible via the &quot;Link to HGMD&quot; at the top of the details page. HGMD variants are originally annotated on RefSeq transcripts. You can show all and only those transcripts annotated by HGMD by activating the HGMD subtrack of the track "NCBI RefSeq". Methods The mappings displayed on this track were obtained from Qiagen and reformatted at UCSC as a bigBed file. Credits Thanks to HGMD, Frank Schacherer and Rupert Yip from Qiagen for making these data available. References Stenson PD, Mort M, Ball EV, Shaw K, Phillips A, Cooper DN. The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine. Hum Genet. 2014 Jan;133(1):1-9. PMID: 24077912; PMC: PMC3898141 
hgnc HGNC HUGO Gene Nomenclature Genes and Gene Predictions Description The HGNC is responsible for approving unique symbols and names for human loci, including protein coding genes, ncRNA genes and pseudogenes, to allow unambiguous scientific communication. For each known human gene, the HGNC approves a gene name and symbol (short-form abbreviation). All approved symbols are stored in the HGNC database, www.genenames.org, a curated online repository of HGNC-approved gene nomenclature, gene groups and associated resources including links to genomic, proteomic, and phenotypic information. Each symbol is unique and we ensure that each gene is only given one approved gene symbol. It is necessary to provide a unique symbol for each gene so that we and others can talk about them, and this also facilitates electronic data retrieval from publications and databases. In preference, each symbol maintains parallel construction in different members of a gene family and can also be used in other species, especially other vertebrates including mouse. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For computational analysis, genome annotations are stored in a bigBigFile file that can be downloaded from the download server. Regional or genome-wide annotations can be converted from binary data to human readable text using our command line utility bigBedToBed which can be compiled from source code or downloaded as a precompiled binary for your system. Files and instructions can be found in the utilities directory. The utility can be used to obtain features within a given range, for example: bigBedToBed -chrom=chr6 -start=0 -end=1000000 http://hgdownload.soe.ucsc.edu/gbdb/hg38/hgnc/hgnc.bb stdout Please refer to our Data Access FAQ for more information or our mailing list for archived user questions. Credits HGNC Database, HUGO Gene Nomenclature Committee (HGNC), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom www.genenames.org. References Tweedie S, Braschi B, Gray KA, Jones TEM, Seal RL, Yates B, Bruford EA. Genenames.org: the HGNC and VGNC resources in 2021. Nucleic Acids Res. PMID: 33152070 PMCID: PMC7779007 DOI: 10.1093/nar/gkaa980 
hicAndMicroC Hi-C and Micro-C Comparison of Micro-C and In situ Hi-C protocols in H1-hESC and HFFc6 Regulation Description These tracks provide heatmaps of chromatin folding data from in situ Hi-C and Micro-C XL experiments on the H1-hESC (embryonic stem cells) and HFFc6 (foreskin fibroblasts) cell lines (Krietenstein et al., 2020). The data indicate how many interactions were detected between regions of the genome. A high score between two regions suggests that they are probably in close proximity in 3D space within the nucleus of a cell. In the track display, this is shown by a more intense color in the heatmap. Display Conventions This is a composite track with data from experiments that compare two protocols on each of two cell lines. Individual subtrack settings can be adjusted by clicking the wrench next to the subtrack name, and all subtracks can be configured simultaneously using the track controls at the top of the page. Note that some controls (specifically, resolution and normalization options) are only available in the subtrack-specific configuration. The proximity data in these tracks are displayed as heatmaps, with high scores (and more intense colors) corresponding to closer proximity. Draw modes There are three display methods available for Hi-C tracks: square, triangle, and arc. Square mode provides a traditional Hi-C display in which chromosome positions are mapped along the top-left-to-bottom-right diagonal, and interaction values are plotted on both sides of that diagonal to form a square. The upper-left corner of the square corresponds to the left-most position of the window in view, while the bottom-right corner corresponds to the right-most position of the window. The color shade at any point within the square shows the proximity score for two genomic regions: the region where a vertical line drawn from that point intersects with the diagonal, and the region where a horizontal line from that point intersects with the diagonal. A point directly on the diagonal shows the score for how proximal a region is to itself (scores on the diagonal are usually quite high unless no data are available). A point at the extreme bottom left of the square shows the score for how proximal the left-most position within the window is to the right-most position within the window. In triangle mode, the display is quite similar to square except that only the top half of the square is drawn (eliminating the redundancy), and the image is rotated so that the diagonal of the square now lies on the horizontal axis. This display consumes less vertical space in the image, although it may be more difficult to ascertain exactly which positions correspond to a point within the triangle. In arc mode, simple arcs are drawn between the centers of interacting regions. The color of each arc corresponds to the proximity score. Self-interactions are not displayed. Score normalization settings Score values for this type of display correspond to how close two genomic regions are in 3D space. A high score indicates more links were formed between them in the experiment, which suggests that the regions are near to each other. A low score suggests that the regions are farther apart. High scores are displayed with a more intense color value; low scores are displayed in paler shades. There are four score values available in this display: NONE, VC, VC_SQRT, and KR. NONE provides raw, un-normalized counts for the number of interactions between regions. VC, or Vanilla Coverage, normalization (Lieberman-Aiden et al., 2009) and the VC_SQRT variant normalize these count values based on the overall count values for each of the two interacting regions. Knight-Ruiz, or KR, matrix balancing (Knight and Ruiz, 2013) provides an alternative normalization method where the row and column sums of the contact matrix equal 1. Color intensity in the heatmap goes up to indicate higher scores, but eventually saturates at a maximum beyond which all scores share the same color intensity. The value of this maximum score for saturation can be set manually by un-checking the &quot;Auto-scale&quot; box. When the &quot;Auto-scale&quot; box is checked, it automatically sets the saturation maximum to be double (2x) the median score in the current display window. Resolution settings The resolution for each track is measured in base pairs and represents the size of the bins into which proximity data are gathered. The list of available resolutions ranges from 1kb to 10MB. There is also an &quot;Auto&quot; setting, which attempts to use the coarsest resolution that still displays at least 500 bins in the current window. Methods Cells from the H1-hESC and HFFc6 cell lines were processed using two protocols and submitted to the 4D Nucleome Data Coordination and Integration Center (4D Nucleome). The data from the experimental replicates were then combined to create a contact matrix for each cell line, which was then processed to create binary heatmap files like the .hic files used by this track. The first protocol, in situ Hi-C, was published in 2014 as a technique for obtaining full-genome proximity data while keeping the cell nucleus intact (Rao et al., 2014). This method uses a restriction enzyme to cleave DNA before linking. The second protocol, Micro-C XL, is an update to the Micro-C method of obtaining chromatin conformation data (Hsieh et al., 2016, Hsieh et al., 2015), and has largely supplanted the original. Both the original Micro-C and the updated version are variants of Hi-C chromatin conformation capture that use micrococcal nuclease to segment the genome before linking. This results in data sets with resolution down to the nucleosome level. The original Micro-C method had difficulty recovering higher order interactions, and the updated protocol makes use of additional cross-linking chemicals to address that issue. We downloaded the .hic contact matrix files with the following accessions from the 4D Nucleome Data Portal: 4DNFI18Q799K, 4DNFI2TK7L2F, 4DNFIFLJLIS5, and 4DNFIQYQWPF5. The files are parsed for display using the Straw library from the Aiden lab at Baylor College of Medicine. Data Access The data for this track can be explored interactively with the Table Browser in the interact format. Direct access to the raw data files in .hic format can be obtained from the 4D Nucleome Data Portal at the URL provided in the Methods section or from our own download server. The following files for this track can be found in the /gbdb/hg38/hic/ subdirectory: 4DNFI18Q799K.hic, 4DNFI2TK7L2F.hic, 4DNFIFLJLIS5.hic, 4DNFIQYQWPF5.hic. The name of each file corresponds to its identifier at the Data Portal. Details on working with .hic files can be found at https://www.aidenlab.org/documentation.html. References Hsieh TS, Fudenberg G, Goloborodko A, Rando OJ. Micro-C XL: assaying chromosome conformation from the nucleosome to the entire genome. Nat Methods. 2016 Dec;13(12):1009-1011. PMID: 27723753 Knight P, Ruiz D. A fast algorithm for matrix balancing. IMA J Numer Anal. 2013 Jul;33(3):1029-1047. Krietenstein N, Abraham S, Venev SV, Abdennur N, Gibcus J, Hsieh TS, Parsi KM, Yang L, Maehr R, Mirny LA et al. Ultrastructural Details of Mammalian Chromosome Architecture. Mol Cell. 2020 May 7;78(3):554-565.e7. PMID: 32213324 Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Ragoczy T, Telling A, Amit I, Lajoie BR, Sabo PJ, Dorschner MO et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science. 2009 Oct 9;326(5950):289-93. PMID: 19815776; PMC: PMC2858594 Rao SS, Huntley MH, Durand NC, Stamenova EK, Bochkov ID, Robinson JT, Sanborn AL, Machol I, Omer AD, Lander ES et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell. 2014 Dec 18;159(7):1665-80. PMID: 25497547; PMC: PMC5635824 
hffc6MicroC HFFc6 Micro-C Micro-C Chromatin Structure on HFFc6 Regulation 
hffc6Insitu HFFc6 In situ In situ Hi-C Chromatin Structure on HFFc6 Regulation 
h1hescMicroC H1-hESC Micro-C Micro-C Chromatin Structure on H1-hESC Regulation 
h1hescInsitu H1-hESC In situ In situ Hi-C Chromatin Structure on H1-hESC Regulation 
hprcDecomposed HPRC All Variants HPRC variants decomposed from hprc-v1.0-mc.grch38.vcfbub.a100k.wave.vcf.gz (Liao et al 2023), no size filtering Human Pangenome - HPRC Description This track shows short nucleotide variants of a few base pairs when aligning HPRC genomes to the hg38 reference assembly. The alignment was made with the Minigraph-cactus approach described in the references below. There are three subtracks in this superTrack: All short variants up to 50bp, without any length filter All short variants &lt;= 3 bp long All short variants &gt; 3 bp long VCF Decomposition from HPRC Pangenome Resources Github: &quot;The Raw VCF files contain a site for each bubble in the graph. Nested bubbles will result in overlapping sites. The nesting relationships are denoted with the PS (parent snarl), LV (level) and AT (allele traversal) tags and need to be taken into account when interpreting the VCF. Alternatively, you can use the &apos;Decomposed VCFs&apos; which have been normalized by using vcfbub to &apos;pop&apos; bubbles with alleles larger than 100k and vcfwave to realign each alt (script). Note that in order to reproduce the PanGenie analyses from the papers, you should instead use the PanGenie HPRC Workflow. This workflow has a CHM13 branch to use when working with that reference. The exact tools and commands used to produce the VCFs are given here.&quot; Display Conventions and Configuration The Name of the items are the pair of node labels that denote the site&apos;s location in the graph, with the &apos;&gt;&apos; and &apos;&lt;&apos; denoting the forward and reverse orientation of the node. Mouseover on items in &quot;squish&quot; and &quot;pack&quot; modes shows the items Name and Genotypes. Mouseover on items in &quot;full&quot; mode shows Alleles. Methods The Minigraph-Cactus HPRC v1.0 graph was converted to VCF using vg deconstruct. This result was further postprocessed using vcfbub to flatten nested sites then vcfwave to normalize by realigning alt alleles to the reference. All steps are described in Hickey et al 2023. The postprocessing command lines and data can be found on Github. Finally, the resulting VCF was filtered by length and split into two VCFs using a cutoff of 3bp. Credits Thanks to Glenn Hickey for providing the HAL file from the HPRC project and for making these VCFs from them. References Armstrong J, Hickey G, Diekhans M, Fiddes IT, Novak AM, Deran A, Fang Q, Xie D, Feng S, Stiller J et al. Progressive Cactus is a multiple-genome aligner for the thousand-genome era. Nature. 2020 Nov;587(7833):246-251. PMID: 33177663; PMC: PMC7673649; DOI: 10.1038/s41586-020-2871-y Glenn Hickey, Jean Monlong, Jana Ebler, Adam M Novak, Jordan M Eizenga, Yan Gao; Human Pangenome Reference Consortium; Tobias Marschall, Heng Li, Benedict Paten Pangenome graph construction from genome alignments with Minigraph-Cactus. Nature Biotechnology. 2023 May 10. doi: 10.1038/s41587-023-01793-w. PMID: 37165083; DOI: 10.1038/s41587-023-01793-w Paten B, Earl D, Nguyen N, Diekhans M, Zerbino D, Haussler D. Cactus: Algorithms for genome multiple sequence alignment. Genome Res. 2011 Sep;21(9):1512-28. PMID: 21665927; PMC: PMC3166836; DOI: 10.1101/gr.123356.111 Wen-Wei Liao, Mobin Asri, Jana Ebler, ...et al, Heng Lin, Benedict Paten A draft human pangenome reference. Nature. 2023 May;617(7960):312-324. PMID: 37165242; PMC: PMC1017212; DOI: 10.1038/s41586-023-05896-x 
hprcVCF Short Variants Short Variants Human Pangenome - HPRC Description This track shows short nucleotide variants of a few base pairs when aligning HPRC genomes to the hg38 reference assembly. The alignment was made with the Minigraph-cactus approach described in the references below. There are three subtracks in this superTrack: All short variants up to 50bp, without any length filter All short variants &lt;= 3 bp long All short variants &gt; 3 bp long VCF Decomposition from HPRC Pangenome Resources Github: &quot;The Raw VCF files contain a site for each bubble in the graph. Nested bubbles will result in overlapping sites. The nesting relationships are denoted with the PS (parent snarl), LV (level) and AT (allele traversal) tags and need to be taken into account when interpreting the VCF. Alternatively, you can use the &apos;Decomposed VCFs&apos; which have been normalized by using vcfbub to &apos;pop&apos; bubbles with alleles larger than 100k and vcfwave to realign each alt (script). Note that in order to reproduce the PanGenie analyses from the papers, you should instead use the PanGenie HPRC Workflow. This workflow has a CHM13 branch to use when working with that reference. The exact tools and commands used to produce the VCFs are given here.&quot; Display Conventions and Configuration The Name of the items are the pair of node labels that denote the site&apos;s location in the graph, with the &apos;&gt;&apos; and &apos;&lt;&apos; denoting the forward and reverse orientation of the node. Mouseover on items in &quot;squish&quot; and &quot;pack&quot; modes shows the items Name and Genotypes. Mouseover on items in &quot;full&quot; mode shows Alleles. Methods The Minigraph-Cactus HPRC v1.0 graph was converted to VCF using vg deconstruct. This result was further postprocessed using vcfbub to flatten nested sites then vcfwave to normalize by realigning alt alleles to the reference. All steps are described in Hickey et al 2023. The postprocessing command lines and data can be found on Github. Finally, the resulting VCF was filtered by length and split into two VCFs using a cutoff of 3bp. Credits Thanks to Glenn Hickey for providing the HAL file from the HPRC project and for making these VCFs from them. References Armstrong J, Hickey G, Diekhans M, Fiddes IT, Novak AM, Deran A, Fang Q, Xie D, Feng S, Stiller J et al. Progressive Cactus is a multiple-genome aligner for the thousand-genome era. Nature. 2020 Nov;587(7833):246-251. PMID: 33177663; PMC: PMC7673649; DOI: 10.1038/s41586-020-2871-y Glenn Hickey, Jean Monlong, Jana Ebler, Adam M Novak, Jordan M Eizenga, Yan Gao; Human Pangenome Reference Consortium; Tobias Marschall, Heng Li, Benedict Paten Pangenome graph construction from genome alignments with Minigraph-Cactus. Nature Biotechnology. 2023 May 10. doi: 10.1038/s41587-023-01793-w. PMID: 37165083; DOI: 10.1038/s41587-023-01793-w Paten B, Earl D, Nguyen N, Diekhans M, Zerbino D, Haussler D. Cactus: Algorithms for genome multiple sequence alignment. Genome Res. 2011 Sep;21(9):1512-28. PMID: 21665927; PMC: PMC3166836; DOI: 10.1101/gr.123356.111 Wen-Wei Liao, Mobin Asri, Jana Ebler, ...et al, Heng Lin, Benedict Paten A draft human pangenome reference. Nature. 2023 May;617(7960):312-324. PMID: 37165242; PMC: PMC1017212; DOI: 10.1038/s41586-023-05896-x 
hprcVCFDecomposedUnder4 HPRC Variants <= 3bp HPRC VCF variants filtered for items size <= 3bp Human Pangenome - HPRC Description This track shows short nucleotide variants of a few base pairs when aligning HPRC genomes to the hg38 reference assembly. The alignment was made with the Minigraph-cactus approach described in the references below. There are three subtracks in this superTrack: All short variants up to 50bp, without any length filter All short variants &lt;= 3 bp long All short variants &gt; 3 bp long VCF Decomposition from HPRC Pangenome Resources Github: &quot;The Raw VCF files contain a site for each bubble in the graph. Nested bubbles will result in overlapping sites. The nesting relationships are denoted with the PS (parent snarl), LV (level) and AT (allele traversal) tags and need to be taken into account when interpreting the VCF. Alternatively, you can use the &apos;Decomposed VCFs&apos; which have been normalized by using vcfbub to &apos;pop&apos; bubbles with alleles larger than 100k and vcfwave to realign each alt (script). Note that in order to reproduce the PanGenie analyses from the papers, you should instead use the PanGenie HPRC Workflow. This workflow has a CHM13 branch to use when working with that reference. The exact tools and commands used to produce the VCFs are given here.&quot; Display Conventions and Configuration The Name of the items are the pair of node labels that denote the site&apos;s location in the graph, with the &apos;&gt;&apos; and &apos;&lt;&apos; denoting the forward and reverse orientation of the node. Mouseover on items in &quot;squish&quot; and &quot;pack&quot; modes shows the items Name and Genotypes. Mouseover on items in &quot;full&quot; mode shows Alleles. Methods The Minigraph-Cactus HPRC v1.0 graph was converted to VCF using vg deconstruct. This result was further postprocessed using vcfbub to flatten nested sites then vcfwave to normalize by realigning alt alleles to the reference. All steps are described in Hickey et al 2023. The postprocessing command lines and data can be found on Github. Finally, the resulting VCF was filtered by length and split into two VCFs using a cutoff of 3bp. Credits Thanks to Glenn Hickey for providing the HAL file from the HPRC project and for making these VCFs from them. References Armstrong J, Hickey G, Diekhans M, Fiddes IT, Novak AM, Deran A, Fang Q, Xie D, Feng S, Stiller J et al. Progressive Cactus is a multiple-genome aligner for the thousand-genome era. Nature. 2020 Nov;587(7833):246-251. PMID: 33177663; PMC: PMC7673649; DOI: 10.1038/s41586-020-2871-y Glenn Hickey, Jean Monlong, Jana Ebler, Adam M Novak, Jordan M Eizenga, Yan Gao; Human Pangenome Reference Consortium; Tobias Marschall, Heng Li, Benedict Paten Pangenome graph construction from genome alignments with Minigraph-Cactus. Nature Biotechnology. 2023 May 10. doi: 10.1038/s41587-023-01793-w. PMID: 37165083; DOI: 10.1038/s41587-023-01793-w Paten B, Earl D, Nguyen N, Diekhans M, Zerbino D, Haussler D. Cactus: Algorithms for genome multiple sequence alignment. Genome Res. 2011 Sep;21(9):1512-28. PMID: 21665927; PMC: PMC3166836; DOI: 10.1101/gr.123356.111 Wen-Wei Liao, Mobin Asri, Jana Ebler, ...et al, Heng Lin, Benedict Paten A draft human pangenome reference. Nature. 2023 May;617(7960):312-324. PMID: 37165242; PMC: PMC1017212; DOI: 10.1038/s41586-023-05896-x 
hprcVCFDecomposedOver3 HPRC Variants > 3bp HPRC VCF variants filtered for items size > 3bp Human Pangenome - HPRC Description This track shows short nucleotide variants of a few base pairs when aligning HPRC genomes to the hg38 reference assembly. The alignment was made with the Minigraph-cactus approach described in the references below. There are three subtracks in this superTrack: All short variants up to 50bp, without any length filter All short variants &lt;= 3 bp long All short variants &gt; 3 bp long VCF Decomposition from HPRC Pangenome Resources Github: &quot;The Raw VCF files contain a site for each bubble in the graph. Nested bubbles will result in overlapping sites. The nesting relationships are denoted with the PS (parent snarl), LV (level) and AT (allele traversal) tags and need to be taken into account when interpreting the VCF. Alternatively, you can use the &apos;Decomposed VCFs&apos; which have been normalized by using vcfbub to &apos;pop&apos; bubbles with alleles larger than 100k and vcfwave to realign each alt (script). Note that in order to reproduce the PanGenie analyses from the papers, you should instead use the PanGenie HPRC Workflow. This workflow has a CHM13 branch to use when working with that reference. The exact tools and commands used to produce the VCFs are given here.&quot; Display Conventions and Configuration The Name of the items are the pair of node labels that denote the site&apos;s location in the graph, with the &apos;&gt;&apos; and &apos;&lt;&apos; denoting the forward and reverse orientation of the node. Mouseover on items in &quot;squish&quot; and &quot;pack&quot; modes shows the items Name and Genotypes. Mouseover on items in &quot;full&quot; mode shows Alleles. Methods The Minigraph-Cactus HPRC v1.0 graph was converted to VCF using vg deconstruct. This result was further postprocessed using vcfbub to flatten nested sites then vcfwave to normalize by realigning alt alleles to the reference. All steps are described in Hickey et al 2023. The postprocessing command lines and data can be found on Github. Finally, the resulting VCF was filtered by length and split into two VCFs using a cutoff of 3bp. Credits Thanks to Glenn Hickey for providing the HAL file from the HPRC project and for making these VCFs from them. References Armstrong J, Hickey G, Diekhans M, Fiddes IT, Novak AM, Deran A, Fang Q, Xie D, Feng S, Stiller J et al. Progressive Cactus is a multiple-genome aligner for the thousand-genome era. Nature. 2020 Nov;587(7833):246-251. PMID: 33177663; PMC: PMC7673649; DOI: 10.1038/s41586-020-2871-y Glenn Hickey, Jean Monlong, Jana Ebler, Adam M Novak, Jordan M Eizenga, Yan Gao; Human Pangenome Reference Consortium; Tobias Marschall, Heng Li, Benedict Paten Pangenome graph construction from genome alignments with Minigraph-Cactus. Nature Biotechnology. 2023 May 10. doi: 10.1038/s41587-023-01793-w. PMID: 37165083; DOI: 10.1038/s41587-023-01793-w Paten B, Earl D, Nguyen N, Diekhans M, Zerbino D, Haussler D. Cactus: Algorithms for genome multiple sequence alignment. Genome Res. 2011 Sep;21(9):1512-28. PMID: 21665927; PMC: PMC3166836; DOI: 10.1101/gr.123356.111 Wen-Wei Liao, Mobin Asri, Jana Ebler, ...et al, Heng Lin, Benedict Paten A draft human pangenome reference. Nature. 2023 May;617(7960):312-324. PMID: 37165242; PMC: PMC1017212; DOI: 10.1038/s41586-023-05896-x 
est Human ESTs Human ESTs Including Unspliced mRNA and EST Description This track shows alignments between human expressed sequence tags (ESTs) in GenBank and the genome. ESTs are single-read sequences, typically about 500 bases in length, that usually represent fragments of transcribed genes. NOTE: As of April, 2007, we no longer include GenBank sequences that contain the following URL as part of the record: http://fulllength.invitrogen.com Some of these entries are the result of alignment to pseudogenes, followed by &quot;correction&quot; of the EST to match the genomic sequence. It is therefore not the sequence of the actual EST and makes it appear that the EST is transcribed. Invitrogen no longer sells the clones. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. In dense display mode, the items that are more darkly shaded indicate matches of better quality. The strand information (+/-) indicates the direction of the match between the EST and the matching genomic sequence. It bears no relationship to the direction of transcription of the RNA with which it might be associated. The description page for this track has a filter that can be used to change the display mode, alter the color, and include/exclude a subset of items within the track. This may be helpful when many items are shown in the track display, especially when only some are relevant to the current task. To use the filter: Type a term in one or more of the text boxes to filter the EST display. For example, to apply the filter to all ESTs expressed in a specific organ, type the name of the organ in the tissue box. To view the list of valid terms for each text box, consult the table in the Table Browser that corresponds to the factor on which you wish to filter. For example, the &quot;tissue&quot; table contains all the types of tissues that can be entered into the tissue text box. Multiple terms may be entered at once, separated by a space. Wildcards may also be used in the filter. If filtering on more than one value, choose the desired combination logic. If &quot;and&quot; is selected, only ESTs that match all filter criteria will be highlighted. If &quot;or&quot; is selected, ESTs that match any one of the filter criteria will be highlighted. Choose the color or display characteristic that should be used to highlight or include/exclude the filtered items. If &quot;exclude&quot; is chosen, the browser will not display ESTs that match the filter criteria. If &quot;include&quot; is selected, the browser will display only those ESTs that match the filter criteria. This track may also be configured to display base labeling, a feature that allows the user to display all bases in the aligning sequence or only those that differ from the genomic sequence. For more information about this option, click here. Several types of alignment gap may also be colored; for more information, click here. Methods To make an EST, RNA is isolated from cells and reverse transcribed into cDNA. Typically, the cDNA is cloned into a plasmid vector and a read is taken from the 5' and/or 3' primer. For most &mdash; but not all &mdash; ESTs, the reverse transcription is primed by an oligo-dT, which hybridizes with the poly-A tail of mature mRNA. The reverse transcriptase may or may not make it to the 5' end of the mRNA, which may or may not be degraded. In general, the 3' ESTs mark the end of transcription reasonably well, but the 5' ESTs may end at any point within the transcript. Some of the newer cap-selected libraries cover transcription start reasonably well. Before the cap-selection techniques emerged, some projects used random rather than poly-A priming in an attempt to retrieve sequence distant from the 3' end. These projects were successful at this, but as a side effect also deposited sequences from unprocessed mRNA and perhaps even genomic sequences into the EST databases. Even outside of the random-primed projects, there is a degree of non-mRNA contamination. Because of this, a single unspliced EST should be viewed with considerable skepticism. To generate this track, human ESTs from GenBank were aligned against the genome using blat. Note that the maximum intron length allowed by blat is 750,000 bases, which may eliminate some ESTs with very long introns that might otherwise align. When a single EST aligned in multiple places, the alignment having the highest base identity was identified. Only alignments having a base identity level within 0.5% of the best and at least 96% base identity with the genomic sequence were kept. Credits This track was produced at UCSC from EST sequence data submitted to the international public sequence databases by scientists worldwide. References Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank: update. Nucleic Acids Res. 2004 Jan 1;32(Database issue):D23-6. Kent WJ. BLAT - The BLAST-Like Alignment Tool. Genome Res. 2002 Apr;12(4):656-64. 
mrna Human mRNAs Human mRNAs from GenBank mRNA and EST Description The mRNA track shows alignments between human mRNAs in GenBank and the genome. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. In dense display mode, the items that are more darkly shaded indicate matches of better quality. The description page for this track has a filter that can be used to change the display mode, alter the color, and include/exclude a subset of items within the track. This may be helpful when many items are shown in the track display, especially when only some are relevant to the current task. To use the filter: Type a term in one or more of the text boxes to filter the mRNA display. For example, to apply the filter to all mRNAs expressed in a specific organ, type the name of the organ in the tissue box. To view the list of valid terms for each text box, consult the table in the Table Browser that corresponds to the factor on which you wish to filter. For example, the &quot;tissue&quot; table contains all the types of tissues that can be entered into the tissue text box. Multiple terms may be entered at once, separated by a space. Wildcards may also be used in the filter. If filtering on more than one value, choose the desired combination logic. If &quot;and&quot; is selected, only mRNAs that match all filter criteria will be highlighted. If &quot;or&quot; is selected, mRNAs that match any one of the filter criteria will be highlighted. Choose the color or display characteristic that should be used to highlight or include/exclude the filtered items. If &quot;exclude&quot; is chosen, the browser will not display mRNAs that match the filter criteria. If &quot;include&quot; is selected, the browser will display only those mRNAs that match the filter criteria. This track may also be configured to display codon coloring, a feature that allows the user to quickly compare mRNAs against the genomic sequence. For more information about this option, go to the Codon and Base Coloring for Alignment Tracks page. Several types of alignment gap may also be colored; for more information, go to the Alignment Insertion/Deletion Display Options page. Methods GenBank human mRNAs were aligned against the genome using the blat program. When a single mRNA aligned in multiple places, the alignment having the highest base identity was found. Only alignments having a base identity level within 0.5% of the best and at least 96% base identity with the genomic sequence were kept. Credits The mRNA track was produced at UCSC from mRNA sequence data submitted to the international public sequence databases by scientists worldwide. References Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW. GenBank. Nucleic Acids Res. 2013 Jan;41(Database issue):D36-42. PMID: 23193287; PMC: PMC3531190 Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank: update. Nucleic Acids Res. 2004 Jan 1;32(Database issue):D23-6. PMID: 14681350; PMC: PMC308779 Kent WJ. BLAT - the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 
hgIkmc IKMC Genes Mapped International Knockout Mouse Consortium Genes Mapped to Human Genome Genes and Gene Predictions Description This track shows genes targeted by International Knockout Mouse Consortium (IKMC) mapped to the human genome. IKMC is a collaboration to generate a public resource of mouse embryonic stem (ES) cells containing a null mutation in every gene in the mouse genome. Gene targets are color-coded by status: Green: Reagent(s) Available Yellow: In Progress Blue: Not Started/On Hold Black: Withdrawn/Problematic The KnockOut Mouse Project Data Coordination Center (KOMP DCC) is the central database resource for coordinating mouse gene targeting within IKMC and provides web-based query and display tools for IKMC data. In addition, the KOMP DCC website provides a tool for the scientific community to nominate genes of interest to be knocked out by the KOMP initiative. IKMC members include KnockOut Mouse Project (KOMP), a trans-NIH initiative (USA) European Conditional Mouse Mutagenesis Program (EUCOMM), funded by the European Union Framework 6 programme (EU) North American Conditional Mouse Mutagenesis Project (NorCOMM), a Genome Prairie Project (Canada) Texas A&amp;M Institute for Genomic Medicine (TIGM) (USA) KOMP includes two production centers: CSD, a collaborative team at the Children's Hospital Oakland Research Institute (CHORI), the Wellcome Trust Sanger Institute and the University of California at Davis School of Veterinary Medicine, and a team at the VelociGene division of Regeneron Pharmaceuticals, Inc. EUCOMM includes 9 participating institutions. NorCOMM includes several participating institutions. Methods Using complementary targeting strategies, the IKMC centers design and create targeting vectors, mutant ES cell lines and, to some extent, mutant mice, embryos or sperm. Materials are distributed to the research community. The KOMP Repository archives, maintains, and distributes IKMC products. Researchers can order products and get product information from the Repository. Researchers can also express interest in products that are still in the pipeline. They will then receive email notification as soon as KOMP generated products are available for distribution. The process for ordering EUCOMM materials can be found here. The process for ordering TIGM materials can be found here. Information on NorCOMM products and services can be found here. Genes were mapped to the human genome by IKMC. Credits Thanks to the International Knockout Mouse Consortium, and Carol Bult in particular, for providing these data. References Austin CP, Battey JF, Bradley A, Bucan M, Capecchi M, Collins FS, Dove WF, Duyk G, Dymecki S, Eppig JT et al. The knockout mouse project. Nat Genet. 2004 Sep;36(9):921-4. PMID: 15340423; PMC: PMC2716027 Collins FS, Finnell RH, Rossant J, Wurst W. A new partner for the international knockout mouse consortium. Cell. 2007 Apr 20;129(2):235. PMID: 17448981 International Mouse Knockout Consortium, Collins FS, Rossant J, Wurst W. A mouse for all reasons. Cell. 2007 Jan 12;128(1):9-13. PMID: 17218247 
ileumWangCellType Ileum Cells Ileum cells binned by cell type from Wang et al 2020 Single Cell RNA-seq Description This track shows data from Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. Droplet-based single-cell RNA sequencing (scRNA-seq) was used to survey gene expression profiles of the epithelium in the human ileum, colon, and rectum. A total of 7 cell clusters were identified: enterocytes (EC), goblet cells (G), paneth-like cells (PLC), enteroendocrine cells (EEC), progenitor cells (PRO), transient-amplifying cells (TA) and stem cells (SC). This track collection contains two bar chart tracks of RNA expression in ileum cells where cells are grouped by cell type (Ileum Cells) or donor (Ileum Donor). The default track displayed is Ileum Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification epithelial secretory stem cell Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Note that the Ileum Donor track is colored by donor for improved clarity. Method Using single-cell RNA sequencing, RNA profiles of intestinal epithelial cells were obtained for 6,167 cells from two human ileum samples. Tissue samples belonged to a male donor age 60 with Neuroendocrine Carcinoma (Ileum-1) and a female donor age 67 with Adenocarcinoma (Ileum-2). The healthy intestinal mucous membranes used for each sample were cut away from the tumor border in surgically removed ileum tissue. Additionally, the intestinal tissues were washed in Hank's balanced salt solution (HBSS) to remove mucus, blood cells, and muscle tissue. The sample was enriched for epithelial cells through centrifugation before being dissociated with Tryple to obtain single-cell suspensions. RNA-seq libraries were prepared using 10x Genomics 3' v2 kit and sequenced on an Illumina Hiseq X Ten PE150. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Yalong Wang, Wanlu Song, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Luis Nassar. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Wang Y, Song W, Wang J, Wang T, Xiong X, Qi Z, Fu W, Yang X, Chen YG. Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. J Exp Med. 2020 Feb 3;217(2). PMID: 31753849; PMC: PMC7041720 
ileumWang Ileum Wang Ileum single cell sequencing from Wang et al 2020 Single Cell RNA-seq Description This track shows data from Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. Droplet-based single-cell RNA sequencing (scRNA-seq) was used to survey gene expression profiles of the epithelium in the human ileum, colon, and rectum. A total of 7 cell clusters were identified: enterocytes (EC), goblet cells (G), paneth-like cells (PLC), enteroendocrine cells (EEC), progenitor cells (PRO), transient-amplifying cells (TA) and stem cells (SC). This track collection contains two bar chart tracks of RNA expression in ileum cells where cells are grouped by cell type (Ileum Cells) or donor (Ileum Donor). The default track displayed is Ileum Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification epithelial secretory stem cell Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Note that the Ileum Donor track is colored by donor for improved clarity. Method Using single-cell RNA sequencing, RNA profiles of intestinal epithelial cells were obtained for 6,167 cells from two human ileum samples. Tissue samples belonged to a male donor age 60 with Neuroendocrine Carcinoma (Ileum-1) and a female donor age 67 with Adenocarcinoma (Ileum-2). The healthy intestinal mucous membranes used for each sample were cut away from the tumor border in surgically removed ileum tissue. Additionally, the intestinal tissues were washed in Hank's balanced salt solution (HBSS) to remove mucus, blood cells, and muscle tissue. The sample was enriched for epithelial cells through centrifugation before being dissociated with Tryple to obtain single-cell suspensions. RNA-seq libraries were prepared using 10x Genomics 3' v2 kit and sequenced on an Illumina Hiseq X Ten PE150. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Yalong Wang, Wanlu Song, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Luis Nassar. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Wang Y, Song W, Wang J, Wang T, Xiong X, Qi Z, Fu W, Yang X, Chen YG. Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. J Exp Med. 2020 Feb 3;217(2). PMID: 31753849; PMC: PMC7041720 
ileumWangDonor Ileum Donor Ileum cells binned by organ donor from Wang et al 2020 Single Cell RNA-seq Description This track shows data from Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. Droplet-based single-cell RNA sequencing (scRNA-seq) was used to survey gene expression profiles of the epithelium in the human ileum, colon, and rectum. A total of 7 cell clusters were identified: enterocytes (EC), goblet cells (G), paneth-like cells (PLC), enteroendocrine cells (EEC), progenitor cells (PRO), transient-amplifying cells (TA) and stem cells (SC). This track collection contains two bar chart tracks of RNA expression in ileum cells where cells are grouped by cell type (Ileum Cells) or donor (Ileum Donor). The default track displayed is Ileum Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification epithelial secretory stem cell Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Note that the Ileum Donor track is colored by donor for improved clarity. Method Using single-cell RNA sequencing, RNA profiles of intestinal epithelial cells were obtained for 6,167 cells from two human ileum samples. Tissue samples belonged to a male donor age 60 with Neuroendocrine Carcinoma (Ileum-1) and a female donor age 67 with Adenocarcinoma (Ileum-2). The healthy intestinal mucous membranes used for each sample were cut away from the tumor border in surgically removed ileum tissue. Additionally, the intestinal tissues were washed in Hank's balanced salt solution (HBSS) to remove mucus, blood cells, and muscle tissue. The sample was enriched for epithelial cells through centrifugation before being dissociated with Tryple to obtain single-cell suspensions. RNA-seq libraries were prepared using 10x Genomics 3' v2 kit and sequenced on an Illumina Hiseq X Ten PE150. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Yalong Wang, Wanlu Song, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Luis Nassar. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Wang Y, Song W, Wang J, Wang T, Xiong X, Qi Z, Fu W, Yang X, Chen YG. Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. J Exp Med. 2020 Feb 3;217(2). PMID: 31753849; PMC: PMC7041720 
ucscToINSDC INSDC Accession at INSDC - International Nucleotide Sequence Database Collaboration Mapping and Sequencing Description This track associates UCSC Genome Browser chromosome names to accession names from the International Nucleotide Sequence Database Collaboration (INSDC). The data were downloaded from the NCBI assembly database. Credits The data for this track was prepared by Hiram Clawson. 
jaspar JASPAR Transcription Factors JASPAR Transcription Factor Binding Site Database Regulation Description This track represents genome-wide predicted binding sites for TF (transcription factor) binding profiles in the JASPAR CORE collection. This open-source database contains a curated, non-redundant set of binding profiles derived from published collections of experimentally defined transcription factor binding sites for eukaryotes. Display Conventions and Configuration Shaded boxes represent predicted binding sites for each of the TF profiles in the JASPAR CORE collection. The shading of the boxes indicates the p-value of the profile's match to that position (scaled between 0-1000 scores, where 0 corresponds to a p-value of 1 and 1000 to a p-value &le; 10-10). Thus, the darker the shade, the lower (better) the p-value. The default view shows only predicted binding sites with scores of 400 or greater but can be adjusted in the track settings. Multi-select filters allow viewing of particular transcription factors. At window sizes of greater than 10,000 base pairs, this track turns to density graph mode. Zoom to a smaller region and click into an item to see more detail. From BED format documentation: shade &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; score in range &le; 166 167-277 278-388 389-499 500-611 612-722 723-833 834-944 &ge; 945 Conversion table: Item score 0 100 131 200 300 400 500 600 700 800 900 1000 p-value 1 0.1 0.049 10-2 10-3 10-4 10-5 10-6 10-7 10-8 10-9 &le; 10-10 Methods The JASPAR 2024 update expanded the JASPAR CORE collection by 20% (329 added and 72 upgraded profiles). The new profiles were introduced after manual curation, in which 26 629 TF binding motifs were curated and obtained as PFMs or discovered from ChIP-seq/-exo or DAP-seq data. 2500 profiles from JASPAR 2022 were revised to either promote them to the CORE collection, update the associated metadata, or remove them because of validation inconsistencies or poor quality. The JASPAR database stores and focuses mostly on PFMs as the model of choice for TF-DNA interactions. More information on the methods can be found in the JASPAR 2024 publication or on the JASPAR website. JASPAR 2022 contains updated transcription factor binding sites with additional transcription factor profiles. More information on the methods can be found in the JASPAR 2022 publication JASPAR 2022 publication or on the JASPAR website. JASPAR 2020 scanned DNA sequences with JASPAR CORE TF-binding profiles for each taxa independently using PWMScan. TFBS predictions were selected with a PWM relative score &ge; 0.8 and a p-value &lt; 0.05. P-values were scaled between 0 (corresponding to a p-value of 1) and 1000 (p-value &le; 10-10) for coloring of the genome tracks and to allow for comparison of prediction confidence between different profiles. JASPAR 2018 used the TFBS Perl module (Lenhard and Wasserman 2002) and FIMO (Grant, Bailey, and Noble 2011), as distributed within the MEME suite (version 4.11.2) (Bailey et al. 2009). For scanning genomes with the BioPerl TFBS module, profiles were converted to PWMs and matches were kept with a relative score &ge; 0.8. For the FIMO scan, profiles were reformatted to MEME motifs and matches with a p-value &lt; 0.05 were kept. TFBS predictions that were not consistent between the two methods (TFBS Perl module and FIMO) were removed. The remaining TFBS predictions were colored according to their FIMO p-value to allow for comparison of prediction confidence between different profiles. Please refer to the JASPAR 2024, 2022, 2020, and 2018 publications for more details (citation below). Data Access JASPAR Transcription Factor Binding data includes billions of items. Limited regions can be explored interactively with the Table Browser and cross-referenced with Data Integrator, although positional queries that are too big can lead to timing out. This results in a black page or truncated output. In this case, you may try reducing the chromosomal query to a smaller window. For programmatic access, the track can be accessed using the Genome Browser's REST API. JASPAR annotations can be downloaded from the Genome Browser's download server as a bigBed file. This compressed binary format can be remotely queried through command line utilities. Please note that some of the download files can be quite large. The utilities for working with bigBed-formatted binary files can be downloaded here. Run a utility with no arguments to see a brief description of the utility and its options. bigBedInfo provides summary statistics about a bigBed file including the number of items in the file. With the -as option, the output includes an autoSql definition of data columns, useful for interpreting the column values. bigBedToBed converts the binary bigBed data to tab-separated text. Output can be restricted to a particular region by using the -chrom, -start and -end options. Example: retrieve all JASPAR items in chr1:200001-200400 bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/jaspar/JASPAR2024.bb -chrom=chr1 -start=200000 -end=200400 stdout All data are freely available. Additional resources are available directly from the JASPAR group: Binding site predictions for all and individual TF profiles are available for download at http://expdata.cmmt.ubc.ca/JASPAR/downloads/UCSC_tracks/. Code and data used to create the UCSC tracks are available at https://github.com/wassermanlab/JASPAR-UCSC-tracks. The underlying JASPAR motif data is available through the JASPAR website at https://jaspar.genereg.net/. Other Genomes The JASPAR group provides TFBS predictions for many additional species and genomes, accessible by connection to their Public Hub or by clicking the assembly links below: Species Genome assembly versions Human - Homo sapiens hg19, hg38 Mouse - Mus musculus mm10, mm39 Zebrafish - Danio rerio danRer11 Fruitfly - Drosophila melanogaster dm6 Nematode - Caenorhabditis elegans ce10, ce11 Vase tunicate - Ciona intestinalis ci3 Thale cress - Arabidopsis thaliana araTha1 Yeast - Saccharomyces cerevisiae sacCer3 Credits The JASPAR database is a joint effort between several labs (please see the latest JASPAR paper, below). Binding site predictions and UCSC tracks were computed by the Wasserman Lab. For enquiries about the data please contact Oriol Fornes ( &#111;&#114;&#105;&#111;l&#64;&#99;&#109;&#109;&#116;. u&#98;&#99;.c&#97; ). Wasserman Lab Centre for Molecular Medicine and Therapeutics BC Children's Hospital Research Institute Department of Medical Genetics University of British Columbia Vancouver, Canada References Castro-Mondragon JA, Riudavets-Puig R, Rauluseviciute I, Berhanu Lemma R, Turchi L, Blanc-Mathieu R, Lucas J, Boddie P, Khan A, Manosalva P&#233;rez N et al. JASPAR 2022: the 9th release of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 2021 Nov 30;. PMID: 34850907 Fornes O, Castro-Mondragon JA, Khan A, van der Lee R, Zhang X, Richmond PA, Modi BP, Correard S, Gheorghe M, Barana&#353;i&#263; D et al. JASPAR 2020: update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 2020 Jan 8;48(D1):D87-D92. PMID: 31701148; PMC: PMC7145627 Khan A, Fornes O, Stigliani A, Gheorghe M, Castro-Mondragon JA, van der Lee R, Bessy A, Ch&#232;neby J, Kulkarni SR, Tan G et al. JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework. Nucleic Acids Res. 2018 Jan 4;46(D1):D260-D266. PMID: 29140473; PMC: PMC5753243 Rauluseviciute I, Riudavets-Puig R, Blanc-Mathieu R, Castro-Mondragon JA, Ferenc K, Kumar V, Lemma RB, Lucas J, Ch&#232;neby J, Baranasic D et al. JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 2023 Nov 14;. PMID: 37962376 
jaspar2018 JASPAR 2018 TFBS JASPAR CORE 2018 - Predicted Transcription Factor Binding Sites Regulation 
jaspar2020 JASPAR 2020 TFBS JASPAR CORE 2020 - Predicted Transcription Factor Binding Sites Regulation 
jaspar2022 JASPAR 2022 TFBS JASPAR CORE 2022 - Predicted Transcription Factor Binding Sites Regulation 
jaspar2024 JASPAR 2024 TFBS JASPAR CORE 2024 - Predicted Transcription Factor Binding Sites Regulation 
kidneyStewartBroadCellType Kidney Broad CT Kidney RNA binned by broad cell type from Stewart et al 2019 Single Cell RNA-seq Description This track displays data from Spatiotemporal immune zonation of the human kidney. Droplet-based single-cell RNA sequencing (scRNA-seq) was used to profile 40,268 mature human kidney cells. After principal component analysis, identified clusters were manually curated into four major cellular compartments using canonical markers as found in Stewart et al., 2019: endothelial, immune, fibroblast, and epithelium. This track collection contains six bar chart tracks of RNA expression in the human kidney where cells are grouped by merged cell type (Kidney Cells), broad cell type (Kidney Broad CT), detailed cell type (Kidney Details), compartment (Kidney Compartment), experiment (Kidney Experiment), and project (Kidney Project). The default track displayed is Kidney Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune kidney specific epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method 14 mature healthy human kidney samples were obtained from individuals (ages 1-72) that either underwent tumor nephrectomy (n=10) or from kidneys donated for transplantation (n=4) but were unsuitable for use. Kidney tissues from tumor nephrectomies were collected from unaffected areas estimated to be corticomedullary. Samples were enzymatically dissociated and enriched for live cells (experiment set 1) or enriched for leukocytes with a density gradient and then for live cells (experiment set 2). Single cell libraries were prepared using 10x Genomics 3' v2 kit and sequenced on an Illumina HiSeq4000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Benjamin J Stewart, John R Ferdinand, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Daniel Schmelter. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Stewart BJ, Ferdinand JR, Young MD, Mitchell TJ, Loudon KW, Riding AM, Richoz N, Frazer GL, Staniforth JUL, Vieira Braga FA et al. Spatiotemporal immune zonation of the human kidney. Science. 2019 Sep 27;365(6460):1461-1466. PMID: 31604275; PMC: PMC7343525 
kidneyStewart Kidney Stewart Kidney single cell data from  Stewart et al 2019 Single Cell RNA-seq Description This track displays data from Spatiotemporal immune zonation of the human kidney. Droplet-based single-cell RNA sequencing (scRNA-seq) was used to profile 40,268 mature human kidney cells. After principal component analysis, identified clusters were manually curated into four major cellular compartments using canonical markers as found in Stewart et al., 2019: endothelial, immune, fibroblast, and epithelium. This track collection contains six bar chart tracks of RNA expression in the human kidney where cells are grouped by merged cell type (Kidney Cells), broad cell type (Kidney Broad CT), detailed cell type (Kidney Details), compartment (Kidney Compartment), experiment (Kidney Experiment), and project (Kidney Project). The default track displayed is Kidney Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune kidney specific epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method 14 mature healthy human kidney samples were obtained from individuals (ages 1-72) that either underwent tumor nephrectomy (n=10) or from kidneys donated for transplantation (n=4) but were unsuitable for use. Kidney tissues from tumor nephrectomies were collected from unaffected areas estimated to be corticomedullary. Samples were enzymatically dissociated and enriched for live cells (experiment set 1) or enriched for leukocytes with a density gradient and then for live cells (experiment set 2). Single cell libraries were prepared using 10x Genomics 3' v2 kit and sequenced on an Illumina HiSeq4000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Benjamin J Stewart, John R Ferdinand, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Daniel Schmelter. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Stewart BJ, Ferdinand JR, Young MD, Mitchell TJ, Loudon KW, Riding AM, Richoz N, Frazer GL, Staniforth JUL, Vieira Braga FA et al. Spatiotemporal immune zonation of the human kidney. Science. 2019 Sep 27;365(6460):1461-1466. PMID: 31604275; PMC: PMC7343525 
kidneyStewartCellType Kidney Cells Kidney RNA binned by merged cell type from Stewart et al 2019 Single Cell RNA-seq Description This track displays data from Spatiotemporal immune zonation of the human kidney. Droplet-based single-cell RNA sequencing (scRNA-seq) was used to profile 40,268 mature human kidney cells. After principal component analysis, identified clusters were manually curated into four major cellular compartments using canonical markers as found in Stewart et al., 2019: endothelial, immune, fibroblast, and epithelium. This track collection contains six bar chart tracks of RNA expression in the human kidney where cells are grouped by merged cell type (Kidney Cells), broad cell type (Kidney Broad CT), detailed cell type (Kidney Details), compartment (Kidney Compartment), experiment (Kidney Experiment), and project (Kidney Project). The default track displayed is Kidney Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune kidney specific epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method 14 mature healthy human kidney samples were obtained from individuals (ages 1-72) that either underwent tumor nephrectomy (n=10) or from kidneys donated for transplantation (n=4) but were unsuitable for use. Kidney tissues from tumor nephrectomies were collected from unaffected areas estimated to be corticomedullary. Samples were enzymatically dissociated and enriched for live cells (experiment set 1) or enriched for leukocytes with a density gradient and then for live cells (experiment set 2). Single cell libraries were prepared using 10x Genomics 3' v2 kit and sequenced on an Illumina HiSeq4000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Benjamin J Stewart, John R Ferdinand, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Daniel Schmelter. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Stewart BJ, Ferdinand JR, Young MD, Mitchell TJ, Loudon KW, Riding AM, Richoz N, Frazer GL, Staniforth JUL, Vieira Braga FA et al. Spatiotemporal immune zonation of the human kidney. Science. 2019 Sep 27;365(6460):1461-1466. PMID: 31604275; PMC: PMC7343525 
kidneyStewartCompartment Kidney Compartment Kidney RNA binned by compartment from Stewart et al 2019 Single Cell RNA-seq Description This track displays data from Spatiotemporal immune zonation of the human kidney. Droplet-based single-cell RNA sequencing (scRNA-seq) was used to profile 40,268 mature human kidney cells. After principal component analysis, identified clusters were manually curated into four major cellular compartments using canonical markers as found in Stewart et al., 2019: endothelial, immune, fibroblast, and epithelium. This track collection contains six bar chart tracks of RNA expression in the human kidney where cells are grouped by merged cell type (Kidney Cells), broad cell type (Kidney Broad CT), detailed cell type (Kidney Details), compartment (Kidney Compartment), experiment (Kidney Experiment), and project (Kidney Project). The default track displayed is Kidney Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune kidney specific epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method 14 mature healthy human kidney samples were obtained from individuals (ages 1-72) that either underwent tumor nephrectomy (n=10) or from kidneys donated for transplantation (n=4) but were unsuitable for use. Kidney tissues from tumor nephrectomies were collected from unaffected areas estimated to be corticomedullary. Samples were enzymatically dissociated and enriched for live cells (experiment set 1) or enriched for leukocytes with a density gradient and then for live cells (experiment set 2). Single cell libraries were prepared using 10x Genomics 3' v2 kit and sequenced on an Illumina HiSeq4000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Benjamin J Stewart, John R Ferdinand, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Daniel Schmelter. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Stewart BJ, Ferdinand JR, Young MD, Mitchell TJ, Loudon KW, Riding AM, Richoz N, Frazer GL, Staniforth JUL, Vieira Braga FA et al. Spatiotemporal immune zonation of the human kidney. Science. 2019 Sep 27;365(6460):1461-1466. PMID: 31604275; PMC: PMC7343525 
kidneyStewartDetailedCellType Kidney Details Kidney RNA binned by detailed cell type from Stewart et al 2019 Single Cell RNA-seq Description This track displays data from Spatiotemporal immune zonation of the human kidney. Droplet-based single-cell RNA sequencing (scRNA-seq) was used to profile 40,268 mature human kidney cells. After principal component analysis, identified clusters were manually curated into four major cellular compartments using canonical markers as found in Stewart et al., 2019: endothelial, immune, fibroblast, and epithelium. This track collection contains six bar chart tracks of RNA expression in the human kidney where cells are grouped by merged cell type (Kidney Cells), broad cell type (Kidney Broad CT), detailed cell type (Kidney Details), compartment (Kidney Compartment), experiment (Kidney Experiment), and project (Kidney Project). The default track displayed is Kidney Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune kidney specific epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method 14 mature healthy human kidney samples were obtained from individuals (ages 1-72) that either underwent tumor nephrectomy (n=10) or from kidneys donated for transplantation (n=4) but were unsuitable for use. Kidney tissues from tumor nephrectomies were collected from unaffected areas estimated to be corticomedullary. Samples were enzymatically dissociated and enriched for live cells (experiment set 1) or enriched for leukocytes with a density gradient and then for live cells (experiment set 2). Single cell libraries were prepared using 10x Genomics 3' v2 kit and sequenced on an Illumina HiSeq4000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Benjamin J Stewart, John R Ferdinand, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Daniel Schmelter. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Stewart BJ, Ferdinand JR, Young MD, Mitchell TJ, Loudon KW, Riding AM, Richoz N, Frazer GL, Staniforth JUL, Vieira Braga FA et al. Spatiotemporal immune zonation of the human kidney. Science. 2019 Sep 27;365(6460):1461-1466. PMID: 31604275; PMC: PMC7343525 
kidneyStewartExperiment Kidney Experiment Kidney RNA binned by Experiment from Stewart et al 2019 Single Cell RNA-seq Description This track displays data from Spatiotemporal immune zonation of the human kidney. Droplet-based single-cell RNA sequencing (scRNA-seq) was used to profile 40,268 mature human kidney cells. After principal component analysis, identified clusters were manually curated into four major cellular compartments using canonical markers as found in Stewart et al., 2019: endothelial, immune, fibroblast, and epithelium. This track collection contains six bar chart tracks of RNA expression in the human kidney where cells are grouped by merged cell type (Kidney Cells), broad cell type (Kidney Broad CT), detailed cell type (Kidney Details), compartment (Kidney Compartment), experiment (Kidney Experiment), and project (Kidney Project). The default track displayed is Kidney Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune kidney specific epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method 14 mature healthy human kidney samples were obtained from individuals (ages 1-72) that either underwent tumor nephrectomy (n=10) or from kidneys donated for transplantation (n=4) but were unsuitable for use. Kidney tissues from tumor nephrectomies were collected from unaffected areas estimated to be corticomedullary. Samples were enzymatically dissociated and enriched for live cells (experiment set 1) or enriched for leukocytes with a density gradient and then for live cells (experiment set 2). Single cell libraries were prepared using 10x Genomics 3' v2 kit and sequenced on an Illumina HiSeq4000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Benjamin J Stewart, John R Ferdinand, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Daniel Schmelter. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Stewart BJ, Ferdinand JR, Young MD, Mitchell TJ, Loudon KW, Riding AM, Richoz N, Frazer GL, Staniforth JUL, Vieira Braga FA et al. Spatiotemporal immune zonation of the human kidney. Science. 2019 Sep 27;365(6460):1461-1466. PMID: 31604275; PMC: PMC7343525 
kidneyStewartProject Kidney Project Kidney RNA binned by project from Stewart et al 2019 Single Cell RNA-seq Description This track displays data from Spatiotemporal immune zonation of the human kidney. Droplet-based single-cell RNA sequencing (scRNA-seq) was used to profile 40,268 mature human kidney cells. After principal component analysis, identified clusters were manually curated into four major cellular compartments using canonical markers as found in Stewart et al., 2019: endothelial, immune, fibroblast, and epithelium. This track collection contains six bar chart tracks of RNA expression in the human kidney where cells are grouped by merged cell type (Kidney Cells), broad cell type (Kidney Broad CT), detailed cell type (Kidney Details), compartment (Kidney Compartment), experiment (Kidney Experiment), and project (Kidney Project). The default track displayed is Kidney Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune kidney specific epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method 14 mature healthy human kidney samples were obtained from individuals (ages 1-72) that either underwent tumor nephrectomy (n=10) or from kidneys donated for transplantation (n=4) but were unsuitable for use. Kidney tissues from tumor nephrectomies were collected from unaffected areas estimated to be corticomedullary. Samples were enzymatically dissociated and enriched for live cells (experiment set 1) or enriched for leukocytes with a density gradient and then for live cells (experiment set 2). Single cell libraries were prepared using 10x Genomics 3' v2 kit and sequenced on an Illumina HiSeq4000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Benjamin J Stewart, John R Ferdinand, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Daniel Schmelter. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Stewart BJ, Ferdinand JR, Young MD, Mitchell TJ, Loudon KW, Riding AM, Richoz N, Frazer GL, Staniforth JUL, Vieira Braga FA et al. Spatiotemporal immune zonation of the human kidney. Science. 2019 Sep 27;365(6460):1461-1466. PMID: 31604275; PMC: PMC7343525 
liftHg19 LiftOver & ReMap UCSC LiftOver and NCBI ReMap: Genome alignments to convert annotations to hg19 Mapping and Sequencing Description This track shows alignments from the hg38 to the hg19 genome assembly, used by the UCSC liftOver tool and NCBI's ReMap service, respectively. Display Conventions and Configuration The track has three subtracks, one for UCSC and two for NCBI alignments. The alignments are shown as &quot;chains&quot; of alignable regions. The display is similar to the other chain tracks, see our chain display documentation for more information. Data access UCSC liftOver chain files for hg19 to hg38 can be obtained from a dedicated directory on our Download server. The NCBI chain file can be obtained from the MySQL tables directory on our download server, the filename is 'chainHg19ReMap.txt.gz'. Both tables can also be explored interactively with the Table Browser or the Data Integrator. Methods ReMap 2.2 alignments were downloaded from the NCBI FTP site and converted with the UCSC kent command line tools. The UCSC tool chainSwap was used to swap target and query genome to show the mappings on the hg38 genome. Like all data processing for the genome browser, the procedure is documented in our hg19 makeDoc file. Credits Thanks to NCBI for making the ReMap data available and to Angie Hinrichs for the file conversion. 
chainHg19ReMapAxtChain ReMap + axtChain hg19 NCBI ReMap alignments to hg19/GRCh37, joined by axtChain Mapping and Sequencing 
chainHg19ReMap NCBI ReMap hg19 NCBI ReMap alignments to hg19/GRCh37 Mapping and Sequencing 
liftOverHg19 UCSC liftOver to hg19 UCSC liftOver alignments to hg19 Mapping and Sequencing 
lincRNAsTranscripts lincRNA TUCP lincRNA and TUCP transcripts Genes and Gene Predictions Description This track displays the Human Body Map lincRNAs (large intergenic non coding RNAs) and TUCPs (transcripts of uncertain coding potential), as well as their expression levels across 22 human tissues and cell lines. The Human Body Map catalog was generated by integrating previously existing annotation sources with transcripts that were de-novo assembled from RNA-Seq data. These transcripts were collected from ~4 billion RNA-Seq reads across 24 tissues and cell types. Expression abundance was estimated by Cufflinks (Trapnell et al., 2010) based on RNA-Seq. Expression abundances were estimated on the gene locus level, rather than for each transcript separately and are given as raw FPKM. The prefixes tcons_ and tcons_l2_ are used to describe lincRNAs and TUCP transcripts, respectively. Specific details about the catalog generation and data sets used for this study can be found in Cabili et al (2011). Extended characterization of each transcript in the human body map catalog can be found at the Human lincRNA Catalog website. Expression abundance scores range from 0 to 1000, and are displayed from light blue to dark blue respectively: 01000 Credits The body map RNA-Seq data was kindly provided by the Gene Expression Applications research group at Illumina. References Cabili MN, Trapnell C, Goff L, Koziol M, Tazon-Vega B, Regev A, Rinn JL. Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev. 2011 Sep 15;25(18):1915-27. PMID: 21890647; PMC: PMC3185964 Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010 May;28(5):511-5. PMID: 20436464; PMC: PMC3146043 
liverMacParlandBroadCellType Liver Broad Liver cells binned by broad cell type from MacParland et al 2018 Single Cell RNA-seq Description This track shows data from Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Liver tissue was analyzed using droplet-based single-cell RNA-sequencing (scRNA-seq) and subsequent clustering distinguished 20 hepatic cell populations based on their identified marker genes found in MacParland et al., 2018. There are three bar chart tracks in this track collection with liver cells grouped by either broad cell type (Liver Broad), specific cell type (Liver Cells) and donor (Liver Donor). The default track displayed is Liver Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification immune endothelial fibroblast epithelial stem cell hepatocyte Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Liver Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Fresh liver samples were taken from 5 neurologically deceased donors (NDD) deemed acceptable for liver transplantation. The caudate lobe of the liver was surgically separated and flushed with HTK solution to leave only tissue resident cells that were used to prepare a cell suspension for scRNA-seq analysis. Samples were prepared using 10x Genomics 3' v2 library kit and sequenced on the Illumina HiSeq 2500. A total of 8,444 transcriptional profiles were obtained for organ specific and non-organ specific cells from healthy hepatic tissue. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Sonya MacParland and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Daniel Schmelter. The UCSC work was paid for by the Chan Zuckerberg Initiative. References MacParland SA, Liu JC, Ma XZ, Innes BT, Bartczak AM, Gage BK, Manuel J, Khuu N, Echeverri J, Linares I et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat Commun. 2018 Oct 22;9(1):4383. PMID: 30348985; PMC: PMC6197289 
liverMacParland Liver MacParland Liver single cell sequencing from MacParland et al 2018 Single Cell RNA-seq Description This track shows data from Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Liver tissue was analyzed using droplet-based single-cell RNA-sequencing (scRNA-seq) and subsequent clustering distinguished 20 hepatic cell populations based on their identified marker genes found in MacParland et al., 2018. There are three bar chart tracks in this track collection with liver cells grouped by either broad cell type (Liver Broad), specific cell type (Liver Cells) and donor (Liver Donor). The default track displayed is Liver Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification immune endothelial fibroblast epithelial stem cell hepatocyte Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Liver Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. The default track displayed is liver RNA grouped by cell type. Method Fresh liver samples were taken from 5 neurologically deceased donors (NDD) deemed acceptable for liver transplantation. The caudate lobe of the liver was surgically separated and flushed with HTK solution to leave only tissue resident cells that were used to prepare a cell suspension for scRNA-seq analysis. Samples were prepared using 10x Genomics 3' v2 library kit and sequenced on the Illumina HiSeq 2500. A total of 8,444 transcriptional profiles were obtained for organ specific and non-organ specific cells from healthy hepatic tissue. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Sonya MacParland and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Daniel Schmelter. The UCSC work was paid for by the Chan Zuckerberg Initiative. References MacParland SA, Liu JC, Ma XZ, Innes BT, Bartczak AM, Gage BK, Manuel J, Khuu N, Echeverri J, Linares I et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat Commun. 2018 Oct 22;9(1):4383. PMID: 30348985; PMC: PMC6197289 
liverMacParlandCellType Liver Cells Liver cells binned by cell type from MacParland et al 2018 Single Cell RNA-seq Description This track shows data from Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Liver tissue was analyzed using droplet-based single-cell RNA-sequencing (scRNA-seq) and subsequent clustering distinguished 20 hepatic cell populations based on their identified marker genes found in MacParland et al., 2018. There are three bar chart tracks in this track collection with liver cells grouped by either broad cell type (Liver Broad), specific cell type (Liver Cells) and donor (Liver Donor). The default track displayed is Liver Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification immune endothelial fibroblast epithelial stem cell hepatocyte Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Liver Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Relevant Figures From MacParland et al., 2018 Map of the human liver and its associated cell types. The liver is constructed of hepatic lobules which are composed of a portal triad (hepatic artery, the portal vein and the bile duct), hepatocytes aligned between a capillary network, and a central vein. MacParland et al. Nat Commun. 2018. / CC BY 4.0 Method Fresh liver samples were taken from 5 neurologically deceased donors (NDD) deemed acceptable for liver transplantation. The caudate lobe of the liver was surgically separated and flushed with HTK solution to leave only tissue resident cells that were used to prepare a cell suspension for scRNA-seq analysis. Samples were prepared using 10x Genomics 3' v2 library kit and sequenced on the Illumina HiSeq 2500. A total of 8,444 transcriptional profiles were obtained for organ specific and non-organ specific cells from healthy hepatic tissue. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Sonya MacParland and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Daniel Schmelter. The UCSC work was paid for by the Chan Zuckerberg Initiative. References MacParland SA, Liu JC, Ma XZ, Innes BT, Bartczak AM, Gage BK, Manuel J, Khuu N, Echeverri J, Linares I et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat Commun. 2018 Oct 22;9(1):4383. PMID: 30348985; PMC: PMC6197289 
liverMacParlandDonor Liver Donor Liver cells binned by organ donor from MacParland et al 2018 Single Cell RNA-seq Description This track shows data from Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Liver tissue was analyzed using droplet-based single-cell RNA-sequencing (scRNA-seq) and subsequent clustering distinguished 20 hepatic cell populations based on their identified marker genes found in MacParland et al., 2018. There are three bar chart tracks in this track collection with liver cells grouped by either broad cell type (Liver Broad), specific cell type (Liver Cells) and donor (Liver Donor). The default track displayed is Liver Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification immune endothelial fibroblast epithelial stem cell hepatocyte Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Liver Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Relevant Figures From MacParland et al., 2018 Contribution of cells from each liver sample to each cell cluster. Note that the liver number corresponds to the donor number (e.g. Liver 1 = Donor 1). MacParland et al. Nat Commun. 2018. / CC BY 4.0 t-SNE plot of human liver resident cells colored by source donor (Liver 1-5) and labeled with cluster number. MacParland et al. Nat Commun. 2018. / CC BY 4.0 Method Fresh liver samples were taken from 5 neurologically deceased donors (NDD) deemed acceptable for liver transplantation. The caudate lobe of the liver was surgically separated and flushed with HTK solution to leave only tissue resident cells that were used to prepare a cell suspension for scRNA-seq analysis. Samples were prepared using 10x Genomics 3' v2 library kit and sequenced on the Illumina HiSeq 2500. A total of 8,444 transcriptional profiles were obtained for organ specific and non-organ specific cells from healthy hepatic tissue. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Sonya MacParland and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Daniel Schmelter. The UCSC work was paid for by the Chan Zuckerberg Initiative. References MacParland SA, Liu JC, Ma XZ, Innes BT, Bartczak AM, Gage BK, Manuel J, Khuu N, Echeverri J, Linares I et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat Commun. 2018 Oct 22;9(1):4383. PMID: 30348985; PMC: PMC6197289 
lovdComp LOVD Variants LOVD: Leiden Open Variation Database Public Variants Phenotype and Literature Description NOTE: LOVD is intended for use primarily by physicians and other professionals concerned with genetic disorders, by genetics researchers, and by advanced students in science and medicine. While the LOVD database is open to the public, users seeking information about a personal medical or genetic condition are urged to consult with a qualified physician for diagnosis and for answers to personal questions. Further, please be sure to visit the LOVD web site for the very latest, as they are continually updating data. DOWNLOADS: LOVD databases are owned by their respective curators and are not available for download or mirroring by any third party without their permission. Batch queries on this track are only available via the UCSC Beacon API (see below). See also the LOVD web site for a list of database installations and the respective curators. This track shows the genomic positions of all public entries in public installations of the Leiden Open Variation Database system (LOVD) and the effect of the variant, if annotated. Due to the copyright restrictions of the LOVD databases, UCSC is not allowed to host any further information. To get details on a variant (bibliographic reference, phenotype, disease, patient, etc.), follow the &quot;Link to LOVD&quot; to the central server at Leiden, which will then redirect you to the details page on the particular LOVD server reporting this variant. Since Apr 2020, similar to the ClinVar track, the data is split into two subtracks, for variants with a length of &lt; 50 bp and &gt;= 50 bp, respectively. LOVD is a flexible, freely-available tool for gene-centered collection and display of DNA variations. It is not a database itself, but rather a platform where curators store and analyze data. While the LOVD team and the biggest LOVD sites are run at the Leiden University Medical Center, LOVD installations and their curators are spread over the whole world. Most LOVD databases report at least some of their content back to Leiden to allow global cross-database search, which is, among others, exported to this UCSC Genome Browser track every month. A few LOVD databases are entirely missing from this track. Reasons include configuration issues and intentionally blocked data search. During the last check in November 2019, the following databases did not export any variants: https://databases.lovd.nl/shared/genes/LDLR http://sysbio.org.cn/ https://ab-openlab.csir.res.in/mitolsdb/ Curators who want to share data in their database so it is present in this track can find more details in the LOVD FAQ. Batch queries The LOVD data is not available for download or for batch queries in the Table Browser. However, it is available for programmatic access via the Global Alliance Beacon API, a web service that accepts queries in the form (genome, chromosome, position, allele) and returns &quot;true&quot; or &quot;false&quot; depending on whether there is information about this allele in the database. For more details see our Beacon Server. To find all LOVD databases that contain variants of a given gene, you can get a list of databases by constructing a url in the format geneSymbol.lovd.nl, for example, tp53.lovd.nl. You can then use the LOVD API to retrieve more detailed information from a particular database. See the LOVD FAQ. Display Conventions and Configuration Genomic locations of LOVD variation entries are labeled with the gene symbol and the description of the mutation according to Human Gene Variation Society standards. For instance, the label AGRN:c.172G&gt;A means that the cDNA of AGRN is mutated from G to A at position 172. Since October 2017, the functional effect for variants is shown on the details page, if annotated. The possible values are: notClassified functionAffected notThisDisease notAnyDisease functionProbablyAffected functionProbablyNotAffected functionNotAffected unknown LOVD does not use the term &quot;pathogenic&quot;, please see the HGVS Terminology page for more details. All other information is shown on the respective LOVD variation page, accessible via the &quot;Link to LOVD&quot; above. Methods The mappings displayed in this track were provided by LOVD. Credits Thanks to the LOVD team, Ivo Fokkema, Peter Taschner, Johan den Dunnen, and all LOVD curators who gave permission to show their data. References Fokkema IF, Taschner PE, Schaafsma GC, Celli J, Laros JF, den Dunnen JT. LOVD v.2.0: the next generation in gene variant databases. Hum Mutat. 2011 May;32(5):557-63. PMID: 21520333 
lovdLong LOVD Variants >= 50 bp LOVD: Leiden Open Variation Database Public Variants, long >= 50 bp variants Phenotype and Literature 
lovdShort LOVD Variants < 50 bp + ins LOVD: Leiden Open Variation Database, short < 50 bp variants and insertions of any length Phenotype and Literature 
lrg LRG Regions Locus Reference Genomic (LRG) / RefSeqGene Sequences Mapped to Dec. 2013 (GRCh38/hg38) Assembly Mapping and Sequencing Description Locus Reference Genomic (LRG) sequences are manually curated, stable DNA sequences that surround a locus (typically a gene) and provide an unchanging coordinate system for reporting sequence variants. They are not necessarily identical to the corresponding sequence in a particular reference genome assembly (such as Dec. 2013 (GRCh38/hg38)), but can be mapped to each version of a reference genome assembly in order to convert between the stable LRG variant coordinates and the various assembly coordinates. We import the data from the LRG database at the EBI. The NCBI RefSeqGene database is almost identical to LRG, but it may contain a few more sequences. See the NCBI documentation. Each LRG record also includes at least one stable transcript on which variants may be reported. These transcripts appear in the LRG Transcripts track in the Gene and Gene Predictions track section. Methods LRG sequences are suggested by the community studying a locus (for example, Locus-Specific Database curators, research laboratories, mutation consortia). LRG curators then examine the submitted transcript as well as other known transcripts at the locus, in the context of alignment and public expression data. For more information on the selection and annotation process, see the LRG FAQ, (Dalgleish, et al.) and (MacArthur, et al.). Credits This track was produced at UCSC using LRG XML files. Thanks to LRG collaborators for making these data available. References Dalgleish R, Flicek P, Cunningham F, Astashyn A, Tully RE, Proctor G, Chen Y, McLaren WM, Larsson P, Vaughan BW et al. Locus Reference Genomic sequences: an improved basis for describing human DNA variants. Genome Med. 2010 Apr 15;2(4):24. PMID: 20398331; PMC: PMC2873802 MacArthur JA, Morales J, Tully RE, Astashyn A, Gil L, Bruford EA, Larsson P, Flicek P, Dalgleish R, Maglott DR et al. Locus Reference Genomic: reference sequences for the reporting of clinically relevant sequence variants. Nucleic Acids Res. 2014 Jan;42(Database issue):D873-8. PMID: 24285302; PMC: PMC3965024 
lrgTranscriptAli LRG Transcripts Locus Reference Genomic (LRG) / RefSeqGene Fixed Transcript Annotations Genes and Gene Predictions Description This track shows the fixed (unchanging) transcript(s) associated with each Locus Reference Genomic (LRG) sequence. LRG sequences are manually curated, stable DNA sequences that surround a locus (typically a gene) and provide an unchanging coordinate system for reporting sequence variants. They are not necessarily identical to the corresponding sequence in a particular reference genome assembly (such as Dec. 2013 (GRCh38/hg38)), but can be mapped to each version of a reference genome assembly in order to convert between the stable LRG variant coordinates and the various assembly coordinates. We import the data from the LRG database at the EBI. The NCBI RefSeqGene database is almost identical to LRG, but it may contain a few more sequences. See the NCBI documentation. The LRG Regions track, in the Mapping and Sequencing Tracks section, includes more information about the LRG including the HGNC gene symbol for the gene at that locus, source of the LRG sequence, and summary of differences between LRG sequence and the genome assembly. Methods LRG sequences are suggested by the community studying a locus (for example, Locus-Specific Database curators, research laboratories, mutation consortia). LRG curators then examine the submitted transcript as well as other known transcripts at the locus, in the context of alignment and public expression data. For more information on the selection and annotation process, see the LRG FAQ, (Dalgleish, et al.) and (MacArthur, et al.). Credits This track was produced at UCSC using LRG XML files. Thanks to LRG collaborators for making these data available. References Dalgleish R, Flicek P, Cunningham F, Astashyn A, Tully RE, Proctor G, Chen Y, McLaren WM, Larsson P, Vaughan BW et al. Locus Reference Genomic sequences: an improved basis for describing human DNA variants. Genome Med. 2010 Apr 15;2(4):24. PMID: 20398331; PMC: PMC2873802 MacArthur JA, Morales J, Tully RE, Astashyn A, Gil L, Bruford EA, Larsson P, Flicek P, Dalgleish R, Maglott DR et al. Locus Reference Genomic: reference sequences for the reporting of clinically relevant sequence variants. Nucleic Acids Res. 2014 Jan;42(Database issue):D873-8. PMID: 24285302; PMC: PMC3965024 
lungTravaglini2020CellType10x Lung Cells Lung cells 10x method binned by merged cell type from Travaglini et al 2020 Single Cell RNA-seq Description This track displays data from A molecular cell atlas of the human lung from single-cell RNA sequencing. Using droplet-based and plate-based single-cell RNA sequencing (scRNA-seq), 58 lung cell type populations were identified: 15 epithelial, 9 endothelial, 9 stromal, and 25 immune. This dataset covers ~75,000 human cells across all lung tissue compartments and circulating blood. This track collection contains 19 bar chart tracks of RNA expression in the human lung where cells are grouped such as by cell type (Lung Cells, Lung Cells FACS), tissue compartments (Lung Compart, Lung Compart FACS), detailed cell type (Lung Detail, Lung Detail FACS), organ donor (Lung Donor, Lung Donor FACS), halfway detailed cell type (Lung Half Det, Lung Half Det FACS), sample location (Lung Locat, Lung Locat FACS), or organ (Lung Organ, Lung Organ FACS). The default track displayed is Lung Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle secretory ciliated epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Lung Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method Healthy lung tissue and peripheral blood was surgically removed from 2 male patients (ages 46 and 75) and 1 female patient (age 51) undergoing lobectomy for focal lung tumors. Lung tissue was sampled from the bronchi (proximal), bronchiole (medial), and alveolar (distal) regions. Lung samples were dissociated and enriched with magnetic columns before being sorted into epithelial, endothelial/immune, and stromal cell suspensions. Lung and peripheral blood libraries were prepared using the 10x Genomics 3' v2 kit. In parallel, Smart-Seq2 (SS2) cDNA libraries were prepared using the Nextera XT library kit. Both 10x and SS2 libraries were sequenced on a NovaSeq 6000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Kyle J. Travaglini, Ahmad N. Nabhan, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, Chang S, Conley SD, Mori Y, Seita J et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature. 2020 Nov;587(7835):619-625. PMID: 33208946; PMC: PMC7704697 
lungTravaglini2020 Lung Travaglini Lung cells from from Travaglini et al 2020 Single Cell RNA-seq Description This track displays data from A molecular cell atlas of the human lung from single-cell RNA sequencing. Using droplet-based and plate-based single-cell RNA sequencing (scRNA-seq), 58 lung cell type populations were identified: 15 epithelial, 9 endothelial, 9 stromal, and 25 immune. This dataset covers ~75,000 human cells across all lung tissue compartments and circulating blood. This track collection contains 19 bar chart tracks of RNA expression in the human lung where cells are grouped such as by cell type (Lung Cells, Lung Cells FACS), tissue compartments (Lung Compart, Lung Compart FACS), detailed cell type (Lung Detail, Lung Detail FACS), organ donor (Lung Donor, Lung Donor FACS), halfway detailed cell type (Lung Half Det, Lung Half Det FACS), sample location (Lung Locat, Lung Locat FACS), or organ (Lung Organ, Lung Organ FACS). The default track displayed is Lung Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle secretory ciliated epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Lung Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method Healthy lung tissue and peripheral blood was surgically removed from 2 male patients (ages 46 and 75) and 1 female patient (age 51) undergoing lobectomy for focal lung tumors. Lung tissue was sampled from the bronchi (proximal), bronchiole (medial), and alveolar (distal) regions. Lung samples were dissociated and enriched with magnetic columns before being sorted into epithelial, endothelial/immune, and stromal cell suspensions. Lung and peripheral blood libraries were prepared using the 10x Genomics 3' v2 kit. In parallel, Smart-Seq2 (SS2) cDNA libraries were prepared using the Nextera XT library kit. Both 10x and SS2 libraries were sequenced on a NovaSeq 6000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Kyle J. Travaglini, Ahmad N. Nabhan, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, Chang S, Conley SD, Mori Y, Seita J et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature. 2020 Nov;587(7835):619-625. PMID: 33208946; PMC: PMC7704697 
lungTravaglini2020CellTypeFacs Lung Cells FACS Lung cells FACS method binned by merged cell type from Travaglini et al 2020 Single Cell RNA-seq Description This track displays data from A molecular cell atlas of the human lung from single-cell RNA sequencing. Using droplet-based and plate-based single-cell RNA sequencing (scRNA-seq), 58 lung cell type populations were identified: 15 epithelial, 9 endothelial, 9 stromal, and 25 immune. This dataset covers ~75,000 human cells across all lung tissue compartments and circulating blood. This track collection contains 19 bar chart tracks of RNA expression in the human lung where cells are grouped such as by cell type (Lung Cells, Lung Cells FACS), tissue compartments (Lung Compart, Lung Compart FACS), detailed cell type (Lung Detail, Lung Detail FACS), organ donor (Lung Donor, Lung Donor FACS), halfway detailed cell type (Lung Half Det, Lung Half Det FACS), sample location (Lung Locat, Lung Locat FACS), or organ (Lung Organ, Lung Organ FACS). The default track displayed is Lung Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle secretory ciliated epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Lung Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method Healthy lung tissue and peripheral blood was surgically removed from 2 male patients (ages 46 and 75) and 1 female patient (age 51) undergoing lobectomy for focal lung tumors. Lung tissue was sampled from the bronchi (proximal), bronchiole (medial), and alveolar (distal) regions. Lung samples were dissociated and enriched with magnetic columns before being sorted into epithelial, endothelial/immune, and stromal cell suspensions. Lung and peripheral blood libraries were prepared using the 10x Genomics 3' v2 kit. In parallel, Smart-Seq2 (SS2) cDNA libraries were prepared using the Nextera XT library kit. Both 10x and SS2 libraries were sequenced on a NovaSeq 6000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Kyle J. Travaglini, Ahmad N. Nabhan, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, Chang S, Conley SD, Mori Y, Seita J et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature. 2020 Nov;587(7835):619-625. PMID: 33208946; PMC: PMC7704697 
lungTravaglini2020Compartment10x Lung Compart Lung cells 10x method binned by compartment from Travaglini et al 2020 Single Cell RNA-seq Description This track displays data from A molecular cell atlas of the human lung from single-cell RNA sequencing. Using droplet-based and plate-based single-cell RNA sequencing (scRNA-seq), 58 lung cell type populations were identified: 15 epithelial, 9 endothelial, 9 stromal, and 25 immune. This dataset covers ~75,000 human cells across all lung tissue compartments and circulating blood. This track collection contains 19 bar chart tracks of RNA expression in the human lung where cells are grouped such as by cell type (Lung Cells, Lung Cells FACS), tissue compartments (Lung Compart, Lung Compart FACS), detailed cell type (Lung Detail, Lung Detail FACS), organ donor (Lung Donor, Lung Donor FACS), halfway detailed cell type (Lung Half Det, Lung Half Det FACS), sample location (Lung Locat, Lung Locat FACS), or organ (Lung Organ, Lung Organ FACS). The default track displayed is Lung Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle secretory ciliated epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Lung Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method Healthy lung tissue and peripheral blood was surgically removed from 2 male patients (ages 46 and 75) and 1 female patient (age 51) undergoing lobectomy for focal lung tumors. Lung tissue was sampled from the bronchi (proximal), bronchiole (medial), and alveolar (distal) regions. Lung samples were dissociated and enriched with magnetic columns before being sorted into epithelial, endothelial/immune, and stromal cell suspensions. Lung and peripheral blood libraries were prepared using the 10x Genomics 3' v2 kit. In parallel, Smart-Seq2 (SS2) cDNA libraries were prepared using the Nextera XT library kit. Both 10x and SS2 libraries were sequenced on a NovaSeq 6000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Kyle J. Travaglini, Ahmad N. Nabhan, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, Chang S, Conley SD, Mori Y, Seita J et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature. 2020 Nov;587(7835):619-625. PMID: 33208946; PMC: PMC7704697 
lungTravaglini2020CompartmentFacs Lung Compart FACS Lung cells FACS method binned by compartment from Travaglini et al 2020 Single Cell RNA-seq Description This track displays data from A molecular cell atlas of the human lung from single-cell RNA sequencing. Using droplet-based and plate-based single-cell RNA sequencing (scRNA-seq), 58 lung cell type populations were identified: 15 epithelial, 9 endothelial, 9 stromal, and 25 immune. This dataset covers ~75,000 human cells across all lung tissue compartments and circulating blood. This track collection contains 19 bar chart tracks of RNA expression in the human lung where cells are grouped such as by cell type (Lung Cells, Lung Cells FACS), tissue compartments (Lung Compart, Lung Compart FACS), detailed cell type (Lung Detail, Lung Detail FACS), organ donor (Lung Donor, Lung Donor FACS), halfway detailed cell type (Lung Half Det, Lung Half Det FACS), sample location (Lung Locat, Lung Locat FACS), or organ (Lung Organ, Lung Organ FACS). The default track displayed is Lung Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle secretory ciliated epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Lung Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method Healthy lung tissue and peripheral blood was surgically removed from 2 male patients (ages 46 and 75) and 1 female patient (age 51) undergoing lobectomy for focal lung tumors. Lung tissue was sampled from the bronchi (proximal), bronchiole (medial), and alveolar (distal) regions. Lung samples were dissociated and enriched with magnetic columns before being sorted into epithelial, endothelial/immune, and stromal cell suspensions. Lung and peripheral blood libraries were prepared using the 10x Genomics 3' v2 kit. In parallel, Smart-Seq2 (SS2) cDNA libraries were prepared using the Nextera XT library kit. Both 10x and SS2 libraries were sequenced on a NovaSeq 6000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Kyle J. Travaglini, Ahmad N. Nabhan, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, Chang S, Conley SD, Mori Y, Seita J et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature. 2020 Nov;587(7835):619-625. PMID: 33208946; PMC: PMC7704697 
lungTravaglini2020DetailedCellType10x Lung Detail Lung cells 10x method binned by detailed cell type from Travaglini et al 2020 Single Cell RNA-seq Description This track displays data from A molecular cell atlas of the human lung from single-cell RNA sequencing. Using droplet-based and plate-based single-cell RNA sequencing (scRNA-seq), 58 lung cell type populations were identified: 15 epithelial, 9 endothelial, 9 stromal, and 25 immune. This dataset covers ~75,000 human cells across all lung tissue compartments and circulating blood. This track collection contains 19 bar chart tracks of RNA expression in the human lung where cells are grouped such as by cell type (Lung Cells, Lung Cells FACS), tissue compartments (Lung Compart, Lung Compart FACS), detailed cell type (Lung Detail, Lung Detail FACS), organ donor (Lung Donor, Lung Donor FACS), halfway detailed cell type (Lung Half Det, Lung Half Det FACS), sample location (Lung Locat, Lung Locat FACS), or organ (Lung Organ, Lung Organ FACS). The default track displayed is Lung Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle secretory ciliated epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Lung Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method Healthy lung tissue and peripheral blood was surgically removed from 2 male patients (ages 46 and 75) and 1 female patient (age 51) undergoing lobectomy for focal lung tumors. Lung tissue was sampled from the bronchi (proximal), bronchiole (medial), and alveolar (distal) regions. Lung samples were dissociated and enriched with magnetic columns before being sorted into epithelial, endothelial/immune, and stromal cell suspensions. Lung and peripheral blood libraries were prepared using the 10x Genomics 3' v2 kit. In parallel, Smart-Seq2 (SS2) cDNA libraries were prepared using the Nextera XT library kit. Both 10x and SS2 libraries were sequenced on a NovaSeq 6000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Kyle J. Travaglini, Ahmad N. Nabhan, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, Chang S, Conley SD, Mori Y, Seita J et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature. 2020 Nov;587(7835):619-625. PMID: 33208946; PMC: PMC7704697 
lungTravaglini2020DetailedCellTypeFacs Lung Detail FACS Lung cells FACS method binned by detailed cell type from Travaglini et al 2020 Single Cell RNA-seq Description This track displays data from A molecular cell atlas of the human lung from single-cell RNA sequencing. Using droplet-based and plate-based single-cell RNA sequencing (scRNA-seq), 58 lung cell type populations were identified: 15 epithelial, 9 endothelial, 9 stromal, and 25 immune. This dataset covers ~75,000 human cells across all lung tissue compartments and circulating blood. This track collection contains 19 bar chart tracks of RNA expression in the human lung where cells are grouped such as by cell type (Lung Cells, Lung Cells FACS), tissue compartments (Lung Compart, Lung Compart FACS), detailed cell type (Lung Detail, Lung Detail FACS), organ donor (Lung Donor, Lung Donor FACS), halfway detailed cell type (Lung Half Det, Lung Half Det FACS), sample location (Lung Locat, Lung Locat FACS), or organ (Lung Organ, Lung Organ FACS). The default track displayed is Lung Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle secretory ciliated epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Lung Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method Healthy lung tissue and peripheral blood was surgically removed from 2 male patients (ages 46 and 75) and 1 female patient (age 51) undergoing lobectomy for focal lung tumors. Lung tissue was sampled from the bronchi (proximal), bronchiole (medial), and alveolar (distal) regions. Lung samples were dissociated and enriched with magnetic columns before being sorted into epithelial, endothelial/immune, and stromal cell suspensions. Lung and peripheral blood libraries were prepared using the 10x Genomics 3' v2 kit. In parallel, Smart-Seq2 (SS2) cDNA libraries were prepared using the Nextera XT library kit. Both 10x and SS2 libraries were sequenced on a NovaSeq 6000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Kyle J. Travaglini, Ahmad N. Nabhan, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, Chang S, Conley SD, Mori Y, Seita J et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature. 2020 Nov;587(7835):619-625. PMID: 33208946; PMC: PMC7704697 
lungTravaglini2020Donor10x Lung Donor Lung cells 10x method binned by organ donor from Travaglini et al 2020 Single Cell RNA-seq Description This track displays data from A molecular cell atlas of the human lung from single-cell RNA sequencing. Using droplet-based and plate-based single-cell RNA sequencing (scRNA-seq), 58 lung cell type populations were identified: 15 epithelial, 9 endothelial, 9 stromal, and 25 immune. This dataset covers ~75,000 human cells across all lung tissue compartments and circulating blood. This track collection contains 19 bar chart tracks of RNA expression in the human lung where cells are grouped such as by cell type (Lung Cells, Lung Cells FACS), tissue compartments (Lung Compart, Lung Compart FACS), detailed cell type (Lung Detail, Lung Detail FACS), organ donor (Lung Donor, Lung Donor FACS), halfway detailed cell type (Lung Half Det, Lung Half Det FACS), sample location (Lung Locat, Lung Locat FACS), or organ (Lung Organ, Lung Organ FACS). The default track displayed is Lung Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle secretory ciliated epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Lung Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method Healthy lung tissue and peripheral blood was surgically removed from 2 male patients (ages 46 and 75) and 1 female patient (age 51) undergoing lobectomy for focal lung tumors. Lung tissue was sampled from the bronchi (proximal), bronchiole (medial), and alveolar (distal) regions. Lung samples were dissociated and enriched with magnetic columns before being sorted into epithelial, endothelial/immune, and stromal cell suspensions. Lung and peripheral blood libraries were prepared using the 10x Genomics 3' v2 kit. In parallel, Smart-Seq2 (SS2) cDNA libraries were prepared using the Nextera XT library kit. Both 10x and SS2 libraries were sequenced on a NovaSeq 6000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Kyle J. Travaglini, Ahmad N. Nabhan, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, Chang S, Conley SD, Mori Y, Seita J et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature. 2020 Nov;587(7835):619-625. PMID: 33208946; PMC: PMC7704697 
lungTravaglini2020DonorFacs Lung Donor FACS Lung cells FACS method binned by organ donor from Travaglini et al 2020 Single Cell RNA-seq Description This track displays data from A molecular cell atlas of the human lung from single-cell RNA sequencing. Using droplet-based and plate-based single-cell RNA sequencing (scRNA-seq), 58 lung cell type populations were identified: 15 epithelial, 9 endothelial, 9 stromal, and 25 immune. This dataset covers ~75,000 human cells across all lung tissue compartments and circulating blood. This track collection contains 19 bar chart tracks of RNA expression in the human lung where cells are grouped such as by cell type (Lung Cells, Lung Cells FACS), tissue compartments (Lung Compart, Lung Compart FACS), detailed cell type (Lung Detail, Lung Detail FACS), organ donor (Lung Donor, Lung Donor FACS), halfway detailed cell type (Lung Half Det, Lung Half Det FACS), sample location (Lung Locat, Lung Locat FACS), or organ (Lung Organ, Lung Organ FACS). The default track displayed is Lung Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle secretory ciliated epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Lung Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method Healthy lung tissue and peripheral blood was surgically removed from 2 male patients (ages 46 and 75) and 1 female patient (age 51) undergoing lobectomy for focal lung tumors. Lung tissue was sampled from the bronchi (proximal), bronchiole (medial), and alveolar (distal) regions. Lung samples were dissociated and enriched with magnetic columns before being sorted into epithelial, endothelial/immune, and stromal cell suspensions. Lung and peripheral blood libraries were prepared using the 10x Genomics 3' v2 kit. In parallel, Smart-Seq2 (SS2) cDNA libraries were prepared using the Nextera XT library kit. Both 10x and SS2 libraries were sequenced on a NovaSeq 6000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Kyle J. Travaglini, Ahmad N. Nabhan, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, Chang S, Conley SD, Mori Y, Seita J et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature. 2020 Nov;587(7835):619-625. PMID: 33208946; PMC: PMC7704697 
lungTravaglini2020GatingFacs Lung Gating FACS Lung cells FACS method binned by gating from Travaglini et al 2020 Single Cell RNA-seq Description This track displays data from A molecular cell atlas of the human lung from single-cell RNA sequencing. Using droplet-based and plate-based single-cell RNA sequencing (scRNA-seq), 58 lung cell type populations were identified: 15 epithelial, 9 endothelial, 9 stromal, and 25 immune. This dataset covers ~75,000 human cells across all lung tissue compartments and circulating blood. This track collection contains 19 bar chart tracks of RNA expression in the human lung where cells are grouped such as by cell type (Lung Cells, Lung Cells FACS), tissue compartments (Lung Compart, Lung Compart FACS), detailed cell type (Lung Detail, Lung Detail FACS), organ donor (Lung Donor, Lung Donor FACS), halfway detailed cell type (Lung Half Det, Lung Half Det FACS), sample location (Lung Locat, Lung Locat FACS), or organ (Lung Organ, Lung Organ FACS). The default track displayed is Lung Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle secretory ciliated epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Lung Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method Healthy lung tissue and peripheral blood was surgically removed from 2 male patients (ages 46 and 75) and 1 female patient (age 51) undergoing lobectomy for focal lung tumors. Lung tissue was sampled from the bronchi (proximal), bronchiole (medial), and alveolar (distal) regions. Lung samples were dissociated and enriched with magnetic columns before being sorted into epithelial, endothelial/immune, and stromal cell suspensions. Lung and peripheral blood libraries were prepared using the 10x Genomics 3' v2 kit. In parallel, Smart-Seq2 (SS2) cDNA libraries were prepared using the Nextera XT library kit. Both 10x and SS2 libraries were sequenced on a NovaSeq 6000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Kyle J. Travaglini, Ahmad N. Nabhan, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, Chang S, Conley SD, Mori Y, Seita J et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature. 2020 Nov;587(7835):619-625. PMID: 33208946; PMC: PMC7704697 
lungTravaglini2020HalfDetailedCellType10x Lung Half Det Lung cells 10x method binned by halfway detailed cell type from Travaglini et al 2020 Single Cell RNA-seq Description This track displays data from A molecular cell atlas of the human lung from single-cell RNA sequencing. Using droplet-based and plate-based single-cell RNA sequencing (scRNA-seq), 58 lung cell type populations were identified: 15 epithelial, 9 endothelial, 9 stromal, and 25 immune. This dataset covers ~75,000 human cells across all lung tissue compartments and circulating blood. This track collection contains 19 bar chart tracks of RNA expression in the human lung where cells are grouped such as by cell type (Lung Cells, Lung Cells FACS), tissue compartments (Lung Compart, Lung Compart FACS), detailed cell type (Lung Detail, Lung Detail FACS), organ donor (Lung Donor, Lung Donor FACS), halfway detailed cell type (Lung Half Det, Lung Half Det FACS), sample location (Lung Locat, Lung Locat FACS), or organ (Lung Organ, Lung Organ FACS). The default track displayed is Lung Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle secretory ciliated epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Lung Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method Healthy lung tissue and peripheral blood was surgically removed from 2 male patients (ages 46 and 75) and 1 female patient (age 51) undergoing lobectomy for focal lung tumors. Lung tissue was sampled from the bronchi (proximal), bronchiole (medial), and alveolar (distal) regions. Lung samples were dissociated and enriched with magnetic columns before being sorted into epithelial, endothelial/immune, and stromal cell suspensions. Lung and peripheral blood libraries were prepared using the 10x Genomics 3' v2 kit. In parallel, Smart-Seq2 (SS2) cDNA libraries were prepared using the Nextera XT library kit. Both 10x and SS2 libraries were sequenced on a NovaSeq 6000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Kyle J. Travaglini, Ahmad N. Nabhan, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, Chang S, Conley SD, Mori Y, Seita J et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature. 2020 Nov;587(7835):619-625. PMID: 33208946; PMC: PMC7704697 
lungTravaglini2020HalfDetailedFacs Lung Half Det FACS Lung cells FACS method binned by merged cell type from Travaglini et al 2020 Single Cell RNA-seq Description This track displays data from A molecular cell atlas of the human lung from single-cell RNA sequencing. Using droplet-based and plate-based single-cell RNA sequencing (scRNA-seq), 58 lung cell type populations were identified: 15 epithelial, 9 endothelial, 9 stromal, and 25 immune. This dataset covers ~75,000 human cells across all lung tissue compartments and circulating blood. This track collection contains 19 bar chart tracks of RNA expression in the human lung where cells are grouped such as by cell type (Lung Cells, Lung Cells FACS), tissue compartments (Lung Compart, Lung Compart FACS), detailed cell type (Lung Detail, Lung Detail FACS), organ donor (Lung Donor, Lung Donor FACS), halfway detailed cell type (Lung Half Det, Lung Half Det FACS), sample location (Lung Locat, Lung Locat FACS), or organ (Lung Organ, Lung Organ FACS). The default track displayed is Lung Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle secretory ciliated epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Lung Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method Healthy lung tissue and peripheral blood was surgically removed from 2 male patients (ages 46 and 75) and 1 female patient (age 51) undergoing lobectomy for focal lung tumors. Lung tissue was sampled from the bronchi (proximal), bronchiole (medial), and alveolar (distal) regions. Lung samples were dissociated and enriched with magnetic columns before being sorted into epithelial, endothelial/immune, and stromal cell suspensions. Lung and peripheral blood libraries were prepared using the 10x Genomics 3' v2 kit. In parallel, Smart-Seq2 (SS2) cDNA libraries were prepared using the Nextera XT library kit. Both 10x and SS2 libraries were sequenced on a NovaSeq 6000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Kyle J. Travaglini, Ahmad N. Nabhan, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, Chang S, Conley SD, Mori Y, Seita J et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature. 2020 Nov;587(7835):619-625. PMID: 33208946; PMC: PMC7704697 
lungTravaglini2020LabelFacs Lung Label FACS Lung cells FACS method binned by label from Travaglini et al 2020 Single Cell RNA-seq Description This track displays data from A molecular cell atlas of the human lung from single-cell RNA sequencing. Using droplet-based and plate-based single-cell RNA sequencing (scRNA-seq), 58 lung cell type populations were identified: 15 epithelial, 9 endothelial, 9 stromal, and 25 immune. This dataset covers ~75,000 human cells across all lung tissue compartments and circulating blood. This track collection contains 19 bar chart tracks of RNA expression in the human lung where cells are grouped such as by cell type (Lung Cells, Lung Cells FACS), tissue compartments (Lung Compart, Lung Compart FACS), detailed cell type (Lung Detail, Lung Detail FACS), organ donor (Lung Donor, Lung Donor FACS), halfway detailed cell type (Lung Half Det, Lung Half Det FACS), sample location (Lung Locat, Lung Locat FACS), or organ (Lung Organ, Lung Organ FACS). The default track displayed is Lung Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle secretory ciliated epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Lung Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method Healthy lung tissue and peripheral blood was surgically removed from 2 male patients (ages 46 and 75) and 1 female patient (age 51) undergoing lobectomy for focal lung tumors. Lung tissue was sampled from the bronchi (proximal), bronchiole (medial), and alveolar (distal) regions. Lung samples were dissociated and enriched with magnetic columns before being sorted into epithelial, endothelial/immune, and stromal cell suspensions. Lung and peripheral blood libraries were prepared using the 10x Genomics 3' v2 kit. In parallel, Smart-Seq2 (SS2) cDNA libraries were prepared using the Nextera XT library kit. Both 10x and SS2 libraries were sequenced on a NovaSeq 6000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Kyle J. Travaglini, Ahmad N. Nabhan, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, Chang S, Conley SD, Mori Y, Seita J et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature. 2020 Nov;587(7835):619-625. PMID: 33208946; PMC: PMC7704697 
lungTravaglini2020Location10x Lung Locat Lung cells 10x method binned by location from Travaglini et al 2020 Single Cell RNA-seq Description This track displays data from A molecular cell atlas of the human lung from single-cell RNA sequencing. Using droplet-based and plate-based single-cell RNA sequencing (scRNA-seq), 58 lung cell type populations were identified: 15 epithelial, 9 endothelial, 9 stromal, and 25 immune. This dataset covers ~75,000 human cells across all lung tissue compartments and circulating blood. This track collection contains 19 bar chart tracks of RNA expression in the human lung where cells are grouped such as by cell type (Lung Cells, Lung Cells FACS), tissue compartments (Lung Compart, Lung Compart FACS), detailed cell type (Lung Detail, Lung Detail FACS), organ donor (Lung Donor, Lung Donor FACS), halfway detailed cell type (Lung Half Det, Lung Half Det FACS), sample location (Lung Locat, Lung Locat FACS), or organ (Lung Organ, Lung Organ FACS). The default track displayed is Lung Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle secretory ciliated epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Lung Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method Healthy lung tissue and peripheral blood was surgically removed from 2 male patients (ages 46 and 75) and 1 female patient (age 51) undergoing lobectomy for focal lung tumors. Lung tissue was sampled from the bronchi (proximal), bronchiole (medial), and alveolar (distal) regions. Lung samples were dissociated and enriched with magnetic columns before being sorted into epithelial, endothelial/immune, and stromal cell suspensions. Lung and peripheral blood libraries were prepared using the 10x Genomics 3' v2 kit. In parallel, Smart-Seq2 (SS2) cDNA libraries were prepared using the Nextera XT library kit. Both 10x and SS2 libraries were sequenced on a NovaSeq 6000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Kyle J. Travaglini, Ahmad N. Nabhan, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, Chang S, Conley SD, Mori Y, Seita J et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature. 2020 Nov;587(7835):619-625. PMID: 33208946; PMC: PMC7704697 
lungTravaglini2020LocationFacs Lung Locat FACS Lung cells FACS method binned by location from Travaglini et al 2020 Single Cell RNA-seq Description This track displays data from A molecular cell atlas of the human lung from single-cell RNA sequencing. Using droplet-based and plate-based single-cell RNA sequencing (scRNA-seq), 58 lung cell type populations were identified: 15 epithelial, 9 endothelial, 9 stromal, and 25 immune. This dataset covers ~75,000 human cells across all lung tissue compartments and circulating blood. This track collection contains 19 bar chart tracks of RNA expression in the human lung where cells are grouped such as by cell type (Lung Cells, Lung Cells FACS), tissue compartments (Lung Compart, Lung Compart FACS), detailed cell type (Lung Detail, Lung Detail FACS), organ donor (Lung Donor, Lung Donor FACS), halfway detailed cell type (Lung Half Det, Lung Half Det FACS), sample location (Lung Locat, Lung Locat FACS), or organ (Lung Organ, Lung Organ FACS). The default track displayed is Lung Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle secretory ciliated epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Lung Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method Healthy lung tissue and peripheral blood was surgically removed from 2 male patients (ages 46 and 75) and 1 female patient (age 51) undergoing lobectomy for focal lung tumors. Lung tissue was sampled from the bronchi (proximal), bronchiole (medial), and alveolar (distal) regions. Lung samples were dissociated and enriched with magnetic columns before being sorted into epithelial, endothelial/immune, and stromal cell suspensions. Lung and peripheral blood libraries were prepared using the 10x Genomics 3' v2 kit. In parallel, Smart-Seq2 (SS2) cDNA libraries were prepared using the Nextera XT library kit. Both 10x and SS2 libraries were sequenced on a NovaSeq 6000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Kyle J. Travaglini, Ahmad N. Nabhan, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, Chang S, Conley SD, Mori Y, Seita J et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature. 2020 Nov;587(7835):619-625. PMID: 33208946; PMC: PMC7704697 
lungTravaglini2020MagneticSelection10x Lung Mag Sel Lung cells 10x method binned by magnetic.selection from Travaglini et al 2020 Single Cell RNA-seq Description This track displays data from A molecular cell atlas of the human lung from single-cell RNA sequencing. Using droplet-based and plate-based single-cell RNA sequencing (scRNA-seq), 58 lung cell type populations were identified: 15 epithelial, 9 endothelial, 9 stromal, and 25 immune. This dataset covers ~75,000 human cells across all lung tissue compartments and circulating blood. This track collection contains 19 bar chart tracks of RNA expression in the human lung where cells are grouped such as by cell type (Lung Cells, Lung Cells FACS), tissue compartments (Lung Compart, Lung Compart FACS), detailed cell type (Lung Detail, Lung Detail FACS), organ donor (Lung Donor, Lung Donor FACS), halfway detailed cell type (Lung Half Det, Lung Half Det FACS), sample location (Lung Locat, Lung Locat FACS), or organ (Lung Organ, Lung Organ FACS). The default track displayed is Lung Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle secretory ciliated epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Lung Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method Healthy lung tissue and peripheral blood was surgically removed from 2 male patients (ages 46 and 75) and 1 female patient (age 51) undergoing lobectomy for focal lung tumors. Lung tissue was sampled from the bronchi (proximal), bronchiole (medial), and alveolar (distal) regions. Lung samples were dissociated and enriched with magnetic columns before being sorted into epithelial, endothelial/immune, and stromal cell suspensions. Lung and peripheral blood libraries were prepared using the 10x Genomics 3' v2 kit. In parallel, Smart-Seq2 (SS2) cDNA libraries were prepared using the Nextera XT library kit. Both 10x and SS2 libraries were sequenced on a NovaSeq 6000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Kyle J. Travaglini, Ahmad N. Nabhan, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, Chang S, Conley SD, Mori Y, Seita J et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature. 2020 Nov;587(7835):619-625. PMID: 33208946; PMC: PMC7704697 
lungTravaglini2020Organ10x Lung Organ Lung cells 10x method binned by organ from Travaglini et al 2020 Single Cell RNA-seq Description This track displays data from A molecular cell atlas of the human lung from single-cell RNA sequencing. Using droplet-based and plate-based single-cell RNA sequencing (scRNA-seq), 58 lung cell type populations were identified: 15 epithelial, 9 endothelial, 9 stromal, and 25 immune. This dataset covers ~75,000 human cells across all lung tissue compartments and circulating blood. This track collection contains 19 bar chart tracks of RNA expression in the human lung where cells are grouped such as by cell type (Lung Cells, Lung Cells FACS), tissue compartments (Lung Compart, Lung Compart FACS), detailed cell type (Lung Detail, Lung Detail FACS), organ donor (Lung Donor, Lung Donor FACS), halfway detailed cell type (Lung Half Det, Lung Half Det FACS), sample location (Lung Locat, Lung Locat FACS), or organ (Lung Organ, Lung Organ FACS). The default track displayed is Lung Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle secretory ciliated epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Lung Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method Healthy lung tissue and peripheral blood was surgically removed from 2 male patients (ages 46 and 75) and 1 female patient (age 51) undergoing lobectomy for focal lung tumors. Lung tissue was sampled from the bronchi (proximal), bronchiole (medial), and alveolar (distal) regions. Lung samples were dissociated and enriched with magnetic columns before being sorted into epithelial, endothelial/immune, and stromal cell suspensions. Lung and peripheral blood libraries were prepared using the 10x Genomics 3' v2 kit. In parallel, Smart-Seq2 (SS2) cDNA libraries were prepared using the Nextera XT library kit. Both 10x and SS2 libraries were sequenced on a NovaSeq 6000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Kyle J. Travaglini, Ahmad N. Nabhan, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, Chang S, Conley SD, Mori Y, Seita J et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature. 2020 Nov;587(7835):619-625. PMID: 33208946; PMC: PMC7704697 
lungTravaglini2020OrganFacs Lung Organ FACS Lung cells FACS method binned by organ from Travaglini et al 2020 Single Cell RNA-seq Description This track displays data from A molecular cell atlas of the human lung from single-cell RNA sequencing. Using droplet-based and plate-based single-cell RNA sequencing (scRNA-seq), 58 lung cell type populations were identified: 15 epithelial, 9 endothelial, 9 stromal, and 25 immune. This dataset covers ~75,000 human cells across all lung tissue compartments and circulating blood. This track collection contains 19 bar chart tracks of RNA expression in the human lung where cells are grouped such as by cell type (Lung Cells, Lung Cells FACS), tissue compartments (Lung Compart, Lung Compart FACS), detailed cell type (Lung Detail, Lung Detail FACS), organ donor (Lung Donor, Lung Donor FACS), halfway detailed cell type (Lung Half Det, Lung Half Det FACS), sample location (Lung Locat, Lung Locat FACS), or organ (Lung Organ, Lung Organ FACS). The default track displayed is Lung Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle secretory ciliated epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Lung Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method Healthy lung tissue and peripheral blood was surgically removed from 2 male patients (ages 46 and 75) and 1 female patient (age 51) undergoing lobectomy for focal lung tumors. Lung tissue was sampled from the bronchi (proximal), bronchiole (medial), and alveolar (distal) regions. Lung samples were dissociated and enriched with magnetic columns before being sorted into epithelial, endothelial/immune, and stromal cell suspensions. Lung and peripheral blood libraries were prepared using the 10x Genomics 3' v2 kit. In parallel, Smart-Seq2 (SS2) cDNA libraries were prepared using the Nextera XT library kit. Both 10x and SS2 libraries were sequenced on a NovaSeq 6000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Kyle J. Travaglini, Ahmad N. Nabhan, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, Chang S, Conley SD, Mori Y, Seita J et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature. 2020 Nov;587(7835):619-625. PMID: 33208946; PMC: PMC7704697 
lungTravaglini2020Sample10x Lung Sample Lung cells 10x method binned by sample from Travaglini et al 2020 Single Cell RNA-seq Description This track displays data from A molecular cell atlas of the human lung from single-cell RNA sequencing. Using droplet-based and plate-based single-cell RNA sequencing (scRNA-seq), 58 lung cell type populations were identified: 15 epithelial, 9 endothelial, 9 stromal, and 25 immune. This dataset covers ~75,000 human cells across all lung tissue compartments and circulating blood. This track collection contains 19 bar chart tracks of RNA expression in the human lung where cells are grouped such as by cell type (Lung Cells, Lung Cells FACS), tissue compartments (Lung Compart, Lung Compart FACS), detailed cell type (Lung Detail, Lung Detail FACS), organ donor (Lung Donor, Lung Donor FACS), halfway detailed cell type (Lung Half Det, Lung Half Det FACS), sample location (Lung Locat, Lung Locat FACS), or organ (Lung Organ, Lung Organ FACS). The default track displayed is Lung Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle secretory ciliated epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Lung Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method Healthy lung tissue and peripheral blood was surgically removed from 2 male patients (ages 46 and 75) and 1 female patient (age 51) undergoing lobectomy for focal lung tumors. Lung tissue was sampled from the bronchi (proximal), bronchiole (medial), and alveolar (distal) regions. Lung samples were dissociated and enriched with magnetic columns before being sorted into epithelial, endothelial/immune, and stromal cell suspensions. Lung and peripheral blood libraries were prepared using the 10x Genomics 3' v2 kit. In parallel, Smart-Seq2 (SS2) cDNA libraries were prepared using the Nextera XT library kit. Both 10x and SS2 libraries were sequenced on a NovaSeq 6000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Kyle J. Travaglini, Ahmad N. Nabhan, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, Chang S, Conley SD, Mori Y, Seita J et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature. 2020 Nov;587(7835):619-625. PMID: 33208946; PMC: PMC7704697 
lungTravaglini2020SampleFacs Lung Sample FACS Lung cells FACS method binned by sample from Travaglini et al 2020 Single Cell RNA-seq Description This track displays data from A molecular cell atlas of the human lung from single-cell RNA sequencing. Using droplet-based and plate-based single-cell RNA sequencing (scRNA-seq), 58 lung cell type populations were identified: 15 epithelial, 9 endothelial, 9 stromal, and 25 immune. This dataset covers ~75,000 human cells across all lung tissue compartments and circulating blood. This track collection contains 19 bar chart tracks of RNA expression in the human lung where cells are grouped such as by cell type (Lung Cells, Lung Cells FACS), tissue compartments (Lung Compart, Lung Compart FACS), detailed cell type (Lung Detail, Lung Detail FACS), organ donor (Lung Donor, Lung Donor FACS), halfway detailed cell type (Lung Half Det, Lung Half Det FACS), sample location (Lung Locat, Lung Locat FACS), or organ (Lung Organ, Lung Organ FACS). The default track displayed is Lung Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle secretory ciliated epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Lung Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Method Healthy lung tissue and peripheral blood was surgically removed from 2 male patients (ages 46 and 75) and 1 female patient (age 51) undergoing lobectomy for focal lung tumors. Lung tissue was sampled from the bronchi (proximal), bronchiole (medial), and alveolar (distal) regions. Lung samples were dissociated and enriched with magnetic columns before being sorted into epithelial, endothelial/immune, and stromal cell suspensions. Lung and peripheral blood libraries were prepared using the 10x Genomics 3' v2 kit. In parallel, Smart-Seq2 (SS2) cDNA libraries were prepared using the Nextera XT library kit. Both 10x and SS2 libraries were sequenced on a NovaSeq 6000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Kyle J. Travaglini, Ahmad N. Nabhan, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, Chang S, Conley SD, Mori Y, Seita J et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature. 2020 Nov;587(7835):619-625. PMID: 33208946; PMC: PMC7704697 
mane MANE MANE Select Plus Clinical: Representative transcript from RefSeq & GENCODE Genes and Gene Predictions Description The Matched Annotation from NCBI and EMBL-EBI (MANE) project aims to produce a matched set of high-confidence transcripts that are identically annotated between RefSeq (NCBI) and Ensembl/GENCODE (led by EMBL-EBI). Transcripts for MANE are chosen by a combination of automated and manual methods based on conservation, expression levels, clinical significance, and other factors. Transcripts are matched between the NCBI RefSeq and Ensembl/GENCODE annotations based on the GRCh38 genome assembly, with precise 5' and 3' ends defined by high-throughput sequencing or other available data. This track is automatically updated, see the source data version above for the current version number. MANE include almost all human protein-coding genes and genes of clinical relevance, including genes in the American College of Medical Genetics and Genomics (ACMG) Secondary Findings list (SF) v3.0. It includes both MANE Select and MANE Plus Clinical transcripts. MANE Plus Clinical items are colored red. For more information on the different gene tracks, including MANE vs GENCODE or RefSeq, see our Genes FAQ. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For computational analysis, genome annotations are stored in a bigGenePred file that can be downloaded from the download server. Regional or genome-wide annotations can be converted from binary data to human readable text using our command line utility bigBedToBed which can be compiled from source code or downloaded as a precompiled binary for your system. Files and instructions can be found in the utilities directory. The utility can be used to obtain features within a given range, for example: bigBedToBed -chrom=chr6 -start=0 -end=1000000 http://hgdownload.soe.ucsc.edu/gbdb/hg38/mane/mane.bb stdout Download links for MANE: ftp://ftp.ncbi.nlm.nih.gov/refseq/MANE Previous MANE versions are also available on our download archive. Please refer to our Data Access FAQ for more information or our mailing list for archived user questions. Credits Thank you to the RefSeq project at NCBI and the Ensembl/GENCODE project at EMBL-EBI. You can contact the authors directly at &#77;A&#78;&#69;&#45;h&#101;&#108;&#112;&#64;&#110;&#99;&#98;&#105;.&#110;&#108;&#109;.&#110;ih.&#103;&#111;&#118; or &#109;&#97;&#110;&#101;&#45;&#104;el&#112;&#64;&#101;&#98;&#105;.a&#99;.&#117;&#107;. References Morales J, Pujar S, Loveland JE, Astashyn A, Bennett R, Berry A, Cox E, Davidson C, Ermolaeva O, Farrell CM et al. A joint NCBI and EMBL-EBI transcript set for clinical genomics and research. Nature. 2022 Apr;604(7905):310-315. PMID: 35388217; PMC: PMC9007741 
mastermind Mastermind Variants Genomenon Mastermind Variants extracted from full text publications Phenotype and Literature Description This track shows most variants found in the full text of scientific publications gathered by Genomenon Mastermind. Mastermind uses a software that searches for disease-gene-variant associations in the scientific literature. The genome browser track shows only if a variant has been indexed by the search engine. To get details on a variant (bibliographic references, disease, etc) click it and follow the &quot;Protein change and link to details&quot; at the top of the details page. Mouse over an item to show the gene and amino acid change and the scores MMCNT1, MMCNT2 and MMCNT3, explained below. Genomenon Mastermind Genomic Search Engine is a commercial database of variants likely to be mentioned in full text scientific articles. A limited number of queries per week is free for healthcare professionals and researchers, if they register on the signup page page. Advanced features require a license for the Mastermind Professional Edition, which contains the same content but allows more comprehensive searches. Display Conventions and Configuration Genomic locations of variants are labeled with the nucleotide change. Hover over the features to see the gene, the amino acid change and the scores MMCNT1, MMCNT2 and MMCNT3, described below. All other information is shown on the respective Mastermind variant detail page, accessible via the &quot;Protein change and link to details&quot; at the top of the details page. The features are colored based on their evidence: As suggested by Genomenom, we added a filter on all variants, so the data are not exactly identical to their website. We skip variants with more than one nucleotide and a MMCNT of 0 and where the variant is not an indel. This means that for longer variants, only variants are shown that are explicitly mentioned in the papers. This makes the data more specific. Color Level of support High: at least one paper mentions this exact cDNA change Medium: at least two papers mention a variant that leads to the same amino acid change Low: only a single paper mentions a variant that leads to the same amino acid change The three numbers that are shown on the mouse-over and the details page have the following meaning (MM=Mastermind): MMCNT1: cDNA-level exact matches. This is the number of articles that mention the variant at the nucleotide level in either the title/abstract or the full-text. MMCNT2: cDNA-level possible matches. This is the number of articles with nucleotide-level matches (from 1) plus articles with protein-level matches in which the publication did not specify the cDNA-level change, meaning they could be referring to this nucleotide-level variant but there is insufficient data in these articles to determine conclusively. MMCNT3: This is the number of articles citing any variant resulting in the same biological effect as this variant. This includes the articles from MMCNT1 and MMCNT2 plus articles with alternative cDNA-level variants that result in the same protein effect. On the track settings page one can filter on these scores under the display mode section by entering a minimum number of articles for each kind of evidence. Data access The raw data can be explored interactively with the Table Browser or the Data Integrator. The data can be accessed from scripts through our API, the track name is "mastermind". For automated download and analysis, the genome annotation is stored in a bigBed file that can be downloaded from our download server. The file for this track is called mastermind.bb. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg19/bbi/mastermind.bb -chrom=chr21 -start=0 -end=100000000 stdout Previous versions of this track can be found on our archive download server. Methods The Mastermind Cited Variants file was downloaded, converted to BED format with scripts that are available in our Git repository and converted to a bigBed file with the UCSC genome browser tool bedToBigBed. This track is automatically updated two weeks after every Mastermind CVR release, which happens every three months. Credits Thanks to Mark Kiel, Steve Schwartz and Clayton Wheeler from Genomenon for making these data available. References Chunn LM, Nefcy DC, Scouten RW, Tarpey RP, Chauhan G, Lim MS, Elenitoba-Johnson KSJ, Schwartz SA, Kiel MJ. Mastermind: A Comprehensive Genomic Association Search Engine for Empirical Evidence Curation and Genetic Variant Interpretation. Front Genet. 2020 Nov 13;11:577152. doi: 10.3389/fgene.2020.577152. PMID: 33281875; PMC: PMC7691534 
mgcFullMrna MGC Genes Mammalian Gene Collection Full ORF mRNAs Genes and Gene Predictions Description This track show alignments of human mRNAs from the Mammalian Gene Collection (MGC) having full-length open reading frames (ORFs) to the genome. The goal of the Mammalian Gene Collection is to provide researchers with unrestricted access to sequence-validated full-length protein-coding cDNA clones for human, mouse, rat, xenopus, and zerbrafish genes. Display Conventions and Configuration The track follows the display conventions for gene prediction tracks. An optional codon coloring feature is available for quick validation and comparison of gene predictions. To display codon colors, select the genomic codons option from the Color track by codons pull-down menu. For more information about this feature, go to the Coloring Gene Predictions and Annotations by Codon page. Methods GenBank human MGC mRNAs identified as having full-length ORFs were aligned against the genome using blat. When a single mRNA aligned in multiple places, the alignment having the highest base identity was found. Only alignments having a base identity level within 1% of the best and at least 95% base identity with the genomic sequence were kept. Credits The human MGC full-length mRNA track was produced at UCSC from mRNA sequence data submitted to GenBank by the Mammalian Gene Collection project. References Mammalian Gene Collection project references. Kent WJ. BLAT--the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 
mgcOrfeomeMrna MGC/ORFeome Genes MGC/ORFeome Full ORF mRNA Clones Genes and Gene Predictions Description These tracks show alignments of human mRNAs from the Mammalian Gene Collection (MGC) and ORFeome Collaboration having full-length open reading frames (ORFs) to the genome. The goal of the Mammalian Gene Collection is to provide researchers with unrestricted access to sequence-validated full-length protein-coding cDNA clones for human, mouse, and rat genes. The ORFeome project extended MGC to provide additional human, mouse, and zebrafish clones. Display Conventions and Configuration The track follows the display conventions for gene prediction tracks. An optional codon coloring feature is available for quick validation and comparison of gene predictions. To display codon colors, select the genomic codons option from the Color track by codons pull-down menu. For more information about this feature, go to the Coloring Gene Predictions and Annotations by Codon page. Methods GenBank human MGC mRNAs identified as having full-length ORFs were aligned against the genome using blat. When a single mRNA aligned in multiple places, the alignment having the highest base identity was found. Only alignments having a base identity level within 1% of the best and at least 95% base identity with the genomic sequence were kept. Credits The human MGC full-length mRNA track was produced at UCSC from mRNA sequence data submitted to GenBank by the Mammalian Gene Collection project. Visit the ORFeome Collaboration members page for a list of credits and references. References Mammalian Gene Collection project references. Kent WJ. BLAT--the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 
miRnaAtlas miRNA Tissue Atlas Tissue-Specific microRNA Expression from Two Individuals Expression Description The Human miRNA Tissue Atlas is a catalog of tissue-specific microRNA (miRNA) expression across 62 tissues. This track contains quantile normalized miRNA expression data sampled from two individuals and mapped to miRBase v21 coordinates. The track contains two subtracks, one for each individual sampled. The Tissue Specificity Index (TSI) is analogous to the &quot;tau&quot; value for mRNA expression, and is calculated as described in the associated publication. Values closer to 0 indicate miRNAs expressed in many or all tissues, while values closer to 1 indicate miRNAs expressed only in a specific tissue or tissues. To browse miRNAs by TSI value, please see the miRNA Tissue Atlas. Display Conventions and Configuration This track is formatted as a barChart track, similar to the GTEx or the TCGA Cancer Expression tracks, where the heights of each bar indicate the expression value for the miRNA in a specific tissue. The tissues sampled are described in the table below: Bar ColorSample 1Sample 2 AdipocyteAdipocyte ArteryArtery ColonColon Dura materDura mater KidneyKidney LiverLiver LungLung MuscleMuscle MyocardiumMyocardium SkinSkin SpleenSpleen StomachStomach TestisTestis ThyroidThyroid Small intestine Bone Gallbladder Fascia Bladder Epididymis Tunica albuginea Nervus intercostalis Arachnoid mater Brain Small intestine duodenum Small intestine jejunum Pancreas Kidney glandula suprarenalis Kidney cortex renalis Esophagus Prostate Bone marrow Vein Lymph node Nerve not specified Pleura Pituitary gland Spinal cord Thalamus Brain white matter Nucleus caudatus Kidney medulla renalis Brain gray_matter Cerebral cortex temporal Cerebral cortex frontal Cerebral cortex occipital Cerebellum The 14 shared tissues sampled across both individuals are presented in the same order for easier comparison. Data Access The underlying expression matrix and TSI values can be obtained from the miRNA tissue atlas website, in the data_matrix_quantile.txt and tsi_quantile.csv files. References Ludwig N, Leidinger P, Becker K, Backes C, Fehlmann T, Pallasch C, Rheinheimer S, Meder B, St&#228;hler C, Meese E et al. Distribution of miRNA expression across human tissues. Nucleic Acids Res. 2016 May 5;44(8):3865-77. PMID: 26921406; PMC: PMC4856985 
miRnaAtlasSample2 miRNA Tissue Atlas Tissue-Specific microRNA Expression from Two Individuals Expression 
miRnaAtlasSample2BarChart Sample 2 miRNA Tissue Atlas microRna Expression Expression Description The Human miRNA Tissue Atlas is a catalog of tissue-specific microRNA (miRNA) expression across 62 tissues. This track contains quantile normalized miRNA expression data sampled from two individuals and mapped to miRBase v21 coordinates. The track contains two subtracks, one for each individual sampled. The Tissue Specificity Index (TSI) is analogous to the &quot;tau&quot; value for mRNA expression, and is calculated as described in the associated publication. Values closer to 0 indicate miRNAs expressed in many or all tissues, while values closer to 1 indicate miRNAs expressed only in a specific tissue or tissues. To browse miRNAs by TSI value, please see the miRNA Tissue Atlas. Display Conventions and Configuration This track is formatted as a barChart track, similar to the GTEx or the TCGA Cancer Expression tracks, where the heights of each bar indicate the expression value for the miRNA in a specific tissue. The tissues sampled are described in the table below: Bar ColorSample 1Sample 2 AdipocyteAdipocyte ArteryArtery ColonColon Dura materDura mater KidneyKidney LiverLiver LungLung MuscleMuscle MyocardiumMyocardium SkinSkin SpleenSpleen StomachStomach TestisTestis ThyroidThyroid Small intestine Bone Gallbladder Fascia Bladder Epididymis Tunica albuginea Nervus intercostalis Arachnoid mater Brain Small intestine duodenum Small intestine jejunum Pancreas Kidney glandula suprarenalis Kidney cortex renalis Esophagus Prostate Bone marrow Vein Lymph node Nerve not specified Pleura Pituitary gland Spinal cord Thalamus Brain white matter Nucleus caudatus Kidney medulla renalis Brain gray_matter Cerebral cortex temporal Cerebral cortex frontal Cerebral cortex occipital Cerebellum The 14 shared tissues sampled across both individuals are presented in the same order for easier comparison. Data Access The underlying expression matrix and TSI values can be obtained from the miRNA tissue atlas website, in the data_matrix_quantile.txt and tsi_quantile.csv files. References Ludwig N, Leidinger P, Becker K, Backes C, Fehlmann T, Pallasch C, Rheinheimer S, Meder B, St&#228;hler C, Meese E et al. Distribution of miRNA expression across human tissues. Nucleic Acids Res. 2016 May 5;44(8):3865-77. PMID: 26921406; PMC: PMC4856985 
miRnaAtlasSample1 miRNA Tissue Atlas Tissue-Specific microRNA Expression from Two Individuals Expression 
miRnaAtlasSample1BarChart Sample 1 miRNA Tissue Atlas microRna Expression Expression Description The Human miRNA Tissue Atlas is a catalog of tissue-specific microRNA (miRNA) expression across 62 tissues. This track contains quantile normalized miRNA expression data sampled from two individuals and mapped to miRBase v21 coordinates. The track contains two subtracks, one for each individual sampled. The Tissue Specificity Index (TSI) is analogous to the &quot;tau&quot; value for mRNA expression, and is calculated as described in the associated publication. Values closer to 0 indicate miRNAs expressed in many or all tissues, while values closer to 1 indicate miRNAs expressed only in a specific tissue or tissues. To browse miRNAs by TSI value, please see the miRNA Tissue Atlas. Display Conventions and Configuration This track is formatted as a barChart track, similar to the GTEx or the TCGA Cancer Expression tracks, where the heights of each bar indicate the expression value for the miRNA in a specific tissue. The tissues sampled are described in the table below: Bar ColorSample 1Sample 2 AdipocyteAdipocyte ArteryArtery ColonColon Dura materDura mater KidneyKidney LiverLiver LungLung MuscleMuscle MyocardiumMyocardium SkinSkin SpleenSpleen StomachStomach TestisTestis ThyroidThyroid Small intestine Bone Gallbladder Fascia Bladder Epididymis Tunica albuginea Nervus intercostalis Arachnoid mater Brain Small intestine duodenum Small intestine jejunum Pancreas Kidney glandula suprarenalis Kidney cortex renalis Esophagus Prostate Bone marrow Vein Lymph node Nerve not specified Pleura Pituitary gland Spinal cord Thalamus Brain white matter Nucleus caudatus Kidney medulla renalis Brain gray_matter Cerebral cortex temporal Cerebral cortex frontal Cerebral cortex occipital Cerebellum The 14 shared tissues sampled across both individuals are presented in the same order for easier comparison. Data Access The underlying expression matrix and TSI values can be obtained from the miRNA tissue atlas website, in the data_matrix_quantile.txt and tsi_quantile.csv files. References Ludwig N, Leidinger P, Becker K, Backes C, Fehlmann T, Pallasch C, Rheinheimer S, Meder B, St&#228;hler C, Meese E et al. Distribution of miRNA expression across human tissues. Nucleic Acids Res. 2016 May 5;44(8):3865-77. PMID: 26921406; PMC: PMC4856985 
consHprc90way Multiple Alignment Multiple Alignment on 90 human genome assemblies Human Pangenome - HPRC Description This track shows multiple alignments of 90 human genomes generated by the Minigraph-Cactus pangenome pipeline, which creates pangenomes directly from whole-genome alignments. This method builds graphs containing all forms of genetic variation while allowing use of current mapping and genotyping tools. Display Conventions and Configuration In full and pack display modes, conservation scores are displayed as a wiggle track (histogram) in which the height reflects the size of the score. The conservation wiggles can be configured in a variety of ways to highlight different aspects of the displayed information. Click the Graph configuration help link for an explanation of the configuration options. Pairwise alignments of each species to the human genome are displayed below the conservation histogram as a grayscale density plot (in pack mode) or as a wiggle (in full mode) that indicates alignment quality. In dense display mode, conservation is shown in grayscale using darker values to indicate higher levels of overall conservation as scored by phastCons. Checkboxes on the track configuration page allow selection of the species to include in the pairwise display. Note that excluding species from the pairwise display does not alter the the conservation score display. To view detailed information about the alignments at a specific position, zoom the display in to 30,000 or fewer bases, then click on the alignment. Gap Annotation The Display chains between alignments configuration option enables display of gaps between alignment blocks in the pairwise alignments in a manner similar to the Chain track display. The following conventions are used: Single line: No bases in the aligned species. Possibly due to a lineage-specific insertion between the aligned blocks in the human genome or a lineage-specific deletion between the aligned blocks in the aligning species. Double line: Aligning species has one or more unalignable bases in the gap region. Possibly due to excessive evolutionary distance between species or independent indels in the region between the aligned blocks in both species. Pale yellow coloring: Aligning species has Ns in the gap region. Reflects uncertainty in the relationship between the DNA of both species, due to lack of sequence in relevant portions of the aligning species. Genomic Breaks Discontinuities in the genomic context (chromosome, scaffold or region) of the aligned DNA in the aligning species are shown as follows: Vertical blue bar: Represents a discontinuity that persists indefinitely on either side, e.g. a large region of DNA on either side of the bar comes from a different chromosome in the aligned species due to a large scale rearrangement. Green square brackets: Enclose shorter alignments consisting of DNA from one genomic context in the aligned species nested inside a larger chain of alignments from a different genomic context. The alignment within the brackets may represent a short misalignment, a lineage-specific insertion of a transposon in the human genome that aligns to a paralogous copy somewhere else in the aligned species, or other similar occurrence. Base Level When zoomed-in to the base-level display, the track shows the base composition of each alignment. The numbers and symbols on the Gaps line indicate the lengths of gaps in the human sequence at those alignment positions relative to the longest non-human sequence. If there is sufficient space in the display, the size of the gap is shown. If the space is insufficient and the gap size is a multiple of 3, a &quot;*&quot; is displayed; other gap sizes are indicated by &quot;+&quot;. Methods The MAF was obtained from the HPRC v1.0 minigraph-cactus HAL file (renamed to replace all &quot;.&quot; characters in sample names with &quot;#&quot; using halRenameGenomes) using cactus v2.6.4 as follows. cactus-hal2maf ./js ./hprc-v1.0-mc-grch38.h al hprc-v1.0-mc-grch38.maf.gz --noAncestors --refGenome GRCh38 --filterGapCausingDupes --chunkSize 100000 --batchCores 96 --batchCount 1 0 --noAncestors --batchParallelTaf 32 --batchSystem slurm --logFile hprc-v1.0-mc-grch38.maf.gz.log zcat hprc-v1.0-mc-grch38.maf.gz | mafDuplicateFilter -m - -k | bgzip > hprc-v1.0-mc-grch38-single-copy.maf.gz Credits Thank you to Glenn Hickey for providing the HAL file from the HPRC project. References Liao WW, Asri M, Ebler J, Doerr D, Haukness M, Hickey G, Lu S, Lucas JK, Monlong J, Abel HJ et al. A draft human pangenome reference. Nature. 2023 May;617(7960):312-324. DOI: 10.1038/s41586-023-05896-x; PMID: 37165242; PMC: PMC10172123 Hickey G, Monlong J, Ebler J, Novak AM, Eizenga JM, Gao Y, Human Pangenome Reference Consortium, Marschall T, Li H, Paten B. Pangenome graph construction from genome alignments with Minigraph-Cactus. Nat Biotechnol. 2023 May 10;. DOI: 10.1038/s41587-023-01793-w; PMID: 37165083; PMC: PMC10638906 Armstrong J, Hickey G, Diekhans M, Fiddes IT, Novak AM, Deran A, Fang Q, Xie D, Feng S, Stiller J et al. Progressive Cactus is a multiple-genome aligner for the thousand-genome era. Nature. 2020 Nov;587(7833):246-251. DOI: 10.1038/s41586-020-2871-y; PMID: 33177663; PMC: PMC7673649 Paten B, Earl D, Nguyen N, Diekhans M, Zerbino D, Haussler D. Cactus: Algorithms for genome multiple sequence alignment. Genome Res. 2011 Sep;21(9):1512-28. DOI: 10.1101/gr.123356.111; PMID: 21665927; PMC: PMC3166836 
consHprc90wayViewalign 90-way Multiple Alignment on 90 human genome assemblies Human Pangenome - HPRC 
hprc90way Multiple Alignment Multiple Alignment on 90 human genome assemblies Human Pangenome - HPRC Description This track shows multiple alignments of 90 human genomes generated by the Minigraph-Cactus pangenome pipeline, which creates pangenomes directly from whole-genome alignments. This method builds graphs containing all forms of genetic variation while allowing use of current mapping and genotyping tools. Display Conventions and Configuration In full and pack display modes, conservation scores are displayed as a wiggle track (histogram) in which the height reflects the size of the score. The conservation wiggles can be configured in a variety of ways to highlight different aspects of the displayed information. Click the Graph configuration help link for an explanation of the configuration options. Pairwise alignments of each species to the human genome are displayed below the conservation histogram as a grayscale density plot (in pack mode) or as a wiggle (in full mode) that indicates alignment quality. In dense display mode, conservation is shown in grayscale using darker values to indicate higher levels of overall conservation as scored by phastCons. Checkboxes on the track configuration page allow selection of the species to include in the pairwise display. Note that excluding species from the pairwise display does not alter the the conservation score display. To view detailed information about the alignments at a specific position, zoom the display in to 30,000 or fewer bases, then click on the alignment. Gap Annotation The Display chains between alignments configuration option enables display of gaps between alignment blocks in the pairwise alignments in a manner similar to the Chain track display. The following conventions are used: Single line: No bases in the aligned species. Possibly due to a lineage-specific insertion between the aligned blocks in the human genome or a lineage-specific deletion between the aligned blocks in the aligning species. Double line: Aligning species has one or more unalignable bases in the gap region. Possibly due to excessive evolutionary distance between species or independent indels in the region between the aligned blocks in both species. Pale yellow coloring: Aligning species has Ns in the gap region. Reflects uncertainty in the relationship between the DNA of both species, due to lack of sequence in relevant portions of the aligning species. Genomic Breaks Discontinuities in the genomic context (chromosome, scaffold or region) of the aligned DNA in the aligning species are shown as follows: Vertical blue bar: Represents a discontinuity that persists indefinitely on either side, e.g. a large region of DNA on either side of the bar comes from a different chromosome in the aligned species due to a large scale rearrangement. Green square brackets: Enclose shorter alignments consisting of DNA from one genomic context in the aligned species nested inside a larger chain of alignments from a different genomic context. The alignment within the brackets may represent a short misalignment, a lineage-specific insertion of a transposon in the human genome that aligns to a paralogous copy somewhere else in the aligned species, or other similar occurrence. Base Level When zoomed-in to the base-level display, the track shows the base composition of each alignment. The numbers and symbols on the Gaps line indicate the lengths of gaps in the human sequence at those alignment positions relative to the longest non-human sequence. If there is sufficient space in the display, the size of the gap is shown. If the space is insufficient and the gap size is a multiple of 3, a &quot;*&quot; is displayed; other gap sizes are indicated by &quot;+&quot;. Methods The MAF was obtained from the HPRC v1.0 minigraph-cactus HAL file (renamed to replace all &quot;.&quot; characters in sample names with &quot;#&quot; using halRenameGenomes) using cactus v2.6.4 as follows. cactus-hal2maf ./js ./hprc-v1.0-mc-grch38.h al hprc-v1.0-mc-grch38.maf.gz --noAncestors --refGenome GRCh38 --filterGapCausingDupes --chunkSize 100000 --batchCores 96 --batchCount 1 0 --noAncestors --batchParallelTaf 32 --batchSystem slurm --logFile hprc-v1.0-mc-grch38.maf.gz.log zcat hprc-v1.0-mc-grch38.maf.gz | mafDuplicateFilter -m - -k | bgzip > hprc-v1.0-mc-grch38-single-copy.maf.gz Credits Thank you to Glenn Hickey for providing the HAL file from the HPRC project. References Liao WW, Asri M, Ebler J, Doerr D, Haukness M, Hickey G, Lu S, Lucas JK, Monlong J, Abel HJ et al. A draft human pangenome reference. Nature. 2023 May;617(7960):312-324. DOI: 10.1038/s41586-023-05896-x; PMID: 37165242; PMC: PMC10172123 Hickey G, Monlong J, Ebler J, Novak AM, Eizenga JM, Gao Y, Human Pangenome Reference Consortium, Marschall T, Li H, Paten B. Pangenome graph construction from genome alignments with Minigraph-Cactus. Nat Biotechnol. 2023 May 10;. DOI: 10.1038/s41587-023-01793-w; PMID: 37165083; PMC: PMC10638906 Armstrong J, Hickey G, Diekhans M, Fiddes IT, Novak AM, Deran A, Fang Q, Xie D, Feng S, Stiller J et al. Progressive Cactus is a multiple-genome aligner for the thousand-genome era. Nature. 2020 Nov;587(7833):246-251. DOI: 10.1038/s41586-020-2871-y; PMID: 33177663; PMC: PMC7673649 Paten B, Earl D, Nguyen N, Diekhans M, Zerbino D, Haussler D. Cactus: Algorithms for genome multiple sequence alignment. Genome Res. 2011 Sep;21(9):1512-28. DOI: 10.1101/gr.123356.111; PMID: 21665927; PMC: PMC3166836 
cons470way Multiz 470-way Multiz Alignment & Conservation (470 mammals) Comparative Genomics Data Access Downloads for data in this track are available: Multiz alignments (bigMaf and MAF format), and phylogenetic trees PhyloP conservation (bigWig and WIG format) PhastCons conservation (bigWig and WIG format) Description This track shows multiple alignments of 470 mammal assemblies and measurements of evolutionary conservation from the Michael Hiller Lab. There is some duplication of different assemblies for the same species, hence there are 431 distinct species in this collection. The multiple alignments were generated using multiz and other tools in the UCSC/Penn State Bioinformatics comparative genomics alignment pipeline. Conserved elements identified by phastCons are also displayed in this track. The base-wise conservation scores are computed using two methods phastCons and phyloP from the PHAST package, for all species. PhastCons (which has been used in previous Conservation tracks) is a hidden Markov model-based method that estimates the probability that each nucleotide belongs to a conserved element, based on the multiple alignment. It considers not just each individual alignment column, but also its flanking columns. By contrast, phyloP separately measures conservation at individual columns, ignoring the effects of their neighbors. As a consequence, the phyloP plots have a less smooth appearance than the phastCons plots, with more "texture" at individual sites. The two methods have different strengths and weaknesses. PhastCons is sensitive to "runs" of conserved sites, and is therefore effective for picking out conserved elements. PhyloP, on the other hand, is more appropriate for evaluating signatures of selection at particular nucleotides or classes of nucleotides (e.g., third codon positions, or first positions of miRNA target sites). The genome assemblies are from a variety of sources. Some are equivalent to UCSC genome browser assemblies, some are from NCBI Genbank assemblies, and some are from the DNA Zoo. When available in the UCSC browser system, links are provided in the table below. Otherwise, links are provided to source locations for the assemblies. count commonname clade scientificname assembly taxon&nbsp;id 1 human primates Homo sapiens Dec. 2013 (GRCh38/hg38) 9606 2 chimpanzee Primates Pan troglodytes Jan. 2018 (Clint_PTRv2/panTro6) 9598 3 pygmy chimpanzee Primates Pan paniscus May 2020 (Mhudiblu_PPA_v0/panPan3) 9597 4 western lowland gorilla Primates Gorilla gorilla gorilla Aug. 2019 (Kamilah_GGO_v0/gorGor6) 9595 5 Sumatran orangutan Primates Pongo abelii Jan. 2018 (Susie_PABv2/ponAbe3) 9601 6 northern white-cheeked gibbon Primates Nomascus leucogenys HLnomLeu4 GCA_006542625.1 61853 7 silvery gibbon Primates Hylobates moloch HLhylMol2 GCA_009828535.2 81572 8 pig-tailed macaque Primates Macaca nemestrina Mar. 2015 (Mnem_1.0/macNem1) 9545 9 gelada Primates Theropithecus gelada HLtheGel1 GCA_003255815.1 9565 10 crab-eating macaque Primates Macaca fascicularis HLmacFas6 GCA_012559485.1 9541 11 Mona monkey Primates Cercopithecus mona HLcerMon1 GCA_014849445.1 36226 12 Ugandan red Colobus Primates Piliocolobus tephrosceles HLpilTep2 GCA_002776525.3 591936 13 Angolan colobus Primates Colobus angolensis palliatus Mar. 2015 (Cang.pa_1.0/colAng1) 336983 14 drill Primates Mandrillus leucophaeus Mar. 2015 (Mleu.le_1.0/manLeu1) 9568 15 sooty mangabey Primates Cercocebus atys Mar. 2015 (Caty_1.0/cerAty1) 9531 16 olive baboon Primates Papio anubis HLpapAnu5 GCA_008728515.1 9555 17 mandrill Primates Mandrillus sphinx HLmanSph1 GCA_004802615.1 9561 18 Hanuman langur Primates Semnopithecus entellus HLsemEnt1 GCA_004025065.1_SemEnt_v1_BIUU 88029 19 Rhesus monkey Primates Macaca mulatta Feb. 2019 (Mmul_10/rheMac10) 9544 20 Japanese macaque Primates Macaca fuscata DNA zoo Macaca fuscata 9542 21 Francois&#39;s langur Primates Trachypithecus francoisi HLtraFra1 GCA_009764315.1 54180 22 black snub-nosed monkey Primates Rhinopithecus bieti Aug. 2016 (ASM169854v1/rhiBie1) 61621 23 golden snub-nosed monkey Primates Rhinopithecus roxellana HLrhiRox2 GCA_007565055.1 61622 24 Red shanked douc langur Primates Pygathrix nemaeus HLpygNem1 GCA_004024825.1_PygNem_v1_BIUU 54133 25 De Brazza&#39;s monkey Primates Cercopithecus neglectus HLcerNeg1 GCA_004027615.1_CertNeg_v1_BIUU 36227 26 proboscis monkey Primates Nasalis larvatus Nov. 2014 (Charlie1.0/nasLar1) 43780 27 Allen&#39;s swamp monkey Primates Allenopithecus nigroviridis DNA zoo Allenopithecus nigroviridis 54135 28 green monkey Primates Chlorocebus sabaeus Mar. 2014 (Chlorocebus_sabeus 1.1/chlSab2) 60711 29 red guenon Primates Erythrocebus patas HLeryPat1 GCA_004027335.1_EryPat_v1_BIUU 9538 30 white-faced saki Primates Pithecia pithecia HLpitPit1 GCA_004026645.1_PitPit_v1_BIUU 43777 31 black-handed spider monkey Primates Ateles geoffroyi HLateGeo1 GCA_004024785.1_AteGeo_v1_BIUU 9509 32 Ma&#39;s night monkey Primates Aotus nancymaae Jun. 2017 (Anan_2.0/aotNan1) 37293 33 Bolivian titi Primates Plecturocebus donacophilus HLpleDon1 GCA_004027715.1_CalDon_v1_BIUU 230833 34 mantled howler monkey Primates Alouatta palliata HLaloPal1 GCA_004027835.1_AloPal_v1_BIUU 30589 35 Bolivian squirrel monkey Primates Saimiri boliviensis DNA zoo Saimiri boliviensis 27679 36 tamarin Primates Saguinus imperator HLsagImp1 GCA_004024885.1_SagImp_v1_BIUU 9491 37 Bolivian squirrel monkey Primates Saimiri boliviensis boliviensis Oct. 2011 (Broad/saiBol1) 39432 38 white-tufted-ear marmoset Primates Callithrix jacchus HLcalJac4 GCA_011100555.1_mCalJac1.pat.X 9483 39 pygmy marmoset Primates Callithrix pygmaea DNA zoo Callithrix pygmaea 9493 40 tufted capuchin Primates Sapajus apella HLsapApe1 GCA_009761245.1 9515 41 Panamanian white-faced capuchin Primates Cebus capucinus imitator Apr. 2016 (Cebus_imitator-1.0/cebCap1) 2715852 42 white-fronted capuchin Primates Cebus albifrons HLcebAlb1 GCA_004027755.1_CebAlb_v1_BIUU 9514 43 aye-aye Primates Daubentonia madagascariensis HLdauMad1 GCA_004027145.1_DauMad_v1_BIUU 31869 44 Coquerel&#39;s sifaka Primates Propithecus coquereli Mar. 2015 (Pcoq_1.0/proCoq1) 379532 45 babakoto Primates Indri indri HLindInd1 GCA_004363605.1_IndInd_v1_BIUU 34827 46 brown lemur Primates Eulemur fulvus HLeulFul1 GCA_004027275.1_EulFul_v1_BIUU 13515 47 Sclater&#39;s lemur Primates Eulemur flavifrons Aug. 2015 (Eflavifronsk33QCA/eulFla1) 87288 48 Ring-tailed lemur Primates Lemur catta HLlemCat1 GCA_004024665.1_LemCat_v1_BIUU 9447 49 greater bamboo lemur Primates Prolemur simus HLproSim1 GCA_003258685.1 1328070 50 mongoose lemur Primates Eulemur mongoz DNA zoo Eulemur mongoz 34828 51 Sclater&#39;s lemur Primates Eulemur flavifrons DNA zoo Eulemur flavifrons 87288 52 Lesser dwarf lemur Primates Cheirogaleus medius HLcheMed1 GCA_008086735.1 9460 53 black lemur Primates Eulemur macaco Aug. 2015 (Emacaco_refEf_BWA_oneround/eulMac1) 30602 54 Philippine tarsier Primates Carlito syrichta Sep. 2013 (Tarsius_syrichta-2.0.1/tarSyr2) 1868482 55 gray mouse lemur Primates Microcebus murinus Feb. 2017 (Mmur_3.0/micMur3) 30608 56 Northern giant mouse lemur Primates Mirza zaza HLmirZaz1 GCA_008750895.1 339999 57 Coquerel&#39;s mouse lemur Primates Mirza coquereli HLmirCoq1 GCA_004024645.1_MizCoq_v1_BIUU 47180 58 mouse lemur Primates Microcebus sp. 3 GT-2019 HLmicSpe31 GCA_008750915.1 2508170 59 Northern rufous mouse lemur Primates Microcebus tavaratra HLmicTav1 GCA_008750935.1 143351 60 slow loris Primates Nycticebus coucang HLnycCou1 GCA_004027815.1_NycCou_v1_BIUU 9470 61 small-eared galago Primates Otolemur garnettii Mar. 2011 (Broad/otoGar3) 30611 62 Sunda flying lemur Euarchontoglires Galeopterus variegatus HLgalVar2 GCA_004027255.2 482537 63 Chinese tree shrew Euarchontoglires Tupaia chinensis Jan 2013 (TupChi_1.0/tupChi1) 246437 64 northern tree shrew Euarchontoglires Tupaia belangeri Dec. 2006 (Broad/tupBel1) 37347 65 puma Carnivora Puma concolor HLpumCon1 GCA_003327715.1_PumCon1.0 9696 66 Amur tiger Carnivora Panthera tigris altaica 06 Sep 2013 (PanTig1.0/panTig1) 74533 67 Clouded leopard Carnivora Neofelis nebulosa DNA zoo Neofelis nebulosa 61452 68 leopard Carnivora Panthera pardus HLpanPar1 GCA_001857705.1_PanPar1.0 9691 69 bearded seal Carnivora Erignathus barbatus DNA zoo Erignathus barbatus 39304 70 jaguar Carnivora Panthera onca HLpanOnc1 GCA_004023805.1_PanOnc_v1_BIUU 9690 71 harbor seal Carnivora Phoca vitulina HLphoVit1 GCA_004348235.1 9720 72 cheetah Carnivora Acinonyx jubatus HLaciJub2 GCF_003709585.1_Aci_jub_2 32536 73 gray seal Carnivora Halichoerus grypus HLhalGry1 GCA_012393455.1 9711 74 Hawaiian monk seal Carnivora Neomonachus schauinslandi Jun. 2017 (ASM220157v1/neoSch1) 29088 75 Weddell seal Carnivora Leptonychotes weddellii Mar 2013 (LepWed1.0/lepWed1) 9713 76 jaguar Carnivora Panthera onca DNA zoo Panthera onca 9690 77 Amur leopard cat Carnivora Prionailurus bengalensis euptilurus HLpriBen1 GCA_005406085.1 300877 78 Asian black bear Carnivora Ursus thibetanus thibetanus HLursThi1 GCA_009660055.1 441215 79 Spanish lynx Carnivora Lynx pardinus HLlynPar1 GCA_900661375.1 191816 80 Southern elephant seal Carnivora Mirounga leonina HLmirLeo1 GCA_011800145.1 9715 81 Canada lynx Carnivora Lynx canadensis HLlynCan1 GCA_007474595.1 61383 82 Northern elephant seal Carnivora Mirounga angustirostris DNA zoo Mirounga angustirostris 9716 83 lion Carnivora Panthera leo HLpanLeo1 GCA_008795835.1 9689 84 walrus Carnivora Odobenus rosmarus DNA zoo Odobenus rosmarus 9707 85 northern fur seal Carnivora Callorhinus ursinus HLcalUrs1 GCA_003265705.1 34884 86 Pacific walrus Carnivora Odobenus rosmarus divergens Jan 2013 (Oros_1.0/odoRosDiv1) 9708 87 giant panda Carnivora Ailuropoda melanoleuca HLailMel2 GCA_002007445.2 9646 88 California sea lion Carnivora Zalophus californianus HLzalCal1 GCA_009762305.1_mZalCal1.pri 9704 89 Steller sea lion Carnivora Eumetopias jubatus HLeumJub1 GCA_004028035.1 34886 90 domestic cat Carnivora Felis catus Nov. 2017 (Felis_catus_9.0/felCat9) 9685 91 jaguarundi Carnivora Puma yagouaroundi HLpumYag1 GCA_014898765.1 1608482 92 grizzly bear Carnivora Ursus arctos horribilis HLursArc1 GCA_003584765.1 116960 93 polar bear Carnivora Ursus maritimus 09 May-2014 (UrsMar_1.0/ursMar1) 29073 94 antarctic fur seal Carnivora Arctocephalus gazella HLarcGaz2 GCA_900642305.1 37190 95 American black bear Carnivora Ursus americanus HLursAme1 GCA_003344425.1 9643 96 American black bear Carnivora Ursus americanus DNA zoo Ursus americanus 9643 97 black-footed cat Carnivora Felis nigripes HLfelNig1 GCA_004023925.1_FelNig_v1_BIUU 61379 98 fossa Carnivora Cryptoprocta ferox DNA zoo Cryptoprocta ferox 94188 99 red fox Carnivora Vulpes vulpes HLvulVul1 GCA_003160815.1 9627 100 dog Carnivora Canis lupus familiaris Mar. 2020 (UU_Cfam_GSD_1.0/canFam4) 9615 101 Arctic fox Carnivora Vulpes lagopus HLvulLag1 GCA_004023825.1_VulLag_v1_BIUU 494514 102 African hunting dog Carnivora Lycaon pictus DNA zoo Lycaon pictus 9622 103 dingo Carnivora Canis lupus dingo HLcanLupDin1 GCA_003254725.1 286419 104 dog Carnivora Canis lupus familiaris May 2019 (UMICH_Zoey_3.1/canFam5) 9615 105 kinkajou Carnivora Potos flavus DNA zoo Potos flavus 29067 106 African hunting dog Carnivora Lycaon pictus HLlycPic2 GCA_004216515.1 9622 107 lesser panda Carnivora Ailurus fulgens DNA zoo Ailurus fulgens 9649 108 spotted hyena Carnivora Crocuta crocuta HLcroCro1 GCA_008692635.1 9678 109 striped hyena Carnivora Hyaena hyaena HLhyaHya1 GCA_003009895.1 95912 110 Asian palm civet Carnivora Paradoxurus hermaphroditus HLparHer1 GCA_004024585.1_ParHer_v1_BIUU 71117 111 White-nosed coati Carnivora Nasua narica DNA zoo Nasua narica 352831 112 sable Carnivora Martes zibellina HLmarZib1 GCA_012583365.1 36722 113 wolverine Carnivora Gulo gulo HLgulGul1 GCA_900006375.2 48420 114 raccoon Carnivora Procyon lotor DNA zoo Procyon lotor 9654 115 Cacomistle Carnivora Bassariscus sumichrasti DNA zoo Bassariscus sumichrasti 392507 116 western spotted skunk Carnivora Spilogale gracilis HLspiGra1 GCA_004023965.1_SpiGra_v1_BIUU 30551 117 North American badger Carnivora Taxidea taxus jeffersonii HLtaxTax1 GCA_003697995.1 2282171 118 ratel Carnivora Mellivora capensis HLmelCap1 GCA_004024625.1_MelCap_v1_BIUU 9664 119 meerkat Carnivora Suricata suricatta HLsurSur2 GCA_004023905.1_SurSur_v1_BIUU 37032 120 meerkat Carnivora Suricata suricatta HLsurSur1 GCA_006229205.1 37032 121 banded mongoose Carnivora Mungos mungo HLmunMug1 GCA_004023785.1_MunMun_v1_BIUU 210652 122 dwarf mongoose Carnivora Helogale parvula HLhelPar1 GCA_004023845.1_HelPar_v1_BIUU 210647 123 Northern American river otter Carnivora Lontra canadensis HLlonCan1 GCA_010015895.1 76717 124 giant otter Carnivora Pteronura brasiliensis DNA zoo Pteronura brasiliensis 9672 125 giant otter Carnivora Pteronura brasiliensis HLpteBra1 GCA_004024605.1_PteBra_v1_BIUU 9672 126 Southern sea otter Carnivora Enhydra lutris nereis Jun. 2019 (ASM641071v1/enhLutNer1) 1049777 127 Northern sea otter Carnivora Enhydra lutris kenyoni Sep. 2017 (ASM228890v2/enhLutKen1) 391180 128 Eurasian river otter Carnivora Lutra lutra HLlutLut1 GCA_902655055.1 9657 129 ermine Carnivora Mustela erminea HLmusErm1 GCA_009829155.1 36723 130 American mink Carnivora Neovison vison HLneoVis1 GCA_900108605.1_NNQGG.v01 452646 131 European polecat Carnivora Mustela putorius HLmusPut1 GCA_902460205.1 9668 132 domestic ferret Carnivora Mustela putorius furo HLmusFur2 GCA_011764305.1 9669 133 Brazilian tapir Laurasiatheria Tapirus terrestris HLtapTer1 GCA_004025025.1_TapTer_v1_BIUU 9801 134 greater Indian rhinoceros Laurasiatheria Rhinoceros unicornis DNA zoo Rhinoceros unicornis 9809 135 Asiatic tapir Laurasiatheria Tapirus indicus HLtapInd1 GCA_004024905.1_TapInd_v1_BIUU 9802 136 Asiatic tapir Laurasiatheria Tapirus indicus DNA zoo Tapirus indicus 9802 137 black rhinoceros Laurasiatheria Diceros bicornis HLdicBic1 GCA_004027315.2 9805 138 Sumatran rhinoceros Laurasiatheria Dicerorhinus sumatrensis sumatrensis HLdicSum1 GCA_002844835.1_ASM284483v1 310712 139 northern white rhinoceros Laurasiatheria Ceratotherium simum cottoni HLcerSimCot1 GCA_004027795.1_CerCot_v1_BIUU 310713 140 southern white rhinoceros Laurasiatheria Ceratotherium simum simum May 2012 (CerSimSim1.0/cerSim1) 73337 141 Equus burchelli boehmi Laurasiatheria Equus burchellii boehmi DNA zoo Equus burchellii boehmi 89250 142 horse Laurasiatheria Equus caballus Jan. 2018 (EquCab3.0/equCab3) 9796 143 Przewalski&#39;s horse Laurasiatheria Equus przewalskii Jun 2014 (Burgud/equPrz1) 9798 144 ass Laurasiatheria Equus asinus HLequAsi1 GCA_001305755.1_ASM130575v1 9793 145 donkey Laurasiatheria Equus asinus asinus HLequAsiAsi2 GCA_003033725.1 83772 146 Tree pangolin Laurasiatheria Manis tricuspis HLmanTri1 GCA_004765945.1 358128 147 Tree pangolin Laurasiatheria Manis tricuspis DNA zoo Manis tricuspis 358128 148 Chinese pangolin Laurasiatheria Manis pentadactyla HLmanPen2 GCA_014570555.1 143292 149 Chinese pangolin Laurasiatheria Manis pentadactyla Aug 2014 (M_pentadactyla-1.1.1/manPen1) 143292 150 Malayan pangolin Laurasiatheria Manis javanica HLmanJav1 GCA_001685135.1_ManJav1.0 9974 151 Malayan pangolin Laurasiatheria Manis javanica HLmanJav2 GCA_014570535.1 9974 152 Hispaniolan solenodon Laurasiatheria Solenodon paradoxus HLsolPar1 GCA_004363575.1_SolPar_v1_BIUU 79805 153 eastern mole Laurasiatheria Scalopus aquaticus HLscaAqu1 GCA_004024925.1_ScaAqu_v1_BIUU 71119 154 Iberian mole Laurasiatheria Talpa occidentalis HLtalOcc1 GCA_014898055.1 50954 155 gracile shrew mole Laurasiatheria Uropsilus gracilis HLuroGra1 GCA_004024945.1_UroGra_v1_BIUU 182669 156 star-nosed mole Laurasiatheria Condylura cristata Mar 2012 (ConCri1.0/conCri1) 143302 157 western European hedgehog Laurasiatheria Erinaceus europaeus May 2012 (EriEur2.0/eriEur2) 9365 158 European shrew Laurasiatheria Sorex araneus Aug. 2008 (Broad/sorAra2) 42254 159 Antarctic minke whale Cetartiodactyla Balaenoptera bonaerensis HLbalBon1 GCA_000978805.1_ASM97880v1 33556 160 grey whale Cetartiodactyla Eschrichtius robustus HLescRob1 GCA_004363415.1_EscRob_v1_BIUU 9764 161 sperm whale Cetartiodactyla Physeter catodon Sep. 2013 (Physeter_macrocephalus-2.0.2/phyCat1) 9755 162 sperm whale Cetartiodactyla Physeter catodon HLphyCat2 GCA_002837175.2 9755 163 Yangtze River dolphin Cetartiodactyla Lipotes vexillifer 31 Jul 2013 (Lipotes_vexillifer_v1/lipVex1) 118797 164 beluga whale Cetartiodactyla Delphinapterus leucas HLdelLeu2 GCA_002288925.3 9749 165 hippopotamus Cetartiodactyla Hippopotamus amphibius HLhipAmp3 GCA_004027065.2 9833 166 hippopotamus Cetartiodactyla Hippopotamus amphibius HLhipAmp1 GCA_002995585.1_ASM299558v1 9833 167 harbor porpoise Cetartiodactyla Phocoena phocoena DNA zoo Phocoena phocoena 9742 168 harbor porpoise Cetartiodactyla Phocoena phocoena HLphoPho1 GCA_004363495.1_PhoPho_v1_BIUU 9742 169 Wild Bactrian camel Cetartiodactyla Camelus ferus HLcamFer3 GCA_009834535.1 419612 170 killer whale Cetartiodactyla Orcinus orca Jan. 2013 (Oorc_1.1/orcOrc1) 9733 171 Bactrian camel Cetartiodactyla Camelus bactrianus HLcamBac1 GCA_000767855.1_Ca_bactrianus_MBC_1.0 9837 172 Indo-pacific humpbacked dolphin Cetartiodactyla Sousa chinensis HLsouChi1 GCA_007760645.1 103600 173 Arabian camel Cetartiodactyla Camelus dromedarius HLcamDro2 GCA_000803125.3 9838 174 alpaca Cetartiodactyla Vicugna pacos Mar. 2013 (Vicugna_pacos-2.0.1/vicPac2) 30538 175 common bottlenose dolphin Cetartiodactyla Tursiops truncatus HLturTru4 GCA_011762595.1_mTurTru1.mat.Y 9739 176 Indo-pacific bottlenose dolphin Cetartiodactyla Tursiops aduncus HLturAdu1 GCA_003227395.1 79784 177 Indo-pacific bottlenose dolphin Cetartiodactyla Tursiops aduncus DNA zoo Tursiops aduncus 79784 178 common bottlenose dolphin Cetartiodactyla Tursiops truncatus Oct. 2011 (Baylor Ttru_1.4/turTru2) 9739 179 common bottlenose dolphin Cetartiodactyla Tursiops truncatus HLturTru3 GCA_001922835.1_NIST_Tur_tru_v1 9739 180 pig Cetartiodactyla Sus scrofa Feb. 2017 (Sscrofa11.1/susScr11) 9823 181 okapi Cetartiodactyla Okapia johnstoni DNA zoo Okapia johnstoni 86973 182 Masai giraffe Cetartiodactyla Giraffa tippelskirchi HLgirTip1 GCA_001651235.1_ASM165123v1 439328 183 water buffalo Cetartiodactyla Bubalus bubalis HLbubBub2 GCA_003121395.1 89462 184 zebu cattle Cetartiodactyla Bos indicus HLbosInd2 GCA_002933975.1 9915 185 cattle Cetartiodactyla Bos taurus Apr. 2018 (ARS-UCD1.2/bosTau9) 9913 186 wild yak Cetartiodactyla Bos mutus HLbosMut2 GCA_007646595.3 72004 187 greater kudu Cetartiodactyla Tragelaphus strepsiceros HLtraStr1 GCA_006410795.1 9946 188 aoudad Cetartiodactyla Ammotragus lervia HLammLer1 GCA_002201775.1_ALER1.0 9899 189 goat Cetartiodactyla Capra hircus HLcapHir2 GCA_001704415.1_ARS1 9925 190 wild goat Cetartiodactyla Capra aegagrus HLcapAeg1 GCA_000765075.1 9923 191 chiru Cetartiodactyla Pantholops hodgsonii May 2013 (PHO1.0/panHod1) 59538 192 white-tailed deer Cetartiodactyla Odocoileus virginianus HLodoVir3 GCA_014726795.1 9874 193 bighorn sheep Cetartiodactyla Ovis canadensis HLoviCan2 GCA_004026945.1_OviCan_v1_BIUU 37174 194 white-tailed deer Cetartiodactyla Odocoileus virginianus DNA zoo Odocoileus virginianus 9874 195 sheep Cetartiodactyla Ovis aries HLoviAri5 GCA_011170295.1 9940 196 Pere David&#39;s deer Cetartiodactyla Elaphurus davidianus HLelaDav1 GCA_002443075.1_Milu1.0 43332 197 argali Cetartiodactyla Ovis ammon HLoviAmm1 GCA_003121645.1 30527 198 North Atlantic right whale Artiodactyla Eubalaena glacialis DNA zoo Eubalaena glacialis 27606 199 North Pacific right whale Artiodactyla Eubalaena japonica HLeubJap1 GCA_004363455.1_EubJap_v1_BIUU 302098 200 minke whale Artiodactyla Balaenoptera acutorostrata scammoni Oct. 2013 (BalAcu1.0/balAcu1) 310752 201 humpback whale Artiodactyla Megaptera novaeangliae HLmegNov1 GCA_004329385.1 9773 202 Fin whale Artiodactyla Balaenoptera physalus HLbalPhy1 GCA_008795845.1 9770 203 bowhead whale Artiodactyla Balaena mysticetus HLbalMys1/http://alfred.liv.ac.uk/downloads/bowhead_whale/bowhead_whale_scaffolds.zip/none 27602 204 Blue whale Artiodactyla Balaenoptera musculus HLbalMus1 GCA_009873245.1 9771 205 pygmy Bryde&#39;s whale Artiodactyla Balaenoptera edeni DNA zoo Balaenoptera edeni 9769 206 Sowerby&#39;s beaked whale Artiodactyla Mesoplodon bidens HLmesBid1 GCA_004027085.1_MesBid_v1_BIUU 48745 207 Indus River dolphin Artiodactyla Platanista minor HLplaMin1 GCA_004363435.1_PlaMin_v1_BIUU 48752 208 Cuvier&#39;s beaked whale Artiodactyla Ziphius cavirostris HLzipCav1 GCA_004364475.1_ZipCav_v1_BIUU 9760 209 boutu Artiodactyla Inia geoffrensis HLlniGeo1 GCA_004363515.1_IniGeo_v1_BIUU 9725 210 narwhal Artiodactyla Monodon monoceros HLmonMon1 GCA_005190385.2 40151 211 Yangtze finless porpoise Artiodactyla Neophocaena asiaeorientalis asiaeorientalis HLneoAsi1 GCA_003031525.1_Neophocaena_asiaeorientalis_V1 1706337 212 pygmy sperm whale Artiodactyla Kogia breviceps HLkogBre1 GCA_004363705.1_KogBre_v1_BIUU 27615 213 vaquita Artiodactyla Phocoena sinus HLphoSin1 GCA_008692025.1 42100 214 franciscana Artiodactyla Pontoporia blainvillei HLponBla1 GCA_011754075.1 48723 215 Lama pacos huacaya Artiodactyla Vicugna pacos huacaya HLvicPacHua3 GCA_000767525.1_Vi_pacos_V1.0 273913 216 llama Artiodactyla Lama glama DNA zoo Lama glama 9844 217 melon-headed whale Artiodactyla Peponocephala electra DNA zoo Peponocephala electra 103596 218 long-finned pilot whale Artiodactyla Globicephala melas HLgloMel1 GCA_006547405.1 9731 219 Pacific white-sided dolphin Artiodactyla Lagenorhynchus obliquidens HLlagObl1 GCA_003676395.1 90247 220 Vicugna mensalis Artiodactyla Vicugna vicugna mensalis HLvicVicMen1 GCA_013265495.1 273917 221 guanaco Artiodactyla Lama guanicoe cacsilensis HLlamGuaCac1 GCA_013239625.1 273908 222 llama Artiodactyla Lama glama chaku HLlamGlaCha1 GCA_013239585.1 273914 223 Chacoan peccary Artiodactyla Catagonus wagneri HLcatWag1 GCA_004024745.2_CatWag_v2_BIUU_UCD 51154 224 giraffe Artiodactyla Giraffa camelopardalis HLgirCam1 GCA_006408565.1 9894 225 giraffe Artiodactyla Giraffa camelopardalis DNA zoo Giraffa camelopardalis 9894 226 African buffalo Artiodactyla Syncerus caffer HLsynCaf1 GCA_902500845.1 9970 227 Bos bison bison Artiodactyla Bison bison bison Oct. 2014 (Bison_UMD1.0/bisBis1) 43346 228 Chinese forest musk deer Artiodactyla Moschus berezovskii HLmosBer1 GCA_006459085.1 68408 229 Siberian musk deer Artiodactyla Moschus moschiferus HLmosMos1 GCA_004024705.2 68415 230 alpine musk deer Artiodactyla Moschus chrysogaster HLmosChr1 GCA_006461725.1 68412 231 Yarkand deer Artiodactyla Cervus hanglu yarkandensis HLcerHanYar1 GCA_010411085.1 84702 232 gaur Artiodactyla Bos gaurus HLbosGau1 GCA_014182915.1 9904 233 gayal Artiodactyla Bos frontalis HLbosFro1 GCA_007844835.1_NRC_Mithun_1 30520 234 white-lipped deer Artiodactyla Przewalskium albirostris HLprzAlb1 GCA_006408465.1 1088058 235 roan antelope Artiodactyla Hippotragus equinus HLhipEqu1 GCA_016433095.1 37186 236 Harvey&#39;s duiker Artiodactyla Cephalophus harveyi HLcepHar1 GCA_006410635.1 129224 237 sable antelope Artiodactyla Hippotragus niger niger HLhipNig1 GCA_006942125.1 82127 238 domestic yak Artiodactyla Bos grunniens HLbosGru1 GCA_005887515.2 30521 239 scimitar-horned oryx Artiodactyla Oryx dammah DNA zoo Oryx dammah 59534 240 bush duiker Artiodactyla Sylvicapra grimmia HLsylGri1 GCA_006408735.1 119562 241 Maxwell&#39;s duiker Artiodactyla Philantomba maxwellii HLphiMax1 GCA_006410695.1 907741 242 gemsbok Artiodactyla Oryx gazella HLoryGaz1 GCA_003945745.1 9958 243 pronghorn Artiodactyla Antilocapra americana HLantAme1 GCA_007570785.1 9891 244 Reeves&#39; muntjac Artiodactyla Muntiacus reevesi HLmunRee1 GCA_008787405.1 9886 245 black muntjac Artiodactyla Muntiacus crinifrons HLmunCri1 GCA_006408485.1 71854 246 Central European red deer Artiodactyla Cervus elaphus hippelaphus HLcerEla1 GCA_002197005.1 46360 247 lesser kudu Artiodactyla Tragelaphus imberbis HLtraImb1 GCA_006410775.1 9947 248 brindled gnu Artiodactyla Connochaetes taurinus DNA zoo Connochaetes taurinus 9927 249 bushbuck Artiodactyla Tragelaphus scriptus HLtraScr1 GCA_006410495.1 66440 250 waterbuck Artiodactyla Kobus ellipsiprymnus HLkobEll1 GCA_006410655.1 9962 251 muntjak Artiodactyla Muntiacus muntjak HLmunMun1 GCA_008782695.1 9888 252 topi Artiodactyla Damaliscus lunatus HLdamLun1 GCA_006408505.1 9929 253 bighorn sheep Artiodactyla Ovis canadensis canadensis HLoviCan1 GCA_001039535.1 112262 254 lechwe Artiodactyla Kobus leche leche HLkobLecLec1 GCA_014926565.1 91880 255 Eastern roe deer Artiodactyla Capreolus pygargus HLcapPyg1 GCA_012922965.1 48560 256 Eurasian elk Artiodactyla Alces alces HLalcAlc1 GCA_007570765.1 9852 257 Cobus hunteri Artiodactyla Beatragus hunteri HLbeaHun1 GCA_004027495.1_BeaHun_v1_BIUU 59527 258 impala Artiodactyla Aepyceros melampus HLaepMel1 GCA_006408695.1 9897 259 mule deer Artiodactyla Odocoileus hemionus hemionus HLodoHem1 GCA_004115125.1 9877 260 Bohar reedbuck Artiodactyla Redunca redunca HLredRed1 GCA_006410935.1 59556 261 Siberian ibex Artiodactyla Capra sibirica HLcapSib1 GCA_003182615.2 72544 262 porcupine caribou Artiodactyla Rangifer tarandus granti HLranTarGra2 GCA_014898785.1 191431 263 reindeer Artiodactyla Rangifer tarandus HLranTar1 GCA_004026565.1_RanTarSib_v1_BIUU 9870 264 klipspringer Artiodactyla Oreotragus oreotragus HLoreOre1 GCA_006410675.1 66444 265 Chinese water deer Artiodactyla Hydropotes inermis HLhydIne1 GCA_006459105.1 9883 266 snow sheep Artiodactyla Ovis nivicola lydekkeri HLoviNivLyd1 GCA_903231385.1 1867112 267 suni Artiodactyla Neotragus moschatus HLneoMos1 GCA_006410615.1 66442 268 white-tailed deer Artiodactyla Odocoileus virginianus texanus HLodoVir1 GCA_002102435.1_Ovir.te_1.0 9880 269 Nilgiri tahr Artiodactyla Hemitragus hylocrius HLhemHyl1 GCA_004026825.1_HemHyl_v1_BIUU 330464 270 Asiatic mouflon Artiodactyla Ovis orientalis HLoviOri1 GCA_014523465.1 469796 271 royal antelope Artiodactyla Neotragus pygmaeus HLneoPyg1 GCA_006410875.1 1027985 272 Grant&#39;s gazelle Artiodactyla Nanger granti HLnanGra1 GCA_006408635.1 27591 273 Przewalski&#39;s gazelle Artiodactyla Procapra przewalskii HLproPrz1 GCA_006410515.1 157668 274 steenbok Artiodactyla Raphicerus campestris HLrapCam1 GCA_006410735.1 59544 275 Thomson&#39;s gazelle Artiodactyla Eudorcas thomsonii HLeudTho1 GCA_006408755.1 69308 276 springbok Artiodactyla Antidorcas marsupialis HLantMar1 GCA_006408585.1 59523 277 gerenuk Artiodactyla Litocranius walleri HLlitWal1 GCA_006410535.1 69311 278 Kirk&#39;s dik-dik Artiodactyla Madoqua kirkii HLmadKir1 GCA_006408675.1 66434 279 Hog deer Artiodactyla Axis porcinus HLaxiPor1 GCA_003798545.1 57737 280 Java mouse-deer Artiodactyla Tragulus javanicus HLtraJav1 GCA_004024965.2 9849 281 lesser mouse-deer Artiodactyla Tragulus kanchil HLtraKan1 GCA_006408655.1 1088131 282 mountain goat Artiodactyla Oreamnos americanus HLoreAme1 GCA_009758055.1 34873 283 saiga antelope Artiodactyla Saiga tatarica HLsaiTat1 GCA_004024985.1_SaiTat_v1_BIUU 34875 284 Alpine ibex Artiodactyla Capra ibex HLcapIbe1 GCA_006410555.1 72542 285 Hoffmann&#39;s two-fingered sloth Xenarthra Choloepus hoffmanni DNA zoo Choloepus hoffmanni 9358 286 southern two-toed sloth Xenarthra Choloepus didactylus HLchoDid2 GCF_015220235.1_mChoDid1.pri 27675 287 southern two-toed sloth Xenarthra Choloepus didactylus HLchoDid1 GCA_004027855.1_ChoDid_v1_BIUU 27675 288 nine-banded armadillo Xenarthra Dasypus novemcinctus Dec. 2011 (Baylor/dasNov3) 9361 289 giant anteater Xenarthra Myrmecophaga tridactyla HLmyrTri1 GCA_004026745.1_MyrTri_v1_BIUU 71006 290 southern tamandua Xenarthra Tamandua tetradactyla HLtamTet1 GCA_004025105.1_TamTet_v1_BIUU 48850 291 Southern three-banded armadillo Xenarthra Tolypeutes matacus HLtolMat1 GCA_004025125.1_TolMat_v1_BIUU 183749 292 Chinese rufous horseshoe bat Chiroptera Rhinolophus sinicus HLrhiSin1 GCA_001888835.1_ASM188883v1 89399 293 great roundleaf bat Chiroptera Hipposideros armiger HLhipArm1 GCA_001890085.1_ASM189008v1 186990 294 black flying fox Chiroptera Pteropus alecto Aug 2012 (ASM32557v1/pteAle1) 9402 295 greater horseshoe bat Chiroptera Rhinolophus ferrumequinum HLrhiFer5/Bat1K published/none 59479 296 Bonin flying fox Chiroptera Pteropus pselaphon HLptePse1 GCA_014363405.1 1496133 297 Brazilian free-tailed bat Chiroptera Tadarida brasiliensis HLtadBra1 GCA_004025005.1_TadBra_v1_BIUU 9438 298 large flying fox Chiroptera Pteropus vampyrus HLpteVam2 GCA_000151845.2 132908 299 Malagasy flying fox Chiroptera Pteropus rufus DNA zoo Pteropus rufus 196297 300 Indian flying fox Chiroptera Pteropus giganteus HLpteGig1 GCA_902729225.1 143291 301 Malagasy straw-colored fruit bat Chiroptera Eidolon dupreanum DNA zoo Eidolon dupreanum 58063 302 straw-colored fruit bat Chiroptera Eidolon helvum HLeidHel2/DNAZoo/none 77214 303 Cantor&#39;s roundleaf bat Chiroptera Hipposideros galeritus HLhipGal1 GCA_004027415.1_HipGal_v1_BIUU 58069 304 lesser short-nosed fruit bat Chiroptera Cynopterus brachyotis HLcynBra1 GCA_009793145.1 58060 305 lesser dawn bat Chiroptera Eonycteris spelaea HLeonSpe1 GCA_003508835.1 58065 306 Leschenault&#39;s rousette Chiroptera Rousettus leschenaultii HLrouLes1 GCA_015472975.1 9408 307 Egyptian rousette Chiroptera Rousettus aegyptiacus HLrouAeg4/Bat1K published/none 9407 308 Madagascan rousette Chiroptera Rousettus madagascariensis DNA zoo Rousettus madagascariensis 77223 309 Indian false vampire Chiroptera Megaderma lyra HLmegLyr2 GCA_004026885.1_MegLyr_v1_BIUU 9413 310 Pallas&#39;s mastiff bat Chiroptera Molossus molossus HLmolMol2/Bat1K published/none 27622 311 long-tongued fruit bat Chiroptera Macroglossus sobrinus HLmacSob1 GCA_004027375.1_MacSob_v1_BIUU 326083 312 Schreibers&#39; long-fingered bat Chiroptera Miniopterus schreibersii HLminSch1 GCA_004026525.1_MinSch_v1_BIUU 9433 313 Miniopterus schreibersii natalensis Chiroptera Miniopterus natalensis HLminNat1 GCA_001595765.1 291302 314 hog-nosed bat Chiroptera Craseonycteris thonglongyai HLcraTho1 GCA_004027555.1_CraTho_v1_BIUU 208972 315 Antillean ghost-faced bat Chiroptera Mormoops blainvillei HLmorBla1 GCA_004026545.1_MorMeg_v1_BIUU 118852 316 Parnell&#39;s mustached bat Chiroptera Pteronotus parnellii Sep. 2013 (ASM46540v1/ptePar1) 59476 317 big brown bat Chiroptera Eptesicus fuscus Jul 2012 (EptFus1.0/eptFus1) 29078 318 greater mouse-eared bat Chiroptera Myotis myotis HLmyoMyo6/Bat1K published/none 51298 319 Brandt&#39;s bat Chiroptera Myotis brandtii 28 Jun 2013 (ASM41265v1/myoBra1) 109478 320 common vampire bat Chiroptera Desmodus rotundus HLdesRot2 9430 321 California big-eared bat Chiroptera Macrotus californicus HLmacCal1 GCA_007922815.1 9419 322 Northern long-eared myotis Chiroptera Myotis septentrionalis DNA zoo Myotis septentrionalis 258941 323 little brown bat Chiroptera Myotis lucifugus DNA zoo Myotis lucifugus 59463 324 little brown bat Chiroptera Myotis lucifugus Jul. 2010 (Broad Institute Myoluc2.0/myoLuc2) 59463 325 Lesser long-nosed bat Chiroptera Leptonycteris yerbabuenae HLlepYer1/GIGADB/none 700936 326 Vespertilio Davidii Chiroptera Myotis davidii Aug 2012 (ASM32734v1/myoDav1) 225400 327 Schizostoma hirsutum Chiroptera Micronycteris hirsuta HLmicHir1 GCA_004026765.1_MicHir_v1_BIUU 148065 328 tailed tailless bat Chiroptera Anoura caudifer HLanoCau1 GCA_004027475.1_AnoCau_v1_BIUU 27642 329 Murina feae Chiroptera Murina aurata feae HLmurAurFea1 GCA_004026665.1_MurFea_v1_BIUU 1453894 330 greater bulldog bat Chiroptera Noctilio leporinus HLnocLep1 GCA_004026585.1_NocLep_v1_BIUU 94963 331 Seba&#39;s short-tailed bat Chiroptera Carollia perspicillata HLcarPer3 GCA_004027735.1_CarPer_v1_BIUU 40233 332 pale spear-nosed bat Chiroptera Phyllostomus discolor HLphyDis3/Bat1K published/none 89673 333 stripe-headed round-eared bat Chiroptera Tonatia saurophila HLtonSau1 GCA_004024845.1_TonSau_v1_BIUU 171122 334 Jamaican fruit-eating bat Chiroptera Artibeus jamaicensis HLartJam1 GCA_004027435.1_ArtJam_v1_BIUU 9417 335 Jamaican fruit-eating bat Chiroptera Artibeus jamaicensis HLartJam2 GCA_014825515.1 9417 336 Honduran yellow-shouldered bat Chiroptera Sturnira hondurensis HLstuHon1 GCA_014824575.1 192404 337 hoary bat Chiroptera Aeorestes cinereus HLaeoCin1 GCA_011751065.1 257879 338 pallid bat Chiroptera Antrozous pallidus HLantPal1 GCA_007922775.1 9440 339 evening bat Chiroptera Nycticeius humeralis HLnycHum2 GCA_007922795.1 27670 340 red bat Chiroptera Lasiurus borealis HLlasBor1 GCA_004026805.1_LasBor_v1_BIUU 258930 341 Kuhl&#39;s pipistrelle Chiroptera Pipistrellus kuhlii HLpipKuh2/Bat1K published/none 59472 342 common pipistrelle Chiroptera Pipistrellus pipistrellus HLpipPip1 GCA_004026625.1_PipPip_v1_BIUU 59474 343 common pipistrelle Chiroptera Pipistrellus pipistrellus HLpipPip2 GCA_903992545.1 59474 344 gray squirrel Glires Sciurus carolinensis HLsciCar1 GCA_902686445.1 30640 345 Eurasian red squirrel Glires Sciurus vulgaris HLsciVul1 GCA_902686455.1_mSciVul1.1 55149 346 South African ground squirrel Glires Xerus inauris HLxerIna1 GCA_004024805.1_XerIna_v1_BIUU 234690 347 mountain beaver Glires Aplodontia rufa HLaplRuf1 GCA_004027875.1_AplRuf_v1_BIUU 51342 348 yellow-bellied marmot Glires Marmota flaviventris HLmarFla1 GCA_003676075.2 93162 349 Alpine marmot Glires Marmota marmota marmota HLmarMar1 GCF_001458135.1_marMar2.1 9994 350 Vancouver Island marmot Glires Marmota vancouverensis HLmarVan1 GCA_005458795.1 93167 351 Himalayan marmot Glires Marmota himalayana HLmarHim1 GCA_005280165.1 93163 352 Daurian ground squirrel Glires Spermophilus dauricus HLspeDau1 GCA_002406435.1_ASM240643v1 99837 353 woodchuck Glires Marmota monax HLmarMon1 GCA_901343595.1_MONAX5 9995 354 woodchuck Glires Marmota monax HLmarMon2 GCA_014533835.1 9995 355 Arctic ground squirrel Glires Urocitellus parryii HLuroPar1 GCA_003426925.1 9999 356 Gunnison&#39;s prairie dog Glires Cynomys gunnisoni HLcynGun1 GCA_011316645.1 45479 357 thirteen-lined ground squirrel Glires Ictidomys tridecemlineatus Nov. 2011 (Broad/speTri2) 43179 358 Fat dormouse Glires Glis glis HLgliGli1 GCA_004027185.1_GliGli_v1_BIUU 41261 359 springhare Glires Pedetes capensis HLpedCap1 GCA_007922755.1 10023 360 American beaver Glires Castor canadensis DNA zoo Castor canadensis 51338 361 woodland dormouse Glires Graphiurus murinus HLgraMur1 GCA_004027655.1_GraMur_v1_BIUU 51346 362 Mountain hare Glires Lepus timidus HLlepTim1 GCA_009760805.1 62621 363 snowshoe hare Glires Lepus americanus HLlepAme1 GCA_004026855.1_LepAme_v1_BIUU 48086 364 European rabbit Glires Oryctolagus cuniculus cuniculus HLoryCunCun4 GCA_013371645.1 568996 365 rabbit Glires Oryctolagus cuniculus Apr. 2009 (Broad/oryCun2) 9986 366 rabbit Glires Oryctolagus cuniculus HLoryCun3 GCA_009806435.1 9986 367 brush rabbit Glires Sylvilagus bachmani DNA zoo Sylvilagus bachmani 365149 368 crested porcupine Glires Hystrix cristata HLhysCri1 GCA_004026905.1_HysCri_v1_BIUU 10137 369 North American porcupine Glires Erethizon dorsatum HLereDor1 GCA_006547115.1 34844 370 Brazilian porcupine Glires Coendou prehensilis DNA zoo Coendou prehensilis 187985 371 hazel dormouse Glires Muscardinus avellanarius HLmusAve1 GCA_004027005.1_MusAve_v1_BIUU 39082 372 naked mole-rat Glires Heterocephalus glaber Jan. 2012 (Broad HetGla_female_1.0/hetGla2) 10181 373 Damara mole-rat Glires Fukomys damarensis HLfukDam2 GCA_012274545.1 885580 374 Upper Galilee mountains blind mole rat Glires Nannospalax galili Jun 2014 (S.galili_v1.0/nanGal1) 1026970 375 long-tailed chinchilla Glires Chinchilla lanigera May 2012 (ChiLan1.0/chiLan1) 34839 376 punctate agouti Glires Dasyprocta punctata HLdasPun1 GCA_004363535.1_DasPun_v1_BIUU 34846 377 northern gundi Glires Ctenodactylus gundi HLcteGun1 GCA_004027205.1_CteGun_v1_BIUU 10166 378 Gobi jerboa Glires Allactaga bullata HLallBul1 GCA_004027895.1_AllBul_v1_BIUU 1041416 379 Stephens&#39;s kangaroo rat Glires Dipodomys stephensi HLdipSte1 GCA_004024685.1_DipSte_v1_BIUU 323379 380 Ord&#39;s kangaroo rat Glires Dipodomys ordii Dec. 2014 (Dord_2.0/dipOrd2) 10020 381 hoary bamboo rat Glires Rhizomys pruinosus HLrhiPru1 GCA_009823505.1 53275 382 pacarana Glires Dinomys branickii HLdinBra1 GCA_004027595.1_DinBra_v1_BIUU 108858 383 lesser Egyptian jerboa Glires Jaculus jaculus May 2012 (JacJac1.0/jacJac1) 51337 384 meadow jumping mouse Glires Zapus hudsonius HLzapHud1 GCA_004024765.1_ZapHud_v1_BIUU 160400 385 Patagonian cavy Glires Dolichotis patagonum HLdolPat1 GCA_004027295.1_DolPat_v1_BIUU 29091 386 Pacific pocket mouse Glires Perognathus longimembris pacificus HLperLonPac1 GCA_004363475.1_PerLonPac_v1_BIUU 214514 387 capybara Glires Hydrochoerus hydrochaeris HLhydHyd1 GCA_004027455.1_HydHyd_v1_BIUU 10149 388 American pika Glires Ochotona princeps May 2012 (OchPri3.0/ochPri3) 9978 389 Brazilian guinea pig Glires Cavia aperea Jan. 2014 (CavAp1.0/cavApe1) 37548 390 dassie-rat Glires Petromus typicus HLpetTyp1 GCA_004026965.1_PetTyp_v1_BIUU 10183 391 Montane guinea pig Glires Cavia tschudii HLcavTsc1 GCA_004027695.1_CavTsc_v1_BIUU 143287 392 domestic guinea pig Glires Cavia porcellus Feb. 2008 (Broad/cavPor3) 10141 393 Greater cane rat Glires Thryonomys swinderianus HLthrSwi1 GCA_004025085.1_ThrSwi_v1_BIUU 10169 394 degu Glires Octodon degus Apr 2012 (OctDeg1.0/octDeg1) 10160 395 Gambian giant pouched rat Glires Cricetomys gambianus HLcriGam1 GCA_004027575.1_CriGam_v1_BIUU 10085 396 desert woodrat Glires Neotoma lepida HLneoLep1 GCA_001675575.1 56216 397 social tuco-tuco Glires Ctenomys sociabilis HLcteSoc1 GCA_004027165.1_CteSoc_v1_BIUU 43321 398 nutria Glires Myocastor coypus HLmyoCoy1 GCA_004027025.1_MyoCoy_v1_BIUU 10157 399 northern rock mouse Glires Peromyscus nasutus DNA zoo Peromyscus nasutus 97212 400 Chinese hamster Glires Cricetulus griseus HLcriGri3 GCA_003668045.1 10029 401 Hesperomys crinitus Glires Peromyscus crinitus DNA zoo Peromyscus crinitus 144753 402 muskrat Glires Ondatra zibethicus HLondZib1 GCA_004026605.1_OndZib_v1_BIUU 10060 403 Peromyscus californicus subsp. insignis Glires Peromyscus californicus insignis HLperCal2 GCA_007827085.2 564181 404 cactus mouse Glires Peromyscus eremicus HLperEre1 GCA_902702925.1 42410 405 southern grasshopper mouse Glires Onychomys torridus HLonyTor1 GCA_903995425.1 38674 406 golden hamster Glires Mesocricetus auratus Mar 2013 (MesAur1.0/mesAur1) 10036 407 white-footed mouse Glires Peromyscus leucopus HLperLeu1 GCA_004664715.1 10041 408 Northern mole vole Glires Ellobius talpinus HLellTal1 GCA_001685095.1_ETalpinus_0.1 329620 409 oldfield mouse Glires Peromyscus polionotus subgriseus HLperPol1 GCA_003704135.2 369710 410 prairie deer mouse Glires Peromyscus maniculatus bairdii HLperManBai2 GCA_003704035.1 230844 411 hispid cotton rat Glires Sigmodon hispidus HLsigHis1 GCA_004025045.1_SigHis_v1_BIUU 42415 412 Transcaucasian mole vole Glires Ellobius lutescens HLellLut1 GCA_001685075.1_ASM168507v1 39086 413 Bank vole Glires Myodes glareolus HLmyoGla2 GCA_902806735.1 447135 414 Eurasian water vole Glires Arvicola amphibius HLarvAmp1 GCA_903992535.1 1047088 415 fat sand rat Glires Psammomys obesus HLpsaObe1 GCA_002215935.2 48139 416 golden spiny mouse Glires Acomys russatus HLacoRus1 GCA_903995435.1 60746 417 African woodland thicket rat Glires Grammomys surdaster HLgraSur1 GCA_004785775.1 491861 418 African grass rat Glires Arvicanthis niloticus HLarvNil1 GCA_011762505.1_mArvNil1.pat.X 61156 419 root vole Glires Microtus oeconomus HLmicOec1 GCA_007455595.1 64717 420 short-tailed field vole Glires Microtus agrestis HLmicAgr2 GCA_902806775.1 29092 421 reed vole Glires Microtus fortis HLmicFor1 GCA_014885135.1 100897 422 Egyptian spiny mouse Glires Acomys cahirinus HLacoCah1 GCA_004027535.1_AcoCah_v1_BIUU 10068 423 Common vole Glires Microtus arvalis HLmicArv1 GCA_007455615.1 47230 424 prairie vole Glires Microtus ochrogaster Oct. 2012 (MicOch1.0/micOch1) 79684 425 great gerbil Glires Rhombomys opimus HLrhoOpi1 GCA_010120015.1 186474 426 southern multimammate mouse Glires Mastomys coucha HLmasCou1 GCA_008632895.1 35658 427 Mongolian gerbil Glires Meriones unguiculatus HLmerUng1 GCA_002204375.1 10047 428 black rat Glires Rattus rattus HLratRat7 GCA_011064425.1 10117 429 Norway rat Glires Rattus norvegicus HLratNor7 GCA_015227675.1 10116 430 Norway rat Glires Rattus norvegicus Jul. 2014 (RGSC 6.0/rn6) 10116 431 shrew mouse Glires Mus pahari HLmusPah1 GCA_900095145.2 10093 432 Ryukyu mouse Glires Mus caroli HLmusCar1 GCA_900094665.2_CAROLI_EIJ_v1.1 10089 433 steppe mouse Glires Mus spicilegus HLmusSpi1 GCA_003336285.1 10103 434 house mouse Glires Mus musculus Jun. 2020 (GRCm39/mm39) 10090 435 house mouse Glires Mus musculus Dec. 2011 (GRCm38/mm10) 10090 436 western wild mouse Glires Mus spretus HLmusSpr1 GCA_001624865.1_SPRET_EiJ_v1 10096 437 European woodmouse Glires Apodemus sylvaticus HLapoSyl1 GCA_001305905.1 10129 438 dugong Afrotheria Dugong dugon HLdugDug1 GCA_015147995.1 29137 439 Florida manatee Afrotheria Trichechus manatus latirostris Oct. 2011 (Broad v1.0/triMan1) 127582 440 Asiatic elephant Afrotheria Elephas maximus DNA zoo Elephas maximus 9783 441 African savanna elephant Afrotheria Loxodonta africana HLloxAfr4/ftp://ftp.broadinstitute.org/pub/assemblies/mammals/elephant/loxAfr4//none 9785 442 aardvark Afrotheria Orycteropus afer afer May 2012 (OryAfe1.0/oryAfe1) 1230840 443 Steller&#39;s sea cow Afrotheria Hydrodamalis gigas HLhydGig1 GCA_013391785.1 63631 444 Cape golden mole Afrotheria Chrysochloris asiatica Aug 2012 (ChrAsi1.0/chrAsi1) 185453 445 yellow-spotted hyrax Afrotheria Heterohyrax brucei HLhetBru1 GCA_004026845.1_HetBruBak_v1_BIUU 77598 446 Cape rock hyrax Afrotheria Procavia capensis HLproCap3 GCA_004026925.2 9813 447 Cape elephant shrew Afrotheria Elephantulus edwardii Aug 2012 (EleEdw1.0/eleEdw1) 28737 448 small Madagascar hedgehog Afrotheria Echinops telfairi Nov. 2012 (Broad/echTel2) 9371 449 Talazac&#39;s shrew tenrec Afrotheria Microgale talazaci HLmicTal1 GCA_004026705.1_MicTal_v1_BIUU 176115 450 common wombat Metatheria Vombatus ursinus HLvomUrs1 GCA_900497805.2 29139 451 koala Metatheria Phascolarctos cinereus HLphaCin1 GCA_002099425.1 38626 452 Agile Gracile Mouse Opossum Metatheria Gracilinanus agilis HLgraAgi1 GCA_016433145.1 191870 453 common brushtail Metatheria Trichosurus vulpecula HLtriVul1 GCA_011100635.1_mTriVul1.pri 9337 454 North American opossum Metatheria Didelphis virginiana DNA zoo Didelphis virginiana 9267 455 ground cuscus Metatheria Phalanger gymnotis DNA zoo Phalanger gymnotis 65615 456 gray short-tailed opossum Metatheria Monodelphis domestica Oct. 2006 (Broad/monDom5) 13616 457 Leadbeater&#39;s possum Metatheria Gymnobelideus leadbeateri HLgymLea1 GCA_011680675.1 38618 458 Tasmanian wolf Metatheria Thylacinus cynocephalus HLthyCyn1 GCA_007646695.1 9275 459 coppery ringtail possum Metatheria Pseudochirops cupreus DNA zoo Pseudochirops cupreus 37702 460 eastern gray kangaroo Metatheria Macropus giganteus DNA zoo Macropus giganteus 9317 461 golden ringtail possum Metatheria Pseudochirops corinnae DNA zoo Pseudochirops corinnae 65629 462 western gray kangaroo Metatheria Macropus fuliginosus DNA zoo Macropus fuliginosus 9316 463 tammar wallaby Metatheria Macropus eugenii DNA zoo Macropus eugenii 9315 464 red kangaroo Metatheria Osphranter rufus DNA zoo Osphranter rufus 9321 465 Western ringtail oppossum Metatheria Pseudocheirus occidentalis DNA zoo Pseudocheirus occidentalis 656515 466 tammar wallaby Metatheria Macropus eugenii Sep. 2009 (TWGS Meug_1.1/macEug2) 9315 467 yellow-footed antechinus Metatheria Antechinus flavipes HLantFla1 GCA_016432865.1_AdamAnt 38775 468 Tasmanian devil Metatheria Sarcophilus harrisii HLsarHar2 GCA_902635505.1 9305 469 platypus Monotremata Ornithorhynchus anatinus HLornAna3 GCA_004115215.1 9258 470 Australian echidna Monotremata Tachyglossus aculeatus HLtacAcu1 GCA_015852505.1 9261 Table 1. Genome assemblies included in the 470-way Conservation track. Display Conventions and Configuration In full and pack display modes, conservation scores are displayed as a wiggle track (histogram) in which the height reflects the size of the score. The conservation wiggles can be configured in a variety of ways to highlight different aspects of the displayed information. Click the Graph configuration help link for an explanation of the configuration options. Pairwise alignments of each species to the human genome are displayed below the conservation histogram as a grayscale density plot (in pack mode) or as a wiggle (in full mode) that indicates alignment quality. In dense display mode, conservation is shown in grayscale using darker values to indicate higher levels of overall conservation as scored by phastCons. Checkboxes on the track configuration page allow selection of the species to include in the pairwise display. Note that excluding species from the pairwise display does not alter the the conservation score display. To view detailed information about the alignments at a specific position, zoom the display in to 30,000 or fewer bases, then click on the alignment. Gap Annotation The Display chains between alignments configuration option enables display of gaps between alignment blocks in the pairwise alignments in a manner similar to the Chain track display. Missing sequence in any assembly is highlighted in the track display by regions of yellow when zoomed out and by Ns when displayed at base level. The following conventions are used: Single line: No bases in the aligned species. Possibly due to a lineage-specific insertion between the aligned blocks in the human genome or a lineage-specific deletion between the aligned blocks in the aligning species. Double line: Aligning species has one or more unalignable bases in the gap region. Possibly due to excessive evolutionary distance between species or independent indels in the region between the aligned blocks in both species. Pale yellow coloring: Aligning species has Ns in the gap region. Reflects uncertainty in the relationship between the DNA of both species, due to lack of sequence in relevant portions of the aligning species. Genomic Breaks Discontinuities in the genomic context (chromosome, scaffold or region) of the aligned DNA in the aligning species are shown as follows: Vertical blue bar: Represents a discontinuity that persists indefinitely on either side, e.g. a large region of DNA on either side of the bar comes from a different chromosome in the aligned species due to a large scale rearrangement. Green square brackets: Enclose shorter alignments consisting of DNA from one genomic context in the aligned species nested inside a larger chain of alignments from a different genomic context. The alignment within the brackets may represent a short misalignment, a lineage-specific insertion of a transposon in the human genome that aligns to a paralogous copy somewhere else in the aligned species, or other similar occurrence. Base Level When zoomed-in to the base-level display, the track shows the base composition of each alignment. The numbers and symbols on the Gaps line indicate the lengths of gaps in the human sequence at those alignment positions relative to the longest non-human sequence. If there is sufficient space in the display, the size of the gap is shown. If the space is insufficient and the gap size is a multiple of 3, a &quot;*&quot; is displayed; other gap sizes are indicated by &quot;+&quot;. Codon translation is available in base-level display mode if the displayed region is identified as a coding segment. To display this annotation, select the species for translation from the pull-down menu in the Codon Translation configuration section at the top of the page. Then, select one of the following modes: No codon translation: The gene annotation is not used; the bases are displayed without translation. Use default species reading frames for translation: The annotations from the genome displayed in the Default species to establish reading frame pull-down menu are used to translate all the aligned species present in the alignment. Use reading frames for species if available, otherwise no translation: Codon translation is performed only for those species where the region is annotated as protein coding. Use reading frames for species if available, otherwise use default species: Codon translation is done on those species that are annotated as being protein coding over the aligned region using species-specific annotation; the remaining species are translated using the default species annotation. Codon translation uses the following gene tracks as the basis for translation: Gene TrackSpecies RefSeq Genesaardvark, American pika, Amur tiger, Angolan colobus, big brown bat, black flying fox, black snub-nosed monkey, Bolivian squirrel monkey, Brandt's bat, Cape elephant shrew, Cape golden mole, cattle, chimpanzee, Chinese tree shrew, Coquerel's sifaka, degu, dog, domestic cat, domestic guinea pig, drill, European shrew, Florida manatee, golden hamster, gray mouse lemur, green monkey, Hawaiian monk seal, horse, house mouse, house mouse, human, killer whale, lesser Egyptian jerboa, little brown bat, long-tailed chinchilla, Ma's night monkey, minke whale, naked mole-rat, nine-banded armadillo, Northern sea otter, Norway rat, Ord's kangaroo rat, Pacific walrus, Panamanian white-faced capuchin, Philippine tarsier, pig, pig-tailed macaque, polar bear, prairie vole, Przewalski's horse, pygmy chimpanzee, rabbit, Rhesus monkey, small Madagascar hedgehog, small-eared galago, sooty mangabey, southern white rhinoceros, star-nosed mole, Sumatran orangutan, thirteen-lined ground squirrel, Upper Galilee mountains blind mole rat, Vespertilio Davidii, Weddell seal, western European hedgehog, western lowland gorilla, Yangtze River dolphin Ensembl GenesBos bison bison, Brazilian guinea pig, dog, gray short-tailed opossum, northern tree shrew Xeno RefGenealpaca, black lemur, Chinese pangolin, common bottlenose dolphin, proboscis monkey, Sclater's lemur, Southern sea otter, tammar wallaby no annotationAfrican buffalo, African grass rat, African hunting dog, African hunting dog, African savanna elephant, African woodland thicket rat, Agile Gracile Mouse Opossum, Allen's swamp monkey, Alpine ibex, Alpine marmot, alpine musk deer, American beaver, American black bear, American black bear, American mink, Amur leopard cat, antarctic fur seal, Antarctic minke whale, Antillean ghost-faced bat, aoudad, Arabian camel, Arctic fox, Arctic ground squirrel, argali, Asian black bear, Asian palm civet, Asiatic elephant, Asiatic mouflon, Asiatic tapir, Asiatic tapir, ass, Australian echidna, aye-aye, babakoto, Bactrian camel, banded mongoose, Bank vole, bearded seal, beluga whale, bighorn sheep, bighorn sheep, black muntjac, black rat, black rhinoceros, black-footed cat, black-handed spider monkey, Blue whale, Bohar reedbuck, Bolivian squirrel monkey, Bolivian titi, Bonin flying fox, boutu, bowhead whale, Brazilian free-tailed bat, Brazilian porcupine, Brazilian tapir, brindled gnu, brown lemur, brush rabbit, bush duiker, bushbuck, Cacomistle, cactus mouse, California big-eared bat, California sea lion, Canada lynx, Cantor's roundleaf bat, Cape rock hyrax, capybara, Central European red deer, Chacoan peccary, cheetah, Chinese forest musk deer, Chinese hamster, Chinese pangolin, Chinese rufous horseshoe bat, Chinese water deer, chiru, Clouded leopard, Cobus hunteri, common bottlenose dolphin, common bottlenose dolphin, common brushtail, common pipistrelle, common pipistrelle, common vampire bat, Common vole, common wombat, coppery ringtail possum, Coquerel's mouse lemur, crab-eating macaque, crested porcupine, Cuvier's beaked whale, Damara mole-rat, dassie-rat, Daurian ground squirrel, De Brazza's monkey, desert woodrat, dingo, domestic ferret, domestic yak, donkey, dugong, dwarf mongoose, eastern gray kangaroo, eastern mole, Eastern roe deer, Egyptian rousette, Egyptian spiny mouse, Equus burchelli boehmi, ermine, Eurasian elk, Eurasian red squirrel, Eurasian river otter, Eurasian water vole, European polecat, European rabbit, European woodmouse, evening bat, Fat dormouse, fat sand rat, Fin whale, fossa, franciscana, Francois's langur, Gambian giant pouched rat, gaur, gayal, gelada, gemsbok, gerenuk, giant anteater, giant otter, giant otter, giant panda, giraffe, giraffe, goat, Gobi jerboa, golden ringtail possum, golden snub-nosed monkey, golden spiny mouse, gracile shrew mole, Grant's gazelle, gray seal, gray squirrel, great gerbil, great roundleaf bat, greater bamboo lemur, greater bulldog bat, Greater cane rat, greater horseshoe bat, greater Indian rhinoceros, greater kudu, greater mouse-eared bat, grey whale, grizzly bear, ground cuscus, guanaco, Gunnison's prairie dog, Hanuman langur, harbor porpoise, harbor porpoise, harbor seal, Harvey's duiker, hazel dormouse, Hesperomys crinitus, Himalayan marmot, hippopotamus, hippopotamus, Hispaniolan solenodon, hispid cotton rat, hoary bamboo rat, hoary bat, Hoffmann's two-fingered sloth, Hog deer, hog-nosed bat, Honduran yellow-shouldered bat, humpback whale, Iberian mole, impala, Indian false vampire, Indian flying fox, Indo-pacific bottlenose dolphin, Indo-pacific bottlenose dolphin, Indo-pacific humpbacked dolphin, Indus River dolphin, jaguar, jaguar, jaguarundi, Jamaican fruit-eating bat, Jamaican fruit-eating bat, Japanese macaque, Java mouse-deer, kinkajou, Kirk's dik-dik, klipspringer, koala, Kuhl's pipistrelle, Lama pacos huacaya, large flying fox, Leadbeater's possum, lechwe, leopard, Leschenault's rousette, lesser dawn bat, Lesser dwarf lemur, lesser kudu, Lesser long-nosed bat, lesser mouse-deer, lesser panda, lesser short-nosed fruit bat, lion, little brown bat, llama, llama, long-finned pilot whale, long-tongued fruit bat, Madagascan rousette, Malagasy flying fox, Malagasy straw-colored fruit bat, Malayan pangolin, Malayan pangolin, mandrill, mantled howler monkey, Masai giraffe, Maxwell's duiker, meadow jumping mouse, meerkat, meerkat, melon-headed whale, Miniopterus schreibersii natalensis, Mona monkey, Mongolian gerbil, mongoose lemur, Montane guinea pig, mountain beaver, mountain goat, Mountain hare, mouse lemur, mule deer, muntjak, Murina feae, muskrat, narwhal, Nilgiri tahr, North American badger, North American opossum, North American porcupine, North Atlantic right whale, North Pacific right whale, Northern American river otter, Northern elephant seal, northern fur seal, Northern giant mouse lemur, northern gundi, Northern long-eared myotis, Northern mole vole, northern rock mouse, Northern rufous mouse lemur, northern white rhinoceros, northern white-cheeked gibbon, Norway rat, nutria, okapi, oldfield mouse, olive baboon, pacarana, Pacific pocket mouse, Pacific white-sided dolphin, pale spear-nosed bat, Pallas's mastiff bat, pallid bat, Parnell's mustached bat, Patagonian cavy, Pere David's deer, Peromyscus californicus subsp. insignis, platypus, porcupine caribou, prairie deer mouse, pronghorn, Przewalski's gazelle, puma, punctate agouti, pygmy Bryde's whale, pygmy marmoset, pygmy sperm whale, rabbit, raccoon, ratel, red bat, red fox, red guenon, red kangaroo, Red shanked douc langur, reed vole, Reeves' muntjac, reindeer, Ring-tailed lemur, roan antelope, root vole, royal antelope, Ryukyu mouse, sable, sable antelope, saiga antelope, Schizostoma hirsutum, Schreibers' long-fingered bat, scimitar-horned oryx, Sclater's lemur, Seba's short-tailed bat, sheep, short-tailed field vole, shrew mouse, Siberian ibex, Siberian musk deer, silvery gibbon, slow loris, snow sheep, snowshoe hare, social tuco-tuco, South African ground squirrel, Southern elephant seal, southern grasshopper mouse, southern multimammate mouse, southern tamandua, Southern three-banded armadillo, southern two-toed sloth, southern two-toed sloth, Sowerby's beaked whale, Spanish lynx, sperm whale, sperm whale, spotted hyena, springbok, springhare, steenbok, Steller sea lion, Steller's sea cow, Stephens's kangaroo rat, steppe mouse, straw-colored fruit bat, stripe-headed round-eared bat, striped hyena, Sumatran rhinoceros, Sunda flying lemur, suni, tailed tailless bat, Talazac's shrew tenrec, tamarin, tammar wallaby, Tasmanian devil, Tasmanian wolf, Thomson's gazelle, topi, Transcaucasian mole vole, Tree pangolin, Tree pangolin, tufted capuchin, Ugandan red Colobus, Vancouver Island marmot, vaquita, Vicugna mensalis, walrus, water buffalo, waterbuck, western gray kangaroo, Western ringtail oppossum, western spotted skunk, western wild mouse, white-faced saki, white-footed mouse, white-fronted capuchin, white-lipped deer, White-nosed coati, white-tailed deer, white-tailed deer, white-tailed deer, white-tufted-ear marmoset, Wild Bactrian camel, wild goat, wild yak, wolverine, woodchuck, woodchuck, woodland dormouse, Yangtze finless porpoise, Yarkand deer, yellow-bellied marmot, yellow-footed antechinus, yellow-spotted hyrax, zebu cattle, Table 2. Gene tracks used for codon translation. Methods Pairwise alignments with the human genome were generated for each species using lastz from repeat-masked genomic sequence. Pairwise alignments were then linked into chains using a dynamic programming algorithm that finds maximally scoring chains of gapless subsections of the alignments organized in a kd-tree. The scoring matrix and parameters for pairwise alignment and chaining were tuned for each species based on phylogenetic distance from the reference. High-scoring chains were then placed along the genome, with gaps filled by lower-scoring chains, to produce an alignment net. Phylogenetic Tree Model The phyloP are phylogenetic methods that rely on a tree model containing the tree topology, branch lengths representing evolutionary distance at neutrally evolving sites, the background distribution of nucleotides, and a substitution rate matrix. The all-species tree model for this track was generated using the phyloFit program from the PHAST package (REV model, EM algorithm, medium precision) using multiple alignments of 4-fold degenerate sites extracted from the 470-way alignment (msa_view). The 4d sites were derived from the RefSeq (Reviewed+Coding) gene set, filtered to select single-coverage long transcripts. This same tree model was used in the phyloP calculations; however, the background frequencies were modified to maintain reversibility. The resulting tree model: all species. PhyloP Conservation The phyloP program supports several different methods for computing p-values of conservation or acceleration, for individual nucleotides or larger elements ( http://compgen.cshl.edu/phast/). Here it was used to produce separate scores at each base (--wig-scores option), considering all branches of the phylogeny rather than a particular subtree or lineage (i.e., the --subtree option was not used). The scores were computed by performing a likelihood ratio test at each alignment column (--method LRT), and scores for both conservation and acceleration were produced (--mode CONACC). Credits This track was created using the following programs: Alignment tools: lastz (formerly blastz) and multiz by Minmei Hou, Scott Schwartz and Webb Miller of the Penn State Bioinformatics Group Chaining and Netting: axtChain, chainNet by Jim Kent at UCSC Conservation scoring: phastCons, phyloP, phyloFit, tree_doctor, msa_view and other programs in PHAST by Adam Siepel at Cold Spring Harbor Laboratory (original development done at the Haussler lab at UCSC). MAF Annotation tools: mafAddIRows by Brian Raney, UCSC; mafAddQRows by Richard Burhans, Penn State; genePredToMafFrames by Mark Diekhans, UCSC Tree image generator: phyloPng by Galt Barber, UCSC Conservation track display: Kate Rosenbloom, Hiram Clawson (wiggle display), and Brian Raney (gap annotation and codon framing) at UCSC References Harris RS. Improved pairwise alignment of genomic DNA. Ph.D. Thesis. Pennsylvania State University, USA. 2007. PhyloP: Cooper GM, Stone EA, Asimenos G, NISC Comparative Sequencing Program., Green ED, Batzoglou S, Sidow A. Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. 2005 Jul;15(7):901-13. PMID: 15965027; PMC: PMC1172034; DOI: 10.1101/gr.3577405 Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 2010 Jan;20(1):110-21. PMID: 19858363; PMC: PMC2798823 Siepel A, Haussler D. Phylogenetic Hidden Markov Models. In: Nielsen R, editor. Statistical Methods in Molecular Evolution. New York: Springer; 2005. pp. 325-351. DOI: 10.1007/0-387-27733-1_12 Siepel A, Pollard KS, and Haussler D. New methods for detecting lineage-specific selection. In Proceedings of the 10th International Conference on Research in Computational Molecular Biology (RECOMB 2006), pp. 190-205. DOI: 10.1007/11732990_17 
cons470wayViewalign Multiz 470-way Multiz Alignment & Conservation (470 mammals) Comparative Genomics 
multiz470way Multiz 470-way Multiz Alignments of 470 mammals Comparative Genomics 
cons470wayViewphastcons Element Conservation (phastCons) Multiz Alignment & Conservation (470 mammals) Comparative Genomics 
phastCons470way Cons 470 Mammals 470 mammals conservation by PhastCons Comparative Genomics 
cons470wayViewelements Conserved Elements Multiz Alignment & Conservation (470 mammals) Comparative Genomics 
phastConsElements470way 470 Mamm. El 470 mammals Conserved Elements Comparative Genomics 
cons470wayViewphyloP Basewise Conservation (phyloP) Multiz Alignment & Conservation (470 mammals) Comparative Genomics 
phyloP470wayBW 470 phyloP 470 mammals Basewise Conservation by PhyloP Comparative Genomics 
muscleDeMicheliCellType Muscle Cells Muscle RNA binned by cell type from De Micheli et al 2020 Single Cell RNA-seq Description This track displays data from A reference single-cell transcriptomic atlas of human skeletal muscle tissue reveals bifurcated muscle stem cell populations. Muscle tissue was analyzed using single-cell RNA-sequencing (scRNA-seq) and subsequent clustering distinguished 16 muscle-resident cell types based on their identified marker genes found in De Micheli et al., 2020. Muscle samples were from surgically discarded tissue taken from a wide variety of anatomical sites. This track collection contains two bar chart tracks of RNA expression in the human muscle where cells are grouped by cell type (Muscle Cells) or biosample (Muscle Sample). The default track displayed is Muscle Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification stem cell adipose fibroblast immune muscle endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Muscle Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Note that the Muscle Sample subtrack is colored based on colors provided from Figure 1 from De Micheli et al., 2020. Relevant Figures From De Micheli et al. 2020 Muscle tissue cell type populations. De Micheli et al. Skelet Muscle. 2020. / CC BY 4.0 Method Muscle samples were taken from 10 healthy donors of ages ranging from 41-81 years old from different sections of the face (F), trunk (T), and leg (L). Excessive fat and connective tissue were removed from the muscle samples prior to enzymatic dissociation. Next, libraries were prepared using the 10x Genomics 3' v2 or v3 library kit and sequenced on the Illumina NextSeq 500. This resulted in libraries with 200-250 million reads which were processed using Cell Ranger version 3.1. In total, over 22,000 RNA transcriptomic profiles were generated from all of the samples after quality control filtering. The single cell transcriptomes from all 10 datasets were integrated using a scRNA-seq integration method called Scanorama as described in the reference below. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Andrea De Micheli of the Cosgrove Laboratory at Cornell University and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed Luis Nassar. The UCSC work was paid for by the Chan Zuckerberg Initiative. References De Micheli AJ, Spector JA, Elemento O, Cosgrove BD. A reference single-cell transcriptomic atlas of human skeletal muscle tissue reveals bifurcated muscle stem cell populations. Skelet Muscle. 2020 Jul 6;10(1):19. PMID: 32624006; PMC: PMC7336639 
muscleDeMicheli Muscle De Micheli Muscle single cell data from De Micheli et al 2020 Single Cell RNA-seq Description This track displays data from A reference single-cell transcriptomic atlas of human skeletal muscle tissue reveals bifurcated muscle stem cell populations. Muscle tissue was analyzed using single-cell RNA-sequencing (scRNA-seq) and subsequent clustering distinguished 16 muscle-resident cell types based on their identified marker genes found in De Micheli et al., 2020. Muscle samples were from surgically discarded tissue taken from a wide variety of anatomical sites. This track collection contains two bar chart tracks of RNA expression in the human muscle where cells are grouped by cell type (Muscle Cells) or biosample (Muscle Sample). The default track displayed is Muscle Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification stem cell adipose fibroblast immune muscle endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Muscle Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Note that the Muscle Sample subtrack is colored based on colors provided from Figure 1 from De Micheli et al., 2020. Method Muscle samples were taken from 10 healthy donors of ages ranging from 41-81 years old from different sections of the face (F), trunk (T), and leg (L). Excessive fat and connective tissue were removed from the muscle samples prior to enzymatic dissociation. Next, libraries were prepared using the 10x Genomics 3' v2 or v3 library kit and sequenced on the Illumina NextSeq 500. This resulted in libraries with 200-250 million reads which were processed using Cell Ranger version 3.1. In total, over 22,000 RNA transcriptomic profiles were generated from all of the samples after quality control filtering. The single cell transcriptomes from all 10 datasets were integrated using a scRNA-seq integration method called Scanorama as described in the reference below. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Andrea De Micheli of the Cosgrove Laboratory at Cornell University and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed Luis Nassar. The UCSC work was paid for by the Chan Zuckerberg Initiative. References De Micheli AJ, Spector JA, Elemento O, Cosgrove BD. A reference single-cell transcriptomic atlas of human skeletal muscle tissue reveals bifurcated muscle stem cell populations. Skelet Muscle. 2020 Jul 6;10(1):19. PMID: 32624006; PMC: PMC7336639 
muscleDeMicheliSample Muscle Sample Muscle RNA binned by biosample from De Micheli et al 2020 Single Cell RNA-seq Description This track displays data from A reference single-cell transcriptomic atlas of human skeletal muscle tissue reveals bifurcated muscle stem cell populations. Muscle tissue was analyzed using single-cell RNA-sequencing (scRNA-seq) and subsequent clustering distinguished 16 muscle-resident cell types based on their identified marker genes found in De Micheli et al., 2020. Muscle samples were from surgically discarded tissue taken from a wide variety of anatomical sites. This track collection contains two bar chart tracks of RNA expression in the human muscle where cells are grouped by cell type (Muscle Cells) or biosample (Muscle Sample). The default track displayed is Muscle Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification stem cell adipose fibroblast immune muscle endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Muscle Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Note that the Muscle Sample subtrack is colored based on colors provided from Figure 1 from De Micheli et al., 2020. Relevant Figures From De Micheli et al. 2020 Details on sex, age, anatomical site, and single-cell transcriptomes after quality control (QC) filtering from 10 donors. Colors represent areas from which samples were taken from. De Micheli et al. Skelet Muscle. 2020. / CC BY 4.0 Cell type proportions across the 10 donors and grouped by leg (donors 02, 07, 08), trunk (donors 01, 05, 06, 09, 10), and face (donors 03, 04). De Micheli et al. Skelet Muscle. 2020. / CC BY 4.0 Method Muscle samples were taken from 10 healthy donors of ages ranging from 41-81 years old from different sections of the face (F), trunk (T), and leg (L). Excessive fat and connective tissue were removed from the muscle samples prior to enzymatic dissociation. Next, libraries were prepared using the 10x Genomics 3' v2 or v3 library kit and sequenced on the Illumina NextSeq 500. This resulted in libraries with 200-250 million reads which were processed using Cell Ranger version 3.1. In total, over 22,000 RNA transcriptomic profiles were generated from all of the samples after quality control filtering. The single cell transcriptomes from all 10 datasets were integrated using a scRNA-seq integration method called Scanorama as described in the reference below. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Andrea De Micheli of the Cosgrove Laboratory at Cornell University and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed Luis Nassar. The UCSC work was paid for by the Chan Zuckerberg Initiative. References De Micheli AJ, Spector JA, Elemento O, Cosgrove BD. A reference single-cell transcriptomic atlas of human skeletal muscle tissue reveals bifurcated muscle stem cell populations. Skelet Muscle. 2020 Jul 6;10(1):19. PMID: 32624006; PMC: PMC7336639 
knownGeneOldV45 Old UCSC Genes Previous Version of UCSC Genes Genes and Gene Predictions Description The Old UCSC Genes track shows genes from the previous version of the UCSC Genes build, which was built with GENCODE v45 models. See the description page for more information on how the new GENCODE v46 track was built. The new release has 278,220 total transcripts, compared with 277,801 in the previous version. The total number of canonical genes has decreased from 70,711 to 70,611. 
oreganno ORegAnno Regulatory elements from ORegAnno Regulation Description This track displays literature-curated regulatory regions, transcription factor binding sites, and regulatory polymorphisms from ORegAnno (Open Regulatory Annotation). For more detailed information on a particular regulatory element, follow the link to ORegAnno from the details page. ORegAnno (Open Regulatory Annotation). --> Display Conventions and Configuration The display may be filtered to show only selected region types, such as: regulatory regions (shown in light blue) regulatory polymorphisms (shown in dark blue) transcription factor binding sites (shown in orange) regulatory haplotypes (shown in red) miRNA binding sites (shown in blue-green) To exclude a region type, uncheck the appropriate box in the list at the top of the Track Settings page. Methods An ORegAnno record describes an experimentally proven and published regulatory region (promoter, enhancer, etc.), transcription factor binding site, or regulatory polymorphism. Each annotation must have the following attributes: A stable ORegAnno identifier. A valid taxonomy ID from the NCBI taxonomy database. A valid PubMed reference. A target gene that is either user-defined, in Entrez Gene or in EnsEMBL. A sequence with at least 40 flanking bases (preferably more) to allow the site to be mapped to any release of an associated genome. At least one piece of specific experimental evidence, including the biological technique used to discover the regulatory sequence. (Currently only the evidence subtypes are supplied with the UCSC track.) A positive, neutral or negative outcome based on the experimental results from the primary reference. (Only records with a positive outcome are currently included in the UCSC track.) The following attributes are optionally included: A transcription factor that is either user-defined, in Entrez Gene or in EnsEMBL. A specific cell type for each piece of experimental evidence, using the eVOC cell type ontology. A specific dataset identifier (e.g. the REDfly dataset) that allows external curators to manage particular annotation sets using ORegAnno's curation tools. A &quot;search space&quot; sequence that specifies the region that was assayed, not just the regulatory sequence. A dbSNP identifier and type of variant (germline, somatic or artificial) for regulatory polymorphisms. Mapping to genome coordinates is performed periodically to current genome builds by BLAST sequence alignment. The information provided in this track represents an abbreviated summary of the details for each ORegAnno record. Please visit the official ORegAnno entry (by clicking on the ORegAnno link on the details page of a specific regulatory element) for complete details such as evidence descriptions, comments, validation score history, etc. Credits ORegAnno core team and principal contacts: Stephen Montgomery, Obi Griffith, and Steven Jones from Canada's Michael Smith Genome Sciences Centre, Vancouver, British Columbia, Canada. The ORegAnno community (please see individual citations for various features): ORegAnno Citation. References Lesurf R, Cotto KC, Wang G, Griffith M, Kasaian K, Jones SJ, Montgomery SB, Griffith OL, Open Regulatory Annotation Consortium.. ORegAnno 3.0: a community-driven resource for curated regulatory annotation. Nucleic Acids Res. 2016 Jan 4;44(D1):D126-32. PMID: 26578589; PMC: PMC4702855 Griffith OL, Montgomery SB, Bernier B, Chu B, Kasaian K, Aerts S, Mahony S, Sleumer MC, Bilenky M, Haeussler M et al. ORegAnno: an open-access community-driven resource for regulatory annotation. Nucleic Acids Res. 2008 Jan;36(Database issue):D107-13. PMID: 18006570; PMC: PMC2239002 Montgomery SB, Griffith OL, Sleumer MC, Bergman CM, Bilenky M, Pleasance ED, Prychyna Y, Zhang X, Jones SJ. ORegAnno: an open access database and curation system for literature-derived promoters, transcription factor binding sites and regulatory variation. Bioinformatics. 2006 Mar 1;22(5):637-40. PMID: 16397004 
orfeomeMrna ORFeome Clones ORFeome Collaboration Gene Clones Genes and Gene Predictions Description This track show alignments of human clones from the ORFeome Collaboration. The goal of the project is to be an &quot;unrestricted source of fully sequence-validated full-ORF human cDNA clones in a format allowing easy transfer of the ORF sequences into virtually any type of expression vector. A major goal is to provide at least one fully-sequenced full-ORF clone for each human, mouse, and zebrafish gene. This track is updated automatically as new clones become available. Display Conventions and Configuration The track follows the display conventions for gene prediction tracks. Methods ORFeome human clones were obtained from GenBank and aligned against the genome using the blat program. When a single clone aligned in multiple places, the alignment having the highest base identity was found. Only alignments having a base identity level within 0.5% of the best and at least 96% base identity with the genomic sequence were kept. Credits and References Visit the ORFeome Collaboration members page for a list of credits and references. 
orphadata Orphanet Orphadata: Aggregated Data From Orphanet Phenotype and Literature Description NOTE: These data are for research purposes only. While the Orphadata data is open to the public, users seeking information about a personal medical or genetic condition are urged to consult with a qualified physician for diagnosis and for answers to personal medical questions. UCSC presents these data for use by qualified professionals, and even such professionals should use caution in interpreting the significance of information found here. No single data point should be taken at face value and such data should always be used in conjunction with as much corroborating data as possible. No treatment protocols should be developed or patient advice given on the basis of these data without careful consideration of all possible sources of information. No attempt to identify individual patients should be undertaken. No one is authorized to attempt to identify patients by any means. The Orphadata: Aggregated data from Orphanet (Orphanet) track shows genomic positions of genes and their association to human disorders, related epidemiological data, and phenotypic annotations. As a consortium of 40 countries throughout the world, Orphanet gathers and improves knowledge regarding rare diseases and maintains the Orphanet rare disease nomenclature (ORPHAcode), essential in improving the visibility of rare diseases in health and research information systems. The data is updated monthly by Orphanet and updated monthly on the UCSC Genome Browser. Display Conventions Mouseover on items shows the gene name, disorder name, modes of inheritance(s) (if available), and age(s) of onset (if available). Tracks can be filtered according to gene-disorder association types, modes of inheritance, and ages of onset. Clicking an item from the browser will return the complete entry, including gene linkouts to Ensembl, OMIM, and HGNC, as well as phenotype information using HPO (human phenotype ontology) terms. For more information on the use of this data, see the Orphadata FAQs. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Data is also freely available through Orphadata datasets. Methods Orphadata files were reformatted at UCSC to the bigBed format. Credits Thank you to the Orphanet and Orphadata team and to Tiana Pereira, Christopher Lee, Daniel Schmelter, and Anna Benet-Pages of the Genome Browser team. References Pavan S, Rommel K, Mateo Marquina ME, H&#246;hn S, Lanneau V, Rath A. Clinical Practice Guidelines for Rare Diseases: The Orphanet Database. PLoS One. 2017;12(1):e0170365. PMID: 28099516; PMC: PMC5242437 Nguengang Wakap S, Lambert DM, Olry A, Rodwell C, Gueydan C, Lanneau V, Murphy D, Le Cam Y, Rath A. Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database. Eur J Hum Genet. 2020 Feb;28(2):165-173. PMID: 31527858; PMC: PMC6974615 
xenoEst Other ESTs Non-Human ESTs from GenBank mRNA and EST Description This track displays translated blat alignments of expressed sequence tags (ESTs) in GenBank from organisms other than human. ESTs are single-read sequences, typically about 500 bases in length, that usually represent fragments of transcribed genes. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. In dense display mode, the items that are more darkly shaded indicate matches of better quality. The strand information (+/-) for this track is in two parts. The first + or - indicates the orientation of the query sequence whose translated protein produced the match. The second + or - indicates the orientation of the matching translated genomic sequence. Because the two orientations of a DNA sequence give different predicted protein sequences, there are four combinations. ++ is not the same as --, nor is +- the same as -+. The description page for this track has a filter that can be used to change the display mode, alter the color, and include/exclude a subset of items within the track. This may be helpful when many items are shown in the track display, especially when only some are relevant to the current task. To use the filter: Type a term in one or more of the text boxes to filter the EST display. For example, to apply the filter to all ESTs expressed in a specific organ, type the name of the organ in the tissue box. To view the list of valid terms for each text box, consult the table in the Table Browser that corresponds to the factor on which you wish to filter. For example, the &quot;tissue&quot; table contains all the types of tissues that can be entered into the tissue text box. Multiple terms may be entered at once, separated by a space. Wildcards may also be used in the filter. If filtering on more than one value, choose the desired combination logic. If &quot;and&quot; is selected, only ESTs that match all filter criteria will be highlighted. If &quot;or&quot; is selected, ESTs that match any one of the filter criteria will be highlighted. Choose the color or display characteristic that should be used to highlight or include/exclude the filtered items. If &quot;exclude&quot; is chosen, the browser will not display ESTs that match the filter criteria. If &quot;include&quot; is selected, the browser will display only those ESTs that match the filter criteria. This track may also be configured to display base labeling, a feature that allows the user to display all bases in the aligning sequence or only those that differ from the genomic sequence. For more information about this option, go to the Base Coloring for Alignment Tracks page. Several types of alignment gap may also be colored; for more information, go to the Alignment Insertion/Deletion Display Options page. Methods To generate this track, the ESTs were aligned against the genome using blat. When a single EST aligned in multiple places, the alignment having the highest base identity was found. Only alignments having a base identity level within 0.5% of the best and at least 96% base identity with the genomic sequence were kept. Credits This track was produced at UCSC from EST sequence data submitted to the international public sequence databases by scientists worldwide. References Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW. GenBank. Nucleic Acids Res. 2013 Jan;41(Database issue):D36-42. PMID: 23193287; PMC: PMC3531190 Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank: update. Nucleic Acids Res. 2004 Jan 1;32(Database issue):D23-6. PMID: 14681350; PMC: PMC308779 Kent WJ. BLAT - the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 
xenoMrna Other mRNAs Non-Human mRNAs from GenBank mRNA and EST Description This track displays translated blat alignments of vertebrate and invertebrate mRNA in GenBank from organisms other than human. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. In dense display mode, the items that are more darkly shaded indicate matches of better quality. The strand information (+/-) for this track is in two parts. The first + indicates the orientation of the query sequence whose translated protein produced the match (here always 5' to 3', hence +). The second + or - indicates the orientation of the matching translated genomic sequence. Because the two orientations of a DNA sequence give different predicted protein sequences, there are four combinations. ++ is not the same as --, nor is +- the same as -+. The description page for this track has a filter that can be used to change the display mode, alter the color, and include/exclude a subset of items within the track. This may be helpful when many items are shown in the track display, especially when only some are relevant to the current task. To use the filter: Type a term in one or more of the text boxes to filter the mRNA display. For example, to apply the filter to all mRNAs expressed in a specific organ, type the name of the organ in the tissue box. To view the list of valid terms for each text box, consult the table in the Table Browser that corresponds to the factor on which you wish to filter. For example, the &quot;tissue&quot; table contains all the types of tissues that can be entered into the tissue text box. Multiple terms may be entered at once, separated by a space. Wildcards may also be used in the filter. If filtering on more than one value, choose the desired combination logic. If &quot;and&quot; is selected, only mRNAs that match all filter criteria will be highlighted. If &quot;or&quot; is selected, mRNAs that match any one of the filter criteria will be highlighted. Choose the color or display characteristic that should be used to highlight or include/exclude the filtered items. If &quot;exclude&quot; is chosen, the browser will not display mRNAs that match the filter criteria. If &quot;include&quot; is selected, the browser will display only those mRNAs that match the filter criteria. This track may also be configured to display codon coloring, a feature that allows the user to quickly compare mRNAs against the genomic sequence. For more information about this option, go to the Codon and Base Coloring for Alignment Tracks page. Several types of alignment gap may also be colored; for more information, go to the Alignment Insertion/Deletion Display Options page. Methods The mRNAs were aligned against the human genome using translated blat. When a single mRNA aligned in multiple places, the alignment having the highest base identity was found. Only those alignments having a base identity level within 1% of the best and at least 25% base identity with the genomic sequence were kept. Credits The mRNA track was produced at UCSC from mRNA sequence data submitted to the international public sequence databases by scientists worldwide. References Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW. GenBank. Nucleic Acids Res. 2013 Jan;41(Database issue):D36-42. PMID: 23193287; PMC: PMC3531190 Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank: update. Nucleic Acids Res. 2004 Jan 1;32(Database issue):D23-6. PMID: 14681350; PMC: PMC308779 Kent WJ. BLAT - the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 
xenoRefGene Other RefSeq Non-Human RefSeq Genes Genes and Gene Predictions Description This track shows known protein-coding and non-protein-coding genes for organisms other than human, taken from the NCBI RNA reference sequences collection (RefSeq). The data underlying this track are updated weekly. Display Conventions and Configuration This track follows the display conventions for gene prediction tracks. The color shading indicates the level of review the RefSeq record has undergone: predicted (light), provisional (medium), reviewed (dark). The item labels and display colors of features within this track can be configured through the controls at the top of the track description page. Label: By default, items are labeled by gene name. Click the appropriate Label option to display the accession name instead of the gene name, show both the gene and accession names, or turn off the label completely. Codon coloring: This track contains an optional codon coloring feature that allows users to quickly validate and compare gene predictions. To display codon colors, select the genomic codons option from the Color track by codons pull-down menu. For more information about this feature, go to the Coloring Gene Predictions and Annotations by Codon page. Hide non-coding genes: By default, both the protein-coding and non-protein-coding genes are displayed. If you wish to see only the coding genes, click this box. Methods The RNAs were aligned against the human genome using blat; those with an alignment of less than 15% were discarded. When a single RNA aligned in multiple places, the alignment having the highest base identity was identified. Only alignments having a base identity level within 0.5% of the best and at least 25% base identity with the genomic sequence were kept. Credits This track was produced at UCSC from RNA sequence data generated by scientists worldwide and curated by the NCBI RefSeq project. References Kent WJ. BLAT--the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 Pruitt KD, Brown GR, Hiatt SM, Thibaud-Nissen F, Astashyn A, Ermolaeva O, Farrell CM, Hart J, Landrum MJ, McGarvey KM et al. RefSeq: an update on mammalian reference sequences. Nucleic Acids Res. 2014 Jan;42(Database issue):D756-63. PMID: 24259432; PMC: PMC3965018 Pruitt KD, Tatusova T, Maglott DR. NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2005 Jan 1;33(Database issue):D501-4. PMID: 15608248; PMC: PMC539979 
hprcChainNet Pairwise Alignments Human Genomes, Chain/Net pairwise alignments, as mapped by the HPRC project Human Pangenome - HPRC Description This track shows regions of the human genome that are alignable to other Homo sapiens genomes. The alignable parts are shown with thick blocks that look like exons. Non-alignable parts between these are shown with thin lines like introns. More description on this display can be found below. Other assemblies included in this track are from the HPRC project. Display Conventions and Configuration Chain Track The chain track shows alignments of the human genome to other Homo sapiens genomes using a gap scoring system that allows longer gaps than traditional affine gap scoring systems. It can also tolerate gaps in both source and target assemblies simultaneously. These &quot;double-sided&quot; gaps can be caused by local inversions and overlapping deletions in both species. The chain track displays boxes joined together by either single or double lines. The boxes represent aligning regions. Single lines indicate gaps that are largely due to a deletion in the query assembly or an insertion in the target assembly. assembly. Double lines represent more complex gaps that involve substantial sequence in both species. This may result from inversions, overlapping deletions, an abundance of local mutation, or an unsequenced gap in one species. In cases where multiple chains align over a particular region of the target genome, the chains with single-lined gaps are often due to processed pseudogenes, while chains with double-lined gaps are more often due to paralogs and unprocessed pseudogenes. In the &quot;pack&quot; and &quot;full&quot; display modes, the individual feature names indicate the chromosome, strand, and location (in thousands) of the match for each matching alignment. By default, the chains to chromosome-based assemblies are colored based on which chromosome they map to in the aligning organism. To turn off the coloring, check the &quot;off&quot; button next to: Color track based on chromosome. To display only the chains of one chromosome in the aligning organism, enter the name of that chromosome (e.g. chr4) in box next to: Filter by chromosome. Methods The bigChain files were obtained from the HPRC S3 bucket (Amazon Web Services). For more information about how the bigChain files were generated, please refer to the HPRC publication below. Credits Thank you to Glenn Hickey for providing the HAL file from the HPRC project. References Liao WW, Asri M, Ebler J, Doerr D, Haukness M, Hickey G, Lu S, Lucas JK, Monlong J, Abel HJ et al. A draft human pangenome reference. Nature. 2023 May;617(7960):312-324. DOI: 10.1038/s41586-023-05896-x; PMID: 37165242; PMC: PMC10172123 Hickey G, Monlong J, Ebler J, Novak AM, Eizenga JM, Gao Y, Human Pangenome Reference Consortium, Marschall T, Li H, Paten B. Pangenome graph construction from genome alignments with Minigraph-Cactus. Nat Biotechnol. 2023 May 10;. DOI: 10.1038/s41587-023-01793-w; PMID: 37165083; PMC: PMC10638906 Armstrong J, Hickey G, Diekhans M, Fiddes IT, Novak AM, Deran A, Fang Q, Xie D, Feng S, Stiller J et al. Progressive Cactus is a multiple-genome aligner for the thousand-genome era. Nature. 2020 Nov;587(7833):246-251. DOI: 10.1038/s41586-020-2871-y; PMID: 33177663; PMC: PMC7673649 Paten B, Earl D, Nguyen N, Diekhans M, Zerbino D, Haussler D. Cactus: Algorithms for genome multiple sequence alignment. Genome Res. 2011 Sep;21(9):1512-28. DOI: 10.1101/gr.123356.111; PMID: 21665927; PMC: PMC3166836 
hprcChainNetViewnet Nets Human Genomes, Chain/Net pairwise alignments, as mapped by the HPRC project Human Pangenome - HPRC 
netHprcGCA_018503285v1 NA18906.pat NA18906.pat NA18906.alt.pat.f1_v2 (May 2021 GCA_018503285.1_NA18906.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018503255v1 NA18906.mat NA18906.mat NA18906.pri.mat.f1_v2 (May 2021 GCA_018503255.1_NA18906.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcHs1 T2T-CHM13v2.0 T2T-CHM13v2.0 T2T-CHM13v2.0 (Jan. 2022 GCF_009914755.1_T2T-CHM13v2.0) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018506955v1 HG00733.pat HG00733.pat HG00733.alt.pat.f1_v2 (May 2021 GCA_018506955.1_HG00733.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018504645v1 HG01109.pat HG01109.pat HG01109.alt.pat.f1_v2 (May 2021 GCA_018504645.1_HG01109.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018504045v1 HG01243.pat HG01243.pat HG01243.alt.pat.f1_v2 (May 2021 GCA_018504045.1_HG01243.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018472725v1 HG01071.pat HG01071.pat HG01071.alt.pat.f1_v2 (May 2021 GCA_018472725.1_HG01071.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018472715v1 HG00735.pat HG00735.pat HG00735.alt.pat.f1_v2 (May 2021 GCA_018472715.1_HG00735.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018471105v1 HG00741.pat HG00741.pat HG00741.alt.pat.f1_v2 (May 2021 GCA_018471105.1_HG00741.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018471075v1 HG01106.pat HG01106.pat HG01106.alt.pat.f1_v2 (May 2021 GCA_018471075.1_HG01106.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018471065v1 HG01175.pat HG01175.pat HG01175.alt.pat.f1_v2 (May 2021 GCA_018471065.1_HG01175.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018506975v1 HG00733.mat HG00733.mat HG00733.pri.mat.f1_v2 (May 2021 GCA_018506975.1_HG00733.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018504375v1 HG01243.mat HG01243.mat HG01243.pri.mat.f1_v2 (May 2021 GCA_018504375.1_HG01243.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018504365v1 HG01109.mat HG01109.mat HG01109.pri.mat.f1_v2 (May 2021 GCA_018504365.1_HG01109.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018472765v1 HG00735.mat HG00735.mat HG00735.pri.mat.f1_v2 (May 2021 GCA_018472765.1_HG00735.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018472685v1 HG01071.mat HG01071.mat HG01071.pri.mat.f1_v2 (May 2021 GCA_018472685.1_HG01071.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018471345v1 HG01106.mat HG01106.mat HG01106.pri.mat.f1_v2 (May 2021 GCA_018471345.1_HG01106.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018471095v1 HG00741.mat HG00741.mat HG00741.pri.mat.f1_v2 (May 2021 GCA_018471095.1_HG00741.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018471085v1 HG01175.mat HG01175.mat HG01175.pri.mat.f1_v2 (May 2021 GCA_018471085.1_HG01175.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018505835v1 HG03492.pat HG03492.pat HG03492.alt.pat.f1_v2 (May 2021 GCA_018505835.1_HG03492.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018505845v1 HG03492.mat HG03492.mat HG03492.pri.mat.f1_v2 (May 2021 GCA_018505845.1_HG03492.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018472845v1 HG01978.pat HG01978.pat HG01978.alt.pat.f1_v2 (May 2021 GCA_018472845.1_HG01978.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018472705v1 HG01928.pat HG01928.pat HG01928.alt.pat.f1_v2 (May 2021 GCA_018472705.1_HG01928.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018471555v1 HG01952.pat HG01952.pat HG01952.alt.pat.f1_v2 (May 2021 GCA_018471555.1_HG01952.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018471525v1 HG02148.pat HG02148.pat HG02148.alt.pat.f1_v2 (May 2021 GCA_018471525.1_HG02148.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018472865v1 HG01978.mat HG01978.mat HG01978.pri.mat.f1_v2 (May 2021 GCA_018472865.1_HG01978.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018472695v1 HG01928.mat HG01928.mat HG01928.pri.mat.f1_v2 (May 2021 GCA_018472695.1_HG01928.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018471545v1 HG01952.mat HG01952.mat HG01952.pri.mat.f1_v2 (May 2021 GCA_018471545.1_HG01952.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018471535v1 HG02148.mat HG02148.mat HG02148.pri.mat.f1_v2 (May 2021 GCA_018471535.1_HG02148.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018506155v1 HG03098.pat HG03098.pat HG03098.alt.pat.f1_v2 (May 2021 GCA_018506155.1_HG03098.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018503245v1 HG03486.pat HG03486.pat HG03486.alt.pat.f1_v2 (May 2021 GCA_018503245.1_HG03486.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018473305v1 HG03453.pat HG03453.pat HG03453.alt.pat.f1_v2 (May 2021 GCA_018473305.1_HG03453.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018472835v1 HG03579.pat HG03579.pat HG03579.alt.pat.f1_v2 (May 2021 GCA_018472835.1_HG03579.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018506165v1 HG03098.mat HG03098.mat HG03098.pri.mat.f1_v2 (May 2021 GCA_018506165.1_HG03098.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018503525v1 HG03486.mat HG03486.mat HG03486.pri.mat.f1_v2 (May 2021 GCA_018503525.1_HG03486.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018472855v1 HG03453.mat HG03453.mat HG03453.pri.mat.f1_v2 (May 2021 GCA_018472855.1_HG03453.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018472825v1 HG03579.mat HG03579.mat HG03579.pri.mat.f1_v2 (May 2021 GCA_018472825.1_HG03579.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018504055v1 HG02080.pat HG02080.pat HG02080.alt.pat.f1_v2 (May 2021 GCA_018504055.1_HG02080.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018504085v1 HG02080.mat HG02080.mat HG02080.pri.mat.f1_v2 (May 2021 GCA_018504085.1_HG02080.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018504665v1 NA21309.pat NA21309.pat NA21309.alt.pat.f1_v2 (May 2021 GCA_018504665.1_NA21309.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018504075v1 HG02723.pat HG02723.pat HG02723.alt.pat.f1_v2 (May 2021 GCA_018504075.1_HG02723.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018503575v1 HG02818.pat HG02818.pat HG02818.alt.pat.f1_v2 (May 2021 GCA_018503575.1_HG02818.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018473315v1 HG03540.pat HG03540.pat HG03540.alt.pat.f1_v2 (May 2021 GCA_018473315.1_HG03540.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018470465v1 HG02886.pat HG02886.pat HG02886.alt.pat.f1_v2 (May 2021 GCA_018470465.1_HG02886.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018470435v1 HG02572.pat HG02572.pat HG02572.alt.pat.f1_v2 (May 2021 GCA_018470435.1_HG02572.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018470425v1 HG02717.pat HG02717.pat HG02717.alt.pat.f1_v2 (May 2021 GCA_018470425.1_HG02717.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018469945v1 HG02630.pat HG02630.pat HG02630.alt.pat.f1_v2 (May 2021 GCA_018469945.1_HG02630.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018469925v1 HG02622.pat HG02622.pat HG02622.alt.pat.f1_v2 (May 2021 GCA_018469925.1_HG02622.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018504065v1 HG02723.mat HG02723.mat HG02723.pri.mat.f1_v2 (May 2021 GCA_018504065.1_HG02723.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018503585v1 HG02818.mat HG02818.mat HG02818.pri.mat.f1_v2 (May 2021 GCA_018503585.1_HG02818.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018473295v1 HG03540.mat HG03540.mat HG03540.pri.mat.f1_v2 (May 2021 GCA_018473295.1_HG03540.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018470455v1 HG02886.mat HG02886.mat HG02886.pri.mat.f1_v2 (May 2021 GCA_018470455.1_HG02886.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018470445v1 HG02572.mat HG02572.mat HG02572.pri.mat.f1_v2 (May 2021 GCA_018470445.1_HG02572.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018469955v1 HG02630.mat HG02630.mat HG02630.pri.mat.f1_v2 (May 2021 GCA_018469955.1_HG02630.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018469935v1 HG02717.mat HG02717.mat HG02717.pri.mat.f1_v2 (May 2021 GCA_018469935.1_HG02717.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018469875v1 HG02622.mat HG02622.mat HG02622.pri.mat.f1_v2 (May 2021 GCA_018469875.1_HG02622.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018469415v1 HG03516.pat HG03516.pat HG03516.alt.pat.f1_v2 (May 2021 GCA_018469415.1_HG03516.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018469425v1 HG03516.mat HG03516.mat HG03516.pri.mat.f1_v2 (May 2021 GCA_018469425.1_HG03516.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018469965v1 HG01358.pat HG01358.pat HG01358.alt.pat.f1_v2.1 (May 2021 GCA_018469965.1_HG01358.alt.pat.f1_v2.1) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018469705v1 HG01361.pat HG01361.pat HG01361.alt.pat.f1_v2 (May 2021 GCA_018469705.1_HG01361.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018469695v1 HG01123.pat HG01123.pat HG01123.alt.pat.f1_v2.1 (May 2021 GCA_018469695.1_HG01123.alt.pat.f1_v2.1) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018469675v1 HG01258.pat HG01258.pat HG01258.alt.pat.f1_v2 (May 2021 GCA_018469675.1_HG01258.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018469865v1 HG01358.mat HG01358.mat HG01358.pri.mat.f1_v2.1 (May 2021 GCA_018469865.1_HG01358.pri.mat.f1_v2.1) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018469685v1 HG01361.mat HG01361.mat HG01361.pri.mat.f1_v2 (May 2021 GCA_018469685.1_HG01361.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018469665v1 HG01123.mat HG01123.mat HG01123.pri.mat.f1_v2.1 (May 2021 GCA_018469665.1_HG01123.pri.mat.f1_v2.1) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018469405v1 HG01258.mat HG01258.mat HG01258.pri.mat.f1_v2 (May 2021 GCA_018469405.1_HG01258.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018472595v1 HG00438.pat HG00438.pat HG00438.alt.pat.f1_v2 (May 2021 GCA_018472595.1_HG00438.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018472585v1 HG00673.pat HG00673.pat HG00673.alt.pat.f1_v2 (May 2021 GCA_018472585.1_HG00673.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018472575v1 HG00621.pat HG00621.pat HG00621.alt.pat.f1_v2 (May 2021 GCA_018472575.1_HG00621.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018472605v1 HG00621.mat HG00621.mat HG00621.pri.mat.f1_v2 (May 2021 GCA_018472605.1_HG00621.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018472565v1 HG00673.mat HG00673.mat HG00673.pri.mat.f1_v2 (May 2021 GCA_018472565.1_HG00673.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018471515v1 HG00438.mat HG00438.mat HG00438.pri.mat.f1_v2 (May 2021 GCA_018471515.1_HG00438.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018504625v1 NA20129.pat NA20129.pat NA20129.alt.pat.f1_v2 (May 2021 GCA_018504625.1_NA20129.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018504635v1 NA20129.mat NA20129.mat NA20129.pri.mat.f1_v2 (May 2021 GCA_018504635.1_NA20129.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018852595v1 HG02145.pat HG02145.pat HG02145.alt.pat.f1_v2 (Jun. 2021 GCA_018852595.1_HG02145.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018505865v1 HG02109.pat HG02109.pat HG02109.alt.pat.f1_v2 (May 2021 GCA_018505865.1_HG02109.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018505855v1 HG02055.pat HG02055.pat HG02055.alt.pat.f1_v2 (May 2021 GCA_018505855.1_HG02055.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018467165v1 HG01891.pat HG01891.pat HG01891.alt.pat.f1_v2 (May 2021 GCA_018467165.1_HG01891.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018467005v1 HG02486.pat HG02486.pat HG02486.alt.pat.f1_v2 (May 2021 GCA_018467005.1_HG02486.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018466855v1 HG02559.pat HG02559.pat HG02559.alt.pat.f1_v2 (May 2021 GCA_018466855.1_HG02559.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018466835v1 HG02257.pat HG02257.pat HG02257.alt.pat.f1_v2 (May 2021 GCA_018466835.1_HG02257.alt.pat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018852585v1 HG02145.mat HG02145.mat HG02145.pri.mat.f1_v2 (Jun. 2021 GCA_018852585.1_HG02145.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018506125v1 HG02055.mat HG02055.mat HG02055.pri.mat.f1_v2 (May 2021 GCA_018506125.1_HG02055.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018505825v1 HG02109.mat HG02109.mat HG02109.pri.mat.f1_v2 (May 2021 GCA_018505825.1_HG02109.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018467155v1 HG01891.mat HG01891.mat HG01891.pri.mat.f1_v2 (May 2021 GCA_018467155.1_HG01891.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018467015v1 HG02486.mat HG02486.mat HG02486.pri.mat.f1_v2 (May 2021 GCA_018467015.1_HG02486.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018466985v1 HG02559.mat HG02559.mat HG02559.pri.mat.f1_v2 (May 2021 GCA_018466985.1_HG02559.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
netHprcGCA_018466845v1 HG02257.mat HG02257.mat HG02257.pri.mat.f1_v2 (May 2021 GCA_018466845.1_HG02257.pri.mat.f1_v2) HPRC project computed Chain Nets Human Pangenome - HPRC 
hprcChainNetViewchain Chains Human Genomes, Chain/Net pairwise alignments, as mapped by the HPRC project Human Pangenome - HPRC 
chainHprcGCA_018503285v1 NA18906.pat NA18906.pat NA18906.alt.pat.f1_v2 (May 2021 GCA_018503285.1_NA18906.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018503255v1 NA18906.mat NA18906.mat NA18906.pri.mat.f1_v2 (May 2021 GCA_018503255.1_NA18906.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcHs1 T2T-CHM13v2.0 T2T-CHM13v2.0 T2T-CHM13v2.0 (Jan. 2022 GCF_009914755.1_T2T-CHM13v2.0) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018506955v1 HG00733.pat HG00733.pat HG00733.alt.pat.f1_v2 (May 2021 GCA_018506955.1_HG00733.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018504645v1 HG01109.pat HG01109.pat HG01109.alt.pat.f1_v2 (May 2021 GCA_018504645.1_HG01109.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018504045v1 HG01243.pat HG01243.pat HG01243.alt.pat.f1_v2 (May 2021 GCA_018504045.1_HG01243.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018472725v1 HG01071.pat HG01071.pat HG01071.alt.pat.f1_v2 (May 2021 GCA_018472725.1_HG01071.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018472715v1 HG00735.pat HG00735.pat HG00735.alt.pat.f1_v2 (May 2021 GCA_018472715.1_HG00735.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018471105v1 HG00741.pat HG00741.pat HG00741.alt.pat.f1_v2 (May 2021 GCA_018471105.1_HG00741.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018471075v1 HG01106.pat HG01106.pat HG01106.alt.pat.f1_v2 (May 2021 GCA_018471075.1_HG01106.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018471065v1 HG01175.pat HG01175.pat HG01175.alt.pat.f1_v2 (May 2021 GCA_018471065.1_HG01175.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018506975v1 HG00733.mat HG00733.mat HG00733.pri.mat.f1_v2 (May 2021 GCA_018506975.1_HG00733.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018504375v1 HG01243.mat HG01243.mat HG01243.pri.mat.f1_v2 (May 2021 GCA_018504375.1_HG01243.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018504365v1 HG01109.mat HG01109.mat HG01109.pri.mat.f1_v2 (May 2021 GCA_018504365.1_HG01109.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018472765v1 HG00735.mat HG00735.mat HG00735.pri.mat.f1_v2 (May 2021 GCA_018472765.1_HG00735.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018472685v1 HG01071.mat HG01071.mat HG01071.pri.mat.f1_v2 (May 2021 GCA_018472685.1_HG01071.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018471345v1 HG01106.mat HG01106.mat HG01106.pri.mat.f1_v2 (May 2021 GCA_018471345.1_HG01106.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018471095v1 HG00741.mat HG00741.mat HG00741.pri.mat.f1_v2 (May 2021 GCA_018471095.1_HG00741.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018471085v1 HG01175.mat HG01175.mat HG01175.pri.mat.f1_v2 (May 2021 GCA_018471085.1_HG01175.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018505835v1 HG03492.pat HG03492.pat HG03492.alt.pat.f1_v2 (May 2021 GCA_018505835.1_HG03492.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018505845v1 HG03492.mat HG03492.mat HG03492.pri.mat.f1_v2 (May 2021 GCA_018505845.1_HG03492.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018472845v1 HG01978.pat HG01978.pat HG01978.alt.pat.f1_v2 (May 2021 GCA_018472845.1_HG01978.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018472705v1 HG01928.pat HG01928.pat HG01928.alt.pat.f1_v2 (May 2021 GCA_018472705.1_HG01928.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018471555v1 HG01952.pat HG01952.pat HG01952.alt.pat.f1_v2 (May 2021 GCA_018471555.1_HG01952.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018471525v1 HG02148.pat HG02148.pat HG02148.alt.pat.f1_v2 (May 2021 GCA_018471525.1_HG02148.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018472865v1 HG01978.mat HG01978.mat HG01978.pri.mat.f1_v2 (May 2021 GCA_018472865.1_HG01978.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018472695v1 HG01928.mat HG01928.mat HG01928.pri.mat.f1_v2 (May 2021 GCA_018472695.1_HG01928.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018471545v1 HG01952.mat HG01952.mat HG01952.pri.mat.f1_v2 (May 2021 GCA_018471545.1_HG01952.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018471535v1 HG02148.mat HG02148.mat HG02148.pri.mat.f1_v2 (May 2021 GCA_018471535.1_HG02148.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018506155v1 HG03098.pat HG03098.pat HG03098.alt.pat.f1_v2 (May 2021 GCA_018506155.1_HG03098.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018503245v1 HG03486.pat HG03486.pat HG03486.alt.pat.f1_v2 (May 2021 GCA_018503245.1_HG03486.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018473305v1 HG03453.pat HG03453.pat HG03453.alt.pat.f1_v2 (May 2021 GCA_018473305.1_HG03453.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018472835v1 HG03579.pat HG03579.pat HG03579.alt.pat.f1_v2 (May 2021 GCA_018472835.1_HG03579.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018506165v1 HG03098.mat HG03098.mat HG03098.pri.mat.f1_v2 (May 2021 GCA_018506165.1_HG03098.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018503525v1 HG03486.mat HG03486.mat HG03486.pri.mat.f1_v2 (May 2021 GCA_018503525.1_HG03486.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018472855v1 HG03453.mat HG03453.mat HG03453.pri.mat.f1_v2 (May 2021 GCA_018472855.1_HG03453.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018472825v1 HG03579.mat HG03579.mat HG03579.pri.mat.f1_v2 (May 2021 GCA_018472825.1_HG03579.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018504055v1 HG02080.pat HG02080.pat HG02080.alt.pat.f1_v2 (May 2021 GCA_018504055.1_HG02080.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018504085v1 HG02080.mat HG02080.mat HG02080.pri.mat.f1_v2 (May 2021 GCA_018504085.1_HG02080.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018504665v1 NA21309.pat NA21309.pat NA21309.alt.pat.f1_v2 (May 2021 GCA_018504665.1_NA21309.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018504075v1 HG02723.pat HG02723.pat HG02723.alt.pat.f1_v2 (May 2021 GCA_018504075.1_HG02723.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018503575v1 HG02818.pat HG02818.pat HG02818.alt.pat.f1_v2 (May 2021 GCA_018503575.1_HG02818.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018473315v1 HG03540.pat HG03540.pat HG03540.alt.pat.f1_v2 (May 2021 GCA_018473315.1_HG03540.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018470465v1 HG02886.pat HG02886.pat HG02886.alt.pat.f1_v2 (May 2021 GCA_018470465.1_HG02886.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018470435v1 HG02572.pat HG02572.pat HG02572.alt.pat.f1_v2 (May 2021 GCA_018470435.1_HG02572.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018470425v1 HG02717.pat HG02717.pat HG02717.alt.pat.f1_v2 (May 2021 GCA_018470425.1_HG02717.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018469945v1 HG02630.pat HG02630.pat HG02630.alt.pat.f1_v2 (May 2021 GCA_018469945.1_HG02630.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018469925v1 HG02622.pat HG02622.pat HG02622.alt.pat.f1_v2 (May 2021 GCA_018469925.1_HG02622.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018504065v1 HG02723.mat HG02723.mat HG02723.pri.mat.f1_v2 (May 2021 GCA_018504065.1_HG02723.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018503585v1 HG02818.mat HG02818.mat HG02818.pri.mat.f1_v2 (May 2021 GCA_018503585.1_HG02818.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018473295v1 HG03540.mat HG03540.mat HG03540.pri.mat.f1_v2 (May 2021 GCA_018473295.1_HG03540.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018470455v1 HG02886.mat HG02886.mat HG02886.pri.mat.f1_v2 (May 2021 GCA_018470455.1_HG02886.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018470445v1 HG02572.mat HG02572.mat HG02572.pri.mat.f1_v2 (May 2021 GCA_018470445.1_HG02572.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018469955v1 HG02630.mat HG02630.mat HG02630.pri.mat.f1_v2 (May 2021 GCA_018469955.1_HG02630.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018469935v1 HG02717.mat HG02717.mat HG02717.pri.mat.f1_v2 (May 2021 GCA_018469935.1_HG02717.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018469875v1 HG02622.mat HG02622.mat HG02622.pri.mat.f1_v2 (May 2021 GCA_018469875.1_HG02622.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018469415v1 HG03516.pat HG03516.pat HG03516.alt.pat.f1_v2 (May 2021 GCA_018469415.1_HG03516.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018469425v1 HG03516.mat HG03516.mat HG03516.pri.mat.f1_v2 (May 2021 GCA_018469425.1_HG03516.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018469965v1 HG01358.pat HG01358.pat HG01358.alt.pat.f1_v2.1 (May 2021 GCA_018469965.1_HG01358.alt.pat.f1_v2.1) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018469705v1 HG01361.pat HG01361.pat HG01361.alt.pat.f1_v2 (May 2021 GCA_018469705.1_HG01361.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018469695v1 HG01123.pat HG01123.pat HG01123.alt.pat.f1_v2.1 (May 2021 GCA_018469695.1_HG01123.alt.pat.f1_v2.1) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018469675v1 HG01258.pat HG01258.pat HG01258.alt.pat.f1_v2 (May 2021 GCA_018469675.1_HG01258.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018469865v1 HG01358.mat HG01358.mat HG01358.pri.mat.f1_v2.1 (May 2021 GCA_018469865.1_HG01358.pri.mat.f1_v2.1) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018469685v1 HG01361.mat HG01361.mat HG01361.pri.mat.f1_v2 (May 2021 GCA_018469685.1_HG01361.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018469665v1 HG01123.mat HG01123.mat HG01123.pri.mat.f1_v2.1 (May 2021 GCA_018469665.1_HG01123.pri.mat.f1_v2.1) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018469405v1 HG01258.mat HG01258.mat HG01258.pri.mat.f1_v2 (May 2021 GCA_018469405.1_HG01258.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018472595v1 HG00438.pat HG00438.pat HG00438.alt.pat.f1_v2 (May 2021 GCA_018472595.1_HG00438.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018472585v1 HG00673.pat HG00673.pat HG00673.alt.pat.f1_v2 (May 2021 GCA_018472585.1_HG00673.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018472575v1 HG00621.pat HG00621.pat HG00621.alt.pat.f1_v2 (May 2021 GCA_018472575.1_HG00621.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018472605v1 HG00621.mat HG00621.mat HG00621.pri.mat.f1_v2 (May 2021 GCA_018472605.1_HG00621.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018472565v1 HG00673.mat HG00673.mat HG00673.pri.mat.f1_v2 (May 2021 GCA_018472565.1_HG00673.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018471515v1 HG00438.mat HG00438.mat HG00438.pri.mat.f1_v2 (May 2021 GCA_018471515.1_HG00438.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018504625v1 NA20129.pat NA20129.pat NA20129.alt.pat.f1_v2 (May 2021 GCA_018504625.1_NA20129.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018504635v1 NA20129.mat NA20129.mat NA20129.pri.mat.f1_v2 (May 2021 GCA_018504635.1_NA20129.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018852595v1 HG02145.pat HG02145.pat HG02145.alt.pat.f1_v2 (Jun. 2021 GCA_018852595.1_HG02145.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018505865v1 HG02109.pat HG02109.pat HG02109.alt.pat.f1_v2 (May 2021 GCA_018505865.1_HG02109.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018505855v1 HG02055.pat HG02055.pat HG02055.alt.pat.f1_v2 (May 2021 GCA_018505855.1_HG02055.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018467165v1 HG01891.pat HG01891.pat HG01891.alt.pat.f1_v2 (May 2021 GCA_018467165.1_HG01891.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018467005v1 HG02486.pat HG02486.pat HG02486.alt.pat.f1_v2 (May 2021 GCA_018467005.1_HG02486.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018466855v1 HG02559.pat HG02559.pat HG02559.alt.pat.f1_v2 (May 2021 GCA_018466855.1_HG02559.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018466835v1 HG02257.pat HG02257.pat HG02257.alt.pat.f1_v2 (May 2021 GCA_018466835.1_HG02257.alt.pat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018852585v1 HG02145.mat HG02145.mat HG02145.pri.mat.f1_v2 (Jun. 2021 GCA_018852585.1_HG02145.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018506125v1 HG02055.mat HG02055.mat HG02055.pri.mat.f1_v2 (May 2021 GCA_018506125.1_HG02055.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018505825v1 HG02109.mat HG02109.mat HG02109.pri.mat.f1_v2 (May 2021 GCA_018505825.1_HG02109.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018467155v1 HG01891.mat HG01891.mat HG01891.pri.mat.f1_v2 (May 2021 GCA_018467155.1_HG01891.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018467015v1 HG02486.mat HG02486.mat HG02486.pri.mat.f1_v2 (May 2021 GCA_018467015.1_HG02486.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018466985v1 HG02559.mat HG02559.mat HG02559.pri.mat.f1_v2 (May 2021 GCA_018466985.1_HG02559.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
chainHprcGCA_018466845v1 HG02257.mat HG02257.mat HG02257.pri.mat.f1_v2 (May 2021 GCA_018466845.1_HG02257.pri.mat.f1_v2) HPRC project computed Chained Alignments Human Pangenome - HPRC 
pancreasBaronBatch Pancreas Batch Pancreas cells binned by batch from Baron et al 2016 Single Cell RNA-seq Description This track shows data from A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. Pancreas tissue was analyzed using droplet-based single-cell RNA-sequencing (scRNA-seq) and subsequent clustering distinguished 14 pancreas-resident cell types based on their identified marker genes found in Baron et al., 2016. There are four bar chart tracks in this track collection with pancreas cells grouped by either batch (Pancreas Batch), cell type (Pancreas Cells), detailed cell type (Pancreas Details) and donor (Pancreas Donor). The default track displayed is pancreas cells grouped by cell type. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification secretory endothelial epithelial fibroblast Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Pancreas Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Human islets were obtained from two female cadaveric donors ages 51 (human2) and 59 (human4) and two male cadaveric donors ages 17 (human1) and 38 (human3). The samples collected from human 1-3 were non-diabetic and human 4 had type 2 diabetes mellitus. Using single-cell RNA-sequencing ~10,000 human pancreatic cells were isolated and sequenced. For each donor, several separate batches of ~800 cells were prepared and sequenced to obtain an average of about 100,000 reads per cell. Cells were barcoded using the inDrop platform which follows the CEL-Seq protocol for library construction. Paired end sequencing was done on the Illumina Hiseq 2500. After filtering out cells with limited numbers of detected genes, the dataset contained 8,629 cells from the four donors. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Mayaan Baron, Adrian Veres, Samuel L. Wolock, Aubrey L. Faust, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Baron M, Veres A, Wolock SL, Faust AL, Gaujoux R, Vetere A, Ryu JH, Wagner BK, Shen-Orr SS, Klein AM et al. A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. Cell Syst. 2016 Oct 26;3(4):346-360.e4. PMID: 27667365; PMC: PMC5228327 
pancreasBaron Pancreas Baron Pancreas single cell sequencing from Baron et al 2016 Single Cell RNA-seq Description This track shows data from A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. Pancreas tissue was analyzed using droplet-based single-cell RNA-sequencing (scRNA-seq) and subsequent clustering distinguished 14 pancreas-resident cell types based on their identified marker genes found in Baron et al., 2016. There are four bar chart tracks in this track collection with pancreas cells grouped by either batch (Pancreas Batch), cell type (Pancreas Cells), detailed cell type (Pancreas Details) and donor (Pancreas Donor). The default track displayed is pancreas cells grouped by cell type. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification secretory endothelial epithelial fibroblast Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Pancreas Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Human islets were obtained from two female cadaveric donors ages 51 (human2) and 59 (human4) and two male cadaveric donors ages 17 (human1) and 38 (human3). The samples collected from human 1-3 were non-diabetic and human 4 had type 2 diabetes mellitus. Using single-cell RNA-sequencing ~10,000 human pancreatic cells were isolated and sequenced. For each donor, several separate batches of ~800 cells were prepared and sequenced to obtain an average of about 100,000 reads per cell. Cells were barcoded using the inDrop platform which follows the CEL-Seq protocol for library construction. Paired end sequencing was done on the Illumina Hiseq 2500. After filtering out cells with limited numbers of detected genes, the dataset contained 8,629 cells from the four donors. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Mayaan Baron, Adrian Veres, Samuel L. Wolock, Aubrey L. Faust, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Baron M, Veres A, Wolock SL, Faust AL, Gaujoux R, Vetere A, Ryu JH, Wagner BK, Shen-Orr SS, Klein AM et al. A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. Cell Syst. 2016 Oct 26;3(4):346-360.e4. PMID: 27667365; PMC: PMC5228327 
pancreasBaronCellType Pancreas Cells Pancreas cells binned by cell type from Baron et al 2016 Single Cell RNA-seq Description This track shows data from A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. Pancreas tissue was analyzed using droplet-based single-cell RNA-sequencing (scRNA-seq) and subsequent clustering distinguished 14 pancreas-resident cell types based on their identified marker genes found in Baron et al., 2016. There are four bar chart tracks in this track collection with pancreas cells grouped by either batch (Pancreas Batch), cell type (Pancreas Cells), detailed cell type (Pancreas Details) and donor (Pancreas Donor). The default track displayed is pancreas cells grouped by cell type. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification secretory endothelial epithelial fibroblast Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Pancreas Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Human islets were obtained from two female cadaveric donors ages 51 (human2) and 59 (human4) and two male cadaveric donors ages 17 (human1) and 38 (human3). The samples collected from human 1-3 were non-diabetic and human 4 had type 2 diabetes mellitus. Using single-cell RNA-sequencing ~10,000 human pancreatic cells were isolated and sequenced. For each donor, several separate batches of ~800 cells were prepared and sequenced to obtain an average of about 100,000 reads per cell. Cells were barcoded using the inDrop platform which follows the CEL-Seq protocol for library construction. Paired end sequencing was done on the Illumina Hiseq 2500. After filtering out cells with limited numbers of detected genes, the dataset contained 8,629 cells from the four donors. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Mayaan Baron, Adrian Veres, Samuel L. Wolock, Aubrey L. Faust, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Baron M, Veres A, Wolock SL, Faust AL, Gaujoux R, Vetere A, Ryu JH, Wagner BK, Shen-Orr SS, Klein AM et al. A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. Cell Syst. 2016 Oct 26;3(4):346-360.e4. PMID: 27667365; PMC: PMC5228327 
pancreasBaronDetailedCellType Pancreas Details Pancreas cells binned by detailed cell type from Baron et al 2016 Single Cell RNA-seq Description This track shows data from A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. Pancreas tissue was analyzed using droplet-based single-cell RNA-sequencing (scRNA-seq) and subsequent clustering distinguished 14 pancreas-resident cell types based on their identified marker genes found in Baron et al., 2016. There are four bar chart tracks in this track collection with pancreas cells grouped by either batch (Pancreas Batch), cell type (Pancreas Cells), detailed cell type (Pancreas Details) and donor (Pancreas Donor). The default track displayed is pancreas cells grouped by cell type. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification secretory endothelial epithelial fibroblast Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Pancreas Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Human islets were obtained from two female cadaveric donors ages 51 (human2) and 59 (human4) and two male cadaveric donors ages 17 (human1) and 38 (human3). The samples collected from human 1-3 were non-diabetic and human 4 had type 2 diabetes mellitus. Using single-cell RNA-sequencing ~10,000 human pancreatic cells were isolated and sequenced. For each donor, several separate batches of ~800 cells were prepared and sequenced to obtain an average of about 100,000 reads per cell. Cells were barcoded using the inDrop platform which follows the CEL-Seq protocol for library construction. Paired end sequencing was done on the Illumina Hiseq 2500. After filtering out cells with limited numbers of detected genes, the dataset contained 8,629 cells from the four donors. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Mayaan Baron, Adrian Veres, Samuel L. Wolock, Aubrey L. Faust, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Baron M, Veres A, Wolock SL, Faust AL, Gaujoux R, Vetere A, Ryu JH, Wagner BK, Shen-Orr SS, Klein AM et al. A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. Cell Syst. 2016 Oct 26;3(4):346-360.e4. PMID: 27667365; PMC: PMC5228327 
pancreasBaronDonor Pancreas Donor Pancreas cells binned by organ donor from Baron et al 2016 Single Cell RNA-seq Description This track shows data from A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. Pancreas tissue was analyzed using droplet-based single-cell RNA-sequencing (scRNA-seq) and subsequent clustering distinguished 14 pancreas-resident cell types based on their identified marker genes found in Baron et al., 2016. There are four bar chart tracks in this track collection with pancreas cells grouped by either batch (Pancreas Batch), cell type (Pancreas Cells), detailed cell type (Pancreas Details) and donor (Pancreas Donor). The default track displayed is pancreas cells grouped by cell type. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification secretory endothelial epithelial fibroblast Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Pancreas Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Human islets were obtained from two female cadaveric donors ages 51 (human2) and 59 (human4) and two male cadaveric donors ages 17 (human1) and 38 (human3). The samples collected from human 1-3 were non-diabetic and human 4 had type 2 diabetes mellitus. Using single-cell RNA-sequencing ~10,000 human pancreatic cells were isolated and sequenced. For each donor, several separate batches of ~800 cells were prepared and sequenced to obtain an average of about 100,000 reads per cell. Cells were barcoded using the inDrop platform which follows the CEL-Seq protocol for library construction. Paired end sequencing was done on the Illumina Hiseq 2500. After filtering out cells with limited numbers of detected genes, the dataset contained 8,629 cells from the four donors. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Mayaan Baron, Adrian Veres, Samuel L. Wolock, Aubrey L. Faust, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Baron M, Veres A, Wolock SL, Faust AL, Gaujoux R, Vetere A, Ryu JH, Wagner BK, Shen-Orr SS, Klein AM et al. A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. Cell Syst. 2016 Oct 26;3(4):346-360.e4. PMID: 27667365; PMC: PMC5228327 
panelApp PanelApp Genomics England PanelApp Diagnostics Phenotype and Literature Description The Genomics England PanelApp tracks show gene panels that are related to human disorders. Originally developed to aid interpretation of participant genomes in the 100,000 Genomes Project, PanelApp is now also being used as the platform for achieving consensus on gene panels in the NHS Genomic Medicine Service (GMS). As panels in PanelApp are publicly available, they can also be used by other groups and projects. Panels are maintained and updated by Genomics England curators. Genes and genomic entities (short tandem repeats/STRs and copy number variants/CNVs) have been reviewed by experts to enable a community consensus to be reached on which genes and genomic entities should appear on a diagnostics grade panel for each disorder. A rating system (confidence level 0 - 3) is used to classify the level of evidence supporting association with phenotypes covered by the gene panel in question. The available data tracks are: Genomics England PanelApp Genes (PanelApp Genes): shows genes with evidence supporting a gene-disease relationship. NOTE: Due to a bug in the PanelApp gene API, between 5 and 20% of gene entries are missing as of 11/2/22. Genomics England PanelApp STRs (PanelApp STRs): shows short tandem repeats that can be disease-causing when a particular number of repeats is present. Only on hg38: Genomics England PanelApp Regions (PanelApp CNV Regions): shows copy-number variants (region-loss and region-gain) with evidence supporting a gene-disease relationship. Display Conventions The individual tracks are colored by confidence level: Score 3 (lime green) - High level of evidence for this gene-disease association. Demonstrates confidence that this gene should be used for genome interpretation. Score 2 (amber) - Moderate evidence for this gene-disease association. This gene should not be used for genomic interpretation. Score 0 or 1 (red) - Not enough evidence for this gene-disease association. This gene should not be used for genomic interpretation. Mouseover on items shows the gene name, panel associated, mode of inheritance (if known), phenotypes related to the gene, and confidence level. Tracks can be filtered according to the confidence level of disease association evidence. For more information on the use of this data, see the PanelApp FAQs. Data Access The raw data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. For automated download and analysis, the genome annotation is stored in a bigBed file that can be downloaded from our download server. The files for this track are called genes.bb, tandRep.bb and cnv.bb. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/panelApp/genes.bb -chrom=chr21 -start=0 -end=100000000 stdout Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Data is also freely available on the PanelApp API. Updates and archiving of old releases This track is updated automatically every week. If you need to access older releases of the data, you can download them from our archive directory on the download server. To load them into the browser, select a week on the archive directory, copy the link to a file, go to My Data &gt; Custom Tracks, click "Add custom track", paste the link into the box and click "Submit". Methods PanelApp files were reformatted at UCSC to the bigBed format. The script that updates the track is called updatePanelApp and can be found in our Github repository. Credits Thank you to Genomics England PanelApp, especially Catherine Snow for technical coordination and consultation. Thank you to Beagan Nguy, Christopher Lee, Daniel Schmelter, Ana Benet-Pag&egrave;s and Maximilian Haeussler of the Genome Browser team for the creation of the tracks. Reference Martin AR, Williams E, Foulger RE, Leigh S, Daugherty LC, Niblock O, Leong IUS, Smith KR, Gerasimenko O, Haraldsdottir E et al. PanelApp crowdsources expert knowledge to establish consensus diagnostic gene panels. Nat Genet. 2019 Nov;51(11):1560-1565. PMID: 31676867 
panelAppTandRep PanelApp STRs Genomics England PanelApp Short Tandem Repeats Phenotype and Literature 
panelAppGenes PanelApp Genes Genomics England PanelApp Genes Phenotype and Literature 
panelAppCNVs PanelApp CNVs Genomics England PanelApp CNV Regions Phenotype and Literature 
ucscGenePfam Pfam in GENCODE Pfam Domains in GENCODE Genes Genes and Gene Predictions Description Most proteins are composed of one or more conserved functional regions called domains. This track shows the high-quality, manually-curated Pfam-A domains found in transcripts located in the GENCODE Genes track by the software HMMER3. Display Conventions and Configuration This track follows the display conventions for gene tracks. Methods The sequences from the knownGenePep table (see GENCODE Genes description page) are submitted to the set of Pfam-A HMMs which annotate regions within the predicted peptide that are recognizable as Pfam protein domains. These regions are then mapped to the transcripts themselves using the pslMap utility. A complete shell script log for every version of UCSC genes can be found in our GitHub repository under hg/makeDb/doc/ucscGenes, e.g. mm10.knownGenes17.csh is for the database mm10 and version 17 of UCSC known genes. Of the several options for filtering out false positives, the &quot;Trusted cutoff (TC)&quot; threshold method is used in this track to determine significance. For more information regarding thresholds and scores, see the HMMER documentation and results interpretation pages. Note: There is currently an undocumented but known HMMER problem which results in lessened sensitivity and possible missed searches for some zinc finger domains. Until a fix is released for HMMER /PFAM thresholds, please also consult the &quot;UniProt Domains&quot; subtrack of the UniProt track for more comprehensive zinc finger annotations. Credits pslMap was written by Mark Diekhans at UCSC. References Finn RD, Mistry J, Tate J, Coggill P, Heger A, Pollington JE, Gavin OL, Gunasekaran P, Ceric G, Forslund K et al. The Pfam protein families database. Nucleic Acids Res. 2010 Jan;38(Database issue):D211-22. PMID: 19920124; PMC: PMC2808889 
placentaVentoTormoCellType10x Placenta Cells Placenta and decidua cells binned by cell type 10x from Vento-Tormo et al 2018 Single Cell RNA-seq Description This track displays data from Single-cell reconstruction of the early maternal-fetal interface in humans. Using droplet-based 10x and plate-based Smart-seq2 single cell RNA-sequencing (scRNA-seq) ~70,000 cells were profiled from first-trimester placentas with matched decidual cells and maternal peripheral blood mononuclear cells (PBMC). This track collection contains nine bar chart tracks of RNA expression in the human placenta, decidua, and maternal PBMCs where cells are grouped by cell type (Placenta Cells, Placenta Cells Ss2), detailed cell type (Placenta Detail, Placenta Detail Ss2), cell location (Placenta Loc, Placenta Loc Ss2), stage (Placenta Stage), and placenta and decidua cells (Placenta Mat/Fet, Placenta Mat/Fet Ss2). The default tracks displayed are Placenta Cells, Placenta Loc, Placenta Loc Ss2, and Placenta Mat/Fet Ss2. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle trophoblast epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Placenta Cells and Placenta Cells Ss2 subtracks, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Tissue was collected from 5 placentas (6-14 gestational weeks) and 11 deciduas. Additionally, blood was drawn from 6 of the donors (D4-D9) and enriched for PBMCs using a Ficoll-Paque gradient. Decidual and placental tissue were both first macroscopically separated. Decidual tissue was then chopped before enzymatic dissociation. Placental villi was scraped from the chorionic membrane before enzymatic dissociation. Decidual and blood cells were enriched for certain populations using an antibody panel prior to Smart-seq2 library preparation. Cells from blood decidua and placenta were enriched using FACS prior to 10x Genomics v2 library preparation. Smart-seq2 libraries were sequenced on an Illumina HiSeq2000. 10x libraries were sequenced on an Illumina HiSeq4000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Roser Vento-Tormo, Mirjana Efremova, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M, Meyer KB, Park JE, Stephenson E, Pola&#324;ski K, Goncalves A et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature. 2018 Nov;563(7731):347-353. PMID: 30429548 
placentaVentoTormo Placenta Vento-Tormo Placenta and decidua cells from from Vento-Tormo et al 2018 Single Cell RNA-seq Description This track displays data from Single-cell reconstruction of the early maternal-fetal interface in humans. Using droplet-based 10x and plate-based Smart-seq2 single cell RNA-sequencing (scRNA-seq) ~70,000 cells were profiled from first-trimester placentas with matched decidual cells and maternal peripheral blood mononuclear cells (PBMC). This track collection contains nine bar chart tracks of RNA expression in the human placenta, decidua, and maternal PBMCs where cells are grouped by cell type (Placenta Cells, Placenta Cells Ss2), detailed cell type (Placenta Detail, Placenta Detail Ss2), cell location (Placenta Loc, Placenta Loc Ss2), stage (Placenta Stage), and placenta and decidua cells (Placenta Mat/Fet, Placenta Mat/Fet Ss2). The default tracks displayed are Placenta Cells, Placenta Loc, Placenta Loc Ss2, and Placenta Mat/Fet Ss2. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle trophoblast epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Placenta Cells and Placenta Cells Ss2 subtracks, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Tissue was collected from 5 placentas (6-14 gestational weeks) and 11 deciduas. Additionally, blood was drawn from 6 of the donors (D4-D9) and enriched for PBMCs using a Ficoll-Paque gradient. Decidual and placental tissue were both first macroscopically separated. Decidual tissue was then chopped before enzymatic dissociation. Placental villi was scraped from the chorionic membrane before enzymatic dissociation. Decidual and blood cells were enriched for certain populations using an antibody panel prior to Smart-seq2 library preparation. Cells from blood decidua and placenta were enriched using FACS prior to 10x Genomics v2 library preparation. Smart-seq2 libraries were sequenced on an Illumina HiSeq2000. 10x libraries were sequenced on an Illumina HiSeq4000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Roser Vento-Tormo, Mirjana Efremova, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M, Meyer KB, Park JE, Stephenson E, Pola&#324;ski K, Goncalves A et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature. 2018 Nov;563(7731):347-353. PMID: 30429548 
placentaVentoTormoCellTypeSs2 Placenta Cells Ss2 Placenta and decidua cells binned by cell type smart-seq2 from Vento-Tormo et al 2018 Single Cell RNA-seq Description This track displays data from Single-cell reconstruction of the early maternal-fetal interface in humans. Using droplet-based 10x and plate-based Smart-seq2 single cell RNA-sequencing (scRNA-seq) ~70,000 cells were profiled from first-trimester placentas with matched decidual cells and maternal peripheral blood mononuclear cells (PBMC). This track collection contains nine bar chart tracks of RNA expression in the human placenta, decidua, and maternal PBMCs where cells are grouped by cell type (Placenta Cells, Placenta Cells Ss2), detailed cell type (Placenta Detail, Placenta Detail Ss2), cell location (Placenta Loc, Placenta Loc Ss2), stage (Placenta Stage), and placenta and decidua cells (Placenta Mat/Fet, Placenta Mat/Fet Ss2). The default tracks displayed are Placenta Cells, Placenta Loc, Placenta Loc Ss2, and Placenta Mat/Fet Ss2. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle trophoblast epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Placenta Cells and Placenta Cells Ss2 subtracks, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Tissue was collected from 5 placentas (6-14 gestational weeks) and 11 deciduas. Additionally, blood was drawn from 6 of the donors (D4-D9) and enriched for PBMCs using a Ficoll-Paque gradient. Decidual and placental tissue were both first macroscopically separated. Decidual tissue was then chopped before enzymatic dissociation. Placental villi was scraped from the chorionic membrane before enzymatic dissociation. Decidual and blood cells were enriched for certain populations using an antibody panel prior to Smart-seq2 library preparation. Cells from blood decidua and placenta were enriched using FACS prior to 10x Genomics v2 library preparation. Smart-seq2 libraries were sequenced on an Illumina HiSeq2000. 10x libraries were sequenced on an Illumina HiSeq4000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Roser Vento-Tormo, Mirjana Efremova, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M, Meyer KB, Park JE, Stephenson E, Pola&#324;ski K, Goncalves A et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature. 2018 Nov;563(7731):347-353. PMID: 30429548 
placentaVentoTormoCellDetailed10x Placenta Detail Placenta and decidua cells binned by detailed cell type 10x from Vento-Tormo et al 2018 Single Cell RNA-seq Description This track displays data from Single-cell reconstruction of the early maternal-fetal interface in humans. Using droplet-based 10x and plate-based Smart-seq2 single cell RNA-sequencing (scRNA-seq) ~70,000 cells were profiled from first-trimester placentas with matched decidual cells and maternal peripheral blood mononuclear cells (PBMC). This track collection contains nine bar chart tracks of RNA expression in the human placenta, decidua, and maternal PBMCs where cells are grouped by cell type (Placenta Cells, Placenta Cells Ss2), detailed cell type (Placenta Detail, Placenta Detail Ss2), cell location (Placenta Loc, Placenta Loc Ss2), stage (Placenta Stage), and placenta and decidua cells (Placenta Mat/Fet, Placenta Mat/Fet Ss2). The default tracks displayed are Placenta Cells, Placenta Loc, Placenta Loc Ss2, and Placenta Mat/Fet Ss2. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle trophoblast epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Placenta Cells and Placenta Cells Ss2 subtracks, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Tissue was collected from 5 placentas (6-14 gestational weeks) and 11 deciduas. Additionally, blood was drawn from 6 of the donors (D4-D9) and enriched for PBMCs using a Ficoll-Paque gradient. Decidual and placental tissue were both first macroscopically separated. Decidual tissue was then chopped before enzymatic dissociation. Placental villi was scraped from the chorionic membrane before enzymatic dissociation. Decidual and blood cells were enriched for certain populations using an antibody panel prior to Smart-seq2 library preparation. Cells from blood decidua and placenta were enriched using FACS prior to 10x Genomics v2 library preparation. Smart-seq2 libraries were sequenced on an Illumina HiSeq2000. 10x libraries were sequenced on an Illumina HiSeq4000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Roser Vento-Tormo, Mirjana Efremova, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M, Meyer KB, Park JE, Stephenson E, Pola&#324;ski K, Goncalves A et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature. 2018 Nov;563(7731):347-353. PMID: 30429548 
placentaVentoTormoCellDetailedSs2 Placenta Detail Ss2 Placenta and decidua cells binned by detailed cell type smart-seq2 from Vento-Tormo et al 2018 Single Cell RNA-seq Description This track displays data from Single-cell reconstruction of the early maternal-fetal interface in humans. Using droplet-based 10x and plate-based Smart-seq2 single cell RNA-sequencing (scRNA-seq) ~70,000 cells were profiled from first-trimester placentas with matched decidual cells and maternal peripheral blood mononuclear cells (PBMC). This track collection contains nine bar chart tracks of RNA expression in the human placenta, decidua, and maternal PBMCs where cells are grouped by cell type (Placenta Cells, Placenta Cells Ss2), detailed cell type (Placenta Detail, Placenta Detail Ss2), cell location (Placenta Loc, Placenta Loc Ss2), stage (Placenta Stage), and placenta and decidua cells (Placenta Mat/Fet, Placenta Mat/Fet Ss2). The default tracks displayed are Placenta Cells, Placenta Loc, Placenta Loc Ss2, and Placenta Mat/Fet Ss2. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle trophoblast epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Placenta Cells and Placenta Cells Ss2 subtracks, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Tissue was collected from 5 placentas (6-14 gestational weeks) and 11 deciduas. Additionally, blood was drawn from 6 of the donors (D4-D9) and enriched for PBMCs using a Ficoll-Paque gradient. Decidual and placental tissue were both first macroscopically separated. Decidual tissue was then chopped before enzymatic dissociation. Placental villi was scraped from the chorionic membrane before enzymatic dissociation. Decidual and blood cells were enriched for certain populations using an antibody panel prior to Smart-seq2 library preparation. Cells from blood decidua and placenta were enriched using FACS prior to 10x Genomics v2 library preparation. Smart-seq2 libraries were sequenced on an Illumina HiSeq2000. 10x libraries were sequenced on an Illumina HiSeq4000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Roser Vento-Tormo, Mirjana Efremova, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M, Meyer KB, Park JE, Stephenson E, Pola&#324;ski K, Goncalves A et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature. 2018 Nov;563(7731):347-353. PMID: 30429548 
placentaVentoTormoLocation10x Placenta Loc Placenta and decidua cells binned by cell location 10x from Vento-Tormo et al 2018 Single Cell RNA-seq Description This track displays data from Single-cell reconstruction of the early maternal-fetal interface in humans. Using droplet-based 10x and plate-based Smart-seq2 single cell RNA-sequencing (scRNA-seq) ~70,000 cells were profiled from first-trimester placentas with matched decidual cells and maternal peripheral blood mononuclear cells (PBMC). This track collection contains nine bar chart tracks of RNA expression in the human placenta, decidua, and maternal PBMCs where cells are grouped by cell type (Placenta Cells, Placenta Cells Ss2), detailed cell type (Placenta Detail, Placenta Detail Ss2), cell location (Placenta Loc, Placenta Loc Ss2), stage (Placenta Stage), and placenta and decidua cells (Placenta Mat/Fet, Placenta Mat/Fet Ss2). The default tracks displayed are Placenta Cells, Placenta Loc, Placenta Loc Ss2, and Placenta Mat/Fet Ss2. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle trophoblast epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Placenta Cells and Placenta Cells Ss2 subtracks, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Tissue was collected from 5 placentas (6-14 gestational weeks) and 11 deciduas. Additionally, blood was drawn from 6 of the donors (D4-D9) and enriched for PBMCs using a Ficoll-Paque gradient. Decidual and placental tissue were both first macroscopically separated. Decidual tissue was then chopped before enzymatic dissociation. Placental villi was scraped from the chorionic membrane before enzymatic dissociation. Decidual and blood cells were enriched for certain populations using an antibody panel prior to Smart-seq2 library preparation. Cells from blood decidua and placenta were enriched using FACS prior to 10x Genomics v2 library preparation. Smart-seq2 libraries were sequenced on an Illumina HiSeq2000. 10x libraries were sequenced on an Illumina HiSeq4000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Roser Vento-Tormo, Mirjana Efremova, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M, Meyer KB, Park JE, Stephenson E, Pola&#324;ski K, Goncalves A et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature. 2018 Nov;563(7731):347-353. PMID: 30429548 
placentaVentoTormoLocationSs2 Placenta Loc Ss2 Placenta and decidua cells binned by cell location smart-seq2 from Vento-Tormo et al 2018 Single Cell RNA-seq Description This track displays data from Single-cell reconstruction of the early maternal-fetal interface in humans. Using droplet-based 10x and plate-based Smart-seq2 single cell RNA-sequencing (scRNA-seq) ~70,000 cells were profiled from first-trimester placentas with matched decidual cells and maternal peripheral blood mononuclear cells (PBMC). This track collection contains nine bar chart tracks of RNA expression in the human placenta, decidua, and maternal PBMCs where cells are grouped by cell type (Placenta Cells, Placenta Cells Ss2), detailed cell type (Placenta Detail, Placenta Detail Ss2), cell location (Placenta Loc, Placenta Loc Ss2), stage (Placenta Stage), and placenta and decidua cells (Placenta Mat/Fet, Placenta Mat/Fet Ss2). The default tracks displayed are Placenta Cells, Placenta Loc, Placenta Loc Ss2, and Placenta Mat/Fet Ss2. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle trophoblast epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Placenta Cells and Placenta Cells Ss2 subtracks, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Tissue was collected from 5 placentas (6-14 gestational weeks) and 11 deciduas. Additionally, blood was drawn from 6 of the donors (D4-D9) and enriched for PBMCs using a Ficoll-Paque gradient. Decidual and placental tissue were both first macroscopically separated. Decidual tissue was then chopped before enzymatic dissociation. Placental villi was scraped from the chorionic membrane before enzymatic dissociation. Decidual and blood cells were enriched for certain populations using an antibody panel prior to Smart-seq2 library preparation. Cells from blood decidua and placenta were enriched using FACS prior to 10x Genomics v2 library preparation. Smart-seq2 libraries were sequenced on an Illumina HiSeq2000. 10x libraries were sequenced on an Illumina HiSeq4000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Roser Vento-Tormo, Mirjana Efremova, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M, Meyer KB, Park JE, Stephenson E, Pola&#324;ski K, Goncalves A et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature. 2018 Nov;563(7731):347-353. PMID: 30429548 
placentaVentoTormoMatFet10x Placenta Mat/Fet Placenta and decidua cells binned by maternal/fetal 10x from Vento-Tormo et al 2018 Single Cell RNA-seq Description This track displays data from Single-cell reconstruction of the early maternal-fetal interface in humans. Using droplet-based 10x and plate-based Smart-seq2 single cell RNA-sequencing (scRNA-seq) ~70,000 cells were profiled from first-trimester placentas with matched decidual cells and maternal peripheral blood mononuclear cells (PBMC). This track collection contains nine bar chart tracks of RNA expression in the human placenta, decidua, and maternal PBMCs where cells are grouped by cell type (Placenta Cells, Placenta Cells Ss2), detailed cell type (Placenta Detail, Placenta Detail Ss2), cell location (Placenta Loc, Placenta Loc Ss2), stage (Placenta Stage), and placenta and decidua cells (Placenta Mat/Fet, Placenta Mat/Fet Ss2). The default tracks displayed are Placenta Cells, Placenta Loc, Placenta Loc Ss2, and Placenta Mat/Fet Ss2. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle trophoblast epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Placenta Cells and Placenta Cells Ss2 subtracks, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Tissue was collected from 5 placentas (6-14 gestational weeks) and 11 deciduas. Additionally, blood was drawn from 6 of the donors (D4-D9) and enriched for PBMCs using a Ficoll-Paque gradient. Decidual and placental tissue were both first macroscopically separated. Decidual tissue was then chopped before enzymatic dissociation. Placental villi was scraped from the chorionic membrane before enzymatic dissociation. Decidual and blood cells were enriched for certain populations using an antibody panel prior to Smart-seq2 library preparation. Cells from blood decidua and placenta were enriched using FACS prior to 10x Genomics v2 library preparation. Smart-seq2 libraries were sequenced on an Illumina HiSeq2000. 10x libraries were sequenced on an Illumina HiSeq4000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Roser Vento-Tormo, Mirjana Efremova, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M, Meyer KB, Park JE, Stephenson E, Pola&#324;ski K, Goncalves A et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature. 2018 Nov;563(7731):347-353. PMID: 30429548 
placentaVentoTormoMatFetSs2 Placenta Mat/Fet Ss2 Placenta and decidua cells binned by maternal/fetal smart-seq2 from Vento-Tormo et al 2018 Single Cell RNA-seq Description This track displays data from Single-cell reconstruction of the early maternal-fetal interface in humans. Using droplet-based 10x and plate-based Smart-seq2 single cell RNA-sequencing (scRNA-seq) ~70,000 cells were profiled from first-trimester placentas with matched decidual cells and maternal peripheral blood mononuclear cells (PBMC). This track collection contains nine bar chart tracks of RNA expression in the human placenta, decidua, and maternal PBMCs where cells are grouped by cell type (Placenta Cells, Placenta Cells Ss2), detailed cell type (Placenta Detail, Placenta Detail Ss2), cell location (Placenta Loc, Placenta Loc Ss2), stage (Placenta Stage), and placenta and decidua cells (Placenta Mat/Fet, Placenta Mat/Fet Ss2). The default tracks displayed are Placenta Cells, Placenta Loc, Placenta Loc Ss2, and Placenta Mat/Fet Ss2. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle trophoblast epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Placenta Cells and Placenta Cells Ss2 subtracks, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Tissue was collected from 5 placentas (6-14 gestational weeks) and 11 deciduas. Additionally, blood was drawn from 6 of the donors (D4-D9) and enriched for PBMCs using a Ficoll-Paque gradient. Decidual and placental tissue were both first macroscopically separated. Decidual tissue was then chopped before enzymatic dissociation. Placental villi was scraped from the chorionic membrane before enzymatic dissociation. Decidual and blood cells were enriched for certain populations using an antibody panel prior to Smart-seq2 library preparation. Cells from blood decidua and placenta were enriched using FACS prior to 10x Genomics v2 library preparation. Smart-seq2 libraries were sequenced on an Illumina HiSeq2000. 10x libraries were sequenced on an Illumina HiSeq4000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Roser Vento-Tormo, Mirjana Efremova, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M, Meyer KB, Park JE, Stephenson E, Pola&#324;ski K, Goncalves A et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature. 2018 Nov;563(7731):347-353. PMID: 30429548 
placentaVentoTormoStage10x Placenta Stage Placenta and decidua cells binned by placental stage 10x from Vento-Tormo et al 2018 Single Cell RNA-seq Description This track displays data from Single-cell reconstruction of the early maternal-fetal interface in humans. Using droplet-based 10x and plate-based Smart-seq2 single cell RNA-sequencing (scRNA-seq) ~70,000 cells were profiled from first-trimester placentas with matched decidual cells and maternal peripheral blood mononuclear cells (PBMC). This track collection contains nine bar chart tracks of RNA expression in the human placenta, decidua, and maternal PBMCs where cells are grouped by cell type (Placenta Cells, Placenta Cells Ss2), detailed cell type (Placenta Detail, Placenta Detail Ss2), cell location (Placenta Loc, Placenta Loc Ss2), stage (Placenta Stage), and placenta and decidua cells (Placenta Mat/Fet, Placenta Mat/Fet Ss2). The default tracks displayed are Placenta Cells, Placenta Loc, Placenta Loc Ss2, and Placenta Mat/Fet Ss2. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune muscle trophoblast epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Placenta Cells and Placenta Cells Ss2 subtracks, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Tissue was collected from 5 placentas (6-14 gestational weeks) and 11 deciduas. Additionally, blood was drawn from 6 of the donors (D4-D9) and enriched for PBMCs using a Ficoll-Paque gradient. Decidual and placental tissue were both first macroscopically separated. Decidual tissue was then chopped before enzymatic dissociation. Placental villi was scraped from the chorionic membrane before enzymatic dissociation. Decidual and blood cells were enriched for certain populations using an antibody panel prior to Smart-seq2 library preparation. Cells from blood decidua and placenta were enriched using FACS prior to 10x Genomics v2 library preparation. Smart-seq2 libraries were sequenced on an Illumina HiSeq2000. 10x libraries were sequenced on an Illumina HiSeq4000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Roser Vento-Tormo, Mirjana Efremova, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M, Meyer KB, Park JE, Stephenson E, Pola&#324;ski K, Goncalves A et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature. 2018 Nov;563(7731):347-353. PMID: 30429548 
platinumGenomes Platinum Genomes Platinum genome variants Variation Description These tracks show high-confidence &quot;Platinum Genome&quot; variant calls for two individuals, NA12877 and NA12878, part of a sequenced 17 member pedigree for family number 1463, from the Centre d'Etude du Polymorphisme Humain (CEPH). The hybrid track displays a merging of the NA12878 results with variant calls produced by Genome in a Bottle, discussed further below. CEPH is an international genetic research center that provides a resource of immortalized cell cultures used to map genetic markers, and pedigree 1463 represents a family lineage from Utah of four grandparents, two parents, and 11 children. The whole pedigree was sequenced to 50x depth on a HiSeq 2000 Illumina system, which is considered a platinum standard, where platinum refers to the quality and completeness of the resulting assembly, such as providing full chromosome scaffolds with phasing and haplotypes resolved across the entire genome. This figure depicts the pedigree of the family sequenced for this study, where the ID for each sample is defined by adding the prefix NA128 to each numbered individual, so that 77 = NA12877 and 78 = NA12878, corresponding to the VCF tracks available in this track set. The dark orange individuals indicate sequences used in the analysis methods, whereas the blue represent the founder generations (grandparents), which were also sequenced and used in validation steps. The genomes of the parent-child trio on the top right side, 91-92-78, were also sequenced during Phase I of the 1000 Genomes Project. These tracks represent a comprehensive genome-wide set of phased small variants that have been validated to high confidence. Sequencing and phasing a larger pedigree, beyond the two parents and one child, increases the ability to detect errors and assess the accuracy of more of the variants compared to a standard trio analysis. The genetic inheritance data enables creating a more comprehensive catalog of &quot;platinum variants&quot; that reflects both high accuracy and completeness. These results are significant as a comprehensive set of valid single-nucleotide variants (SNVs) and insertions and deletions (indels), in both the easy and difficult parts of the genome, provides a vital resource for software developers creating the next generation of variant callers, because these are the areas where the current methods most need training data to improve their methods. Since every one of the variants in this catalog is phased, this data set provides a resource to better assess emerging technologies designed to generate valid phasing information. To generate the calls, six analysis pipelines to call SNVs and indels were used and merged into one catalog, where the sensitivity of the genetic inheritance aided to detect genotyping errors and maximize the chance of only including true variants, that might otherwise be removed by suboptimal filtering. Read more about the detailed methods in the referenced paper, further describing this variant catalog of 4.7 million SNVs plus 0.7 million small (1-50 bp) indels, that are all consistent with the pattern of inheritance in the parents and 11 children of this pedigree. The hybrid track in this set extends the characterization of NA12878 by incorporating high confidence calls produced by Genome in a Bottle analysis. The resulting merged files contain more comprehensive coverage of variation than either set independently, for instance, the hg19 version contains over 80,000 more indels than either input set. Read more about the hybrid methods at the following link: https://github.com/Illumina/PlatinumGenomes/wiki/Hybrid-truthset Data Access The VCF files for this track can be obtained from the download server: https://hgdownload.soe.ucsc.edu/gbdb/hg38/platinumGenomes/. These files were obtained from the Platinum genomes source archive: https://s3.eu-central-1.amazonaws.com/platinum-genomes/2017-1.0/ReleaseNotes.txt. Reference Eberle MA, Fritzilas E, Krusche P, K&#228;llberg M, Moore BL, Bekritsky MA, Iqbal Z, Chuang HY, Humphray SJ, Halpern AL et al. A reference data set of 5.4 million phased human variants validated by genetic inheritance from sequencing a three-generation 17-member pedigree. Genome Res. 2017 Jan;27(1):157-164. PMID: 27903644; PMC: PMC5204340 
platinumNA12878 NA12878 Platinum genome variant NA12878 Variation 
platinumNA12877 NA12877 Platinum genome variant NA12877 Variation 
platinumHybrid hybrid Platinum genome hybrid Variation 
hprcArrV1 Rearrangements Rearrangements including indels, inversions, and duplications Human Pangenome - HPRC Description This track shows various rearrangements in the HPRC assemblies with respect to hg38. The types include indels, duplications, inversions, and other more complicated rearrangements. There are five tracks in the Rearrangement composite track: Insertions in hg38 with respect to the HPRC genomes Deletions in hg38 with respect to the HPRC genomes Inversion in hg38 with respect to the HPRC genomes Duplications in the HPRC genomes with respect to hg38 Other Rearrangements: Unalignable sequences in both genomes (inversions, partial transpositions) Display Conventions All items are labeled by the number of HPRC assemblies that have the rearrangement. The indel tracks have one or two additional fields that specify how large the indel is in base pairs. For the Insertions and Deletions track there's only one number with "bp" after it. For insertions, it is the size of the insertion in hg38. For deletions, it is the size of the sequence deleted in hg38. For the Other Rearrangements track, there are two numbers given: the number of unaligned bases in hg38 and the number of unaligned bases in the HPRC assemblies. Methods All these tracks are built from the HPRC chains and nets. The actual instructions used to create these tracks are in the files hprcRearrange.txt and hprcInDel.txt. The first step for all the tracks is to find the orthologous sequences in each HPRC assembly for each chromosome in hg38. These sequences are called the query sequences. For each query sequence, we select the longest chain to the hg38 sequence. This is called the orthologous chain. Following are the specific methods for each track. Insertions, Deletions, and Others In each orthologous chain we look for any gaps in either the reference or the query sequence. There are two basic types of gaps. One type is when the gap contains no bases in one of the two sequences, but one or more unaligned bases in the other. These indicate a standard insertion in one sequence or a deletion in the other. There are also gaps where there are unaligned bases in both sequences. These may be alignment errors or sites where more than one rearrangement occurred between the two sequences. This type of gap is in the "Other Rearrangements" track. This gap identification is done for each of the HPRC assemblies resulting in a set of indels that are clustered based on exact boundaries of the gap in both sequences. This kind of clustering often results in indels that "pile up" with a different number of inserted or deleted bases. Inversions and Duplications For each orthologous chain, we look for any other chain between the same query sequence and the sequence in hg38 that overlaps the orthologous chain. Each of those overlaps is determined to be either an inversion or a local duplication in the HPRC genome by the chainArrange utility. This is done for each of the HPRC assemblies resulting in a set of inversion/duplications that are then clustered over all the assemblies. The clustering is by simple overlap such that no cluster overlaps any other and is done by the chainArrangeCollect utility. References Wen-Wei Liao, Mobin Asri, Jana Ebler, ...et al, Heng Lin, Benedict Paten A draft human pangenome reference. Nature. 2023 May;617(7960):312-324. PMID: 37165242; PMC: PMC1017212; DOI: 10.1038/s41586-023-05896-x Glenn Hickey, Jean Monlong, Jana Ebler, Adam M Novak, Jordan M Eizenga, Yan Gao; Human Pangenome Reference Consortium; Tobias Marschall, Heng Li, Benedict Paten Pangenome graph construction from genome alignments with Minigraph-Cactus. Nature Biotechnology. 2023 May 10. doi: 10.1038/s41587-023-01793-w. PMID: 37165083; DOI: 10.1038/s41587-023-01793-w Armstrong J, Hickey G, Diekhans M, Fiddes IT, Novak AM, Deran A, Fang Q, Xie D, Feng S, Stiller J et al. Progressive Cactus is a multiple-genome aligner for the thousand-genome era. Nature. 2020 Nov;587(7833):246-251. PMID: 33177663; PMC: PMC7673649; DOI: 10.1038/s41586-020-2871-y Paten B, Earl D, Nguyen N, Diekhans M, Zerbino D, Haussler D. Cactus: Algorithms for genome multiple sequence alignment. Genome Res. 2011 Sep;21(9):1512-28. PMID: 21665927; PMC: PMC3166836; DOI: 10.1101/gr.123356.111 
hprcDoubleV1 Other Rearrangements Other Rearrangements: Unalignable sequences in both assemblies (inversions, partial transpositions) Human Pangenome - HPRC 
hprcArrDupBedV1 Duplications Duplications with respect to hg38 in HPRC assemblies Human Pangenome - HPRC 
hprcArrInvBedV1 Inversions Inversions with respect to hg38 in HPRC assemblies Human Pangenome - HPRC 
hprcDeletionsV1 Deletions Insertions in hg38 = Deletion in the HPRC assemblies Human Pangenome - HPRC 
hprcInsertsV1 Insertions Deletions in hg38 = Insertion in the HPRC assemblies Human Pangenome - HPRC 
rectumWangCellType Rectum Cells Rectum cells binned by cell type from Wang et al 2020 Single Cell RNA-seq Description This track shows data from Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. Droplet-based single-cell RNA sequencing (scRNA-seq) was used to survey gene expression profiles of the epithelium in the human ileum, colon, and rectum. A total of 7 cell clusters were identified: enterocytes (EC), goblet cells (G), paneth-like cells (PLC), enteroendocrine cells (EEC), progenitor cells (PRO), transient-amplifying cells (TA) and stem cells (SC). This track collection contains two bar chart tracks of RNA expression in rectum cells where cells are grouped by cell type (Rectum Cells) or donor (Rectum Donor). The default track displayed is Rectum Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification epithelial secretory stem cell Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Note that the Rectum Donor track is colored by donor for improved clarity. Method Using scRNA-seq, RNA profiles of intestinal epithelial cells were obtained for 3,898 cells from two human rectum samples. Tissue samples belonged to two female donors diagnosed with Adenocarcinoma age 66 (Rectum-1) and age 50 (Rectum-2). The healthy intestinal mucous membranes used for each sample were cut away from the tumor border in surgically removed rectal tissue. Additionally, the intestinal tissues were washed in Hank's balanced salt solution (HBSS) to remove mucus, blood cells, and muscle tissue. The sample was enriched for epithelial cells through centrifugation before being dissociated with Tryple to obtain single-cell suspensions. RNA-seq libraries were prepared using 10x Genomics 3' v2 kit and sequenced on an Illumina Hiseq X Ten PE150. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Yalong Wang, Wanlu Song, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Luis Nassar. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Wang Y, Song W, Wang J, Wang T, Xiong X, Qi Z, Fu W, Yang X, Chen YG. Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. J Exp Med. 2020 Feb 3;217(2). PMID: 31753849; PMC: PMC7041720 
rectumWang Rectum Wang Rectum single cell sequencing from Wang et al 2020 Single Cell RNA-seq Description This track shows data from Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. Droplet-based single-cell RNA sequencing (scRNA-seq) was used to survey gene expression profiles of the epithelium in the human ileum, colon, and rectum. A total of 7 cell clusters were identified: enterocytes (EC), goblet cells (G), paneth-like cells (PLC), enteroendocrine cells (EEC), progenitor cells (PRO), transient-amplifying cells (TA) and stem cells (SC). This track collection contains two bar chart tracks of RNA expression in rectum cells where cells are grouped by cell type (Rectum Cells) or donor (Rectum Donor). The default track displayed is Rectum Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification epithelial secretory stem cell Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Note that the Rectum Donor track is colored by donor for improved clarity. Method Using scRNA-seq, RNA profiles of intestinal epithelial cells were obtained for 3,898 cells from two human rectum samples. Tissue samples belonged to two female donors diagnosed with Adenocarcinoma age 66 (Rectum-1) and age 50 (Rectum-2). The healthy intestinal mucous membranes used for each sample were cut away from the tumor border in surgically removed rectal tissue. Additionally, the intestinal tissues were washed in Hank's balanced salt solution (HBSS) to remove mucus, blood cells, and muscle tissue. The sample was enriched for epithelial cells through centrifugation before being dissociated with Tryple to obtain single-cell suspensions. RNA-seq libraries were prepared using 10x Genomics 3' v2 kit and sequenced on an Illumina Hiseq X Ten PE150. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Yalong Wang, Wanlu Song, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Luis Nassar. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Wang Y, Song W, Wang J, Wang T, Xiong X, Qi Z, Fu W, Yang X, Chen YG. Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. J Exp Med. 2020 Feb 3;217(2). PMID: 31753849; PMC: PMC7041720 
rectumWangDonor Rectum Donor Rectum cells binned by organ donor from Wang et al 2020 Single Cell RNA-seq Description This track shows data from Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. Droplet-based single-cell RNA sequencing (scRNA-seq) was used to survey gene expression profiles of the epithelium in the human ileum, colon, and rectum. A total of 7 cell clusters were identified: enterocytes (EC), goblet cells (G), paneth-like cells (PLC), enteroendocrine cells (EEC), progenitor cells (PRO), transient-amplifying cells (TA) and stem cells (SC). This track collection contains two bar chart tracks of RNA expression in rectum cells where cells are grouped by cell type (Rectum Cells) or donor (Rectum Donor). The default track displayed is Rectum Cells. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification epithelial secretory stem cell Cells that fall into multiple classes will be colored by blending the colors associated with those classes. Note that the Rectum Donor track is colored by donor for improved clarity. Method Using scRNA-seq, RNA profiles of intestinal epithelial cells were obtained for 3,898 cells from two human rectum samples. Tissue samples belonged to two female donors diagnosed with Adenocarcinoma age 66 (Rectum-1) and age 50 (Rectum-2). The healthy intestinal mucous membranes used for each sample were cut away from the tumor border in surgically removed rectal tissue. Additionally, the intestinal tissues were washed in Hank's balanced salt solution (HBSS) to remove mucus, blood cells, and muscle tissue. The sample was enriched for epithelial cells through centrifugation before being dissociated with Tryple to obtain single-cell suspensions. RNA-seq libraries were prepared using 10x Genomics 3' v2 kit and sequenced on an Illumina Hiseq X Ten PE150. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Yalong Wang, Wanlu Song, and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Luis Nassar. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Wang Y, Song W, Wang J, Wang T, Xiong X, Qi Z, Fu W, Yang X, Chen YG. Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. J Exp Med. 2020 Feb 3;217(2). PMID: 31753849; PMC: PMC7041720 
ucscToRefSeq RefSeq Acc RefSeq Accession Mapping and Sequencing Description This track associates UCSC Genome Browser chromosome names to accession identifiers from the NCBI Reference Sequence Database (RefSeq). The data were downloaded from the NCBI assembly database. Credits The data for this track was prepared by Hiram Clawson. 
refSeqFuncElems RefSeq Func Elems NCBI RefSeq Functional Elements Regulation Description NCBI recently announced a new release of functional regulatory elements. NCBI is now providing RefSeq and Gene records for non-genic functional elements that have been described in the literature and are experimentally validated. Elements in scope include experimentally-verified gene regulatory regions (e.g., enhancers, silencers, locus control regions), known structural elements (e.g., insulators, DNase I hypersensitive sites, matrix/scaffold-associated regions), well-characterized DNA replication origins, and clinically-significant sites of DNA recombination and genomic instability. Priority is given to genomic regions that are implicated in human disease or are otherwise of significant interest to the research community. Currently, the scope of this project is restricted to human and mouse. The current scope does not include functional elements predicted from large-scale epigenomic mapping studies, nor elements based on disease-associated variation. Display Conventions and Configuration Functional elements are colored by Sequence Ontology (SO) term using the same scheme as NCBI's Genome Data Viewer: Regulatory elements (items labeled by INSDC regulatory class) Protein binding sites (items labeled by bound moiety) Mobile elements Recombination features Sequence features Other Methods NCBI manually curated features in accordance with International Nucleotide Sequence Database Collaboration (INSDC) standards. Features that are supported by direct experimental evidence include at least one experiment qualifier with an evidence code (ECO ID) from the Evidence and Conclusion Ontology, and at least one citation from PubMed. Currently 971 distinct PubMed citations are included in this track. Contact This track was made with assistance from Terence Murphy at NCBI. Data access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API, and the genome annotations are stored in files that can be downloaded from our download server, with more information available on our blog. New Version Available Several new enhancements to the RefSeq Functional Elements dataset are available as a Public Hub. The hub can be found on the Public Hub page. The track hub was prepared by Dr. Catherine M. Farrell, NCBI/NLM/NIH with further insights discussed in a related NCBI blog post. References Pruitt KD, Brown GR, Hiatt SM, Thibaud-Nissen F, Astashyn A, Ermolaeva O, Farrell CM, Hart J, Landrum MJ, McGarvey KM et al. RefSeq: an update on mammalian reference sequences. Nucleic Acids Res. 2014 Jan;42(Database issue):D756-63. PMID: 24259432; PMC: PMC3965018 Pruitt KD, Tatusova T, Maglott DR. NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2005 Jan 1;33(Database issue):D501-4. PMID: 15608248; PMC: PMC539979 
ReMap ReMap ChIP-seq ReMap Atlas of Regulatory Regions Regulation Description This track represents the ReMap Atlas of regulatory regions, which consists of a large-scale integrative analysis of all Public ChIP-seq data for transcriptional regulators from GEO, ArrayExpress, and ENCODE. Below is a schematic diagram of the types of regulatory regions: ReMap 2022 Atlas (all peaks for each analyzed data set) ReMap 2022 Non-redundant peaks (merged similar target) ReMap 2022 Cis Regulatory Modules Display Conventions and Configuration Each transcription factor follows a specific RGB color. ChIP-seq peak summits are represented by vertical bars. Hsap: A data set is defined as a ChIP/Exo-seq experiment in a given GEO/ArrayExpress/ENCODE series (e.g. GSE41561), for a given TF (e.g. ESR1), in a particular biological condition (e.g. MCF-7). Data sets are labeled with the concatenation of these three pieces of information (e.g. GSE41561.ESR1.MCF-7). Atha: The data set is defined as a ChIP-seq experiment in a given series (e.g. GSE94486), for a given target (e.g. ARR1), in a particular biological condition (i.e. ecotype, tissue type, experimental conditions; e.g. Col-0_seedling_3d-6BA-4h). Data sets are labeled with the concatenation of these three pieces of information (e.g. GSE94486.ARR1.Col-0_seedling_3d-6BA-4h). Methods This 4th release of ReMap (2022) presents the analysis of a total of 8,103 quality controlled ChIP-seq (n=7,895) and ChIP-exo (n=208) data sets from public sources (GEO, ArrayExpress, ENCODE). The ChIP-seq/exo data sets have been mapped to the GRCh38/hg38 human assembly. The data set is defined as a ChIP-seq experiment in a given series (e.g. GSE46237), for a given TF (e.g. NR2C2), in a particular biological condition (i.e. cell line, tissue type, disease state, or experimental conditions; e.g. HELA). Data sets were labeled by concatenating these three pieces of information, such as GSE46237.NR2C2.HELA. Those merged analyses cover a total of 1,211 DNA-binding proteins (transcriptional regulators) such as a variety of transcription factors (TFs), transcription co-activators (TCFs), and chromatin-remodeling factors (CRFs) for 182 million peaks. GEO & ArrayExpress Public ChIP-seq data sets were extracted from Gene Expression Omnibus (GEO) and ArrayExpress (AE) databases. For GEO, the query &apos;(&apos;chip seq&apos; OR &apos;chipseq&apos; OR &apos;chip sequencing&apos;) AND &apos;Genome binding/occupancy profiling by high throughput sequencing&apos; AND &apos;homo sapiens&apos;[organism] AND NOT &apos;ENCODE&apos;[project]&apos; was used to return a list of all potential data sets to analyze, which were then manually assessed for further analyses. Data sets involving polymerases (i.e. Pol2 and Pol3), and some mutated or fused TFs (e.g. KAP1 N/C terminal mutation, GSE27929) were excluded. ENCODE Available ENCODE ChIP-seq data sets for transcriptional regulators from the ENCODE portal were processed with the standardized ReMap pipeline. The list of ENCODE data was retrieved as FASTQ files from the ENCODE portal using the following filters: Assay: &quot;ChIP-seq&quot; Organism: &quot;Homo sapiens&quot; Target of assay: &quot;transcription factor&quot; Available data: &quot;fastq&quot; on 2016 June 21st Metadata information in JSON format and FASTQ files were retrieved using the Python requests module. ChIP-seq processing Both Public and ENCODE data were processed similarly. Bowtie 2 (PMC3322381) (version 2.2.9) with options -end-to-end -sensitive was used to align all reads on the genome. Biological and technical replicates for each unique combination of GSE/TF/Cell type or Biological condition were used for peak calling. TFBS were identified using MACS2 peak-calling tool (PMC3120977) (version 2.1.1.2) in order to follow ENCODE ChIP-seq guidelines, with stringent thresholds (MACS2 default thresholds, p-value: 1e-5). An input data set was used when available. Quality assessment To assess the quality of public data sets, a score was computed based on the cross-correlation and the FRiP (fraction of reads in peaks) metrics developed by the ENCODE Consortium (https://genome.ucsc.edu/ENCODE/qualityMetrics.html). Two thresholds were defined for each of the two cross-correlation ratios (NSC, normalized strand coefficient: 1.05 and 1.10; RSC, relative strand coefficient: 0.8 and 1.0). Detailed descriptions of the ENCODE quality coefficients can be found at https://genome.ucsc.edu/ENCODE/qualityMetrics.html. The phantompeak tools suite was used (https://code.google.com/p/phantompeakqualtools/) to compute RSC and NSC. Please refer to the ReMap 2022, 2020, and 2018 publications for more details (citation below). This is a detailled view of the data increase in ReMap v2 with FOXA1 peaks at a specific location. --> Data Access ReMap Atlas of regulatory regions data can be explored interactively with the Table Browser and cross-referenced with the Data Integrator. For programmatic access, the track can be accessed using the Genome Browser&apos;s REST API. ReMap annotations can be downloaded from the Genome Browser's download server as a bigBed file. This compressed binary format can be remotely queried through command line utilities. Please note that some of the download files can be quite large. Individual BED files for specific TFs, cells/biotypes, or data sets can be found and downloaded on the ReMap website. References Ch&#232;neby J, Gheorghe M, Artufel M, Mathelier A, Ballester B. ReMap 2018: an updated atlas of regulatory regions from an integrative analysis of DNA-binding ChIP- seq experiments. Nucleic Acids Res. 2018 Jan 4;46(D1):D267-D275. PMID: 29126285; PMC: PMC5753247 Ch&#232;neby J, M&#233;n&#233;trier Z, Mestdagh M, Rosnet T, Douida A, Rhalloussi W, Bergon A, Lopez F, Ballester B. ReMap 2020: a database of regulatory regions from an integrative analysis of Human and Arabidopsis DNA-binding sequencing experiments. Nucleic Acids Res. 2020 Jan 8;48(D1):D180-D188. PMID: 31665499; PMC: PMC7145625 Griffon A, Barbier Q, Dalino J, van Helden J, Spicuglia S, Ballester B. Integrative analysis of public ChIP-seq experiments reveals a complex multi-cell regulatory landscape. Nucleic Acids Res. 2015 Feb 27;43(4):e27. PMID: 25477382; PMC: PMC4344487 Hammal F, de Langen P, Bergon A, Lopez F, Ballester B. ReMap 2022: a database of Human, Mouse, Drosophila and Arabidopsis regulatory regions from an integrative analysis of DNA-binding sequencing experiments. Nucleic Acids Res. 2022 Jan 7;50(D1):D316-D325. PMID: 34751401; PMC: PMC8728178 
ReMapTFs ReMap ChIP-seq ReMap Atlas of Regulatory Regions Regulation Description This track represents the ReMap Atlas of regulatory regions, which consists of a large-scale integrative analysis of all Public ChIP-seq data for transcriptional regulators from GEO, ArrayExpress, and ENCODE. Below is a schematic diagram of the types of regulatory regions: ReMap 2022 Atlas (all peaks for each analyzed data set) ReMap 2022 Non-redundant peaks (merged similar target) ReMap 2022 Cis Regulatory Modules Display Conventions and Configuration Each transcription factor follows a specific RGB color. ChIP-seq peak summits are represented by vertical bars. Hsap: A data set is defined as a ChIP/Exo-seq experiment in a given GEO/ArrayExpress/ENCODE series (e.g. GSE41561), for a given TF (e.g. ESR1), in a particular biological condition (e.g. MCF-7). Data sets are labeled with the concatenation of these three pieces of information (e.g. GSE41561.ESR1.MCF-7). Atha: The data set is defined as a ChIP-seq experiment in a given series (e.g. GSE94486), for a given target (e.g. ARR1), in a particular biological condition (i.e. ecotype, tissue type, experimental conditions; e.g. Col-0_seedling_3d-6BA-4h). Data sets are labeled with the concatenation of these three pieces of information (e.g. GSE94486.ARR1.Col-0_seedling_3d-6BA-4h). Methods This 4th release of ReMap (2022) presents the analysis of a total of 8,103 quality controlled ChIP-seq (n=7,895) and ChIP-exo (n=208) data sets from public sources (GEO, ArrayExpress, ENCODE). The ChIP-seq/exo data sets have been mapped to the GRCh38/hg38 human assembly. The data set is defined as a ChIP-seq experiment in a given series (e.g. GSE46237), for a given TF (e.g. NR2C2), in a particular biological condition (i.e. cell line, tissue type, disease state, or experimental conditions; e.g. HELA). Data sets were labeled by concatenating these three pieces of information, such as GSE46237.NR2C2.HELA. Those merged analyses cover a total of 1,211 DNA-binding proteins (transcriptional regulators) such as a variety of transcription factors (TFs), transcription co-activators (TCFs), and chromatin-remodeling factors (CRFs) for 182 million peaks. GEO & ArrayExpress Public ChIP-seq data sets were extracted from Gene Expression Omnibus (GEO) and ArrayExpress (AE) databases. For GEO, the query &apos;(&apos;chip seq&apos; OR &apos;chipseq&apos; OR &apos;chip sequencing&apos;) AND &apos;Genome binding/occupancy profiling by high throughput sequencing&apos; AND &apos;homo sapiens&apos;[organism] AND NOT &apos;ENCODE&apos;[project]&apos; was used to return a list of all potential data sets to analyze, which were then manually assessed for further analyses. Data sets involving polymerases (i.e. Pol2 and Pol3), and some mutated or fused TFs (e.g. KAP1 N/C terminal mutation, GSE27929) were excluded. ENCODE Available ENCODE ChIP-seq data sets for transcriptional regulators from the ENCODE portal were processed with the standardized ReMap pipeline. The list of ENCODE data was retrieved as FASTQ files from the ENCODE portal using the following filters: Assay: &quot;ChIP-seq&quot; Organism: &quot;Homo sapiens&quot; Target of assay: &quot;transcription factor&quot; Available data: &quot;fastq&quot; on 2016 June 21st Metadata information in JSON format and FASTQ files were retrieved using the Python requests module. ChIP-seq processing Both Public and ENCODE data were processed similarly. Bowtie 2 (PMC3322381) (version 2.2.9) with options -end-to-end -sensitive was used to align all reads on the genome. Biological and technical replicates for each unique combination of GSE/TF/Cell type or Biological condition were used for peak calling. TFBS were identified using MACS2 peak-calling tool (PMC3120977) (version 2.1.1.2) in order to follow ENCODE ChIP-seq guidelines, with stringent thresholds (MACS2 default thresholds, p-value: 1e-5). An input data set was used when available. Quality assessment To assess the quality of public data sets, a score was computed based on the cross-correlation and the FRiP (fraction of reads in peaks) metrics developed by the ENCODE Consortium (https://genome.ucsc.edu/ENCODE/qualityMetrics.html). Two thresholds were defined for each of the two cross-correlation ratios (NSC, normalized strand coefficient: 1.05 and 1.10; RSC, relative strand coefficient: 0.8 and 1.0). Detailed descriptions of the ENCODE quality coefficients can be found at https://genome.ucsc.edu/ENCODE/qualityMetrics.html. The phantompeak tools suite was used (https://code.google.com/p/phantompeakqualtools/) to compute RSC and NSC. Please refer to the ReMap 2022, 2020, and 2018 publications for more details (citation below). This is a detailled view of the data increase in ReMap v2 with FOXA1 peaks at a specific location. --> Data Access ReMap Atlas of regulatory regions data can be explored interactively with the Table Browser and cross-referenced with the Data Integrator. For programmatic access, the track can be accessed using the Genome Browser&apos;s REST API. ReMap annotations can be downloaded from the Genome Browser's download server as a bigBed file. This compressed binary format can be remotely queried through command line utilities. Please note that some of the download files can be quite large. Individual BED files for specific TFs, cells/biotypes, or data sets can be found and downloaded on the ReMap website. References Ch&#232;neby J, Gheorghe M, Artufel M, Mathelier A, Ballester B. ReMap 2018: an updated atlas of regulatory regions from an integrative analysis of DNA-binding ChIP- seq experiments. Nucleic Acids Res. 2018 Jan 4;46(D1):D267-D275. PMID: 29126285; PMC: PMC5753247 Ch&#232;neby J, M&#233;n&#233;trier Z, Mestdagh M, Rosnet T, Douida A, Rhalloussi W, Bergon A, Lopez F, Ballester B. ReMap 2020: a database of regulatory regions from an integrative analysis of Human and Arabidopsis DNA-binding sequencing experiments. Nucleic Acids Res. 2020 Jan 8;48(D1):D180-D188. PMID: 31665499; PMC: PMC7145625 Griffon A, Barbier Q, Dalino J, van Helden J, Spicuglia S, Ballester B. Integrative analysis of public ChIP-seq experiments reveals a complex multi-cell regulatory landscape. Nucleic Acids Res. 2015 Feb 27;43(4):e27. PMID: 25477382; PMC: PMC4344487 Hammal F, de Langen P, Bergon A, Lopez F, Ballester B. ReMap 2022: a database of Human, Mouse, Drosophila and Arabidopsis regulatory regions from an integrative analysis of DNA-binding sequencing experiments. Nucleic Acids Res. 2022 Jan 7;50(D1):D316-D325. PMID: 34751401; PMC: PMC8728178 
ReMapDensity ReMap density ReMap density Regulation Description This track represents the ReMap Atlas of regulatory regions, which consists of a large-scale integrative analysis of all Public ChIP-seq data for transcriptional regulators from GEO, ArrayExpress, and ENCODE. Below is a schematic diagram of the types of regulatory regions: ReMap 2022 Atlas (all peaks for each analyzed data set) ReMap 2022 Non-redundant peaks (merged similar target) ReMap 2022 Cis Regulatory Modules Display Conventions and Configuration Each transcription factor follows a specific RGB color. ChIP-seq peak summits are represented by vertical bars. Hsap: A data set is defined as a ChIP/Exo-seq experiment in a given GEO/ArrayExpress/ENCODE series (e.g. GSE41561), for a given TF (e.g. ESR1), in a particular biological condition (e.g. MCF-7). Data sets are labeled with the concatenation of these three pieces of information (e.g. GSE41561.ESR1.MCF-7). Atha: The data set is defined as a ChIP-seq experiment in a given series (e.g. GSE94486), for a given target (e.g. ARR1), in a particular biological condition (i.e. ecotype, tissue type, experimental conditions; e.g. Col-0_seedling_3d-6BA-4h). Data sets are labeled with the concatenation of these three pieces of information (e.g. GSE94486.ARR1.Col-0_seedling_3d-6BA-4h). Methods This 4th release of ReMap (2022) presents the analysis of a total of 8,103 quality controlled ChIP-seq (n=7,895) and ChIP-exo (n=208) data sets from public sources (GEO, ArrayExpress, ENCODE). The ChIP-seq/exo data sets have been mapped to the GRCh38/hg38 human assembly. The data set is defined as a ChIP-seq experiment in a given series (e.g. GSE46237), for a given TF (e.g. NR2C2), in a particular biological condition (i.e. cell line, tissue type, disease state, or experimental conditions; e.g. HELA). Data sets were labeled by concatenating these three pieces of information, such as GSE46237.NR2C2.HELA. Those merged analyses cover a total of 1,211 DNA-binding proteins (transcriptional regulators) such as a variety of transcription factors (TFs), transcription co-activators (TCFs), and chromatin-remodeling factors (CRFs) for 182 million peaks. GEO & ArrayExpress Public ChIP-seq data sets were extracted from Gene Expression Omnibus (GEO) and ArrayExpress (AE) databases. For GEO, the query &apos;(&apos;chip seq&apos; OR &apos;chipseq&apos; OR &apos;chip sequencing&apos;) AND &apos;Genome binding/occupancy profiling by high throughput sequencing&apos; AND &apos;homo sapiens&apos;[organism] AND NOT &apos;ENCODE&apos;[project]&apos; was used to return a list of all potential data sets to analyze, which were then manually assessed for further analyses. Data sets involving polymerases (i.e. Pol2 and Pol3), and some mutated or fused TFs (e.g. KAP1 N/C terminal mutation, GSE27929) were excluded. ENCODE Available ENCODE ChIP-seq data sets for transcriptional regulators from the ENCODE portal were processed with the standardized ReMap pipeline. The list of ENCODE data was retrieved as FASTQ files from the ENCODE portal using the following filters: Assay: &quot;ChIP-seq&quot; Organism: &quot;Homo sapiens&quot; Target of assay: &quot;transcription factor&quot; Available data: &quot;fastq&quot; on 2016 June 21st Metadata information in JSON format and FASTQ files were retrieved using the Python requests module. ChIP-seq processing Both Public and ENCODE data were processed similarly. Bowtie 2 (PMC3322381) (version 2.2.9) with options -end-to-end -sensitive was used to align all reads on the genome. Biological and technical replicates for each unique combination of GSE/TF/Cell type or Biological condition were used for peak calling. TFBS were identified using MACS2 peak-calling tool (PMC3120977) (version 2.1.1.2) in order to follow ENCODE ChIP-seq guidelines, with stringent thresholds (MACS2 default thresholds, p-value: 1e-5). An input data set was used when available. Quality assessment To assess the quality of public data sets, a score was computed based on the cross-correlation and the FRiP (fraction of reads in peaks) metrics developed by the ENCODE Consortium (https://genome.ucsc.edu/ENCODE/qualityMetrics.html). Two thresholds were defined for each of the two cross-correlation ratios (NSC, normalized strand coefficient: 1.05 and 1.10; RSC, relative strand coefficient: 0.8 and 1.0). Detailed descriptions of the ENCODE quality coefficients can be found at https://genome.ucsc.edu/ENCODE/qualityMetrics.html. The phantompeak tools suite was used (https://code.google.com/p/phantompeakqualtools/) to compute RSC and NSC. Please refer to the ReMap 2022, 2020, and 2018 publications for more details (citation below). This is a detailled view of the data increase in ReMap v2 with FOXA1 peaks at a specific location. --> Data Access ReMap Atlas of regulatory regions data can be explored interactively with the Table Browser and cross-referenced with the Data Integrator. For programmatic access, the track can be accessed using the Genome Browser&apos;s REST API. ReMap annotations can be downloaded from the Genome Browser's download server as a bigBed file. This compressed binary format can be remotely queried through command line utilities. Please note that some of the download files can be quite large. Individual BED files for specific TFs, cells/biotypes, or data sets can be found and downloaded on the ReMap website. References Ch&#232;neby J, Gheorghe M, Artufel M, Mathelier A, Ballester B. ReMap 2018: an updated atlas of regulatory regions from an integrative analysis of DNA-binding ChIP- seq experiments. Nucleic Acids Res. 2018 Jan 4;46(D1):D267-D275. PMID: 29126285; PMC: PMC5753247 Ch&#232;neby J, M&#233;n&#233;trier Z, Mestdagh M, Rosnet T, Douida A, Rhalloussi W, Bergon A, Lopez F, Ballester B. ReMap 2020: a database of regulatory regions from an integrative analysis of Human and Arabidopsis DNA-binding sequencing experiments. Nucleic Acids Res. 2020 Jan 8;48(D1):D180-D188. PMID: 31665499; PMC: PMC7145625 Griffon A, Barbier Q, Dalino J, van Helden J, Spicuglia S, Ballester B. Integrative analysis of public ChIP-seq experiments reveals a complex multi-cell regulatory landscape. Nucleic Acids Res. 2015 Feb 27;43(4):e27. PMID: 25477382; PMC: PMC4344487 Hammal F, de Langen P, Bergon A, Lopez F, Ballester B. ReMap 2022: a database of Human, Mouse, Drosophila and Arabidopsis regulatory regions from an integrative analysis of DNA-binding sequencing experiments. Nucleic Acids Res. 2022 Jan 7;50(D1):D316-D325. PMID: 34751401; PMC: PMC8728178 
ucscRetroAli9 RetroGenes V9 Retroposed Genes V9, Including Pseudogenes Genes and Gene Predictions Description Retrotransposition is a process involving the copying of DNA by a group of enzymes that have the ability to reverse transcribe spliced mRNAs, and the insertion of these processed mRNAs back into the genome resulting in single-exon copies of genes and sometime chimeric genes. Retrogenes are mostly non-functional pseudogenes but some are functional genes that have acquired a promoter from a neighboring gene, or transcribed pseudogenes, and some are anti-sense transcripts that may impede mRNA translation. Methods All mRNAs of a species from GenBank were aligned to the genome using lastz (Miller lab, Pennsylvania State University). mRNAs that aligned twice in the genome (once with introns and once without introns) were initially screened. Next, a series of features were scored to determine candidates for retrotransposition events. These features included position and length of the polyA tail, percent coverage of the retrogene alignment to the parent, degree of synteny with mouse, coverage of repetitive elements, number of exons that can still be aligned to the retrogene, number of putative introns removed at the retrogene locus and degree of divergence from the parent gene. Retrogenes were classified using a threshold score function that is a linear combination of this set of features. Retrogenes in the final set were selected using a score threshold based on a ROC plot against the Vega annotated pseudogenes. Retrogene Statistics table: Expression of Retrogene: The following values are possible where those that are not expressed are classed as pseudogene or mrna: pseudogene indicates that the parent gene has been annotated by one of NCBI's RefSeq, UCSC Genes or Mammalian Gene Collection (MGC). mrna indicates that the parent gene is a spliced mrna that has no annotation in NCBI's RefSeq, UCSC Genes or Mammalian Gene Collection (MGC). Therefore, the retrogene is a product of a potentially non-annotated parent gene and is a putative pseudogene of that putative parent gene. expressed weak indicates that there is a mRNA overlapping the retrogene, indicating possible transcription. noOrf indicates that an ORF was not identified by BESTORF. expressed indicates that there is a medium level of mRNAs/ESTs mapping to the retrogene locus, indicating possible transcription. expressed strong indicates that there is a mRNA overlapping the retrogene, and at least five spliced ESTs indicating probable transcription. noOrf indicates that an ORF was not identified by BESTORF. expressed shuffle indicates that the retrogene was inserted into a pre-existing annotated gene. Score: Weighted sum of features (mentioned above) of the potential retrogene. Percent Gene Alignment Coverage (Bases Matching Parent): Shows the percentage of the parent gene aligning to this region. Intron Count: Number of introns is the number of gaps in the alignment between the parent mRNA and the genome where gaps are &gt;80 bp and the ratio of the mRNA alignment gap to the genome alignment gap is less than 30% after removing repeats. Gap Count: Numer of gaps in the alignment of between the parent mRNA and the genome after removing repeats. Gaps are not counted if the gap on the mRNA side of the alignment is a similar size to the gap in the genome alignment. BESTORF Score: BESTORF (written by Victor Solovyev) predicts potential open reading frames (ORFs) in mRNAs/ESTs with very high accuracy using a Markov chain model of coding regions and a probabilistic model of translation start codon potential. The score threshold for finding an ORF is 50 (Jim Kent, personal communication). Break in Orthology table: Retrogenes inserted into the genome since the mouse/human divergence show a break in the human genome syntenic net alignments to the mouse genome. A break in orthology score is calculated and weighted before contributing to the final retrogene score. The break in orthology score ranges from 0-130 and it represents the portion of the genome that is missing in each species relative to the reference genome (human hg38) at the retrogene locus as defined by syntenic alignment nets. If the score is 0, there is orthologous DNA and no break in ortholog with the other species; this could be an ancient retrogene; duplicated pseudogenes may also score low because they are often generated via large segmental duplication events so the size of the pseudogene is small relative to the size of the inserted duplicated sequence. Scores greater than 100 represent cases where the retrogene alignment has no flanking alignment resulting from an ancient insertion or other complex rearrangement. Breaks in orthology with human and dog tend to be due to genomic insertions in the rodent lineage so sequence gaps are not treated as orthology breaks. Relative orthology of human/mouse and dog/mouse nets are used to avoid false positives due to deletions in the human genome. Since older retrogenes will not show a break in orthology, this feature is weighted lower than other features when scoring putative retrogenes. Credits The RetroFinder program and browser track were developed by Robert Baertsch at UCSC. References Baertsch R, Diekhans M, Kent WJ, Haussler D, Brosius J. Retrocopy contributions to the evolution of the human genome. BMC Genomics. 2008 Oct 8;9:466. PMID: 18842134; PMC: PMC2584115 Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Pei B, Sisu C, Frankish A, Howald C, Habegger L, Mu XJ, Harte R, Balasubramanian S, Tanzer A, Diekhans M et al. The GENCODE pseudogene resource. Genome Biol. 2012 Sep 26;13(9):R51. PMID: 22951037; PMC: PMC3491395 Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 Zheng D, Frankish A, Baertsch R, Kapranov P, Reymond A, Choo SW, Lu Y, Denoeud F, Antonarakis SE, Snyder M et al. Pseudogenes in the ENCODE regions: consensus annotation, analysis of transcription, and evolution. Genome Res. 2007 Jun;17(6):839-51. PMID: 17568002; PMC: PMC1891343 
revel REVEL Scores REVEL Pathogenicity Score for single-base coding mutations (zoom for exact score) Phenotype and Literature Description This track collection shows Rare Exome Variant Ensemble Learner (REVEL) scores for predicting the deleteriousness of each nucleotide change in the genome. REVEL is an ensemble method for predicting the pathogenicity of missense variants based on a combination of scores from 13 individual tools: MutPred, FATHMM v2.3, VEST 3.0, PolyPhen-2, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP++, SiPhy, phyloP, and phastCons. REVEL was trained using recently discovered pathogenic and rare neutral missense variants, excluding those previously used to train its constituent tools. The REVEL score for an individual missense variant can range from 0 to 1, with higher scores reflecting greater likelihood that the variant is disease-causing. Most authors of deleteriousness scores argue against using fixed cutoffs in diagnostics. But to give an idea of the meaning of the score value, the REVEL authors note: "For example, 75.4% of disease mutations but only 10.9% of neutral variants (and 12.4% of all ESVs) have a REVEL score above 0.5, corresponding to a sensitivity of 0.754 and specificity of 0.891. Selecting a more stringent REVEL score threshold of 0.75 would result in higher specificity but lower sensitivity, with 52.1% of disease mutations, 3.3% of neutral variants, and 4.1% of all ESVs being classified as pathogenic". (Figure S1 of the reference below) Display Conventions and Configuration There are five subtracks for this track: Four lettered subtracks, one for every nucleotide, showing scores for mutation from the reference to that nucleotide. All subtracks show the REVEL ensemble score on mouseover. Across the exome, there are three values per position, one for every possible nucleotide mutation. The fourth value, &quot;no mutation&quot;, representing the reference allele, e.g. A to A, is always set to zero, "0.0". REVEL only takes into account amino acid changes, so a nucleotide change that results in no amino acid change (synonymous) also receives the score "0.0". In rare cases, two scores are output for the same variant at a genome position. This happens when there are two transcripts with different splicing patterns and since some input scores for REVEL take into account the sequence context, the same mutation can get two different scores. In these cases, only the maximum score is shown in the four per-nucleotide subtracks. The complete set of scores are shown in the Overlaps track. One subtrack, Overlaps, shows alternate REVEL scores when applicable. In rare cases (0.05% of genome positions), multiple scores exist with a single variant, due to multiple, overlapping transcripts. For example, if there are two transcripts and one covers only half of an exon, then the amino acids that overlap both transcripts will get two different REVEL scores, since some of the underlying scores (polyPhen for example) take into account the amino acid sequence context and this context is different depending on the transcript. For these cases, this subtrack contains at least two graphical features, for each affected genome position. Each feature is labeled with the mutation (A, C, T or G). The transcript IDs and resulting score is shown when hovering over the feature or clicking it. For the large majority of the genome, this subtrack has no features. This is because REVEL usually outputs only a single score per nucleotide and most transcript-derived amino acid sequence contexts are identical. Note that in most diagnostic assays, variants are called using WGS pipelines, not RNA-seq. As a result, variants are originally located on the genome, not on transcripts, and the choice of transcript is made by a variant calling software using a heuristic. In addition, clinically, in the field, some transcripts have been agreed-on as more relevant for a disease, e.g. because only certain transcripts may be expressed in the relevant tissue. So the choice of the most relevant transcript, and as such the REVEL score, may be a question of manual curation standards rather than a result of the variant itself. When using this track, zoom in until you can see every basepair at the top of the display. Otherwise, there are several nucleotides per pixel under your mouse cursor and no score will be shown on the mouseover tooltip. For hg38, note that the data was converted from the hg19 data using the UCSC liftOver program, by the REVEL authors. This can lead to missing values or duplicated values. When a hg38 position is annotated with two scores due to the lifting, the authors removed all the scores for this position. They did the same when the reference allele has changed from hg19 to hg38. Also, on hg38, the track has the "lifted" icon to indicate this. You can double-check if a nucleotide position is possibly affected by the lifting procedure by activating the track "Hg19 Mapping" under "Mapping and Sequencing". Data access REVEL scores are available at the REVEL website. The site provides precomputed REVEL scores for all possible human missense variants to facilitate the identification of pathogenic variants among the large number of rare variants discovered in sequencing studies. The REVEL data on the UCSC Genome Browser can be explored interactively with the Table Browser or the Data Integrator. For automated download and analysis, the genome annotation is stored at UCSC in bigWig files that can be downloaded from our download server. The files for this track are called a.bw, c.bw, g.bw, t.bw. Individual regions or the whole genome annotation can be obtained using our tool bigWigToWig which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tools can also be used to obtain features confined to given range, e.g. &nbsp; bigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/revel/a.bw stdout Methods Data were converted from the files provided on the REVEL Downloads website. As with all other tracks, a full log of all commands used for the conversion is available in our source repository, for hg19 and hg38. The release used for each assembly is shown on the track description page. Credits Thanks to the REVEL development team for providing precomputed data and fixing duplicated values in the hg38 files. References Ioannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK, Baheti S, Musolf A, Li Q, Holzinger E, Karyadi D, et al. REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants Am J Hum Genet. 2016 Oct 6;99(4):877-885. PMID: 27666373; PMC: PMC5065685 
revelOverlaps REVEL overlaps REVEL: Positions with >1 score due to overlapping transcripts (mouseover for details) Phenotype and Literature 
revelT Mutation: T REVEL: Mutation is T Phenotype and Literature 
revelG Mutation: G REVEL: Mutation is G Phenotype and Literature 
revelC Mutation: C REVEL: Mutation is C Phenotype and Literature 
revelA Mutation: A REVEL: Mutation is A Phenotype and Literature 
scaffolds Scaffolds GRCh38 Defined Scaffold Identifiers Mapping and Sequencing Description This track shows the Genome Reference Consortium (GRC) names for the scaffolds in the GRCh38 (hg38) assembly, downloaded from the GRCh38 acc2name file in GenBank. 
sgpGene SGP Genes SGP Gene Predictions Using Mouse/Human Homology Genes and Gene Predictions Description This track shows gene predictions from the SGP2 homology-based gene prediction program developed by Roderic Guig&oacute;'s &quot;Computational Biology of RNA Processing&quot; group, which is part of the Centre de Regulaci&oacute; Gen&ograve;mica (CRG) in Barcelona, Catalunya, Spain. To predict genes in a genomic query, SGP2 combines geneid predictions with tblastx comparisons of the genome of the target species against genomic sequences of other species (reference genomes) deemed to be at an appropriate evolutionary distance from the target. Credits Thanks to the &quot;Computational Biology of RNA Processing&quot; group for providing these data. 
sibTxGraph SIB Alt-Splicing Alternative Splicing Graph from Swiss Institute of Bioinformatics mRNA and EST Description This track shows the graphs constructed by analyzing experimental RNA transcripts and serves as basis for the predicted alternative splicing transcripts shown in the SIB Genes track. The blocks represent exons; lines indicate introns. The graphical display is drawn such that no exons overlap, making alternative events easier to view when the track is in full display mode and the resolution is set to approximately gene-level. Further information on the graphs can be found on the Transcriptome Web interface. Methods The splicing graphs were generated using a multi-step pipeline: RefSeq and GenBank RNAs and ESTs are aligned to the genome with SIBsim4, keeping only the best alignments for each RNA. Alignments are broken up at non-intronic gaps, with small isolated fragments thrown out. A splicing graph is created for each set of overlapping alignments. This graph has an edge for each exon or intron, and a vertex for each splice site, start, and end. Each RNA that contributes to an edge is kept as evidence for that edge. Graphs consisting solely of unspliced ESTs are discarded. Credits The SIB Alternative Splicing Graphs track was produced on the Vital-IT high-performance computing platform using a computational pipeline developed by Christian Iseli with help from colleagues at the Ludwig Institute for Cancer Research and the Swiss Institute of Bioinformatics. It is based on data from NCBI RefSeq and GenBank/EMBL. Our thanks to the people running these databases and to the scientists worldwide who have made contributions to them. 
sibGene SIB Genes Swiss Institute of Bioinformatics Gene Predictions from mRNA and ESTs Genes and Gene Predictions Description The SIB Genes track is a transcript-based set of gene predictions based on data from RefSeq and EMBL/GenBank. Genes all have the support of at least one GenBank full length RNA sequence, one RefSeq RNA, or one spliced EST. The track includes both protein-coding and non-coding transcripts. The coding regions are predicted using ESTScan. Display Conventions and Configuration This track in general follows the display conventions for gene prediction tracks. The exons for putative non-coding genes and untranslated regions are represented by relatively thin blocks while those for coding open reading frames are thicker. This track contains an optional codon coloring feature that allows users to quickly validate and compare gene predictions. To display codon colors, select the genomic codons option from the Color track by codons pull-down menu. Go to the Coloring Gene Predictions and Annotations by Codon page for more information about this feature. Further information on the predicted transcripts can be found on the Transcriptome Web interface. Methods The SIB Genes are built using a multi-step pipeline: RefSeq and GenBank RNAs and ESTs are aligned to the genome with SIBsim4, keeping only the best alignments for each RNA. Alignments are broken up at non-intronic gaps, with small isolated fragments thrown out. A splicing graph is created for each set of overlapping alignments. This graph has an edge for each exon or intron, and a vertex for each splice site, start, and end. Each RNA that contributes to an edge is kept as evidence for that edge. The graph is traversed to generate all unique transcripts. The traversal is guided by the initial RNAs to avoid a combinatorial explosion in alternative splicing. Protein predictions are generated. Credits The SIB Genes track was produced on the Vital-IT high-performance computing platform using a computational pipeline developed by Christian Iseli with help from colleagues at the Ludwig Institute for Cancer Research and the Swiss Institute of Bioinformatics. It is based on data from NCBI RefSeq and GenBank/EMBL. Our thanks to the people running these databases and to the scientists worldwide who have made contributions to them. References Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank: update. Nucleic Acids Res. 2004 Jan 1;32(Database issue):D23-6. PMID: 14681350; PMC: PMC308779 
singleCellMerged Single Cell Expression Single cell RNA expression levels cell types from many organs Expression Description This track displays single-cell data from 12 papers covering 14 organs. Cells are grouped together by organ and cell type. The cell types are based on annotations published alongside the papers. These were curated at UCSC as much as possible to use the same cell type terminologies across papers and organs. In some cases, we merged together small populations of cells annotated as distinct and related types into a single type so as to have enough cells to call gene expression levels accurate. The gene expression levels are normalized so that the total level of expression for all genes in a single cell or cell type adds up to one million. Display Conventions and Configuration The cell types are colored by which class they belong to according to the following table. Please note, the coloring algorithm allows cells that show some mixed characteristics to = show blended colors so there will be some color variation within a class. In addition, cells with less than 100 transcripts will be a lighter shade and less concentrated in color to represent a low number of transcripts. Color Cell classification neural adipose fibroblast immune muscle hepatocyte trophoblast secretory ciliated epithelial endothelial glia stem cell or progenitor cell Methods Each organ or tissue was integrated and curated into the Genome Browser indiviually. Blood (PBMC) Hao - This track displays peripheral blood mononuclear cell expression data from Hao et al., 2020 for 3 levels of cell type annotations, donor, phase, and time. Colon Wang - This track shows colon expression data from Wang et al., 2020 grouped by cell type and donor. Cortex Velmeshev - This track shows cortex expression data from Velmeshev et al., 2019 grouped by cell type, sex, sample, donor, and diagnosis. Fetal Gene Atlas - This track shows expression data from Cao et al., 2020 binned by cell type and other categories including sex, organ, experiment, donor, etc. Heart Cell Atlas - This track shows heart expression data from Litvi&#328;ukov&#225 et al., 2020 binned by cell type and various categories including cell state, sample, region, donor, age, etc. Ileum Wang - This track shows ileum expression data from Wang et al., 2020 grouped by cell type and donor. Kidney Stewart - This track shows kidney expression data from Stewart et al., 2019 grouped by cell type, detailed cell type, project, experiment, etc. Liver MacParland - This track shows liver expression data from MacParland et al., 2018 grouped by cell type, broad cell type, and donor. Lung Travaglini - This track shows lung expression data from Travaglini et al., 2020 binned by categories such as cell type, sample, donor, compartment, etc. using both 10x and Smart-seq2 library preparation methods. Muscle De Micheli - This track shows muscle expression data from De Micheli et al., 2020 grouped by cell type and sample. Pancreas Baron - This track shows pancreas expression data from Baron et al., 2016 grouped by cell type, detailed cell type, donor, and batch. Placenta Vento-Tormo - This track shows placenta and matched decidua and maternal PBMCs expression data from Vento-Tormo et al., 2018 grouped by cell type, detailed cell type, stage, etc. using both 10x and Smart-seq2 library preparation methods. Rectum Wang - This track shows rectum expression data from Wang et al., 2020 grouped by cell type and donor. Skin Sole-Boldo - This track shows skin expression data from Sol&#233-Boldo et al., 2020 grouped by cell type, cell type with donor's age, donor, and age. All components were normalized to be in parts per million using the matrixNormalize command available from UCSC. Metadata was cleaned up using the tabToTabDir tool. The major clean-ups were unpacking abbreviations, replacing jargon with standard English, choosing shorted terms to shorten long labels, labeling outliers, etc. Before integration we invited the original data producers as well as local biologists and informaticions to view the data. Data Access The raw barChart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. The expScores field for this track contains a comma-separated list of values for each cell type, and the expCount field is the total cell count. The value in the expScores field corresponds to the read count for that cell type, and the order of the cell types is defined by the barChartBars line in the trackDb file for this track. Credits Many thanks to the data contributing labs for sharing their high quality research. Thanks to the Cell Browser team including Matt Speir and Max Haeussler, for their work in integratinging these datasets into the Cell Browser. In most cases, their efforts were ahead of our own and we could leverage their work making the job much easier. Within the Genome Browser group, Jim Kent did the initial wrangling, and Brittney Wick did substantial data cleanup and coordination with the labs. References Baron M, Veres A, Wolock SL, Faust AL, Gaujoux R, Vetere A, Ryu JH, Wagner BK, Shen-Orr SS, Klein AM et al. A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. Cell Syst. 2016 Oct 26;3(4):346-360.e4. PMID: 27667365; PMC: PMC5228327 Cao J, O'Day DR, Pliner HA, Kingsley PD, Deng M, Daza RM, Zager MA, Aldinger KA, Blecher-Gonen R, Zhang F et al. A human cell atlas of fetal gene expression. Science. 2020 Nov 13;370(6518). PMID: 33184181; PMC: PMC7780123 Cao J, Spielmann M, Qiu X, Huang X, Ibrahim DM, Hill AJ, Zhang F, Mundlos S, Christiansen L, Steemers FJ et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019 Feb;566(7745):496-502. PMID: 30787437; PMC: PMC6434952 De Micheli AJ, Spector JA, Elemento O, Cosgrove BD. A reference single-cell transcriptomic atlas of human skeletal muscle tissue reveals bifurcated muscle stem cell populations. Skelet Muscle. 2020 Jul 6;10(1):19. PMID: 32624006; PMC: PMC7336639 Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M et al. Integrated analysis of multimodal single-cell data. Cell. 2021 Jun 24;184(13):3573-3587.e29. PMID: 34062119; PMC: PMC8238499 Litvi&#328;ukov&#225; M, Talavera-L&#243;pez C, Maatz H, Reichart D, Worth CL, Lindberg EL, Kanda M, Polanski K, Heinig M, Lee M et al. Cells of the adult human heart. Nature. 2020 Dec;588(7838):466-472. PMID: 32971526; PMC: PMC7681775 MacParland SA, Liu JC, Ma XZ, Innes BT, Bartczak AM, Gage BK, Manuel J, Khuu N, Echeverri J, Linares I et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat Commun. 2018 Oct 22;9(1):4383. PMID: 30348985; PMC: PMC6197289 Sol&#233;-Boldo L, Raddatz G, Sch&#252;tz S, Mallm JP, Rippe K, Lonsdorf AS, Rodr&#237;guez-Paredes M, Lyko F. Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming. Commun Biol. 2020 Apr 23;3(1):188. PMID: 32327715; PMC: PMC7181753 Stewart BJ, Ferdinand JR, Young MD, Mitchell TJ, Loudon KW, Riding AM, Richoz N, Frazer GL, Staniforth JUL, Vieira Braga FA et al. Spatiotemporal immune zonation of the human kidney. Science. 2019 Sep 27;365(6460):1461-1466. PMID: 31604275; PMC: PMC7343525 Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, Chang S, Conley SD, Mori Y, Seita J et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature. 2020 Nov;587(7835):619-625. PMID: 33208946; PMC: PMC7704697 Velmeshev D, Schirmer L, Jung D, Haeussler M, Perez Y, Mayer S, Bhaduri A, Goyal N, Rowitch DH, Kriegstein AR. Single-cell genomics identifies cell type-specific molecular changes in autism. Science. 2019 May 17;364(6441):685-689. PMID: 31097668; PMC: PMC7678724 Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M, Meyer KB, Park JE, Stephenson E, Pola&#324;ski K, Goncalves A et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature. 2018 Nov;563(7731):347-353. PMID: 30429548 Wang Y, Song W, Wang J, Wang T, Xiong X, Qi Z, Fu W, Yang X, Chen YG. Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. J Exp Med. 2020 Feb 3;217(2). PMID: 31753849; PMC: PMC7041720 
skinSoleBoldoAge Skin Age Skin single cell RNA binned by skin donor's age from Sole-Boldo et al 2020 Single Cell RNA-seq Description This track displays data from Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming. Single cell RNA sequencing (scRNA-seq) was performed on sun-protected skin samples prepared using droplet-sequencing (drop-seq). RNA profiles were generated for 15,457 cells after quality control and subsequent clustering identified 17 clusters with distinct expression profiles as found in Sol&#233;-Boldo et al., 2020. This track collection contains four bar chart tracks of RNA expression in the human skin where cells are grouped by cell type (Skin Cell), age (Skin Age), donor (Skin Donor), and cell type and donor's age (Skin Cell+Age). The default track displayed is Skin Cell. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Skin Cell subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Healthy skin samples were obtained from whole-skin specimens belonging to 5 male donors (ages 25-70) with fair skin. Donors underwent full body skin examinations by a dermatologist and medical records were checked for skin diseases and/or comorbidities that affect the skin. 4-mm punch biopsies were taken from surgically removed skin belonging to the inguinal region of the body also known as the groin. Skin samples were kept in MACS Tissue Storage Solution for less than 1 hour to avoid necrosis and apoptosis. Enzymatical and mechanical dissociation was done using the Miltenyi Biotec Whole Skin Dissociation kit for human material and the Miltenyi Biotec Gentle MACS dissociator. Drop-seq libraries were prepared using a 10x Genomics 3' v2 kit and sequenced on an Illumina HiSeq4000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Lloren&#231 Sol&#233-Boldo and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Sol&#233;-Boldo L, Raddatz G, Sch&#252;tz S, Mallm JP, Rippe K, Lonsdorf AS, Rodr&#237;guez-Paredes M, Lyko F. Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming. Commun Biol. 2020 Apr 23;3(1):188. PMID: 32327715; PMC: PMC7181753 
skinSoleBoldo Skin Sole-Boldo Skin single cell data from  Sole-Boldo et al 2020 Single Cell RNA-seq Description This track displays data from Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming. Single cell RNA sequencing (scRNA-seq) was performed on sun-protected skin samples prepared using droplet-sequencing (drop-seq). RNA profiles were generated for 15,457 cells after quality control and subsequent clustering identified 17 clusters with distinct expression profiles as found in Sol&#233;-Boldo et al., 2020. This track collection contains four bar chart tracks of RNA expression in the human skin where cells are grouped by cell type (Skin Cell), age (Skin Age), donor (Skin Donor), and cell type and donor's age (Skin Cell+Age). The default track displayed is Skin Cell. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Skin Cell subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Healthy skin samples were obtained from whole-skin specimens belonging to 5 male donors (ages 25-70) with fair skin. Donors underwent full body skin examinations by a dermatologist and medical records were checked for skin diseases and/or comorbidities that affect the skin. 4-mm punch biopsies were taken from surgically removed skin belonging to the inguinal region of the body also known as the groin. Skin samples were kept in MACS Tissue Storage Solution for less than 1 hour to avoid necrosis and apoptosis. Enzymatical and mechanical dissociation was done using the Miltenyi Biotec Whole Skin Dissociation kit for human material and the Miltenyi Biotec Gentle MACS dissociator. Drop-seq libraries were prepared using a 10x Genomics 3' v2 kit and sequenced on an Illumina HiSeq4000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Lloren&#231 Sol&#233-Boldo and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Sol&#233;-Boldo L, Raddatz G, Sch&#252;tz S, Mallm JP, Rippe K, Lonsdorf AS, Rodr&#237;guez-Paredes M, Lyko F. Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming. Commun Biol. 2020 Apr 23;3(1):188. PMID: 32327715; PMC: PMC7181753 
skinSoleBoldoCellType Skin Cell Skin single cell RNA binned by cell type from Sole-Boldo et al 2020 Single Cell RNA-seq Description This track displays data from Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming. Single cell RNA sequencing (scRNA-seq) was performed on sun-protected skin samples prepared using droplet-sequencing (drop-seq). RNA profiles were generated for 15,457 cells after quality control and subsequent clustering identified 17 clusters with distinct expression profiles as found in Sol&#233;-Boldo et al., 2020. This track collection contains four bar chart tracks of RNA expression in the human skin where cells are grouped by cell type (Skin Cell), age (Skin Age), donor (Skin Donor), and cell type and donor's age (Skin Cell+Age). The default track displayed is Skin Cell. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Skin Cell subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Healthy skin samples were obtained from whole-skin specimens belonging to 5 male donors (ages 25-70) with fair skin. Donors underwent full body skin examinations by a dermatologist and medical records were checked for skin diseases and/or comorbidities that affect the skin. 4-mm punch biopsies were taken from surgically removed skin belonging to the inguinal region of the body also known as the groin. Skin samples were kept in MACS Tissue Storage Solution for less than 1 hour to avoid necrosis and apoptosis. Enzymatical and mechanical dissociation was done using the Miltenyi Biotec Whole Skin Dissociation kit for human material and the Miltenyi Biotec Gentle MACS dissociator. Drop-seq libraries were prepared using a 10x Genomics 3' v2 kit and sequenced on an Illumina HiSeq4000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Lloren&#231 Sol&#233-Boldo and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Sol&#233;-Boldo L, Raddatz G, Sch&#252;tz S, Mallm JP, Rippe K, Lonsdorf AS, Rodr&#237;guez-Paredes M, Lyko F. Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming. Commun Biol. 2020 Apr 23;3(1):188. PMID: 32327715; PMC: PMC7181753 
skinSoleBoldoAgeCellType Skin Cell+Age Skin single cell RNA binned by cell type and donor's age from Sole-Boldo et all 2020 Single Cell RNA-seq Description This track displays data from Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming. Single cell RNA sequencing (scRNA-seq) was performed on sun-protected skin samples prepared using droplet-sequencing (drop-seq). RNA profiles were generated for 15,457 cells after quality control and subsequent clustering identified 17 clusters with distinct expression profiles as found in Sol&#233;-Boldo et al., 2020. This track collection contains four bar chart tracks of RNA expression in the human skin where cells are grouped by cell type (Skin Cell), age (Skin Age), donor (Skin Donor), and cell type and donor's age (Skin Cell+Age). The default track displayed is Skin Cell. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Skin Cell subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Healthy skin samples were obtained from whole-skin specimens belonging to 5 male donors (ages 25-70) with fair skin. Donors underwent full body skin examinations by a dermatologist and medical records were checked for skin diseases and/or comorbidities that affect the skin. 4-mm punch biopsies were taken from surgically removed skin belonging to the inguinal region of the body also known as the groin. Skin samples were kept in MACS Tissue Storage Solution for less than 1 hour to avoid necrosis and apoptosis. Enzymatical and mechanical dissociation was done using the Miltenyi Biotec Whole Skin Dissociation kit for human material and the Miltenyi Biotec Gentle MACS dissociator. Drop-seq libraries were prepared using a 10x Genomics 3' v2 kit and sequenced on an Illumina HiSeq4000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Lloren&#231 Sol&#233-Boldo and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Sol&#233;-Boldo L, Raddatz G, Sch&#252;tz S, Mallm JP, Rippe K, Lonsdorf AS, Rodr&#237;guez-Paredes M, Lyko F. Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming. Commun Biol. 2020 Apr 23;3(1):188. PMID: 32327715; PMC: PMC7181753 
skinSoleBoldoDonor Skin Donor Skin single cell RNA binned by skin donor from Sole-Boldo et al 2020 Single Cell RNA-seq Description This track displays data from Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming. Single cell RNA sequencing (scRNA-seq) was performed on sun-protected skin samples prepared using droplet-sequencing (drop-seq). RNA profiles were generated for 15,457 cells after quality control and subsequent clustering identified 17 clusters with distinct expression profiles as found in Sol&#233;-Boldo et al., 2020. This track collection contains four bar chart tracks of RNA expression in the human skin where cells are grouped by cell type (Skin Cell), age (Skin Age), donor (Skin Donor), and cell type and donor's age (Skin Cell+Age). The default track displayed is Skin Cell. Display Conventions The cell types are colored by which class they belong to according to the following table. Color Cell classification fibroblast immune epithelial endothelial Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Skin Cell subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well. Method Healthy skin samples were obtained from whole-skin specimens belonging to 5 male donors (ages 25-70) with fair skin. Donors underwent full body skin examinations by a dermatologist and medical records were checked for skin diseases and/or comorbidities that affect the skin. 4-mm punch biopsies were taken from surgically removed skin belonging to the inguinal region of the body also known as the groin. Skin samples were kept in MACS Tissue Storage Solution for less than 1 hour to avoid necrosis and apoptosis. Enzymatical and mechanical dissociation was done using the Miltenyi Biotec Whole Skin Dissociation kit for human material and the Miltenyi Biotec Gentle MACS dissociator. Drop-seq libraries were prepared using a 10x Genomics 3' v2 kit and sequenced on an Illumina HiSeq4000. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server. Data Access The raw bar chart data can be explored interactively with the Table Browser or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credit Thanks to Lloren&#231 Sol&#233-Boldo and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by Gerardo Perez. The UCSC work was paid for by the Chan Zuckerberg Initiative. References Sol&#233;-Boldo L, Raddatz G, Sch&#252;tz S, Mallm JP, Rippe K, Lonsdorf AS, Rodr&#237;guez-Paredes M, Lyko F. Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming. Commun Biol. 2020 Apr 23;3(1):188. PMID: 32327715; PMC: PMC7181753 
wgRna sno/miRNA C/D and H/ACA Box snoRNAs, scaRNAs, and microRNAs from snoRNABase and miRBase Genes and Gene Predictions Description This track displays positions of four different types of RNA in the human genome: microRNAs from the miRBase at the Wellcome Trust Sanger Institute(WTSI). small nucleolar RNAs (C/D box and H/ACA box snoRNAs) and Cajal body-specific RNAs (scaRNAs) from the snoRNABase maintained at the Laboratoire de Biologie Mol&eacute;culaire Eucaryote C/D box and H/ACA box snoRNAs are guides for the 2'O-ribose methylation and the pseudouridilation, respectively, of rRNAs and snRNAs, although many of them have no documented target RNA. The scaRNAs guide modifications of the spliceosomal snRNAs transcribed by RNA polymerase II, and often contain both C/D and H/ACA domains. Display Conventions and Configuration This track follows the general display conventions for gene prediction tracks. The miRNA precursor forms (pre-miRNA) are represented by red blocks. C/D box snoRNAs, H/ACA box snoRNAs and scaRNAs are represented by blue, green and magenta blocks, respectively. At a zoomed-in resolution, arrows superimposed on the blocks indicate the sense orientation of the snoRNAs. Methods Precursor miRNA genomic locations from miRBase were calculated using wublastn for sequence alignment with the requirement of 100% identity. The extents of the precursor sequences were not generally known and were predicted based on base-paired hairpin structure. miRBase is described in Griffiths-Jones, S. (2004) and Weber, M.J. (2005) in the References section below. The snoRNAs and scaRNAs from the snoRNABase were aligned against the human genome using blat. Credits Genome coordinates for this track were obtained from the miRBase sequences FTP site and from snoRNABase coordinates download page. References When making use of these data, please cite the folowing articles in addition to the primary sources of the miRNA sequences: Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ. miRBase: tools for microRNA genomics. Nucleic Acids Res. 2008 Jan 1;36(Database issue):D154-8. Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ. miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 2006 Jan 1;34(Database issue):D140-4. Griffiths-Jones S. The microRNA Registry. Nucleic Acids Res. 2004 Jan 1;32(Database issue):D109-11. Weber MJ. New human and mouse microRNA genes found by homology search. You may also want to cite The Wellcome Trust Sanger Institute miRBase and The Laboratoire de Biologie Moleculaire Eucaryote snoRNABase. The following publication provides guidelines on miRNA annotation: Ambros V. et al., A uniform system for microRNA annotation. RNA. 2003;9(3):277-9. 
snpedia SNPedia SNPedia Phenotype and Literature Description SNPedia is a wiki investigating human genetics with information about the effects of variations in DNA, citing peer-reviewed scientific publications. SNPedia all: SNPedia all SNPs (including empty pages) The track &#34;SNPedia all&#34; shows all SNPs that exist as a page in SNPedia.com. As SNPedia's user collaboration grows, more detail will be added to SNPedia.com pages. For now, most of the pages are auto-generated by bots and have empty pages. According to Mike Carioso (SNPedia.com founder), SNPedia entries are mostly ClinVar entries marked as pathogenic with at least 4 stars as defined by the ClinVar review status. SNPedia with text: SNPedia pages with manually typed text The track &#34;SNPedia with text&#34; is a subset of the &#34;SNPedia all&#34; track. This track displays only SNPedia entries with a text page that was created manually by a user who typed in some text (approximately 5,000 entries). In the browser, click on the &#34;configure&#34; button and select &#34;next/previous item navigation&#34; to show clickable arrows in the browser which will jump to the next or previous item. Clicks on the features show the text from the SNPedia.com page and a link to the original page. Display Conventions and Configuration Genomic locations of SNPedia entries are labeled with the dbSNP ID. In the track &#34;SNPedia all SNPs&#34;, the features are colored based on the SNPedia microarray annotation: grey for SNPs that are on no microarray, dark blue for Affymetrix, dark purple for Illumina and black for features on both arrays. Methods The mappings displayed in this track were used as provided in the SNPedia GFF file. For the &#34;SNPedia with text&#34; track, all SNPedia pages were downloaded and their content checked with a script that tries to remove pages that were auto-generated and not created manually by a user. Credits Thanks to Mike Cariaso for help with the GFF download and Max Haeussler at UCSC for building this track. References Cariaso Michael; Lennon Greg. SNPedia: a wiki supporting personal genome annotation, interpretation and analysis. Nucleic acids research. 2012 40Database issue:D1308-12. PMID: 22140107; PMC: PMC3245045 
snpediaText SNPedia with text SNPedia pages with manually typed text Phenotype and Literature 
snpediaAll SNPedia all SNPedia all SNPs (including empty pages) Phenotype and Literature 
intronEst Spliced ESTs Human ESTs That Have Been Spliced mRNA and EST Description This track shows alignments between human expressed sequence tags (ESTs) in GenBank and the genome that show signs of splicing when aligned against the genome. ESTs are single-read sequences, typically about 500 bases in length, that usually represent fragments of transcribed genes. To be considered spliced, an EST must show evidence of at least one canonical intron (i.e., the genomic sequence between EST alignment blocks must be at least 32 bases in length and have GT/AG ends). By requiring splicing, the level of contamination in the EST databases is drastically reduced at the expense of eliminating many genuine 3' ESTs. For a display of all ESTs (including unspliced), see the human EST track. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. In dense display mode, darker shading indicates a larger number of aligned ESTs. The strand information (+/-) indicates the direction of the match between the EST and the matching genomic sequence. It bears no relationship to the direction of transcription of the RNA with which it might be associated. The description page for this track has a filter that can be used to change the display mode, alter the color, and include/exclude a subset of items within the track. This may be helpful when many items are shown in the track display, especially when only some are relevant to the current task. To use the filter: Type a term in one or more of the text boxes to filter the EST display. For example, to apply the filter to all ESTs expressed in a specific organ, type the name of the organ in the tissue box. To view the list of valid terms for each text box, consult the table in the Table Browser that corresponds to the factor on which you wish to filter. For example, the &quot;tissue&quot; table contains all the types of tissues that can be entered into the tissue text box. Multiple terms may be entered at once, separated by a space. Wildcards may also be used in the filter. If filtering on more than one value, choose the desired combination logic. If &quot;and&quot; is selected, only ESTs that match all filter criteria will be highlighted. If &quot;or&quot; is selected, ESTs that match any one of the filter criteria will be highlighted. Choose the color or display characteristic that should be used to highlight or include/exclude the filtered items. If &quot;exclude&quot; is chosen, the browser will not display ESTs that match the filter criteria. If &quot;include&quot; is selected, the browser will display only those ESTs that match the filter criteria. This track may also be configured to display base labeling, a feature that allows the user to display all bases in the aligning sequence or only those that differ from the genomic sequence. For more information about this option, go to the Base Coloring for Alignment Tracks page. Several types of alignment gap may also be colored; for more information, go to the Alignment Insertion/Deletion Display Options page. Methods To make an EST, RNA is isolated from cells and reverse transcribed into cDNA. Typically, the cDNA is cloned into a plasmid vector and a read is taken from the 5' and/or 3' primer. For most &mdash; but not all &mdash; ESTs, the reverse transcription is primed by an oligo-dT, which hybridizes with the poly-A tail of mature mRNA. The reverse transcriptase may or may not make it to the 5' end of the mRNA, which may or may not be degraded. In general, the 3' ESTs mark the end of transcription reasonably well, but the 5' ESTs may end at any point within the transcript. Some of the newer cap-selected libraries cover transcription start reasonably well. Before the cap-selection techniques emerged, some projects used random rather than poly-A priming in an attempt to retrieve sequence distant from the 3' end. These projects were successful at this, but as a side effect also deposited sequences from unprocessed mRNA and perhaps even genomic sequences into the EST databases. Even outside of the random-primed projects, there is a degree of non-mRNA contamination. Because of this, a single unspliced EST should be viewed with considerable skepticism. To generate this track, human ESTs from GenBank were aligned against the genome using blat. Note that the maximum intron length allowed by blat is 750,000 bases, which may eliminate some ESTs with very long introns that might otherwise align. When a single EST aligned in multiple places, the alignment having the highest base identity was identified. Only alignments having a base identity level within 0.5% of the best and at least 96% base identity with the genomic sequence are displayed in this track. Credits This track was produced at UCSC from EST sequence data submitted to the international public sequence databases by scientists worldwide. References Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW. GenBank. Nucleic Acids Res. 2013 Jan;41(Database issue):D36-42. PMID: 23193287; PMC: PMC3531190 Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank: update. Nucleic Acids Res. 2004 Jan 1;32(Database issue):D23-6. PMID: 14681350; PMC: PMC308779 Kent WJ. BLAT - the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 
stsMap STS Markers STS Markers on Genetic (blue) and Radiation Hybrid (black) Maps Mapping and Sequencing Description This track shows locations of Sequence Tagged Site (STS) markers along the draft assembly. These markers have been mapped using either genetic mapping (Genethon, Marshfield, and deCODE maps), radiation hybridization mapping (Stanford, Whitehead RH, and GeneMap99 maps) or YAC mapping (the Whitehead YAC map) techniques. Since August 2001, this track no longer displays fluorescent in situ hybridization (FISH) clones, which are now displayed in a separate track. Genetic map markers are shown in blue; radiation hybrid map markers are shown in black. When a marker maps to multiple positions in the genome, it is shown in a lighter color. Methods Positions of STS markers are determined using both full sequences and primer information. Full sequences are aligned using blat, while isPCR (Jim Kent) and ePCR are used to find locations using primer information. Both sets of placements are combined to give final positions. In nearly all cases, full sequence and primer-based locations are in agreement, but in cases of disagreement, full sequence positions are used. Sequence and primer information for the markers were obtained from the primary sites for each of the maps, and from NCBI UniSTS (now part of NCBI Probe). Using the Filter The track filter can be used to change the color or include/exclude a set of map data within the track. This is helpful when many items are shown in the track display, especially when only some are relevant to the current task. To use the filter: In the pulldown menu, select the map whose data you would like to highlight or exclude in the display. By default, the &quot;All Genetic&quot; option is selected. Choose the color or display characteristic that will be used to highlight or include/exclude the filtered items. If &quot;exclude&quot; is chosen, the browser will not display data from the map selected in the pulldown list. If &quot;include&quot; is selected, the browser will display only data from the selected map. When you have finished configuring the filter, click the Submit button. Credits This track was designed and implemented by Terry Furey. Many thanks to the researchers who worked on these maps, and to Greg Schuler, Arek Kasprzyk, Wonhee Jang, and Sanja Rogic for helping process the data. Additional data on the individual maps can be found at the following links: Genethon map Marshfield map deCODE map GeneMap99 GB4 and G3 maps Stanford TNG (Center has closed) Whitehead YAC and RH maps 
tabulaSapiensFullDetails Tabula Details Tabula sapiens full details view Single Cell RNA-seq Description This track shows data from The Tabula Sapiens: a multiple organ single cell transcriptomic atlas of humans. The dataset covers ~500,000 cells from a total of 24 human tissues and organs from all regions of the body using both droplet-based and plate-based single-cell RNA-sequencing (scRNA-seq). Samples were taken from the human bladder, blood, bone marrow, eye, fat, heart, kidney, large intestine, liver, lung, lymph node, mammary, muscle, pancreas, prostate, salivary gland, skin, small intestine, spleen, thymus, tongue, trachea, uterus, and vasculature. The dataset includes 264,009 immune cells, 102,580 epithelial cells, 32,701 endothelial cells, and 81,529 stromal cells. A total of 475 distinct cell types were identified. This track collection contains two bar chart tracks of RNA expression. The first track, Tabula Tissue Cell allows cells to be grouped together and faceted on up to 3 categories: tissue, cell class, and cell type. The second track, Tabula Details allows cells to be grouped together and faceted on up to 7 categories: tissue, cell class, cell type, subtissue, sex, donor, and assay. Please see tabula-sapiens-portal.ds.czbiohub.org for further interactive displays and additional data. Display Conventions and Configuration The cell types are colored by which compartment they belong to according to the following table. In addition, cells found in the Tabula Details track with less than 100 transcripts will be a lighter shade and less concentrated in color to represent a low number of transcripts. Color Cell Compartment epithelial endothelial germline immune stromal Methods All tissues 36 tissue specimens comprising 24 unique tissues and organs were collected from 15 human donors (TSP1-15) with a mean age of 51 years. Tissue specimens were collected at various hospital locations in the Northern California region and transported on ice in less than one hour to preserve cell viability. Single cell suspensions from each organ were prepared in tissue expert laboratories at Stanford and UCSF. For each tissue, the dissociated cells were sorted using MACS and FACS to balance immune, stromal, epithelial, and endothelial cell types. Sequencing libraries for all tissues were prepared using 10x 3' v3.1, 10x 5' v2, and Smart-seq2 (SS2) protocols for Illumina sequencing. Two 10x reactions per organ were loaded with 7,000 cells each with the goal to yield 10,000 QC-passed cells. Four 384-well Smartseq2 plates were run per organ. In most organs, one plate was used for each compartment (epithelial, endothelial, immune, and stromal), however, to capture rare cells, some organ experts allocated cells across the four plates differently. Sequencing runs for droplet libraries were loaded onto the NovaSeq S4 flow cell in sets of 16 to 20 libraries of approximately 5,000 cells per library with the goal of generating 50,000 to 75,000 reads per cell. Plate libraries were run in sets of 20 plates on Novaseq S4 flow cells to allow generating 1M reads per cell, depending on library quality. 152 10x reactions were performed, yielding 454,069 cells passing QC, and 161 smartseq2 plates were processed, yielding 27,051 cells passing QC. Tissues collected from the same donor were used to study the clonal distribution of T cells between tissues, to understand the tissue specific mutation rate in B cells, and to analyze the cell cycle state and proliferative potential of shared cell types across tissues. RNA splicing analysis was also used to characterize cell type specific splicing and its variation across individuals. For detailed methods and information on donors for each organ or tissue please refer to Quake et al, 2021 or the Tabula Sapiens website. Errata Some cell types, particularly in the intestines, are duplicated due to the use of multiple ontologies for the same cell type. In a future version, we plan to pool the data from these duplicates. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The UCSC utilities can be found on our download server. Credits Thanks to the Tabula Sapiens Consortium who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent, Brittney Wick, and Rachel Schwartz. References The Tabula Sapiens Consortium, Quake SR., The Tabula Sapiens: A Multiple Organ Single Cell Transcriptomic Atlas of Humans. bioRxiv. 2021 March 4.; doi: https://doi.org/10.1101/2021.07.19.452956. 
tabulaSapiens Tabula Sapiens Tabula Sapiens single cell RNA data from many tissues Single Cell RNA-seq Description This track shows data from The Tabula Sapiens: a multiple organ single cell transcriptomic atlas of humans. The dataset covers ~500,000 cells from a total of 24 human tissues and organs from all regions of the body using both droplet-based and plate-based single-cell RNA-sequencing (scRNA-seq). Samples were taken from the human bladder, blood, bone marrow, eye, fat, heart, kidney, large intestine, liver, lung, lymph node, mammary, muscle, pancreas, prostate, salivary gland, skin, small intestine, spleen, thymus, tongue, trachea, uterus, and vasculature. The dataset includes 264,009 immune cells, 102,580 epithelial cells, 32,701 endothelial cells, and 81,529 stromal cells. A total of 475 distinct cell types were identified. This track collection contains two bar chart tracks of RNA expression. The first track, Tabula Tissue Cell allows cells to be grouped together and faceted on up to 3 categories: tissue, cell class, and cell type. The second track, Tabula Details allows cells to be grouped together and faceted on up to 7 categories: tissue, cell class, cell type, subtissue, sex, donor, and assay. Please see tabula-sapiens-portal.ds.czbiohub.org for further interactive displays and additional data. Display Conventions and Configuration The cell types are colored by which compartment they belong to according to the following table. In addition, cells found in the Tabula Details track with less than 100 transcripts will be a lighter shade and less concentrated in color to represent a low number of transcripts. Color Cell Compartment epithelial endothelial germline immune stromal Methods All tissues 36 tissue specimens comprising 24 unique tissues and organs were collected from 15 human donors (TSP1-15) with a mean age of 51 years. Tissue specimens were collected at various hospital locations in the Northern California region and transported on ice in less than one hour to preserve cell viability. Single cell suspensions from each organ were prepared in tissue expert laboratories at Stanford and UCSF. For each tissue, the dissociated cells were sorted using MACS and FACS to balance immune, stromal, epithelial, and endothelial cell types. Sequencing libraries for all tissues were prepared using 10x 3' v3.1, 10x 5' v2, and Smart-seq2 (SS2) protocols for Illumina sequencing. Two 10x reactions per organ were loaded with 7,000 cells each with the goal to yield 10,000 QC-passed cells. Four 384-well Smartseq2 plates were run per organ. In most organs, one plate was used for each compartment (epithelial, endothelial, immune, and stromal), however, to capture rare cells, some organ experts allocated cells across the four plates differently. Sequencing runs for droplet libraries were loaded onto the NovaSeq S4 flow cell in sets of 16 to 20 libraries of approximately 5,000 cells per library with the goal of generating 50,000 to 75,000 reads per cell. Plate libraries were run in sets of 20 plates on Novaseq S4 flow cells to allow generating 1M reads per cell, depending on library quality. 152 10x reactions were performed, yielding 454,069 cells passing QC, and 161 smartseq2 plates were processed, yielding 27,051 cells passing QC. Tissues collected from the same donor were used to study the clonal distribution of T cells between tissues, to understand the tissue specific mutation rate in B cells, and to analyze the cell cycle state and proliferative potential of shared cell types across tissues. RNA splicing analysis was also used to characterize cell type specific splicing and its variation across individuals. For detailed methods and information on donors for each organ or tissue please refer to Quake et al, 2021 or the Tabula Sapiens website. Errata Some cell types, particularly in the intestines, are duplicated due to the use of multiple ontologies for the same cell type. In a future version, we plan to pool the data from these duplicates. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The UCSC utilities can be found on our download server. Credits Thanks to the Tabula Sapiens Consortium who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent, Brittney Wick, and Rachel Schwartz. References The Tabula Sapiens Consortium, Quake SR., The Tabula Sapiens: A Multiple Organ Single Cell Transcriptomic Atlas of Humans. bioRxiv. 2021 March 4.; doi: https://doi.org/10.1101/2021.07.19.452956. 
tabulaSapiensTissueCellType Tabula Tissue Cell Tabula sapiens RNA by tissue and cell type Single Cell RNA-seq Description This track shows data from The Tabula Sapiens: a multiple organ single cell transcriptomic atlas of humans. The dataset covers ~500,000 cells from a total of 24 human tissues and organs from all regions of the body using both droplet-based and plate-based single-cell RNA-sequencing (scRNA-seq). Samples were taken from the human bladder, blood, bone marrow, eye, fat, heart, kidney, large intestine, liver, lung, lymph node, mammary, muscle, pancreas, prostate, salivary gland, skin, small intestine, spleen, thymus, tongue, trachea, uterus, and vasculature. The dataset includes 264,009 immune cells, 102,580 epithelial cells, 32,701 endothelial cells, and 81,529 stromal cells. A total of 475 distinct cell types were identified. This track collection contains two bar chart tracks of RNA expression. The first track, Tabula Tissue Cell allows cells to be grouped together and faceted on up to 3 categories: tissue, cell class, and cell type. The second track, Tabula Details allows cells to be grouped together and faceted on up to 7 categories: tissue, cell class, cell type, subtissue, sex, donor, and assay. Please see tabula-sapiens-portal.ds.czbiohub.org for further interactive displays and additional data. Display Conventions and Configuration The cell types are colored by which compartment they belong to according to the following table. In addition, cells found in the Tabula Details track with less than 100 transcripts will be a lighter shade and less concentrated in color to represent a low number of transcripts. Color Cell Compartment epithelial endothelial germline immune stromal Methods All tissues 36 tissue specimens comprising 24 unique tissues and organs were collected from 15 human donors (TSP1-15) with a mean age of 51 years. Tissue specimens were collected at various hospital locations in the Northern California region and transported on ice in less than one hour to preserve cell viability. Single cell suspensions from each organ were prepared in tissue expert laboratories at Stanford and UCSF. For each tissue, the dissociated cells were sorted using MACS and FACS to balance immune, stromal, epithelial, and endothelial cell types. Sequencing libraries for all tissues were prepared using 10x 3' v3.1, 10x 5' v2, and Smart-seq2 (SS2) protocols for Illumina sequencing. Two 10x reactions per organ were loaded with 7,000 cells each with the goal to yield 10,000 QC-passed cells. Four 384-well Smartseq2 plates were run per organ. In most organs, one plate was used for each compartment (epithelial, endothelial, immune, and stromal), however, to capture rare cells, some organ experts allocated cells across the four plates differently. Sequencing runs for droplet libraries were loaded onto the NovaSeq S4 flow cell in sets of 16 to 20 libraries of approximately 5,000 cells per library with the goal of generating 50,000 to 75,000 reads per cell. Plate libraries were run in sets of 20 plates on Novaseq S4 flow cells to allow generating 1M reads per cell, depending on library quality. 152 10x reactions were performed, yielding 454,069 cells passing QC, and 161 smartseq2 plates were processed, yielding 27,051 cells passing QC. Tissues collected from the same donor were used to study the clonal distribution of T cells between tissues, to understand the tissue specific mutation rate in B cells, and to analyze the cell cycle state and proliferative potential of shared cell types across tissues. RNA splicing analysis was also used to characterize cell type specific splicing and its variation across individuals. For detailed methods and information on donors for each organ or tissue please refer to Quake et al, 2021 or the Tabula Sapiens website. Errata Some cell types, particularly in the intestines, are duplicated due to the use of multiple ontologies for the same cell type. In a future version, we plan to pool the data from these duplicates. Data Access The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The UCSC utilities can be found on our download server. Credits Thanks to the Tabula Sapiens Consortium who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent, Brittney Wick, and Rachel Schwartz. References The Tabula Sapiens Consortium, Quake SR., The Tabula Sapiens: A Multiple Organ Single Cell Transcriptomic Atlas of Humans. bioRxiv. 2021 March 4.; doi: https://doi.org/10.1101/2021.07.19.452956. 
gdcCancer TCGA Pan-Cancer TCGA Pan-Cancer mutations: 33 TCGA Cancer Projects Summary (Pan-Can 33) Phenotype and Literature Description This track shows the genomic positions of somatic variants found through whole genome sequencing of tumors as part of The Cancer Genome Atlas (TCGA) by the National Cancer Institute, made available through the Genomic Data Commons Portal. The data shown here is sometimes called the &quot;Pan-Cancer dataset&quot;, a collection of thirty-three TCGA projects processed in a uniform way. Display Conventions and Configuration Variants can be filtered by project ID and gender from the track details page. Pressing the &quot;All&quot; button allows the user to specify whether the checked values all have to be true of a particular variant, or if only one of them need be present to satisfy the filter. The vertical viewing range in full mode can also be used to filter what variants are shown. Variants that have a sampleCount more or less than the min and max values specificed in the viewing range are not displayed. Data access The raw data can be explored interactively with the Table Browser or the Data Integrator. For automated download and analysis, the genome annotation for all the thirty-three projects is stored in a bigBed file that can be downloaded from our download server. There are also bigBed files for each of the thirty-three projects in that directory. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g., bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/gdcCancer/gdcCancer.bb -chrom=chr21 -start=0 -end=100000000 stdout Methods All MuTect Variant calls were downloaded from the GDC portal in January 2019 and reformatted at UCSC to the bigBed format with a short script, cancerMafToBigBed. Credits Thanks to GDC for making the TCGA data available on their web site. 
KICH KICH Kidney Chromophobe Phenotype and Literature 
UVM UVM Uveal Melanoma Phenotype and Literature 
UCS UCS Uterine Carcinosarcoma Phenotype and Literature 
UCEC UCEC Uterine Corpus Endometrial Carcinoma Phenotype and Literature 
THYM THYM Thymoma Phenotype and Literature 
THCA THCA Thyroid carcinoma Phenotype and Literature 
TGCT TGCT Testicular Germ Cell Tumors Phenotype and Literature 
STAD STAD Stomach adenocarcinoma Phenotype and Literature 
SKCM SKCM Skin Cutaneous Melanoma Phenotype and Literature 
SARC SARC Sarcoma Phenotype and Literature 
READ READ Rectum adenocarcinoma Phenotype and Literature 
PRAD PRAD Prostate adenocarcinoma Phenotype and Literature 
PCPG PCPG Pheochromocytoma and Paraganglioma Phenotype and Literature 
PAAD PAAD Pancreatic adenocarcinoma Phenotype and Literature 
OV OV Ovarian serous cystadenocarcinoma Phenotype and Literature 
MESO MESO Mesothelioma Phenotype and Literature 
LUSC LUSC Lung squamous cell carcinoma Phenotype and Literature 
LUAD LUAD Lung adenocarcinoma Phenotype and Literature 
LIHC LIHC Liver hepatocellular carcinoma Phenotype and Literature 
LGG LGG Brain Lower Grade Glioma Phenotype and Literature 
LAML LAML Acute Myeloid Leukemia Phenotype and Literature 
KIRP KIRP Kidney renal papillary cell carcinoma Phenotype and Literature 
KIRC KIRC Kidney renal clear cell carcinoma Phenotype and Literature 
HNSC HNSC Head and Neck squamous cell carcinoma Phenotype and Literature 
GBM GBM Glioblastoma multiforme Phenotype and Literature 
ESCA ESCA Esophageal carcinoma Phenotype and Literature 
DLBC DLBC Lymphoid Neoplasm Diffuse Large B-cell Lymphoma Phenotype and Literature 
COAD COAD Colon adenocarcinoma Phenotype and Literature 
CHOL CHOL Cholangiocarcinoma Phenotype and Literature 
CESC CESC Cervical squamous cell carcinoma and endocervical adenocarcinoma Phenotype and Literature 
BRCA BRCA Breast invasive carcinoma Phenotype and Literature 
BLCA BLCA Bladder Urothelial Carcinoma Phenotype and Literature 
ACC ACC Adrenocortical carcinoma Phenotype and Literature 
allCancer All Cancers All TCGA Pan-Cancer mutations: 33 TCGA Cancer Projects Summary (Pan-Can 33) Phenotype and Literature 
tRNAs tRNA Genes Transfer RNA Genes Identified with tRNAscan-SE Genes and Gene Predictions Description This track displays tRNA genes predicted by using tRNAscan-SE v.1.23. tRNAscan-SE is an integrated program that uses tRNAscan (Fichant) and an A/B box motif detection algorithm (Pavesi) as pre-filters to obtain an initial list of tRNA candidates. The program then filters these candidates with a covariance model-based search program COVE (Eddy) to obtain a highly specific set of primary sequence and secondary structure predictions that represent 99-100% of true tRNAs with a false positive rate of fewer than 1 per 15 gigabases. Detailed tRNA annotations for eukaryotes, bacteria, and archaea are available at Genomic tRNA Database (GtRNAdb). What does the tRNAscan-SE score mean? Anything with a score above 20 bits is likely to be derived from a tRNA, although this does not indicate whether the tRNA gene still encodes a functional tRNA molecule (i.e. tRNA-derived SINES probably do not function in the ribosome in translation). Vertebrate tRNAs with scores of &gt;60.0 (bits) are likely to encode functional tRNA genes, and those with scores below ~45 have sequence or structural features that indicate they probably are no longer involved in translation. tRNAs with scores between 45-60 bits are in the &quot;grey&quot; zone, and may or may not have all the required features to be functional. In these cases, tRNAs should be inspected carefully for loss of specific primary or secondary structure features (usually in alignments with other genes of the same isotype), in order to make a better educated guess. These rough score range guides are not exact, nor are they based on specific biochemical studies of atypical tRNA features, so please treat them accordingly. Please note that tRNA genes marked as &quot;Pseudo&quot; are low scoring predictions that are mostly pseudogenes or tRNA-derived elements. These genes do not usually fold into a typical cloverleaf tRNA secondary structure and the provided images of the predicted secondary structures may appear rotated. Credits Both tRNAscan-SE and GtRNAdb are maintained by the Lowe Lab at UCSC. Cove-predicted tRNA secondary structures were rendered by NAVIEW (c) 1988 Robert E. Bruccoleri. References When making use of these data, please cite the following articles: Chan PP, Lowe TM. GtRNAdb: a database of transfer RNA genes detected in genomic sequence. Nucleic Acids Res. 2009 Jan;37(Database issue):D93-7. PMID: 18984615; PMC: PMC2686519 Eddy SR, Durbin R. RNA sequence analysis using covariance models. Nucleic Acids Res. 1994 Jun 11;22(11):2079-88. PMID: 8029015; PMC: PMC308124 Fichant GA, Burks C. Identifying potential tRNA genes in genomic DNA sequences. J Mol Biol. 1991 Aug 5;220(3):659-71. PMID: 1870126 Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997 Mar 1;25(5):955-64. PMID: 9023104; PMC: PMC146525 Pavesi A, Conterio F, Bolchi A, Dieci G, Ottonello S. Identification of new eukaryotic tRNA genes in genomic DNA databases by a multistep weight matrix analysis of transcriptional control regions. Nucleic Acids Res. 1994 Apr 11;22(7):1247-56. PMID: 8165140; PMC: PMC523650 
knownAlt UCSC Alt Events Alternative Splicing, Alternative Promoter and Similar Events in UCSC Genes Genes and Gene Predictions Description This track shows various types of alternative splicing and other events that result in more than a single transcript from the same gene. The label by an item describes the type of event. The events are: Alternate Promoter (altPromoter) - Transcription starts at multiple places. The altPromoter extends from 100 bases before to 50 bases after transcription start. Alternate Finish Site (altFinish) - Transcription ends at multiple places. Cassette Exon (cassetteExon) - Exon is present in some transcripts but not others. These are found by looking for exons that overlap an intron in the same transcript. Retained Intron (retainedIntron) - Introns are spliced out in some transcripts but not others. In some cases, particularly when the intron is near the 3' end, this can reflect an incompletely processed transcript rather than a true alt-splicing event. Overlapping Exon (bleedingExon) - Initial or terminal exons overlap in an intron in another transcript. These often are associated with incompletely processed transcripts. Alternate 3' End (altThreePrime) - Variations on the 3' end of an intron. Alternate 5' End (altFivePrime) - Variations on the 5' end of an intron. Intron Ends have AT/AC (atacIntron) - An intron with AT/AC ends rather than the usual GT/AG. These are associated with the minor spliceosome. Strange Intron Ends (strangeSplice) - An intron with ends that are not GT/AG, GC/AG, or AT/AC. These are usually artifacts of some sort due to sequencing error or polymorphism. Credits This track is based on an analysis by the txgAnalyse program of splicing graphs produced by the txGraph program. Both of these programs were written by Jim Kent at UCSC. 
umap Umap Single-read and multi-read mappability by Umap Mapping and Sequencing Description These tracks indicate regions with uniquely mappable reads of particular lengths before and after bisulfite conversion. Both Umap and Bismap tracks contain single-read mappability and multi-read mappability tracks for four different read lengths: 24 bp, 36 bp, 50 bp, and 100 bp. You can use these tracks for many purposes, including filtering unreliable signal from sequencing assays. The Bismap track can help filter unreliable signal from sequencing assays involving bisulfite conversion, such as whole-genome bisulfite sequencing or reduced representation bisulfite sequencing. Bismap single-read and multi-read mappability Bismap single-read mappability These tracks mark any region of the bisulfite-converted genome that is uniquely mappable by at least one k-mer on the specified strand. Mappability of the forward strand was generated by converting all instances of cytosine to thymine. Similarly, mappability of the reverse strand was generated by converting all instances of guanine to adenine. To calculate the single-read mappability, you must find the overlap of a given region with the region that is uniquely mappable on both strands. Regions not uniquely mappable on both strands or have a low multi-read mappability might bias the downstream analysis. Bismap multi-read mappability These tracks represent the probability that a randomly selected k-mer which overlaps with a given position is uniquely mappable. Multi-read mappability track is calculated for k-mers that are uniquely mappable on both strands, and thus there is no strand specification. Umap single-read and multi-read mappability Umap single-read mappability These tracks mark any region of the genome that is uniquely mappable by at least one k-mer. To calculate the single-read mappability, you must find the overlap of a given region with this track. Umap multi-read mappability These tracks represent the probability that a randomly selected k-mer which overlaps with a given position is uniquely mappable. For greater detail and explanatory diagrams, see the preprint, the Umap and Bismap project website, or the Umap and Bismap software documentation. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, genome annotation is stored in a bigBed or bigWig file that can be downloaded from the download server. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed or bigWigToWig, which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed -chrom=chr6 -start=0 -end=1000000 http://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k24.Unique.Mappability.bb stdout bigWigToWig -chrom=chr6 -start=0 -end=1000000 http://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k24.Umap.MultiTrackMappability.bw stdout Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits Anshul Kundaje (Stanford University) created the original Umap software in MATLAB. The original Umap repository is available here. Mehran Karimzadeh (Michael Hoffman lab, Princess Margaret Cancer Centre) implemented the Python version of Umap and added features, including Bismap. References Karimzadeh M, Ernst C, Kundaje A, Hoffman MM., Umap and Bismap: quantifying genome and methylome mappability bioRxiv bioRxiv, p. 095463, 2016.; doi: https://doi.org/10.1101/095463. 
umapBigWig Umap Single-read and multi-read mappability by Umap Mapping and Sequencing 
umap100Quantitative Umap M100 Multi-read mappability with 100-mers Mapping and Sequencing 
umap50Quantitative Umap M50 Multi-read mappability with 50-mers Mapping and Sequencing 
umap36Quantitative Umap M36 Multi-read mappability with 36-mers Mapping and Sequencing 
umap24Quantitative Umap M24 Multi-read mappability with 24-mers Mapping and Sequencing 
umapBigBed Umap Single-read and multi-read mappability by Umap Mapping and Sequencing 
umap100 Umap S100 Single-read mappability with 100-mers Mapping and Sequencing 
umap50 Umap S50 Single-read mappability with 50-mers Mapping and Sequencing 
umap36 Umap S36 Single-read mappability with 36-mers Mapping and Sequencing 
umap24 Umap S24 Single-read mappability with 24-mers Mapping and Sequencing 
uniprot UniProt UniProt SwissProt/TrEMBL Protein Annotations Genes and Gene Predictions Description This track shows protein sequences and annotations on them from the UniProt/SwissProt database, mapped to genomic coordinates. UniProt/SwissProt data has been curated from scientific publications by the UniProt staff, UniProt/TrEMBL data has been predicted by various computational algorithms. The annotations are divided into multiple subtracks, based on their &quot;feature type&quot; in UniProt. The first two subtracks below - one for SwissProt, one for TrEMBL - show the alignments of protein sequences to the genome, all other tracks below are the protein annotations mapped through these alignments to the genome. Track Name Description UCSC Alignment, SwissProt = curated protein sequences Protein sequences from SwissProt mapped to the genome. All other tracks are (start,end) SwissProt annotations on these sequences mapped through this alignment. Even protein sequences without a single curated annotation (splice isoforms) are visible in this track. Each UniProt protein has one main isoform, which is colored in dark. Alternative isoforms are sequences that do not have annotations on them and are colored in light-blue. They can be hidden with the TrEMBL/Isoform filter (see below). UCSC Alignment, TrEMBL = predicted protein sequences Protein sequences from TrEMBL mapped to the genome. All other tracks below are (start,end) TrEMBL annotations mapped to the genome using this track. This track is hidden by default. To show it, click its checkbox on the track configuration page. UniProt Signal Peptides Regions found in proteins destined to be secreted, generally cleaved from mature protein. UniProt Extracellular Domains Protein domains with the comment &quot;Extracellular&quot;. UniProt Transmembrane Domains Protein domains of the type &quot;Transmembrane&quot;. UniProt Cytoplasmic Domains Protein domains with the comment &quot;Cytoplasmic&quot;. UniProt Polypeptide Chains Polypeptide chain in mature protein after post-processing. UniProt Regions of Interest Regions that have been experimentally defined, such as the role of a region in mediating protein-protein interactions or some other biological process. UniProt Domains Protein domains, zinc finger regions and topological domains. UniProt Disulfide Bonds Disulfide bonds. UniProt Amino Acid Modifications Glycosylation sites, modified residues and lipid moiety-binding regions. UniProt Amino Acid Mutations Mutagenesis sites and sequence variants. UniProt Protein Primary/Secondary Structure Annotations Beta strands, helices, coiled-coil regions and turns. UniProt Sequence Conflicts Differences between Genbank sequences and the UniProt sequence. UniProt Repeats Regions of repeated sequence motifs or repeated domains. UniProt Other Annotations All other annotations, e.g. compositional bias For consistency and convenience for users of mutation-related tracks, the subtrack &quot;UniProt/SwissProt Variants&quot; is a copy of the track &quot;UniProt Variants&quot; in the track group &quot;Phenotype and Literature&quot;, or &quot;Variation and Repeats&quot;, depending on the assembly. Display Conventions and Configuration Genomic locations of UniProt/SwissProt annotations are labeled with a short name for the type of annotation (e.g. &quot;glyco&quot;, &quot;disulf bond&quot;, &quot;Signal peptide&quot; etc.). A click on them shows the full annotation and provides a link to the UniProt/SwissProt record for more details. TrEMBL annotations are always shown in light blue, except in the Signal Peptides, Extracellular Domains, Transmembrane Domains, and Cytoplamsic domains subtracks. Mouse over a feature to see the full UniProt annotation comment. For variants, the mouse over will show the full name of the UniProt disease acronym. The subtracks for domains related to subcellular location are sorted from outside to inside of the cell: Signal peptide, extracellular, transmembrane, and cytoplasmic. Features in the &quot;UniProt Modifications&quot; (modified residues) track are drawn in light green. Disulfide bonds are shown in dark grey. Topological domains in maroon and zinc finger regions in olive green. Duplicate annotations are removed as far as possible: if a TrEMBL annotation has the same genome position and same feature type, comment, disease and mutated amino acids as a SwissProt annotation, it is not shown again. Two annotations mapped through different protein sequence alignments but with the same genome coordinates are only shown once. On the configuration page of this track, you can choose to hide any TrEMBL annotations. This filter will also hide the UniProt alternative isoform protein sequences because both types of information are less relevant to most users. Please contact us if you want more detailed filtering features. Note that for the human hg38 assembly and SwissProt annotations, there also is a public track hub prepared by UniProt itself, with genome annotations maintained by UniProt using their own mapping method based on those Gencode/Ensembl gene models that are annotated in UniProt for a given protein. For proteins that differ from the genome, UniProt's mapping method will, in most cases, map a protein and its annotations to an unexpected location (see below for details on UCSC's mapping method). Methods Briefly, UniProt protein sequences were aligned to the transcripts associated with the protein, the top-scoring alignments were retained, and the result was projected to the genome through a transcript-to-genome alignment. Depending on the genome, the transcript-genome alignments was either provided by the source database (NBCI RefSeq), created at UCSC (UCSC RefSeq) or derived from the transcripts (Ensembl/Augustus). The transcript set is NCBI RefSeq for hg38, UCSC RefSeq for hg19 (due to alt/fix haplotype misplacements in the NCBI RefSeq set on hg19). For other genomes, RefSeq, Ensembl and Augustus are tried, in this order. The resulting protein-genome alignments of this process are available in the file formats for liftOver or pslMap from our data archive (see "Data Access" section below). An important step of the mapping process protein -&gt; transcript -&gt; genome is filtering the alignment from protein to transcript. Due to differences between the UniProt proteins and the transcripts (proteins were made many years before the transcripts were made, and human genomes have variants), the transcript with the highest BLAST score when aligning the protein to all transcripts is not always the correct transcript for a protein sequence. Therefore, the protein sequence is aligned to only a very short list of one or sometimes more transcripts, selected by a three-step procedure: Use transcripts directly annotated by UniProt: for organisms that have a RefSeq transcript track, proteins are aligned to the RefSeq transcripts that are annotated by UniProt for this particular protein. Use transcripts for NCBI Gene ID annotated by UniProt: If no transcripts are annotated on the protein, or the annotated ones have been deprecated by NCBI, but a NCBI Gene ID is annotated, the RefSeq transcripts for this Gene ID are used. This can result in multiple matching transcripts for a protein. Use best matching transcript: If no NCBI Gene is annotated, then BLAST scores are used to pick the transcripts. There can be multiple transcripts for one protein, as their coding sequences can be identical. All transcripts within 1% of the highest observed BLAST score are used. For strategy 2 and 3, many of the transcripts found do not differ in coding sequence, so the resulting alignments on the genome will be identical. Therefore, any identical alignments are removed in a final filtering step. The details page of these alignments will contain a list of all transcripts that result in the same protein-genome alignment. On hg38, only a handful of edge cases (pseudogenes, very recently added proteins) remain in 2023 where strategy 3 has to be used. In other words, when an NCBI or UCSC RefSeq track is used for the mapping and to align a protein sequence to the correct transcript, we use a three stage process: If UniProt has annotated a given RefSeq transcript for a given protein sequence, the protein is aligned to this transcript. Any difference in the version suffix is tolerated in this comparison. If no transcript is annotated or the transcript cannot be found in the NCBI/UCSC RefSeq track, the UniProt-annotated NCBI Gene ID is resolved to a set of NCBI RefSeq transcript IDs via the most current version of NCBI genes tables. Only the top match of the resulting alignments and all others within 1% of its score are used for the mapping. If no transcript can be found after step (2), the protein is aligned to all transcripts, the top match, and all others within 1% of its score are used. This system was designed to resolve the problem of incorrect mappings of proteins, mostly on hg38, due to differences between the SwissProt sequences and the genome reference sequence, which has changed since the proteins were defined. The problem is most pronounced for gene families composed of either very repetitive or very similar proteins. To make sure that the alignments always go to the best chromosome location, all _alt and _fix reference patch sequences are ignored for the alignment, so the patches are entirely free of UniProt annotations. Please contact us if you have feedback on this process or example edge cases. We are not aware of a way to evaluate the results completely and in an automated manner. Proteins were aligned to transcripts with TBLASTN, converted to PSL, filtered with pslReps (93% query coverage, keep alignments within top 1% score), lifted to genome positions with pslMap and filtered again with pslReps. UniProt annotations were obtained from the UniProt XML file. The UniProt annotations were then mapped to the genome through the alignment described above using the pslMap program. This approach draws heavily on the LS-SNP pipeline by Mark Diekhans. Like all Genome Browser source code, the main script used to build this track can be found on Github. Older releases This track is automatically updated on an ongoing basis, every 2-3 months. The current version name is always shown on the track details page, it includes the release of UniProt, the version of the transcript set and a unique MD5 that is based on the protein sequences, the transcript sequences, the mapping file between both and the transcript-genome alignment. The exact transcript that was used for the alignment is shown when clicking a protein alignment in one of the two alignment tracks. For reproducibility of older analysis results and for manual inspection, previous versions of this track are available for browsing in the form of the UCSC UniProt Archive Track Hub (click this link to connect the hub now). The underlying data of all releases of this track (past and current) can be obtained from our downloads server, including the UniProt protein-to-genome alignment. Data Access The raw data of the current track can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the genome annotation is stored in a bigBed file that can be downloaded from the download server. The exact filenames can be found in the track configuration file. Annotations can be converted to ASCII text by our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/uniprot/unipStruct.bb -chrom=chr6 -start=0 -end=1000000 stdout Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Lifting from UniProt to genome coordinates in pipelines To facilitate mapping protein coordinates to the genome, we provide the alignment files in formats that are suitable for our command line tools. Our command line programs liftOver or pslMap can be used to map coordinates on protein sequences to genome coordinates. The filenames are unipToGenome.over.chain.gz (liftOver) and unipToGenomeLift.psl.gz (pslMap). Example commands: wget -q https://hgdownload.soe.ucsc.edu/goldenPath/archive/hg38/uniprot/2022_03/unipToGenome.over.chain.gz wget -q https://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/liftOver chmod a+x liftOver echo 'Q99697 1 10 annotationOnProtein' &gt; prot.bed liftOver prot.bed unipToGenome.over.chain.gz genome.bed cat genome.bed Credits This track was created by Maximilian Haeussler at UCSC, with a lot of input from Chris Lee, Mark Diekhans and Brian Raney, feedback from the UniProt staff, Alejo Mujica, Regeneron Pharmaceuticals and Pia Riestra, GeneDx. Thanks to UniProt for making all data available for download. References UniProt Consortium. Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res. 2012 Jan;40(Database issue):D71-5. PMID: 22102590; PMC: PMC3245120 Yip YL, Scheib H, Diemand AV, Gattiker A, Famiglietti LM, Gasteiger E, Bairoch A. The Swiss-Prot variant page and the ModSNP database: a resource for sequence and structure information on human protein variants. Hum Mutat. 2004 May;23(5):464-70. PMID: 15108278 
unipConflict Seq. Conflicts UniProt Sequence Conflicts Genes and Gene Predictions 
unipRepeat Repeats UniProt Repeats Genes and Gene Predictions 
unipStruct Structure UniProt Protein Primary/Secondary Structure Annotations Genes and Gene Predictions 
unipOther Other Annot. UniProt Other Annotations Genes and Gene Predictions 
unipMut Mutations UniProt Amino Acid Mutations Genes and Gene Predictions 
unipModif AA Modifications UniProt Amino Acid Modifications Genes and Gene Predictions 
unipDomain Domains UniProt Domains Genes and Gene Predictions 
unipDisulfBond Disulf. Bonds UniProt Disulfide Bonds Genes and Gene Predictions 
unipChain Chains UniProt Mature Protein Products (Polypeptide Chains) Genes and Gene Predictions 
unipLocCytopl Cytoplasmic UniProt Cytoplasmic Domains Genes and Gene Predictions 
unipLocTransMemb Transmembrane UniProt Transmembrane Domains Genes and Gene Predictions 
unipInterest Interest UniProt Regions of Interest Genes and Gene Predictions 
unipLocExtra Extracellular UniProt Extracellular Domain Genes and Gene Predictions 
unipLocSignal Signal Peptide UniProt Signal Peptides Genes and Gene Predictions 
unipAliTrembl TrEMBL Aln. UCSC alignment of TrEMBL proteins to genome Genes and Gene Predictions 
unipAliSwissprot SwissProt Aln. UCSC alignment of SwissProt proteins to genome (dark blue: main isoform, light blue: alternative isoforms) Genes and Gene Predictions 
spMut UniProt Variants UniProt/SwissProt Amino Acid Substitutions Phenotype and Literature Description NOTE: This track is intended for use primarily by physicians and other professionals concerned with genetic disorders, by genetics researchers, and by advanced students in science and medicine. While the genome browser database is open to the public, users seeking information about a personal medical or genetic condition are urged to consult with a qualified physician for diagnosis and for answers to personal questions. This track shows the genomic positions of natural and artifical amino acid variants in the UniProt/SwissProt database. The data has been curated from scientific publications by the UniProt staff. Display Conventions and Configuration Genomic locations of UniProt/SwissProt variants are labeled with the amino acid change at a given position and, if known, the abbreviated disease name. A &quot;?&quot; is used if there is no disease annotated at this location, but the protein is described as being linked to only a single disease in UniProt. Mouse over a mutation to see the UniProt comments. Artificially-introduced mutations are colored green and naturally-occurring variants are colored red. For full information about a particular variant, click the &quot;UniProt variant&quot; linkout. The &quot;UniProt record&quot; linkout lists all variants of a particular protein sequence. The &quot;Source articles&quot; linkout lists the articles in PubMed that originally described the variant(s) and were used as evidence by the UniProt curators. Methods UniProt sequences were aligned to RefSeq sequences first with BLAT, then lifted to genome positions with pslMap. UniProt variants were parsed from the UniProt XML file. The variants were then mapped to the genome through the alignment using the pslMap program. This mapping approach draws heavily on the LS-SNP pipeline by Mark Diekhans. The complete script is part of the kent source tree and is located in src/hg/utils/uniprotMutations. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the genome annotation is stored in a bigBed file that can be downloaded from the download server. The underlying data file for this track is called spMut.bb. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/uniprot/spMut.bb -chrom=chr6 -start=0 -end=1000000 stdout Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits This track was created by Maximilian Haeussler, with advice from Mark Diekhans and Brian Raney. References UniProt Consortium. Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res. 2012 Jan;40(Database issue):D71-5. PMID: 22102590; PMC: PMC3245120 Yip YL, Scheib H, Diemand AV, Gattiker A, Famiglietti LM, Gasteiger E, Bairoch A. The Swiss-Prot variant page and the ModSNP database: a resource for sequence and structure information on human protein variants. Hum Mutat. 2004 May;23(5):464-70. PMID: 15108278 
vistaEnhancersBb VISTA Enhancers VISTA Enhancers Regulation Description This track shows potential enhancers whose activity was experimentally validated in transgenic mice. Most of these noncoding elements were selected for testing based on their extreme conservation in other vertebrates or epigenomic evidence (ChIP-Seq) of putative enhancer marks. More information can be found on the VISTA Enhancer Browser page. Display Conventions and Configuration Items appearing in red (positive) indicate that a reproducible pattern was observed in the in vivo enhancer assay. Items appearing in blue (negative) indicate that NO reproducible pattern was observed in the in vivo enhancer assay. Note that this annotation refers only to the single developmental timepoint that was tested in this screen (e11.5) and does not exclude the possibility that this region is a reproducible enhancer active at earlier or later timepoints in development. Methods Excerpted from the Vista Enhancer Mouse Enhancer Screen Handbook and Methods page at the Lawrence Berkeley National Laboratory (LBNL) website: Enhancer Candidate Identification Most enhancer candidate sequences are identified by extreme evolutionary sequence conservation or by ChIP-seq. Detailed information related to enhancer identification by extreme evolutionary conservation can be found in the following publications: Pennacchio et al., Genomic strategies to identify mammalian regulatory sequences. Nature Rev Genet 2001 Nobrega et al., Nobrega et al., Scanning human gene deserts for long-range enhancers. Science 2003 Pennacchio et al., In vivo enhancer analysis of human conserved non-coding sequences. Nature 2006 Visel et al., Enhancer identification through comparative genomics. Semin Cell Dev Biol. 2007 Visel et al., Ultraconservation identifies a small subset of extremely constrained developmental enhancers. Nature Genet 2008 Detailed information related to enhancer identification by ChIP-seq can be found in the following publications: Visel et al., ChIP-seq accurately predicts tissue-specific activity of enhancers. Nature 2009 Visel et al., Genomic views of distant-acting enhancers. Nature 2009 See the Transgenic Mouse Assay section for experimental procedures that were used to perform the transgenic assays: Mouse Enhancer Screen Handbook and Methods UCSC converted the Experimental Data for hg19 and mm9 into bigBed format using the bedToBigBed utility. The data for hg38 was lifted over from hg19. The data for mm10 and mm39 were lifted over from mm9. Data Access VISTA Enhancers data can be explored interactively with the Table Browser and cross-referenced with the Data Integrator. For programmatic access, the track can be accessed using the Genome Browser's REST API. ReMap annotations can be downloaded from the Genome Browser's download server as a bigBed file. This compressed binary format can be remotely queried through command line utilities. Please note that some of the download files can be quite large. Credits Thanks to the Lawrence Berkeley National Laboratory for providing this data References Visel A, Minovitsky S, Dubchak I, Pennacchio LA. VISTA Enhancer Browser--a database of tissue-specific human enhancers. Nucleic Acids Res. 2007 Jan;35(Database issue):D88-92. PMID: 17130149; PMC: PMC1716724