refSeqComposite NCBI RefSeq RefSeq genes from NCBI Genes and Gene Predictions Description The NCBI RefSeq Genes composite track shows S. cerevisiae 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 – all curated and predicted annotations provided by RefSeq. RefSeq Curated – 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.) They were manually curated, based on publications describing transcripts and manual reviews of evidence which includes EST and full-length cDNA alignments, protein sequences, splice sites and any other evidence available in databases or the scientific literature. The resulting sequences can differ from the genome, they exist independently from a particular human genome build, and so must be aligned to the genome to create a track. The "RefSeq Curated" track is NCBI's mapping of these transcripts to the genome. Another alignment track exists for these, the "UCSC RefSeq" track (see beloow). RefSeq Predicted – subset of RefSeq All that includes those annotations whose accessions begin with XM or XR. They were predicted based on protein, cDNA, EST and RNA-seq alignments to the genome assembly by the NCBI Gnomon prediction software. RefSeq Other – 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. Examples are untranscribed pseudogenes or gene clusters, such as HOX or protocadherin alpha. They were manually curated from publications or databases but are not typical transcribed genes. RefSeq Alignments – alignments of RefSeq RNAs to the S. cerevisiae genome provided by the RefSeq group, following the display conventions for PSL tracks. RefSeq Diffs – alignment differences between the S. cerevisiae reference genome(s) and RefSeq curated transcripts. (Track not currently available for every assembly.) UCSC RefSeq – annotations generated from UCSC's realignment of RNAs with NM and NR accessions to the S. cerevisiae genome. This track was previously known as the "RefSeq Genes" track. RefSeq Select (subset, only on hg38) – Subset of RefSeq Curated, transcripts marked as part of the RefSeq Select dataset. A single Select transcript is chosen as representative for each protein-coding gene. See NCBI RefSeq Select. RefSeq HGMD (subset) – 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, 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 – 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 – genomic gaps that are too small to be introns (arbitrary cutoff of < 45 bp), most likely insertions/deletion variants or errors, with HGVS g. and c./n. showing differences. shift gap – 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 – 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 – 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 S. cerevisiae 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 UCSC RefSeq - refGene PSL format: RefSeq Alignments - ncbiRefSeqPsl The first column of each of these tables is "bin". 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/sacCer3/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 sacCer3 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 
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 
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 
sgdClone WashU Clones Washington University Clones Mapping and Sequencing Description This track displays the location of clones (mostly lambda and cosmid clones) from Washington University in St. Louis using the names assigned by that group. This information was downloaded from the Saccharomyces Genome Database (SGD) from the file https://downloads.yeastgenome.org/curation/chromosomal_feature/clone.tab. Credits Thanks to Washington University in St. Louis and the SGD for the data used in this track. 
sgdGene SGD Genes Protein-Coding Genes from Saccharomyces Genome Database Genes and Gene Predictions Description This track shows annotated genes and open reading frames (ORFs) of Saccharomyces cerevisiae obtained from the Saccharomyces Genome Database (SGD). Clicking on an item in this track brings up a display that synthesizes available data on the gene from a wide variety of sources. The data were downloaded from the SGD: saccharomyces_cerevisiae.gff (accessed 29 Aug. 2011) This track excludes the ORFs classified as dubious by SGD. Credits Thanks to the SGD for providing the data used in this annotation. 
sgdOther SGD Other Other Features from Saccharomyces Genome Database Genes and Gene Predictions Description This track shows a variety of features in the Saccharomyces cerevisiae genome, including tRNAs, transposons, centromeres, and open reading frames (ORFs) classified as dubious. Click on an item in this track to display details about it. The data were downloaded from the Saccharomyces Genome Database (SGD): saccharomyces_cerevisiae.gff (accessed 29 Aug. 2011). Credits Thanks to the SGD for providing the data used in this annotation. 
transRegCode Regulatory Code Transcriptional Regulatory Code from Harbison Gordon et al. Expression and Regulation Description This track shows putative regulatory elements in Saccharomyces cerevisiae that are supported by cross-species evidence (Harbison, Gordon, et al., 2004). Harbison, Gordon, et al. performed a genome-wide location analysis with 203 known DNA-binding transcriptional regulators (some under multiple environmental conditions) and identified 11,000 high-confidence interactions between regulators and promoter regions. They then compiled a compendium of motifs for 102 transcriptional regulators based on a combination of their experimental results, cross-species conservation data for four species of yeast and motifs from the literature. Finally, they mapped these motifs to the S. cerevisiae genome. This track shows positions at which these motifs matched the genome with high confidence and at which the matching sequence was well conserved across yeast species. The details page for each putative binding site shows the sequence at that site compared to the position-specific probability matrix for the associated transcriptional regulator (shown as both a table and a graphical logo). It also indicates whether the binding site is supported by experimental (ChIP-chip) results and the number of other yeast species in which it is conserved. See also the "Reg. ChIP-chip" track for additional related information. Display Conventions The scoring ranges from 200 to 1000 and is based on the number of lines of evidence that support the motif being active. Each of the two sensu stricto species in which the motif was conserved counts as a line of evidence. If the ChIP-chip data showed good (P ≤ 0.001) evidence of binding to the transcription factor associated with the motif, that counts as two lines of evidence. If the ChIP-chip data showed weaker (P ≤ 0.005) evidence of binding, that counts as just one line of evidence. The following table shows the relationship between lines of evidence and score: EvidenceScore 41000 3500 2333 1250 0200 Credits The data for this track was provided by the Young and Fraenkel labs at MIT/Whitehead/Broad. The track was created by Jim Kent. References Harbison CT, Gordon DB, Lee TI, Rinaldi NJ, MacIsaac KD, Danford TW, Hannett NM, Tagne JB, Reynolds DB, Yoo J et al. Transcriptional regulatory code of a eukaryotic genome. Nature. 2004 Sep 2;431(7004):99-104. PMID: 15343339; PMC: PMC3006441 Supplementary data at http://younglab.wi.mit.edu/regulatory_code/ and http://fraenkel.mit.edu/Harbison/. 
transRegCodeProbe Reg. ChIP-chip ChIP-chip Results from Harbison Gordon et al. Expression and Regulation Description This track shows the location of the probes spotted on a slide in the chromatin immunoprecipitation/microarray hybridization (ChIP-chip) experiments described in Harbison, Gordon et al. below. Click on an item in this track to display a page showing which transcription factors pulled down DNA that is enriched for this probe sequence, which transcription factor binding site motifs are present in the probe and whether these motifs are conserved in related yeast species. See also the "Regulatory Code" track for the position of the individual motifs. Credits The data for this track was provided by the Young and Fraenkel labs at MIT/Whitehead/Broad. The track was created by Jim Kent. References Harbison CT, Gordon DB, Lee TI, Rinaldi NJ, MacIsaac KD, Danford TW, Hannett NM, Tagne JB, Reynolds DB, Yoo J et al. Transcriptional regulatory code of a eukaryotic genome. Nature. 2004 Sep 2;431(7004):99-104. PMID: 15343339; PMC: PMC3006441 Supplementary data at http://younglab.wi.mit.edu/regulatory_code/ and http://fraenkel.mit.edu/Harbison/. 
gold Assembly Assembly from Fragments Mapping and Sequencing Description This track shows the final assembly of the Saccharomyces cerevisiae S288c assembly (GCA_000146045.2) as of April 2011. of the S. cerevisiae genome. Please note the sequencing status at: SGD™. Chromosomes available in this assembly: chrI, chrII, chrIII, chrIV ... etc ... chrXVI, and chrM. See also: SGD™ Genome Snapshot/Overview. Credits The April 2011 Saccharomyces cerevisiae genome assembly is based on sequence from the NCBI R64-1-1 download directory. 
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 König 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 
crisprRanges CRISPR Regions Genome regions processed to find CRISPR/Cas9 target sites (exons +/- 200 bp) Genes and Gene Predictions Description This track shows regions of the genome within 200 bp of transcribed regions and DNA sequences targetable by CRISPR RNA guides using the Cas9 enzyme from S. pyogenes (PAM: NGG). CRISPR target sites were annotated with predicted specificity (off-target effects) and predicted efficiency (on-target cleavage) by various algorithms through the tool CRISPOR. Display Conventions and Configuration The track "CRISPR Regions" shows the regions of the genome where target sites were analyzed, i.e. within 200 bp of transcribed regions as annotated by Ensembl transcript models. The track "CRISPR Targets" shows the target sites in these regions. 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 "uniqueness" 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 <= 50 (see below) Colors highlight targets that are specific in the genome (MIT specificity > 50) but have different predicted efficiencies: unable to calculate Doench/Fusi 2016 efficiency score low predicted cleavage: Doench/Fusi 2016 Efficiency percentile <= 30 medium predicted cleavage: Doench/Fusi 2016 Efficiency percentile > 30 and < 55 high predicted cleavage: Doench/Fusi 2016 Efficiency > 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 > 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. "70%" 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 > 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 < 0.023 are not shown on this page, but are availble 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 < 0.023 were false-positives. 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 Exons as predicted by Ensembl Gene models were used, extended by 200 basepairs on each side, searched 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. Data Access The raw data can be explored interactively with the Table Browser. For automated 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 crispr.bb and crisprDetails.tab and are located in the /gbdb/sacCer3/crispr directory of our downloads 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/hg19/crisprRanges/crispr.bb -chrom=chr21 -start=0 -end=10000000 stdout The file crisprDetails.tab includes the details of the off-targets. The last column of the bigBed file is the offset of the respective line in crisprDetails.tab. E.g. if the last column is 14227033723, then the following command will extract the line with the corresponding off-target details: curl -s -r 14227033723-14227043723 http://hgdownload.soe.ucsc.edu/gbdb/hg19/crispr/crisprDetails.tab | head -n1. The off-target details can currently not be joined with the table browser. The file crisprDetails.tab is a tab-separated text file with two fields. The first field contains the numbers of off-targets for each mismatch, e.g. "0,0,1,3,49" means 0 off-targets at zero mismatches, 1 at two mismatches, 3 at three and 49 off-targets at four mismatches. The second field is a pipe-separated list of semicolon-separated tuples with the genome coordinates and the CFD score. E.g. "chr10;123376795+;42|chr5;148353274-;39" describes two off-targets, with the first at chr1:123376795 on the positive strand and a CFD score 0.42 Credits Track created by Maximilian Haeussler and Hiram Clawson, with helpful input from Jean-Paul Concordet (MNHN Paris) and Alberto Stolfi (NYU). References Haeussler M, Schönig K, Eckert H, Eschstruth A, Mianné 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 
crispr CRISPR CRISPR/Cas9 Sp. Pyog. target sites Genes and Gene Predictions Description This track shows regions of the genome within 200 bp of transcribed regions and DNA sequences targetable by CRISPR RNA guides using the Cas9 enzyme from S. pyogenes (PAM: NGG). CRISPR target sites were annotated with predicted specificity (off-target effects) and predicted efficiency (on-target cleavage) by various algorithms through the tool CRISPOR. Display Conventions and Configuration The track "CRISPR Regions" shows the regions of the genome where target sites were analyzed, i.e. within 200 bp of transcribed regions as annotated by Ensembl transcript models. The track "CRISPR Targets" shows the target sites in these regions. 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 "uniqueness" 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 <= 50 (see below) Colors highlight targets that are specific in the genome (MIT specificity > 50) but have different predicted efficiencies: unable to calculate Doench/Fusi 2016 efficiency score low predicted cleavage: Doench/Fusi 2016 Efficiency percentile <= 30 medium predicted cleavage: Doench/Fusi 2016 Efficiency percentile > 30 and < 55 high predicted cleavage: Doench/Fusi 2016 Efficiency > 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 > 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. "70%" 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 > 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 < 0.023 are not shown on this page, but are availble 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 < 0.023 were false-positives. 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 Exons as predicted by Ensembl Gene models were used, extended by 200 basepairs on each side, searched 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. Data Access The raw data can be explored interactively with the Table Browser. For automated 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 crispr.bb and crisprDetails.tab and are located in the /gbdb/sacCer3/crispr directory of our downloads 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/hg19/crispr/crispr.bb -chrom=chr21 -start=0 -end=10000000 stdout The file crisprDetails.tab includes the details of the off-targets. The last column of the bigBed file is the offset of the respective line in crisprDetails.tab. E.g. if the last column is 14227033723, then the following command will extract the line with the corresponding off-target details: curl -s -r 14227033723-14227043723 http://hgdownload.soe.ucsc.edu/gbdb/hg19/crispr/crisprDetails.tab | head -n1. The off-target details can currently not be joined with the table browser. The file crisprDetails.tab is a tab-separated text file with two fields. The first field contains the numbers of off-targets for each mismatch, e.g. "0,0,1,3,49" means 0 off-targets at zero mismatches, 1 at two mismatches, 3 at three and 49 off-targets at four mismatches. The second field is a pipe-separated list of semicolon-separated tuples with the genome coordinates and the CFD score. E.g. "chr10;123376795+;42|chr5;148353274-;39" describes two off-targets, with the first at chr1:123376795 on the positive strand and a CFD score 0.42 Credits Track created by Maximilian Haeussler and Hiram Clawson, with helpful input from Jean-Paul Concordet (MNHN Paris) and Alberto Stolfi (NYU). References Haeussler M, Schönig K, Eckert H, Eschstruth A, Mianné 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 
crisprTargets CRISPR Targets CRISPR/Cas9 -NGG Targets Genes and Gene Predictions Description This track shows regions of the genome within 200 bp of transcribed regions and DNA sequences targetable by CRISPR RNA guides using the Cas9 enzyme from S. pyogenes (PAM: NGG). CRISPR target sites were annotated with predicted specificity (off-target effects) and predicted efficiency (on-target cleavage) by various algorithms through the tool CRISPOR. Display Conventions and Configuration The track "CRISPR Regions" shows the regions of the genome where target sites were analyzed, i.e. within 200 bp of transcribed regions as annotated by Ensembl transcript models. The track "CRISPR Targets" shows the target sites in these regions. 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 "uniqueness" 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 <= 50 (see below) Colors highlight targets that are specific in the genome (MIT specificity > 50) but have different predicted efficiencies: unable to calculate Doench/Fusi 2016 efficiency score low predicted cleavage: Doench/Fusi 2016 Efficiency percentile <= 30 medium predicted cleavage: Doench/Fusi 2016 Efficiency percentile > 30 and < 55 high predicted cleavage: Doench/Fusi 2016 Efficiency > 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 > 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. "70%" 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 > 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 < 0.023 are not shown on this page, but are availble 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 < 0.023 were false-positives. 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 Exons as predicted by Ensembl Gene models were used, extended by 200 basepairs on each side, searched 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. Data Access The raw data can be explored interactively with the Table Browser. For automated 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 crispr.bb and crisprDetails.tab and are located in the /gbdb/sacCer3/crispr directory of our downloads 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/hg19/crisprTargets/crispr.bb -chrom=chr21 -start=0 -end=10000000 stdout The file crisprDetails.tab includes the details of the off-targets. The last column of the bigBed file is the offset of the respective line in crisprDetails.tab. E.g. if the last column is 14227033723, then the following command will extract the line with the corresponding off-target details: curl -s -r 14227033723-14227043723 http://hgdownload.soe.ucsc.edu/gbdb/hg19/crispr/crisprDetails.tab | head -n1. The off-target details can currently not be joined with the table browser. The file crisprDetails.tab is a tab-separated text file with two fields. The first field contains the numbers of off-targets for each mismatch, e.g. "0,0,1,3,49" means 0 off-targets at zero mismatches, 1 at two mismatches, 3 at three and 49 off-targets at four mismatches. The second field is a pipe-separated list of semicolon-separated tuples with the genome coordinates and the CFD score. E.g. "chr10;123376795+;42|chr5;148353274-;39" describes two off-targets, with the first at chr1:123376795 on the positive strand and a CFD score 0.42 Credits Track created by Maximilian Haeussler and Hiram Clawson, with helpful input from Jean-Paul Concordet (MNHN Paris) and Alberto Stolfi (NYU). References Haeussler M, Schönig K, Eckert H, Eschstruth A, Mianné 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 
ensGene Ensembl Genes Ensembl Genes Genes and Gene Predictions Description These gene predictions were generated by Ensembl. For more information on the different gene tracks, see our Genes FAQ. Methods For a description of the methods used in Ensembl gene predictions, please refer to Hubbard et al. (2002), also listed in the References section below. Data access Ensembl Gene data can be explored interactively using the Table Browser or the Data Integrator. For local downloads, the genePred format files for sacCer3 are available in our downloads directory as ensGene.txt.gz or in our genes download directory in GTF format. For programmatic access, the data can be queried from the REST API or directly from our public MySQL servers. Instructions on this method are available on our MySQL help page and on our blog. Previous versions of this track can be found on our archive download server. Credits We would like to thank Ensembl for providing these gene annotations. For more information, please see Ensembl&#39s genome annotation page. 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 
evaSnpContainer EVA SNP Short Genetic Variants from European Variant Archive Variation and Repeats Description These tracks contain mappings of single nucleotide variants and small insertions and deletions (indels) from the European Variation Archive (EVA) for the S. cerevisiae sacCer3 genome. The dbSNP database at NCBI no longer hosts non-human variants. 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 variant 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. The display is set to automatically collapse to dense visibility when there are more than 100k variants in the window. When the window size is more than 250k bp, the display is switched to density graph mode. Searching, details, and filtering Navigation to an individual variant can be accomplished by typing or copying the variant identifier (rsID) or the genomic coordinates into the Position/Search box on the Browser. A click on an item in the graphical display displays a page with data about that variant. Data fields include the Reference and Alternate Alleles, the class of the variant as reported by EVA, the source of the data, the amino acid change, if any, and the functional class as determined by UCSC's Variant Annotation Integrator. Variants can be filtered using the track controls to show subsets of the data by either EVA Sequence Ontology (SO) term, UCSC-generated functional effect, or by color, which bins the UCSC functional effects into general classes. Mouse-over Mousing over an item shows the ucscClass, which is the consequence according to the Variant Annotation Integrator, and the aaChange when one is available, which is the change in amino acid in HGVS.p terms. Items may have multiple ucscClasses, which will all be shown in the mouse-over in a comma-separated list. Likewise, multiple HGVS.p terms may be shown for each rsID separated by spaces describing all possible AA changes. Multiple items may appear due to different variant predictions on multiple gene transcripts. For all organisms the gene models used were the NCBI RefSeq curated when available, if not then ensembl genes, or finally UCSC mappings of RefSeq if neither of the previous models was possible. Track colors Variants are colored according to the most potentially deleterious functional effect prediction according to the Variant Annotation Integrator. Specific bins can be seen in the Methods section below. Color Variant Type Protein-altering variants and splice site variants Synonymous codon variants Non-coding transcript or Untranslated Region (UTR) variants Intergenic and intronic variants Sequence ontology (SO) Variants are classified by EVA into one of the following sequence ontology terms: substitution — A single nucleotide in the reference is replaced by another, alternate allele deletion — One or more nucleotides is deleted. The representation in the database is to display one additional nucleotide in both the Reference field (Ref) and the Alternate Allele field (Alt). E.g. a variant that is a deletion of an A maybe be represented as Ref = GA and Alt = G. insertion — One or more nucleotides is inserted. The representation in the database is to display one additional nucleotide in both the Reference field (Ref) and the Alternate Allele field (Alt). E.g. a variant that is an insertion of a T maybe be represented as Ref = G and Alt = GT delins — Similar to tandemRepeat, in that the runs of Ref and Alt Alleles are of different length, except that there is more than one type of nucleotide, e.g., Ref = CCAAAAACAAAAACA, Alt = ACAAAAAC. multipleNucleotideVariant — More than one nucleotide is substituted by an equal number of different nucleotides, e.g., Ref = AA, Alt = GC. sequence alteration — A parent term meant to signify a deviation from another sequence. Can be assigned to variants that have not been characterized yet. Methods Data were downloaded from the European Variation Archive EVA current_ids.vcf.gz files corresponding to the proper assembly. Chromosome names were converted to UCSC-style and the variants passed through the Variant Annotation Integrator to predict consequence. For every organism the NCBI RefSeq curated models were used when available, followed by ensembl genes, and finally UCSC mapping of RefSeq when neither of the previous models were possible. Variants were then colored according to their predicted consequence in the following fashion: Protein-altering variants and splice site variants - exon_loss_variant, frameshift_variant, inframe_deletion, inframe_insertion, initiator_codon_variant, missense_variant, splice_acceptor_variant, splice_donor_variant, splice_region_variant, stop_gained, stop_lost, coding_sequence_variant, transcript_ablation Synonymous codon variants - synonymous_variant, stop_retained_variant Non-coding transcript or Untranslated Region (UTR) variants - 5_prime_UTR_variant, 3_prime_UTR_variant, complex_transcript_variant, non_coding_transcript_exon_variant Intergenic and intronic variants - upstream_gene_variant, downstream_gene_variant, intron_variant, intergenic_variant, NMD_transcript_variant, no_sequence_alteration Sequence Ontology ("SO:") terms were converted to the variant classes, then the files were converted to BED, and then bigBed format. No functional annotations were provided by the EVA (e.g., missense, nonsense, etc). These were computed using UCSC's Variant Annotation Integrator (Hinrichs, et al., 2016). Amino-acid substitutions for missense variants are based on RefSeq alignments of mRNA transcripts, which do not always match the amino acids predicted from translating the genomic sequence. Therefore, in some instances, the variant and the genomic nucleotide and associated amino acid may be reversed. E.g., a Pro > Arg change from the perspective of the mRNA would be Arg > Pro from the persepective the genomic sequence. Also, in bosTau9, galGal5, rheMac8, danRer10 and danRer11 the mitochondrial sequence was removed or renamed to match UCSC. For complete documentation of the processing of these tracks, see the makedoc corresponding to the version of interest. For example, the EVA Release 6 MakeDoc. 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 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. For automated download and analysis, this annotation is stored in a bigBed file that can be downloaded from our download server. Use the corresponding version number for the track of interest, e.g. evaSnp6.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 https://hgdownload.soe.ucsc.edu/gbdb/sacCer3/bbi/evaSnp6.bb -chrom=chr21 -start=0 -end=100000000 stdout Credits This track was produced from the European Variation Archive release data. Consequences were predicted using UCSC's Variant Annotation Integrator and NCBI's RefSeq as well as ensembl gene models. References Cezard T, Cunningham F, Hunt SE, Koylass B, Kumar N, Saunders G, Shen A, Silva AF, Tsukanov K, Venkataraman S et al. The European Variation Archive: a FAIR resource of genomic variation for all species. Nucleic Acids Res. 2021 Oct 28:gkab960. doi:10.1093/nar/gkab960. Epub ahead of print. PMID: 34718739. PMID: PMC8728205. Hinrichs AS, Raney BJ, Speir ML, Rhead B, Casper J, Karolchik D, Kuhn RM, Rosenbloom KR, Zweig AS, Haussler D, Kent WJ. UCSC Data Integrator and Variant Annotation Integrator. Bioinformatics. 2016 May 1;32(9):1430-2. PMID: 26740527; PMC: PMC4848401 
evaSnp6 EVA SNP Release 6 Short Genetic Variants from European Variant Archive Release 6 Variation and Repeats Description These tracks contain mappings of single nucleotide variants and small insertions and deletions (indels) from the European Variation Archive (EVA) for the S. cerevisiae sacCer3 genome. The dbSNP database at NCBI no longer hosts non-human variants. 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 variant 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. The display is set to automatically collapse to dense visibility when there are more than 100k variants in the window. When the window size is more than 250k bp, the display is switched to density graph mode. Searching, details, and filtering Navigation to an individual variant can be accomplished by typing or copying the variant identifier (rsID) or the genomic coordinates into the Position/Search box on the Browser. A click on an item in the graphical display displays a page with data about that variant. Data fields include the Reference and Alternate Alleles, the class of the variant as reported by EVA, the source of the data, the amino acid change, if any, and the functional class as determined by UCSC's Variant Annotation Integrator. Variants can be filtered using the track controls to show subsets of the data by either EVA Sequence Ontology (SO) term, UCSC-generated functional effect, or by color, which bins the UCSC functional effects into general classes. Mouse-over Mousing over an item shows the ucscClass, which is the consequence according to the Variant Annotation Integrator, and the aaChange when one is available, which is the change in amino acid in HGVS.p terms. Items may have multiple ucscClasses, which will all be shown in the mouse-over in a comma-separated list. Likewise, multiple HGVS.p terms may be shown for each rsID separated by spaces describing all possible AA changes. Multiple items may appear due to different variant predictions on multiple gene transcripts. For all organisms the gene models used were the NCBI RefSeq curated when available, if not then ensembl genes, or finally UCSC mappings of RefSeq if neither of the previous models was possible. Track colors Variants are colored according to the most potentially deleterious functional effect prediction according to the Variant Annotation Integrator. Specific bins can be seen in the Methods section below. Color Variant Type Protein-altering variants and splice site variants Synonymous codon variants Non-coding transcript or Untranslated Region (UTR) variants Intergenic and intronic variants Sequence ontology (SO) Variants are classified by EVA into one of the following sequence ontology terms: substitution — A single nucleotide in the reference is replaced by another, alternate allele deletion — One or more nucleotides is deleted. The representation in the database is to display one additional nucleotide in both the Reference field (Ref) and the Alternate Allele field (Alt). E.g. a variant that is a deletion of an A maybe be represented as Ref = GA and Alt = G. insertion — One or more nucleotides is inserted. The representation in the database is to display one additional nucleotide in both the Reference field (Ref) and the Alternate Allele field (Alt). E.g. a variant that is an insertion of a T maybe be represented as Ref = G and Alt = GT delins — Similar to tandemRepeat, in that the runs of Ref and Alt Alleles are of different length, except that there is more than one type of nucleotide, e.g., Ref = CCAAAAACAAAAACA, Alt = ACAAAAAC. multipleNucleotideVariant — More than one nucleotide is substituted by an equal number of different nucleotides, e.g., Ref = AA, Alt = GC. sequence alteration — A parent term meant to signify a deviation from another sequence. Can be assigned to variants that have not been characterized yet. Methods Data were downloaded from the European Variation Archive EVA current_ids.vcf.gz files corresponding to the proper assembly. Chromosome names were converted to UCSC-style and the variants passed through the Variant Annotation Integrator to predict consequence. For every organism the NCBI RefSeq curated models were used when available, followed by ensembl genes, and finally UCSC mapping of RefSeq when neither of the previous models were possible. Variants were then colored according to their predicted consequence in the following fashion: Protein-altering variants and splice site variants - exon_loss_variant, frameshift_variant, inframe_deletion, inframe_insertion, initiator_codon_variant, missense_variant, splice_acceptor_variant, splice_donor_variant, splice_region_variant, stop_gained, stop_lost, coding_sequence_variant, transcript_ablation Synonymous codon variants - synonymous_variant, stop_retained_variant Non-coding transcript or Untranslated Region (UTR) variants - 5_prime_UTR_variant, 3_prime_UTR_variant, complex_transcript_variant, non_coding_transcript_exon_variant Intergenic and intronic variants - upstream_gene_variant, downstream_gene_variant, intron_variant, intergenic_variant, NMD_transcript_variant, no_sequence_alteration Sequence Ontology ("SO:") terms were converted to the variant classes, then the files were converted to BED, and then bigBed format. No functional annotations were provided by the EVA (e.g., missense, nonsense, etc). These were computed using UCSC's Variant Annotation Integrator (Hinrichs, et al., 2016). Amino-acid substitutions for missense variants are based on RefSeq alignments of mRNA transcripts, which do not always match the amino acids predicted from translating the genomic sequence. Therefore, in some instances, the variant and the genomic nucleotide and associated amino acid may be reversed. E.g., a Pro > Arg change from the perspective of the mRNA would be Arg > Pro from the persepective the genomic sequence. Also, in bosTau9, galGal5, rheMac8, danRer10 and danRer11 the mitochondrial sequence was removed or renamed to match UCSC. For complete documentation of the processing of these tracks, see the makedoc corresponding to the version of interest. For example, the EVA Release 6 MakeDoc. 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 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. For automated download and analysis, this annotation is stored in a bigBed file that can be downloaded from our download server. Use the corresponding version number for the track of interest, e.g. evaSnp6.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 https://hgdownload.soe.ucsc.edu/gbdb/sacCer3/bbi/evaSnp6.bb -chrom=chr21 -start=0 -end=100000000 stdout Credits This track was produced from the European Variation Archive release data. Consequences were predicted using UCSC's Variant Annotation Integrator and NCBI's RefSeq as well as ensembl gene models. References Cezard T, Cunningham F, Hunt SE, Koylass B, Kumar N, Saunders G, Shen A, Silva AF, Tsukanov K, Venkataraman S et al. The European Variation Archive: a FAIR resource of genomic variation for all species. Nucleic Acids Res. 2021 Oct 28:gkab960. doi:10.1093/nar/gkab960. Epub ahead of print. PMID: 34718739. PMID: PMC8728205. Hinrichs AS, Raney BJ, Speir ML, Rhead B, Casper J, Karolchik D, Kuhn RM, Rosenbloom KR, Zweig AS, Haussler D, Kent WJ. UCSC Data Integrator and Variant Annotation Integrator. Bioinformatics. 2016 May 1;32(9):1430-2. PMID: 26740527; PMC: PMC4848401 
gap Gap Gap Locations Mapping and Sequencing Description There are no gaps in the S. cerevisiae assembly. Credits The April 2011 Saccharomyces cerevisiae genome assembly is based on sequence from the: NCBI genbank/genomes/Eukaryotes/fungi/Saccharomyces_cerevisiae/R64-1-1/ download directory. 
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 "Graph configuration help" link for an explanation of the configuration options. Credits The data and presentation of this graph were prepared by Hiram Clawson. 
blastHg18KG Human Proteins Human Proteins Mapped by Chained tBLASTn Genes and Gene Predictions Description This track contains tBLASTn alignments of the peptides from the predicted and known genes identified in the hg18 UCSC Genes track. Methods First, the predicted proteins from the human Known Genes track were aligned with the human genome using the Blat program to discover exon boundaries. Next, the amino acid sequences that make up each exon were aligned with the S. cerevisiae sequence using the tBLASTn program. Finally, the putative S. cerevisiae exons were chained together using an organism-specific maximum gap size but no gap penalty. The single best exon chains extending over more than 60% of the query protein were included. Exon chains that extended over 60% of the query and matched at least 60% of the protein's amino acids were also included. Credits tBLASTn is part of the NCBI BLAST tool set. For more information on BLAST, see Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990 Oct 5;215(3):403-10. PMID: 2231712 Blat was written by Jim Kent. The remaining utilities used to produce this track were written by Jim Kent or Brian Raney. 
microsat Microsatellite Microsatellites - Di-nucleotide and Tri-nucleotide Repeats Variation and 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 
oreganno ORegAnno Regulatory elements from ORegAnno Expression and 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 "search space" 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 
xenoRefGene Other RefSeq Non-S. cerevisiae RefSeq Genes Genes and Gene Predictions Description This track shows known protein-coding and non-protein-coding genes for organisms other than S. cerevisiae, 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 S. cerevisiae 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 
esRegGeneToMotif Reg. Module Eran Segal Regulatory Module Expression and Regulation Description This track shows predicted transcription factor binding sites based on sequence similarities upstream of coordinately expressed genes. In dense display mode the gold areas indicate the extent of the area searched for binding sites; black boxes indicate the actual binding sites. In other modes the gold areas disappear and only the binding sites are displayed. Clicking on a particular predicted binding site displays a page that shows the sequence motif associated with the predicted transcription factor and the sequence at the predicted binding site. Where known motifs have been identified by this method, they are named; otherwise, they are assigned a motif number. Methods This analysis was performed according to Genome-wide discovery of transcriptional modules from DNA sequence and gene expression on various pre-existing microarray datasets. A regulatory module is comprised of a set of genes predicted to be regulated by the same combination of DNA sequence motifs. The predictions are based on the co-expression of the set of genes in the module and on the appearance of common combinations of motifs in the upstream regions of genes assigned to the same module. Credits Thanks to Eran Segal for providing the data analysis that forms the basis for this track. The display was programmed by Jim Kent. References Segal E, Yelensky R, Koller D. Genome-wide discovery of transcriptional modules from DNA sequence and gene expression. Bioinformatics. 2003;19 Suppl 1:i273-82. PMID: 12855470 
est S. cer. ESTs S. cerevisiae ESTs Including Unspliced mRNA and EST Description This track shows alignments between S. cerevisiae 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. 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 "tissue" 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 "and" is selected, only ESTs that match all filter criteria will be highlighted. If "or" 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 "exclude" is chosen, the browser will not display ESTs that match the filter criteria. If "include" 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 — but not all — 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, S. cerevisiae 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, 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 
mrna S. cer. mRNAs S. cerevisiae mRNAs from GenBank mRNA and EST Description The mRNA track shows alignments between S. cerevisiae 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 submitted by a specific author, type the name of the individual in the author 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 "author" table contains the names of all individuals who can be entered into the author text box. Wildcards may also be used in the filter. If filtering on more than one value, choose the desired combination logic. If "and" is selected, only mRNAs that match all filter criteria will be displayed. If "or" is selected, only mRNAs that match any one of the filter criteria will be displayed. Choose the color or display characteristic that should be used to highlight or include/exclude the filtered items. If "exclude" is chosen, the browser will not display mRNAs that match the filter criteria. If "include" 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, click here. Methods GenBank S. cerevisiae 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, 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 
simpleRepeat Simple Repeats Simple Tandem Repeats by TRF Variation and 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 
intronEst Spliced ESTs S. cerevisiae ESTs That Have Been Spliced mRNA and EST Description This track shows alignments between S. cerevisiae 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 S. cerevisiae 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 "tissue" 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 "and" is selected, only ESTs that match all filter criteria will be highlighted. If "or" 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 "exclude" is chosen, the browser will not display ESTs that match the filter criteria. If "include" 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 — but not all — 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, S. cerevisiae 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 
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 "feature type" 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 "Extracellular". UniProt Transmembrane Domains Protein domains of the type "Transmembrane". UniProt Cytoplasmic Domains Protein domains with the comment "Cytoplasmic". 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 "UniProt/SwissProt Variants" is a copy of the track "UniProt Variants" in the track group "Phenotype and Literature", or "Variation and Repeats", 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. "glyco", "disulf bond", "Signal peptide" 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 "UniProt Modifications" (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 -> transcript -> 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/sacCer3/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' > 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 
cons7way Conservation Multiz Alignment & Conservation (7 Yeasts) Comparative Genomics Description This track shows a measure of evolutionary conservation in seven species of the genus Saccharomyces based on a phylogenetic hidden Markov model (phastCons). The graphic display shows the alignment projected onto S. cerevisiae. The genomes were downloaded from: S. cerevisiae - https://downloads.yeastgenome.org/sequence/S288C_reference/genome_releases/ S. paradoxus - ftp://ftp.broadinstitute.org/pub/annotation/fungi/comp_yeasts/S1a.Assembly/Spar_contigs.fasta S. mikatae - ftp://ftp.broadinstitute.org/pub/annotation/fungi/comp_yeasts/S1a.Assembly/Smik_contigs.fasta S. kudriavzevii - http://www.genetics.wustl.edu/saccharomycesgenomes/Contigs/YM6553.fsa.gz S. bayanus - ftp://ftp.broadinstitute.org/pub/annotation/fungi/comp_yeasts/S1a.Assembly/Sbay_contigs.fasta S. castelli - http://www.genetics.wustl.edu/saccharomycesgenomes/Contigs/YM476.fsa.gz S. kluyveri - http://www.genetics.wustl.edu/saccharomycesgenomes/Contigs/YM479.fsa.gz 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 S. cerevisiae 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 bases or fewer, 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 S. cerevisiae 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. Downloads for data in this track are available: Multiz alignments (MAF format), and phylogenetic trees PhastCons conservation (WIG format) 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 S. cerevisiae sequence at those alignment positions relative to the longest non-S. cerevisiae 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, depending on the species chosen (Table 2). Species listed in the row labeled "None" do not have species-specific reading frames for gene translation. Gene TrackSpecies SGD GenesS. cerevisae No annotationall the other yeast strains Table 2. Gene tracks used for codon translation. Methods Best-in-genome pairwise alignments were generated for each species using lastz, followed by chaining and netting. The pairwise alignments were then multiply aligned using multiz, and the resulting multiple alignments were assigned conservation scores by phastCons. 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. Note that, unlike many conservation-scoring programs, phastCons does not rely on a sliding window of fixed size, so 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). PhastCons currently treats alignment gaps as missing data, which sometimes has the effect of producing undesirably high conservation scores in gappy regions of the alignment. We are looking at several possible ways of improving the handling of alignment gaps. Credits This track was created at UCSC using the following programs: Lastz (formerly Blastz) and multiz by Minmei Hou, Scott Schwartz and Webb Miller of the Penn State Bioinformatics Group. AxtBest, axtChain, chainNet, netSyntenic, and netClass by Jim Kent at UCSC. PhastCons by Adam Siepel at Cornell University. "Wiggle track" plotting software by Hiram Clawson at UCSC. The phylogenetic tree is based on the Saccharomyces Phylogeny page from the Department of Genetics at Washington University in St. Louis. References Phylo-HMMs and phastCons: 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 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: 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. 
cons7wayViewalign Multiz Alignments Multiz Alignment & Conservation (7 Yeasts) Comparative Genomics 
multiz7way Multiz Align Multiz Alignments of 7 Yeasts Comparative Genomics Description This track shows a measure of evolutionary conservation in seven species of the genus Saccharomyces based on a phylogenetic hidden Markov model (phastCons). The graphic display shows the alignment projected onto S. cerevisiae. The genomes were downloaded from: S. cerevisiae - https://downloads.yeastgenome.org/sequence/S288C_reference/genome_releases/ S. paradoxus - ftp://ftp.broadinstitute.org/pub/annotation/fungi/comp_yeasts/S1a.Assembly/Spar_contigs.fasta S. mikatae - ftp://ftp.broadinstitute.org/pub/annotation/fungi/comp_yeasts/S1a.Assembly/Smik_contigs.fasta S. kudriavzevii - http://www.genetics.wustl.edu/saccharomycesgenomes/Contigs/YM6553.fsa.gz S. bayanus - ftp://ftp.broadinstitute.org/pub/annotation/fungi/comp_yeasts/S1a.Assembly/Sbay_contigs.fasta S. castelli - http://www.genetics.wustl.edu/saccharomycesgenomes/Contigs/YM476.fsa.gz S. kluyveri - http://www.genetics.wustl.edu/saccharomycesgenomes/Contigs/YM479.fsa.gz 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 S. cerevisiae 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 bases or fewer, 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 S. cerevisiae 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. Downloads for data in this track are available: Multiz alignments (MAF format), and phylogenetic trees PhastCons conservation (WIG format) 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 S. cerevisiae sequence at those alignment positions relative to the longest non-S. cerevisiae 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, depending on the species chosen (Table 2). Species listed in the row labeled "None" do not have species-specific reading frames for gene translation. Gene TrackSpecies SGD GenesS. cerevisae No annotationall the other yeast strains Table 2. Gene tracks used for codon translation. Methods Best-in-genome pairwise alignments were generated for each species using lastz, followed by chaining and netting. The pairwise alignments were then multiply aligned using multiz, and the resulting multiple alignments were assigned conservation scores by phastCons. 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. Note that, unlike many conservation-scoring programs, phastCons does not rely on a sliding window of fixed size, so 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). PhastCons currently treats alignment gaps as missing data, which sometimes has the effect of producing undesirably high conservation scores in gappy regions of the alignment. We are looking at several possible ways of improving the handling of alignment gaps. Credits This track was created at UCSC using the following programs: Lastz (formerly Blastz) and multiz by Minmei Hou, Scott Schwartz and Webb Miller of the Penn State Bioinformatics Group. AxtBest, axtChain, chainNet, netSyntenic, and netClass by Jim Kent at UCSC. PhastCons by Adam Siepel at Cornell University. "Wiggle track" plotting software by Hiram Clawson at UCSC. The phylogenetic tree is based on the Saccharomyces Phylogeny page from the Department of Genetics at Washington University in St. Louis. References Phylo-HMMs and phastCons: 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 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: 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. 
cons7wayViewphastcons Element Conservation (phastCons) Multiz Alignment & Conservation (7 Yeasts) Comparative Genomics 
phastCons7way PhastCons 7 Yeast Conservation by PhastCons Comparative Genomics 
cons7wayViewelements Conserved Elements Multiz Alignment & Conservation (7 Yeasts) Comparative Genomics 
phastConsElements7way Elements 7 Yeasts Conserved Elements Comparative Genomics