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). The data were downloaded from the SGD: saccharomyces_cerevisiae.gff (accessed 29 Aug. 2011). This track excludes the ORFs classified as dubious by the 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. 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. The data were downloaded from the Saccharomyces Genome Database (SGD): saccharomyces_cerevisiae.gff (accessed 29 Aug. 2011). Click on an item in this track to display details about it. 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 S. cerevisiae genome as of June 2008. Please note the sequencing status at: SGD. Chromosomes available in this assembly: chrI, chrII, chrIII, chrIV ... etc ... chrXVI, chrM, 2micron. The 2micron sequence is the 2-micron plasmid. See also: SGD genome snapshot/overview Credits The June 2008 Saccharomyces cerevisiae genome assembly is based on sequence dated June 2008 in the Saccharomyces Genome Database (SGD). 
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 
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 sacCer2 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 
gap Gap Gap Locations Mapping and Sequencing Description There are no gaps in the S. cerevisiae assembly. Credits The June 2008 Saccharomyces cerevisiae genome assembly is based on sequence dated June 2008 in the Saccharomyces Genome Database (SGD). 
gc5Base 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 
pubs Publications Publications: Sequences in Scientific Articles Literature Description This track is based on text-mining of full-text biomedical articles and includes two types of subtracks: Sequences found in publications, grouped by article and searched in genomes with BLAT Identifiers in publications that directly relate to chromosome locations (e.g., gene symbols, SNP identifiers, etc) Both sources of information are linked to the respective articles. Background information on how permission to full-text data was obtained can be found on the project website. Display Convention and Configuration The sequence subtrack indicates the location of sequences in publications mapped back to the genome, annotated with the first author and the year of the publication. All matches of one article are grouped ("chained") together. Article titles are shown when you move the mouse cursor over the features. Thicker parts of the features (exons) represent matching sequences, connected by thin lines to matches from the same article within 30 kbp. The subtrack "individual sequence matches" activates automatically when the user clicks a sequence match and follows the link "Show sequence matches individually" from the details page. Mouse-overs show flanking text around the sequence, and clicking features links to BLAT alignments. All other subtracks (i.e. bands, genes, SNPs) show the number of matching articles as the feature description. Clicking on them shows the sentences and sections in articles where the identifiers were found. The track configuration includes a keyword and year filter. Keywords are space-separated and are searched in the article's title, author list, and abstract. Data The track is based on text from biomedical research articles, obtained as part of the UCSC Genocoding Project. The current dataset consists of about 600,000 files (main text and supplementary files) from PubMed Central (Open-Access set) and around 6 million text files (main text) from Elsevier (as part of the Sciverse Apps program). Methods All file types (including XML, raw ASCII, PDFs and various Microsoft Office formats (Excel, Word, PowerPoint)) were converted to text. The results were processed to find groups of words that look like DNA/RNA sequences or words that look like protein sequences. These were then mapped with BLAT to the human genome and these model organisms: mouse (mm9), rat (rn4), zebrafish (danRer6), Drosophila melanogaster (dm3), X. tropicalis (xenTro2), Medaka (oryLat2), C. intestinalis (ci2), C. elegans (ce6) and yeast (sacCer2). The pipeline roughly proceeds through these steps: For sequences, the best match across all genomes is used, if it is longer than 17 bp and matches at 90% identity. Two sets of BLAT parameters are tried, the default ones for sequences longer than 25 bp, very sensitive ones (stepSize=5) for shorter sequences. Sequences are mapped to genomic DNA. Those that do not match are mapped to RefSeq cDNAs. Hits from the same article that are closer than 30 kbp are joined into one feature (shown as exon-blocks on the browser). All parts of a joined feature have to match at least 25 bp. Non-unique hits are kept in the joined feature with the most members. Joined features with identical members in two different genomes are kept in both genomes. Note that due to the 90% identity filter, some sequences do not match anywhere in the genome. Examples include primers with added restriction sites, mutation primers, or any other sequence that joins or mixes two pieces of genomic DNA not part of RefSeq. Also note that some gene symbols correspond to English words which can sometimes lead to many false positives. Credits Software and processing by Maximilian Haeussler. UCSC Track visualisation by Larry Meyer and Hiram Clawson. Elsevier support by Max Berenstein, Raphael Sidi, Judd Dunham, Scott Robbins and colleagues. Original version written at the Bergman Lab, University of Manchester, UK. Testing by Mary Mangan, OpenHelix Inc, and Greg Roe, UCSC. Feedback Please send ideas, comments or feedback on this track to max@soe.ucsc.edu. We are very interested in getting access to more articles from publishers for this dataset; see the project website. References Aerts S, Haeussler M, van Vooren S, Griffith OL, Hulpiau P, Jones SJ, Montgomery SB, Bergman CM, Open Regulatory Annotation Consortium. Text-mining assisted regulatory annotation. Genome Biol. 2008;9(2):R31. PMID: 18271954; PMC: PMC2374703 Haeussler M, Gerner M, Bergman CM. Annotating genes and genomes with DNA sequences extracted from biomedical articles. Bioinformatics. 2011 Apr 1;27(7):980-6. PMID: 21325301; PMC: PMC3065681 Van Noorden R. Trouble at the text mine. Nature. 2012 Mar 7;483(7388):134-5. 
pubsBlat Sequences Sequences in Articles: PubmedCentral and Elsevier Literature 
pubsBlatPsl Indiv. Seq. Matches Individual Sequence Matches of One Selected Article from Sequences Track Literature 
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. In the "UniProt Modifications" track, lipoification sites are highlighted in dark blue, glycosylation sites in dark green, and phosphorylation in light 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/sacCer2/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 
pubsBingBlat Web Sequences DNA Sequences in Web Pages Indexed by Bing.com / Microsoft Research Literature Description This track is powered by Bing! and Microsoft Research. UCSC collaborators at Microsoft Research (Bob Davidson, David Heckerman) implemented a DNA sequence detector and processed thirty days of web crawler updates, which covers roughly 40 billion webpages. The results were mapped with BLAT to the genome. Display Convention and Configuration The track indicates the location of sequences on web pages mapped to the genome, labelled with the web page URL. If the web page includes invisible meta data, then the first author and a year of publication is shown instead of the URL. All matches of one web page are grouped ("chained") together. Web page titles are shown when you move the mouse cursor over the features. Thicker parts of the features (exons) represent matching sequences, connected by thin lines to matches from the same web page within 30 kbp. The subtrack "individual sequence matches" activates automatically when the user clicks a sequence match and follows the link "Show sequence matches individually" from the details page. Mouse-overs show flanking text around the sequence, and clicking features links to BLAT alignments. - --> Methods All file types (PDFs and various Microsoft Office formats) were converted to text. The results were processed to find groups of words that look like DNA/RNA sequences. These were then mapped with BLAT to the human genome using the same software as used in the Publication track. Credits DNA sequence detection by Bob Davidson at Microsoft Research. HTML parsing and sequence mapping by Maximilian Haeussler at UCSC. References Aerts S, Haeussler M, van Vooren S, Griffith OL, Hulpiau P, Jones SJ, Montgomery SB, Bergman CM, Open Regulatory Annotation Consortium. Text-mining assisted regulatory annotation. Genome Biol. 2008;9(2):R31. PMID: 18271954; PMC: PMC2374703 Haeussler M, Gerner M, Bergman CM. Annotating genes and genomes with DNA sequences extracted from biomedical articles. Bioinformatics. 2011 Apr 1;27(7):980-6. PMID: 21325301; PMC: PMC3065681 Van Noorden R. Trouble at the text mine. Nature. 2012 Mar 7;483(7388):134-5. 
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 - http://downloads.yeastgenome.org/sequence/S288C_reference/genome_releases/ S. paradoxus - http://www.broadinstitute.org/ftp/pub/annotation/fungi/comp_yeasts/S1a.Assembly/Spar_contigs.fasta S. mikatae - http://www.broadinstitute.org/ftp/pub/annotation/fungi/comp_yeasts/S1a.Assembly/Smik_contigs.fasta S. kudriavzevii - http://www.genetics.wustl.edu/saccharomycesgenomes/Contigs/YM6553.fsa.gz S. bayanus - http://www.broadinstitute.org/ftp/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 or fewer bases, then click on the alignment. Gap Annotation The Display chains between alignments configuration option enables display of gaps between alignment blocks in the pairwise alignments in a manner similar to the Chain track display. The following conventions are used: Single line: No bases in the aligned species. Possibly due to a lineage-specific insertion between the aligned blocks in the 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 - http://downloads.yeastgenome.org/sequence/S288C_reference/genome_releases/ S. paradoxus - http://www.broadinstitute.org/ftp/pub/annotation/fungi/comp_yeasts/S1a.Assembly/Spar_contigs.fasta S. mikatae - http://www.broadinstitute.org/ftp/pub/annotation/fungi/comp_yeasts/S1a.Assembly/Smik_contigs.fasta S. kudriavzevii - http://www.genetics.wustl.edu/saccharomycesgenomes/Contigs/YM6553.fsa.gz S. bayanus - http://www.broadinstitute.org/ftp/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 or fewer bases, then click on the alignment. Gap Annotation The Display chains between alignments configuration option enables display of gaps between alignment blocks in the pairwise alignments in a manner similar to the Chain track display. The following conventions are used: Single line: No bases in the aligned species. Possibly due to a lineage-specific insertion between the aligned blocks in the 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 
uwFootprints UW Footprints UW Protein/DNA Interaction Footprints Expression and Regulation Description The orchestrated binding of transcriptional activators and repressors to specific DNA sequences in the context of chromatin defines the regulatory program of eukaryotic genomes. We developed a digital approach to assay regulatory protein occupancy on genomic DNA in vivo by dense mapping of individual DNase I cleavages from intact nuclei using massively parallel DNA sequencing. Analysis of >23 million cleavages across the Saccharomyces cerevisiae genome revealed thousands of protected regulatory protein footprints, enabling de novo derivation of factor binding motifs as well as the identification of hundreds of novel binding sites for major regulators. We observed striking correspondence between nucleotide-level DNase I cleavage patterns and protein-DNA interactions determined by crystallography. The data also yielded a detailed view of larger chromatin features including positioned nucleosomes flanking factor binding regions. Digital genomic footprinting provides a powerful approach to delineate the cis-regulatory framework of any organism with an available genome sequence. Display Conventions and Configuration DNaseI-seq cleavage counts are displayed at nucleotide resolution, along with a 'mappability' track that indicates whether tag sequences starting at that location on both the forward and the reverse strands can be uniquely mapped to the yeast genome. Finally, the set of footprints with q values <0.1 are included, where the q value is defined as the minimal false discovery rate threshold at which the given footprint is deemed significant. The name associated with each footprint is its q value. Methods To visualize regulatory protein occupancy across the genome of Saccharomyces cerevisiae, DNase I digestion of yeast nuclei was coupled with massively parallel DNA sequencing to create a dense whole-genome map of DNA template accessibility at the nucleotide-level. Yeast nuclei were isolated and treated with a DNase I concentration sufficient to release short (<300 bp) DNA fragments. Small fragments were derived from two DNase I "hits" in close proximity. Each end of those fragments represents an in vivo DNase I cleavage site. The sequence and hence genomic location of these sites were then determined by DNA sequencing. Footprints were identified using a computational algorithm that evaluates short regions (between 8 and 30 bp) over which the DNase I cleavage density was significantly reduced compared with the immediately flanking regions. FDR thresholds were assigned to each footprint by comparing p-values obtained from real and shuffled cleavage data. Detailed methods are given in Hesselberth et al. (2009), and supplementary data and source code are available here. Credits This track was produced at the University of Washington by Jay R. Hesselberth, Xiaoyu Chen, Zhihong Zhang, Peter J. Sabo, Richard Sandstrom, Alex P. Reynolds, Robert E. Thurman, Shane Neph, Michael S. Kuehn, William S. Noble (william-noble@u.washington.edu), Stanley Fields (fields@u.washington.edu) and John A. Stamatoyannopoulos (jstam@stamlab.org). References Hesselberth JR, Chen X, Zhang Z, Sabo PJ, Sandstrom R, Reynolds AP, Thurman RE, Neph S, Kuehn MS, Noble WS et al. Global mapping of protein-DNA interactions in vivo by digital genomic footprinting. Nat Methods. 2009 Apr;6(4):283-9. PMID: 19305407; PMC: PMC2668528 
uwFootprintsViewCounts Tag Counts UW Protein/DNA Interaction Footprints Expression and Regulation 
uwFootprintsTagCounts Tag Counts UW Footprints Tag Counts Expression and Regulation Description The orchestrated binding of transcriptional activators and repressors to specific DNA sequences in the context of chromatin defines the regulatory program of eukaryotic genomes. We developed a digital approach to assay regulatory protein occupancy on genomic DNA in vivo by dense mapping of individual DNase I cleavages from intact nuclei using massively parallel DNA sequencing. Analysis of >23 million cleavages across the Saccharomyces cerevisiae genome revealed thousands of protected regulatory protein footprints, enabling de novo derivation of factor binding motifs as well as the identification of hundreds of novel binding sites for major regulators. We observed striking correspondence between nucleotide-level DNase I cleavage patterns and protein-DNA interactions determined by crystallography. The data also yielded a detailed view of larger chromatin features including positioned nucleosomes flanking factor binding regions. Digital genomic footprinting provides a powerful approach to delineate the cis-regulatory framework of any organism with an available genome sequence. Display Conventions and Configuration DNaseI-seq cleavage counts are displayed at nucleotide resolution, along with a 'mappability' track that indicates whether tag sequences starting at that location on both the forward and the reverse strands can be uniquely mapped to the yeast genome. Finally, the set of footprints with q values <0.1 are included, where the q value is defined as the minimal false discovery rate threshold at which the given footprint is deemed significant. The name associated with each footprint is its q value. Methods To visualize regulatory protein occupancy across the genome of Saccharomyces cerevisiae, DNase I digestion of yeast nuclei was coupled with massively parallel DNA sequencing to create a dense whole-genome map of DNA template accessibility at the nucleotide-level. Yeast nuclei were isolated and treated with a DNase I concentration sufficient to release short (<300 bp) DNA fragments. Small fragments were derived from two DNase I "hits" in close proximity. Each end of those fragments represents an in vivo DNase I cleavage site. The sequence and hence genomic location of these sites were then determined by DNA sequencing. Footprints were identified using a computational algorithm that evaluates short regions (between 8 and 30 bp) over which the DNase I cleavage density was significantly reduced compared with the immediately flanking regions. FDR thresholds were assigned to each footprint by comparing p-values obtained from real and shuffled cleavage data. Detailed methods are given in Hesselberth et al. (2009), and supplementary data and source code are available here. Credits This track was produced at the University of Washington by Jay R. Hesselberth, Xiaoyu Chen, Zhihong Zhang, Peter J. Sabo, Richard Sandstrom, Alex P. Reynolds, Robert E. Thurman, Shane Neph, Michael S. Kuehn, William S. Noble (william-noble@u.washington.edu), Stanley Fields (fields@u.washington.edu) and John A. Stamatoyannopoulos (jstam@stamlab.org). References Hesselberth JR, Chen X, Zhang Z, Sabo PJ, Sandstrom R, Reynolds AP, Thurman RE, Neph S, Kuehn MS, Noble WS et al. Global mapping of protein-DNA interactions in vivo by digital genomic footprinting. Nat Methods. 2009 Apr;6(4):283-9. PMID: 19305407; PMC: PMC2668528 
uwFootprintsViewMap Mappability UW Protein/DNA Interaction Footprints Expression and Regulation 
uwFootprintsMappability Mappability UW Footprints Mappability Expression and Regulation Description The orchestrated binding of transcriptional activators and repressors to specific DNA sequences in the context of chromatin defines the regulatory program of eukaryotic genomes. We developed a digital approach to assay regulatory protein occupancy on genomic DNA in vivo by dense mapping of individual DNase I cleavages from intact nuclei using massively parallel DNA sequencing. Analysis of >23 million cleavages across the Saccharomyces cerevisiae genome revealed thousands of protected regulatory protein footprints, enabling de novo derivation of factor binding motifs as well as the identification of hundreds of novel binding sites for major regulators. We observed striking correspondence between nucleotide-level DNase I cleavage patterns and protein-DNA interactions determined by crystallography. The data also yielded a detailed view of larger chromatin features including positioned nucleosomes flanking factor binding regions. Digital genomic footprinting provides a powerful approach to delineate the cis-regulatory framework of any organism with an available genome sequence. Display Conventions and Configuration DNaseI-seq cleavage counts are displayed at nucleotide resolution, along with a 'mappability' track that indicates whether tag sequences starting at that location on both the forward and the reverse strands can be uniquely mapped to the yeast genome. Finally, the set of footprints with q values <0.1 are included, where the q value is defined as the minimal false discovery rate threshold at which the given footprint is deemed significant. The name associated with each footprint is its q value. Methods To visualize regulatory protein occupancy across the genome of Saccharomyces cerevisiae, DNase I digestion of yeast nuclei was coupled with massively parallel DNA sequencing to create a dense whole-genome map of DNA template accessibility at the nucleotide-level. Yeast nuclei were isolated and treated with a DNase I concentration sufficient to release short (<300 bp) DNA fragments. Small fragments were derived from two DNase I "hits" in close proximity. Each end of those fragments represents an in vivo DNase I cleavage site. The sequence and hence genomic location of these sites were then determined by DNA sequencing. Footprints were identified using a computational algorithm that evaluates short regions (between 8 and 30 bp) over which the DNase I cleavage density was significantly reduced compared with the immediately flanking regions. FDR thresholds were assigned to each footprint by comparing p-values obtained from real and shuffled cleavage data. Detailed methods are given in Hesselberth et al. (2009), and supplementary data and source code are available here. Credits This track was produced at the University of Washington by Jay R. Hesselberth, Xiaoyu Chen, Zhihong Zhang, Peter J. Sabo, Richard Sandstrom, Alex P. Reynolds, Robert E. Thurman, Shane Neph, Michael S. Kuehn, William S. Noble (william-noble@u.washington.edu), Stanley Fields (fields@u.washington.edu) and John A. Stamatoyannopoulos (jstam@stamlab.org). References Hesselberth JR, Chen X, Zhang Z, Sabo PJ, Sandstrom R, Reynolds AP, Thurman RE, Neph S, Kuehn MS, Noble WS et al. Global mapping of protein-DNA interactions in vivo by digital genomic footprinting. Nat Methods. 2009 Apr;6(4):283-9. PMID: 19305407; PMC: PMC2668528 
uwFootprintsViewPrint Footprints UW Protein/DNA Interaction Footprints Expression and Regulation 
uwFootprintsPrints Footprints UW Protein-binding Footprints Expression and Regulation Description The orchestrated binding of transcriptional activators and repressors to specific DNA sequences in the context of chromatin defines the regulatory program of eukaryotic genomes. We developed a digital approach to assay regulatory protein occupancy on genomic DNA in vivo by dense mapping of individual DNase I cleavages from intact nuclei using massively parallel DNA sequencing. Analysis of >23 million cleavages across the Saccharomyces cerevisiae genome revealed thousands of protected regulatory protein footprints, enabling de novo derivation of factor binding motifs as well as the identification of hundreds of novel binding sites for major regulators. We observed striking correspondence between nucleotide-level DNase I cleavage patterns and protein-DNA interactions determined by crystallography. The data also yielded a detailed view of larger chromatin features including positioned nucleosomes flanking factor binding regions. Digital genomic footprinting provides a powerful approach to delineate the cis-regulatory framework of any organism with an available genome sequence. Display Conventions and Configuration DNaseI-seq cleavage counts are displayed at nucleotide resolution, along with a 'mappability' track that indicates whether tag sequences starting at that location on both the forward and the reverse strands can be uniquely mapped to the yeast genome. Finally, the set of footprints with q values <0.1 are included, where the q value is defined as the minimal false discovery rate threshold at which the given footprint is deemed significant. The name associated with each footprint is its q value. Methods To visualize regulatory protein occupancy across the genome of Saccharomyces cerevisiae, DNase I digestion of yeast nuclei was coupled with massively parallel DNA sequencing to create a dense whole-genome map of DNA template accessibility at the nucleotide-level. Yeast nuclei were isolated and treated with a DNase I concentration sufficient to release short (<300 bp) DNA fragments. Small fragments were derived from two DNase I "hits" in close proximity. Each end of those fragments represents an in vivo DNase I cleavage site. The sequence and hence genomic location of these sites were then determined by DNA sequencing. Footprints were identified using a computational algorithm that evaluates short regions (between 8 and 30 bp) over which the DNase I cleavage density was significantly reduced compared with the immediately flanking regions. FDR thresholds were assigned to each footprint by comparing p-values obtained from real and shuffled cleavage data. Detailed methods are given in Hesselberth et al. (2009), and supplementary data and source code are available here. Credits This track was produced at the University of Washington by Jay R. Hesselberth, Xiaoyu Chen, Zhihong Zhang, Peter J. Sabo, Richard Sandstrom, Alex P. Reynolds, Robert E. Thurman, Shane Neph, Michael S. Kuehn, William S. Noble (william-noble@u.washington.edu), Stanley Fields (fields@u.washington.edu) and John A. Stamatoyannopoulos (jstam@stamlab.org). References Hesselberth JR, Chen X, Zhang Z, Sabo PJ, Sandstrom R, Reynolds AP, Thurman RE, Neph S, Kuehn MS, Noble WS et al. Global mapping of protein-DNA interactions in vivo by digital genomic footprinting. Nat Methods. 2009 Apr;6(4):283-9. PMID: 19305407; PMC: PMC2668528