\n
\n
![\"Table](\"https://genome.ucsc.edu/images/bergquist25.png\")
\n\nThis track shows AlphaMissense predictions for all possible single amino acid substitutions in \nthe human proteome.\n
\n\nAlphaMissense is a deep learning method for predicting the pathogenicity of missense variants\nin human proteins. It classifies 32% of all missense variants as likely pathogenic and 57% \nas likely benign using a cutoff yielding 90% precision on the ClinVar dataset.\n
\n\n\nThere are four lettered subtracks, one for every nucleotide, showing\nscores for mutation from the reference to that\nnucleotide. All subtracks show the AlphaMissense score on mouseover. Across the exome, \nthere are three values per position, one for every possible\nnucleotide mutation. The fourth value, "no mutation", representing\nthe reference allele, e.g. A to A, is always set to zero, \"0.0\". AlphaMissense only\ntakes into account amino acid changes, so a nucleotide change that results in no\namino acid change (synonymous) is not scored. These are shown in the tracks\nwith score \"0.0\". \n\n
\nWhen using this track, zoom in until you can see every base at the\ntop of the display. Otherwise, there are several nucleotides per pixel under \nyour mouse cursor and no score will be shown on the mouseover tooltip.\n
\n\nTrack colors
\n\nThis track is colored according to the am_class column in the AlphaMissense_hg38.tsv file.\n\n
| Range | \nClassification | \n
|---|---|
| ≥ .564 | \nLikely Pathogenic | \n
| .565 - .340 | \nLikely Neutral | \n
| ≤ .340 | \nLikely Benign | \n
\nAlphaMissense scores are available at the \n\nAlphaMissense cloud storage site. \nThe site provides precomputed AlphaMissense scores for all possible human missense variants \nto facilitate the identification of pathogenic variants among the large number of \nrare variants discovered in sequencing studies.\n
\n\n\nThe AlphaMissense data on the UCSC Genome Browser can be explored interactively with the\nTable Browser or the\nData Integrator.\n
\n\n\nFor automated download and analysis, the genome annotation is stored at UCSC in\nbigWig format that can be downloaded from\nour download server.\nThe files for this track are called a.bw, c.bw, g.bw, t.bw. Individual\nregions or the whole genome annotation can be obtained using our tool bigWigToWig\nwhich can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nFor example, to extract only annotations in a given region, you could use the following command:\n
\n\n\nbigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/alphaMissense/a.bw stdout\n
\n\n\nData were converted from the files provided on\nthe AlphaMissense Downloads website. As with all other tracks,\na full log of all commands used for the conversion is available in our \nsource\nrepository, for hg19\nand hg38.\nThe release used for each assembly is shown on the track description page.\n
\n\n\nThanks to \n
\n\n\nCheng J, Novati G, Pan J, Bycroft C, Žemgulytė A, Applebaum T, Pritzel A, Wong LH,\nZielinski M, Sargeant T et al.\nAccurate proteome-wide missense variant effect prediction with\nAlphaMissense.\nScience. 2023 Sep 22;381(6664):eadg7492.\nPMID: 37733863\n
\n\n", "longLabel": "AlphaMissense Score for all possible single-basepair mutations (zoom in for scores)", "maxWindowToDraw": "10000000", "mouseOverFunction": "noAverage", "shortLabel": "AlphaMissense", "track": "alphaMissense", "type": "bigWig", "visibility": "hide" } }, "description": "AlphaMissense Score for all possible single-basepair mutations (zoom in for scores)" }, { "type": "MultiQuantitativeTrack", "trackId": "hg38-cadd", "name": "CADD 1.6", "category": [ "Phenotypes, Variants, and Literature" ], "assemblyNames": [ "hg38" ], "adapter": { "type": "MultiWiggleAdapter", "subadapters": [ { "type": "BigWigAdapter", "bigWigLocation": { "locationType": "UriLocation", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd/a.bw" }, "source": "Mutation: A" }, { "type": "BigWigAdapter", "bigWigLocation": { "locationType": "UriLocation", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd/c.bw" }, "source": "Mutation: C" }, { "type": "BigWigAdapter", "bigWigLocation": { "locationType": "UriLocation", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd/g.bw" }, "source": "Mutation: G" }, { "type": "BigWigAdapter", "bigWigLocation": { "locationType": "UriLocation", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd/t.bw" }, "source": "Mutation: T" } ] }, "metadata": { "addedByJBrowseTeam": true, "ucsc": { "color": "100,130,160", "compositeTrack": "on", "group": "phenDis", "html": "This track collection shows Combined Annotation Dependent Depletion scores.\nCADD is a tool for scoring the deleteriousness of single nucleotide variants as\nwell as insertion/deletion variants in the human genome.
\n\n\nSome mutation annotations\ntend to exploit a single information type (e.g., phastCons or phyloP for\nconservation) and/or are restricted in scope (e.g., to missense changes). Thus,\na broadly applicable metric that objectively weights and integrates diverse\ninformation is needed. Combined Annotation Dependent Depletion (CADD) is a\nframework that integrates multiple annotations into one metric by contrasting\nvariants that survived natural selection with simulated mutations.\n
\n\n\nCADD scores strongly correlate with allelic diversity, pathogenicity of both\ncoding and non-coding variants, experimentally measured regulatory effects,\nand also rank causal variants within individual genome sequences with a higher\nvalue than non-causal variants. \nFinally, CADD scores of complex trait-associated variants from genome-wide\nassociation studies (GWAS) are significantly higher than matched controls and\ncorrelate with study sample size, likely reflecting the increased accuracy of\nlarger GWAS.\n
\n\n\nA CADD score represents a ranking not a prediction, and no threshold is defined\nfor a specific purpose. Higher scores are more likely to be deleterious: \nScores are \n\n
10 * -log of the rank\n\nso that variants with scores above 20 are \npredicted to be among the 1.0% most deleterious possible substitutions in \nthe human genome. We recommend thinking carefully about what threshold is \nappropriate for your application.\n\n\n
\nThere are six subtracks of this track: four for single-nucleotide mutations,\none for each base, showing all possible substitutions, \none for insertions and one for deletions. All subtracks show the CADD Phred\nscore on mouseover. Zooming in shows the exact score on mouseover, same\nbase = score 0.0.
\n\nPHRED-scaled scores are normalized to all potential ~9 billion SNVs, and\nthereby provide an externally comparable unit for analysis. For example, a\nscaled score of 10 or greater indicates a raw score in the top 10% of all\npossible reference genome SNVs, and a score of 20 or greater indicates a raw\nscore in the top 1%, regardless of the details of the annotation set, model\nparameters, etc.\n
\n\nThe four single-nucleotide mutation tracks have a default viewing range of\nscore 10 to 50. As explained in the paragraph above, that results in\nslightly less than 10% of the data displayed. The \ndeletion and insertion tracks have a default filter of 10-100, because they\ndisplay discrete items and not graphical data.\n
\n\n\nSingle nucleotide variants (SNV): For SNVs, at every\ngenome position, there are three values per position, one for every possible\nnucleotide mutation. The fourth value, "no mutation", representing \nthe reference allele, e.g., A to A, is always set to zero.\n
\n\nWhen using this track, zoom in until you can see every base at the\ntop of the display. Otherwise, there are several nucleotides per pixel under \nyour mouse cursor and instead of an actual score, the tooltip text will show\nthe average score of all nucleotides under the cursor. This is indicated by\nthe prefix "~" in the mouseover. Averages of scores are not useful for any\napplication of CADD.\n
\n\nInsertions and deletions: Scores are also shown on mouseover for a\nset of insertions and deletions. On hg38, the set has been obtained from\ngnomAD3. On hg19, the set of indels has been obtained from various sources\n(gnomAD2, ExAC, 1000 Genomes, ESP). If your insertion or deleletion of interest\nis not in the track, you will need to use CADD's\nonline scoring tool\nto obtain them.
\n\n\nCADD scores are freely available for all non-commercial applications from\nthe CADD website.\nFor commercial applications, see\nthe license instructions there.\n
\n\n\nThe CADD data on the UCSC Genome Browser can be explored interactively with the\nTable Browser or the\nData Integrator.\nFor automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed\nfiles that can be downloaded from\nour download server.\nThe files for this track are called a.bw, c.bw, g.bw, t.bw, ins.bb and del.bb. Individual\nregions or the whole genome annotation can be obtained using our tools bigWigToWig\nor bigBedToBed which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tools can also be used to obtain features confined to a given range, e.g.,\n
\nbigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd/a.bw stdout\n
\nor\n
\nbigBedToBed -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd/ins.bb stdout
\nData were converted from the files provided on\nthe CADD Downloads website,\nprovided by the Kircher lab, using\n\ncustom Python scripts, \ndocumented in our \nmakeDoc files.\n
\n\n\nThanks to the CADD development team for providing precomputed data as simple tab-separated files.\n
\n\n\nKircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J.\n\nA general framework for estimating the relative pathogenicity of human genetic variants.\nNat Genet. 2014 Mar;46(3):310-5.\nPMID: 24487276;\nPMC: PMC3992975\n
\n\n\nRentzsch P, Witten D, Cooper GM, Shendure J, Kircher M.\n\nCADD: predicting the deleteriousness of variants throughout the human genome.\nNucleic Acids Res. 2019 Jan 8;47(D1):D886-D894.\nPMID: 30371827;\nPMC: PMC6323892\n
\n", "longLabel": "CADD 1.6 Score for all possible single-basepair mutations (zoom in for scores)", "maxWindowToDraw": "10000000", "mouseOverFunction": "noAverage", "parent": "caddSuper", "shortLabel": "CADD 1.6", "track": "cadd", "type": "bigWig", "visibility": "dense" } }, "description": "CADD 1.6 Score for all possible single-basepair mutations (zoom in for scores)" }, { "type": "MultiQuantitativeTrack", "trackId": "hg38-cadd1_7", "name": "CADD 1.7", "category": [ "Phenotypes, Variants, and Literature" ], "assemblyNames": [ "hg38" ], "adapter": { "type": "MultiWiggleAdapter", "subadapters": [ { "type": "BigWigAdapter", "bigWigLocation": { "locationType": "UriLocation", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd1.7/a.bw" }, "source": "Mutation: A" }, { "type": "BigWigAdapter", "bigWigLocation": { "locationType": "UriLocation", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd1.7/c.bw" }, "source": "Mutation: C" }, { "type": "BigWigAdapter", "bigWigLocation": { "locationType": "UriLocation", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd1.7/g.bw" }, "source": "Mutation: G" }, { "type": "BigWigAdapter", "bigWigLocation": { "locationType": "UriLocation", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd1.7/t.bw" }, "source": "Mutation: T" } ] }, "metadata": { "addedByJBrowseTeam": true, "ucsc": { "color": "100,130,160", "compositeTrack": "on", "group": "phenDis", "html": "This track collection shows Combined Annotation Dependent Depletion scores.\nCADD is a tool for scoring the deleteriousness of single nucleotide variants as\nwell as insertion/deletion variants in the human genome.
\n\n\nSome mutation annotations\ntend to exploit a single information type (e.g., phastCons or phyloP for\nconservation) and/or are restricted in scope (e.g., to missense changes). Thus,\na broadly applicable metric that objectively weights and integrates diverse\ninformation is needed. Combined Annotation Dependent Depletion (CADD) is a\nframework that integrates multiple annotations into one metric by contrasting\nvariants that survived natural selection with simulated mutations.\n
\n\n\nCADD scores strongly correlate with allelic diversity, pathogenicity of both\ncoding and non-coding variants, experimentally measured regulatory effects,\nand also rank causal variants within individual genome sequences with a higher\nvalue than non-causal variants. \nFinally, CADD scores of complex trait-associated variants from genome-wide\nassociation studies (GWAS) are significantly higher than matched controls and\ncorrelate with study sample size, likely reflecting the increased accuracy of\nlarger GWAS.\n
\n\n\nA CADD score represents a ranking not a prediction, and no threshold is defined\nfor a specific purpose. Higher scores are more likely to be deleterious: \nScores are \n\n
10 * -log of the rank\n\nso that variants with scores above 20 are \npredicted to be among the 1.0% most deleterious possible substitutions in \nthe human genome. We recommend thinking carefully about what threshold is \nappropriate for your application.\n\n\n
\nThere are six subtracks of this track: four for single-nucleotide mutations,\none for each base, showing all possible substitutions, \none for insertions and one for deletions. All subtracks show the CADD Phred\nscore on mouseover. Zooming in shows the exact score on mouseover, same\nbase = score 0.0.
\n\nPHRED-scaled scores are normalized to all potential ~9 billion SNVs, and\nthereby provide an externally comparable unit for analysis. For example, a\nscaled score of 10 or greater indicates a raw score in the top 10% of all\npossible reference genome SNVs, and a score of 20 or greater indicates a raw\nscore in the top 1%, regardless of the details of the annotation set, model\nparameters, etc.\n
\n\nThe four single-nucleotide mutation tracks have a default viewing range of\nscore 10 to 50. As explained in the paragraph above, that results in\nslightly less than 10% of the data displayed. The \ndeletion and insertion tracks have a default filter of 10-100, because they\ndisplay discrete items and not graphical data.\n
\n\n\nSingle nucleotide variants (SNV): For SNVs, at every\ngenome position, there are three values per position, one for every possible\nnucleotide mutation. The fourth value, "no mutation", representing \nthe reference allele, e.g., A to A, is always set to zero.\n
\n\nWhen using this track, zoom in until you can see every base at the\ntop of the display. Otherwise, there are several nucleotides per pixel under \nyour mouse cursor and instead of an actual score, the tooltip text will show\nthe average score of all nucleotides under the cursor. This is indicated by\nthe prefix "~" in the mouseover. Averages of scores are not useful for any\napplication of CADD.\n
\n\nInsertions and deletions: Scores are also shown on mouseover for a\nset of insertions and deletions. On hg38, the set has been obtained from\ngnomAD3. On hg19, the set of indels has been obtained from various sources\n(gnomAD2, ExAC, 1000 Genomes, ESP). If your insertion or deleletion of interest\nis not in the track, you will need to use CADD's\nonline scoring tool\nto obtain them.
\n\nTrack colors
\n\nThis track is colored according to Table 2 in Vikas et al. The colors represent the recommended ACMG/AMP score cutoffs. \n\n
| Range | \nClassification | \n
|---|---|
| ≥ 25.3 | \nPathogenic | \n
| 25.2 - 22.6 | \nNeutral | \n
| ≤ 22.7 | \nBenign | \n
\nIn CADD version 1.7, new features have been added to improve CADD scores for certain variant\neffects, boosting the overall performance of CADD and bringing new developments to the community.\nCADD v1.7 integrates annotations from recent efforts to assess variant effects, along with new\nconservation and mutation scores.
\n\nCADD v1.7 supports only the major chromosomes of the hg38/GRCh38 reference genome (chromosomes 1-22,\nX, and Y) and may be the last version to support the hg19/GRCh37 human reference genome.
\n\nThis version includes scores derived from Evolutionary Scale Modeling (ESM) for assessing variants\nin protein-coding regions, along with scores from a convolutional neural network (CNN) trained on\nopen chromatin sequences, used as a proxy for regulatory regions in the genome. The previously\nincluded conservation scores have been updated with data from the Zoonomia project. New annotations\nhave also been added for 3' Untranslated Regions (3' UTRs), along with models of genome-wide\nmutational rates. The gene and transcript models have been updated by advancing from Ensembl version\n95 to version 110, and the Ensembl Variant Effect Predictor (VEP) has been upgraded accordingly.
\n\nThe models in CADD v1.7 have been trained similarly to the version 1.6 release. The logistic\nregression uses an L2 penalty with C = 1, and training was completed after thirteen L-BFGS\niterations using the sklearn library The new models exhibit a high degree of similarity to the\nprevious release, with a Spearman correlation of 0.946 for CADD scores calculated for 100,000\nrandomly selected variants between CADD GRCh38-v1.6 and CADD GRCh38-v1.7. The v1.7 models perform\ncomparably to earlier versions in distinguishing known pathogenic variants (ClinVar) from common\nvariants (gnomAD) across the genome. Improvements in CADD v1.7 are particularly evident when\nfocusing on specific variant categories, such as missense or 3' UTR variants, where the latest\nrelease includes updated annotations.
\n\nMore information can be found at the\nCADD site\nand the Schubach et al., Nucleic Acids Res, 2024 publication.\n\n\nData were converted from the files provided on\nthe CADD Downloads website,\nprovided by the Kircher lab, using\n\ncustom Python scripts,\ndocumented in our \nmakeDoc files.\n
\n\n\n\nCADD scores are freely available for all non-commercial applications from\nthe CADD website.\nFor commercial applications, see\nthe license instructions there.\n
\n\n\nThe CADD data on the UCSC Genome Browser can be explored interactively with the\nTable Browser or the\nData Integrator.\nFor automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed\nfiles that can be downloaded from\nour download server.\nThe files for this track are called a.bw, c.bw, g.bw, t.bw, ins.bb and del.bb. Individual\nregions or the whole genome annotation can be obtained using our tools bigWigToWig\nor bigBedToBed which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tools can also be used to obtain features confined to a given range, e.g.,\n
\nbigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd1.7/a.bw stdout\n
\nor\n
\nbigBedToBed -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd1.7/ins.bb stdout
\nThanks to the CADD development team for providing precomputed data as simple tab-separated files.\n
\n\n\nKircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J.\n\nA general framework for estimating the relative pathogenicity of human genetic variants.\nNat Genet. 2014 Mar;46(3):310-5.\nPMID: 24487276;\nPMC: PMC3992975\n
\n\n\nRentzsch P, Witten D, Cooper GM, Shendure J, Kircher M.\n\nCADD: predicting the deleteriousness of variants throughout the human genome.\nNucleic Acids Res. 2019 Jan 8;47(D1):D886-D894.\nPMID: 30371827;\nPMC: PMC6323892\n
\n\n\nSchubach M, Maass T, Nazaretyan L, Röner S, Kircher M.\n\nCADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to\nimprove genome-wide variant predictions.\nNucleic Acids Res. 2024 Jan 5;52(D1):D1143-D1154.\nPMID: 38183205; PMC:
This track collection shows Rare Exome Variant Ensemble Learner (REVEL) scores that can be\nused as evidence for pathogenicity classifications.\n
\n\n\nREVEL is an ensemble method for predicting a score for missense variants \nbased on a combination of scores from 13 individual tools: MutPred, FATHMM v2.3, \nVEST 3.0, PolyPhen-2, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP++, \nSiPhy, phyloP, and phastCons. REVEL was trained using recently discovered pathogenic \nand rare neutral missense variants, excluding those previously used to train its \nconstituent tools. The REVEL score for an individual missense variant can range \nfrom 0 to 1, with higher scores reflecting greater likelihood that the variant is \ndamaging.\n
\n\nMost authors of deleteriousness scores argue against using fixed cutoffs in\ndiagnostics. But to give an idea of the meaning of the score value, the REVEL\nauthors note: \"For example, 75.4% of disease mutations but only 10.9% of\nneutral variants (and 12.4% of all ESVs) have a REVEL score above 0.5,\ncorresponding to a sensitivity of 0.754 and specificity of 0.891. Selecting a\nmore stringent REVEL score threshold of 0.75 would result in higher specificity\nbut lower sensitivity, with 52.1% of disease mutations, 3.3% of neutral\nvariants, and 4.1% of all ESVs being classified as pathogenic\". (Figure S1 of\nthe reference below)\n
\n\n\nThere are five subtracks for this track:\n
Four lettered subtracks, one for every nucleotide, showing\nscores for the variant from the reference to that\nnucleotide. All subtracks show the REVEL ensemble score on mouseover. Across the exome, \nthere are three values per position, one for every possible\nnucleotide variant. The fourth value, "no variant", representing\nthe reference allele, e.g. A to A, is always set to zero, \"0.0\". REVEL only\ntakes into account amino acid changes, so a nucleotide variant that predicts no\namino acid change (synonymous) also receives the score \"0.0\". \n
\nIn rare cases, two scores are output for the same variant at a \ngenome position. This happens when there are two transcripts with\ndistinct splicing patterns and since some input scores for REVEL take into account\nthe sequence context, the same variant can get two different scores. In these cases,\nonly the maximum score is shown in the four per-nucleotide subtracks. The complete set of \nscores are shown in the Overlaps track.\n
\n\nOne subtrack, Overlaps, shows alternate REVEL scores when applicable. \nIn rare cases (0.05% of genome positions), multiple scores exist with a single variant, \ndue to multiple, overlapping transcripts. For example, if there are \ntwo transcripts and one covers only half of an exon, then the amino acids\nthat overlap both transcripts will get two distinct REVEL scores, since some of the underlying\nscores (polyPhen for example) take into account the amino acid sequence context and \nthis context is different depending on the transcript.\nFor these cases, this subtrack contains at least two\ngraphical features, for each affected genome position. Each feature is labeled\nwith the reference or variant (A, C, T, or G). The transcript IDs and resulting score is\nshown when hovering over the feature or clicking\nit. For the large majority of the genome, this subtrack has no features.\nThis is because REVEL usually outputs only a single score per nucleotide and \nmost transcript-derived amino acid sequence contexts are identical.\n
\n\nNote that in most diagnostic testing scenarios, variants are called using WGS\npipelines, not RNA-seq. As a result, variants are originally located on the\ngenome, not on transcripts, and the choice of transcript is made by\na variant calling software using a heuristic. In addition, clinically, in the\nfield, some transcripts have been agreed-on as more relevant for a disease, e.g.\nbecause only certain transcripts may be expressed in the relevant tissue. So\nthe choice of the most relevant transcript, and as such the REVEL score, may be\na question of manual curation standards rather than a result of the variant itself.\n
\n\nNote further that these thresholds represent the recommended score\ncutoffs for genes with no Variant Curation Expert Panel (VCEP) rules.\nFor genes with published VCEP rules, the VCEP might\nselect different thresholds, which are adjusted for the frequency of the\nrelevant disorders. These are available in the ClinGen Criteria\nSpecification.
\n\nWhen using this track, zoom in until you can see every base at the\ntop of the display. Otherwise, there are several nucleotides per pixel under \nyour mouse cursor and no score will be shown on the mouseover tooltip.\n
\n\nTrack colors
\n\nThis track is colored according to Table 2 in Pejaver et al. The colors represent the recommended\nClinGen score cutoffs.\n\n
| Range | \nClassification | \n
|---|---|
| ≥ 0.644 | \nPathogenic supporting | \n
| 0.643 - 0.291 | \nNeutral | \n
| ≤ 0.290 | \nBenign supporting | \n
\nMore details on these scoring ranges can be found in Bergquist et al. Genet Med 2025, Table 2:
\n
\n\n
For hg38, note that the data were converted from the hg19 data using the UCSC\nliftOver program, by the REVEL authors. This can lead to missing values or\nduplicated values. When a hg38 position is annotated with two scores due to the\nlifting, the authors removed all the scores for this position. They did the same when\nthe reference nucleotide has changed from hg19 to hg38. Also, on hg38, the track has\nthe ![](\"https://genome.ucsc.edu/images/19.jpg\")
"lifted" icon to indicate\nthis. You can double-check if a nucleotide\nposition is possibly affected by the lifting procedure by activating the track\n"Hg19 Mapping" under "Mapping and Sequencing".\n
\nREVEL scores are available at the \n\nREVEL website. \nThe site provides precomputed REVEL scores for all possible human missense variants \nto facilitate the identification of pathogenic variants among the large number of \nrare variants discovered in sequencing studies.\n\n
\n\n\nThe REVEL data on the UCSC Genome Browser can be explored interactively with the\nTable Browser or the\nData Integrator. The previous overlap bigBed version file is\navailable in the\narchives of our downloads server.\nFor automated download and analysis, the genome annotation is stored at UCSC in bigWig\nfiles that can be downloaded from\nour download server.\nThe files for this track are called a.bw, c.bw, g.bw, t.bw. Individual\nregions or the genome annotation can be obtained using our tool bigWigToWig,\nwhich can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tools can also be used to obtain features confined to given range, e.g.\n
\n
\nbigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/revel/a.bw stdout\n
\n\n
\nData were converted from the files provided on\nthe REVEL Downloads website. As with all other tracks,\na full log of all commands used for the conversion is available in our \nsource repository for hg19 and hg38. The release used for each assembly is shown on the track description page.\n
\n\n\nThanks to the REVEL development team for providing precomputed data and fixing duplicated values in the hg38 files.\n
\n\n\nIoannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK, Baheti S, \nMusolf A, Li Q, Holzinger E, Karyadi D, et al.\n\nREVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants\nAm J Hum Genet. 2016 Oct 6;99(4):877-885.\nPMID: 27666373;\nPMC: PMC5065685\n
\n\n\nBergquist T, Stenton SL, Nadeau EAW, Byrne AB, Greenblatt MS, Harrison SM, Tavtigian SV,\nO'Donnell-Luria A, Biesecker LG, Radivojac P et al.\n\nCalibration of additional computational tools expands ClinGen recommendation options for variant\nclassification with PP3/BP4 criteria.\nGenet Med. 2025 Mar 10;27(6):101402.\nPMID: 40084623\n
\n\n" } }, "description": "REVEL Pathogenicity Score for single-base coding mutations (zoom for exact score)" }, { "type": "MultiQuantitativeTrack", "trackId": "hg38-umap", "name": "Multi-read mappability - Umap (multi)", "assemblyNames": [ "hg38" ], "category": [ "Mapping and Sequencing" ], "metadata": { "source": "https://bismap.hoffmanlab.org/", "addedByJBrowseTeam": true, "ucsc": { "compositeTrack": "on", "group": "map", "html": "\nThese tracks indicate regions with uniquely mappable reads of particular lengths before and after\nbisulfite conversion. Both Umap and Bismap tracks contain single-read mappability and multi-read\nmappability tracks for four different read lengths: 24 bp, 36 bp, 50 bp, and 100 bp.
\n\nYou can use these tracks for many purposes, including filtering unreliable signal from\nsequencing assays. The Bismap track can help filter unreliable signal from sequencing assays\ninvolving bisulfite conversion, such as whole-genome bisulfite sequencing or reduced representation\nbisulfite sequencing.
\n\n\nThese tracks mark any region of the bisulfite-converted genome that is uniquely mappable by\n at least one k-mer on the specified strand. Mappability of the forward strand was\n generated by converting all instances of cytosine to thymine. Similarly, mappability of the\n reverse strand was generated by converting all instances of guanine to adenine.
\nTo calculate the single-read mappability, you must find the overlap of a given region with\n the region that is uniquely mappable on both strands. Regions not uniquely mappable on both\n strands or have a low multi-read mappability might bias the downstream analysis.
These tracks represent the probability that a randomly selected k-mer which overlaps\n with a given position is uniquely mappable. Multi-read mappability track is calculated for\n k-mers that are uniquely mappable on both strands, and thus there is no strand\n specification.
These tracks mark any region of the genome that is uniquely mappable by at least one\n k-mer. To calculate the single-read mappability, you must find the overlap of a given\n region with this track.
These tracks represent the probability that a randomly selected k-mer which overlaps\n with a given position is uniquely mappable.
For greater detail and explanatory diagrams, see the\npreprint, the\nUmap and Bismap project website, or the\nUmap and Bismap software\ndocumentation.\n\n
\nThe raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, genome annotation is stored in a bigBed\nor bigWig file that can be downloaded from the\ndownload\nserver. Individual regions or the whole genome annotation can be obtained using our tool\nbigBedToBed or bigWigToWig, which can be compiled from the source code or\ndownloaded as a precompiled binary for your system. Instructions for downloading source code and\nbinaries can be found here.\nThe tool can also be used to obtain only features within a given range, for example:
\nbigBedToBed -chrom=chr6 -start=0 -end=1000000\nhttp://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k24.Unique.Mappability.bb stdout\n\nPlease refer to our mailing list archives for questions, or our\nData Access FAQ for more\ninformation.
\n\n\nAnshul Kundaje (Stanford\nUniversity) created the original Umap software in MATLAB. The original Umap repository is available\nhere.\nMehran Karimzadeh (Michael Hoffman\nlab, Princess Margaret Cancer Centre) implemented the Python version of Umap and added features,\nincluding Bismap.
\n\n\nKarimzadeh M, Ernst C, Kundaje A, Hoffman MM.,\nUmap and Bismap:\nquantifying genome and methylome mappability\nbioRxiv bioRxiv, p. 095463, 2016.; doi: https://doi.org/10.1101/095463.
\n", "longLabel": "Single-read and multi-read mappability by Umap", "parent": "mappability", "shortLabel": "Umap", "subGroup1": "view Views SR=Single-read MR=Multi-read", "track": "umap", "type": "bigWig", "visibility": "full" } }, "adapter": { "type": "MultiWiggleAdapter", "subadapters": [ { "type": "BigWigAdapter", "bigWigLocation": { "locationType": "UriLocation", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k24.Umap.MultiTrackMappability.bw" }, "source": "Umap M24" }, { "type": "BigWigAdapter", "bigWigLocation": { "locationType": "UriLocation", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k36.Umap.MultiTrackMappability.bw" }, "source": "Umap M36" }, { "type": "BigWigAdapter", "bigWigLocation": { "locationType": "UriLocation", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k50.Umap.MultiTrackMappability.bw" }, "source": "Umap M50" }, { "type": "BigWigAdapter", "bigWigLocation": { "locationType": "UriLocation", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k100.Umap.MultiTrackMappability.bw" }, "source": "Umap M100" } ] }, "description": "Single-read and multi-read mappability by Umap" }, { "type": "MultiQuantitativeTrack", "trackId": "hg38-bismap", "name": "Multi-read mappability - Bismap (multi)", "category": [ "Mapping and Sequencing" ], "assemblyNames": [ "hg38" ], "metadata": { "source": "https://bismap.hoffmanlab.org/", "addedByJBrowseTeam": true, "ucsc": { "compositeTrack": "on", "group": "map", "html": "\nThese tracks indicate regions with uniquely mappable reads of particular lengths before and after\nbisulfite conversion. Both Umap and Bismap tracks contain single-read mappability and multi-read\nmappability tracks for four different read lengths: 24 bp, 36 bp, 50 bp, and 100 bp.
\n\nYou can use these tracks for many purposes, including filtering unreliable signal from\nsequencing assays. The Bismap track can help filter unreliable signal from sequencing assays\ninvolving bisulfite conversion, such as whole-genome bisulfite sequencing or reduced representation\nbisulfite sequencing.
\n\n\nThese tracks mark any region of the bisulfite-converted genome that is uniquely mappable by\n at least one k-mer on the specified strand. Mappability of the forward strand was\n generated by converting all instances of cytosine to thymine. Similarly, mappability of the\n reverse strand was generated by converting all instances of guanine to adenine.
\nTo calculate the single-read mappability, you must find the overlap of a given region with\n the region that is uniquely mappable on both strands. Regions not uniquely mappable on both\n strands or have a low multi-read mappability might bias the downstream analysis.
These tracks represent the probability that a randomly selected k-mer which overlaps\n with a given position is uniquely mappable. Multi-read mappability track is calculated for\n k-mers that are uniquely mappable on both strands, and thus there is no strand\n specification.
These tracks mark any region of the genome that is uniquely mappable by at least one\n k-mer. To calculate the single-read mappability, you must find the overlap of a given\n region with this track.
These tracks represent the probability that a randomly selected k-mer which overlaps\n with a given position is uniquely mappable.
For greater detail and explanatory diagrams, see the\npreprint, the\nUmap and Bismap project website, or the\nUmap and Bismap software\ndocumentation.\n\n
\nThe raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, genome annotation is stored in a bigBed\nor bigWig file that can be downloaded from the\ndownload\nserver. Individual regions or the whole genome annotation can be obtained using our tool\nbigBedToBed or bigWigToWig, which can be compiled from the source code or\ndownloaded as a precompiled binary for your system. Instructions for downloading source code and\nbinaries can be found here.\nThe tool can also be used to obtain only features within a given range, for example:
\nbigBedToBed -chrom=chr6 -start=0 -end=1000000\nhttp://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k24.Unique.Mappability.bb stdout\n\nPlease refer to our mailing list archives for questions, or our\nData Access FAQ for more\ninformation.
\n\n\nAnshul Kundaje (Stanford\nUniversity) created the original Umap software in MATLAB. The original Umap repository is available\nhere.\nMehran Karimzadeh (Michael Hoffman\nlab, Princess Margaret Cancer Centre) implemented the Python version of Umap and added features,\nincluding Bismap.
\n\n\nKarimzadeh M, Ernst C, Kundaje A, Hoffman MM.,\nUmap and Bismap:\nquantifying genome and methylome mappability\nbioRxiv bioRxiv, p. 095463, 2016.; doi: https://doi.org/10.1101/095463.
\n", "longLabel": "Single-read and multi-read mappability after bisulfite conversion", "noInherit": "on", "parent": "mappability", "shortLabel": "Bismap", "subGroup1": "view Views SR=Single-read MR=Multi-read", "track": "bismap", "type": "bigWig", "visibility": "full" } }, "adapter": { "type": "MultiWiggleAdapter", "subadapters": [ { "type": "BigWigAdapter", "bigWigLocation": { "locationType": "UriLocation", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k24.Bismap.MultiTrackMappability.bw" }, "source": "Bismap M24" }, { "type": "BigWigAdapter", "bigWigLocation": { "locationType": "UriLocation", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k36.Bismap.MultiTrackMappability.bw" }, "source": "Bismap M36" }, { "type": "BigWigAdapter", "bigWigLocation": { "locationType": "UriLocation", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k50.Bismap.MultiTrackMappability.bw" }, "source": "Bismap M50" }, { "type": "BigWigAdapter", "bigWigLocation": { "locationType": "UriLocation", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k100.Bismap.MultiTrackMappability.bw" }, "source": "Bismap M100" } ] }, "description": "Single-read and multi-read mappability after bisulfite conversion" }, { "trackId": "hg38-wgEncodeReg4Dnase", "name": "DNase (Layered)", "type": "MultiQuantitativeTrack", "assemblyNames": [ "hg38" ], "metadata": { "multiWigContainer": true, "ucsc": { "aggregate": "transparentOverlay", "allButtonPair": "on", "autoScale": "on", "container": "multiWig", "dragAndDrop": "subtracks", "html": "\nDNase I hypersensitivity identifies regions of open chromatin, which are often associated with\nregulatory elements such as promoters, enhancers, and insulators. This track displays\ngenome-wide DNase I hypersensitivity signal, as determined by ENCODE DNase-seq data across all\nphases of the project. Higher signal intensity indicates greater chromatin accessibility,\nsuggesting potential regulatory activity. Regulatory elements -- especially promoters -- tend\nto be strongly DNase-sensitive. The data are processed following the\nENCODE\nDNase-seq pipeline. Additional chromatin accessibility and transcription factor binding\ndatasets are available at the\nENCODE portal.
\n\n\nFor each organ, this track provides up to two subtracks averaging DNase signal:
\n\nWhether one or two subtracks appear for an organ depends on which kinds of biosamples have\nbeen assayed:
\n| Organ/Tissue | \nTissue and Primary Cell Subtrack | \nAll Biosamples Subtrack | \n
|---|---|---|
| adipose | – | ✓ |
| adrenal gland | – | ✓ |
| blood | ✓ | ✓ |
| blood vessel | – | ✓ |
| bone | ✓ | ✓ |
| bone marrow | ✓ | ✓ |
| brain | ✓ | ✓ |
| breast | ✓ | ✓ |
| connective tissue | ✓ | ✓ |
| embryo | ✓ | ✓ |
| epithelium | ✓ | ✓ |
| esophagus | – | ✓ |
| eye | ✓ | ✓ |
| gallbladder | – | ✓ |
| heart | ✓ | ✓ |
| kidney | ✓ | ✓ |
| large intestine | ✓ | ✓ |
| limb | – | ✓ |
| liver | ✓ | ✓ |
| lung | ✓ | ✓ |
| lymphoid tissue | ✓ | – |
| mouth | ✓ | ✓ |
| muscle | ✓ | ✓ |
| nerve | – | ✓ |
| nose | – | ✓ |
| ovary | – | ✓ |
| pancreas | ✓ | ✓ |
| penis | ✓ | ✓ |
| placenta | ✓ | ✓ |
| prostate | ✓ | ✓ |
| skin | ✓ | ✓ |
| small intestine | – | ✓ |
| spinal cord | – | ✓ |
| spleen | – | ✓ |
| stomach | – | ✓ |
| testis | ✓ | ✓ |
| thymus | – | ✓ |
| thyroid | – | ✓ |
| urinary bladder | – | ✓ |
| uterus | ✓ | ✓ |
| vagina | – | ✓ |
\nBy default, this track uses a transparent overlay to visualize data from multiple organs or tissues within\nthe same vertical space. For each organ or tissue, signals from all associated experiments were\naveraged to generate the displayed track. Each organ or tissue is assigned a distinct\ncolor following the\nENCODE color\nmapping convention,\nselected to be light and saturated to maintain clarity when overlaid. Initially, each layered\ntrack displays an overlay of five representative organs: blood, brain, kidney, liver, and\nmuscle. Clicking on the track opens a details page where you can view and select organs or\ntissues.
\n\n\n\nThe ENCODE 4 Regulation data on the UCSC Genome Browser can be explored interactively with the\nTable Browser or the\nData Integrator.\nFor automated download and analysis, the genome annotation is stored in bigWig\nfiles that can be downloaded from\nour download server.\nThe data may also be explored interactively using our\nREST API.\nThe original data files are also available from the\nENCODE portal.
\n\n\nThese files may also be locally explored using our tool bigWigToWig,\nwhich can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tool can also be used to obtain data confined to a given range, e.g.,\n
\nbigWigToWig -chrom=chr1 -start=100000 -end=100500 https://hgdownload.soe.ucsc.edu/gbdb/hg38/encode4/regulation/organAve/adiposeDNase.bw stdout
\nData were generated by the ENCODE Consortium. We thank the production labs for generating the\ndata: Drs. Gregory Crawford (Duke) and John Stamatoyannopoulos (UW). The data were further\nprocessed for visualization through a collaborative effort between the\nWeng lab and the\nMoore lab\nat UMass Chan Medical School (funded by NIH grant HG012343). Integration and visualization\nwere developed by Drs. Mingshi Gao, Jill Moore, and Zhiping Weng at UMass Chan Medical School,\nwho were part of the ENCODE Data Analysis Center.
\n\n\nENCODE Project Consortium, Moore JE, Purcaro MJ, Pratt HE, Epstein CB, Shoresh N, Adrian J,\nKawli T, Davis CA, Dobin A et al.\n\nExpanded encyclopaedias of DNA elements in the human and mouse genomes.\nNature. 2020 Jul;583(7818):699-710.\nPMID: 32728249; PMC:
\nMoore JE, Pratt HE, Fan K, Phalke N, Fisher J, Elhajjajy SI, Andrews G, Gao M, Shedd N,\nFu Y et al.\n\nAn Expanded Registry of Candidate cis-Regulatory Elements for Studying Transcriptional\nRegulation.\nNature. 2026 January 7.\nPMID: 39763870; PMC:
\nThis track shows transcription levels for several cell types as assayed by high-throughput\nsequencing of polyadenylated RNA (RNA-seq).\nAdditional views of this dataset and additional documentation on the methods used\nfor this track are available at the\nENCODE Caltech RNA-seq\npage. The data shown here are derived from the Raw Signal view from the paired \n75-mer 200 bp insert size reads. The two replicates of the signal were pooled and normalized\nso that the total genome-wide signal sums to 10 billion.\n
\n\n\nBy default, this track uses a transparent overlay method of displaying data from a number of cell\nlines in the same vertical space. Each of the cell lines in this track\nis associated with a particular color, and these colors are relatively light and saturated so\nas to work best with the transparent overlay. The color of these tracks\nmatch their versions from their lifted source on the hg19 assembly. The colors are consistent with the\nother hg19 lifted tracks located in the ENCODE Regulation\nsupertrack, with the exception being the DNase tracks, as they were not lifted from hg19 and are\ncolored to reflect similarity of cell types.\n
\n\n\nThis track shows data from the\nWold Lab at Caltech,\nas part of the ENCODE Consortium. \n
\n\n\nThis is release 2 (July 2012) of this track which includes two new subtracks for HeLa-S3 and HepG2.\n
\n\n\nPrimary ENCODE data produced during the 2007-2012 production phase were subject to a restriction\nperiod. However, the data here are past those restrictions and are freely available.\nThe full data release policy for ENCODE is available\nhere.\n
\n" } }, "displays": [ { "type": "MultiLinearWiggleDisplay", "defaultRendering": "multixyplot" } ], "adapter": { "type": "MultiWiggleAdapter", "subadapters": [ { "type": "BigWigAdapter", "name": "GM12878", "color": "rgb(255,128,128)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegTxn/wgEncodeRegTxnCaltechRnaSeqGm12878R2x75Il200SigPooled.bigWig" } }, { "type": "BigWigAdapter", "name": "H1-hESC", "color": "rgb(255,212,128)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegTxn/wgEncodeRegTxnCaltechRnaSeqH1hescR2x75Il200SigPooled.bigWig" } }, { "type": "BigWigAdapter", "name": "HeLa-S3", "color": "rgb(227,255,128)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegTxn/wgEncodeRegTxnCaltechRnaSeqHelas3R2x75Il200SigPooled.bigWig" } }, { "type": "BigWigAdapter", "name": "HepG2", "color": "rgb(128,255,149)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegTxn/wgEncodeRegTxnCaltechRnaSeqHepg2R2x75Il200SigPooled.bigWig" } }, { "type": "BigWigAdapter", "name": "HSMM", "color": "rgb(128,255,242)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegTxn/wgEncodeRegTxnCaltechRnaSeqHsmmR2x75Il200SigPooled.bigWig" } }, { "type": "BigWigAdapter", "name": "HUVEC", "color": "rgb(128,199,255)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegTxn/wgEncodeRegTxnCaltechRnaSeqHuvecR2x75Il200SigPooled.bigWig" } }, { "type": "BigWigAdapter", "name": "K562", "color": "rgb(149,128,255)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegTxn/wgEncodeRegTxnCaltechRnaSeqK562R2x75Il200SigPooled.bigWig" } }, { "type": "BigWigAdapter", "name": "NHEK", "color": "rgb(227,128,255)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegTxn/wgEncodeRegTxnCaltechRnaSeqNhekR2x75Il200SigPooled.bigWig" } }, { "type": "BigWigAdapter", "name": "NHLF", "color": "rgb(255,128,212)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegTxn/wgEncodeRegTxnCaltechRnaSeqNhlfR2x75Il200SigPooled.bigWig" } } ] }, "description": "Transcription Levels Assayed by RNA-seq on 9 Cell Lines from ENCODE", "category": [ "Regulation" ] }, { "trackId": "hg38-wgEncodeReg4Atac", "name": "ATAC (Layered)", "type": "MultiQuantitativeTrack", "assemblyNames": [ "hg38" ], "metadata": { "multiWigContainer": true, "ucsc": { "aggregate": "transparentOverlay", "allButtonPair": "on", "autoScale": "on", "container": "multiWig", "dragAndDrop": "subtracks", "html": "\nThe Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) identifies regions\nof open chromatin, which are often associated with active regulatory elements such as promoters\nand enhancers. This track displays genome-wide chromatin accessibility signal, as determined by\nENCODE ATAC-seq data across all phases of the project. Higher signal intensity indicates\nincreased chromatin accessibility, suggesting potential regulatory activity. The data are\nprocessed following the\nENCODE\nATAC-seq pipeline. Additional chromatin accessibility and transcription factor binding\ndatasets are available at the\nENCODE portal.
\n\n\nFor each organ, this track provides up to two subtracks averaging ATAC signal:
\n\nWhether one or two subtracks appear for an organ depends on which kinds of biosamples have\nbeen assayed:
\n| Organ/Tissue | \nTissue and Primary Cell Subtrack | \nAll Biosamples Subtrack | \n
|---|---|---|
| adipose | – | ✓ |
| adrenal gland | – | ✓ |
| blood | ✓ | ✓ |
| blood vessel | – | ✓ |
| bone marrow | – | ✓ |
| brain | ✓ | ✓ |
| breast | ✓ | ✓ |
| esophagus | – | ✓ |
| gallbladder | – | ✓ |
| heart | – | ✓ |
| large intestine | ✓ | ✓ |
| liver | ✓ | ✓ |
| lung | ✓ | ✓ |
| muscle | – | ✓ |
| nerve | – | ✓ |
| ovary | – | ✓ |
| pancreas | ✓ | ✓ |
| prostate | ✓ | ✓ |
| skin | – | ✓ |
| small intestine | – | ✓ |
| spleen | – | ✓ |
| stomach | – | ✓ |
| testis | – | ✓ |
| thyroid | – | ✓ |
| urinary bladder | – | ✓ |
| uterus | – | ✓ |
\nBy default, this track uses a transparent overlay to visualize data from multiple organs or tissues within\nthe same vertical space. For each organ or tissue, signals from all associated experiments were\naveraged to generate the displayed track. Each organ or tissue is assigned a distinct\ncolor following the\nENCODE color\nmapping convention,\nselected to be light and saturated to maintain clarity when overlaid. Initially, each layered\ntrack displays an overlay of five representative organs: blood, brain, kidney, liver, and\nmuscle. Clicking on the track opens a details page where you can view and select organs or\ntissues.
\n\n\n\nThe ENCODE 4 Regulation data on the UCSC Genome Browser can be explored interactively with the\nTable Browser or the\nData Integrator.\nFor automated download and analysis, the genome annotation is stored in bigWig\nfiles that can be downloaded from\nour download server.\nThe data may also be explored interactively using our\nREST API.\nThe original data files are also available from the\nENCODE portal.
\n\n\nThese files may also be locally explored using our tool bigWigToWig,\nwhich can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tool can also be used to obtain data confined to a given range, e.g.,\n
\nbigWigToWig -chrom=chr1 -start=100000 -end=100500 https://hgdownload.soe.ucsc.edu/gbdb/hg38/encode4/regulation/organAve/adiposeATAC.bw stdout
\nData were generated by the ENCODE Consortium. We thank the production labs for generating the\ndata: Drs. Barbara Wold (Caltech), Michael Snyder, Stephen Montgomery,\nand Will Greenleaf (Stanford), and Yin Shen (UCSF). The data were further processed for\nvisualization through a\ncollaborative effort between the\nWeng lab and the\nMoore lab\nat UMass Chan Medical School (funded by NIH grant HG012343). Integration and visualization\nwere developed by Drs. Mingshi Gao, Jill Moore, and Zhiping Weng at UMass Chan Medical School,\nwho were part of the ENCODE Data Analysis Center.
\n\n\nENCODE Project Consortium, Moore JE, Purcaro MJ, Pratt HE, Epstein CB, Shoresh N, Adrian J,\nKawli T, Davis CA, Dobin A et al.\n\nExpanded encyclopaedias of DNA elements in the human and mouse genomes.\nNature. 2020 Jul;583(7818):699-710.\nPMID: 32728249; PMC:
\nMoore JE, Pratt HE, Fan K, Phalke N, Fisher J, Elhajjajy SI, Andrews G, Gao M, Shedd N,\nFu Y et al.\n\nAn Expanded Registry of Candidate cis-Regulatory Elements for Studying Transcriptional\nRegulation.\nNature. 2026 January 7.\nPMID: 39763870; PMC:
\nChemical modifications (e.g., methylation and acetylation) to the histone proteins\npresent in chromatin influence gene expression by changing how\naccessible the chromatin is to transcription. A specific modification of\na specific histone protein is called a histone mark.\nThis track shows the levels of enrichment of the H3K4Me1 histone mark across the genome as\ndetermined by a ChIP-seq assay. The H3K4me1 histone mark is the mono-methylation of lysine 4\nof the H3 histone protein, and it is associated with enhancers and with DNA regions downstream of\ntranscription starts. Additional histone marks and other chromatin associated ChIP-seq data is\navailable at the\nBroad Histone page.\n
\n\n\nBy default, this track uses a transparent overlay method of displaying data from a number of cell\nlines in the same vertical space. Each of the cell lines in this track\nis associated with a particular color, and these colors are relatively light and saturated so\nas to work best with the transparent overlay. The color of these tracks\nmatch their versions from their lifted source on the hg19 assembly. The colors are consistent with the\nother hg19 lifted tracks located in the ENCODE Regulation\nsupertrack, with the exception being the DNase tracks, as they were not lifted from hg19 and are\ncolored to reflect similarity of cell types.\n
\n\n\nThis track shows data from the Bernstein Lab at the Broad Institute, as part of\nthe ENCODE Consortium.\n
\n\n\nPrimary ENCODE data produced during the 2007-2012 production phase were subject to a restriction\nperiod. However, the data here are past those restrictions and are freely available.\nThe full data release policy for ENCODE is available\nhere.\n
\n" } }, "displays": [ { "type": "MultiLinearWiggleDisplay", "defaultRendering": "multixyplot" } ], "adapter": { "type": "MultiWiggleAdapter", "subadapters": [ { "type": "BigWigAdapter", "name": "GM12878", "color": "rgb(255,128,128)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegMarkH3k4me1/wgEncodeBroadHistoneGm12878H3k4me1StdSig.bigWig" } }, { "type": "BigWigAdapter", "name": "H1-hESC", "color": "rgb(255,212,128)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegMarkH3k4me1/wgEncodeBroadHistoneH1hescH3k4me1StdSig.bigWig" } }, { "type": "BigWigAdapter", "name": "HSMM", "color": "rgb(120,235,204)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegMarkH3k4me1/wgEncodeBroadHistoneHsmmH3k4me1StdSig.bigWig" } }, { "type": "BigWigAdapter", "name": "HUVEC", "color": "rgb(128,212,255)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegMarkH3k4me1/wgEncodeBroadHistoneHuvecH3k4me1StdSig.bigWig" } }, { "type": "BigWigAdapter", "name": "K562", "color": "rgb(128,128,255)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegMarkH3k4me1/wgEncodeBroadHistoneK562H3k4me1StdSig.bigWig" } }, { "type": "BigWigAdapter", "name": "NHEK", "color": "rgb(212,128,255)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegMarkH3k4me1/wgEncodeBroadHistoneNhekH3k4me1StdSig.bigWig" } }, { "type": "BigWigAdapter", "name": "NHLF", "color": "rgb(255,128,212)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegMarkH3k4me1/wgEncodeBroadHistoneNhlfH3k4me1StdSig.bigWig" } } ] }, "description": "H3K4Me1 Mark (Often Found Near Regulatory Elements) on 7 cell lines from ENCODE", "category": [ "Regulation" ] }, { "trackId": "hg38-wgEncodeReg4MarkH3k4me3", "name": "H3K4me3 (Layered)", "type": "MultiQuantitativeTrack", "assemblyNames": [ "hg38" ], "metadata": { "multiWigContainer": true, "ucsc": { "aggregate": "transparentOverlay", "allButtonPair": "on", "autoScale": "on", "container": "multiWig", "dragAndDrop": "subtracks", "html": "\nChemical modifications (e.g., methylation and acetylation) to the histone proteins present in\nchromatin influence gene expression by changing how accessible the chromatin is to transcription.\nA specific modification of a specific histone protein is called a histone mark. This track\ndisplays genome-wide enrichment levels of the H3K4me3 histone mark, as determined by ENCODE\nChIP-seq data across all phases of the project. H3K4me3 refers to the tri-methylation of\nlysine 4 on the H3 histone protein and is associated with active or poised promoters. The data\nare processed following the\nENCODE Histone\nChIP-seq pipeline. Additional histone marks and other chromatin-associated ChIP-seq datasets\nare available at the\nENCODE portal.
\n\n\nFor each organ, this track provides up to two subtracks averaging H3K4me3 signal:
\n\nWhether one or two subtracks appear for an organ depends on which kinds of biosamples have\nbeen assayed:
\n| Organ/Tissue | \nTissue and Primary Cell Subtrack | \nAll Biosamples Subtrack | \n
|---|---|---|
| adipose | – | ✓ |
| adrenal gland | – | ✓ |
| blood | ✓ | ✓ |
| blood vessel | – | ✓ |
| bone | – | ✓ |
| bone marrow | ✓ | ✓ |
| brain | ✓ | ✓ |
| breast | ✓ | ✓ |
| connective tissue | ✓ | ✓ |
| embryo | ✓ | ✓ |
| epithelium | – | ✓ |
| esophagus | – | ✓ |
| eye | ✓ | ✓ |
| heart | ✓ | ✓ |
| kidney | ✓ | ✓ |
| large intestine | ✓ | ✓ |
| liver | ✓ | ✓ |
| lung | ✓ | ✓ |
| mouth | – | ✓ |
| muscle | ✓ | ✓ |
| nerve | – | ✓ |
| ovary | – | ✓ |
| pancreas | ✓ | ✓ |
| parathyroid gland | – | ✓ |
| penis | ✓ | ✓ |
| placenta | – | ✓ |
| prostate | ✓ | ✓ |
| skin | ✓ | ✓ |
| small intestine | – | ✓ |
| spinal cord | – | ✓ |
| spleen | – | ✓ |
| stomach | – | ✓ |
| testis | ✓ | ✓ |
| thymus | – | ✓ |
| thyroid | – | ✓ |
| urinary bladder | – | ✓ |
| uterus | ✓ | ✓ |
| vagina | – | ✓ |
\nBy default, this track uses a transparent overlay to visualize data from multiple organs or tissues within\nthe same vertical space. For each organ or tissue, signals from all associated experiments were\naveraged to generate the displayed track. Each organ or tissue is assigned a distinct\ncolor following the\nENCODE color\nmapping convention,\nselected to be light and saturated to maintain clarity when overlaid. Initially, each layered\ntrack displays an overlay of five representative organs: blood, brain, kidney, liver, and\nmuscle. Clicking on the track opens a details page where you can view and select organs or\ntissues.
\n\n\n\nThe ENCODE 4 Regulation data on the UCSC Genome Browser can be explored interactively with the\nTable Browser or the\nData Integrator.\nFor automated download and analysis, the genome annotation is stored in bigWig\nfiles that can be downloaded from\nour download server.\nThe data may also be explored interactively using our\nREST API.\nThe original data files are also available from the\nENCODE portal.
\n\n\nThese files may also be locally explored using our tool bigWigToWig,\nwhich can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tool can also be used to obtain data confined to a given range, e.g.,\n
\nbigWigToWig -chrom=chr1 -start=100000 -end=100500 https://hgdownload.soe.ucsc.edu/gbdb/hg38/encode4/regulation/organAve/adiposeH3K4me3.bw stdout
\nData were generated by the ENCODE Consortium. We thank the production labs for generating the\ndata: Drs. Bing Ren (UCSD), Bradley Bernstein (Broad),\nJohn Stamatoyannopoulos (UW), Joseph Costello (UCSF), Michael Snyder (Stanford),\nand Peggy Farnham (USC). The data were further processed for visualization through a collaborative effort between the\nWeng lab and the\nMoore lab\nat UMass Chan Medical School (funded by NIH grant HG012343). Integration and visualization\nwere developed by Drs. Mingshi Gao, Jill Moore, and Zhiping Weng at UMass Chan Medical School,\nwho were part of the ENCODE Data Analysis Center.
\n\n\nENCODE Project Consortium, Moore JE, Purcaro MJ, Pratt HE, Epstein CB, Shoresh N, Adrian J,\nKawli T, Davis CA, Dobin A et al.\n\nExpanded encyclopaedias of DNA elements in the human and mouse genomes.\nNature. 2020 Jul;583(7818):699-710.\nPMID: 32728249; PMC:
\nMoore JE, Pratt HE, Fan K, Phalke N, Fisher J, Elhajjajy SI, Andrews G, Gao M, Shedd N,\nFu Y et al.\n\nAn Expanded Registry of Candidate cis-Regulatory Elements for Studying Transcriptional\nRegulation.\nNature. 2026 January 7.\nPMID: 39763870; PMC:
\nChemical modifications (e.g., methylation and acetylation) to the histone proteins\npresent in chromatin influence gene expression by changing how\naccessible the chromatin is to transcription. A specific modification of\na specific histone protein is called a histone mark.\nThis track shows the levels of enrichment of the H3K4Me3 histone mark across the genome as\ndetermined by a ChIP-seq assay. The H3K4Me3 histone mark is the tri-methylation of lysine 4 of the\nH3 histone protein, and it is associated with promoters that are active or poised to be\nactivated. Additional histone marks and other chromatin associated ChIP-seq data is available at \nthe Broad Histone\npage.\n
\n\n\nBy default, this track uses a transparent overlay method of displaying data from a number of cell\nlines in the same vertical space. Each of the cell lines in this track\nis associated with a particular color, and these colors are relatively light and saturated so\nas to work best with the transparent overlay. The color of these tracks\nmatch their versions from their lifted source on the hg19 assembly. The colors are consistent with the\nother hg19 lifted tracks located in the ENCODE Regulation\nsupertrack, with the exception being the DNase tracks, as they were not lifted from hg19 and are\ncolored to reflect similarity of cell types.\n
\n\n\nThis track shows data from the Bernstein Lab at the Broad Institute, as part of\nthe ENCODE Consortium.\n
\n\n\nPrimary ENCODE data produced during the 2007-2012 production phase were subject to a restriction\nperiod. However, the data here are past those restrictions and are freely available.\nThe full data release policy for ENCODE is available\nhere.\n
\n" } }, "displays": [ { "type": "MultiLinearWiggleDisplay", "defaultRendering": "multixyplot" } ], "adapter": { "type": "MultiWiggleAdapter", "subadapters": [ { "type": "BigWigAdapter", "name": "GM12878", "color": "rgb(255,128,128)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegMarkH3k4me3/wgEncodeBroadHistoneGm12878H3k4me3StdSig.bigWig" } }, { "type": "BigWigAdapter", "name": "H1-hESC", "color": "rgb(255,212,128)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegMarkH3k4me3/wgEncodeBroadHistoneH1hescH3k4me3StdSig.bigWig" } }, { "type": "BigWigAdapter", "name": "HSMM", "color": "rgb(120,235,204)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegMarkH3k4me3/wgEncodeBroadHistoneHsmmH3k4me3StdSig.bigWig" } }, { "type": "BigWigAdapter", "name": "HUVEC", "color": "rgb(128,212,255)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegMarkH3k4me3/wgEncodeBroadHistoneHuvecH3k4me3StdSig.bigWig" } }, { "type": "BigWigAdapter", "name": "K562", "color": "rgb(128,128,255)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegMarkH3k4me3/wgEncodeBroadHistoneK562H3k4me3StdSig.bigWig" } }, { "type": "BigWigAdapter", "name": "NHEK", "color": "rgb(212,128,255)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegMarkH3k4me3/wgEncodeBroadHistoneNhekH3k4me3StdSig.bigWig" } }, { "type": "BigWigAdapter", "name": "NHLF", "color": "rgb(255,128,212)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegMarkH3k4me3/wgEncodeBroadHistoneNhlfH3k4me3StdSig.bigWig" } } ] }, "description": "H3K4Me3 Mark (Often Found Near Promoters) on 7 cell lines from ENCODE", "category": [ "Regulation" ] }, { "trackId": "hg38-Total_counts_multiwig", "name": "Total counts of CAGE reads", "type": "MultiQuantitativeTrack", "assemblyNames": [ "hg38" ], "metadata": { "multiWigContainer": true, "ucsc": { "aggregate": "transparentOverlay", "autoScale": "off", "configurable": "on", "container": "multiWig", "dataVersion": "FANTOM5 reprocessed7", "dragAndDrop": "subTracks", "html": "\nThe FANTOM5 track shows mapped transcription start sites (TSS) and their usage in primary cells,\ncell lines, and tissues to produce a comprehensive overview of gene expression across the human\nbody by using single molecule sequencing.\n
\n\nItems in this track are colored according to their strand orientation. Blue\nindicates alignment to the negative strand, and red indicates\nalignment to the positive strand.\n
\n\nIndividual biological states are profiled by HeliScopeCAGE, which is a variation of the CAGE\n(Cap Analysis Gene Expression) protocol based on a single molecule sequencer. The standard protocol\nrequiring 5 µg of total RNA as a starting material is referred to as hCAGE, and an\noptimized version for a lower quantity (~ 100 ng) is referred to as LQhCAGE (Kanamori-Katyama\net al. 2011).\n
Transcription start sites (TSSs) were mapped and their usage in human and mouse primary cells,\ncell lines, and tissues was to produce a comprehensive overview of mammalian gene expression across the\nhuman body. 5′-end of the mapped CAGE reads are counted at a single base pair resolution\n(CTSS, CAGE tag starting sites) on the genomic coordinates, which represent TSS activities in the\nsample. Individual samples shown in \"TSS activity\" tracks are grouped as below.\n
TSS (CAGE) peaks across the panel of the biological states (samples) are identified by DPI\n(decomposition based peak identification, Forrest et al. 2014), where each of the peaks consists of\nneighboring and related TSSs. The peaks are used as anchors to define promoters and units of\npromoter-level expression analysis. Two subsets of the peaks are defined based on evidence of read\ncounts, depending on scopes of subsequent analyses, and the first subset (referred as a\nrobust set of the peaks, thresholded for expression analysis is shown as TSS peaks. They are\nnamed \"p#@GENE_SYMBOL\" if associated with 5'-end of known genes, or \"p@CHROM:START..END,STRAND\"\notherwise. The summary tracks consist of the TSS (CAGE) peaks and summary profiles of TSS\nactivities (total and maximum values). The summary track consists of the following tracks.\n
\n5′-end of the mapped CAGE reads are counted at a single base pair resolution (CTSS, CAGE tag starting sites) on the genomic coordinates, which represent TSS activities in the sample. The read counts tracks indicate raw counts of CAGE reads, and the TPM tracks indicate normalized counts as TPM (tags per million).\n
\n\n\nFANTOM5 data can be explored interactively with the\nTable Browser and cross-referenced with the \nData Integrator. For programmatic access,\nthe track can be accessed using the Genome Browser's\nREST API.\nReMap annotations can be downloaded from the\nGenome Browser's download server\nas a bigBed file. This compressed binary format can be remotely queried through\ncommand line utilities. Please note that some of the download files can be quite large.
\n\n\nThe FANTOM5 reprocessed data can be found and downloaded on the FANTOM website.
\n\n\nThanks to the FANTOM5 consortium,\nthe Large Scale Data Managing Unit and Preventive Medicine and\nApplied Genomics Unit, the Center for Integrative Medical Sciences (IMS), and\nRIKEN for providing this data\nand its analysis.
\n\n\nFANTOM Consortium and the RIKEN PMI and CLST (DGT), Forrest AR, Kawaji H, Rehli M, Baillie JK, de\nHoon MJ, Haberle V, Lassmann T, Kulakovskiy IV, Lizio M et al.\n\nA promoter-level mammalian expression atlas.\nNature. 2014 Mar 27;507(7493):462-70.\nPMID: 24670764; PMC:
\nKanamori-Katayama M, Itoh M, Kawaji H, Lassmann T, Katayama S, Kojima M, Bertin N, Kaiho A, Ninomiya\nN, Daub CO et al.\n\nUnamplified cap analysis of gene expression on a single-molecule sequencer.\nGenome Res. 2011 Jul;21(7):1150-9.\nPMID: 21596820; PMC:
\nLizio M, Harshbarger J, Shimoji H, Severin J, Kasukawa T, Sahin S, Abugessaisa I, Fukuda S, Hori F,\nIshikawa-Kato S et al.\n\nGateways to the FANTOM5 promoter level mammalian expression atlas.\nGenome Biol. 2015 Jan 5;16(1):22.\nPMID: 25723102; PMC:
\nChemical modifications (e.g., methylation and acetylation) to the histone proteins present in\nchromatin influence gene expression by changing how accessible the chromatin is to transcription.\nA specific modification of a specific histone protein is called a histone mark. This track\ndisplays genome-wide enrichment levels of the H3K27ac histone mark, as determined by ENCODE\nChIP-seq data across all phases of the project. H3K27ac refers to the acetylation of lysine 27\non the H3 histone protein and is associated with active enhancers and promoters. The data are\nprocessed following the\nENCODE Histone\nChIP-seq pipeline. Additional histone marks and other chromatin-associated ChIP-seq datasets\nare available at the\nENCODE portal.
\n\n\nFor each organ, this track provides up to two subtracks averaging H3K27ac signal:
\n\nWhether one or two subtracks appear for an organ depends on which kinds of biosamples have\nbeen assayed:
\n| Organ/Tissue | \nTissue and Primary Cell Subtrack | \nAll Biosamples Subtrack | \n
|---|---|---|
| adipose | – | ✓ |
| adrenal gland | – | ✓ |
| blood | ✓ | ✓ |
| blood vessel | – | ✓ |
| bone | – | ✓ |
| bone marrow | ✓ | ✓ |
| brain | ✓ | ✓ |
| breast | ✓ | ✓ |
| connective tissue | ✓ | ✓ |
| embryo | ✓ | ✓ |
| epithelium | – | ✓ |
| esophagus | – | ✓ |
| eye | – | ✓ |
| heart | ✓ | ✓ |
| kidney | ✓ | ✓ |
| large intestine | ✓ | ✓ |
| liver | ✓ | ✓ |
| lung | ✓ | ✓ |
| mouth | – | ✓ |
| muscle | ✓ | ✓ |
| nerve | – | ✓ |
| ovary | – | ✓ |
| pancreas | ✓ | ✓ |
| parathyroid gland | – | ✓ |
| penis | ✓ | ✓ |
| placenta | – | ✓ |
| prostate | ✓ | ✓ |
| skin | ✓ | ✓ |
| small intestine | – | ✓ |
| spinal cord | – | ✓ |
| spleen | – | ✓ |
| stomach | – | ✓ |
| testis | – | ✓ |
| thymus | – | ✓ |
| thyroid | – | ✓ |
| urinary bladder | – | ✓ |
| uterus | ✓ | ✓ |
| vagina | – | ✓ |
\nBy default, this track uses a transparent overlay to visualize data from multiple organs or tissues within\nthe same vertical space. For each organ or tissue, signals from all associated experiments were\naveraged to generate the displayed track. Each organ or tissue is assigned a distinct\ncolor following the\nENCODE color\nmapping convention,\nselected to be light and saturated to maintain clarity when overlaid. Initially, each layered\ntrack displays an overlay of five representative organs: blood, brain, kidney, liver, and\nmuscle. Clicking on the track opens a details page where you can view and select organs or\ntissues.
\n\n\n\nThe ENCODE 4 Regulation data on the UCSC Genome Browser can be explored interactively with the\nTable Browser or the\nData Integrator.\nFor automated download and analysis, the genome annotation is stored in bigWig\nfiles that can be downloaded from\nour download server.\nThe data may also be explored interactively using our\nREST API.\nThe original data files are also available from the\nENCODE portal.
\n\n\nThese files may also be locally explored using our tool bigWigToWig,\nwhich can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tool can also be used to obtain data confined to a given range, e.g.,\n
\nbigWigToWig -chrom=chr1 -start=100000 -end=100500 https://hgdownload.soe.ucsc.edu/gbdb/hg38/encode4/regulation/organAve/adiposeH3K27ac.bw stdout
\nData were generated by the ENCODE Consortium. We thank the production labs for generating the\ndata: Drs. Bing Ren (UCSD), Bradley Bernstein (Broad),\nJohn Stamatoyannopoulos (UW), Joseph Costello (UCSF), Michael Snyder (Stanford),\nand Peggy Farnham (USC). The data were further processed for visualization through a collaborative effort between the\nWeng lab and the\nMoore lab\nat UMass Chan Medical School (funded by NIH grant HG012343). Integration and visualization\nwere developed by Drs. Mingshi Gao, Jill Moore, and Zhiping Weng at UMass Chan Medical School,\nwho were part of the ENCODE Data Analysis Center.
\n\n\nENCODE Project Consortium, Moore JE, Purcaro MJ, Pratt HE, Epstein CB, Shoresh N, Adrian J,\nKawli T, Davis CA, Dobin A et al.\n\nExpanded encyclopaedias of DNA elements in the human and mouse genomes.\nNature. 2020 Jul;583(7818):699-710.\nPMID: 32728249; PMC:
\nMoore JE, Pratt HE, Fan K, Phalke N, Fisher J, Elhajjajy SI, Andrews G, Gao M, Shedd N,\nFu Y et al.\n\nAn Expanded Registry of Candidate cis-Regulatory Elements for Studying Transcriptional\nRegulation.\nNature. 2026 January 7.\nPMID: 39763870; PMC:
\nChemical modifications (e.g., methylation and acetylation) to the histone proteins\npresent in chromatin influence gene expression by changing how\naccessible the chromatin is to transcription. A specific modification of\na specific histone protein is called a histone mark.\nThis track shows the levels of enrichment of the H3K27Ac histone mark across the genome as\ndetermined by a ChIP-seq assay. The H3K27Ac histone mark is the acetylation of lysine 27 of the H3\nhistone protein, and it is thought to enhance transcription possibly by blocking the\nspread of the repressive histone mark H3K27Me3. Additional histone marks and other chromatin \nassociated ChIP-seq data is available at the \nBroad Histone page.\n
\n\n\nBy default, this track uses a transparent overlay method of displaying data from a number of cell\nlines in the same vertical space. Each of the cell lines in this track\nis associated with a particular color, and these colors are relatively light and saturated so\nas to work best with the transparent overlay. The color of these tracks\nmatch their versions from their lifted source on the hg19 assembly. The colors are consistent with the \nother hg19 lifted tracks located in the ENCODE Regulation\nsupertrack, with the exception being the DNase tracks, as they were not lifted from hg19 and are\ncolored to reflect similarity of cell types. \n
\n\n\nThis track shows data from the Bernstein Lab at the Broad Institute, as part of\nthe ENCODE Consortium.\n
\n\n\nPrimary ENCODE data produced during the 2007-2012 production phase were subject to a restriction\nperiod. However, the data here are past those restrictions and are freely available.\nThe full data release policy for ENCODE is available\nhere.\n
\n" } }, "displays": [ { "type": "MultiLinearWiggleDisplay", "defaultRendering": "multixyplot" } ], "adapter": { "type": "MultiWiggleAdapter", "subadapters": [ { "type": "BigWigAdapter", "name": "GM12878", "color": "rgb(255,128,128)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegMarkH3k27ac/wgEncodeBroadHistoneGm12878H3k27acStdSig.bigWig" } }, { "type": "BigWigAdapter", "name": "H1-hESC", "color": "rgb(255,212,128)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegMarkH3k27ac/wgEncodeBroadHistoneH1hescH3k27acStdSig.bigWig" } }, { "type": "BigWigAdapter", "name": "HSMM", "color": "rgb(120,235,204)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegMarkH3k27ac/wgEncodeBroadHistoneHsmmH3k27acStdSig.bigWig" } }, { "type": "BigWigAdapter", "name": "HUVEC", "color": "rgb(128,212,255)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegMarkH3k27ac/wgEncodeBroadHistoneHuvecH3k27acStdSig.bigWig" } }, { "type": "BigWigAdapter", "name": "K562", "color": "rgb(128,128,255)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegMarkH3k27ac/wgEncodeBroadHistoneK562H3k27acStdSig.bigWig" } }, { "type": "BigWigAdapter", "name": "NHEK", "color": "rgb(212,128,255)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegMarkH3k27ac/wgEncodeBroadHistoneNhekH3k27acStdSig.bigWig" } }, { "type": "BigWigAdapter", "name": "NHLF", "color": "rgb(255,128,212)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeReg/wgEncodeRegMarkH3k27ac/wgEncodeBroadHistoneNhlfH3k27acStdSig.bigWig" } } ] }, "description": "H3K27Ac Mark (Often Found Near Regulatory Elements) on 7 cell lines from ENCODE", "category": [ "Regulation" ] }, { "trackId": "hg38-Max_counts_multiwig", "name": "Max counts of CAGE reads", "type": "MultiQuantitativeTrack", "assemblyNames": [ "hg38" ], "metadata": { "multiWigContainer": true, "ucsc": { "aggregate": "transparentOverlay", "autoScale": "off", "configurable": "on", "container": "multiWig", "dataVersion": "FANTOM5 reprocessed7", "dragAndDrop": "subTracks", "html": "\nThe FANTOM5 track shows mapped transcription start sites (TSS) and their usage in primary cells,\ncell lines, and tissues to produce a comprehensive overview of gene expression across the human\nbody by using single molecule sequencing.\n
\n\nItems in this track are colored according to their strand orientation. Blue\nindicates alignment to the negative strand, and red indicates\nalignment to the positive strand.\n
\n\nIndividual biological states are profiled by HeliScopeCAGE, which is a variation of the CAGE\n(Cap Analysis Gene Expression) protocol based on a single molecule sequencer. The standard protocol\nrequiring 5 µg of total RNA as a starting material is referred to as hCAGE, and an\noptimized version for a lower quantity (~ 100 ng) is referred to as LQhCAGE (Kanamori-Katyama\net al. 2011).\n
Transcription start sites (TSSs) were mapped and their usage in human and mouse primary cells,\ncell lines, and tissues was to produce a comprehensive overview of mammalian gene expression across the\nhuman body. 5′-end of the mapped CAGE reads are counted at a single base pair resolution\n(CTSS, CAGE tag starting sites) on the genomic coordinates, which represent TSS activities in the\nsample. Individual samples shown in \"TSS activity\" tracks are grouped as below.\n
TSS (CAGE) peaks across the panel of the biological states (samples) are identified by DPI\n(decomposition based peak identification, Forrest et al. 2014), where each of the peaks consists of\nneighboring and related TSSs. The peaks are used as anchors to define promoters and units of\npromoter-level expression analysis. Two subsets of the peaks are defined based on evidence of read\ncounts, depending on scopes of subsequent analyses, and the first subset (referred as a\nrobust set of the peaks, thresholded for expression analysis is shown as TSS peaks. They are\nnamed \"p#@GENE_SYMBOL\" if associated with 5'-end of known genes, or \"p@CHROM:START..END,STRAND\"\notherwise. The summary tracks consist of the TSS (CAGE) peaks and summary profiles of TSS\nactivities (total and maximum values). The summary track consists of the following tracks.\n
\n5′-end of the mapped CAGE reads are counted at a single base pair resolution (CTSS, CAGE tag starting sites) on the genomic coordinates, which represent TSS activities in the sample. The read counts tracks indicate raw counts of CAGE reads, and the TPM tracks indicate normalized counts as TPM (tags per million).\n
\n\n\nFANTOM5 data can be explored interactively with the\nTable Browser and cross-referenced with the \nData Integrator. For programmatic access,\nthe track can be accessed using the Genome Browser's\nREST API.\nReMap annotations can be downloaded from the\nGenome Browser's download server\nas a bigBed file. This compressed binary format can be remotely queried through\ncommand line utilities. Please note that some of the download files can be quite large.
\n\n\nThe FANTOM5 reprocessed data can be found and downloaded on the FANTOM website.
\n\n\nThanks to the FANTOM5 consortium,\nthe Large Scale Data Managing Unit and Preventive Medicine and\nApplied Genomics Unit, the Center for Integrative Medical Sciences (IMS), and\nRIKEN for providing this data\nand its analysis.
\n\n\nFANTOM Consortium and the RIKEN PMI and CLST (DGT), Forrest AR, Kawaji H, Rehli M, Baillie JK, de\nHoon MJ, Haberle V, Lassmann T, Kulakovskiy IV, Lizio M et al.\n\nA promoter-level mammalian expression atlas.\nNature. 2014 Mar 27;507(7493):462-70.\nPMID: 24670764; PMC:
\nKanamori-Katayama M, Itoh M, Kawaji H, Lassmann T, Katayama S, Kojima M, Bertin N, Kaiho A, Ninomiya\nN, Daub CO et al.\n\nUnamplified cap analysis of gene expression on a single-molecule sequencer.\nGenome Res. 2011 Jul;21(7):1150-9.\nPMID: 21596820; PMC:
\nLizio M, Harshbarger J, Shimoji H, Severin J, Kasukawa T, Sahin S, Abugessaisa I, Fukuda S, Hori F,\nIshikawa-Kato S et al.\n\nGateways to the FANTOM5 promoter level mammalian expression atlas.\nGenome Biol. 2015 Jan 5;16(1):22.\nPMID: 25723102; PMC:
\nCTCF (CCCTC-binding factor) is a multifunctional DNA-binding protein involved in chromatin\norganization, transcriptional regulation, and insulation of regulatory elements. This track\ndisplays genome-wide CTCF binding signal, as determined by CTCF ChIP-seq data across all\nphases of the ENCODE project. CTCF plays a key role in establishing chromatin loops and\nboundary elements that influence gene expression and higher-order genome architecture. CTCF\nbinding sites often occur at insulators and chromatin loop anchors, which help define\ntopologically associating domains (TADs) and mediate enhancer-promoter interactions. The data\nare processed following the\nENCODE\ntranscription factor ChIP-seq pipeline. Additional transcription factor binding and\nchromatin accessibility datasets are available at the\nENCODE portal.
\n\n\nFor each organ, this track provides up to two subtracks averaging CTCF signal:
\n\nWhether one or two subtracks appear for an organ depends on which kinds of biosamples have\nbeen assayed:
\n| Organ/Tissue | \nTissue and Primary Cell Subtrack | \nAll Biosamples Subtrack | \n
|---|---|---|
| adipose | – | ✓ |
| adrenal gland | – | ✓ |
| blood | ✓ | ✓ |
| blood vessel | – | ✓ |
| bone | – | ✓ |
| bone marrow | – | ✓ |
| brain | ✓ | ✓ |
| breast | ✓ | ✓ |
| connective tissue | ✓ | ✓ |
| embryo | – | ✓ |
| epithelium | – | ✓ |
| esophagus | – | ✓ |
| eye | ✓ | ✓ |
| heart | ✓ | ✓ |
| kidney | ✓ | ✓ |
| large intestine | ✓ | ✓ |
| liver | ✓ | ✓ |
| lung | ✓ | ✓ |
| mouth | – | ✓ |
| muscle | ✓ | ✓ |
| nerve | – | ✓ |
| ovary | – | ✓ |
| pancreas | ✓ | ✓ |
| parathyroid gland | – | ✓ |
| penis | – | ✓ |
| placenta | – | ✓ |
| prostate | ✓ | ✓ |
| skin | ✓ | ✓ |
| small intestine | – | ✓ |
| spinal cord | – | ✓ |
| spleen | – | ✓ |
| stomach | – | ✓ |
| testis | – | ✓ |
| thyroid | – | ✓ |
| uterus | ✓ | ✓ |
| vagina | – | ✓ |
\nBy default, this track uses a transparent overlay to visualize data from multiple organs or tissues within\nthe same vertical space. For each organ or tissue, signals from all associated experiments were\naveraged to generate the displayed track. Each organ or tissue is assigned a distinct\ncolor following the\nENCODE color\nmapping convention,\nselected to be light and saturated to maintain clarity when overlaid. Initially, each layered\ntrack displays an overlay of five representative organs: blood, brain, kidney, liver, and\nmuscle. Clicking on the track opens a details page where you can view and select organs or\ntissues.
\n\n\n\nThe ENCODE 4 Regulation data on the UCSC Genome Browser can be explored interactively with the\nTable Browser or the\nData Integrator.\nFor automated download and analysis, the genome annotation is stored in bigWig\nfiles that can be downloaded from\nour download server.\nThe data may also be explored interactively using our\nREST API.\nThe original data files are also available from the\nENCODE portal.
\n\n\nThese files may also be locally explored using our tool bigWigToWig,\nwhich can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tool can also be used to obtain data confined to a given range, e.g.,\n
\nbigWigToWig -chrom=chr1 -start=100000 -end=100500 https://hgdownload.soe.ucsc.edu/gbdb/hg38/encode4/regulation/organAve/adiposeCTCF.bw stdout
\nData were generated by the ENCODE Consortium. We thank the production labs for generating the\ndata: Drs. Bradley Bernstein (Broad), John Stamatoyannopoulos (UW),\nMichael Snyder (Stanford), Richard Myers (HAIB), and Vishwanath Iyer (UTA). The data were\nfurther processed for visualization through a collaborative effort between the\nWeng lab and the\nMoore lab\nat UMass Chan Medical School (funded by NIH grant HG012343). Integration and visualization\nwere developed by Drs. Mingshi Gao, Jill Moore, and Zhiping Weng at UMass Chan Medical School,\nwho were part of the ENCODE Data Analysis Center.
\n\n\nENCODE Project Consortium, Moore JE, Purcaro MJ, Pratt HE, Epstein CB, Shoresh N, Adrian J,\nKawli T, Davis CA, Dobin A et al.\n\nExpanded encyclopaedias of DNA elements in the human and mouse genomes.\nNature. 2020 Jul;583(7818):699-710.\nPMID: 32728249; PMC:
\nMoore JE, Pratt HE, Fan K, Phalke N, Fisher J, Elhajjajy SI, Andrews G, Gao M, Shedd N,\nFu Y et al.\n\nAn Expanded Registry of Candidate cis-Regulatory Elements for Studying Transcriptional\nRegulation.\nNature. 2026 January 7.\nPMID: 39763870; PMC:
\nFor each organ, this track provides up to two subtracks averaging total RNA-seq signal per\nstrand:
\n\nEach subtrack provides separate signal tracks for the plus and minus genomic strands.\nWhether one or two pairs of strand subtracks appear for an organ depends on which kinds of\nbiosamples have been assayed:
\n| Organ/Tissue | \nTissue and Primary Cell Subtrack | \nAll Biosamples Subtrack | \n
|---|---|---|
| adipose | ✓ | – |
| adrenal gland | ✓ | – |
| blood | ✓ | ✓ |
| blood vessel | ✓ | – |
| brain | ✓ | ✓ |
| breast | ✓ | ✓ |
| connective tissue | ✓ | ✓ |
| embryo | ✓ | ✓ |
| epithelium | ✓ | ✓ |
| esophagus | ✓ | – |
| eye | ✓ | – |
| gallbladder | ✓ | – |
| heart | ✓ | ✓ |
| kidney | ✓ | ✓ |
| large intestine | ✓ | ✓ |
| liver | ✓ | ✓ |
| lung | ✓ | ✓ |
| mouth | ✓ | – |
| muscle | ✓ | ✓ |
| nerve | ✓ | – |
| nose | ✓ | – |
| ovary | ✓ | – |
| pancreas | ✓ | ✓ |
| placenta | ✓ | – |
| prostate | ✓ | ✓ |
| skin | ✓ | ✓ |
| small intestine | ✓ | – |
| spinal cord | ✓ | – |
| spleen | ✓ | – |
| stomach | ✓ | – |
| testis | ✓ | – |
| thyroid | ✓ | – |
| trachea | ✓ | – |
| urinary bladder | ✓ | – |
| uterus | ✓ | – |
| vagina | ✓ | – |
\nBy default, this track uses a transparent overlay to visualize data from multiple organs or tissues within\nthe same vertical space. For each organ or tissue, signals from all associated experiments were\naveraged to generate the displayed track. Each organ or tissue is assigned a distinct\ncolor following the\nENCODE color\nmapping convention,\nselected to be light and saturated to maintain clarity when overlaid. Initially, each layered\ntrack displays an overlay of five representative organs: blood, brain, kidney, liver, and\nmuscle. Clicking on the track opens a details page where you can view and select organs or\ntissues. Subtracks can be further filtered by strandedness (+ strand or - strand) and life\nstage of the biosample.
\n\n\nThe ENCODE 4 Regulation data on the UCSC Genome Browser can be explored interactively with the\nTable Browser or the\nData Integrator.\nFor automated download and analysis, the genome annotation is stored in bigWig\nfiles that can be downloaded from\nour download server.\nThe data may also be explored interactively using our\nREST API.\nThe original data files are also available from the\nENCODE portal.
\n\n\nThese files may also be locally explored using our tool bigWigToWig,\nwhich can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tool can also be used to obtain data confined to a given range, e.g.,\n
\nbigWigToWig -chrom=chr1 -start=100000 -end=100500 https://hgdownload.soe.ucsc.edu/gbdb/hg38/encode4/regulation/organAve/adiposePlus.bw stdout
\nData were generated by the ENCODE Consortium. We thank the production labs for generating the\ndata: Drs. Barbara Wold (Caltech) and Thomas Gingeras (CSHL). The data were further processed\nfor visualization through a collaborative effort between the\nWeng lab and the\nMoore lab\nat UMass Chan Medical School (funded by NIH grant HG012343). Integration and visualization\nwere developed by Drs. Mingshi Gao, Jill Moore, and Zhiping Weng at UMass Chan Medical School,\nwho were part of the ENCODE Data Analysis Center.
\n\n\nENCODE Project Consortium, Moore JE, Purcaro MJ, Pratt HE, Epstein CB, Shoresh N, Adrian J,\nKawli T, Davis CA, Dobin A et al.\n\nExpanded encyclopaedias of DNA elements in the human and mouse genomes.\nNature. 2020 Jul;583(7818):699-710.\nPMID: 32728249; PMC:
\nMoore JE, Pratt HE, Fan K, Phalke N, Fisher J, Elhajjajy SI, Andrews G, Gao M, Shedd N,\nFu Y et al.\n\nAn Expanded Registry of Candidate cis-Regulatory Elements for Studying Transcriptional\nRegulation.\nNature. 2026 January 7.\nPMID: 39763870; PMC:
\nThis track provides an integrated display of DNase hypersensitivity in multiple\ncell types using overlapping colored graphs of signal density with graph colors\nassigned to cell types based on similarity of signal. The track is based on\nresults of experiments performed by the John Stamatoyannapoulos lab at the\nUniversity of Washington from September 2007 to January 2011 as part of the\nENCODE project first production phase.
\n\nThe signal graphs displayed here are also included in the comprehensive\nDNaseI HS track,\nwhich also provides peak and region calls and uses the same coloring based on\nsimiliarity of cell types (please note there is different coloring on the ENCODE hg38\nTranscription track,\nLayered H3K4Me1 track,\nLayered H3K4Me3 track, and\nLayered H3K27Ac track,\nwhich match the coloring used in their previous versions lifted from the hg19 assembly). \n
\n\n\nRaw sequence data files were processed by the UCSC ENCODE DNase analysis pipeline\ndescribed in the \nDNaseI HS\ntrack description.\nSignal graphs were normalized so the average value genome-wide is 1.\nColors for the signal graphs were assigned by the UCSC BigWigCluster tool.\n\n
\nThe cell types were clustered into a binary tree, a rainbow was cast to the leaf nodes providing coloring based on similarity. \n
\n![\"ENCODE](\"/images/encode95CellsDendrogram.png\")
\nCredit: Chris Eisenhart, J. Kent lab \n\nThe processed data for this track were generated at UCSC.\nCredits for the primary data underlying this track are included in the\nDNaseI HS\ntrack description.\n
\n\n\nMiga KH, Eisenhart C, Kent WJ.\n\nUtilizing mapping targets of sequences underrepresented in the reference assembly to reduce false\npositive alignments.\nNucleic Acids Res. 2015 Nov 16;43(20):e133.\nPMID: 26163063\n
\n\nThurman RE, Rynes E, Humbert R, Vierstra J, Maurano MT, Haugen E, Sheffield NC, Stergachis AB, Wang\nH, Vernot B et al.\n\nThe accessible chromatin landscape of the human genome.\nNature. 2012 Sep 6;489(7414):75-82.\nPMID: 22955617; PMC:
\nSee also the references in the\nDNaseI HS\ntrack.\n
\n", "longLabel": "DNase I Hypersensitivity Signal Colored by Similarity from ENCODE", "maxHeightPixels": "128:64:11", "priority": "1.8", "shortLabel": "DNase Signal", "showSubtrackColorOnUi": "on", "sortOrder": "subtrackColor=+ cellType=+ tissue=+", "subGroup1": "cellType Cell_Type A549=A549 AG04449=AG04449 AG04450=AG04450 AG09309=AG09309 AG09319=AG09319 AG10803=AG10803 AoAF=AoAF bone_marrow_MSC=bone_marrow_MSC BE2_C=BE2_C BJ=BJ CD20_RO01778=CD20+_RO01778 Caco-2=Caco-2 GM04503=GM04503 GM04504=GM04504 GM06990=GM06990 GM12865=GM12865 GM12878=GM12878 H7-hESC=H7-hESC HA-h=HA-h HA-sp=HA-sp HAEpiC=HAEpiC HAc=HAc HBMEC=HBMEC HBVSMC=HBVSMC HCF=HCF HCFaa=HCFaa HCM=HCM HCPEpiC=HCPEpiC HCT-116=HCT-116 HConF=HConF HEEpiC=HEEpiC HFF-Myc=HFF-Myc HFF=HFF HGF=HGF HIPEpiC=HIPEpiC HL-60=HL-60 HMEC=HMEC HMF=HMF HMVEC-LBl=HMVEC-LBl HMVEC-LLy=HMVEC-LLy HMVEC-dAd=HMVEC-dAd HMVEC-dBl-Ad=HMVEC-dBl-Ad HMVEC-dBl-Neo=HMVEC-dBl-Neo HMVEC-dLy-Ad=HMVEC-dLy-Ad HMVEC-dLy-Neo=HMVEC-dLy-Neo HMVEC-dNeo=HMVEC-dNeo HNPCEpiC=HNPCEpiC HPAF=HPAF HPF=HPF HPdLF=HPdLF HRCEpiC=HRCEpiC HRE=HRE HRGEC=HRGEC HRPEpiC=HRPEpiC HSMM=HSMM HSMMtube=HSMMtube HUVEC=HUVEC HVMF=HVMF HeLa-S3=HeLa-S3 HepG2=HepG2 Jurkat=Jurkat K562=K562 LHCN-M2=LHCN-M2 LNCaP=LNCaP M059J=M059J MCF-7=MCF-7 Monocytes_CD14_RO01746=Monocytes-CD14+_RO01746 NB4=NB4 NH-A=NH-A NHBE_RA=NHBE_RA NHDF-Ad=NHDF-Ad NHDF-neo=NHDF-neo NHEK=NHEK NHLF=NHLF NT2-D1=NT2-D1 PANC-1=PANC-1 PrEC=PrEC RPMI-7951=RPMI-7951 RPTEC=RPTEC SAEC=SAEC SK-N-MC=SK-N-MC SK-N-SH_RA=SK-N-SH_RA SKMC=SKMC T-47D=T-47D Th1=Th1 Th1_Wb54553204=Th1_Wb54553204 Th2=Th2 WERI-Rb-1=WERI-Rb-1 WI-38=WI-38", "subGroup2": "treatment Treatment diffProtA_5d=diffProtA_5d diffProtA_14d=diffProtA_14d DIFF_4d=DIFF_4d n_a=n/a 4OHTAM_20nM_72hr=4OHTAM_20nM_72hr Estradiol_ctrl_0hr=Estradiol_ctrl_0hr Estradiol_100nM_1hr=Estradiol_100nM_1hr", "subGroup3": "tissue Tissue blood=blood blood_vessel=blood_vessel bone_marrow=bone_marrow brain=brain breast=breast cervix=cervix colon=colon embryo=embryo esophagus=esophagus eye=eye heart=heart kidney=kidney liver=liver lung=lung muscle=muscle pancreas=pancreas periodontium=periodontium periodontium=periodontium placenta=placenta prostate=prostate skin=skin spinal_cord=spinal_cord testis=testis", "subGroup4": "cancer Cancer cancer=cancer normal=normal unknown=unknown", "subGroup5": "subtrackColor Similarity", "superTrack": "wgEncodeReg hide", "track": "wgEncodeRegDnaseWig", "type": "bigWig 0 10000", "viewLimits": "0:200", "visibility": "hide" } }, "displays": [ { "type": "MultiLinearWiggleDisplay", "defaultRendering": "multixyplot" } ], "adapter": { "type": "MultiWiggleAdapter", "subadapters": [ { "type": "BigWigAdapter", "name": "K562 Sg", "color": "rgb(255,85,85)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeRegDnase/wgEncodeRegDnaseUwK562Signal.bw" } }, { "type": "BigWigAdapter", "name": "A549 Sg", "color": "rgb(254,93,85)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeRegDnase/wgEncodeRegDnaseUwA549Signal.bw" } }, { "type": "BigWigAdapter", "name": "LNCaP Sg", "color": "rgb(255,102,85)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeRegDnase/wgEncodeRegDnaseUwLncapSignal.bw" } }, { "type": "BigWigAdapter", "name": "HMEC Sg", "color": "rgb(255,112,85)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeRegDnase/wgEncodeRegDnaseUwHmecSignal.bw" } }, { "type": "BigWigAdapter", "name": "T-47D Sg", "color": "rgb(255,124,85)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeRegDnase/wgEncodeRegDnaseUwT47dSignal.bw" } }, { "type": "BigWigAdapter", "name": "PANC-1 Sg", "color": "rgb(255,141,85)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeRegDnase/wgEncodeRegDnaseUwPanc1Signal.bw" } }, { "type": "BigWigAdapter", "name": "HCT-116 Sg", "color": "rgb(255,150,85)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeRegDnase/wgEncodeRegDnaseUwHct116Signal.bw" } }, { "type": "BigWigAdapter", "name": "NHBE_RA Sg", "color": "rgb(255,154,85)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeRegDnase/wgEncodeRegDnaseUwNhberaSignal.bw" } }, { "type": "BigWigAdapter", "name": "HFF Sg", "color": "rgb(255,163,85)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeRegDnase/wgEncodeRegDnaseUwHffSignal.bw" } }, { "type": "BigWigAdapter", "name": "HFF-Myc Sg", "color": "rgb(255,165,85)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeRegDnase/wgEncodeRegDnaseUwHffmycSignal.bw" } }, { "type": "BigWigAdapter", "name": "NT2-D1 Sg", "color": "rgb(255,173,85)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeRegDnase/wgEncodeRegDnaseUwNt2d1Signal.bw" } }, { "type": "BigWigAdapter", "name": "BJ Sg", "color": "rgb(255,184,85)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeRegDnase/wgEncodeRegDnaseUwBjSignal.bw" } }, { "type": "BigWigAdapter", "name": "AG09309 Sg", "color": "rgb(255,186,85)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeRegDnase/wgEncodeRegDnaseUwAg09309Signal.bw" } }, { "type": "BigWigAdapter", "name": "HNPCEpiC Sg", "color": "rgb(255,188,85)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeRegDnase/wgEncodeRegDnaseUwHnpcepicSignal.bw" } }, { "type": "BigWigAdapter", "name": "WI-38 Sg", "color": "rgb(255,192,85)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeRegDnase/wgEncodeRegDnaseUwWi38Signal.bw" } }, { "type": "BigWigAdapter", "name": "GM04503 Sg", "color": "rgb(255,200,85)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeRegDnase/wgEncodeRegDnaseUwGm04503Signal.bw" } }, { "type": "BigWigAdapter", "name": "GM04504 Sg", "color": "rgb(255,204,85)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeRegDnase/wgEncodeRegDnaseUwGm04504Signal.bw" } }, { "type": "BigWigAdapter", "name": "NHLF Sg", "color": "rgb(255,209,85)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeRegDnase/wgEncodeRegDnaseUwNhlfSignal.bw" } }, { "type": "BigWigAdapter", "name": "NH-A Sg", "color": "rgb(255,210,85)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeRegDnase/wgEncodeRegDnaseUwNhaSignal.bw" } }, { "type": "BigWigAdapter", "name": "HBMEC Sg", "color": "rgb(255,214,85)", "bigWigLocation": { "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/wgEncodeRegDnase/wgEncodeRegDnaseUwHbmecSignal.bw" } }, { "type": "BigWigAdapter", "name": "AG09319 Sg", "color": "rgb(255,221,85)", "bigWigLocation": { "uri": 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"/gbdb/hg38/gnomAD/coverage/v4-exome/gnomad.coverage.median.bw", "color": "0,0,255", "longLabel": "gnomAD Median Exome Sample Coverage v4.0", "parent": "gnomad4ExomeCoverage off", "priority": "0.2", "shortLabel": "Median Coverage", "track": "gnomad4ExomeMedianCoverage", "html": "" } }, "description": "gnomAD Median Exome Sample Coverage v4.0", "category": [ "Variation and Repeats" ] }, { "trackId": "hg38-phyloP447wayBW", "name": "Basewise Conservation (phyloP) - 447 phyloP REV", "type": "QuantitativeTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigWigAdapter", "uri": "https://hgdownload.soe.ucsc.edu/goldenPath/hg38/phyloP447way/hg38.phyloP447way.bw" }, "metadata": { "ucsc": { "altColor": "140,60,60", "autoScale": "off", "bigDataUrl": "https://hgdownload.soe.ucsc.edu/goldenPath/hg38/phyloP447way/hg38.phyloP447way.bw", "color": "60,60,140", "configurable": "on", "logo": "on", "longLabel": "447 mammals / 233 primates Basewise Conservation by PhyloP phyloFit REV model", "maxHeightPixels": "100:50:11", "noInherit": "on", "parent": "cons447wayViewphyloP", "priority": "1", "shortLabel": "447 phyloP REV", "spanList": "1", "subGroups": "view=phyloP", "track": "phyloP447wayBW", "type": "bigWig -20 11.936", "viewLimits": "-4.5:7.5", "windowingFunction": "mean", "html": "" } }, "description": "447 mammals / 233 primates Basewise Conservation by PhyloP phyloFit REV model", "category": [ "Comparative Genomics" ] }, { "trackId": "hg38-alldifficultregions", "name": "GIAB Problematic Regions - All difficult regions", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/problematic/GIAB/alldifficultregions.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/problematic/GIAB/alldifficultregions.bb", "longLabel": "Genome In a Bottle: all difficult regions", "parent": "problematicGIAB on", "shortLabel": "All difficult regions", "track": "alldifficultregions", "type": "bigBed 3", "visibility": "dense", "html": "" } }, "description": "Genome In a Bottle: all difficult regions", "category": [ "Mapping and Sequencing" ] }, { "trackId": "hg38-bismap24Pos", "name": "Single-read mappability - Bismap S24 +", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k24.C2T-Converted.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/hoffmanMappability/k24.C2T-Converted.bb", "color": "240,20,80", "longLabel": "Single-read mappability with 24-mers after bisulfite conversion (forward strand)", "parent": "bismapBigBed on", "priority": "1", "shortLabel": "Bismap S24 +", "subGroups": "view=SR", "track": "bismap24Pos", "visibility": "dense", "html": "" } }, "description": "Single-read mappability with 24-mers after bisulfite conversion (forward strand)", "category": [ "Mapping and Sequencing" ] }, { "trackId": "hg38-clinGenHaplo", "name": "ClinGen - ClinGen Haploinsufficiency", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/clinGen/clinGenHaplo.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/bbi/clinGen/clinGenHaplo.bb", "dataVersion": "/gbdb/$D/bbi/clinGen/clinGenDosageVersion.txt", "filterLabel.haploScore": "Dosage Sensitivity Score", "filterValues.haploScore": "0|No evidence available,1|Little evidence for dosage pathogenicity,2|Some evidence for dosage pathogenicity,3|Sufficient evidence for dosage pathogenicity,30|Gene associated with autosomal recessive phenotype,40|Dosage sensitivity unlikely", "longLabel": "ClinGen Dosage Sensitivity Map - Haploinsufficiency", "mouseOver": "Gene/ISCA ID: $name\nThis track shows structural variants (SVs) from the \nConsortium of Long-Read Sequencing database (CoLoRSdb).\nThe sequencing data was contributed by labs and research groups around the world and covers 1,427 individuals in total, all sequenced with PacBio HiFi.\nThe track contains 426,239 SVs: 232,973 insertions,\n192,534 deletions and 732 inversions, with per-site allele frequencies,\ngenotype counts and Hardy-Weinberg statistics across the cohort.\n
\n\nNote that CoLoRSdb also published short variants, in the Genome Browser,\nthese can be found in the Variants Frequencies track.\n
\n\n\nItems are colored by SV type:\n
\nInsertions are placed at the insertion site; deletions and inversions span\nthe affected reference interval. Filters are available for SV type, SV\nlength and alternate allele count. Mousing over an item shows the SV type,\nlength, allele frequency, allele counts (homozygous / heterozygous /\nhemizygous) and the number of carrier samples.\n
\n\nThe detail page additionally shows the total allele number (AN), the\nHardy-Weinberg equilibrium p-value (HWE), the excess-heterozygosity p-value\n(ExcHet) and the REF / ALT allele sequences.\n
\n\n\nSVs were called on each sample's long-read alignments with\npbsv\nand then merged across the CoLoRSdb cohort with\nJasmine\nto produce a site-level joint callset. Per-site allele counts, allele\nfrequencies, HWE and ExcHet p-values were computed from the joint VCF. The\nVCF was converted to a bigBed for display in the Genome Browser.\n
\n\nThe step-by-step build commands are recorded in the UCSC makeDoc,\ndoc/hg38/lrSv.txt;\nthe conversion scripts and autoSql schemas live in\nmakeDb/scripts/lrSv,\nand the track configuration is in\ntrackDb/human/lrSv.ra.\n
\n\n\nThe data can be explored interactively in table format with the\nTable Browser or the\nData Integrator, and accessed\nprogrammatically through our API,\ntrack=colorsDbSv.\n
\n\nThe bigBed is available from\nour\ndownload server as sv.hg38.bb. Example:\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/lrSv/colorsDb/sv.hg38.bb -chrom=chr21 -start=0 -end=100000000 stdout.\n
\n\nThe original VCF files and full release documentation are available from\nthe CoLoRSdb v1.2.0 dataset on Zenodo:\nzenodo.org/records/14814308.\n
\n\n\nThanks to Mike Schatz, Evan Eichler, and all\nCoLoRSdb investigators\nfor generating and making the data publicly available.\n
\n\n\nLake, J. A., & Consortium of Long-Read Sequencing (CoLoRS).\nConsortium of Long-Read\nSequencing Database (CoLoRSdb) (v1.2.0) [Data set].\nZenodo. 2025 Feb 5.\nDOI: 10.5281/zenodo.14814308\n
\n\n\nKirsche M, Prabhu G, Sherman R, Ni B, Battle A, Aganezov S, Schatz MC.\n\nJasmine and Iris: population-scale structural variant comparison and analysis.\nNat Methods. 2023 Mar;20(3):408-417.\nPMID: 36658279;\nPMC: PMC10006329\n
\n\n\nEisfeldt J, Ameur A, Lenner F, Ten Berk de Boer E, Ek M, Wincent J, Vaz R, Ottosson J, Jonson T,\nIvarsson S et al.\n\nA national long-read sequencing study on chromosomal rearrangements uncovers hidden complexities.\nGenome Res. 2024 Nov 20;34(11):1774-1784.\nPMID: 39472022;\nPMC: PMC11610602\n
\n" } }, "description": "Structural Variants from 1,427 CoLoRSdb samples (Consortium of Long-Read Sequencing, PacBio HiFi)", "category": [ "Variation and Repeats" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-colorsDbSv-LinearBasicDisplay", "mouseover": "jexl:`Var: ${get(feature,'name')} (${get(feature,'svType')})\nThis track displays the ENCODE Registry of candidate cis-Regulatory Elements (cCREs)\nin the human genome from ENCODE 4. A total of 2,348,854 elements were identified and classified by the\nENCODE Data Analysis Center according to biochemical signatures. Most cCREs are anchored on\nDNase hypersensitive sites further annotated with histone modifications (H3K4me3 and H3K27ac)\nor CTCF binding measured by ChIP-seq experiments. In this latest version of the Registry (V4),\nthe representative DNase hypersensitive sites (rDHSs) were supplemented\nwith 86,748 representative transcription factor ChIP-seq peaks (TF\nrPeaks), which represent binding sites for at least five TFs. The Registry of cCREs is\none of the core components of the integrative level of the ENCODE Encyclopedia of DNA Elements.
\n\nAdditional exploration of the cCREs and underlying raw ENCODE signal data can be done with the\nCore Collection track. The data is also available on the
\nEach cCRE is color-coded by its classification type, which reflects its putative functional\nassignment based on biochemical signatures and genomic context:
\n\nMousing over an item displays the element ID with a linkout to SCREEN, the cCRE class,\nand the max-Z scores for DNase, H3K4me3, H3K27ac, and CTCF. A max-Z score above 1.64 is\nconsidered "high" signal, while a score of 1.64 or below is considered "low" signal\nfor classification purposes (Moore et al., 2026). A score of -10.00 indicates the assay was not\navailable in any surveyed biosample.\nA track filter is also available\nto selectively show items based on their cCRE class type.
\n\n\nCandidate cis-regulatory elements (cCREs) were first anchored on nucleosome-sized DNase\nhypersensitive sites (rDHSs) identified from DNase-seq data. These rDHSs were then annotated\nusing ChIP-seq data for histone modifications (H3K4me3 and H3K27ac, marking promoters and\nenhancers, respectively) and CTCF, marking insulators. To supplement rDHS-anchored cCRE\ndefinitions, transcription factor ChIP-seq peaks were incorporated, enabling identification\nof cCREs even in regions of low chromatin accessibility. Although not used for anchoring,\nATAC-seq data were used to assess chromatin accessibility in biosamples lacking DNase-seq.\nBecause ATAC-seq was not standardized across all biosamples, the ATAC max-Z score is not included\nin the registry bigBed fields or mouseOver. Only the four core assays (DNase, H3K4me3, H3K27ac,\nand CTCF) are reported as max-Z scores. ATAC-seq signal tracks are available in the\nENCODE4 Regulation track.
\n\n\nThe following diagram illustrates the biochemical signal patterns used to classify each cCRE type:
\n\n![\"Graphic](\"https://genome.ucsc.edu/images/encode4cCREs.png\")
\nThe ENCODE accession numbers of the constituent datasets at the ENCODE Portal are available from the cCRE details page.
\n\nThe data in this track can be interactively explored with the Table Browser or the Data Integrator. The data can be accessed from\nscripts through our API,\nthe track name is \"cCREregistry\".
\n\nFor automated download and analysis, this annotation is stored in a bigBed file\nthat can be downloaded from our download server.\nThe file for this track is called cCREregistry.bb. Individual regions or the whole genome\nannotation can be obtained using our tool bigBedToBed which can be compiled from the source\ncode or downloaded as a precompiled binary for your system. Instructions for downloading\nsource code and binaries can be found here.\nThe tool can also be used to obtain only features within a given range, e.g.
\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/encode4/ccre/cCREregistry.bb -chrom=chr21 -start=0 -end=100000000 stdout
\nData were generated by the ENCODE Consortium. The data were further processed for \nvisualization through a collaborative effort between the Weng lab and the Moore lab at UMass Chan Medical \nSchool (funded by NIH grant HG012343). Integration and visualization were developed \nby Drs. Mingshi Gao, Jill Moore, and Zhiping Weng at UMass Chan Medical School, who were \npart of the ENCODE Data Analysis Center. We thank the ENCODE production labs \nfor generating the data.
\n\n\nENCODE Project Consortium, Moore JE, Purcaro MJ, Pratt HE, Epstein CB, Shoresh N, Adrian J, Kawli T,\nDavis CA, Dobin A et al.\n\nExpanded encyclopaedias of DNA elements in the human and mouse genomes.\nNature. 2020 Jul;583(7818):699-710.\nPMID: 32728249; PMC:
\nMoore JE, Pratt HE, Fan K, Phalke N, Fisher J, Elhajjajy SI, Andrews G, Gao M, Shedd N, Fu Y et\nal.\n\nAn Expanded Registry of Candidate cis-Regulatory Elements for Studying Transcriptional\nRegulation.\nNature. 2026 January 7.\nPMID: 39763870; PMC:
\nThese tracks represent the experimentally validated promoters generated by \nthe Eukaryotic Promoter Database.\n
\n\n\nEach item in the track is a representation of the promoter sequence identified by EPD. The\n"thin" part of the element represents the 49 bp upstream of the annotated transcription\nstart site (TSS) whereas the "thick" part represents the TSS plus 10 bp downstream. The\nrelative position of the thick and thin parts define the orientation of the promoter.
\n\nNote that the EPD team has created a public track hub containing\npromoter and supporting annotations for human, mouse, and other vertebrate and model organism\ngenomes.
\n\n\nBriefly, gene transcript coordinates were obtained from multiple sources (HGNC, GENCODE, Ensembl,\nRefSeq) and validated using data from CAGE and RAMPAGE experimental studies obtained from FANTOM 5,\nUCSC, and ENCODE. Peak calling, clustering and filtering based on relative expression were applied\nto identify the most expressed promoters and those present in the largest number of samples.
\n\nFor the methodology and principles used by EPD to predict TSSs, refer to Dreos et al.\n(2013) in the References section below. A more detailed description of how this data was\ngenerated can be found at the following links:\n\n
\nData was generated by the EPD team at the \nSwiss Institute of Bioinformatics. \nFor inquiries, contact the EPD team using this on-line form \nor email \n\nphilipp.\nbucher@epfl.\nch\n\n.\n
\n\n\nDreos R, Ambrosini G, Perier RC, Bucher P.\n\nEPD and EPDnew, high-quality promoter resources in the\nnext-generation sequencing era. Nucleic Acids\nRes. 2013 Jan 1;41(D1):D157-64. PMID:
\nThe GENCODE Genes track (version 50, June 2026) shows high-quality manual\nannotations merged with evidence-based automated annotations across the entire\nhuman genome generated by the\nGENCODE project.\nBy default, only the basic gene set is\ndisplayed, which is a subset of the comprehensive gene set. The basic set represents transcripts\nthat GENCODE believes will be useful to the majority of users.
\n\n\nThe track includes protein-coding genes, non-coding RNA genes, and pseudo-genes, though pseudo-genes\nare not displayed by default. It contains annotations on the reference chromosomes as well as\nassembly patches and alternative loci (haplotypes).
\n\n\nThe v50 release was derived from the GTF file that contains annotations only on the main\nchromosomes. Statistics for this build and information on how they were generated can be found on\nthe GENCODE site.
\n\n\nFor more information on the different gene tracks, see our Genes FAQ.
\n\n\nBy default, this track displays only the basic GENCODE set, splice variants, and non-coding genes.\nIt includes options to display the entire GENCODE set and pseudogenes. To customize these\noptions, the respective boxes can be checked or unchecked at the top of this description page. \n\n
\nThis track also includes a variety of labels which identify the transcripts when visibility is set\nto "full" or "pack". Gene symbols (e.g. NIPA1) are displayed by default, but\nadditional options include GENCODE Transcript ID (ENST00000561183.5), UCSC Known Gene ID\n(uc001yve.4), UniProt Display ID (Q7RTP0). Additional information about gene\nand transcript names can be found in our\nFAQ.
\n\n\nThis track, in general, follows the display conventions for gene prediction tracks. The exons for\nputative non-coding genes and untranslated regions are represented by relatively thin blocks, while\nthose for coding open reading frames are thicker. \n
Coloring for the gene annotations is mostly based on the annotation type:
\n\nThis track contains an optional codon coloring feature that allows users to\nquickly validate and compare gene predictions. There is also an option to display the data as\na density graph, which\ncan be helpful for visualizing the distribution of items over a region.
\n\n\n\nWithin a gene using the pack display mode, transcripts below a specified rank will be\ncondensed into a view similar to squish mode. The transcript ranking approach is\npreliminary and will change in future releases. The transcripts rankings are defined by the\nfollowing criteria for protein-coding and non-coding genes:
\nProtein_coding genes\n\nThe GENCODE v50 track was built from the GENCODE downloads file \ngencode.v50.chr_patch_hapl_scaff.annotation.gff3.gz. Data from other sources\nwere correlated with the GENCODE data to build association tables.
\nThe GENCODE Genes transcripts are annotated in numerous tables, each of which is also available as a\ndownloadable\nfile.\n\n
\nOne can see a full list of the associated tables in the Table Browser by selecting GENCODE Genes from the track menu; this list\nis then available on the table menu.\n\n\n
\nGENCODE Genes and its associated tables can be explored interactively using the\nREST API, the\nTable Browser or the\nData Integrator. \nThe genePred format files for hg38 are available from our \n\ndownloads directory or in our\n\nGTF download directory. \nAll the tables can also be queried directly from our public MySQL\nservers, with more information available on our\nhelp page as well as on\nour blog.
\n\n\nThe GENCODE Genes track was produced at UCSC from the GENCODE comprehensive gene set using a\ncomputational pipeline developed by Jim Kent and Brian Raney. This version of the track was\ngenerated by Jonathan Casper.
\n\n\nMudge JM, Carbonell-Sala S, Diekhans M, Martinez JG, Hunt T, Jungreis I, Loveland JE, Arnan C,\nBarnes I, Bennett R et al.\n\nGENCODE 2025: reference gene annotation for human and mouse.\nNucleic Acids Res. 2025 Jan 6;53(D1):D966-D975.\nPMID: 39565199; PMC:
A full list of GENCODE publications is available\nat The GENCODE\nProject web site.\n
\n\nGENCODE data are available for use without restrictions.
\n", "idXref": "kgAlias kgID alias", "intronGap": "12", "isGencode3": "on", "itemRgb": "on", "labelFields": "geneName,name,geneName2,name2", "longLabel": "GENCODE V50", "maxItems": "50000", "priority": "1", "searchIndex": "name", "shortLabel": "GENCODE V50", "squishyPackField": "rank", "squishyPackLabel": "Number of transcripts shown at full height (ranked by GENCODE transcript ranking)", "squishyPackPoint": "1", "table": "knownGene", "track": "knownGene", "type": "bigGenePred knownGenePep knownGeneMrna", "visibility": "pack" } }, "description": "GENCODE V50", "category": [ "Genes and Gene Predictions" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-knownGene-LinearBasicDisplay", "labels": { "name": "jexl:get(feature,'name')" } } ] }, { "trackId": "hg38-pliByGene", "name": "Constraint V2 - Gene LoF", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/gnomAD/pLI/pliByGene.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/gnomAD/pLI/pliByGene.bb", "defaultLabelFields": "geneName", "filter._pli": "0:1", "filterByRange._pli": "on", "filterLabel._pli": "Show only items between this pLI range", "itemRgb": "on", "labelFields": "name,geneName", "longLabel": "gnomAD Predicted Loss of Function Constraint Metrics By Gene (LOEUF and pLI) v2.1.1", "mouseOver": "LOEUF: $_loeuf\nGnomAD 4 used the whole-genome data from gnomAD 3 and added more exomes.\nThe v4.1 release included a fix for the allele number\nissue.\nThe current v4.1.1 release, from March 30, 2026, revises the LOFTEE END_TRUNC GERP distance\nthreshold from -58.0 to 0.0. This reclassifies about 79,920 predicted loss-of-function (pLoF)\nvariants from high-confidence to low-confidence. For more information, see the related
\nThe track shows variants from 807,162 individuals, including 730,947\nexomes and 76,215 genomes. This includes the 76,156 genomes from the gnomAD v3.1.2 release as well\nas exome data from 416,555 UK Biobank individuals.\n
\n\n\nFollowing the conventions on the gnomAD browser, items are shaded according to their Annotation\ntype:\n
| pLoF | |
| Missense | |
| Synonymous | |
| Other |
\nMouse hover on an item will display the following details about each variant:
\n\nClicking on an item will display additional details on the variant, including a population frequency\ntable showing allele count in each sub-population.\n
\n\n\nTo maintain consistency with the gnomAD website, variants are by default labeled according\nto their chromosomal start position followed by the reference and alternate alleles,\nfor example "chr1-1234-T-CAG". dbSNP rsID's are also available as an additional\nlabel, if the variant is present in dbSnp.\n
\n\n\nThree filters are available for this track:\n
\n\nThe gnomAD v4.1.1 data is unfiltered.
\n\n\nFor the full steps used to create the gnomAD tracks at UCSC, please see the\n
\nThe raw data can be explored interactively with the \nTable Browser, or the Data Integrator. For\nautomated analysis, the data may be queried from our REST API, and the genome annotations are stored in files that\ncan be downloaded from our
\nThe underlying bigBed only contains enough information necessary to use the track in the browser.\nThe extra data like VEP annotations and CADD scores are available in the\nsame directory\nas the bigBed but in the files details.tab.gz and details.tab.gz.gzi. The\ndetails.tab.gz contains the gzip compressed extra data in JSON format, and the .gzi file is\navailable to speed searching of this data. Each variant has an associated md5sum in the name field\nof the bigBed which can be used along with the _dataOffset and _dataLen fields to get the\nassociated external data. For example:
\n\n\n# find an item of interest, the last two fields are _dataOffset and _dataLen:\nbigBedToBed genomes.bb stdout | head -4 | tail -1\nchr1 12416 12417 854246d79dc5d02dcdbd5f5438542b6e [..omitted..] 67293 902\n\n# use _dataOffset and _dataLen (add one to _dataLen for the newline character):\nbgzip -b 67293 -s 903 gnomad.v4.1.1.genomes.details.tab.gz\n854246d79dc5d02dcdbd5f5438542b6e {\"DDX11L1\": {\"cons\": [\"non_coding_transcript_variant\"...\n\n\n\nThe data can also be found directly from the gnomAD downloads page. Please refer to\nour mailing list archives for questions, or our Data Access FAQ for more information.
\n\n\nThanks to the Genome Aggregation\nDatabase Consortium for making these data available. The data are released under the
\nPlease note that some annotations within the provided files may have restrictions on usage. See here for more information.\n
\n\n\nChen S, Francioli LC, Goodrich JK, Collins RL, Kanai M, Wang Q, Alföldi J, Watts NA, Vittal C,\nGauthier LD et al.\n\n A genomic mutational constraint map using variation in 76,156 human genomes.\nNature. 2024 Jan;625(7993):92-100.\nPMID: 38057664\n
\n\nKarczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, Collins RL, Laricchia KM, Ganna\nA, Birnbaum DP et al.\n\nThe mutational constraint spectrum quantified from variation in 141,456 humans.\nNature. 2020 May;581(7809):434-443.\nPMID: 32461654; PMC:
\nLek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill\nAJ, Cummings BB et al.\nAnalysis of protein-coding\ngenetic variation in 60,706 humans. Nature. 2016 Aug 17;536(7616):285-91.\nPMID: 27535533;\nPMC: PMC5018207\n
\n", "itemRgb": "on", "labelFields": "rsId,_displayName", "longLabel": "Genome Aggregation Database (gnomAD) Genome Variants v4.1", "mouseOver": "Position: $chrom:${chromStart}-${chromEnd} ($ref/$alt)\nThe \"Constraint scores\" container track includes several subtracks showing the results of\nconstraint prediction algorithms. These try to find regions of negative\nselection, where variations likely have functional impact. The algorithms do\nnot use multi-species alignments to derive evolutionary constraint, but use\nprimarily human variation, usually from variants collected by gnomAD (see the\ngnomAD V2 or V3 tracks on hg19 and hg38) or TOPMED (contained in our dbSNP\ntracks and available as a filter). One of the subtracks is based on UK Biobank\nvariants, which are not available publicly, so we have no track with the raw data.\nThe number of human genomes that are used as the input for these scores are\n76k, 53k and 110k for gnomAD, TOPMED and UK Biobank, respectively.\n
\n\nNote that another important constraint score, gnomAD\nconstraint, is not part of this container track but can be found in the hg38 gnomAD\ntrack.\n
\n\nThe algorithms included in this track are:\n\nJARVIS scores are shown as a signal (\"wiggle\") track, with one score per genome position.\nMousing over the bars displays the exact values. The scores were downloaded and converted to a single bigWig file.\nMove the mouse over the bars to display the exact values. A horizontal line is shown at the 0.733\nvalue which signifies the 90th percentile.
\nSee hg19 makeDoc and\nhg38 makeDoc.\n\nInterpretation: The authors offer a suggested guideline of > 0.9998 for identifying\nhigher confidence calls and minimizing false positives. In addition to that strict threshold, the \nfollowing two more relaxed cutoffs can be used to explore additional hits. Note that these\nthresholds are offered as guidelines and are not necessarily representative of pathogenicity.
\n\n\n
| Percentile | JARVIS score threshold |
|---|---|
| 99th | 0.9998 |
| 95th | 0.9826 |
| 90th | 0.7338 |
\nHMC scores are displayed as a signal (\"wiggle\") track, with one score per genome position.\nMousing over the bars displays the exact values. The highly-constrained cutoff\nof 0.8 is indicated with a line.
\n\nInterpretation: \nA protein residue with HMC score <1 indicates that missense variants affecting\nthe homologous residues are significantly under negative selection (P-value <\n0.05) and likely to be deleterious. A more stringent score threshold of HMC<0.8\nis recommended to prioritize predicted disease-associated variants.\n
\n\n\nInterpretation: The authors suggest the following guidelines for evaluating\nintolerance. By default, the MetaDome track displays a horizontal line at 0.7 which \nsignifies the first intolerant bin. For more information see the MetaDome publication.
\n\n\n
| Classification | MetaDome Tolerance Score |
|---|---|
| Highly intolerant | ≤ 0.175 |
| Intolerant | ≤ 0.525 |
| Slightly intolerant | ≤ 0.7 |
\nMTR data can be found on two tracks, MTR All data and MTR Scores. In the\nMTR Scores track the data has been converted into 4 separate signal tracks\nrepresenting each base pair mutation, with the lowest possible score shown when\nmultiple transcripts overlap at a position. Overlaps can happen since this score\nis derived from transcripts and multiple transcripts can overlap. \nA horizontal line is drawn on the 0.8 score line\nto roughly represent the 25th percentile, meaning the items below may be of particular\ninterest. It is recommended that the data be explored using\nthis version of the track, as it condenses the information substantially while\nretaining the magnitude of the data.
\n\nAny specific point mutations of interest can then be researched in the \nMTR All data track. This track contains all of the information from\n\nMTRV2 including more than 3 possible scores per base when transcripts overlap.\nA mouse-over on this track shows the ref and alt allele, as well as the MTR score\nand the MTR score percentile. Filters are available for MTR score, False Discovery Rate\n(FDR), MTR percentile, and variant consequence. By default, only items in the bottom\n25 percentile are shown. Items in the track are colored according\nto their MTR percentile:
\n\nInterpretation: Regions with low MTR scores were seen to be enriched with\npathogenic variants. For example, ClinVar pathogenic variants were seen to\nhave an average score of 0.77 whereas ClinVar benign variants had an average score\nof 0.92. Further validation using the FATHMM cancer-associated training dataset saw\nthat scores less than 0.5 contained 8.6% of the pathogenic variants while only containing\n0.9% of neutral variants. In summary, lower scores are more likely to represent\npathogenic variants whereas higher scores could be pathogenic, but have a higher chance\nto be a false positive. For more information see the MTR-Viewer publication.
\n\n\nScores were downloaded and converted to a single bigWig file. See the\nhg19 makeDoc and the\nhg38 makeDoc for more info.\n
\n\n\nScores were downloaded and converted to .bedGraph files with a custom Python \nscript. The bedGraph files were then converted to bigWig files, as documented in our \nmakeDoc hg19 build log.
\n\n\nThe authors provided a bed file containing codon coordinates along with the scores. \nThis file was parsed with a python script to create the two tracks. For the first track\nthe scores were aggregated for each coordinate, then the lowest score chosen for any\noverlaps and the result written out to bedGraph format. The file was then converted\nto bigWig with the bedGraphToBigWig utility. For the second track the file\nwas reorganized into a bed 4+3 and conveted to bigBed with the bedToBigBed\nutility.
\nSee the hg19 makeDoc for details including the build script.
\n\nThe raw MetaDome data can also be accessed via their Zenodo handle.
\n\n\nV2\nfile was downloaded and columns were reshuffled as well as itemRgb added for the\nMTR All data track. For the MTR Scores track the file was parsed with a python\nscript to pull out the highest possible MTR score for each of the 3 possible mutations\nat each base pair and 4 tracks built out of these values representing each mutation.
\n\nSee the hg19 makeDoc entry on MTR for more info.
\n\n\nThe raw data can be explored interactively with the Table Browser, or\nthe Data Integrator. For automated access, this track, like all\nothers, is available via our API. However, for bulk\nprocessing, it is recommended to download the dataset.\n
\n\n\nFor automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed\nfiles that can be downloaded from\nour download server.\nIndividual regions or the whole genome annotation can be obtained using our tools bigWigToWig\nor bigBedToBed which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tools can also be used to obtain features confined to a given range, e.g.,\n
\nbigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/hmc/hmc.bw stdout\n
\n
\nPlease refer to our\nData Access FAQ\nfor more information.\n
\n\n\n\nThanks to Jean-Madeleine Desainteagathe (APHP Paris, France) for suggesting the JARVIS, MTR, HMC tracks. Thanks to Xialei Zhang for providing the HMC data file and to Dimitrios Vitsios and Slave Petrovski for helping clean up the hg38 JARVIS files for providing guidance on interpretation. Additional\nthanks to Laurens van de Wiel for providing the MetaDome data as well as guidance on the track development and interpretation. \n
\n\n\n\nVitsios D, Dhindsa RS, Middleton L, Gussow AB, Petrovski S.\n\n Prioritizing non-coding regions based on human genomic constraint and sequence context with deep\n learning.\nNat Commun. 2021 Mar 8;12(1):1504.\nPMID: 33686085; PMC:
\nXiaolei Zhang, Pantazis I. Theotokis, Nicholas Li, the SHaRe Investigators, Caroline F. Wright, Kaitlin E. Samocha, Nicola Whiffin, James S. Ware\n\nGenetic constraint at single amino acid resolution improves missense variant prioritisation and gene discovery.\nMedrxiv 2022.02.16.22271023\n
\n\n\nWiel L, Baakman C, Gilissen D, Veltman JA, Vriend G, Gilissen C.\n\nMetaDome: Pathogenicity analysis of genetic variants through aggregation of homologous human protein\ndomains.\nHum Mutat. 2019 Aug;40(8):1030-1038.\nPMID: 31116477; PMC:
\nSilk M, Petrovski S, Ascher DB.\n\nMTR-Viewer: identifying regions within genes under purifying selection.\nNucleic Acids Res. 2019 Jul 2;47(W1):W121-W126.\nPMID: 31170280; PMC:
\nHalldorsson BV, Eggertsson HP, Moore KHS, Hauswedell H, Eiriksson O, Ulfarsson MO, Palsson G,\nHardarson MT, Oddsson A, Jensson BO et al.\n\n The sequences of 150,119 genomes in the UK Biobank.\nNature. 2022 Jul;607(7920):732-740.\nPMID: 35859178; PMC:
\nHuang YF, Gulko B, Siepel A.\n\nFast, scalable prediction of deleterious noncoding variants from functional and population genomic\ndata.\nNat Genet. 2017 Apr;49(4):618-624.\nPMID: 28288115; PMC:
\nMassively Parallel Reporter Assays (MPRAs) and related methods such as STARR-seq\nenable quantitative testing of thousands of candidate regulatory DNA sequences in\nparallel by linking each sequence to a reporter gene and measuring transcriptional\noutput using sequencing.\n
\n\n\nThe MPRA Base track shows 40,938 experimentally tested cis-regulatory elements\ncurated from the MPRA Base\ndatabase\n(Zhao et al., 2023),\ndrawn from MPRA, STARR-seq, and related reporter assay experiments.\nThe database integrates data from multiple studies, assay platforms (lentiMPRA,\nplasmidMPRA, STARR-seq, CRE-seq, and others), and cell types while preserving\nexperiment-level resolution. Only elements derived from genomic fragments that can\nbe mapped to the reference genome are included; synthetic or designed oligonucleotide\nlibraries without genomic coordinates are excluded.\n
\n\nThe track is a curated union of study-specific libraries rather than a uniform\ngenome-wide enhancer catalog: each contributing study targeted a distinct set of\ncandidate regions, including HepG2 liver-enhancer panels, melanoma GWAS variants,\nhuman/mouse pluripotent TSSs, and ASD-associated promoter variants. Each item\nrepresents one experimental measurement, not a full enhancer; longer regulatory\nelements may be represented by multiple adjacent tiles. Item width corresponds\nto the assayed DNA fragment for tile-based studies (most items, 144–200 bp;\nsome Klein et al., 2020 elements 354–678 bp) but collapses to a\nsingle base for variant-centered studies that mark the SNP location rather than\nthe surrounding tested window (Choi et al., 2020).\n
\n\nNote on cell lines: The cell line shown for each element is the reporter\ncell line in which the genomic fragment was assayed. Most rows test human DNA in\nhuman cells; the exception is Mattioli et al., 2020, where mESC rows assay the\nmouse orthologous sequence in mouse cells, with hg38 coordinates derived from the\nhuman ortholog by liftOver.\n
\n\nThe biological context of each cell line is summarized below:\n
\n| Cell line | Biological context |
|---|---|
| HepG2 | Hepatocellular carcinoma; liver enhancer studies |
| HUES64 | Human embryonic stem cells; pluripotent |
| mESC | Mouse embryonic stem cells; pluripotent |
| NPC | H1-derived neural progenitor cells; developing brain |
| HEK293FT | Embryonic kidney; high-transfection-efficiency reference |
| UACC903 | Melanoma cell line |
\nEach item represents a genomic fragment tested within a specific experiment, defined\nas a unique combination of cell line, assay type, and publication (PMID). The same\ngenomic region may appear multiple times if tested in different experiments.\n
\n\n\nItems are colored by percentile rank of the mean raw activity score within each experiment:\n
\n\nThe mouse-over shows the cell line, assay type, raw activity score, percentile rank,\nand citation for each element.\n
\n\n\nThe details page additionally shows the variant allele type for each\nrow (reference or alternate for a row that is part of a\nvariant comparison, NA for a standard enhancer element that is not a\nvariant test) and the tested oligo sequence — the exact DNA\nfragment assayed in the MPRA experiment.\n
\n\n\nFor most studies in this track, the raw score is the log2 ratio of reporter\nRNA to input DNA from the source experiment. A score of 0 means the fragment produced\nRNA in proportion to the input plasmid copies (no measurable activity above baseline),\npositive scores indicate the fragment drove the reporter above baseline (enhancer-like\nactivity in the assay), and negative scores indicate sub-baseline output (treated as\ninactive, not as validated transcriptional repression). Linear fold change relative to\nbaseline is approximately 2raw_score — for example, a raw score of 0.18\ncorresponds to roughly 1.13× baseline output, 1.0 to 2×, and 2.0 to 4×.\n
\n\nTwo studies use a different scale: Mattioli et al., 2020 and Koesterich\net al., 2023 report the MPRAnalyze induced-transcription rate\n(α), which is a positive-only quantity not directly convertible to a fold\nchange. As noted in the Methods section, scoring methodology and the threshold\nused to call an element \"active\" differ between studies, so the percentile rank\nreflects within-experiment ranking only and does not by itself indicate the\nabsolute strength of an element.\n
\n\n\nWithin each experiment, replicate measurements for the same genomic fragment were\naggregated by computing the mean raw activity score, yielding 40,938 unique\nexperiment-level genomic elements.\n
\n\n\nElements are ranked by mean raw activity score independently within each experiment,\nand a percentile rank (0–100) is computed per experiment to avoid cross-study\ndistortions caused by differing assay dynamic ranges.\n
\n\n\nScoring methodology and the threshold used to call an element "active"\ndiffer between studies, so percentile-rank comparisons across experiments are\napproximate. Lower scores indicate that the fragment did not measurably activate\ntranscription in the assay, rather than that it actively represses transcription.\nFor any element of interest, users should consult the source publication for the\noriginal significance and effect-size calls.\n
\n\n\nOriginal genomic coordinates from the source studies (mostly hg19, with some\nmm9 and mm10) were lifted to hg38 by the MPRA Base pipeline using the UCSC\nliftOver tool.\n
\n\n\nThe following table lists the experiments represented in this track.\n
\n\n| PMID | \nAuthor | \nYear | \nLab | \nCell type | \nAssay | \nElements | \n
|---|---|---|---|---|---|---|
| 27831498 | Inoue et al. | 2017 | Shendure Lab | HepG2 | lentiMPRA | 2,241 |
| 30045748 | Klein et al. | 2018 | Shendure Lab | HepG2 | STARR-seq | 6,728 |
| 32483191 | Choi et al. | 2020 | Brown Lab | HEK293FT | lentiMPRA | 840 |
| 32483191 | Choi et al. | 2020 | Brown Lab | UACC903 | lentiMPRA | 840 |
| 32819422 | Mattioli et al. | 2020 | Mele Lab | HUES64 | plasmidMPRA | 6,954 |
| 32819422 | Mattioli et al. | 2020 | Mele Lab | mESC | plasmidMPRA | 6,954 |
| 33046894 | Klein et al. | 2020 | Shendure Lab | HepG2 | lentiMPRA | 8,116 |
| 33046894 | Klein et al. | 2020 | Shendure Lab | HepG2 | plasmidMPRA | 2,228 |
| 33046894 | Klein et al. | 2020 | Shendure Lab | HepG2 | STARR-seq | 2,230 |
| 36834916 | Koesterich et al. | 2023 | Kreimer Lab | NPC | lentiMPRA | 3,807 |
\nThe data can be explored interactively in table format with the\nTable Browser or the\nData Integrator\nand exported from there to spreadsheet or tab-sep tables.\nFrom scripts, the data can be accessed through our\nAPI, track=mprabase.\n
\n\nFor automated download and analysis, the genome annotation is stored in a bigBed\nfile that can be downloaded from\nour download server.\nThe file for this track is called mprabase.bb. Individual\nregions or the whole genome annotation can be obtained using our tool\nbigBedToBed, which can be compiled from the source code or downloaded as a\nprecompiled binary for your system. Instructions for downloading source code and\nbinaries can be found\nhere.\nThe tool can also be used to obtain features within a given range, e.g.\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/mpra/mprabase/mprabase.bb -chrom=chr21 -start=0 -end=100000000 stdout\n
\n\nThe original data can be downloaded from the\nMPRA Base web application.\n
\n\n\nThanks to Varda Singhal, Jianyu Zhao, and the\nAhituv Lab\nat the University of California San Francisco for creating and curating MPRA Base and for creating this track.\n
\n\n\nChoi J, Zhang T, Vu A, Ablain J, Makowski MM, Colli LM, Xu M, Hennessey RC, Yin J, Rothschild H\net al.\n\nMassively parallel reporter assays of melanoma risk variants identify MX2 as a gene promoting\nmelanoma.\nNat Commun. 2020 Jun 1;11(1):2718.\nPMID: 32483191; PMC:
\nInoue F, Kircher M, Martin B, Cooper GM, Witten DM, McManus MT, Ahituv N, Shendure J.\n\nA systematic comparison reveals substantial differences in chromosomal versus episomal encoding of\nenhancer activity.\nGenome Res. 2017 Jan;27(1):38-52.\nPMID: 27831498; PMC:
\nKlein JC, Keith A, Agarwal V, Durham T, Shendure J.\n\nFunctional characterization of enhancer evolution in the primate lineage.\nGenome Biol. 2018 Jul 25;19(1):99.\nPMID: 30045748; PMC:
\nKlein JC, Agarwal V, Inoue F, Keith A, Martin B, Kircher M, Ahituv N, Shendure J.\n\nA systematic evaluation of the design and context dependencies of massively parallel reporter\nassays.\nNat Methods. 2020 Nov;17(11):1083-1091.\nPMID: 33046894; PMC:
\nKoesterich J, An JY, Inoue F, Sohota A, Ahituv N, Sanders SJ, Kreimer A.\n\nCharacterization of De Novo Promoter Variants in Autism Spectrum Disorder with Massively Parallel\nReporter Assays.\nInt J Mol Sci. 2023 Feb 9;24(4).\nPMID: 36834916; PMC:
\nMattioli K, Oliveros W, Gerhardinger C, Andergassen D, Maass PG, Rinn JL, Melé M.\n\nCis and trans effects differentially contribute to the evolution of promoters and enhancers.\nGenome Biol. 2020 Aug 20;21(1):210.\nPMID: 32819422; PMC:
\nZhao J, Baltoumas FA, Konnaris MA, Mouratidis I, Liu Z, Sims J, Agarwal V, Pavlopoulos GA,\nGeorgakopoulos-Soares I, Ahituv N.\n\nMPRAbase: A Massively Parallel Reporter Assay Database.\nbioRxiv. 2023 Nov 22;.\nPMID: 38045264; PMC:
\nThese tracks contain pseudogene predictions and their parents as identified by PseudoPipe.\nPseudoPipe is a homology-based\ncomputational pipeline that can search a mammalian genome and identify pseudogene sequences\ncomprehensively and consistently.\n
\n\nPseudogenes are genomic sequences that bear similarity to specific protein-coding genes, but are\nunable to produce functional proteins due to the existence of frameshifts, premature stop codons, or\nother deleterious mutations. They arise from gene duplication or retrotransposition events and are\nimportant resources in understanding the evolutionary history of genes and genomes.
\n\nThis composite track consists of two subtracks: the Pseudogenes track and the Pseudogene\nParents track.
\n\nThe Pseudogene Parents track displays parent genes and pseudogenes\nlabeled with their HUGO\nIDs, which were derived from Ensembl gene IDs provided by the Gerstein lab after dataset creation. It includes indicators for pseudogenes. \nThese indicators do not show pseudogene locations directly but instead indicate how many pseudogenes\nare associated with each gene and link to their genomic regions in the Pseudogenes track.
\n\nThe Pseudogenes track shows pseudogenes labeled with their parent HUGO ID and colored\naccording to pseudogene type. The authors assigned PGOHUMG IDs to genes and PGOHUMT IDs to\ntranscripts. Note: Not all PseudoPipe IDs could be mapped back to their original Ensembl\nIDs. In these cases, the gene ID is listed as NA.
\n\nPseudogene types:\nEach parent gene is shown with associated pseudogenes represented as grey blocks. These blocks\ndo not reflect actual pseudogene locations but rather indicate the count of pseudogenes linked to\nthe gene.\n
\n\nIf a parent gene has four grey blocks beneath it, this indicates the presence of four pseudogenes\nelsewhere in the genome. Hovering over an item displays the gene type, ID (Ensembl transcript ID\nor PseudoPipe transcript ID), and the genome position of the gene or pseudogene, with a link to\nthat genomic region.\n
\n\nPseudogenes are colored by type.
\n\nHovering over a pseudogene item shows the pseudogene type, parent HUGO gene symbol, and the Ensembl\nparent transcript ID, which links to the genome position of the parent gene.
\n\n\nThe PseudoPipe pipeline identifies pseudogenes through a series of steps. It first uses BLAST to\nrapidly cross-reference potential parent proteins against the intergenic regions of the genome. The\nresulting raw hits are then processed by removing redundancies, clustering neighboring sequences,\nand aligning each cluster with a unique parent gene. Finally, pseudogenes are classified based on a\ncombination of criteria, including homology, intron-exon structure, and the presence of stop codons\nor frameshifts. This method is designed to detect pseudogenes that are unable to be translated into\nproteins.
\n\nThese tracks were generated using a Bash script that processes a GTF file with pseudogene\nannotations by removing duplicates, correcting overlapping exons, and converting the data to BED\nformat with pseudoPipeToBed.py. This script extracts gene and transcript IDs, merges overlapping\nexons, assigns colors based on pseudogene type, and outputs a BED file with gene and parent\nannotations. PseudoPipeParents.py then links pseudogenes to their functional genes by determining\nparent gene coordinates, updating pseudogene entries with interactive browser links and generating a\nparent BED file. The final data are formatted into pseudoPipePgenes.bb and pseudoPipeParents.bb BigBed\nfiles. The detailed documentation (makeDoc) and \nPython scripts are available in our GitHub repository.\n
\n\nThe raw data can be explored interactively with the\nTable Browser or the\nData Integrator.\nThe data may also be explored interactively using our\nREST API.
\nFor automated download and analysis, the genome annotation is stored at UCSC in bigBed files\nthat can be downloaded from the\ndownload server.\nIndividual regions or the whole genome annotation can be obtained using our tool\nbigBedToBed which can be compiled from the source code or downloaded as a precompiled\nbinary for your system.
\n\nInstructions for downloading source code and binaries can be found\nhere.\nThe tool can also be used to obtain only features within a given range, e.g.
\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/hg38/pseudogenes/pseudoPipePgenes.bb -chrom=chr21 -start=0 -end=10000000 stdout\n\n\nThanks to the Gerstein lab at Yale University for making this data available, and to Cristina\nSisu for providing data in GTF format with parent annotations.
\n\n\nZhang Z, Carriero N, Zheng D, Karro J, Harrison PM, Gerstein M.\n\nPseudoPipe: an automated pseudogene identification pipeline.\nBioinformatics. 2006 Jun 15;22(12):1437-9.\nPMID: 16574694\n
\n", "itemRgb": "on", "labelFields": "hugo", "labelSeparator": "\" \"", "longLabel": "Yale Pseudogene Parents", "mouseOver": "Gene type: ${geneType}\nThe recombination rate track represents calculated rates of recombination based\non the genetic maps from deCODE (Halldorsson et al., 2019) and 1000 Genomes\n(2013 Phase 3 release, lifted from hg19). The deCODE map is more recent, has a higher \nresolution and was natively created on hg38 and therefore recommended. \nFor the Recomb. deCODE average track, the recombination rates for chrX represent the female rate.\n
\n\nThis track also includes a subtrack with all the\nindividual deCODE recombination events and another subtrack with several thousand\nde-novo mutations found in the deCODE sequencing data. These two tracks are hidden by\ndefault and have to be switched on explicitly on the configuration page.\n
\n\n\nThis is a super track that contains different subtracks, three with the deCODE\nrecombination rates (paternal, maternal and average) and one with the 1000\nGenomes recombination rate (average). These tracks are in \nsignal graph\n(wiggle) format. By default, to show most recombination hotspots, their maximum\nvalue is set to 100 cM, even though many regions have values higher than 100.\nThe maximum value can be changed on the configuration pages of the tracks.\n
\n\n\nThere are two more tracks that show additional details provided by deCODE: one\nsubtrack with the raw data of all cross-overs tagged with their proband ID and\nanother one with around 8000 human de-novo mutation variants that are linked to\ncross-over changes.\n
\n\n\nThe deCODE genetic map was created at \ndeCODE Genetics. It is based \non microarrays assaying 626,828 SNP markers that allowed to identify 1,476,140 crossovers in\n56,321 paternal meioses and 3,055,395 crossovers in 70,086 maternal meioses.\nIn total, the data is based on 4,531,535 crossovers in 126,427 meioses. By\nusing WGS data with 9,305,070 SNPs, the boundaries for 761,981 crossovers were\nrefined: 247,942 crossovers in 9423 paternal meioses and 514,039 crossovers in\n11,750 maternal meioses. The average resolution of the genetic map is 682 base\npairs (bp): 655 and 708 bp for the paternal and maternal maps, respectively.\n
\n\nThe 1000 Genomes genetic map is based on the
As with all other tracks, the data conversion commands and pointers to the\noriginal data files are documented in the \nmakeDoc file of this track.
\n\n\nThe raw data can be explored interactively with the Table Browser, or\nthe Data Integrator. For automated access, this track, like all\nothers, is available via our API. However, for bulk\nprocessing, it is recommended to download the dataset.\n
\n\n\nFor automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed\nfiles that can be downloaded from\nour download server.\nIndividual regions or the whole genome annotation can be obtained using our tools bigWigToWig\nor bigBedToBed which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tools can also be used to obtain features confined to a given range, e.g.,\n
\nbigWigToBedGraph -chrom=chr17 -start=45941345 -end=45942345 http://hgdownload.soe.ucsc.edu/gbdb/hg38/recombRate/recombAvg.bw stdout\n
\n
\nPlease refer to our\nData Access FAQ\nfor more information.\n
\n\n\nThis track was produced at UCSC using data that are freely available for\nthe deCODE\nand 1000 Genomes genetic maps. Thanks to Po-Ru Loh at the\nBroad Institute for providing the code to lift the hg19 1000 Genomes map data to hg38.\n
\n\n\n1000 Genomes Project Consortium., Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA,\nHurles ME, McVean GA.\n\n A map of human genome variation from population-scale sequencing.\nNature. 2010 Oct 28;467(7319):1061-73.\nPMID: 20981092; PMC:
\nHalldorsson BV, Palsson G, Stefansson OA, Jonsson H, Hardarson MT, Eggertsson HP, Gunnarsson B,\nOddsson A, Halldorsson GH, Zink F et al.\n\n Characterizing mutagenic effects of recombination through a sequence-level genetic map.\nScience. 2019 Jan 25;363(6425).\nPMID: 30679340\n
\n", "longLabel": "Recombination rate: deCODE Genetics, average from paternal and maternal (mat for chrX)", "maxHeightPixels": "128:60:8", "parent": "recombRate2", "priority": "1", "shortLabel": "Recomb. deCODE Avg", "track": "recombAvg", "type": "bigWig", "viewLimits": "0.0:100", "viewLimitsMax": "0:150000", "visibility": "full" } }, "description": "Recombination rate: deCODE Genetics, average from paternal and maternal (mat for chrX)", "category": [ "Mapping and Sequencing" ] }, { "trackId": "hg38-ReMapDensity", "name": "ReMap ChIP-seq - ReMap density", "type": "QuantitativeTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigWigAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/reMap/reMapDensity2022.bw" }, "metadata": { "ucsc": { "autoScale": "on", "bigDataUrl": "/gbdb/hg38/reMap/reMapDensity2022.bw", "html": "\nThis track represents the ReMap Atlas of regulatory regions, which consists of a\nlarge-scale integrative analysis of all Public ChIP-seq data for transcriptional\nregulators from GEO, ArrayExpress, and ENCODE. \n
\n\n\nBelow is a schematic diagram of the types of regulatory regions: \n
\nThis 4th release of ReMap (2022) presents the analysis of a total of 8,103 \nquality controlled ChIP-seq (n=7,895) and ChIP-exo (n=208) data sets from public\nsources (GEO, ArrayExpress, ENCODE). The ChIP-seq/exo data sets have been mapped\nto the GRCh38/hg38 human assembly. The data set is defined as a ChIP-seq \nexperiment in a given series (e.g. GSE46237), for a given TF (e.g. NR2C2), in a\nparticular biological condition (i.e. cell line, tissue type, disease state, or\nexperimental conditions; e.g. HELA). Data sets were labeled by concatenating\nthese three pieces of information, such as GSE46237.NR2C2.HELA. \n
\nThose merged analyses cover a total of 1,211 DNA-binding proteins\n(transcriptional regulators) such as a variety of transcription factors (TFs),\ntranscription co-activators (TCFs), and chromatin-remodeling factors (CRFs) for\n182 million peaks. \n
\n\n\nPublic ChIP-seq data sets were extracted from Gene Expression Omnibus (GEO) and\nArrayExpress (AE) databases. For GEO, the query\n\n'('chip seq' OR 'chipseq' OR\n'chip sequencing') AND 'Genome binding/occupancy profiling by high throughput\nsequencing' AND 'homo sapiens'[organism] AND NOT 'ENCODE'[project]'\n\nwas used to return a list of all potential data sets to analyze, which were then manually \nassessed for further analyses. Data sets involving polymerases (i.e. Pol2 and\nPol3), and some mutated or fused TFs (e.g. KAP1 N/C terminal mutation, GSE27929)\nwere excluded.\n
\nAvailable ENCODE ChIP-seq data sets for transcriptional regulators from the\nENCODE portal were processed with the\nstandardized ReMap pipeline. The list of ENCODE data was retrieved as FASTQ files from the\nENCODE portal\nusing the following filters:\n
\nBoth Public and ENCODE data were processed similarly. Bowtie 2 (PMC3322381) (version 2.2.9) with options -end-to-end -sensitive was used to align all\nreads on the genome. Biological and technical\nreplicates for each unique combination of GSE/TF/Cell type or Biological condition\nwere used for peak calling. TFBS were identified using MACS2 peak-calling tool\n(PMC3120977) (version 2.1.1.2) in order to follow ENCODE ChIP-seq guidelines,\nwith stringent thresholds (MACS2 default thresholds, p-value: 1e-5). An input data\nset was used when available.\n
\n\n\n\nTo assess the quality of public data sets, a score was computed based on the\ncross-correlation and the FRiP (fraction of reads in peaks) metrics developed by\nthe ENCODE Consortium (https://genome.ucsc.edu/ENCODE/qualityMetrics.html). Two\nthresholds were defined for each of the two cross-correlation ratios (NSC,\nnormalized strand coefficient: 1.05 and 1.10; RSC, relative strand coefficient:\n0.8 and 1.0). Detailed descriptions of the ENCODE quality coefficients can be\nfound at https://genome.ucsc.edu/ENCODE/qualityMetrics.html. The\nphantompeak tools suite was used\n(https://code.google.com/p/phantompeakqualtools/) to compute\nRSC and NSC.\n
\n\nPlease refer to the ReMap 2022, 2020, and 2018 publications for more details\n(citation below).\n
\n\n\n\n\nReMap Atlas of regulatory regions data can be explored interactively with the\nTable Browser and cross-referenced with the \nData Integrator. For programmatic access,\nthe track can be accessed using the Genome Browser's\nREST API.\nReMap annotations can be downloaded from the\nGenome Browser's download server\nas a bigBed file. This compressed binary format can be remotely queried through\ncommand line utilities. Please note that some of the download files can be quite large.
\n\n\nIndividual BED files for specific TFs, cells/biotypes, or data sets can be\nfound and downloaded on the ReMap website.\n
\n\n\nChèneby J, Gheorghe M, Artufel M, Mathelier A, Ballester B.\n\nReMap 2018: an updated atlas of regulatory regions from an integrative analysis of DNA-binding ChIP-\nseq experiments.\nNucleic Acids Res. 2018 Jan 4;46(D1):D267-D275.\nPMID: 29126285; PMC:
\nChèneby J, Ménétrier Z, Mestdagh M, Rosnet T, Douida A, Rhalloussi W, Bergon A, Lopez\nF, Ballester B.\n\nReMap 2020: a database of regulatory regions from an integrative analysis of Human and Arabidopsis\nDNA-binding sequencing experiments.\nNucleic Acids Res. 2020 Jan 8;48(D1):D180-D188.\nPMID: 31665499; PMC:
\nGriffon A, Barbier Q, Dalino J, van Helden J, Spicuglia S, Ballester B.\n\nIntegrative analysis of public ChIP-seq experiments reveals a complex multi-cell regulatory\nlandscape.\nNucleic Acids Res. 2015 Feb 27;43(4):e27.\nPMID: 25477382; PMC:
\nHammal F, de Langen P, Bergon A, Lopez F, Ballester B.\n\nReMap 2022: a database of Human, Mouse, Drosophila and Arabidopsis regulatory regions from an\nintegrative analysis of DNA-binding sequencing experiments.\nNucleic Acids Res. 2022 Jan 7;50(D1):D316-D325.\nPMID: 34751401; PMC:
This track displays regulatory regions in the human genome identified using ENCODE \ndata, specifically spanning ENCODE phases 2 through 4. It highlights genomic \nregions bound by DNA-associated proteins involved in transcriptional regulation, \nsuch as RNA polymerase, transcription factors (TFs), and chromatin remodeling \nproteins. Sequence-specific TFs bind directly to short DNA motifs via their \nDNA-binding domains, while other DNA-associated proteins interact with DNA \nindirectly through protein-protein interactions with sequence-specific TFs. Chromatin \nimmunoprecipitation followed by sequencing (ChIP-seq) is a high-throughput method \nfor mapping genome-wide protein-DNA interactions. Regions of high ChIP signal, \ncommonly referred to as ChIP-seq peaks, indicate protein binding sites. For each DNA\n-associated protein, all ENCODE ChIP-seq peaks across biosamples were integrated to generate \na set of representative peaks (rPeaks). This track displays these rPeaks alongside \ndetected DNA motif sites.
\n\nEach rPeak is represented as a gray box, with the shade of gray corresponding \nto the maximum ChIP-seq signal observed across contributing biosamples. The HGNC \ngene name of the associated protein is displayed to the left of the box. If the \nrPeak overlaps a cognate TF motif site in the collection built previously (PMID: \n37104580 DOI: 10.1126/science.abn7930), \nthe motif site is highlighted in green.
\n\nClicking on an rPeak provides detailed information about the biosamples where the \nrPeak was detected, including the count of biosamples with contributing ChIP-seq peaks \nand the total number of biosamples assayed for the protein. Links to relevant ENCODE \nChIP-seq experiments and overlapping ENCODE candidate cis-regulatory elements (cCREs) \nare also provided.
\n\nBy default, rPeaks for all 912 DNA-associated proteins with ENCODE ChIP-seq data \nare displayed. Users can customize the display by selecting specific DNA-associated \nproteins in the track settings.
\n\n2,509 ENCODE ChIP-seq experiments were integrated from 912 DNA-associated \nproteins across 1,152 unique biosamples to produce representative peaks (rPeaks) \nfor each protein. The processing steps were as follows:
\n\nThe raw data for the ENCODE TF rPeak track will soon be available.
\n\n\nThe raw data can be explored interactively with the Table Browser,\nfor download, intersection or correlations with other tracks. To join this track with others\nbased on the chromosome positions, use the Data Integrator.\n\n
\nRegarding access to this data track in the Genome Browser, for automated download \nand analysis, the genome annotation is stored in a bigBed file that\ncan be downloaded from\nour download server.\nThe file for this track is called TFrPeakClusters.bb. Individual\nregions or the whole genome annotation can be obtained using our tool bigBedToBed\nwhich can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tool\ncan also be used to obtain only features within a given range, e.g.\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/ENCODE4/TFrPeakClusters.bb -chrom=chr21 -start=0 -end=100000000 stdout
\n\n\nFor automated access, this track like all others, is also available via our\nAPI. However, for bulk processing in\npipelines, downloading the data and/or using bigBed files as described above is\nusually faster.
\n\nThis track was made possible thanks to the efforts of the ENCODE Consortium, \nENCODE ChIP-seq production laboratories, and the ENCODE Data Coordination Center \nfor generating and processing the ChIP-seq datasets. The ENCODE accession numbers \nfor the constituent datasets are accessible from the peak details page. Special thanks \nto Drs. Mingshi Gao, Greg Andrews, Jill Moore, and Zhiping Weng at UMass Chan Medical \nSchool, who were members of the ENCODE Data Analysis Center, for developing this track, \nincluding providing the rPeak and motif datasets and associated metadata and building the \ntrack. We also extend our gratitude to Max Haeussler and Jonathan Casper from the UCSC \nGenome Browser Project Team for their assistance in developing this track. For updates \non the track, please contact the Weng lab.
\n", "itemRgb": "on", "labelFields": "factor", "longLabel": "Transcription Factor Representative Peak (rPeak) Clusters (912 factors in 1152 biosamples) from ENCODE 4", "mouseOverField": "factor", "parent": "wgEncodeReg", "scoreMax": "1000", "scoreMin": "1", "shortLabel": "TF rPeak Clusters", "spectrum": "on", "track": "TFrPeakClusters", "type": "bigBed 12 +", "urls": "exp=\"https://www.encodeproject.org/experiments/$$/\" cCRE=\"https://screen.wenglab.org/search/?q=$$&assembly=GRCh38\" factor=\"https://www.factorbook.org/tf/human/$$/function\"", "visibility": "hide" } }, "description": "Transcription Factor Representative Peak (rPeak) Clusters (912 factors in 1152 biosamples) from ENCODE 4", "category": [ "Regulation" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-TFrPeakClusters-LinearBasicDisplay", "labels": { "name": "jexl:get(feature,'factor')" }, "mouseover": "jexl:get(feature,'factor')" } ] }, { "trackId": "hg38-comments", "name": "Problematic Regions - UCSC Unusual Regions", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/problematic/comments.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/problematic/comments.bb", "longLabel": "UCSC unusual regions on assembly structure (manually annotated)", "mouseOverField": "note", "noScoreFilter": "on", "parent": "problematic", "priority": "1", "searchIndex": "name", "searchTrix": "/gbdb/hg38/problematic/notes.ix", "shortLabel": "UCSC Unusual Regions", "track": "comments", "type": "bigBed 9 +", "html": "" } }, "description": "UCSC unusual regions on assembly structure (manually annotated)", "category": [ "Mapping and Sequencing" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-comments-LinearBasicDisplay", "mouseover": "jexl:get(feature,'note')" } ] }, { "trackId": "hg38-umap24", "name": "Single-read mappability - Umap S24", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k24.Unique.Mappability.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/hoffmanMappability/k24.Unique.Mappability.bb", "color": "80,20,240", "longLabel": "Single-read mappability with 24-mers", "parent": "umapBigBed on", "priority": "1", "shortLabel": "Umap S24", "subGroups": "view=SR", "track": "umap24", "html": "" } }, "description": "Single-read mappability with 24-mers", "category": [ "Mapping and Sequencing" ] }, { "trackId": "hg38-nmdEscMane", "name": "NMD Escape - NMD Escape MANE", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/nmd/nmdEscMane.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/nmd/nmdEscMane.bb", "dataVersion": "MANE 1.5", "defaultLabelFields": "ncbiIds", "filterLabel.ncbiIds": "RefSeq accession (e.g. \"*NM_000546*\")", "filterLabel.transcripts": "Gencode accession (e.g. \"*ENST00000269305*\")", "filterText.ncbiIds": "*", "filterText.transcripts": "*", "filterType.ncbiIds": "wildcard", "filterType.transcripts": "wildcard", "html": "\nThe NMD escape ruleset tracks show predicted regions where a premature termination\ncodon (PTC) or frameshift variant is likely to cause the transcript to\nescape nonsense-mediated decay (NMD), leading to the production of an\naberrant truncated protein rather than degradation of the mRNA.\n
\n\n\nThe following rules were applied to transcript annotations to define predicted\nNMD escape regions (Nagy et al, Trends Biochem Sci 1998 and Lindeboom et al, Nat Genet 2016):\n
\n\n\nNon-coding transcripts (where CDS start equals CDS end) are excluded.\nOverlapping regions from multiple transcripts with identical coordinates and\nthe same rule are collapsed into a single item, with the contributing\ntranscript IDs stored as a comma-separated list.\n
\n\n\nThree versions of this track are available, based on different transcript annotation sets:\n
\n\nNMD escape regions were predicted based on the Exon Junction Complex\n(EJC)-dependent model of NMD. During normal translation, EJCs are deposited at\nexon-exon junctions after splicing. As the ribosome translates the mRNA, it\ndisplaces each EJC it encounters. When a PTC causes the ribosome to stall\nprematurely, any remaining downstream EJCs recruit surveillance factors\n(notably UPF1) that trigger mRNA degradation via NMD.\n
\n\n\nHowever, PTCs located in the last coding exon or within approximately 50 bp\nupstream of the last exon-exon junction are too close to the final EJC (or\nhave no downstream EJC at all) for NMD to be triggered—the transcript\nescapes degradation. Conversely, PTCs located more than 50–55 bp\nupstream of the last exon-exon junction are predicted to elicit NMD.\n
\n\n\nAdditional escape mechanisms, supported by Lindeboom et al. 2016 and other\nstudies, are captured by three further rules:\n
\n\nRegions from overlapping transcripts with the same coordinates are collapsed into\na single item. The gene symbol is shown as the item name. Mouseover displays the\nNMD escape rule and the number of transcripts. The details page lists all\ncontributing transcript IDs.\n
\n\n\nItems are colored by the NMD escape rule that applies:\n
\n\nThe data underlying this track can be explored interactively with the\nTable Browser or the\nData Integrator. For automated analysis,\nthe data may be queried from our\nREST API. Please refer to our\nmailing list archives for questions, or our\nData Access FAQ for more\ninformation.\n
\n\n\nThanks to Guido Neidhardt for suggesting this track at HUGO VEPTC 2025 and Andreas Lahner\nfor feedback. Thanks to the Decipher Genome Browser team for introducing the idea of a\ntrack.\n
\n\n\nKurosaki T, Popp MW, Maquat LE.\n\nQuality and quantity control of gene expression by nonsense-mediated mRNA decay.\nNat Rev Mol Cell Biol. 2019 Jul;20(7):406-420.\nPMID: 30992545; PMC:
\nLindeboom RGH, Supek F, Lehner B.\n\nThe rules and impact of nonsense-mediated mRNA decay in human cancers.\nNat Genet. 2016 Oct;48(10):1112-8.\nPMID: 27618451; PMC:
\nNagy E, Maquat LE.\n\nA rule for termination-codon position within intron-containing genes: when nonsense affects RNA\nabundance.\nTrends Biochem Sci. 1998 Jun;23(6):198-9.\nPMID: 9644970\n
\n\n\n", "labelFields": "ncbiIds,name,transcripts", "labelSeparator": "\" / \"", "longLabel": "NMD escape predictions: MANE Select Plus Clinical transcripts", "mouseOverField": "mouseover", "parent": "nmd on", "priority": "1.4", "shortLabel": "NMD Escape MANE", "track": "nmdEscMane", "type": "bigBed 9 +", "visibility": "pack" } }, "description": "NMD escape predictions: MANE Select Plus Clinical transcripts", "category": [ "Genes and Gene Predictions" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-nmdEscMane-LinearBasicDisplay", "labels": { "name": "jexl:get(feature,'ncbiIds')" }, "mouseover": "jexl:get(feature,'mouseover')" } ] }, { "trackId": "hg38-jaspar2024", "name": "JASPAR Transcription Factors - JASPAR 2024 TFBS", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/jaspar/JASPAR2024.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/jaspar/JASPAR2024.bb", "filter.score": "400", "filterByRange.score": "0:1000", "filterValues.TFName": 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"labelFields": "TFName", "longLabel": "JASPAR CORE 2024 - Predicted Transcription Factor Binding Sites", "maxItems": "100000", "motifPwmTable": "hgFixed.jasparCore2024", "parent": "jaspar off", "priority": "1.5", "shortLabel": "JASPAR 2024 TFBS", "track": "jaspar2024", "type": "bigBed 6 +", "visibility": "pack", "html": "" } }, "description": "JASPAR CORE 2024 - Predicted Transcription Factor Binding Sites", "category": [ "Regulation" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-jaspar2024-LinearBasicDisplay", "labels": { "name": "jexl:get(feature,'TFName')" }, "jexlFilters": [ "get(feature,'gbkey')!='Src'", "get(feature,'score') >= 400" ] } ] }, { "trackId": "hg38-nmdEscGencode", "name": "NMD Escape - NMD Escape Gencode", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/nmd/nmdEscRegions.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/nmd/nmdEscRegions.bb", "dataVersion": "Gencode V49", "filterLabel.transcripts": "Filter on transcript ID (e.g. \"*ENST00000269305*\")", "filterText.transcripts": "*", "filterType.transcripts": "wildcard", "html": "\nThe NMD escape ruleset tracks show predicted regions where a premature termination\ncodon (PTC) or frameshift variant is likely to cause the transcript to\nescape nonsense-mediated decay (NMD), leading to the production of an\naberrant truncated protein rather than degradation of the mRNA.\n
\n\n\nThe following rules were applied to transcript annotations to define predicted\nNMD escape regions (Nagy et al, Trends Biochem Sci 1998 and Lindeboom et al, Nat Genet 2016):\n
\n\n\nNon-coding transcripts (where CDS start equals CDS end) are excluded.\nOverlapping regions from multiple transcripts with identical coordinates and\nthe same rule are collapsed into a single item, with the contributing\ntranscript IDs stored as a comma-separated list.\n
\n\n\nThree versions of this track are available, based on different transcript annotation sets:\n
\n\nNMD escape regions were predicted based on the Exon Junction Complex\n(EJC)-dependent model of NMD. During normal translation, EJCs are deposited at\nexon-exon junctions after splicing. As the ribosome translates the mRNA, it\ndisplaces each EJC it encounters. When a PTC causes the ribosome to stall\nprematurely, any remaining downstream EJCs recruit surveillance factors\n(notably UPF1) that trigger mRNA degradation via NMD.\n
\n\n\nHowever, PTCs located in the last coding exon or within approximately 50 bp\nupstream of the last exon-exon junction are too close to the final EJC (or\nhave no downstream EJC at all) for NMD to be triggered—the transcript\nescapes degradation. Conversely, PTCs located more than 50–55 bp\nupstream of the last exon-exon junction are predicted to elicit NMD.\n
\n\n\nAdditional escape mechanisms, supported by Lindeboom et al. 2016 and other\nstudies, are captured by three further rules:\n
\n\nRegions from overlapping transcripts with the same coordinates are collapsed into\na single item. The gene symbol is shown as the item name. Mouseover displays the\nNMD escape rule and the number of transcripts. The details page lists all\ncontributing transcript IDs.\n
\n\n\nItems are colored by the NMD escape rule that applies:\n
\n\nThe data underlying this track can be explored interactively with the\nTable Browser or the\nData Integrator. For automated analysis,\nthe data may be queried from our\nREST API. Please refer to our\nmailing list archives for questions, or our\nData Access FAQ for more\ninformation.\n
\n\n\nThanks to Guido Neidhardt for suggesting this track at HUGO VEPTC 2025 and Andreas Lahner\nfor feedback. Thanks to the Decipher Genome Browser team for introducing the idea of a\ntrack.\n
\n\n\nKurosaki T, Popp MW, Maquat LE.\n\nQuality and quantity control of gene expression by nonsense-mediated mRNA decay.\nNat Rev Mol Cell Biol. 2019 Jul;20(7):406-420.\nPMID: 30992545; PMC:
\nLindeboom RGH, Supek F, Lehner B.\n\nThe rules and impact of nonsense-mediated mRNA decay in human cancers.\nNat Genet. 2016 Oct;48(10):1112-8.\nPMID: 27618451; PMC:
\nNagy E, Maquat LE.\n\nA rule for termination-codon position within intron-containing genes: when nonsense affects RNA\nabundance.\nTrends Biochem Sci. 1998 Jun;23(6):198-9.\nPMID: 9644970\n
\n\n\n", "longLabel": "NMD escape predictions: Gencode transcripts", "mouseOverField": "mouseover", "parent": "nmd on", "priority": "1.5", "shortLabel": "NMD Escape Gencode", "track": "nmdEscGencode", "type": "bigBed 9 +", "visibility": "dense" } }, "description": "NMD escape predictions: Gencode transcripts", "category": [ "Genes and Gene Predictions" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-nmdEscGencode-LinearBasicDisplay", "mouseover": "jexl:get(feature,'mouseover')" } ] }, { "trackId": "hg38-nmdEscNcbiRefSeq", "name": "NMD Escape - NMD Escape RefSeq", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/nmd/nmdEscNcbiRefSeq.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/nmd/nmdEscNcbiRefSeq.bb", "dataVersion": "GCF_000001405.40-RS_2025_08", "filterLabel.transcripts": "Filter on transcript ID (e.g. \"NM_005228*\")", "filterText.transcripts": "*", "filterType.transcripts": "wildcard", "html": "\nThe NMD escape ruleset tracks show predicted regions where a premature termination\ncodon (PTC) or frameshift variant is likely to cause the transcript to\nescape nonsense-mediated decay (NMD), leading to the production of an\naberrant truncated protein rather than degradation of the mRNA.\n
\n\n\nThe following rules were applied to transcript annotations to define predicted\nNMD escape regions (Nagy et al, Trends Biochem Sci 1998 and Lindeboom et al, Nat Genet 2016):\n
\n\n\nNon-coding transcripts (where CDS start equals CDS end) are excluded.\nOverlapping regions from multiple transcripts with identical coordinates and\nthe same rule are collapsed into a single item, with the contributing\ntranscript IDs stored as a comma-separated list.\n
\n\n\nThree versions of this track are available, based on different transcript annotation sets:\n
\n\nNMD escape regions were predicted based on the Exon Junction Complex\n(EJC)-dependent model of NMD. During normal translation, EJCs are deposited at\nexon-exon junctions after splicing. As the ribosome translates the mRNA, it\ndisplaces each EJC it encounters. When a PTC causes the ribosome to stall\nprematurely, any remaining downstream EJCs recruit surveillance factors\n(notably UPF1) that trigger mRNA degradation via NMD.\n
\n\n\nHowever, PTCs located in the last coding exon or within approximately 50 bp\nupstream of the last exon-exon junction are too close to the final EJC (or\nhave no downstream EJC at all) for NMD to be triggered—the transcript\nescapes degradation. Conversely, PTCs located more than 50–55 bp\nupstream of the last exon-exon junction are predicted to elicit NMD.\n
\n\n\nAdditional escape mechanisms, supported by Lindeboom et al. 2016 and other\nstudies, are captured by three further rules:\n
\n\nRegions from overlapping transcripts with the same coordinates are collapsed into\na single item. The gene symbol is shown as the item name. Mouseover displays the\nNMD escape rule and the number of transcripts. The details page lists all\ncontributing transcript IDs.\n
\n\n\nItems are colored by the NMD escape rule that applies:\n
\n\nThe data underlying this track can be explored interactively with the\nTable Browser or the\nData Integrator. For automated analysis,\nthe data may be queried from our\nREST API. Please refer to our\nmailing list archives for questions, or our\nData Access FAQ for more\ninformation.\n
\n\n\nThanks to Guido Neidhardt for suggesting this track at HUGO VEPTC 2025 and Andreas Lahner\nfor feedback. Thanks to the Decipher Genome Browser team for introducing the idea of a\ntrack.\n
\n\n\nKurosaki T, Popp MW, Maquat LE.\n\nQuality and quantity control of gene expression by nonsense-mediated mRNA decay.\nNat Rev Mol Cell Biol. 2019 Jul;20(7):406-420.\nPMID: 30992545; PMC:
\nLindeboom RGH, Supek F, Lehner B.\n\nThe rules and impact of nonsense-mediated mRNA decay in human cancers.\nNat Genet. 2016 Oct;48(10):1112-8.\nPMID: 27618451; PMC:
\nNagy E, Maquat LE.\n\nA rule for termination-codon position within intron-containing genes: when nonsense affects RNA\nabundance.\nTrends Biochem Sci. 1998 Jun;23(6):198-9.\nPMID: 9644970\n
\n\n\n", "longLabel": "NMD escape predictions: NCBI RefSeq Curated transcripts", "mouseOverField": "mouseover", "parent": "nmd off", "priority": "1.6", "shortLabel": "NMD Escape RefSeq", "track": "nmdEscNcbiRefSeq", "type": "bigBed 9 +", "visibility": "hide" } }, "description": "NMD escape predictions: NCBI RefSeq Curated transcripts", "category": [ "Genes and Gene Predictions" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-nmdEscNcbiRefSeq-LinearBasicDisplay", "mouseover": "jexl:get(feature,'mouseover')" } ] }, { "trackId": "hg38-nmdDetectiveAi", "name": "NMD Escape - NMDetective-AI", "type": "QuantitativeTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigWigAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/nmd/nmdDetectAi.bw" }, "metadata": { "ucsc": { "autoScale": "off", "bigDataUrl": "/gbdb/hg38/nmd/nmdDetectAi.bw", "color": "128,0,128", "html": "\nThe NMDetective-AI tracks display deep-learning predictions of\nnonsense-mediated mRNA decay (NMD) efficiency for every possible stop-gain\nsingle-nucleotide variant in MANE Select transcripts. The model was trained on\n~14,000 somatic premature termination codons (PTCs) measured by allele-specific\nexpression in large human cohorts (TCGA) and was tested on ~1,800 held-out\ngermline PTCs (TCGA germline and GTEx) (Veiner et al.).\n
\n\n\nPredictions are continuous: higher values indicate that a PTC at that codon is\npredicted to trigger NMD (the mRNA is degraded); lower values indicate that the\nPTC is predicted to evade NMD (the truncated mRNA may be translated into an\naberrant protein). The output is normalized against canonical controls so that\n+0.5 corresponds to full NMD efficiency at a PTC and −0.5\ncorresponds to no NMD efficiency (a last-exon PTC). The scale is not strictly\nbounded: due to measurement and prediction noise, observed values fall in\nroughly −1.1 to +1.5, with the bulk of items inside the nominal\n−0.5 to +0.5 interval.\n
\n\n| Track | Description |
|---|---|
| NMDetective-AI | \nSignal track (bigWig) showing the position-averaged prediction across\n all stop-gain SNVs at each codon. Useful for browsing efficiency along a\n transcript at a glance. |
| NMDetective-AI variants | \nPer-stop-gain track (bigBed) with one item per (transcript, codon,\n mutant codon) combination. Each item is colored by its prediction and\n carries the reference and mutant codon, amino-acid position, transcript\n accession, and a pre-rendered mouseover summary. |
\nThe NMDetective-AI signal track is drawn with a default y-axis range of\n−1.1 to +1.5. Positions with positive values (predicted NMD-triggering)\nare shown above the baseline; positions with negative values (predicted NMD\nescape) are shown below.\n
\n\n\nThe NMDetective-AI variants track colors each item along a continuous\ndiverging Okabe-Ito palette running from blue (most NMD-evading) through grey\n(near zero) to vermillion (most NMD-triggering). The mouseover verdict groups\nitems into three categories using the binarization thresholds derived in the\nVeiner et al. Methods (Gaussian mixture model fit to gnomAD\npredictions):\n
\n\nMouseover for each variant shows the codon change, the prediction value with\nits NMD verdict, and the MANE Select transcript accession. Click an item to\nsee the full set of fields on the details page.\n
\n\n\nNMDetective-AI is a fine-tuned version of the Orthrus mRNA foundation model\n(Mamba architecture, ~10M parameters), trained on full-length transcript\nsequences encoded as a six-track representation (four nucleotide channels,\none CDS-start channel, one splice-site channel). The model integrates\nallele-specific PTC expression from large-scale genomic data with mRNA\nlanguage-model embeddings and high-throughput deep mutational scanning, and\npredicts NMD efficiency for every possible stop-gain mutation in every codon\nof a MANE Select transcript.\n
\n\n\nThe training set comprised 14,337 somatic PTCs from TCGA, with chromosomes 1\nand 20 held out as a validation set. The held-out test set comprised 1,065\ngermline PTCs from TCGA and 763 germline PTCs from GTEx. The authors report\nthat the model's accuracy on the somatic validation set approaches the\nempirical reproducibility ceiling of the underlying allele-specific\nexpression measurements.\n
\n\n\nThe publicly released predictions cover MANE Select transcripts at Gencode\nv46. Predictions for transcripts outside the MANE Select set are not yet\navailable; broader coverage is planned by the authors after peer review.\n
\n\n\nSource files were obtained from the\nVejni/NMDetectiveAI\nGitHub repository (supplementary files\nNMDetectiveAI_MANE.bw.gz and NMDetectiveAI_MANE.bed.gz) and\nprocessed at UCSC: the bigWig is used as supplied; the BED was recolored with\nthe diverging Okabe-Ito palette described above, rescored into the\n0–1000 BED range, and augmented with a pre-rendered mouseover column\nbefore conversion to bigBed.\n
\n\n\nNote: the manuscript is currently a bioRxiv preprint and has not yet\ncompleted peer review. Predictions may be refreshed when the final version\nof the data is released.\n
\n\n\nThe data underlying these tracks can be explored interactively with the\nTable Browser or the\nData Integrator. For automated analysis,\nthe data may be queried from our\nREST API. Please refer to our\nmailing list archives for questions, or our\nData Access FAQ for more\ninformation.\n
\n\n\nThanks to Marcell Veiner and Fran Supek for sharing the NMDetective-AI\npredictions ahead of publication, and to the wider Veiner et al.\nauthor group for developing the model.\n
\n\n\nVeiner M, Toledano I, Palou-Márquez G, Lehner B, Supek F.\n\nQuantitative prediction of nonsense-mediated mRNA decay across human genes by\ngenomic language model and large-scale mutational scanning.\nbioRxiv. 2026 Mar 26.\ndoi: 10.64898/2026.03.24.714003.\nSupplementary prediction files at\ngithub.com/Vejni/NMDetectiveAI.\n
\n", "longLabel": "NMDetective-AI: Deep-learning NMD efficiency prediction per position (MANE Select only)", "maxHeightPixels": "128:120:8", "parent": "nmd off", "priority": "1.7", "shortLabel": "NMDetective-AI", "track": "nmdDetectiveAi", "type": "bigWig", "viewLimits": "-1.1:1.5", "visibility": "hide" } }, "description": "NMDetective-AI: Deep-learning NMD efficiency prediction per position (MANE Select only)", "category": [ "Genes and Gene Predictions" ] }, { "trackId": "hg38-nmdDetectiveAiBed", "name": "NMD Escape - NMDetective-AI variants", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/nmd/nmdDetectAi.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/nmd/nmdDetectAi.bb", "html": "\nThe NMDetective-AI tracks display deep-learning predictions of\nnonsense-mediated mRNA decay (NMD) efficiency for every possible stop-gain\nsingle-nucleotide variant in MANE Select transcripts. The model was trained on\n~14,000 somatic premature termination codons (PTCs) measured by allele-specific\nexpression in large human cohorts (TCGA) and was tested on ~1,800 held-out\ngermline PTCs (TCGA germline and GTEx) (Veiner et al.).\n
\n\n\nPredictions are continuous: higher values indicate that a PTC at that codon is\npredicted to trigger NMD (the mRNA is degraded); lower values indicate that the\nPTC is predicted to evade NMD (the truncated mRNA may be translated into an\naberrant protein). The output is normalized against canonical controls so that\n+0.5 corresponds to full NMD efficiency at a PTC and −0.5\ncorresponds to no NMD efficiency (a last-exon PTC). The scale is not strictly\nbounded: due to measurement and prediction noise, observed values fall in\nroughly −1.1 to +1.5, with the bulk of items inside the nominal\n−0.5 to +0.5 interval.\n
\n\n| Track | Description |
|---|---|
| NMDetective-AI | \nSignal track (bigWig) showing the position-averaged prediction across\n all stop-gain SNVs at each codon. Useful for browsing efficiency along a\n transcript at a glance. |
| NMDetective-AI variants | \nPer-stop-gain track (bigBed) with one item per (transcript, codon,\n mutant codon) combination. Each item is colored by its prediction and\n carries the reference and mutant codon, amino-acid position, transcript\n accession, and a pre-rendered mouseover summary. |
\nThe NMDetective-AI signal track is drawn with a default y-axis range of\n−1.1 to +1.5. Positions with positive values (predicted NMD-triggering)\nare shown above the baseline; positions with negative values (predicted NMD\nescape) are shown below.\n
\n\n\nThe NMDetective-AI variants track colors each item along a continuous\ndiverging Okabe-Ito palette running from blue (most NMD-evading) through grey\n(near zero) to vermillion (most NMD-triggering). The mouseover verdict groups\nitems into three categories using the binarization thresholds derived in the\nVeiner et al. Methods (Gaussian mixture model fit to gnomAD\npredictions):\n
\n\nMouseover for each variant shows the codon change, the prediction value with\nits NMD verdict, and the MANE Select transcript accession. Click an item to\nsee the full set of fields on the details page.\n
\n\n\nNMDetective-AI is a fine-tuned version of the Orthrus mRNA foundation model\n(Mamba architecture, ~10M parameters), trained on full-length transcript\nsequences encoded as a six-track representation (four nucleotide channels,\none CDS-start channel, one splice-site channel). The model integrates\nallele-specific PTC expression from large-scale genomic data with mRNA\nlanguage-model embeddings and high-throughput deep mutational scanning, and\npredicts NMD efficiency for every possible stop-gain mutation in every codon\nof a MANE Select transcript.\n
\n\n\nThe training set comprised 14,337 somatic PTCs from TCGA, with chromosomes 1\nand 20 held out as a validation set. The held-out test set comprised 1,065\ngermline PTCs from TCGA and 763 germline PTCs from GTEx. The authors report\nthat the model's accuracy on the somatic validation set approaches the\nempirical reproducibility ceiling of the underlying allele-specific\nexpression measurements.\n
\n\n\nThe publicly released predictions cover MANE Select transcripts at Gencode\nv46. Predictions for transcripts outside the MANE Select set are not yet\navailable; broader coverage is planned by the authors after peer review.\n
\n\n\nSource files were obtained from the\nVejni/NMDetectiveAI\nGitHub repository (supplementary files\nNMDetectiveAI_MANE.bw.gz and NMDetectiveAI_MANE.bed.gz) and\nprocessed at UCSC: the bigWig is used as supplied; the BED was recolored with\nthe diverging Okabe-Ito palette described above, rescored into the\n0–1000 BED range, and augmented with a pre-rendered mouseover column\nbefore conversion to bigBed.\n
\n\n\nNote: the manuscript is currently a bioRxiv preprint and has not yet\ncompleted peer review. Predictions may be refreshed when the final version\nof the data is released.\n
\n\n\nThe data underlying these tracks can be explored interactively with the\nTable Browser or the\nData Integrator. For automated analysis,\nthe data may be queried from our\nREST API. Please refer to our\nmailing list archives for questions, or our\nData Access FAQ for more\ninformation.\n
\n\n\nThanks to Marcell Veiner and Fran Supek for sharing the NMDetective-AI\npredictions ahead of publication, and to the wider Veiner et al.\nauthor group for developing the model.\n
\n\n\nVeiner M, Toledano I, Palou-Márquez G, Lehner B, Supek F.\n\nQuantitative prediction of nonsense-mediated mRNA decay across human genes by\ngenomic language model and large-scale mutational scanning.\nbioRxiv. 2026 Mar 26.\ndoi: 10.64898/2026.03.24.714003.\nSupplementary prediction files at\ngithub.com/Vejni/NMDetectiveAI.\n
\n", "itemRgb": "on", "longLabel": "NMDetective-AI: Per-stop-gain predictions for every codon (MANE Select only)", "mouseOverField": "mouseOver", "parent": "nmd off", "priority": "1.8", "shortLabel": "NMDetective-AI variants", "track": "nmdDetectiveAiBed", "type": "bigBed 9 +", "visibility": "hide" } }, "description": "NMDetective-AI: Per-stop-gain predictions for every codon (MANE Select only)", "category": [ "Genes and Gene Predictions" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-nmdDetectiveAiBed-LinearBasicDisplay", "mouseover": "jexl:get(feature,'mouseOver')" } ] }, { "trackId": "hg38-aou1kSv", "name": "Long-read SVs - AoU 1027 SVs", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/lrSv/aou1k.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/lrSv/aou1k.bb", "filter.AC": "0:2054", "filter.insLen": "0:9998", "filter.svLen": "0:9905", "filterByRange.AC": "on", "filterByRange.afAfr": "on", "filterByRange.afEas": "on", "filterByRange.afEur": "on", "filterByRange.insLen": "on", "filterByRange.svLen": "on", "filterLabel.AC": "Allele Count (approx)", "filterLabel.afAfr": "AF African", "filterLabel.afEas": "AF East Asian", "filterLabel.afEur": "AF European", "filterLabel.insLen": "Insertion Length", "filterLabel.svLen": "SV Length", "filterLabel.svType": "SV Type", "filterLimits.afAfr": "0:1", "filterLimits.afEas": "0:1", "filterLimits.afEur": "0:1", "filterType.svType": "multipleListOr", "filterValues.svType": "DEL,INS", "itemRgb": "on", "longLabel": "Structural Variants from 1,027 All of Us samples (PacBio HiFi)", "mouseOver": "Var: $name ($svType)\nThis track shows structural variants (SVs) identified by PacBio HiFi long-read\nsequencing of 1,027 individuals from the All of Us (AoU) Research Program.\nParticipants self-identified as Black or African American and were sequenced\nto ~8x coverage. The track contains 540,155 SVs (443,630 insertions and\n96,525 deletions) on autosomes, after removing byte-identical duplicate records\nfrom the 541,049-row release.\n
\n\nSVs are annotated with population-specific allele frequencies across five\nancestry groups (African, Admixed American, East Asian, European, South Asian),\ngene intersections from curated disease gene lists (OMIM, ACMG, cancer genes),\nregulatory element overlaps, and associations with eQTLs, GWAS loci, and\nclinical phenotypes from the AoU electronic health records.\n
\n\n\nItems are colored by SV type:\n
\nFilters are available for SV type, SV length, and population-specific allele\nfrequencies. For insertions, the item is placed at the insertion site with a\nwidth of 1 bp; for deletions, the item spans the deleted region.\n
\n\nThe detail page shows the following annotations when available:\n
\nGarimella et al. 2025 performed PacBio HiFi long-read sequencing on 1,027\nAll of Us participants self-identifying as Black or African American, to\n~8x per-sample coverage at HudsonAlpha Discovery. SVs (≥50 bp) were\ncalled per sample with an ensemble of three methods: two alignment-based\ncallers, \nPBSV v2.6.0 (with Tandem Repeat Finder context) and\nSniffles2\nv2.0.6, plus the assembly-based PAV v1.2.1 (hifiasm haplotype-resolved contigs aligned\nto GRCh38 with minimap2 -x asm20). Per-caller VCFs were normalized,\nmerged within and across samples and filtered into stringent and lenient\ntiers, and the callset was re-genotyped across the cohort to produce the\nfinal release: 541,049 autosomal SVs (444,524 insertions, 96,525 deletions)\nwith per-ancestry allele frequencies (AFR, AMR, EAS, EUR, SAS) and gene,\nregulatory, eQTL, GWAS and EHR-phenotype annotations.\n
\n\nThis track was built from the supplementary media-2 table of the AoU\nlong-read sequencing preprint\n(\ndoi:10.1101/2025.10.02.25336942). Access to the underlying AoU\nlong-read data requires registration through the\nAll of Us\nResearch Hub.\n
\n\nThe step-by-step build commands (download, format conversion, bigBed build)\nare recorded in the UCSC makeDoc for this track container:\n\ndoc/hg38/lrSv.txt. The conversion scripts and autoSql schemas live in\n\nmakeDb/scripts/lrSv, and the track configuration is in trackDb/human/lrSv.ra.\n
\n\n\nThis track was built from supplementary data (media-2) of the AoU long-read\nsequencing preprint. Access to the full AoU dataset requires registration\nthrough the All of\nUs Research Hub.\n
\n\n\nThanks to Garimella et al. and the All of Us Research Program for making their\nstructural variant annotations publicly available.\n
\n\n\nGarimella KV, Li Q, Wertz J, Lee SK, Cunial F, Huang Y, Mostovoy Y, Lorig-Roach R, English A, Su H\net al.\n\nPopulation-scale Long-read Sequencing in the All of Us Research Program.\nmedRxiv. 2025 Oct 5;.\nPMID: 41256123; PMC:
\nGnomAD 4 used the whole-genome data from gnomAD 3 and added more exomes.\nThe v4.1 release included a fix for the allele number\nissue.\nThe current v4.1.1 release, from March 30, 2026, revises the LOFTEE END_TRUNC GERP distance\nthreshold from -58.0 to 0.0. This reclassifies about 79,920 predicted loss-of-function (pLoF)\nvariants from high-confidence to low-confidence. For more information, see the related
\nThe track shows variants from 807,162 individuals, including 730,947\nexomes and 76,215 genomes. This includes the 76,156 genomes from the gnomAD v3.1.2 release as well\nas exome data from 416,555 UK Biobank individuals.\n
\n\n\nFollowing the conventions on the gnomAD browser, items are shaded according to their Annotation\ntype:\n
| pLoF | |
| Missense | |
| Synonymous | |
| Other |
\nMouse hover on an item will display the following details about each variant:
\n\nClicking on an item will display additional details on the variant, including a population frequency\ntable showing allele count in each sub-population.\n
\n\n\nTo maintain consistency with the gnomAD website, variants are by default labeled according\nto their chromosomal start position followed by the reference and alternate alleles,\nfor example "chr1-1234-T-CAG". dbSNP rsID's are also available as an additional\nlabel, if the variant is present in dbSnp.\n
\n\n\nThree filters are available for this track:\n
\n\nThe gnomAD v4.1.1 data is unfiltered.
\n\n\nFor the full steps used to create the gnomAD tracks at UCSC, please see the\n
\nThe raw data can be explored interactively with the \nTable Browser, or the Data Integrator. For\nautomated analysis, the data may be queried from our REST API, and the genome annotations are stored in files that\ncan be downloaded from our
\nThe underlying bigBed only contains enough information necessary to use the track in the browser.\nThe extra data like VEP annotations and CADD scores are available in the\nsame directory\nas the bigBed but in the files details.tab.gz and details.tab.gz.gzi. The\ndetails.tab.gz contains the gzip compressed extra data in JSON format, and the .gzi file is\navailable to speed searching of this data. Each variant has an associated md5sum in the name field\nof the bigBed which can be used along with the _dataOffset and _dataLen fields to get the\nassociated external data. For example:
\n\n\n# find an item of interest, the last two fields are _dataOffset and _dataLen:\nbigBedToBed genomes.bb stdout | head -4 | tail -1\nchr1 12416 12417 854246d79dc5d02dcdbd5f5438542b6e [..omitted..] 67293 902\n\n# use _dataOffset and _dataLen (add one to _dataLen for the newline character):\nbgzip -b 67293 -s 903 gnomad.v4.1.1.genomes.details.tab.gz\n854246d79dc5d02dcdbd5f5438542b6e {\"DDX11L1\": {\"cons\": [\"non_coding_transcript_variant\"...\n\n\n\nThe data can also be found directly from the gnomAD downloads page. Please refer to\nour mailing list archives for questions, or our Data Access FAQ for more information.
\n\n\nThanks to the Genome Aggregation\nDatabase Consortium for making these data available. The data are released under the
\nPlease note that some annotations within the provided files may have restrictions on usage. See here for more information.\n
\n\n\nChen S, Francioli LC, Goodrich JK, Collins RL, Kanai M, Wang Q, Alföldi J, Watts NA, Vittal C,\nGauthier LD et al.\n\n A genomic mutational constraint map using variation in 76,156 human genomes.\nNature. 2024 Jan;625(7993):92-100.\nPMID: 38057664\n
\n\nKarczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, Collins RL, Laricchia KM, Ganna\nA, Birnbaum DP et al.\n\nThe mutational constraint spectrum quantified from variation in 141,456 humans.\nNature. 2020 May;581(7809):434-443.\nPMID: 32461654; PMC:
\nLek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill\nAJ, Cummings BB et al.\nAnalysis of protein-coding\ngenetic variation in 60,706 humans. Nature. 2016 Aug 17;536(7616):285-91.\nPMID: 27535533;\nPMC: PMC5018207\n
\n", "itemRgb": "on", "labelFields": "rsId,_displayName", "longLabel": "Genome Aggregation Database (gnomAD) Exomes Variants v4.1", "mouseOver": "Position: $chrom:${chromStart}-${chromEnd} ($ref/$alt)\nThe \"Constraint scores\" container track includes several subtracks showing the results of\nconstraint prediction algorithms. These try to find regions of negative\nselection, where variations likely have functional impact. The algorithms do\nnot use multi-species alignments to derive evolutionary constraint, but use\nprimarily human variation, usually from variants collected by gnomAD (see the\ngnomAD V2 or V3 tracks on hg19 and hg38) or TOPMED (contained in our dbSNP\ntracks and available as a filter). One of the subtracks is based on UK Biobank\nvariants, which are not available publicly, so we have no track with the raw data.\nThe number of human genomes that are used as the input for these scores are\n76k, 53k and 110k for gnomAD, TOPMED and UK Biobank, respectively.\n
\n\nNote that another important constraint score, gnomAD\nconstraint, is not part of this container track but can be found in the hg38 gnomAD\ntrack.\n
\n\nThe algorithms included in this track are:\n\nJARVIS scores are shown as a signal (\"wiggle\") track, with one score per genome position.\nMousing over the bars displays the exact values. The scores were downloaded and converted to a single bigWig file.\nMove the mouse over the bars to display the exact values. A horizontal line is shown at the 0.733\nvalue which signifies the 90th percentile.
\nSee hg19 makeDoc and\nhg38 makeDoc.\n\nInterpretation: The authors offer a suggested guideline of > 0.9998 for identifying\nhigher confidence calls and minimizing false positives. In addition to that strict threshold, the \nfollowing two more relaxed cutoffs can be used to explore additional hits. Note that these\nthresholds are offered as guidelines and are not necessarily representative of pathogenicity.
\n\n\n
| Percentile | JARVIS score threshold |
|---|---|
| 99th | 0.9998 |
| 95th | 0.9826 |
| 90th | 0.7338 |
\nHMC scores are displayed as a signal (\"wiggle\") track, with one score per genome position.\nMousing over the bars displays the exact values. The highly-constrained cutoff\nof 0.8 is indicated with a line.
\n\nInterpretation: \nA protein residue with HMC score <1 indicates that missense variants affecting\nthe homologous residues are significantly under negative selection (P-value <\n0.05) and likely to be deleterious. A more stringent score threshold of HMC<0.8\nis recommended to prioritize predicted disease-associated variants.\n
\n\n\nInterpretation: The authors suggest the following guidelines for evaluating\nintolerance. By default, the MetaDome track displays a horizontal line at 0.7 which \nsignifies the first intolerant bin. For more information see the MetaDome publication.
\n\n\n
| Classification | MetaDome Tolerance Score |
|---|---|
| Highly intolerant | ≤ 0.175 |
| Intolerant | ≤ 0.525 |
| Slightly intolerant | ≤ 0.7 |
\nMTR data can be found on two tracks, MTR All data and MTR Scores. In the\nMTR Scores track the data has been converted into 4 separate signal tracks\nrepresenting each base pair mutation, with the lowest possible score shown when\nmultiple transcripts overlap at a position. Overlaps can happen since this score\nis derived from transcripts and multiple transcripts can overlap. \nA horizontal line is drawn on the 0.8 score line\nto roughly represent the 25th percentile, meaning the items below may be of particular\ninterest. It is recommended that the data be explored using\nthis version of the track, as it condenses the information substantially while\nretaining the magnitude of the data.
\n\nAny specific point mutations of interest can then be researched in the \nMTR All data track. This track contains all of the information from\n\nMTRV2 including more than 3 possible scores per base when transcripts overlap.\nA mouse-over on this track shows the ref and alt allele, as well as the MTR score\nand the MTR score percentile. Filters are available for MTR score, False Discovery Rate\n(FDR), MTR percentile, and variant consequence. By default, only items in the bottom\n25 percentile are shown. Items in the track are colored according\nto their MTR percentile:
\n\nInterpretation: Regions with low MTR scores were seen to be enriched with\npathogenic variants. For example, ClinVar pathogenic variants were seen to\nhave an average score of 0.77 whereas ClinVar benign variants had an average score\nof 0.92. Further validation using the FATHMM cancer-associated training dataset saw\nthat scores less than 0.5 contained 8.6% of the pathogenic variants while only containing\n0.9% of neutral variants. In summary, lower scores are more likely to represent\npathogenic variants whereas higher scores could be pathogenic, but have a higher chance\nto be a false positive. For more information see the MTR-Viewer publication.
\n\n\nScores were downloaded and converted to a single bigWig file. See the\nhg19 makeDoc and the\nhg38 makeDoc for more info.\n
\n\n\nScores were downloaded and converted to .bedGraph files with a custom Python \nscript. The bedGraph files were then converted to bigWig files, as documented in our \nmakeDoc hg19 build log.
\n\n\nThe authors provided a bed file containing codon coordinates along with the scores. \nThis file was parsed with a python script to create the two tracks. For the first track\nthe scores were aggregated for each coordinate, then the lowest score chosen for any\noverlaps and the result written out to bedGraph format. The file was then converted\nto bigWig with the bedGraphToBigWig utility. For the second track the file\nwas reorganized into a bed 4+3 and conveted to bigBed with the bedToBigBed\nutility.
\nSee the hg19 makeDoc for details including the build script.
\n\nThe raw MetaDome data can also be accessed via their Zenodo handle.
\n\n\nV2\nfile was downloaded and columns were reshuffled as well as itemRgb added for the\nMTR All data track. For the MTR Scores track the file was parsed with a python\nscript to pull out the highest possible MTR score for each of the 3 possible mutations\nat each base pair and 4 tracks built out of these values representing each mutation.
\n\nSee the hg19 makeDoc entry on MTR for more info.
\n\n\nThe raw data can be explored interactively with the Table Browser, or\nthe Data Integrator. For automated access, this track, like all\nothers, is available via our API. However, for bulk\nprocessing, it is recommended to download the dataset.\n
\n\n\nFor automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed\nfiles that can be downloaded from\nour download server.\nIndividual regions or the whole genome annotation can be obtained using our tools bigWigToWig\nor bigBedToBed which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tools can also be used to obtain features confined to a given range, e.g.,\n
\nbigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/hmc/hmc.bw stdout\n
\n
\nPlease refer to our\nData Access FAQ\nfor more information.\n
\n\n\n\nThanks to Jean-Madeleine Desainteagathe (APHP Paris, France) for suggesting the JARVIS, MTR, HMC tracks. Thanks to Xialei Zhang for providing the HMC data file and to Dimitrios Vitsios and Slave Petrovski for helping clean up the hg38 JARVIS files for providing guidance on interpretation. Additional\nthanks to Laurens van de Wiel for providing the MetaDome data as well as guidance on the track development and interpretation. \n
\n\n\n\nVitsios D, Dhindsa RS, Middleton L, Gussow AB, Petrovski S.\n\n Prioritizing non-coding regions based on human genomic constraint and sequence context with deep\n learning.\nNat Commun. 2021 Mar 8;12(1):1504.\nPMID: 33686085; PMC:
\nXiaolei Zhang, Pantazis I. Theotokis, Nicholas Li, the SHaRe Investigators, Caroline F. Wright, Kaitlin E. Samocha, Nicola Whiffin, James S. Ware\n\nGenetic constraint at single amino acid resolution improves missense variant prioritisation and gene discovery.\nMedrxiv 2022.02.16.22271023\n
\n\n\nWiel L, Baakman C, Gilissen D, Veltman JA, Vriend G, Gilissen C.\n\nMetaDome: Pathogenicity analysis of genetic variants through aggregation of homologous human protein\ndomains.\nHum Mutat. 2019 Aug;40(8):1030-1038.\nPMID: 31116477; PMC:
\nSilk M, Petrovski S, Ascher DB.\n\nMTR-Viewer: identifying regions within genes under purifying selection.\nNucleic Acids Res. 2019 Jul 2;47(W1):W121-W126.\nPMID: 31170280; PMC:
\nHalldorsson BV, Eggertsson HP, Moore KHS, Hauswedell H, Eiriksson O, Ulfarsson MO, Palsson G,\nHardarson MT, Oddsson A, Jensson BO et al.\n\n The sequences of 150,119 genomes in the UK Biobank.\nNature. 2022 Jul;607(7920):732-740.\nPMID: 35859178; PMC:
\nHuang YF, Gulko B, Siepel A.\n\nFast, scalable prediction of deleterious noncoding variants from functional and population genomic\ndata.\nNat Genet. 2017 Apr;49(4):618-624.\nPMID: 28288115; PMC:
\nThe MPRAVarDB track shows 239,028 variants successfully mapped to hg38\n(from 242,818 total) across 18 MPRA studies compiled in the MPRAVarDB database\n(Jin et al., 2024).\nEach variant was experimentally tested in an MPRA experiment to evaluate whether it\naffects regulatory activity. The database covers over 30 cell lines and 30 human\ndiseases and traits, including neurodegenerative diseases, immune disorders,\nmelanoma, multiple myeloma, and autoimmune diseases.\n
\n\nNote on cell lines: The cell line shown for each variant is the reporter\ncell line in which the human regulatory element was assayed. Several studies\nused mouse cell lines (e.g. Neuro-2a, N2A, NIH/3T3, MIN6) as reporter systems\nfor human sequences; these variants retain human (hg38) coordinates.\n
\n\n\nNote on study type: Not all studies measure transcriptional regulation\nin the same sense. Two of the larger contributors,\nGriesemer\net al., 2021 (72,546 variants) and\nSchuster\net al., 2023 (26,546 variants), test 3'UTR variants placed downstream\nof the reporter, where the log2 fold change between alleles reflects changes\nin mRNA stability, decay, RBP or miRNA binding, or translation efficiency\nrather than transcriptional activation. The remaining studies test 5'\nregulatory elements (promoters and enhancers) where log2FC reflects changes\nin transcription. Together, the 3'UTR studies account for 99,092 of the\n239,028 variants in the track (~41%).\n
\n\n\nItems are colored by statistical significance:\n
\nEach item shows the variant name (rsID when available, otherwise chr:pos:ref>alt),\nthe reference and alternate alleles, the associated disease or trait, cell line,\nlog2 fold change, p-value, and FDR.\n
\n\n\nCell-type specificity: MPRA results are typically cell-type-specific,\nand significance in one cell line does not imply activity in another. For\nexample, Tewhey\net al., 2016 found only modest correlation (R ≈ 0.63)\nbetween LCL and HepG2 measurements of the same eQTL variants, and\nMcAfee\net al., 2023 reported that only 205 of 1,004 HEK293-positive\nvariants overlapped HNP-positive variants. The cell line filter can be used\nto narrow results to a relevant context.\n
\n\n\nNote on Kircher et al., 2019:\nThis study\ncontributes 44,647 variants (~19% of the track) using a saturation mutagenesis\ndesign that tests nearly every possible nucleotide substitution at each\nposition of 20 disease-associated regulatory elements at single-base-pair\nresolution: 10 promoters (TERT, LDLR, HBB, HBG1, HNF4A, MSMB, PKLR, F9,\nFOXE1, GP1BB) and 10 enhancers (SORT1, ZRS, BCL11A, IRF4, IRF6, MYC tested\nwith two distinct enhancers, RET, TCF7L2, and the UC88 ultraconserved\nenhancer). Regions over those elements show many densely-packed Kircher\nvariants that may dominate visualization at those loci.\n
\n\n\nThe log2 fold change is computed as\nlog2(alt RNA/DNA) − log2(ref RNA/DNA).\nA positive value means the alternate allele drove more reporter activity than\nthe reference allele in this assay; a negative value means the reverse. The\nlinear allelic ratio is approximately 2log2FC: log2FC = 0.5\ncorresponds to roughly 1.41× allelic difference, log2FC = 1.0\nto 2×, and log2FC = 2.0 to 4×. As noted in the\nDescription section, log2FC reflects transcriptional activation for\n5'-regulatory studies and steady-state mRNA abundance, decay, or translation\nefficiency for 3'UTR studies (Griesemer et al., 2021; Schuster\net al., 2023).\n
\n\n\nThe following table lists the 18 MPRA studies included in MPRAVarDB, with the number of\ntested variants, diseases/traits, cell lines, and a brief description of the variant selection.\n
\n\n| Study | \nVariants | \nDisease/Trait | \nCell Line(s) | \nDescription | \n
|---|---|---|---|---|
| Griesemer et al., 2021 | \n72,546 | \nNHGRI-EBI GWAS catalog | \nGM12878, HEK293FT, HMEC, HepG2, K562, SKNSH | \n3'UTR SNPs and indels in LD with GWAS catalog variants, variants under positive selection, and rare outlier expression variants from GTEx | \n
| Kircher et al., 2019 | \n44,647 | \nVarious (18 diseases including diabetes, cancer, blood disorders, limb malformations) | \nHEK293T, HEL92.1.7, HaCaT, HeLa, HepG2, K562, LNCaP, MIN6, NIH/3T3, Neuro-2a, SK-MEL-28, SF7996 | \nSaturation mutagenesis of 20 disease-associated regulatory elements at single base-pair resolution | \n
| Abell et al., 2022 | \n29,564 | \neQTL (no specific disease) | \nGM12878 | \n30,893 variants in LD with independent, common, top-ranked eQTL across 744 eGenes in the CEU cohort | \n
| Tewhey et al., 2016 | \n23,430 | \neQTL (no specific disease) | \nGM12878 | \n32,373 variants associated with eQTLs in lymphoblastoid cell lines | \n
| Schuster et al., 2023 | \n26,546 | \nProstate cancer | \nPC3 | \n14,497 single-nucleotide mutations enriched in oncogenic pathways and 3'UTR regulatory elements | \n
| Mouri et al., 2022 | \n14,549 | \nAutoimmune diseases (Crohn's, IBD, psoriasis, MS, RA, T1D, ulcerative colitis) | \nJurkat | \nGWAS variants from autoimmune disease loci tested for regulatory element activity in T cells | \n
| McAfee et al., 2023 | \n10,302 | \nSchizophrenia | \nHEK293s, HNPS | \n5,173 fine-mapped schizophrenia GWAS variants | \n
| Cooper et al., 2022 | \n5,330 | \nAlzheimer's disease, Progressive supranuclear palsy | \nHEK293T | \n5,706 noncoding SNVs from 25 AD and 9 PSP genome-wide significant loci | \n
| Long et al., 2022 | \n3,980 | \nMelanoma | \nC283T, UACC903 | \n1,992 risk-associated variants in tight LD (r2>0.8) from 54 melanoma risk loci | \n
| Myint et al., 2020 | \n2,158 | \nSchizophrenia, Alzheimer's disease | \nK562, SH-SY5Y | \n1,049 SZ and 30 AD variants in 64 SZ loci and 9 AD loci | \n
| Choi et al., 2020 | \n1,664 | \nMelanoma | \nHEK293FT, UACC903 | \nGWAS melanoma risk variants | \n
| Ajore et al., 2022 | \n1,582 | \nMultiple myeloma | \nL363, MOLP8 | \n1,039 variants in high LD (r2>0.8) at 23 MM risk loci | \n
| Klein et al., 2019 | \n1,119 | \nOsteoarthritis | \nSaos-2 | \n1,605 SNPs in high LD (r2>0.8) at 35 lead SNPs associated with OA via GWAS | \n
| Lu et al., 2021 | \n1,036 | \nSystemic lupus erythematosus | \nGM12878, Jurkat | \n18,312 variants in tight LD (r2>0.8) with 578 GWAS index variants at 531 loci | \n
| Mulvey & Dougherty, 2021 | \n275 | \nMajor depressive disorder | \nN2A | \nOver 1,000 SNPs from 39 neuropsychiatric GWAS loci, selected by overlap with eQTL and histone marks | \n
| Ferraro et al., 2020 | \n150 | \nRare variant expression (no specific disease) | \nGM12878 | \nRare variants contributing to extreme expression, allelic expression, and splicing across 49 GTEx tissues | \n
| Rao et al., 2021 | \n88 | \nAlcohol use disorder | \nBLA, CE, NAC, SFC | \nSNPs in 3'UTR of 88 genes from allele-specific expression analysis (30 AUD subjects vs 30 controls) | \n
| Ulirsch et al., 2016 | \n62 | \nRed blood cell traits | \nK562, K562+GATA1 | \n2,756 variants in strong LD with 75 sentinel variants associated with RBC traits | \n
\nVariant counts above are from the source publications (pre-liftOver totals).\nOf 242,818 total source variants, 239,028 lifted successfully to hg38; see Methods.\n
\n\n\nData was downloaded from the\nMPRAVarDB web server.\nVariants originally mapped to hg19 (213,689 of 242,818) were lifted to hg38\nusing liftOver. 114 variants could not be mapped and were excluded.\nThe remaining variants were merged with the 29,129 natively hg38-mapped variants\nto produce a total of 239,028 hg38 records.\n
\nSignificance thresholds across studies: The source studies in MPRAVarDB\ndo not all use the same significance framework. Most studies apply a\nBenjamini-Hochberg FDR threshold (commonly 0.05 or 0.10), but some report only\nnominal regression p-values. For example,\nTewhey\net al., 2016 uses BH FDR < 0.05 to call \"emVars\",\nGriesemer\net al., 2021 and\nMcAfee\net al., 2023 use BH FDR < 0.10, and\nKircher\net al., 2019 reports raw regression p-values rather than FDR. The\ntrack applies a uniform FDR < 0.05 / nominal\np < 0.05 color cutoff for visual consistency, which is the\nmore conservative of the FDR thresholds reported by the source studies. For\nany variant of interest, consult the source publication for the original\nsignificance call.\n
\n\n\nThe data can be explored interactively in table format with the\nTable Browser or the\nData Integrator\nand exported from there to spreadsheet or tab-sep tables.\nFrom scripts, the data can be accessed through our\nAPI, track=mpraVarDb.\n
\n\nFor automated download and analysis, the genome annotation is stored in a bigBed\nfile that can be downloaded from\nour download server.\nThe file for this track is called mpravardb.bb. Individual\nregions or the whole genome annotation can be obtained using our tool\nbigBedToBed, which can be compiled from the source code or downloaded as a\nprecompiled binary for your system. Instructions for downloading source code and\nbinaries can be found\nhere.\nThe tool can also be used to obtain features within a given range, e.g.\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/mpra/mpravardb/mpravardb.bb -chrom=chr21 -start=0 -end=100000000 stdout\n
\n\nThe original annotation source data can be downloaded from the\nMPRAVarDB web server.\n
\n\n\nThanks to Weijia Jin and colleagues at the University of Florida for creating\nand maintaining the MPRAVarDB database.\n
\n\n\nAbell NS, DeGorter MK, Gloudemans MJ, Greenwald E, Smith KS, He Z, Montgomery SB.\n\nMultiple causal variants underlie genetic associations in humans.\nScience. 2022 Mar 18;375(6586):1247-1254.\nPMID: 35298243; PMC:
\nAjore R, Niroula A, Pertesi M, Cafaro C, Thodberg M, Went M, Bao EL, Duran-Lozano L, Lopez de\nLapuente Portilla A, Olafsdottir T et al.\n\nFunctional dissection of inherited non-coding variation influencing multiple myeloma risk.\nNat Commun. 2022 Jan 10;13(1):151.\nPMID: 35013207; PMC:
\nChoi J, Zhang T, Vu A, Ablain J, Makowski MM, Colli LM, Xu M, Hennessey RC, Yin J, Rothschild H\net al.\n\nMassively parallel reporter assays of melanoma risk variants identify MX2 as a gene promoting\nmelanoma.\nNat Commun. 2020 Jun 1;11(1):2718.\nPMID: 32483191; PMC:
\nCooper YA, Teyssier N, Dräger NM, Guo Q, Davis JE, Sattler SM, Yang Z, Patel A, Wu S, Kosuri S\net al.\n\nFunctional regulatory variants implicate distinct transcriptional networks in dementia.\nScience. 2022 Aug 19;377(6608):eabi8654.\nPMID: 35981026\n
\n\n\nFerraro NM, Strober BJ, Einson J, Abell NS, Aguet F, Barbeira AN, Brandt M, Bucan M, Castel SE,\nDavis JR et al.\n\nTranscriptomic signatures across human tissues identify functional rare genetic variation.\nScience. 2020 Sep 11;369(6509).\nPMID: 32913073; PMC:
\nGriesemer D, Xue JR, Reilly SK, Ulirsch JC, Kukreja K, Davis JR, Kanai M, Yang DK, Butts JC, Guney\nMH et al.\n\nGenome-wide functional screen of 3'UTR variants uncovers causal variants for human disease and\nevolution.\nCell. 2021 Sep 30;184(20):5247-5260.e19.\nPMID: 34534445; PMC:
\nJin W, Xia Y, Nizomov J, Liu Y, Li Z, Lu Q, Chen L.\n\nMPRAVarDB: an online database and web server for exploring regulatory effects of genetic variants.\nBioinformatics. 2024 Oct 1;40(10).\nPMID: 39325859; PMC:
\nKircher M, Xiong C, Martin B, Schubach M, Inoue F, Bell RJA, Costello JF, Shendure J, Ahituv N.\n\nSaturation mutagenesis of twenty disease-associated regulatory elements at single base-pair\nresolution.\nNat Commun. 2019 Aug 8;10(1):3583.\nPMID: 31395865; PMC:
\nKlein JC, Keith A, Rice SJ, Shepherd C, Agarwal V, Loughlin J, Shendure J.\n\nFunctional testing of thousands of osteoarthritis-associated variants for regulatory activity.\nNat Commun. 2019 Jun 4;10(1):2434.\nPMID: 31164647; PMC:
\nLong E, Yin J, Funderburk KM, Xu M, Feng J, Kane A, Zhang T, Myers T, Golden A, Thakur R et\nal.\n\nMassively parallel reporter assays and variant scoring identified functional variants and target\ngenes for melanoma loci and highlighted cell-type specificity.\nAm J Hum Genet. 2022 Dec 1;109(12):2210-2229.\nPMID: 36423637; PMC:
\nLu X, Chen X, Forney C, Donmez O, Miller D, Parameswaran S, Hong T, Huang Y, Pujato M, Cazares T\net al.\n\nGlobal discovery of lupus genetic risk variant allelic enhancer activity.\nNat Commun. 2021 Mar 12;12(1):1611.\nPMID: 33712590; PMC:
\nMcAfee JC, Lee S, Lee J, Bell JL, Krupa O, Davis J, Insigne K, Bond ML, Zhao N, Boyle AP et\nal.\n\nSystematic investigation of allelic regulatory activity of schizophrenia-associated common\nvariants.\nCell Genom. 2023 Oct 11;3(10):100404.\nPMID: 37868037; PMC:
\nMouri K, Guo MH, de Boer CG, Lissner MM, Harten IA, Newby GA, DeBerg HA, Platt WF, Gentili M, Liu DR\net al.\n\nPrioritization of autoimmune disease-associated genetic variants that perturb regulatory element\nactivity in T cells.\nNat Genet. 2022 May;54(5):603-612.\nPMID: 35513721; PMC:
\nMulvey B, Dougherty JD.\n\nTranscriptional-regulatory convergence across functional MDD risk variants identified by massively\nparallel reporter assays.\nTransl Psychiatry. 2021 Jul 22;11(1):403.\nPMID: 34294677; PMC:
\nMyint L, Wang R, Boukas L, Hansen KD, Goff LA, Avramopoulos D.\n\nA screen of 1,049 schizophrenia and 30 Alzheimer's-associated variants for regulatory\npotential.\nAm J Med Genet B Neuropsychiatr Genet. 2020 Jan;183(1):61-73.\nPMID: 31503409; PMC:
\nRao X, Thapa KS, Chen AB, Lin H, Gao H, Reiter JL, Hargreaves KA, Ipe J, Lai D, Xuei X et\nal.\n\nAllele-specific expression and high-throughput reporter assay reveal functional genetic variants\nassociated with alcohol use disorders.\nMol Psychiatry. 2021 Apr;26(4):1142-1151.\nPMID: 31477794; PMC:
\nSchuster SL, Arora S, Wladyka CL, Itagi P, Corey L, Young D, Stackhouse BL, Kollath L, Wu QV, Corey\nE et al.\n\nMulti-level functional genomics reveals molecular and cellular oncogenicity of patient-based\n3'-untranslated region mutations.\nCell Rep. 2023 Aug 29;42(8):112840.\nPMID: 37516102; PMC:
\nTewhey R, Kotliar D, Park DS, Liu B, Winnicki S, Reilly SK, Andersen KG, Mikkelsen TS, Lander ES,\nSchaffner SF et al.\n\nDirect Identification of Hundreds of Expression-Modulating Variants using a Multiplexed Reporter\nAssay.\nCell. 2016 Jun 2;165(6):1519-1529.\nPMID: 27259153; PMC:
\nUlirsch JC, Nandakumar SK, Wang L, Giani FC, Zhang X, Rogov P, Melnikov A, McDonel P, Do R,\nMikkelsen TS et al.\n\nSystematic Functional Dissection of Common Genetic Variation Affecting Red Blood Cell Traits.\nCell. 2016 Jun 2;165(6):1530-1545.\nPMID: 27259154; PMC:
\nThe NMDetective tracks display genome-wide predictions of nonsense-mediated mRNA\ndecay (NMD) efficiency using the NMDetective-A and NMDetective-B models from\nLindeboom et al. 2019,\ntrained on the NMD efficiency measure from\nLindeboom et al. 2016.\nNMDetective scores predict whether a premature termination codon (PTC) at a given position\nwill trigger NMD and mRNA degradation, or whether the transcript will escape NMD and\npotentially produce a truncated protein.\n
\n\n\nScores range from 0 to 1. Values near 1 indicate that a PTC at\nthat position is predicted to trigger NMD (the mRNA is degraded). Values near 0 indicate\nthat the PTC is predicted to escape NMD (the truncated mRNA may be translated into an\naberrant protein). Values in between indicate intermediate NMD efficiency.\n
\n\n| Track | Description |
|---|---|
| NMDetective-A | \nRandom forest model predicting NMD efficiency for all possible PTCs introduced\n by single-nucleotide variants. Explains ~71% of systematic variance in NMD\n efficiency. |
| NMDetective-B | \nSimplified decision tree model for all possible PTCs. Slightly lower accuracy\n (~68% variance explained) but more interpretable, making it suitable for\n clinical applications. |
| NMDetective-A PTC | \nRandom forest model predicting NMD efficiency specifically for the first\n out-of-frame PTC introduced by frameshifting indel mutations. |
| NMDetective-B PTC | \nDecision tree model for the first out-of-frame PTC from frameshifting\n indels. |
\nEach subtrack is displayed as a signal (bigWig) track. Values closer to 1 indicate\npredicted NMD-triggering, while values closer to 0 indicate predicted NMD escape.\n
\n\nNMDetective-A and NMDetective-B were introduced in\nLindeboom et al. 2019,\ntrained on NMD efficiency scores derived from somatic nonsense mutation data from 9,769 cancer\npatients\n(Lindeboom et al. 2016),\nand tested on an independent set of frameshift mutations.\nThe models incorporate the following features to predict NMD efficiency:\n
\n\nNMDetective-A (random forest regression) captures non-linear interactions among\nthese features and achieves the highest predictive accuracy.\nNMDetective-B (decision tree) applies a simpler rule-based classification that\nis more transparent, with a modest reduction in accuracy.\n
\n\n\nThe predictions were generated for every possible PTC-introducing single-nucleotide\nvariant and for the first out-of-frame PTC from every possible single-nucleotide\nframeshifting indel across all human protein-coding transcripts. The original bedGraph\ncustom track files were downloaded from the\nNMDetective Figshare page\nresource and converted to bigWig format at UCSC.\n
\n\n\nThe data underlying these tracks can be explored interactively with the\nTable Browser or the\nData Integrator. For automated analysis,\nthe data may be queried from our\nREST API. Please refer to our\nmailing list archives for questions, or our\nData Access FAQ for more\ninformation.\n
\n\n\nThanks to Rik Lindeboom for providing custom tracks and the original NMDetective data\non Figshare.\n
\n\n\nLindeboom RG, Supek F, Lehner B.\n\nThe rules and impact of nonsense-mediated mRNA decay in human cancers.\nNat Genet. 2016 Oct;48(10):1112-8.\nPMID: 27618451; PMC:
\nLindeboom RGH, Vermeulen M, Lehner B, Supek F.\n\nThe impact of nonsense-mediated mRNA decay on genetic disease, gene editing and cancer\nimmunotherapy.\nNat Genet. 2019 Nov;51(11):1645-1651.\nPMID: 31659324; PMC:
\nThese tracks contain pseudogene predictions and their parents as identified by PseudoPipe.\nPseudoPipe is a homology-based\ncomputational pipeline that can search a mammalian genome and identify pseudogene sequences\ncomprehensively and consistently.\n
\n\nPseudogenes are genomic sequences that bear similarity to specific protein-coding genes, but are\nunable to produce functional proteins due to the existence of frameshifts, premature stop codons, or\nother deleterious mutations. They arise from gene duplication or retrotransposition events and are\nimportant resources in understanding the evolutionary history of genes and genomes.
\n\nThis composite track consists of two subtracks: the Pseudogenes track and the Pseudogene\nParents track.
\n\nThe Pseudogene Parents track displays parent genes and pseudogenes\nlabeled with their HUGO\nIDs, which were derived from Ensembl gene IDs provided by the Gerstein lab after dataset creation. It includes indicators for pseudogenes. \nThese indicators do not show pseudogene locations directly but instead indicate how many pseudogenes\nare associated with each gene and link to their genomic regions in the Pseudogenes track.
\n\nThe Pseudogenes track shows pseudogenes labeled with their parent HUGO ID and colored\naccording to pseudogene type. The authors assigned PGOHUMG IDs to genes and PGOHUMT IDs to\ntranscripts. Note: Not all PseudoPipe IDs could be mapped back to their original Ensembl\nIDs. In these cases, the gene ID is listed as NA.
\n\nPseudogene types:\nEach parent gene is shown with associated pseudogenes represented as grey blocks. These blocks\ndo not reflect actual pseudogene locations but rather indicate the count of pseudogenes linked to\nthe gene.\n
\n\nIf a parent gene has four grey blocks beneath it, this indicates the presence of four pseudogenes\nelsewhere in the genome. Hovering over an item displays the gene type, ID (Ensembl transcript ID\nor PseudoPipe transcript ID), and the genome position of the gene or pseudogene, with a link to\nthat genomic region.\n
\n\nPseudogenes are colored by type.
\n\nHovering over a pseudogene item shows the pseudogene type, parent HUGO gene symbol, and the Ensembl\nparent transcript ID, which links to the genome position of the parent gene.
\n\n\nThe PseudoPipe pipeline identifies pseudogenes through a series of steps. It first uses BLAST to\nrapidly cross-reference potential parent proteins against the intergenic regions of the genome. The\nresulting raw hits are then processed by removing redundancies, clustering neighboring sequences,\nand aligning each cluster with a unique parent gene. Finally, pseudogenes are classified based on a\ncombination of criteria, including homology, intron-exon structure, and the presence of stop codons\nor frameshifts. This method is designed to detect pseudogenes that are unable to be translated into\nproteins.
\n\nThese tracks were generated using a Bash script that processes a GTF file with pseudogene\nannotations by removing duplicates, correcting overlapping exons, and converting the data to BED\nformat with pseudoPipeToBed.py. This script extracts gene and transcript IDs, merges overlapping\nexons, assigns colors based on pseudogene type, and outputs a BED file with gene and parent\nannotations. PseudoPipeParents.py then links pseudogenes to their functional genes by determining\nparent gene coordinates, updating pseudogene entries with interactive browser links and generating a\nparent BED file. The final data are formatted into pseudoPipePgenes.bb and pseudoPipeParents.bb BigBed\nfiles. The detailed documentation (makeDoc) and \nPython scripts are available in our GitHub repository.\n
\n\nThe raw data can be explored interactively with the\nTable Browser or the\nData Integrator.\nThe data may also be explored interactively using our\nREST API.
\nFor automated download and analysis, the genome annotation is stored at UCSC in bigBed files\nthat can be downloaded from the\ndownload server.\nIndividual regions or the whole genome annotation can be obtained using our tool\nbigBedToBed which can be compiled from the source code or downloaded as a precompiled\nbinary for your system.
\n\nInstructions for downloading source code and binaries can be found\nhere.\nThe tool can also be used to obtain only features within a given range, e.g.
\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/hg38/pseudogenes/pseudoPipePgenes.bb -chrom=chr21 -start=0 -end=10000000 stdout\n\n\nThanks to the Gerstein lab at Yale University for making this data available, and to Cristina\nSisu for providing data in GTF format with parent annotations.
\n\n\nZhang Z, Carriero N, Zheng D, Karro J, Harrison PM, Gerstein M.\n\nPseudoPipe: an automated pseudogene identification pipeline.\nBioinformatics. 2006 Jun 15;22(12):1437-9.\nPMID: 16574694\n
\n", "itemRgb": "on", "labelFields": "pgenehugo", "labelSeparator": "\" \"", "longLabel": "Yale Pseudogenes", "mouseOver": "Pseudogene type: ${pgeneType}\nThe recombination rate track represents calculated rates of recombination based\non the genetic maps from deCODE (Halldorsson et al., 2019) and 1000 Genomes\n(2013 Phase 3 release, lifted from hg19). The deCODE map is more recent, has a higher \nresolution and was natively created on hg38 and therefore recommended. \nFor the Recomb. deCODE average track, the recombination rates for chrX represent the female rate.\n
\n\nThis track also includes a subtrack with all the\nindividual deCODE recombination events and another subtrack with several thousand\nde-novo mutations found in the deCODE sequencing data. These two tracks are hidden by\ndefault and have to be switched on explicitly on the configuration page.\n
\n\n\nThis is a super track that contains different subtracks, three with the deCODE\nrecombination rates (paternal, maternal and average) and one with the 1000\nGenomes recombination rate (average). These tracks are in \nsignal graph\n(wiggle) format. By default, to show most recombination hotspots, their maximum\nvalue is set to 100 cM, even though many regions have values higher than 100.\nThe maximum value can be changed on the configuration pages of the tracks.\n
\n\n\nThere are two more tracks that show additional details provided by deCODE: one\nsubtrack with the raw data of all cross-overs tagged with their proband ID and\nanother one with around 8000 human de-novo mutation variants that are linked to\ncross-over changes.\n
\n\n\nThe deCODE genetic map was created at \ndeCODE Genetics. It is based \non microarrays assaying 626,828 SNP markers that allowed to identify 1,476,140 crossovers in\n56,321 paternal meioses and 3,055,395 crossovers in 70,086 maternal meioses.\nIn total, the data is based on 4,531,535 crossovers in 126,427 meioses. By\nusing WGS data with 9,305,070 SNPs, the boundaries for 761,981 crossovers were\nrefined: 247,942 crossovers in 9423 paternal meioses and 514,039 crossovers in\n11,750 maternal meioses. The average resolution of the genetic map is 682 base\npairs (bp): 655 and 708 bp for the paternal and maternal maps, respectively.\n
\n\nThe 1000 Genomes genetic map is based on the
As with all other tracks, the data conversion commands and pointers to the\noriginal data files are documented in the \nmakeDoc file of this track.
\n\n\nThe raw data can be explored interactively with the Table Browser, or\nthe Data Integrator. For automated access, this track, like all\nothers, is available via our API. However, for bulk\nprocessing, it is recommended to download the dataset.\n
\n\n\nFor automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed\nfiles that can be downloaded from\nour download server.\nIndividual regions or the whole genome annotation can be obtained using our tools bigWigToWig\nor bigBedToBed which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tools can also be used to obtain features confined to a given range, e.g.,\n
\nbigWigToBedGraph -chrom=chr17 -start=45941345 -end=45942345 http://hgdownload.soe.ucsc.edu/gbdb/hg38/recombRate/recombAvg.bw stdout\n
\n
\nPlease refer to our\nData Access FAQ\nfor more information.\n
\n\n\nThis track was produced at UCSC using data that are freely available for\nthe deCODE\nand 1000 Genomes genetic maps. Thanks to Po-Ru Loh at the\nBroad Institute for providing the code to lift the hg19 1000 Genomes map data to hg38.\n
\n\n\n1000 Genomes Project Consortium., Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA,\nHurles ME, McVean GA.\n\n A map of human genome variation from population-scale sequencing.\nNature. 2010 Oct 28;467(7319):1061-73.\nPMID: 20981092; PMC:
\nHalldorsson BV, Palsson G, Stefansson OA, Jonsson H, Hardarson MT, Eggertsson HP, Gunnarsson B,\nOddsson A, Halldorsson GH, Zink F et al.\n\n Characterizing mutagenic effects of recombination through a sequence-level genetic map.\nScience. 2019 Jan 25;363(6425).\nPMID: 30679340\n
\n", "longLabel": "Recombination rate: deCODE Genetics, paternal", "maxHeightPixels": "128:60:8", "parent": "recombRate2", "priority": "2", "shortLabel": "Recomb. deCODE Pat", "track": "recombPat", "type": "bigWig", "viewLimits": "0.0:100", "viewLimitsMax": "0:150000", "visibility": "full" } }, "description": "Recombination rate: deCODE Genetics, paternal", "category": [ "Mapping and Sequencing" ] }, { "trackId": "hg38-ReMapTFs", "name": "ReMap ChIP-seq", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/reMap/reMap2022.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/reMap/reMap2022.bb", "denseCoverage": "100", "filterLabel.Biotypes": "Biotypes (cell lines, tissues...)", "filterLabel.TF": "Transcriptional regulators", "filterText.Biotypes": "*", "filterText.TF": "*", "filterType.Biotypes": "multipleListOnlyOr", "filterType.TF": "multipleListOnlyOr", "filterValues.Biotypes": 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"html": "\nThis track represents the ReMap Atlas of regulatory regions, which consists of a\nlarge-scale integrative analysis of all Public ChIP-seq data for transcriptional\nregulators from GEO, ArrayExpress, and ENCODE. \n
\n\n\nBelow is a schematic diagram of the types of regulatory regions: \n
\nThis 4th release of ReMap (2022) presents the analysis of a total of 8,103 \nquality controlled ChIP-seq (n=7,895) and ChIP-exo (n=208) data sets from public\nsources (GEO, ArrayExpress, ENCODE). The ChIP-seq/exo data sets have been mapped\nto the GRCh38/hg38 human assembly. The data set is defined as a ChIP-seq \nexperiment in a given series (e.g. GSE46237), for a given TF (e.g. NR2C2), in a\nparticular biological condition (i.e. cell line, tissue type, disease state, or\nexperimental conditions; e.g. HELA). Data sets were labeled by concatenating\nthese three pieces of information, such as GSE46237.NR2C2.HELA. \n
\nThose merged analyses cover a total of 1,211 DNA-binding proteins\n(transcriptional regulators) such as a variety of transcription factors (TFs),\ntranscription co-activators (TCFs), and chromatin-remodeling factors (CRFs) for\n182 million peaks. \n
\n\n\nPublic ChIP-seq data sets were extracted from Gene Expression Omnibus (GEO) and\nArrayExpress (AE) databases. For GEO, the query\n\n'('chip seq' OR 'chipseq' OR\n'chip sequencing') AND 'Genome binding/occupancy profiling by high throughput\nsequencing' AND 'homo sapiens'[organism] AND NOT 'ENCODE'[project]'\n\nwas used to return a list of all potential data sets to analyze, which were then manually \nassessed for further analyses. Data sets involving polymerases (i.e. Pol2 and\nPol3), and some mutated or fused TFs (e.g. KAP1 N/C terminal mutation, GSE27929)\nwere excluded.\n
\nAvailable ENCODE ChIP-seq data sets for transcriptional regulators from the\nENCODE portal were processed with the\nstandardized ReMap pipeline. The list of ENCODE data was retrieved as FASTQ files from the\nENCODE portal\nusing the following filters:\n
\nBoth Public and ENCODE data were processed similarly. Bowtie 2 (PMC3322381) (version 2.2.9) with options -end-to-end -sensitive was used to align all\nreads on the genome. Biological and technical\nreplicates for each unique combination of GSE/TF/Cell type or Biological condition\nwere used for peak calling. TFBS were identified using MACS2 peak-calling tool\n(PMC3120977) (version 2.1.1.2) in order to follow ENCODE ChIP-seq guidelines,\nwith stringent thresholds (MACS2 default thresholds, p-value: 1e-5). An input data\nset was used when available.\n
\n\n\n\nTo assess the quality of public data sets, a score was computed based on the\ncross-correlation and the FRiP (fraction of reads in peaks) metrics developed by\nthe ENCODE Consortium (https://genome.ucsc.edu/ENCODE/qualityMetrics.html). Two\nthresholds were defined for each of the two cross-correlation ratios (NSC,\nnormalized strand coefficient: 1.05 and 1.10; RSC, relative strand coefficient:\n0.8 and 1.0). Detailed descriptions of the ENCODE quality coefficients can be\nfound at https://genome.ucsc.edu/ENCODE/qualityMetrics.html. The\nphantompeak tools suite was used\n(https://code.google.com/p/phantompeakqualtools/) to compute\nRSC and NSC.\n
\n\nPlease refer to the ReMap 2022, 2020, and 2018 publications for more details\n(citation below).\n
\n\n\n\n\nReMap Atlas of regulatory regions data can be explored interactively with the\nTable Browser and cross-referenced with the \nData Integrator. For programmatic access,\nthe track can be accessed using the Genome Browser's\nREST API.\nReMap annotations can be downloaded from the\nGenome Browser's download server\nas a bigBed file. This compressed binary format can be remotely queried through\ncommand line utilities. Please note that some of the download files can be quite large.
\n\n\nIndividual BED files for specific TFs, cells/biotypes, or data sets can be\nfound and downloaded on the ReMap website.\n
\n\n\nChèneby J, Gheorghe M, Artufel M, Mathelier A, Ballester B.\n\nReMap 2018: an updated atlas of regulatory regions from an integrative analysis of DNA-binding ChIP-\nseq experiments.\nNucleic Acids Res. 2018 Jan 4;46(D1):D267-D275.\nPMID: 29126285; PMC:
\nChèneby J, Ménétrier Z, Mestdagh M, Rosnet T, Douida A, Rhalloussi W, Bergon A, Lopez\nF, Ballester B.\n\nReMap 2020: a database of regulatory regions from an integrative analysis of Human and Arabidopsis\nDNA-binding sequencing experiments.\nNucleic Acids Res. 2020 Jan 8;48(D1):D180-D188.\nPMID: 31665499; PMC:
\nGriffon A, Barbier Q, Dalino J, van Helden J, Spicuglia S, Ballester B.\n\nIntegrative analysis of public ChIP-seq experiments reveals a complex multi-cell regulatory\nlandscape.\nNucleic Acids Res. 2015 Feb 27;43(4):e27.\nPMID: 25477382; PMC:
\nHammal F, de Langen P, Bergon A, Lopez F, Ballester B.\n\nReMap 2022: a database of Human, Mouse, Drosophila and Arabidopsis regulatory regions from an\nintegrative analysis of DNA-binding sequencing experiments.\nNucleic Acids Res. 2022 Jan 7;50(D1):D316-D325.\nPMID: 34751401; PMC:
\nThis track shows rare variants associated with monogenic congenital defects of immunity to \nthe SARS-CoV-2 virus identified by the \nCOVID Human Genetic Effort. \nThis international consortium aims to discover truly causative variations: those underlying \nsevere forms of COVID-19 in previously healthy individuals, and those that make certain \nindividuals resistant to infection by the SARS-CoV2 virus despite repeated exposure.\n
\n\nThe major feature of the small set of variants in this track is that they are functionally tested\nto be deleterious and genetically tested to be disease-causing. \nSpecifically, rare variants were predicted to be loss-of-function at human loci known to govern\ninterferon (IFN) immunity to influenza virus in patients with life-threatening COVID-19 pneumonia, \nrelative to subjects with asymptomatic or benign infection.\nThese genetic defects display incomplete penetrance for influenza respiratory distress and only\nappear clinically upon infection with the more virulent SARS-CoV-2.\n
\n\n\nOnly eight genes with 23 variants are contained in this track. \nUse the links below to navigate to the gene of interest or view \nall eight genes together using the following sessions for \nhg38 or\nhg19.\n
\n\n| Gene Name | \nHuman GRCh37/hg19 Assembly | \nHuman GRCh38/hg38 Assembly | \n
|---|---|---|
| TLR3 | \n\nchr4:186990309-187006252 | \n\nchr4:186069152-186088069 | \n
| IRF7 | \n\nchr11:612555-615999 | \n\nchr11:612591-615970 | \n
| UNC93B1 | \n\nchr11:67758575-67771593 | \n\nchr11:67991100-68004097 | \n
| TBK1 | \n\nchr12:64845840-64895899 | \n\nchr12:64452120-64502114 | \n
| TICAM1 | \n\nchr19:4815936-4831754 | \n\nchr19:4815932-4831704 | \n
| IRF3 | \n\nchr19:50162826-50169132 | \n\nchr19:49659570-49665875 | \n
| IFNAR1 | \n\nchr21:34697214-34732128 | \n\nchr21:33324970-33359864 | \n
| IFNAR2 | \n\nchr21:34602231-34636820 | \n\nchr21:33229974-33264525 | \n
\nThis track uses variant calls in autosomal IFN-related genes from whole exome and genome data \nwith a MAF lower than 0.001 (gnomAD v2.1.1) and experimental demonstration of loss-of-function.\nThe patient population studied consisted of 659 patients with life-threatening COVID-19 pneumonia \nrelative to 534 subjects with asymptomatic or benign infection of varying ethnicities. \nVariants underlying autosomal-recessive or autosomal-dominant deficiencies were identified in \n23 patients (3.5%) 17 to 77 years of age.\nThe proportion of individuals carrying at least one variant was compared between severe cases \nand control cases by means of logistic regression with the likelihood ratio test.\nPrincipal Component Analysis (PCA) was conducted with Plink v1.9 software on whole exome and \ngenome sequencing data with the 1000 Genomes (1kG) Project phase 3 public database as reference.\nAnalysis of enrichment in rare synonymous variants of the genes was performed to check the \ncalibration of the burden test. \nThe odds ratio was also estimated by logistic regression and adjusted for ethnic heterogeneity.\n
\n\n\nThe raw data can be explored interactively with the \nTable Browser, or the Data Integrator.\nPlease refer to\nour mailing list archives for questions, or our Data Access FAQ for more information.\n
\n\n\nThanks to the COVID Human Genetic Effort contributors for making these data available, and in\nparticular to Qian Zhang at the Rockefeller University for review and input during browser track\ndevelopment.\n
\n\n\nZhang Q, Bastard P, Liu Z, Le Pen J, Moncada-Velez M, Chen J, Ogishi M, Sabli IKD, Hodeib S, Korol C\net al.\n\nInborn errors of type I IFN immunity in patients with life-threatening COVID-19.\nScience. 2020 Sep 24;.\nPMID: 32972995\n
\n\n" } }, "description": "Rare variants underlying COVID-19 severity and susceptibility from the COVID Human Genetics Effort", "category": [ "Phenotypes, Variants, and Literature" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-covidMuts-LinearBasicDisplay", "labels": { "name": "jexl:get(feature,'gene')" }, "mouseover": "jexl:`${get(feature,'gene')} ${get(feature,'name')} ${get(feature,'rsId')} Genotype: ${get(feature,'genotype')}; Zygosity: ${get(feature,'zygo')} ; Inheritance: ${get(feature,'inhMode')}`" } ] }, { "trackId": "hg38-bismap50Pos", "name": "Single-read mappability - Bismap S50 +", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k50.C2T-Converted.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/hoffmanMappability/k50.C2T-Converted.bb", "color": "240,120,80", "longLabel": "Single-read mappability with 50-mers after bisulfite conversion (forward strand)", "parent": "bismapBigBed off", "priority": "3", "shortLabel": "Bismap S50 +", "subGroups": "view=SR", "track": "bismap50Pos", "visibility": "hide", "html": "" } }, "description": "Single-read mappability with 50-mers after bisulfite conversion (forward strand)", "category": [ "Mapping and Sequencing" ] }, { "trackId": "hg38-clinGenGeneDisease", "name": "ClinGen - ClinGen Validity", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/clinGen/clinGenGeneDisease.bb" }, "metadata": { "ucsc": { "bedNameLabel": "Associated Disease", "bigDataUrl": "/gbdb/hg38/bbi/clinGen/clinGenGeneDisease.bb", "filterLabel.Classification": "ClinGen Gene-Disease Validity Classification", "filterLabel.Inheritance": "Inheritance Pattern", "filterLabel.SOPversion": "ClinGen SOP Version Number", "filterValues.Classification": "Definitive,Strong,Moderate,Limited,Animal Model Only,No Reported Evidence,Disputed,Refuted", "filterValues.Inheritance": "Autosomal Dominant,Autosomal Recessive,Semidominant,X-Linked,X-linked recessive,Other", "filterValues.SOPversion": "SOP4,SOP5,SOP6,SOP7", "itemRgb": "on", "longLabel": "ClinGen Gene-Disease Validity Classification", "mouseOver": "Gene/ISCA ID: $geneSymbol\nThe NMDetective tracks display genome-wide predictions of nonsense-mediated mRNA\ndecay (NMD) efficiency using the NMDetective-A and NMDetective-B models from\nLindeboom et al. 2019,\ntrained on the NMD efficiency measure from\nLindeboom et al. 2016.\nNMDetective scores predict whether a premature termination codon (PTC) at a given position\nwill trigger NMD and mRNA degradation, or whether the transcript will escape NMD and\npotentially produce a truncated protein.\n
\n\n\nScores range from 0 to 1. Values near 1 indicate that a PTC at\nthat position is predicted to trigger NMD (the mRNA is degraded). Values near 0 indicate\nthat the PTC is predicted to escape NMD (the truncated mRNA may be translated into an\naberrant protein). Values in between indicate intermediate NMD efficiency.\n
\n\n| Track | Description |
|---|---|
| NMDetective-A | \nRandom forest model predicting NMD efficiency for all possible PTCs introduced\n by single-nucleotide variants. Explains ~71% of systematic variance in NMD\n efficiency. |
| NMDetective-B | \nSimplified decision tree model for all possible PTCs. Slightly lower accuracy\n (~68% variance explained) but more interpretable, making it suitable for\n clinical applications. |
| NMDetective-A PTC | \nRandom forest model predicting NMD efficiency specifically for the first\n out-of-frame PTC introduced by frameshifting indel mutations. |
| NMDetective-B PTC | \nDecision tree model for the first out-of-frame PTC from frameshifting\n indels. |
\nEach subtrack is displayed as a signal (bigWig) track. Values closer to 1 indicate\npredicted NMD-triggering, while values closer to 0 indicate predicted NMD escape.\n
\n\nNMDetective-A and NMDetective-B were introduced in\nLindeboom et al. 2019,\ntrained on NMD efficiency scores derived from somatic nonsense mutation data from 9,769 cancer\npatients\n(Lindeboom et al. 2016),\nand tested on an independent set of frameshift mutations.\nThe models incorporate the following features to predict NMD efficiency:\n
\n\nNMDetective-A (random forest regression) captures non-linear interactions among\nthese features and achieves the highest predictive accuracy.\nNMDetective-B (decision tree) applies a simpler rule-based classification that\nis more transparent, with a modest reduction in accuracy.\n
\n\n\nThe predictions were generated for every possible PTC-introducing single-nucleotide\nvariant and for the first out-of-frame PTC from every possible single-nucleotide\nframeshifting indel across all human protein-coding transcripts. The original bedGraph\ncustom track files were downloaded from the\nNMDetective Figshare page\nresource and converted to bigWig format at UCSC.\n
\n\n\nThe data underlying these tracks can be explored interactively with the\nTable Browser or the\nData Integrator. For automated analysis,\nthe data may be queried from our\nREST API. Please refer to our\nmailing list archives for questions, or our\nData Access FAQ for more\ninformation.\n
\n\n\nThanks to Rik Lindeboom for providing custom tracks and the original NMDetective data\non Figshare.\n
\n\n\nLindeboom RG, Supek F, Lehner B.\n\nThe rules and impact of nonsense-mediated mRNA decay in human cancers.\nNat Genet. 2016 Oct;48(10):1112-8.\nPMID: 27618451; PMC:
\nLindeboom RGH, Vermeulen M, Lehner B, Supek F.\n\nThe impact of nonsense-mediated mRNA decay on genetic disease, gene editing and cancer\nimmunotherapy.\nNat Genet. 2019 Nov;51(11):1645-1651.\nPMID: 31659324; PMC:
\nNuclear mitochondrial DNA segments (NUMTs) are a kind of insertion from the mitochondrion to the\nnucleus, which is an ongoing and frequent process that happens in all eukaryotes. In previous\nstudies, NUMTs have been reported to increase genetic diversity, promote gene and genome evolution,\nand generate novel nuclear exons. NUMTs can also affect the accuracy when nuclear genomes are\nassembled.
\n\nThis track is a collection of Nuclear mitochondrial DNA segments, provided in BED format.
\nNotice: Alignments to incompletely assembled or unmapped chromosome locations are omitted\nin this track.
\n\nIn this track, the BED score is calculated by -10log10(E-value), representing the alignment\nconfidence and is reflected in the level of gray. Scores >=100 (E-values <= 1e-10) are\ncolored black. It is important to note that when a NUMT is a merged result, the score is taken as the\nhighest score among all results.
\n\n\n\nThis dataset identifies nuclear mitochondrial genome segments (NUMTs) by comparing nuclear and\nmitochondrial genomes and proteins using LAST alignment tools. The method involves several steps:\nnuclear genome-mitochondrial genome comparison, nuclear genome-mitochondrial protein comparison,\nand exclusion of overlapping nuclear ribosomal RNA regions using maf-Bed and seg-suite tools.\nResults are merged if alignments are consistent across both comparisons, with sequences under 30bp\nexcluded. Bedtools and LAST are used throughout the process for efficient alignment and merging.\n
\n\nFor more detailed information on the methods used for detecting NUMTs, please visit the following\nwebpage:
\nhttps://github.com/Koumokuyou/NUMTs\n\nIf you have questions or comments, please write to:\n
Huang Muyao, \n\n2171272903@edu.k.u-tokyo.ac.jp\n\n
\n\n\nKleine T, Maier UG, Leister D.\n\nDNA transfer from organelles to the nucleus: the idiosyncratic genetics of endosymbiosis.\nAnnu Rev Plant Biol. 2009;60:115-38.\nDOI: 10.1146/annurev.arplant.043008.092119; PMID:
\nZhang GJ, Dong R, Lan LN, Li SF, Gao WJ, Niu HX.\n\nNuclear Integrants of Organellar DNA Contribute to Genome Structure and Evolution in Plants.\nInt J Mol Sci. 2020 Jan 21;21(3).\nDOI: 10.3390/ijms21030707; PMID:\n31973163; PMC:
\nYao Y, Frith MC.\n\nImproved DNA-Versus-Protein Homology Search for Protein Fossils.\nIEEE/ACM Trans Comput Biol Bioinform. 2023 May-Jun;20(3):1691-1699.\nDOI: 10.1109/TCBB.2022.3177855; PMID:
\nFrith MC.\n\nA simple method for finding related sequences by adding probabilities of alternative alignments.\nGenome Res. 2024 Sep 13;.\nDOI: 10.1101/gr.279464.124;\nPMID: 39152037\n
\n" } }, "description": "Nuclear mitochondrial DNA segments", "category": [ "Repeats" ] }, { "trackId": "hg38-panelAppTandRep", "name": "PanelApp - PanelApp GE STRs", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/panelApp/tandRep.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/panelApp/tandRep.bb", "filter.version": "1", "filterLabel.version": "Minimum panel version to display", "filterValues.confidenceLevel": "3,2,1,0", "itemRgb": "on", "labelFields": "hgncSymbol", "longLabel": "Genomics England PanelApp Short Tandem Repeats", "mouseOver": "Gene name: $geneName\nThe recombination rate track represents calculated rates of recombination based\non the genetic maps from deCODE (Halldorsson et al., 2019) and 1000 Genomes\n(2013 Phase 3 release, lifted from hg19). The deCODE map is more recent, has a higher \nresolution and was natively created on hg38 and therefore recommended. \nFor the Recomb. deCODE average track, the recombination rates for chrX represent the female rate.\n
\n\nThis track also includes a subtrack with all the\nindividual deCODE recombination events and another subtrack with several thousand\nde-novo mutations found in the deCODE sequencing data. These two tracks are hidden by\ndefault and have to be switched on explicitly on the configuration page.\n
\n\n\nThis is a super track that contains different subtracks, three with the deCODE\nrecombination rates (paternal, maternal and average) and one with the 1000\nGenomes recombination rate (average). These tracks are in \nsignal graph\n(wiggle) format. By default, to show most recombination hotspots, their maximum\nvalue is set to 100 cM, even though many regions have values higher than 100.\nThe maximum value can be changed on the configuration pages of the tracks.\n
\n\n\nThere are two more tracks that show additional details provided by deCODE: one\nsubtrack with the raw data of all cross-overs tagged with their proband ID and\nanother one with around 8000 human de-novo mutation variants that are linked to\ncross-over changes.\n
\n\n\nThe deCODE genetic map was created at \ndeCODE Genetics. It is based \non microarrays assaying 626,828 SNP markers that allowed to identify 1,476,140 crossovers in\n56,321 paternal meioses and 3,055,395 crossovers in 70,086 maternal meioses.\nIn total, the data is based on 4,531,535 crossovers in 126,427 meioses. By\nusing WGS data with 9,305,070 SNPs, the boundaries for 761,981 crossovers were\nrefined: 247,942 crossovers in 9423 paternal meioses and 514,039 crossovers in\n11,750 maternal meioses. The average resolution of the genetic map is 682 base\npairs (bp): 655 and 708 bp for the paternal and maternal maps, respectively.\n
\n\nThe 1000 Genomes genetic map is based on the
As with all other tracks, the data conversion commands and pointers to the\noriginal data files are documented in the \nmakeDoc file of this track.
\n\n\nThe raw data can be explored interactively with the Table Browser, or\nthe Data Integrator. For automated access, this track, like all\nothers, is available via our API. However, for bulk\nprocessing, it is recommended to download the dataset.\n
\n\n\nFor automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed\nfiles that can be downloaded from\nour download server.\nIndividual regions or the whole genome annotation can be obtained using our tools bigWigToWig\nor bigBedToBed which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tools can also be used to obtain features confined to a given range, e.g.,\n
\nbigWigToBedGraph -chrom=chr17 -start=45941345 -end=45942345 http://hgdownload.soe.ucsc.edu/gbdb/hg38/recombRate/recombAvg.bw stdout\n
\n
\nPlease refer to our\nData Access FAQ\nfor more information.\n
\n\n\nThis track was produced at UCSC using data that are freely available for\nthe deCODE\nand 1000 Genomes genetic maps. Thanks to Po-Ru Loh at the\nBroad Institute for providing the code to lift the hg19 1000 Genomes map data to hg38.\n
\n\n\n1000 Genomes Project Consortium., Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA,\nHurles ME, McVean GA.\n\n A map of human genome variation from population-scale sequencing.\nNature. 2010 Oct 28;467(7319):1061-73.\nPMID: 20981092; PMC:
\nHalldorsson BV, Palsson G, Stefansson OA, Jonsson H, Hardarson MT, Eggertsson HP, Gunnarsson B,\nOddsson A, Halldorsson GH, Zink F et al.\n\n Characterizing mutagenic effects of recombination through a sequence-level genetic map.\nScience. 2019 Jan 25;363(6425).\nPMID: 30679340\n
\n", "longLabel": "Recombination rate: deCODE Genetics, maternal", "maxHeightPixels": "128:60:8", "parent": "recombRate2", "priority": "3", "shortLabel": "Recomb. deCODE Mat", "track": "recombMat", "type": "bigWig", "viewLimits": "0.0:100", "viewLimitsMax": "0:150000", "visibility": "full" } }, "description": "Recombination rate: deCODE Genetics, maternal", "category": [ "Mapping and Sequencing" ] }, { "trackId": "hg38-gnomad310XPercentage", "name": "gnomAD v3 Genome Coverage - Sample % > 10X", "type": "QuantitativeTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigWigAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/gnomAD/coverage/v3-genome/gnomad.coverage.over_10.bw" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/gnomAD/coverage/v3-genome/gnomad.coverage.over_10.bw", "color": "195,0,60", "longLabel": "gnomAD Percentage of Genome Samples with at least 10X Coverage v3.0.1", "parent": "gnomad3Coverage off", "priority": "3", "shortLabel": "Sample % > 10X", "track": "gnomad310XPercentage", "viewLimits": "0:1", "html": "" } }, "description": "gnomAD Percentage of Genome Samples with at least 10X Coverage v3.0.1", "category": [ "Variation and Repeats" ] }, { "trackId": "hg38-gnomad4Exome10XPercentage", "name": "gnomAD v4 Exome Coverage - Sample % > 10X", "type": "QuantitativeTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigWigAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/gnomAD/coverage/v4-exome/gnomad.coverage.over_10.bw" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/gnomAD/coverage/v4-exome/gnomad.coverage.over_10.bw", "color": "195,0,60", "longLabel": "gnomAD Percentage of Exome Samples with at least 10X Coverage v4.0", "parent": "gnomad4ExomeCoverage off", "priority": "3", "shortLabel": "Sample % > 10X", "track": "gnomad4Exome10XPercentage", "viewLimits": "0:1", "html": "" } }, "description": "gnomAD Percentage of Exome Samples with at least 10X Coverage v4.0", "category": [ "Variation and Repeats" ] }, { "trackId": "hg38-unipLocSignal", "name": "UniProt - Signal Peptide", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/uniprot/unipLocSignal.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/uniprot/unipLocSignal.bb", "color": "255,0,150", "filterValues.status": "Manually reviewed (Swiss-Prot),Unreviewed (TrEMBL)", "itemRgb": "off", "longLabel": "UniProt Signal Peptides", "mouseOver": "UniProt record: $uniProtId\ngnomAD 3 was a genomes-only release. The gnomAD v3.1.1 track is the current version of gnomAD 3\nand shows variants from 76,156 whole genomes (and no exomes), all mapped to the GRCh38/hg38\nreference sequence. 4,454 genomes were added to the number of genomes in the previous v3 release.\nFor more detailed information on gnomAD v3.1, see the related blog post.\nA bugfix to v3.1 resulted in gnomAD v3.1.1, see\nchangelog.\nDo not use gnomAD v3.1 anymore, we will remove the 3.1 track soon.\n
\n\n\nThe gnomAD v3.1 track is deprecated. Please use v3.1.1 instead.\n
\n\n\nThe gnomAD v3 track shows variants from 71,702 whole genomes (and no exomes), all mapped to the\nGRCh38/hg38 reference sequence. For more detailed\ninformation on gnomAD v3, see the related blog post.
\n\n\nFor questions on the gnomAD data, also see the gnomAD FAQ.
\n\nMore details on the Variant type(s) can be found on the Sequence Ontology page.
\n\n\nThe gnomAD v3.1.1 track version follows the same conventions and configuration as the v3.1 track,\nexcept as noted below.
\n\n\nBy default, a maximum of 50,000 variants can be displayed at a time (before applying the filters\ndescribed below), before the track switches to dense display mode.\n
\n\n\nMouse hover on an item will display many details about each variant, including the affected gene(s),\nthe variant type, and annotation (missense, synonymous, etc).\n
\n\n\nClicking on an item will display additional details on the variant, including a population frequency\ntable showing allele count in each sub-population.\n
\n\n\nFollowing the conventions on the gnomAD browser, items are shaded according to their Annotation\ntype:\n
| pLoF | |
| Missense | |
| Synonymous | |
| Other |
\nTo maintain consistency with the gnomAD website, variants are by default labeled according\nto their chromosomal start position followed by the reference and alternate alleles,\nfor example "chr1-1234-T-CAG". dbSNP rsID's are also available as an additional\nlabel, if the variant is present in dbSnp.\n
\n\n\nThree filters are available for these tracks:\n
\n\nThe gnomAD v3.1.1 data is unfiltered.
\n\n\nFor the deprecated v3.1 update only, in order to cut\ndown on the amount of displayed data, the following variant\ntypes have been filtered out, but are still viewable in the gnomAD browser:\n
\nFor the full steps used to create the gnomAD tracks at UCSC, please see the\n
\nThe raw data can be explored interactively with the \nTable Browser, or the Data Integrator. For\nautomated analysis, the data may be queried from our REST API, and the genome annotations are stored in files that\ncan be downloaded from our
\nFor the v3.1.1 variants in particular, the underlying bigBed only contains enough information\nnecessary to use the track in the browser. The extra data like VEP annotations and CADD scores are\navailable in the same directory\nas the bigBed but in the files gnomad.v3.1.1.details.tab.gz and\ngnomad.v3.1.1.details.tab.gz.gzi. The gnomad.v3.1.1.details.tab.gz contains the gzip\ncompressed extra data in JSON format, and the .gzi file is available to speed searching of\nthis data. Each variant has an associated md5sum in the name field of the bigBed which can be\nused along with the _dataOffset and _dataLen fields to get the associated external data, as show\nbelow:\n
\n# find item of interest:\nbigBedToBed genomes.bb stdout | head -4 | tail -1\nchr1 12416 12417 854246d79dc5d02dcdbd5f5438542b6e [..omitted for brevity..] chr1-12417-G-A 67293 902\n\n# use the final two fields, _dataOffset and _dataLen (add one to _dataLen to include a newline), to get the extra data:\nbgzip -b 67293 -s 903 gnomad.v3.1.1.details.tab.gz\n854246d79dc5d02dcdbd5f5438542b6e {\"DDX11L1\": {\"cons\": [\"non_coding_transcript_variant\", [..omitted for brevity..]\n\n\n\nThe data can also be found directly from the gnomAD downloads page. Please refer to\nour mailing list archives for questions, or our Data Access FAQ for more information.
\n\n\nThanks to the Genome Aggregation\nDatabase Consortium for making these data available. The data are released under the
\nPlease note that some annotations within the provided files may have restrictions on usage. See here for more information.\n
\n\n\nChen S, Francioli LC, Goodrich JK, Collins RL, Kanai M, Wang Q, Alföldi J, Watts NA, Vittal C,\nGauthier LD et al.\n\n A genomic mutational constraint map using variation in 76,156 human genomes.\nNature. 2024 Jan;625(7993):92-100.\nPMID: 38057664\n
\n\nKarczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, Collins RL, Laricchia KM, Ganna\nA, Birnbaum DP et al.\n\nThe mutational constraint spectrum quantified from variation in 141,456 humans.\nNature. 2020 May;581(7809):434-443.\nPMID: 32461654; PMC:
\nLek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill\nAJ, Cummings BB et al.\nAnalysis of protein-coding\ngenetic variation in 60,706 humans. Nature. 2016 Aug 17;536(7616):285-91.\nPMID: 27535533;\nPMC: PMC5018207\n
\n", "itemRgb": "on", "labelFields": "rsId,_displayName", "longLabel": "Genome Aggregation Database (gnomAD) Genome Variants v3.1.1", "maxItems": "50000", "mouseOver": "Position: $chrom:${chromStart}-${chromEnd} ($ref/$alt)\ngnomAD 3 was a genomes-only release. The gnomAD v3.1.1 track is the current version of gnomAD 3\nand shows variants from 76,156 whole genomes (and no exomes), all mapped to the GRCh38/hg38\nreference sequence. 4,454 genomes were added to the number of genomes in the previous v3 release.\nFor more detailed information on gnomAD v3.1, see the related blog post.\nA bugfix to v3.1 resulted in gnomAD v3.1.1, see\nchangelog.\nDo not use gnomAD v3.1 anymore, we will remove the 3.1 track soon.\n
\n\n\nThe gnomAD v3.1 track is deprecated. Please use v3.1.1 instead.\n
\n\n\nThe gnomAD v3 track shows variants from 71,702 whole genomes (and no exomes), all mapped to the\nGRCh38/hg38 reference sequence. For more detailed\ninformation on gnomAD v3, see the related blog post.
\n\n\nFor questions on the gnomAD data, also see the gnomAD FAQ.
\n\nMore details on the Variant type(s) can be found on the Sequence Ontology page.
\n\n\nThe gnomAD v3.1.1 track version follows the same conventions and configuration as the v3.1 track,\nexcept as noted below.
\n\n\nBy default, a maximum of 50,000 variants can be displayed at a time (before applying the filters\ndescribed below), before the track switches to dense display mode.\n
\n\n\nMouse hover on an item will display many details about each variant, including the affected gene(s),\nthe variant type, and annotation (missense, synonymous, etc).\n
\n\n\nClicking on an item will display additional details on the variant, including a population frequency\ntable showing allele count in each sub-population.\n
\n\n\nFollowing the conventions on the gnomAD browser, items are shaded according to their Annotation\ntype:\n
| pLoF | |
| Missense | |
| Synonymous | |
| Other |
\nTo maintain consistency with the gnomAD website, variants are by default labeled according\nto their chromosomal start position followed by the reference and alternate alleles,\nfor example "chr1-1234-T-CAG". dbSNP rsID's are also available as an additional\nlabel, if the variant is present in dbSnp.\n
\n\n\nThree filters are available for these tracks:\n
\n\nThe gnomAD v3.1.1 data is unfiltered.
\n\n\nFor the deprecated v3.1 update only, in order to cut\ndown on the amount of displayed data, the following variant\ntypes have been filtered out, but are still viewable in the gnomAD browser:\n
\nFor the full steps used to create the gnomAD tracks at UCSC, please see the\n
\nThe raw data can be explored interactively with the \nTable Browser, or the Data Integrator. For\nautomated analysis, the data may be queried from our REST API, and the genome annotations are stored in files that\ncan be downloaded from our
\nFor the v3.1.1 variants in particular, the underlying bigBed only contains enough information\nnecessary to use the track in the browser. The extra data like VEP annotations and CADD scores are\navailable in the same directory\nas the bigBed but in the files gnomad.v3.1.1.details.tab.gz and\ngnomad.v3.1.1.details.tab.gz.gzi. The gnomad.v3.1.1.details.tab.gz contains the gzip\ncompressed extra data in JSON format, and the .gzi file is available to speed searching of\nthis data. Each variant has an associated md5sum in the name field of the bigBed which can be\nused along with the _dataOffset and _dataLen fields to get the associated external data, as show\nbelow:\n
\n# find item of interest:\nbigBedToBed genomes.bb stdout | head -4 | tail -1\nchr1 12416 12417 854246d79dc5d02dcdbd5f5438542b6e [..omitted for brevity..] chr1-12417-G-A 67293 902\n\n# use the final two fields, _dataOffset and _dataLen (add one to _dataLen to include a newline), to get the extra data:\nbgzip -b 67293 -s 903 gnomad.v3.1.1.details.tab.gz\n854246d79dc5d02dcdbd5f5438542b6e {\"DDX11L1\": {\"cons\": [\"non_coding_transcript_variant\", [..omitted for brevity..]\n\n\n\nThe data can also be found directly from the gnomAD downloads page. Please refer to\nour mailing list archives for questions, or our Data Access FAQ for more information.
\n\n\nThanks to the Genome Aggregation\nDatabase Consortium for making these data available. The data are released under the
\nPlease note that some annotations within the provided files may have restrictions on usage. See here for more information.\n
\n\n\nChen S, Francioli LC, Goodrich JK, Collins RL, Kanai M, Wang Q, Alföldi J, Watts NA, Vittal C,\nGauthier LD et al.\n\n A genomic mutational constraint map using variation in 76,156 human genomes.\nNature. 2024 Jan;625(7993):92-100.\nPMID: 38057664\n
\n\nKarczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, Collins RL, Laricchia KM, Ganna\nA, Birnbaum DP et al.\n\nThe mutational constraint spectrum quantified from variation in 141,456 humans.\nNature. 2020 May;581(7809):434-443.\nPMID: 32461654; PMC:
\nLek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill\nAJ, Cummings BB et al.\nAnalysis of protein-coding\ngenetic variation in 60,706 humans. Nature. 2016 Aug 17;536(7616):285-91.\nPMID: 27535533;\nPMC: PMC5018207\n
\n", "itemRgb": "on", "labelFields": "name,rsId", "longLabel": "Deprecated: Genome Aggregation Database (gnomAD) Genome Variants v3.1", "maxItems": "50000", "mouseOver": "Position: $chrom:${chromStart}-${chromEnd} ($ref/$alt)\nThis track shows structural variants (SVs) identified by Oxford Nanopore long-read\nsequencing of 1,019 individuals from the 1000 Genomes Project, representing 26\npopulations across 5 continental regions: Africa (275 samples), East Asia (192),\nSouth Asia (199), Europe (189), and Americas (164). Median sequencing coverage\nwas 16.9x per sample with a median N50 read length of 20.3 kb.\n
\n\nSVs were discovered using the SAGA framework (SV Analysis by Graph Augmentation)\nand annotated with SVAN, which classifies insertions and deletions by their\nmechanism of origin. The full release is native to the T2T-CHM13 assembly\n(hs1) and contains 161,332 annotated SVs (75,324 insertions, 66,192 deletions,\nand 19,816 complex rearrangements). For GRCh38 (hg38), coordinates were converted\nusing liftOver and 148,375 records mapped successfully (73,298 insertions,\n58,637 deletions, and 16,440 complex rearrangements).\n
\n\nThe 1,019 samples sequenced here are distinct from those in the\n1KG ONT 100 track (Gustafson et al. 2024);\nthe two releases were produced by separate consortia (Vienna and the 1000 Genomes\nONT Sequencing Consortium, respectively) and there is no sample overlap between\nthe two.\n
\n\n\nItems are colored by SV class:\n
\nFilters are available for SV type, insertion/deletion type, transposon family,\nand SV length. For insertions, the item is placed at the insertion site with a\nwidth of 1 bp; for deletions, the item spans the deleted region.\n
\n\nThe detail page for each item shows SVAN annotation fields including:\n
\nSchloissnig et al. 2025 generated intermediate-coverage Oxford Nanopore\nlong-read sequencing of 1,019 samples from the 1000 Genomes Project on\nPromethION 48 instruments with R9.4.1 (FLO-PRO002) flow cells (SQK-LSK110\nlibraries, 24-h runs with flow-cell wash and reload). SVs were discovered\nwith the SAGA framework (SV Analysis by Graph Augmentation), which combines\nlinear-reference callers (Sniffles and DELLY, run against both GRCh38 and\nT2T-CHM13), graph-aware discovery with SVarp (local long-read assembly of\nSV-supporting graph-aligned reads) and graph-based joint genotyping with\nGiggles across a pangenome graph. Insertions and deletions were then\nannotated with \nSVAN v1.3, which classifies SVs by mechanism of origin. The release\ncontains 161,332 SVAN-annotated SVs: 75,324 insertions, 66,192 deletions\nand 19,816 complex rearrangements. The original VCF is on T2T-CHM13 contig\ncoordinates; for the hg38 version of this track, SVs were lifted with\nliftOver (148,375 of 161,332 records mapped), while the hs1 version uses\nthe native coordinates.\n
\n\nThe SVAN-annotated unphased VCF (final-vcf.unphased.SVAN_1.3.vcf.gz)\nwas downloaded from\n\nthe IGSR 1KG_ONT_VIENNA v1.1 SVAN-annotation directory; allele counts\nwere added from the companion shapeit5-phased-callset\n(shapeit5-phased-callset_final-vcf.phased.vcf.gz) in the same\nrelease tree.\n
\n\nThe step-by-step build commands (download, liftOver, format conversion,\nbigBed build) are recorded in the UCSC makeDoc for this track container:\n\ndoc/hg38/lrSv.txt. The conversion scripts and autoSql schemas live in\n\nmakeDb/scripts/lrSv, and the track configuration is in trackDb/human/lrSv.ra.\n
\n\n\nSource data is available from the\n1000 Genomes ONT Vienna data collection at IGSR.\n
\n\n\nThanks to the 1000 Genomes ONT Vienna consortium for making their structural\nvariant calls and SVAN annotations publicly available.\n
\n\n\nSchloissnig S, Pani S, Ebler J, Hain C, Tsapalou V, Söylev A, Hüther P, Ashraf H, Prodanov T,\nAsparuhova M et al.\n\nStructural variation in 1,019 diverse humans based on long-read sequencing.\nNature. 2025 Aug;644(8076):442-452.\nPMID: 40702182; PMC:
\nThe NMDetective tracks display genome-wide predictions of nonsense-mediated mRNA\ndecay (NMD) efficiency using the NMDetective-A and NMDetective-B models from\nLindeboom et al. 2019,\ntrained on the NMD efficiency measure from\nLindeboom et al. 2016.\nNMDetective scores predict whether a premature termination codon (PTC) at a given position\nwill trigger NMD and mRNA degradation, or whether the transcript will escape NMD and\npotentially produce a truncated protein.\n
\n\n\nScores range from 0 to 1. Values near 1 indicate that a PTC at\nthat position is predicted to trigger NMD (the mRNA is degraded). Values near 0 indicate\nthat the PTC is predicted to escape NMD (the truncated mRNA may be translated into an\naberrant protein). Values in between indicate intermediate NMD efficiency.\n
\n\n| Track | Description |
|---|---|
| NMDetective-A | \nRandom forest model predicting NMD efficiency for all possible PTCs introduced\n by single-nucleotide variants. Explains ~71% of systematic variance in NMD\n efficiency. |
| NMDetective-B | \nSimplified decision tree model for all possible PTCs. Slightly lower accuracy\n (~68% variance explained) but more interpretable, making it suitable for\n clinical applications. |
| NMDetective-A PTC | \nRandom forest model predicting NMD efficiency specifically for the first\n out-of-frame PTC introduced by frameshifting indel mutations. |
| NMDetective-B PTC | \nDecision tree model for the first out-of-frame PTC from frameshifting\n indels. |
\nEach subtrack is displayed as a signal (bigWig) track. Values closer to 1 indicate\npredicted NMD-triggering, while values closer to 0 indicate predicted NMD escape.\n
\n\nNMDetective-A and NMDetective-B were introduced in\nLindeboom et al. 2019,\ntrained on NMD efficiency scores derived from somatic nonsense mutation data from 9,769 cancer\npatients\n(Lindeboom et al. 2016),\nand tested on an independent set of frameshift mutations.\nThe models incorporate the following features to predict NMD efficiency:\n
\n\nNMDetective-A (random forest regression) captures non-linear interactions among\nthese features and achieves the highest predictive accuracy.\nNMDetective-B (decision tree) applies a simpler rule-based classification that\nis more transparent, with a modest reduction in accuracy.\n
\n\n\nThe predictions were generated for every possible PTC-introducing single-nucleotide\nvariant and for the first out-of-frame PTC from every possible single-nucleotide\nframeshifting indel across all human protein-coding transcripts. The original bedGraph\ncustom track files were downloaded from the\nNMDetective Figshare page\nresource and converted to bigWig format at UCSC.\n
\n\n\nThe data underlying these tracks can be explored interactively with the\nTable Browser or the\nData Integrator. For automated analysis,\nthe data may be queried from our\nREST API. Please refer to our\nmailing list archives for questions, or our\nData Access FAQ for more\ninformation.\n
\n\n\nThanks to Rik Lindeboom for providing custom tracks and the original NMDetective data\non Figshare.\n
\n\n\nLindeboom RG, Supek F, Lehner B.\n\nThe rules and impact of nonsense-mediated mRNA decay in human cancers.\nNat Genet. 2016 Oct;48(10):1112-8.\nPMID: 27618451; PMC:
\nLindeboom RGH, Vermeulen M, Lehner B, Supek F.\n\nThe impact of nonsense-mediated mRNA decay on genetic disease, gene editing and cancer\nimmunotherapy.\nNat Genet. 2019 Nov;51(11):1645-1651.\nPMID: 31659324; PMC:
\nThe recombination rate track represents calculated rates of recombination based\non the genetic maps from deCODE (Halldorsson et al., 2019) and 1000 Genomes\n(2013 Phase 3 release, lifted from hg19). The deCODE map is more recent, has a higher \nresolution and was natively created on hg38 and therefore recommended. \nFor the Recomb. deCODE average track, the recombination rates for chrX represent the female rate.\n
\n\nThis track also includes a subtrack with all the\nindividual deCODE recombination events and another subtrack with several thousand\nde-novo mutations found in the deCODE sequencing data. These two tracks are hidden by\ndefault and have to be switched on explicitly on the configuration page.\n
\n\n\nThis is a super track that contains different subtracks, three with the deCODE\nrecombination rates (paternal, maternal and average) and one with the 1000\nGenomes recombination rate (average). These tracks are in \nsignal graph\n(wiggle) format. By default, to show most recombination hotspots, their maximum\nvalue is set to 100 cM, even though many regions have values higher than 100.\nThe maximum value can be changed on the configuration pages of the tracks.\n
\n\n\nThere are two more tracks that show additional details provided by deCODE: one\nsubtrack with the raw data of all cross-overs tagged with their proband ID and\nanother one with around 8000 human de-novo mutation variants that are linked to\ncross-over changes.\n
\n\n\nThe deCODE genetic map was created at \ndeCODE Genetics. It is based \non microarrays assaying 626,828 SNP markers that allowed to identify 1,476,140 crossovers in\n56,321 paternal meioses and 3,055,395 crossovers in 70,086 maternal meioses.\nIn total, the data is based on 4,531,535 crossovers in 126,427 meioses. By\nusing WGS data with 9,305,070 SNPs, the boundaries for 761,981 crossovers were\nrefined: 247,942 crossovers in 9423 paternal meioses and 514,039 crossovers in\n11,750 maternal meioses. The average resolution of the genetic map is 682 base\npairs (bp): 655 and 708 bp for the paternal and maternal maps, respectively.\n
\n\nThe 1000 Genomes genetic map is based on the
As with all other tracks, the data conversion commands and pointers to the\noriginal data files are documented in the \nmakeDoc file of this track.
\n\n\nThe raw data can be explored interactively with the Table Browser, or\nthe Data Integrator. For automated access, this track, like all\nothers, is available via our API. However, for bulk\nprocessing, it is recommended to download the dataset.\n
\n\n\nFor automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed\nfiles that can be downloaded from\nour download server.\nIndividual regions or the whole genome annotation can be obtained using our tools bigWigToWig\nor bigBedToBed which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tools can also be used to obtain features confined to a given range, e.g.,\n
\nbigWigToBedGraph -chrom=chr17 -start=45941345 -end=45942345 http://hgdownload.soe.ucsc.edu/gbdb/hg38/recombRate/recombAvg.bw stdout\n
\n
\nPlease refer to our\nData Access FAQ\nfor more information.\n
\n\n\nThis track was produced at UCSC using data that are freely available for\nthe deCODE\nand 1000 Genomes genetic maps. Thanks to Po-Ru Loh at the\nBroad Institute for providing the code to lift the hg19 1000 Genomes map data to hg38.\n
\n\n\n1000 Genomes Project Consortium., Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA,\nHurles ME, McVean GA.\n\n A map of human genome variation from population-scale sequencing.\nNature. 2010 Oct 28;467(7319):1061-73.\nPMID: 20981092; PMC:
\nHalldorsson BV, Palsson G, Stefansson OA, Jonsson H, Hardarson MT, Eggertsson HP, Gunnarsson B,\nOddsson A, Halldorsson GH, Zink F et al.\n\n Characterizing mutagenic effects of recombination through a sequence-level genetic map.\nScience. 2019 Jan 25;363(6425).\nPMID: 30679340\n
\n", "longLabel": "Recombination events in deCODE Genetic Map (zoom to < 10kbp to see the events)", "parent": "recombRate2", "priority": "4", "shortLabel": "Recomb. deCODE Evts", "track": "recombEvents", "type": "bigBed 4 +", "visibility": "hide" } }, "description": "Recombination events in deCODE Genetic Map (zoom to < 10kbp to see the events)", "category": [ "Mapping and Sequencing" ] }, { "trackId": "hg38-ncbiRefSeqOther", "name": "NCBI RefSeq - RefSeq Other", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/ncbiRefSeq/ncbiRefSeqOther.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/ncbiRefSeq/ncbiRefSeqOther.bb", "color": "32,32,32", "labelFields": "gene", "longLabel": "NCBI RefSeq Other Annotations (not NM_*, NR_*, XM_*, XR_*, NP_* or YP_*)", "parent": "refSeqComposite off", "priority": "4", "searchIndex": "name", "searchTrix": "/gbdb/hg38/ncbiRefSeq/ncbiRefSeqOther.ix", "shortLabel": "RefSeq Other", "skipEmptyFields": "on", "track": "ncbiRefSeqOther", "type": "bigBed 12 +", "urls": "GeneID=\"https://www.ncbi.nlm.nih.gov/gene/$$\" MIM=\"https://www.ncbi.nlm.nih.gov/omim/612091\" HGNC=\"https://www.genenames.org/data/gene-symbol-report/#!/hgnc_id/$$\" FlyBase=\"https://flybase.org/reports/$$\" WormBase=\"http://www.wormbase.org/db/gene/gene?name=$$\" RGD=\"https://rgd.mcw.edu/rgdweb/search/search.html?term=$$\" SGD=\"https://www.yeastgenome.org/locus/$$\" miRBase=\"http://www.mirbase.org/cgi-bin/mirna_entry.pl?acc=$$\" ZFIN=\"https://zfin.org/$$\" MGI=\"https://www.informatics.jax.org//marker/$$\"", "html": "" } }, "description": "NCBI RefSeq Other Annotations (not NM_*, NR_*, XM_*, XR_*, NP_* or YP_*)", "category": [ "Genes and Gene Predictions" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-ncbiRefSeqOther-LinearBasicDisplay", "labels": { "name": "jexl:get(feature,'gene')" } } ] }, { "trackId": "hg38-gnomad315XPercentage", "name": "gnomAD v3 Genome Coverage - Sample % > 15X", "type": "QuantitativeTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigWigAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/gnomAD/coverage/v3-genome/gnomad.coverage.over_15.bw" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/gnomAD/coverage/v3-genome/gnomad.coverage.over_15.bw", "color": "165,0,90", "longLabel": "gnomAD Percentage of Genome Samples with at least 15X Coverage v3.0.1", "parent": "gnomad3Coverage off", "priority": "4", "shortLabel": "Sample % > 15X", "track": "gnomad315XPercentage", "viewLimits": "0:1", "html": "" } }, "description": "gnomAD Percentage of Genome Samples with at least 15X Coverage v3.0.1", "category": [ "Variation and Repeats" ] }, { "trackId": "hg38-gnomad4Exome15XPercentage", "name": "gnomAD v4 Exome Coverage - Sample % > 15X", "type": "QuantitativeTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigWigAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/gnomAD/coverage/v4-exome/gnomad.coverage.over_15.bw" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/gnomAD/coverage/v4-exome/gnomad.coverage.over_15.bw", "color": "165,0,90", "longLabel": "gnomAD Percentage of Exome Samples with at least 15X Coverage v4.0", "parent": "gnomad4ExomeCoverage off", "priority": "4", "shortLabel": "Sample % > 15X", "track": "gnomad4Exome15XPercentage", "viewLimits": "0:1", "html": "" } }, "description": "gnomAD Percentage of Exome Samples with at least 15X Coverage v4.0", "category": [ "Variation and Repeats" ] }, { "trackId": "hg38-shorthcondels", "name": "Unusually Conserved - Short hConDels", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/unusualcons/hcondels.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/unusualcons/hcondels.bb", "longLabel": "short hConDels: 10032 Short Human hCondels - Human Conserved Deletions < 40bp", "parent": "unusualcons on", "shortLabel": "Short hConDels", "track": "shorthcondels", "type": "bigBed 4 +", "html": "" } }, "description": "short hConDels: 10032 Short Human hCondels - Human Conserved Deletions < 40bp", "category": [ "Comparative Genomics" ] }, { "trackId": "hg38-spliceAiDonorMinus", "name": "SpliceAI Wildtype - SpliceAI Donor Minus", "type": "QuantitativeTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigWigAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/spliceAi/wildtype/spliceAiDonorMinus.bw" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/bbi/spliceAi/wildtype/spliceAiDonorMinus.bw", "longLabel": "SpliceAI Splice Donor Sites, Minus Strand", "parent": "spliceAIWt on", "priority": "4", "shortLabel": "SpliceAI Donor Minus", "track": "spliceAiDonorMinus", "type": "bigWig 0 1", "html": "" } }, "description": "SpliceAI Splice Donor Sites, Minus Strand", "category": [ "Phenotypes, Variants, and Literature" ] }, { "trackId": "hg38-umap100", "name": "Single-read mappability - Umap S100", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k100.Unique.Mappability.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/hoffmanMappability/k100.Unique.Mappability.bb", "color": "80,170,240", "longLabel": "Single-read mappability with 100-mers", "parent": "umapBigBed off", "priority": "4", "shortLabel": "Umap S100", "subGroups": "view=SR", "track": "umap100", "visibility": "hide", "html": "" } }, "description": "Single-read mappability with 100-mers", "category": [ "Mapping and Sequencing" ] }, { "trackId": "hg38-gustafsonSv", "name": "Long-read SVs - 1KG UW ONT SVs", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/lrSv/gustafson.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/lrSv/gustafson.bb", "filter.AC": "0:200", "filter.insLen": "0:25094", "filter.sampleCount": "1:100", "filter.svLen": "0:98289", "filterByRange.AC": "on", "filterByRange.insLen": "on", "filterByRange.sampleCount": "on", "filterByRange.svLen": "on", "filterLabel.AC": "Allele Count (placeholder)", "filterLabel.insLen": "Insertion Length", "filterLabel.sampleCount": "Number of Carrier Samples", "filterLabel.svLen": "SV Length", "filterLabel.svType": "SV Type", "filterType.svType": "multipleListOr", "filterValues.svType": "DEL,INS,DUP,INV", "itemRgb": "on", "longLabel": "Structural Variants from 100 1000 Genomes samples (University of Washington ONT; Gustafson et al. 2024)", "mouseOver": "Var: $name ($svType)\nThis track shows structural variants (SVs) from Oxford Nanopore long-read\nwhole-genome sequencing of 100 individuals in the 1000 Genomes Project,\ngenerated by the University of Washington-led 1000 Genomes Project ONT\nsequencing effort and described in Gustafson et al. 2024. The cohort spans\nall five 1000 Genomes superpopulations and 19 subpopulations. Samples were\nsequenced with ONT R9.4.1 pores at ~37x coverage with median read N50 of\n~54 kb. This is the initial 100-sample release; sequencing of the 1000\nGenomes collection is ongoing.\n
\n\nThe track contains 113,159 SVs (63,177 insertions, 49,700 deletions,\n211 inversions, 71 duplications; byte-identical duplicate records have been\nremoved). Each variant was called by up to five\nindependent methods (three alignment-based: Sniffles2, cuteSV, SVIM;\nand assembly-based hapdiff on Flye or Shasta/Hapdup assemblies) and then\nmerged across callers and samples with Jasmine to produce a\ncross-sample consensus catalog.\n
\n\nThis 100-sample Gustafson cohort is distinct from the Vienna\n1000-Genomes-ONT release (1KG ONT SVs),\nwhich uses different samples, pore chemistry and callers; the two\nreleases share neither samples nor calls.\n
\n\n\nItems are colored by SV type:\n
\nInsertions are placed at the insertion site with a width of 1 bp; deletions,\nduplications and inversions span the affected reference interval. Filters\nare available for SV type, SV length and carrier-sample count. The detail\npage also shows the number of per-caller calls supporting each site\n(VARCALLS) and whether the source caller marked the breakpoints as precise.\n
\n\n\nGustafson et al. 2024 performed Oxford Nanopore long-read sequencing on\n100 samples from the 1000 Genomes Project (all five superpopulations and\n19 subpopulations) using R9.4.1 flow cells, at a median per-sample\ncoverage of ~37x and read N50 of ~54 kb. Per-sample SV calls were\ngenerated through the Napu pipeline with five independent methods: three\nalignment-based callers (Sniffles2, cuteSV and SVIM run on minimap2\nalignments to GRCh38) and two assembly-based callers (hapdiff run on Flye\nand on Shasta/Hapdup assemblies). The five per-sample VCFs were merged\nwith Jasmine\nin two stages (intra-sample consensus, then cross-sample merge). The\nreleased confident site-level callset is defined as variants supported by\nhapdiff and at least two unique alignment-based callers, yielding 113,696\nSVs (63,177 insertions, 49,704 deletions, 744 inversions, 71\nduplications). SV counts per sample and multicaller concordance were\nbenchmarked against the HPRC Sniffles2 truth and the GIAB HG002 Tier1\nregion with Truvari v4.1.0.\n
\n\nThe source Jasmine-merged VCF was downloaded from the 1000 Genomes ONT S3\nbucket:\n\n20240423_jasmine_intrasample_noBND_custom_suppvec_alphanumeric_header_JASMINE.vcf.gz.\n
\n\nThe step-by-step build commands (download, format conversion, bigBed build)\nare recorded in the UCSC makeDoc for this track container:\n\ndoc/hg38/lrSv.txt. The conversion scripts and autoSql schemas live in\n\nmakeDb/scripts/lrSv, and the track configuration is in trackDb/human/lrSv.ra.\n
\n\n\nThe data can be explored interactively in table format with the\nTable Browser or the\nData Integrator, and accessed\nprogrammatically through our API,\ntrack=gustafsonSv.\n
\n\nThe bigBed is available from\nour\ndownload server as gustafson.bb. Example:\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/lrSv/gustafson.bb -chrom=chr21 -start=0 -end=100000000 stdout.\n
\n\nThe original VCF is available from the 1000 Genomes ONT S3 bucket:\n\n20240423_jasmine_intrasample_noBND_custom_suppvec_alphanumeric_header_JASMINE.vcf.gz.\n
\n\n\nThanks to Gustafson and colleagues and the 1000 Genomes Project ONT\nSequencing Consortium for releasing this dataset.\n
\n\n\nGustafson JA, Gibson SB, Damaraju N, Zalusky MPG, Hoekzema K, Twesigomwe D, Yang L, Snead AA,\nRichmond PA, De Coster W et al.\n\nHigh-coverage nanopore sequencing of samples from the 1000 Genomes Project to build a comprehensive\ncatalog of human genetic variation.\nGenome Res. 2024 Nov 20;34(11):2061-2073.\nPMID: 39358015; PMC:
\nThe NMDetective tracks display genome-wide predictions of nonsense-mediated mRNA\ndecay (NMD) efficiency using the NMDetective-A and NMDetective-B models from\nLindeboom et al. 2019,\ntrained on the NMD efficiency measure from\nLindeboom et al. 2016.\nNMDetective scores predict whether a premature termination codon (PTC) at a given position\nwill trigger NMD and mRNA degradation, or whether the transcript will escape NMD and\npotentially produce a truncated protein.\n
\n\n\nScores range from 0 to 1. Values near 1 indicate that a PTC at\nthat position is predicted to trigger NMD (the mRNA is degraded). Values near 0 indicate\nthat the PTC is predicted to escape NMD (the truncated mRNA may be translated into an\naberrant protein). Values in between indicate intermediate NMD efficiency.\n
\n\n| Track | Description |
|---|---|
| NMDetective-A | \nRandom forest model predicting NMD efficiency for all possible PTCs introduced\n by single-nucleotide variants. Explains ~71% of systematic variance in NMD\n efficiency. |
| NMDetective-B | \nSimplified decision tree model for all possible PTCs. Slightly lower accuracy\n (~68% variance explained) but more interpretable, making it suitable for\n clinical applications. |
| NMDetective-A PTC | \nRandom forest model predicting NMD efficiency specifically for the first\n out-of-frame PTC introduced by frameshifting indel mutations. |
| NMDetective-B PTC | \nDecision tree model for the first out-of-frame PTC from frameshifting\n indels. |
\nEach subtrack is displayed as a signal (bigWig) track. Values closer to 1 indicate\npredicted NMD-triggering, while values closer to 0 indicate predicted NMD escape.\n
\n\nNMDetective-A and NMDetective-B were introduced in\nLindeboom et al. 2019,\ntrained on NMD efficiency scores derived from somatic nonsense mutation data from 9,769 cancer\npatients\n(Lindeboom et al. 2016),\nand tested on an independent set of frameshift mutations.\nThe models incorporate the following features to predict NMD efficiency:\n
\n\nNMDetective-A (random forest regression) captures non-linear interactions among\nthese features and achieves the highest predictive accuracy.\nNMDetective-B (decision tree) applies a simpler rule-based classification that\nis more transparent, with a modest reduction in accuracy.\n
\n\n\nThe predictions were generated for every possible PTC-introducing single-nucleotide\nvariant and for the first out-of-frame PTC from every possible single-nucleotide\nframeshifting indel across all human protein-coding transcripts. The original bedGraph\ncustom track files were downloaded from the\nNMDetective Figshare page\nresource and converted to bigWig format at UCSC.\n
\n\n\nThe data underlying these tracks can be explored interactively with the\nTable Browser or the\nData Integrator. For automated analysis,\nthe data may be queried from our\nREST API. Please refer to our\nmailing list archives for questions, or our\nData Access FAQ for more\ninformation.\n
\n\n\nThanks to Rik Lindeboom for providing custom tracks and the original NMDetective data\non Figshare.\n
\n\n\nLindeboom RG, Supek F, Lehner B.\n\nThe rules and impact of nonsense-mediated mRNA decay in human cancers.\nNat Genet. 2016 Oct;48(10):1112-8.\nPMID: 27618451; PMC:
\nLindeboom RGH, Vermeulen M, Lehner B, Supek F.\n\nThe impact of nonsense-mediated mRNA decay on genetic disease, gene editing and cancer\nimmunotherapy.\nNat Genet. 2019 Nov;51(11):1645-1651.\nPMID: 31659324; PMC:
\nThe recombination rate track represents calculated rates of recombination based\non the genetic maps from deCODE (Halldorsson et al., 2019) and 1000 Genomes\n(2013 Phase 3 release, lifted from hg19). The deCODE map is more recent, has a higher \nresolution and was natively created on hg38 and therefore recommended. \nFor the Recomb. deCODE average track, the recombination rates for chrX represent the female rate.\n
\n\nThis track also includes a subtrack with all the\nindividual deCODE recombination events and another subtrack with several thousand\nde-novo mutations found in the deCODE sequencing data. These two tracks are hidden by\ndefault and have to be switched on explicitly on the configuration page.\n
\n\n\nThis is a super track that contains different subtracks, three with the deCODE\nrecombination rates (paternal, maternal and average) and one with the 1000\nGenomes recombination rate (average). These tracks are in \nsignal graph\n(wiggle) format. By default, to show most recombination hotspots, their maximum\nvalue is set to 100 cM, even though many regions have values higher than 100.\nThe maximum value can be changed on the configuration pages of the tracks.\n
\n\n\nThere are two more tracks that show additional details provided by deCODE: one\nsubtrack with the raw data of all cross-overs tagged with their proband ID and\nanother one with around 8000 human de-novo mutation variants that are linked to\ncross-over changes.\n
\n\n\nThe deCODE genetic map was created at \ndeCODE Genetics. It is based \non microarrays assaying 626,828 SNP markers that allowed to identify 1,476,140 crossovers in\n56,321 paternal meioses and 3,055,395 crossovers in 70,086 maternal meioses.\nIn total, the data is based on 4,531,535 crossovers in 126,427 meioses. By\nusing WGS data with 9,305,070 SNPs, the boundaries for 761,981 crossovers were\nrefined: 247,942 crossovers in 9423 paternal meioses and 514,039 crossovers in\n11,750 maternal meioses. The average resolution of the genetic map is 682 base\npairs (bp): 655 and 708 bp for the paternal and maternal maps, respectively.\n
\n\nThe 1000 Genomes genetic map is based on the
As with all other tracks, the data conversion commands and pointers to the\noriginal data files are documented in the \nmakeDoc file of this track.
\n\n\nThe raw data can be explored interactively with the Table Browser, or\nthe Data Integrator. For automated access, this track, like all\nothers, is available via our API. However, for bulk\nprocessing, it is recommended to download the dataset.\n
\n\n\nFor automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed\nfiles that can be downloaded from\nour download server.\nIndividual regions or the whole genome annotation can be obtained using our tools bigWigToWig\nor bigBedToBed which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tools can also be used to obtain features confined to a given range, e.g.,\n
\nbigWigToBedGraph -chrom=chr17 -start=45941345 -end=45942345 http://hgdownload.soe.ucsc.edu/gbdb/hg38/recombRate/recombAvg.bw stdout\n
\n
\nPlease refer to our\nData Access FAQ\nfor more information.\n
\n\n\nThis track was produced at UCSC using data that are freely available for\nthe deCODE\nand 1000 Genomes genetic maps. Thanks to Po-Ru Loh at the\nBroad Institute for providing the code to lift the hg19 1000 Genomes map data to hg38.\n
\n\n\n1000 Genomes Project Consortium., Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA,\nHurles ME, McVean GA.\n\n A map of human genome variation from population-scale sequencing.\nNature. 2010 Oct 28;467(7319):1061-73.\nPMID: 20981092; PMC:
\nHalldorsson BV, Palsson G, Stefansson OA, Jonsson H, Hardarson MT, Eggertsson HP, Gunnarsson B,\nOddsson A, Halldorsson GH, Zink F et al.\n\n Characterizing mutagenic effects of recombination through a sequence-level genetic map.\nScience. 2019 Jan 25;363(6425).\nPMID: 30679340\n
\n", "longLabel": "Recombination rate: De-novo mutations found in deCODE samples", "parent": "recombRate2", "priority": "5", "shortLabel": "Recomb. deCODE Dmn", "track": "recombDnm", "type": "bigBed 4 +", "visibility": "hide" } }, "description": "Recombination rate: De-novo mutations found in deCODE samples", "category": [ "Mapping and Sequencing" ] }, { "trackId": "hg38-revelOverlaps", "name": "REVEL Scores - REVEL overlaps", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/revel/overlap.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/revel/overlap.bb", "extraTableFields": "_jsonTable|Title", "longLabel": "REVEL: Positions with >1 score due to overlapping transcripts (mouseover for details)", "mouseOverField": "_mouseOver", "parent": "revel on", "shortLabel": "REVEL overlaps", "track": "revelOverlaps", "type": "bigBed 9 +", "visibility": "dense", "html": "" } }, "description": "REVEL: Positions with >1 score due to overlapping transcripts (mouseover for details)", "category": [ "Phenotypes, Variants, and Literature" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-revelOverlaps-LinearBasicDisplay", "mouseover": "jexl:get(feature,'_mouseOver')" } ] }, { "trackId": "hg38-gnomad320XPercentage", "name": "gnomAD v3 Genome Coverage - Sample % > 20X", "type": "QuantitativeTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigWigAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/gnomAD/coverage/v3-genome/gnomad.coverage.over_20.bw" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/gnomAD/coverage/v3-genome/gnomad.coverage.over_20.bw", "color": "135,0,120", "longLabel": "gnomAD Percentage of Genome Samples with at least 20X Coverage v3.0.1", "parent": "gnomad3Coverage off", "priority": "5", "shortLabel": "Sample % > 20X", "track": "gnomad320XPercentage", "viewLimits": "0:1", "html": "" } }, "description": "gnomAD Percentage of Genome Samples with at least 20X Coverage v3.0.1", "category": [ "Variation and Repeats" ] }, { "trackId": "hg38-gnomad4Exome20XPercentage", "name": "gnomAD v4 Exome Coverage - Sample % > 20X", "type": "QuantitativeTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigWigAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/gnomAD/coverage/v4-exome/gnomad.coverage.over_20.bw" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/gnomAD/coverage/v4-exome/gnomad.coverage.over_20.bw", "color": "135,0,120", "longLabel": "gnomAD Percentage of Exome Samples with at least 20X Coverage v4.0", "parent": "gnomad4ExomeCoverage off", "priority": "5", "shortLabel": "Sample % > 20X", "track": "gnomad4Exome20XPercentage", "viewLimits": "0:1", "html": "" } }, "description": "gnomAD Percentage of Exome Samples with at least 20X Coverage v4.0", "category": [ "Variation and Repeats" ] }, { "trackId": "hg38-unipLocTransMemb", "name": "UniProt - Transmembrane", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/uniprot/unipLocTransMemb.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/uniprot/unipLocTransMemb.bb", "color": "0,150,0", "filterValues.status": "Manually reviewed (Swiss-Prot),Unreviewed (TrEMBL)", "itemRgb": "off", "longLabel": "UniProt Transmembrane Domains", "mouseOver": "UniProt record: $uniProtId\nThe structural variants (SVs) in this dataset were identified using Oxford\nNanopore long-read whole-genome sequencing of 888 individuals from the 1000\nGenomes Project, representing five ancestry groups. This callset was created\nprimarily for SV imputation. To generate a multi-ancestry SV imputation\nreference panel, SVs observed in only one individual (singletons) were excluded,\nand the remaining SVs were merged with previously identified short variants from\nthe same individuals. This panel was used to impute SVs in approximately 500,000\nUK Biobank participants and to test their associations with 32 disease-relevant\ntraits.\n
\n\nThe track contains all 107,445 SVs in the reference panel: 59,953 insertions,\n38,459 deletions, 5,729 inversions, 2,696 breakends, and 608 duplications. Each\nvariant is annotated with its overall allele frequency; allele frequencies\nacross five superpopulations (African, Admixed American, East Asian, European,\nand South Asian); Hardy-Weinberg equilibrium p-values; and imputation accuracy\nmetrics from internal leave-one-out validation and UK Biobank imputation. For\nSVs reaching genome-wide significance, the associated traits, p-values, and INFO\nscores are listed on the corresponding variant details page.\n
\n\nThe 888 samples in this dataset are a subset of the 1,019 samples included in\nthe 1KG Vienna ONT track (Schloissnig et al.\n2025). Although the two studies share the same raw sequencing data, they applied\ndifferent data-processing and SV-calling pipelines to address distinct research\nobjectives, so the individual calls are only partially concordant. The\nimputation reference panel, UK Biobank imputation results, and SV-wide\nassociation study (SV-WAS) results described here are specific to this track.\n
\n\n\nItems are colored by SV type, matching the other subtracks of the container:\n
\n| \n | Deletion (DEL) |
|---|---|
| \n | Insertion (INS) |
| \n | Duplication (DUP) |
| \n | Inversion (INV) |
| \n | Breakend (BND), a single junction of a larger rearrangement |
\nInsertions and breakends are drawn at a single reference base; the length of\ninserted sequence is reported for insertions, and the mate locus of the\nrearrangement junction is reported for breakends. Deletions, inversions and\nduplications span the affected reference interval. Because the source table\ndoes not report an allele count, the allele count and allele number shown here\nare approximate values derived from the reported allele frequency and the\ngenotype missing rate (allele number = 2 × 888 × (1 − missing\nrate); allele count = allele frequency × allele number).\n
\n\nThe mouseover shows the variant name, SV type, reference and insertion lengths,\nallele frequency, approximate allele count, and the number of UK Biobank trait\nassociations. Filters are available for SV type, SV length, insertion length,\napproximate allele count, overall and per-population allele frequency, the\nnumber of UK Biobank GWAS hits, and the leave-one-out imputation r² and\nminor-allele concordance.\n
\n\n\n888 individuals from the 1000 Genomes Project (164 European, 144 Admixed\nAmerican, 168 East Asian, 171 South Asian and 241 African), out of 906\nsequenced, passed quality control. They were sequenced on the Oxford Nanopore\nPromethION P48 platform with R9.4.1 flow cells and the SQK-LSK110 ligation kit,\nto a median read length of about 6.2 kb and 15x median coverage. Reads were\naligned to GRCh38 with minimap2 v2.24 and structural variants were jointly\ncalled across all samples with Sniffles2 v2.0.7 using tandem-repeat\nannotations. Variants were retained if they were 50 bp to 30 Mb long, present\nin at least two individuals and had a genotype missing rate below 20%, yielding\n107,445 SVs. This SV panel was merged with about 45 million short variants from\n1000 Genomes Phase 3 and phased with Beagle to build a multi-ancestry\nimputation reference panel. Leave-one-out cross-validation with Beagle v5.4\nprovided per-variant imputation accuracy (r²) and minor-allele\nconcordance. The panel was then used to impute SVs into 488,130 UK Biobank\nparticipants, and an SV-wide association study (SV-WAS) with Regenie v3 tested\n32 disease-relevant phenotypes and 1,463 protein levels in European-ancestry\nparticipants, using a genome-wide significance threshold of p<5×10-8.\nSee Noyvert et al. 2025 for full details.\n
\n\nThe per-variant summary table (allele frequencies, quality metrics, imputation\naccuracy and significant UK Biobank associations for all 107,445 SVs) was\nprovided by the authors. At UCSC it was converted to the shared long-read SV\nschema (signed lengths made positive, an explicit insertion-length field added,\nallele count and allele number approximated from allele frequency and missing\nrate, and colors assigned from the container's shared palette). The\nstep-by-step commands are recorded in the UCSC makeDoc for this track\ncontainer:\n\ndoc/hg38/lrSv.txt. The conversion script and autoSql schema live in\n\nmakeDb/scripts/lrSv, and the track configuration is in\ntrackDb/human/lrSv.ra.\n
\n\n\nThe data can be explored interactively in table format with the\nTable Browser or the\nData Integrator and exported from there\nto spreadsheet or tab-sep tables. From scripts, the data can be accessed\nthrough our API, track=noyvertSv.\n
\n\nThe annotation is stored as a bigBed file that can be downloaded from\nour\ndownload server as noyvert.bb. Individual regions or the whole\nannotation can be obtained with the bigBedToBed utility, available\nfrom our\nutilities\npage. Example:\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/lrSv/noyvert.bb -chrom=chr21 -start=0 -end=100000000 stdout.\n
\n\n\nThanks to Boris Noyvert and colleagues at Boehringer Ingelheim and the wider\nstudy team for generating this multi-ancestry long-read SV panel and for\nsharing the per-variant summary table, and to the 1000 Genomes Project and the\nUK Biobank participants whose data made the study possible.\n
\n\n\nNoyvert B, Erzurumluoglu AM, Drichel D, Omland S, Andlauer TFM et al.\n\nImputation of structural variants using a multi-ancestry long-read sequencing panel enables\nidentification of disease associations.\neLife. 2025. doi:10.7554/eLife.106115.1\n
\n\nA continuously updated preprint version of this study is available on medRxiv:\n\ndoi:10.1101/2023.12.20.23300308.\n
\n" } }, "description": "Structural Variants from 888 1000 Genomes samples (Boehringer ONT; Noyvert et al. 2025)", "category": [ "Variation and Repeats" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-noyvertSv-LinearBasicDisplay", "mouseover": "jexl:`Var: ${get(feature,'name')} (${get(feature,'svType')})\nThe recombination rate track represents calculated rates of recombination based\non the genetic maps from deCODE (Halldorsson et al., 2019) and 1000 Genomes\n(2013 Phase 3 release, lifted from hg19). The deCODE map is more recent, has a higher \nresolution and was natively created on hg38 and therefore recommended. \nFor the Recomb. deCODE average track, the recombination rates for chrX represent the female rate.\n
\n\nThis track also includes a subtrack with all the\nindividual deCODE recombination events and another subtrack with several thousand\nde-novo mutations found in the deCODE sequencing data. These two tracks are hidden by\ndefault and have to be switched on explicitly on the configuration page.\n
\n\n\nThis is a super track that contains different subtracks, three with the deCODE\nrecombination rates (paternal, maternal and average) and one with the 1000\nGenomes recombination rate (average). These tracks are in \nsignal graph\n(wiggle) format. By default, to show most recombination hotspots, their maximum\nvalue is set to 100 cM, even though many regions have values higher than 100.\nThe maximum value can be changed on the configuration pages of the tracks.\n
\n\n\nThere are two more tracks that show additional details provided by deCODE: one\nsubtrack with the raw data of all cross-overs tagged with their proband ID and\nanother one with around 8000 human de-novo mutation variants that are linked to\ncross-over changes.\n
\n\n\nThe deCODE genetic map was created at \ndeCODE Genetics. It is based \non microarrays assaying 626,828 SNP markers that allowed to identify 1,476,140 crossovers in\n56,321 paternal meioses and 3,055,395 crossovers in 70,086 maternal meioses.\nIn total, the data is based on 4,531,535 crossovers in 126,427 meioses. By\nusing WGS data with 9,305,070 SNPs, the boundaries for 761,981 crossovers were\nrefined: 247,942 crossovers in 9423 paternal meioses and 514,039 crossovers in\n11,750 maternal meioses. The average resolution of the genetic map is 682 base\npairs (bp): 655 and 708 bp for the paternal and maternal maps, respectively.\n
\n\nThe 1000 Genomes genetic map is based on the
As with all other tracks, the data conversion commands and pointers to the\noriginal data files are documented in the \nmakeDoc file of this track.
\n\n\nThe raw data can be explored interactively with the Table Browser, or\nthe Data Integrator. For automated access, this track, like all\nothers, is available via our API. However, for bulk\nprocessing, it is recommended to download the dataset.\n
\n\n\nFor automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed\nfiles that can be downloaded from\nour download server.\nIndividual regions or the whole genome annotation can be obtained using our tools bigWigToWig\nor bigBedToBed which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tools can also be used to obtain features confined to a given range, e.g.,\n
\nbigWigToBedGraph -chrom=chr17 -start=45941345 -end=45942345 http://hgdownload.soe.ucsc.edu/gbdb/hg38/recombRate/recombAvg.bw stdout\n
\n
\nPlease refer to our\nData Access FAQ\nfor more information.\n
\n\n\nThis track was produced at UCSC using data that are freely available for\nthe deCODE\nand 1000 Genomes genetic maps. Thanks to Po-Ru Loh at the\nBroad Institute for providing the code to lift the hg19 1000 Genomes map data to hg38.\n
\n\n\n1000 Genomes Project Consortium., Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA,\nHurles ME, McVean GA.\n\n A map of human genome variation from population-scale sequencing.\nNature. 2010 Oct 28;467(7319):1061-73.\nPMID: 20981092; PMC:
\nHalldorsson BV, Palsson G, Stefansson OA, Jonsson H, Hardarson MT, Eggertsson HP, Gunnarsson B,\nOddsson A, Halldorsson GH, Zink F et al.\n\n Characterizing mutagenic effects of recombination through a sequence-level genetic map.\nScience. 2019 Jan 25;363(6425).\nPMID: 30679340\n
\n", "longLabel": "Recombination rate: 1000 Genomes, lifted from hg19 (PR Loh)", "maxHeightPixels": "128:60:8", "parent": "recombRate2", "priority": "6", "shortLabel": "Recomb. 1k Genomes", "track": "recomb1000GAvg", "type": "bigWig", "viewLimits": "0.0:100", "viewLimitsMax": "0:150000", "visibility": "full" } }, "description": "Recombination rate: 1000 Genomes, lifted from hg19 (PR Loh)", "category": [ "Mapping and Sequencing" ] }, { "trackId": "hg38-ncbiRefSeqGenomicDiff", "name": "NCBI RefSeq - RefSeq Diffs", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/ncbiRefSeq/ncbiRefSeqGenomicDiff.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/ncbiRefSeq/ncbiRefSeqGenomicDiff.bb", "itemRgb": "on", "longLabel": "Differences between NCBI RefSeq Transcripts and the Reference Genome", "parent": "refSeqComposite off", "priority": "6", "shortLabel": "RefSeq Diffs", "skipEmptyFields": "on", "track": "ncbiRefSeqGenomicDiff", "type": "bigBed 9 +", "html": "" } }, "description": "Differences between NCBI RefSeq Transcripts and the Reference Genome", "category": [ "Genes and Gene Predictions" ] }, { "trackId": "hg38-gnomad325XPercentage", "name": "gnomAD v3 Genome Coverage - Sample % > 25X", "type": "QuantitativeTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigWigAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/gnomAD/coverage/v3-genome/gnomad.coverage.over_25.bw" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/gnomAD/coverage/v3-genome/gnomad.coverage.over_25.bw", "color": "105,0,150", "longLabel": "gnomAD Percentage of Genome Samples with at least 25X Coverage v3.0.1", "parent": "gnomad3Coverage off", "priority": "6", "shortLabel": "Sample % > 25X", "track": "gnomad325XPercentage", "viewLimits": "0:1", "html": "" } }, "description": "gnomAD Percentage of Genome Samples with at least 25X Coverage v3.0.1", "category": [ "Variation and Repeats" ] }, { "trackId": "hg38-gnomad4Exome25XPercentage", "name": "gnomAD v4 Exome Coverage - Sample % > 25X", "type": "QuantitativeTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigWigAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/gnomAD/coverage/v4-exome/gnomad.coverage.over_25.bw" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/gnomAD/coverage/v4-exome/gnomad.coverage.over_25.bw", "color": "105,0,150", "longLabel": "gnomAD Percentage of Exome Samples with at least 25X Coverage v4.0", "parent": "gnomad4ExomeCoverage off", "priority": "6", "shortLabel": "Sample % > 25X", "track": "gnomad4Exome25XPercentage", "viewLimits": "0:1", "html": "" } }, "description": "gnomAD Percentage of Exome Samples with at least 25X Coverage v4.0", "category": [ "Variation and Repeats" ] }, { "trackId": "hg38-ucneClusters", "name": "Unusually Conserved - UCNE Clusters", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/unusualcons/clusters.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/unusualcons/clusters.bb", "longLabel": "UCNEBase: 239 Cluster of UCNE elements", "parent": "unusualcons on", "shortLabel": "UCNE Clusters", "track": "ucneClusters", "type": "bigBed 4 +", "url": "https://epd.expasy.org/ucnebase/view.php?data=cluster&entry=$$", "html": "" } }, "description": "UCNEBase: 239 Cluster of UCNE elements", "category": [ "Comparative Genomics" ] }, { "trackId": "hg38-umap36Quantitative", "name": "Multi-read mappability - Umap M36", "type": "QuantitativeTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigWigAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k36.Umap.MultiTrackMappability.bw" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/hoffmanMappability/k36.Umap.MultiTrackMappability.bw", "color": "80,70,240", "longLabel": "Multi-read mappability with 36-mers", "parent": "umapBigWig off", "priority": "6", "shortLabel": "Umap M36", "subGroups": "view=MR", "track": "umap36Quantitative", "type": "bigWig 0.027778 1.0", "visibility": "hide", "html": "" } }, "description": "Multi-read mappability with 36-mers", "category": [ "Mapping and Sequencing" ] }, { "trackId": "hg38-bismap100Neg", "name": "Single-read mappability - Bismap S100 -", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k100.G2A-Converted.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/hoffmanMappability/k100.G2A-Converted.bb", "color": "240,170,80", "longLabel": "Single-read mappability with 100-mers after bisulfite conversion (reverse strand)", "parent": "bismapBigBed off", "priority": "7", "shortLabel": "Bismap S100 -", "subGroups": "view=SR", "track": "bismap100Neg", "visibility": "hide", "html": "" } }, "description": "Single-read mappability with 100-mers after bisulfite conversion (reverse strand)", "category": [ "Mapping and Sequencing" ] }, { "trackId": "hg38-bismap50Neg", "name": "Single-read mappability - Bismap S50 -", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/hoffmanMappability/k50.G2A-Converted.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/hoffmanMappability/k50.G2A-Converted.bb", "color": "240,120,80", "longLabel": "Single-read mappability with 50-mers after bisulfite conversion (reverse strand)", "parent": "bismapBigBed off", "priority": "7", "shortLabel": "Bismap S50 -", "subGroups": "view=SR", "track": "bismap50Neg", "visibility": "hide", "html": "" } }, "description": "Single-read mappability with 50-mers after bisulfite conversion (reverse strand)", "category": [ "Mapping and Sequencing" ] }, { "trackId": "hg38-unipChain", "name": "UniProt - Chains", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/uniprot/unipChain.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/uniprot/unipChain.bb", "filterValues.status": "Manually reviewed (Swiss-Prot),Unreviewed (TrEMBL)", "longLabel": "UniProt Mature Protein Products (Polypeptide Chains)", "mouseOver": "UniProt record: $uniProtId\nA pangenome graph holds many human genomes at once. Sequence that the\ngenomes share collapses onto common paths, and the places where they\ndiffer show up as bubbles in the graph. This track shows the structural\nvariants found in version 2.1 of the Human Pangenome Reference Consortium\n(HPRC) minigraph-cactus graph, which was built from haplotype-resolved\nPacBio HiFi assemblies of 233 samples. Only larger events are shown here:\ninsertions and deletions of at least 50 bp. HPRC produces one variant file\nper reference path, so the events are measured against GRCh38 on hg38 and\nagainst T2T-CHM13 on hs1, and each assembly shows its own native callset.\n
\n\nOn hg38 there are about 550,000 such alleles (roughly 422,000 insertions and\n128,000 deletions). On hs1 there are about 541,000 (roughly 348,000\ninsertions and 193,000 deletions). The two sets are not lifted between\nassemblies; the counts differ because an insertion against one reference can\nbe a deletion against the other.\n
\n\nA linear callset with conventional SV callers, not the pangenome graph approach, was\ncreated by Wenwei Liao and is available \nfrom GitHub.\n
\n\n\nItems are colored by SV type:\n
\n| \n | Insertion (INS) |
|---|---|
| \n | Deletion (DEL) |
\nAn insertion is drawn as a 1 bp anchor at the point where the extra\nsequence goes in. A deletion spans the stretch of reference that is\nmissing. Each variant keeps its allele count, allele frequency, the\nnumber of samples with data, and the level it sits at in the graph's\nsnarl tree. A snarl level of 0 is a top-level bubble; higher numbers are\nbubbles nested inside a parent bubble. All of these can be used as\nfilters.\n
\n\n\nHPRC release 2 does not yet have a peer-reviewed paper. The graph was\nbuilt with minigraph-cactus from haplotype-resolved PacBio HiFi assemblies\nof 233 samples, including T2T-CHM13 and the diverse 1000 Genomes Project\npanel, using GRCh38 as the reference path. Variants were called from the\ngraph with vg deconstruct. HPRC keeps the sample list and assembly\nprovenance in\n\nalignments_v2.0.csv.\n
\n\nWe started from the per-reference files provided by the HPRC graph team,\nhprc-v2.1-mc-grch38.gref95.ro.vcf.gz for hg38 and\nhprc-v2.1-mc-chm13.gref95.ro.vcf.gz for hs1. These are the raw\nvg deconstruct output: each graph bubble is one multi-allelic\nrecord with its graph traversals attached, and there are no per-allele type\nor length fields. To turn a file into a track, we compared every alternate\nallele to the reference allele after trimming the sequence they share at\neach end. An allele was kept when the net length change was at least 50 bp,\nand labeled an insertion when the alternate is longer or a deletion when it\nis shorter. At this size no balanced, equal-length substitutions came up,\nand the files carry no inversion calls, so the track has only insertions and\ndeletions. On hg38, 549,649 alleles were kept (40,678 at nested snarl\nlevels); on hs1, 541,176 (70,200 nested), after removing byte-identical\nduplicate records. Because these files are not broken\ndown into atomic indels, one bubble can appear as a single large allele\nrather than several small ones, so the counts are not comparable to a\nwave-decomposed callset. Allele counts, frequencies and sample counts come\nstraight from the VCF.\n
\n\nThe conversion script and autoSql schema are in\n\nmakeDb/scripts/lrSv and the build steps are in the makeDoc at\n\ndoc/hg38/lrSv.txt, and the track configuration is in\ntrackDb/human/lrSv.ra.\n
\n\n\nThe data can be explored interactively in table format with the\nTable Browser or the\nData Integrator, and read programmatically\nthrough our API,\ntrack=hprc2v21Sv. For automated download and analysis the variants\nare in a bigBed file on our download server, one per assembly:\n\nhg38 and\n\nhs1. You can pull out one region or the whole set with\nbigBedToBed, for example\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/lrSv/hprc2v21.bb -chrom=chr21 -start=0 -end=100000000 stdout.\n
\n\n\nThanks to the Human Pangenome Reference Consortium for building and\nreleasing the release-2 minigraph-cactus pangenome, and to Glenn Hickey\nfor the v2.1 deconstructed VCF.\n
\n\n\nHPRC release 2 is not yet described in a peer-reviewed publication. The\nrelease announcement has background and data-access details:\n\nHPRC data release 2.\n
\n" } }, "description": "Structural Variants from 233 HPRC v2.1 assemblies (minigraph-cactus pangenome graph)", "category": [ "Variation and Repeats" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-hprc2v21Sv-LinearBasicDisplay", "mouseover": "jexl:`Var: ${get(feature,'name')} (${get(feature,'svType')})\nThis track shows structural variants (SVs) from the third phase of the\nHuman Genome Structural Variation Consortium (HGSVC3). The callset comes\nfrom 65 diverse individuals across five continental groups, each sequenced\nwith PacBio HiFi (~47x), Oxford Nanopore ultra-long reads (~56x) and\ncomplemented with Strand-seq, optical mapping, Hi-C and Iso-Seq for\nhaplotype-resolved assembly. SVs were discovered from the de novo assemblies\nwith PAV v2.4.0.1 and cross-validated by ten additional orthogonal callers.\n
\n\nThe track merges the two final SV annotation tables from the HGSVC3 v1.0\nrelease on GRCh38: 176,231 insertions/deletions and 300 inversions, for a\ntotal of 176,531 SVs. Each row is a site-level variant with the list of\ncarrier haplotypes and additional structural annotations.\n
\n\nThe same track is also available natively on the T2T-CHM13 (hs1)\nassembly: HGSVC3 independently aligned all haplotype-resolved assemblies\nto both GRCh38 and T2T-CHM13 and released a separate set of annotation\ntables per reference. The hs1 track is built directly from the\n\nHGSVC3 T2T-CHM13 annotation tables (188,224 DEL+INS and 276 INV;\n188,500 SVs total); no liftOver is involved.\n
\n\n\nItems are colored by SV type:\n
\nInsertions are placed at the insertion site with a width of 1 bp; deletions\nand inversions span the affected reference interval. Filters are available\nfor SV type, SV length, carrier-haplotype count, distinct sample count,\nwhether the site falls in a Tandem Repeat Finder region and the fraction\nof the variant overlapping segmental duplications.\n
\n\nThe detail page shows, where available:\n
\nLogsdon et al. 2025 produced fully phased hybrid de novo assemblies for 65\ndiverse individuals (63 from 1kGP, NA21487 from HapMap, and HG002 from\nGIAB), using PacBio HiFi (Sequel II/Revio, 30-h movies), Oxford Nanopore\nultra-long sequencing (R9.4.1 PromethION, 96-h runs), Bionano optical\nmapping (DLE-1 on Saphyr 2nd-gen), Strand-seq, Hi-C (Proximo) and Iso-Seq.\nAssemblies were generated with Verkko v1.4.1 (primary) and hifiasm-UL\nv0.19.6 (complementary, especially for centromeres and Yq12), phased with\nthe Graphasing pipeline v0.3.1-alpha, and produced 130 haplotype\nassemblies with median N50 of 130 Mbp that close 92% of previous assembly\ngaps (39% of chromosomes at telomere-to-telomere status). SVs were called\nagainst GRCh38 and T2T-CHM13 with PAV v2.4.1 (plus DipCall and SVIM-asm\nfrom the same alignments) and cross-validated with an additional ten\ncallers (PBSV, Sniffles, Delly, cuteSV, DeBreak, SVIM, DeepVariant,\nClair3, PEPPER-Margin-DeepVariant for ONT and MELT-LRA/PALMER2 for MEIs).\nCalls were merged with SV-Pop and centromere-satellite / telomere hits\nwere filtered. The final GRCh38 release contains 176,231 DEL+INS plus 300\nINV (176,531 SVs total); the T2T-CHM13 release contains 188,224 DEL+INS\nplus 276 INV (188,500 SVs total).\n
\n\nFor display, the two final HGSVC3 v1.0 annotation tables\nvariants_GRCh38_sv_insdel_HGSVC2024v1.0.tsv.gz and\nvariants_GRCh38_sv_inv_HGSVC2024v1.0.tsv.gz were downloaded from\nthe \nIGSR HGSVC3 GRCh38 release directory and merged into a single bigBed.\nThe hs1 version uses the parallel\nvariants_T2T-CHM13_sv_insdel_HGSVC2024v1.0.tsv.gz and\nvariants_T2T-CHM13_sv_inv_HGSVC2024v1.0.tsv.gz tables from the\n\nHGSVC3 T2T-CHM13 release directory; no liftOver is involved on hs1.\nType-specific columns (HOM_REF/HOM_TIG/TE for insdel; RGN_REF_INNER for\ninversions) are empty on the detail page when they do not apply.\n
\n\nThe step-by-step build commands (download, format conversion, bigBed build)\nare recorded in the UCSC makeDoc for this track container:\n\ndoc/hg38/lrSv.txt and\n\ndoc/hs1/lrSv.txt. The conversion scripts and autoSql schemas live in\n\nmakeDb/scripts/lrSv, and the track configuration is in trackDb/human/lrSv.ra.\n
\n\n\nThe data can be explored interactively in table format with the\nTable Browser or the\nData Integrator, and accessed\nprogrammatically through our API,\ntrack=hgsvc3Sv.\n
\n\nThe bigBed is available from our download server for both assemblies:\n
\nThe original annotation tables are available from the\n\nHGSVC3 release on the IGSR FTP site.\n
\n\n\nThanks to the Human Genome Structural Variation Consortium (HGSVC) and all\nparticipating sequencing and analysis centers for making the HGSVC3\nannotation tables publicly available.\n
\n\n\nLogsdon GA, Ebert P, Audano PA, Loftus M, Porubsky D, Ebler J, Yilmaz F, Hallast P, Prodanov T, Yoo\nD et al.\n\nComplex genetic variation in nearly complete human genomes.\nNature. 2025 Aug;644(8076):430-441.\nPMID: 40702183; PMC:
\nThis track shows structural variants (SVs) from the second phase of the\nHuman Genome Structural Variation Consortium (HGSVC2). The callset is\nderived from 32 haplotype-resolved diploid genomes (64 phased haplotypes)\nspanning five 1000 Genomes superpopulations (African, Admixed American,\nEast Asian, European, South Asian). Each genome was sequenced with\nPacBio long reads (continuous long-read and HiFi) and phased with\nStrand-seq.\n
\n\nThe track merges the two SV annotation tables from the HGSVC2 v2.0\nintegrated callset freeze 4: 111,330 insertions/deletions and 416\ninversions, for a total of 111,746 SVs. Each row is a site-level variant\nwith per-site allele count, carrier haplotypes, population-scale allele\nfrequencies (imputed from the phased callset back into 1000 Genomes,\ninsertions and deletions only) and structural annotations.\n
\n\n\nItems are colored by SV type:\n
\nInsertions are placed at the insertion site with a width of 1 bp; deletions\nand inversions span the affected reference interval. Filters are available\nfor SV type, SV length, carrier-haplotype count, distinct sample count,\nwhether the site falls in a Tandem Repeat Finder region and the fraction\nof the variant overlapping segmental duplications.\n
\n\nThe detail page shows, where available:\n
\nEbert et al. 2021 produced phased haplotype-resolved de novo assemblies for\n32 diploid samples (64 unrelated haplotypes) across five 1000 Genomes\nsuperpopulations on the PacBio Sequel II platform, using continuous\nlong-read sequencing (CLR, >40x) and high-fidelity sequencing (HiFi,\n>20x). Single-cell Strand-seq data from the same samples were used to\nphase the assemblies without parental trios, yielding N50 contigs >25 Mbp\nat QV > 40. SVs were discovered from the two haplotype assemblies of\neach sample with the Phased Assembly Variant (PAV) caller against GRCh38,\nand candidate SVs were orthogonally supported by at least one of seven\nother sources (read-based callers MELT, PBSV and PALMER; Bionano optical\nmapping; breakpoint k-mer analysis; PAV replication with LRA). This\nyielded the integrated nonredundant callset of 107,590 insertion/deletion\nSVs and 316 inversions. Population-scale allele frequencies (POP_*_AF) were\nobtained by graph-based re-genotyping of the HGSVC2 SVs into the\n3,202-sample 1000 Genomes short-read cohort with PanGenie (insertions and\ndeletions only).\n
\n\nFor display, the HGSVC2 v2.0 freeze-4 annotation tables\nvariants_freeze4_sv_insdel.tsv.gz (111,330 DEL+INS) and\nvariants_freeze4_sv_inv.tsv.gz (416 INV) were downloaded from the\n\nIGSR HGSVC2 v2.0 integrated-callset directory and merged into a single\nbigBed; type-specific columns (POP_*_AF for insdel, RGN_REF_INNER for\ninversions) are empty on the detail page when they do not apply.\n
\n\nThe step-by-step build commands (download, format conversion, bigBed build)\nare recorded in the UCSC makeDoc for this track container:\n\ndoc/hg38/lrSv.txt. The conversion scripts and autoSql schemas live in\n\nmakeDb/scripts/lrSv, and the track configuration is in trackDb/human/lrSv.ra.\n
\n\n\nThe data can be explored interactively in table format with the\nTable Browser or the\nData Integrator, and accessed\nprogrammatically through our API,\ntrack=hgsvc2Sv.\n
\n\nThe bigBed is available from\nour\ndownload server as hgsvc2.bb. Example:\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/lrSv/hgsvc2.bb -chrom=chr21 -start=0 -end=100000000 stdout.\n
\n\nThe original annotation tables and VCFs are available from the\n\nHGSVC2 v2.0 integrated callset on the IGSR FTP site.\n
\n\n\nThanks to the Human Genome Structural Variation Consortium (HGSVC) and\nthe 1000 Genomes Project for releasing this dataset. Later HGSVC releases\nare also available as UCSC tracks:\nHGSVC3 65 SVs.\n
\n\n\nEbert P, Audano PA, Zhu Q, Rodriguez-Martin B, Porubsky D, Bonder MJ, Sulovari A, Ebler J, Zhou W,\nSerra Mari R et al.\n\nHaplotype-resolved diverse human genomes and integrated analysis of structural variation.\nScience. 2021 Apr 2;372(6537).\nPMID: 33632895; PMC:
\nThis track shows structural variants (SVs) identified by Oxford Nanopore\nlong-read sequencing of post-mortem brain tissue (prefrontal cortex) from\n351 individuals, generated by the NIH Center for Alzheimer's and Related\nDementias (NIH CARD) Long-Read Initiative. Structural variants are genomic\nrearrangements larger than about 50 bp, such as deletions, insertions,\ninversions and duplications; because they alter or move large stretches of\nDNA at once they can have outsized effects on gene dosage, gene regulation\nand DNA methylation compared with single-nucleotide changes.\n
\n\nThe cohort combines two studies: 205 samples of European ancestry from the\nNorth American Brain Expression Consortium (NABEC, dbGaP phs001300) and 146\nsamples of African and African-admixed ancestry from the NIMH Human Brain\nCollection Core (HBCC, dbGaP phs000979). The track contains more than 228,000 SVs called\nagainst GRCh38: about 127,000 insertions, 102,000 deletions, 431 inversions\nand one tandem duplication. Each record carries the alternate allele count\noverall and split by cohort (NABEC and HBCC), together with the allele frequency\nreported by the source project. None of these samples are Alzheimer's disease\ncases; the cohorts are population brain-tissue collections.\n
\n\n\nItems are colored by SV type, matching the other subtracks of the container:\n
\nInsertions are placed at the insertion site with a width of 1 bp, and the\nlength of the inserted sequence is shown as the insertion length; deletions,\ninversions and the duplication span the affected reference interval. The\nmouseover shows the variant name, SV type, reference and insertion lengths,\nallele frequency and the alternate allele count split into the NABEC and\nHBCC cohorts. Filters are available for SV type, SV length, insertion length,\nallele count, allele frequency, and the allele count in each cohort.\n
\n\n\nNABEC samples were sequenced on Oxford Nanopore R9.4.1 and HBCC samples on\nR10.4.1 PromethION flow cells, with a median read N50 of 27 kb and about 40x\naverage genome coverage. Structural variants were called both from read\nalignments (minimap2 alignments processed with Sniffles2 v2.3) and from\nde novo assemblies (Shasta v0.11.1 assemblies phased with HapDup v0.12 and\ncompared to the reference with Hapdiff). Assembly-based calls were merged\nacross samples with Truvari, read-based calls were merged across samples with\nSniffles2, and the read and assembly sets were then merged together and across\nthe two cohorts with Truvari. All processing used the Nanopore Analysis\nPipeline (NAPU) workflows on the AnVIL/Terra platform; see Kolmogorov et al.\n2023 and Billingsley et al. 2024 for details.\n
\n\nThe display bigBed NIH_CARD_longReadSVs.bb was obtained from the NIH\nCARD browser-track\nGitHub\nrepository. At UCSC it was converted to the shared long-read SV schema\n(signed lengths made positive, an explicit insertion-length field added, the\nsingle DUP:TANDEM call folded to DUP, and colors reassigned to\nthe container's shared palette) so it matches the other subtracks. The\nstep-by-step commands are recorded in the UCSC makeDoc for this track\ncontainer:\n\ndoc/hg38/lrSv.txt. The conversion script and autoSql schema live in\n\nmakeDb/scripts/lrSv, and the track configuration is in\ntrackDb/human/lrSv.ra.\n
\n\n\nThe data can be explored interactively in table format with the\nTable Browser or the\nData Integrator and exported from there\nto spreadsheet or tab-sep tables. From scripts, the data can be accessed\nthrough our API, track=cardSv.\n
\n\nThe annotation is stored as a bigBed file that can be downloaded from\nour\ndownload server as card.bb. Individual regions or the whole\nannotation can be obtained with the bigBedToBed utility, available\nfrom our\nutilities\npage. Example:\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/lrSv/card.bb -chrom=chr21 -start=0 -end=100000000 stdout.\n
\n\nThe underlying genotype-level calls in VCF format are available under\ncontrolled access through dbGaP (NABEC phs001300, substudy phs003181; HBCC\nphs000979) and can be requested through the AnVIL Data Explorer for the\nNABEC\nand\nHBCC\ndatasets.\n
\n\n\nThanks to the North American Brain Expression Consortium (NABEC), the NIMH\nHuman Brain Collection Core (HBCC), the Banner Sun Health Research Institute\nBrain and Body Donation Program, and the NIH CARD Long-Read Initiative for\ngenerating and sharing this dataset, and to Melissa Meredith for preparing the\nbrowser track. This work was supported by the Intramural Research Programs of\nthe NIA, NINDS, NCI, NHGRI and NIMH, and used the NIH STRIDES Initiative and\nthe NIH HPC Biowulf cluster.\n
\n\n\nBillingsley KJ, Meredith M, Daida K, Jerez PA, Negi S, Malik L, Genner RM, Moller A, Zheng X, Gibson\nSB et al.\n\nLong-read sequencing of hundreds of diverse brains provides insight into the impact of structural\nvariation on gene expression and DNA methylation.\nbioRxiv. 2024 Dec 17;.\nPMID: 39764002; PMC:
\nKolmogorov M, Billingsley KJ, Mastoras M, Meredith M, Monlong J, Lorig-Roach R, Asri M, Alvarez\nJerez P, Malik L, Dewan R et al.\n\nScalable Nanopore sequencing of human genomes provides a comprehensive view of haplotype-resolved\nvariation and methylation.\nNat Methods. 2023 Oct;20(10):1483-1492.\nPMID: 37710018; PMC:
GnomAD Genome Mutational Constraint, also known as \"Genome non-coding constraint of\nhaploinsufficient variation (Gnocchi)\", is based on v3.1.2 and is available only on hg38.\nIt shows the reduced variation caused by purifying\nnatural selection. This is similar to negative selection on loss-of-function\n(LoF) for genes, but can be calculated for non-coding regions too.\nPositive values are red and reflect stronger mutation constraint (and less variation), indicating\nhigher natural selection pressure in a region. Negative values are green and\nreflect lower mutation constraint\n(and more variation), indicating less selection pressure and less functional effect.\nBriefly, for any 1kbp window in\nthe genome, a model based on trinucleotide sequence context, base-level\nmethylation, and regional genomic features predicts expected number of mutations,\nand compares this number to the observed number of mutations using a Z-score (see Chen et al 2024\nin the Reference section for details). The chrX scores were added as received from the authors,\nas there are no de novo mutation data available on chrX (for estimating the effects of regional\ngenomic features on mutation rates), they are more speculative than the ones on the autosomes.
\n\n\nThe raw data can be explored interactively with the \nTable Browser, or the Data Integrator. For\nautomated analysis, the data may be queried from our REST API, and the genome annotations are stored in files that\ncan be downloaded from our
The mutational constraints score was updated in October 2022 from a previous,\nnow deprecated, pre-publication version. The old version can be found in our\narchive\ndirectory on the download server. It can be loaded by copying the URL into\nour \"Custom tracks\" input box.
\n\n\nThe data can also be found directly from the gnomAD downloads page. Please refer to\nour mailing list archives for questions, or our Data Access FAQ for more information.
\n\n\nThanks to the Genome Aggregation\nDatabase Consortium for making these data available. The data are released under the
\nPlease note that some annotations within the provided files may have restrictions on usage. See here for more information.\n
\n\n\nChen S, Francioli LC, Goodrich JK, Collins RL, Kanai M, Wang Q, Alföldi J, Watts NA, Vittal C,\nGauthier LD et al.\n\n A genomic mutational constraint map using variation in 76,156 human genomes.\nNature. 2024 Jan;625(7993):92-100.\nPMID: 38057664\n
\n", "longLabel": "Gnocchi: Genome Aggregation Database (gnomAD) non-coding constraint of haploinsufficient variation, includes chrX", "maxHeightPixels": "128:40:8", "parent": "gnomadVariants on", "priority": "10", "setColorWith": "/gbdb/hg38/gnomAD/mutConstraint/mutConstraint.color.bb", "shortLabel": "gnomAD Mut Constraint", "track": "gnomadConstraint", "type": "bigWig", "viewLimitsMax": "-3:3", "windowingFunction": "minimum" } }, "description": "Gnocchi: Genome Aggregation Database (gnomAD) non-coding constraint of haploinsufficient variation, includes chrX", "category": [ "Variation and Repeats" ] }, { "trackId": "hg38-unipMut", "name": "UniProt - Mutations", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/uniprot/unipMut.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/uniprot/unipMut.bb", "longLabel": "UniProt Amino Acid Mutations", "mouseOver": "UniProt record: $uniProtId\nThe \"Constraint scores\" container track includes several subtracks showing the results of\nconstraint prediction algorithms. These try to find regions of negative\nselection, where variations likely have functional impact. The algorithms do\nnot use multi-species alignments to derive evolutionary constraint, but use\nprimarily human variation, usually from variants collected by gnomAD (see the\ngnomAD V2 or V3 tracks on hg19 and hg38) or TOPMED (contained in our dbSNP\ntracks and available as a filter). One of the subtracks is based on UK Biobank\nvariants, which are not available publicly, so we have no track with the raw data.\nThe number of human genomes that are used as the input for these scores are\n76k, 53k and 110k for gnomAD, TOPMED and UK Biobank, respectively.\n
\n\nNote that another important constraint score, gnomAD\nconstraint, is not part of this container track but can be found in the hg38 gnomAD\ntrack.\n
\n\nThe algorithms included in this track are:\n\nJARVIS scores are shown as a signal (\"wiggle\") track, with one score per genome position.\nMousing over the bars displays the exact values. The scores were downloaded and converted to a single bigWig file.\nMove the mouse over the bars to display the exact values. A horizontal line is shown at the 0.733\nvalue which signifies the 90th percentile.
\nSee hg19 makeDoc and\nhg38 makeDoc.\n\nInterpretation: The authors offer a suggested guideline of > 0.9998 for identifying\nhigher confidence calls and minimizing false positives. In addition to that strict threshold, the \nfollowing two more relaxed cutoffs can be used to explore additional hits. Note that these\nthresholds are offered as guidelines and are not necessarily representative of pathogenicity.
\n\n\n
| Percentile | JARVIS score threshold |
|---|---|
| 99th | 0.9998 |
| 95th | 0.9826 |
| 90th | 0.7338 |
\nHMC scores are displayed as a signal (\"wiggle\") track, with one score per genome position.\nMousing over the bars displays the exact values. The highly-constrained cutoff\nof 0.8 is indicated with a line.
\n\nInterpretation: \nA protein residue with HMC score <1 indicates that missense variants affecting\nthe homologous residues are significantly under negative selection (P-value <\n0.05) and likely to be deleterious. A more stringent score threshold of HMC<0.8\nis recommended to prioritize predicted disease-associated variants.\n
\n\n\nInterpretation: The authors suggest the following guidelines for evaluating\nintolerance. By default, the MetaDome track displays a horizontal line at 0.7 which \nsignifies the first intolerant bin. For more information see the MetaDome publication.
\n\n\n
| Classification | MetaDome Tolerance Score |
|---|---|
| Highly intolerant | ≤ 0.175 |
| Intolerant | ≤ 0.525 |
| Slightly intolerant | ≤ 0.7 |
\nMTR data can be found on two tracks, MTR All data and MTR Scores. In the\nMTR Scores track the data has been converted into 4 separate signal tracks\nrepresenting each base pair mutation, with the lowest possible score shown when\nmultiple transcripts overlap at a position. Overlaps can happen since this score\nis derived from transcripts and multiple transcripts can overlap. \nA horizontal line is drawn on the 0.8 score line\nto roughly represent the 25th percentile, meaning the items below may be of particular\ninterest. It is recommended that the data be explored using\nthis version of the track, as it condenses the information substantially while\nretaining the magnitude of the data.
\n\nAny specific point mutations of interest can then be researched in the \nMTR All data track. This track contains all of the information from\n\nMTRV2 including more than 3 possible scores per base when transcripts overlap.\nA mouse-over on this track shows the ref and alt allele, as well as the MTR score\nand the MTR score percentile. Filters are available for MTR score, False Discovery Rate\n(FDR), MTR percentile, and variant consequence. By default, only items in the bottom\n25 percentile are shown. Items in the track are colored according\nto their MTR percentile:
\n\nInterpretation: Regions with low MTR scores were seen to be enriched with\npathogenic variants. For example, ClinVar pathogenic variants were seen to\nhave an average score of 0.77 whereas ClinVar benign variants had an average score\nof 0.92. Further validation using the FATHMM cancer-associated training dataset saw\nthat scores less than 0.5 contained 8.6% of the pathogenic variants while only containing\n0.9% of neutral variants. In summary, lower scores are more likely to represent\npathogenic variants whereas higher scores could be pathogenic, but have a higher chance\nto be a false positive. For more information see the MTR-Viewer publication.
\n\n\nScores were downloaded and converted to a single bigWig file. See the\nhg19 makeDoc and the\nhg38 makeDoc for more info.\n
\n\n\nScores were downloaded and converted to .bedGraph files with a custom Python \nscript. The bedGraph files were then converted to bigWig files, as documented in our \nmakeDoc hg19 build log.
\n\n\nThe authors provided a bed file containing codon coordinates along with the scores. \nThis file was parsed with a python script to create the two tracks. For the first track\nthe scores were aggregated for each coordinate, then the lowest score chosen for any\noverlaps and the result written out to bedGraph format. The file was then converted\nto bigWig with the bedGraphToBigWig utility. For the second track the file\nwas reorganized into a bed 4+3 and conveted to bigBed with the bedToBigBed\nutility.
\nSee the hg19 makeDoc for details including the build script.
\n\nThe raw MetaDome data can also be accessed via their Zenodo handle.
\n\n\nV2\nfile was downloaded and columns were reshuffled as well as itemRgb added for the\nMTR All data track. For the MTR Scores track the file was parsed with a python\nscript to pull out the highest possible MTR score for each of the 3 possible mutations\nat each base pair and 4 tracks built out of these values representing each mutation.
\n\nSee the hg19 makeDoc entry on MTR for more info.
\n\n\nThe raw data can be explored interactively with the Table Browser, or\nthe Data Integrator. For automated access, this track, like all\nothers, is available via our API. However, for bulk\nprocessing, it is recommended to download the dataset.\n
\n\n\nFor automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed\nfiles that can be downloaded from\nour download server.\nIndividual regions or the whole genome annotation can be obtained using our tools bigWigToWig\nor bigBedToBed which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tools can also be used to obtain features confined to a given range, e.g.,\n
\nbigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/hmc/hmc.bw stdout\n
\n
\nPlease refer to our\nData Access FAQ\nfor more information.\n
\n\n\n\nThanks to Jean-Madeleine Desainteagathe (APHP Paris, France) for suggesting the JARVIS, MTR, HMC tracks. Thanks to Xialei Zhang for providing the HMC data file and to Dimitrios Vitsios and Slave Petrovski for helping clean up the hg38 JARVIS files for providing guidance on interpretation. Additional\nthanks to Laurens van de Wiel for providing the MetaDome data as well as guidance on the track development and interpretation. \n
\n\n\n\nVitsios D, Dhindsa RS, Middleton L, Gussow AB, Petrovski S.\n\n Prioritizing non-coding regions based on human genomic constraint and sequence context with deep\n learning.\nNat Commun. 2021 Mar 8;12(1):1504.\nPMID: 33686085; PMC:
\nXiaolei Zhang, Pantazis I. Theotokis, Nicholas Li, the SHaRe Investigators, Caroline F. Wright, Kaitlin E. Samocha, Nicola Whiffin, James S. Ware\n\nGenetic constraint at single amino acid resolution improves missense variant prioritisation and gene discovery.\nMedrxiv 2022.02.16.22271023\n
\n\n\nWiel L, Baakman C, Gilissen D, Veltman JA, Vriend G, Gilissen C.\n\nMetaDome: Pathogenicity analysis of genetic variants through aggregation of homologous human protein\ndomains.\nHum Mutat. 2019 Aug;40(8):1030-1038.\nPMID: 31116477; PMC:
\nSilk M, Petrovski S, Ascher DB.\n\nMTR-Viewer: identifying regions within genes under purifying selection.\nNucleic Acids Res. 2019 Jul 2;47(W1):W121-W126.\nPMID: 31170280; PMC:
\nHalldorsson BV, Eggertsson HP, Moore KHS, Hauswedell H, Eiriksson O, Ulfarsson MO, Palsson G,\nHardarson MT, Oddsson A, Jensson BO et al.\n\n The sequences of 150,119 genomes in the UK Biobank.\nNature. 2022 Jul;607(7920):732-740.\nPMID: 35859178; PMC:
\nHuang YF, Gulko B, Siepel A.\n\nFast, scalable prediction of deleterious noncoding variants from functional and population genomic\ndata.\nNat Genet. 2017 Apr;49(4):618-624.\nPMID: 28288115; PMC:
\nThis track shows high-confidence structural variants (SVs) identified by\nOxford Nanopore long-read sequencing of 3,622 Icelanders recruited through\nthe deCODE genetics population cohort. The track contains 119,453 high-confidence\nSVs (41,216 deletions, 75,050 insertions and 3,187 combined insertion/deletion\nevents), deduplicated from a 133,886-record upstream release. Variants are\nsite-level (no per-sample genotypes) and have been\nfiltered to a high-confidence subset validated in the accompanying\npopulation-scale analysis.\n
\n\nNote that this release does not include allele counts or allele frequencies:\neach row represents a site that was called with high confidence in the\ncohort, but the number of carrier samples is not provided, so the track\ncannot be filtered by AF/AC.\n
\n\n\nItems are colored by SV type:\n
\nInsertions are placed at the insertion site with a width of 1 bp; deletions\nspan the deleted interval; INSDEL events span the affected reference region\nand have SVLEN=0 because the reference and alternate alleles differ in both\nsequence and length. Filters are available for SV type and SV length.\n
\n\nWhere a variant falls inside an annotated tandem-repeat region, the detail\npage also shows the coordinates of that region (TRRBEGIN / TRREND from the\nsource VCF), which can be useful context for repeat-mediated insertions and\ndeletions.\n
\n\n\nBeyter et al. 2021 performed Oxford Nanopore long-read sequencing of 3,622\nIcelanders recruited through deCODE genetics and detected a median of\n22,636 SVs per individual (13,353 insertions and 9,474 deletions). Across\nthe cohort they derived a set of 133,886 reliably genotyped SV alleles,\nimputed those alleles into 166,281 chip-typed Icelanders, and tested them\nfor association with disease and quantitative traits (notably including a\nrare PCSK9 deletion associated with lower LDL-cholesterol and a\nmulti-allelic 57-bp VNTR in ACAN associated with adult height). The\ntrack shown here displays 119,453 unique high-confidence SV sites (exact-duplicate\nrecords present in the release have been collapsed): 41,216 deletions, 75,050\ninsertions and 3,187 combined insertion/deletion events.\nThe release is site-only (no per-sample genotypes or allele frequencies),\nso the track cannot be filtered by AF/AC.\n
\n\nThe VCF ont_sv_high_confidence_SVs.sorted.vcf.gz was downloaded\nfrom the deCODE genetics\n\nLRS_SV_sets GitHub repository.\n
\n\nThe step-by-step build commands (download, format conversion, bigBed build)\nare recorded in the UCSC makeDoc for this track container:\n\ndoc/hg38/lrSv.txt. The conversion scripts and autoSql schemas live in\n\nmakeDb/scripts/lrSv, and the track configuration is in trackDb/human/lrSv.ra.\n
\n\n\nThe data can be explored interactively in table format with the\nTable Browser or the\nData Integrator and exported from there\nto spreadsheet or tab-sep tables. From scripts, the data can be accessed\nthrough our API, track=decodeSv.\n
\n\nThe annotation is stored as a bigBed file that can be downloaded from\nour\ndownload server as decodeSv.bb. Individual regions or the whole\nannotation can be obtained with the bigBedToBed utility, available\nfrom our\nutilities\npage. Example:\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/lrSv/decodeSv.bb -chrom=chr21 -start=0 -end=100000000 stdout.\n
\n\nThe original VCF is available from the deCODE genetics\nLRS_SV_sets\nGitHub repository.\n
\n\n\nThanks to the deCODE genetics team and the Icelandic study participants for\nmaking this dataset publicly available.\n
\n\n\nBeyter D, Ingimundardottir H, Oddsson A, Eggertsson HP, Bjornsson E, Jonsson H, Atlason BA,\nKristmundsdottir S, Mehringer S, Hardarson MT et al.\n\nLong-read sequencing of 3,622 Icelanders provides insight into the role of structural variants in\nhuman diseases and other traits.\nNat Genet. 2021 Jun;53(6):779-786.\nPMID: 33972781\n
\n\n" } }, "description": "Structural Variants from 3,622 deCODE samples (Icelandic; Oxford Nanopore)", "category": [ "Variation and Repeats" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-decodeSv-LinearBasicDisplay", "mouseover": "jexl:`Var: ${get(feature,'name')} (${get(feature,'svType')})\nThis track shows structural variants (SVs) identified by long-read sequencing\nof 945 Han Chinese individuals. The dataset contains 111,288 SVs merged across\nsamples using SURVIVOR, including 49,518 deletions, 42,300 insertions,\n13,503 duplications, 5,595 inversions, and 372 translocations.\n
\n\n\nItems are colored by SV type:\n
\nFilters are available for SV type, SV length, allele frequency, and number of\nsupporting samples. For insertions, the item is placed at the insertion site\nwith a width of 1 bp. For translocations, only the first breakpoint is shown;\nthe second breakpoint chromosome and position are listed in the item details.\n
\n\n\nGong et al. 2025 performed Oxford Nanopore long-read sequencing of 945\nHan Chinese individuals on PromethION instruments with R9.4 flow cells.\nReads were aligned to GRCh38.p13 with NGMLR v0.2.7 using ONT-tuned\nparameters, and a joint-calling strategy was used to call SVs at moderate\ncoverage: per-sample discovery with\ncuteSV\nv1.0.13, merging of breakpoints within 500 bp across individuals with\nSURVIVOR\nv1.0.6, per-sample re-genotyping of the merged set with LRcaller v1.0, and\na final BCFtools merge. SVs in centromeric, pericentromeric and gap regions\nwere filtered out, yielding 111,288 high-quality SVs: 49,518 deletions,\n42,300 insertions, 13,503 duplications, 5,595 inversions and 372\ntranslocations.\n
\n\nThe site-only VCF released at\n\nOMIX accession OED00945268 (OED00945268_Han_945samples_SV.vcf.gz)\nwas converted to BED for this track.\n
\n\nThe step-by-step build commands (download, format conversion, bigBed build)\nare recorded in the UCSC makeDoc for this track container:\n\ndoc/hg38/lrSv.txt. The conversion scripts and autoSql schemas live in\n\nmakeDb/scripts/lrSv, and the track configuration is in trackDb/human/lrSv.ra.\n
\n\n\nThe raw VCF data was obtained from the\nOMIX\nrepository (accession OED00945268) at the National Genomics Data Center (NGDC),\nChina National Center for Bioinformation.\n
\n\nThe source VCF also encodes phased per-sample genotypes: the sampleList\nfield on the detail page is derived from the SURVIVOR SUPP_VEC bitmask\nand is an ordered list of the 1-based indices of the 945 samples carrying\neach SV. The full per-sample phased VCF can be browsed as a separate track in\nthe SVs from 945 Han Chinese entry of\nthe Phased Variants track collection.\n
\n\n\nThanks to Gong et al. for making their structural variant calls publicly available.\n
\n\n\nGong J, Sun H, Wang K, Zhao Y, Huang Y, Chen Q, Qiao H, Gao Y, Zhao J, Ling Y et al.\n\nLong-read sequencing of 945 Han individuals identifies structural variants associated with\nphenotypic diversity and disease susceptibility.\nNat Commun. 2025 Feb 10;16(1):1494.\nPMID: 39929826; PMC:
\nThis track displays structural variants (SVs) at least 50 bp long\n(deletions, insertions, and complex substitutions) identified by the\nChinese Pangenome Consortium (CPC) in 58 samples representing 36 Chinese\nminority ethnic groups.
\n\n\nThe upstream release combined the 58 CPC samples with 47 samples from\nPhase 1 of the Human Pangenome Reference Consortium (HPRC) into a single\npangenome graph built on the T2T-CHM13v2 assembly with Minigraph-Cactus.\nFor this track we recomputed allele counts (AC), allele numbers (AN) and\nsample counts (NS) using only the 58 CPC sample columns (those with\nHIFI032* or RY* prefixes in the source VCF) and dropped\nall snarls that no CPC sample carries (HPRC-specific SVs). To see the\nHPRC data on its own, use the HPRC SV tracks elsewhere in this collection.
\n\n\nA pangenome is a graph that represents many genomes simultaneously, letting\nvariants that are missing from a single linear reference be captured and\ntyped directly. Variants are shown natively on the hs1 browser and lifted\nto hg38 using the UCSC hs1ToHg38.over.chain.gz chain. The track\ncontains 46,092 snarl sites on hs1 and 36,030 lifted to hg38 (10,062 did\nnot lift, typically in T2T-added repetitive regions).
\n\nItems are colored by SV type:
\n\nEach bed item spans from the start of the REF allele to its end on the\nreference. Pure insertions (where REF is a single base) therefore appear\nas narrow single-base marks; DELs and CPX items span the affected reference\ninterval.
\n\n\nThe name field is the graph snarl ID (two node identifiers separated\nby strand arrows, e.g. >2541>2547). It is stable across the\ngraph but has no meaning outside the CPC pangenome graph file.
\n\n\nThe source VCF was decomposed with bcftools norm -m -any, so each\ngraph snarl appears as one VCF row per alternative allele (a single\nbubble in the graph may have 2-20+ alt paths). For this track we first\ncompute the CPC-only allele count per alt, drop any alt that no CPC sample\ncarries, then collapse all remaining alts sharing the same snarl ID into\none track item:
\nAvailable filters:
\n\nGao et al. 2023 generated PacBio HiFi long reads (mean ~30.65x,\nSequel II/IIe platforms) for 58 QC-passed samples representing 36\nminority Chinese ethnic groups, complemented with Illumina short reads\nand Oxford Nanopore ultralong reads. Haplotype-phased de novo assemblies\nwere produced with\nhifiasm\nv0.16.1 (116 high-quality haplotype assemblies retained after QC) and\ncombined with 47 HPRC Phase 1 assemblies into a single variation graph\nbuilt on T2T-CHM13v2 with the Minigraph-Cactus pipeline (Minigraph v0.19\nfor the SV skeleton, Cactus v2.1.1 base alignment, hal2vg).\nGraph bubbles were decomposed into variant records with vcfwave\nand normalized with bcftools norm -m -any, yielding the source\nVCF (CPC.HPRC.Phase1.processed.SVs.normed.vcf.gz). The upstream\nGao et al. release identified 78,072 SVs across the combined 105-sample\ngraph. For this track we restrict to the 58 CPC samples (columns matching\nHIFI032* or RY*), recompute AC/AN/NS from those columns\nonly, drop snarls with no CPC carrier (HPRC-specific sites), filter to\nalts with ≥50 bp REF/ALT length difference, and collapse by graph snarl\nID. The final track contains 46,092 snarl sites on hs1; the hg38 version\nis lifted with the UCSC hs1ToHg38.over.chain.gz chain (36,030\nsites, 10,062 did not lift).
\n\n\nThe source VCF is distributed by the\n\nChinese-Pangenome-Consortium-Phase-I GitHub repository.
\n\n\nThe step-by-step build commands (CPC-only recount, liftOver, snarl\ncollapse, bigBed build) are recorded in the UCSC makeDoc for this track\ncontainer:\n\ndoc/hg38/lrSv.txt. The conversion scripts and autoSql schemas live in\n\nmakeDb/scripts/lrSv, and the track configuration is in trackDb/human/lrSv.ra.\n
\n\nThe data can be explored interactively with the\nTable Browser or\nData Integrator, and accessed from\nscripts via our API\n(track=cpc1Sv).
\n\nFor automated download, the bigBed files are at\n\nhttp://hgdownload.soe.ucsc.edu/gbdb/hs1/lrSv/cpc1.bb (native) and\n\nhttp://hgdownload.soe.ucsc.edu/gbdb/hg38/lrSv/cpc1.bb (lifted).\nUse bigBedToBed to extract features: e.g.\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hs1/lrSv/cpc1.bb -chrom=chr21 -start=0 -end=100000000 stdout
\n\nThe original pangenome VCF is distributed by the Chinese Pangenome\nConsortium; see the\n\nCPC Phase I repository.
\n\nThanks to the Chinese Pangenome Consortium and the HPRC Phase 1 team\nfor producing and releasing the combined pangenome and its decomposed\nvariant calls.
\n\n\nGao Y, Yang X, Chen H, Tan X, Yang Z, Deng L, Wang B, Kong S, Li S, Cui Y et al.\n\nA pangenome reference of 36 Chinese populations.\nNature. 2023 Jul;619(7968):112-121.\nPMID: 37316654; PMC:
\nThis track shows structural variants (SVs) identified by Oxford Nanopore long-read\nsequencing of 333 Japanese individuals from the Tohoku Medical Megabank (ToMMo)\nproject. The 333 individuals form 111 parent-offspring trios, enabling\nMendelian consistency checks on the SV calls. Activated T lymphocytes were used\nas a source of high-molecular-weight DNA for nanopore sequencing at a median\ncoverage of 22.2x with an N50 read length of 25.8 kb.\n
\n\nThe dataset contains 74,201 SVs (37,981 deletions and 36,220 insertions),\nmerged across individuals using SURVIVOR v1.0.6. Over 95% of the SVs are\nconcordant with Mendelian inheritance in the trio families.\n
\n\n\nItems are colored by SV type:\n
\nFilters are available for SV type, SV length, and allele frequency.\nFor insertions, the item is placed at the insertion site with a width of 1 bp;\nfor deletions, the item spans the deleted region.\n
\n\nThe detail page for each item shows:\n
\nOtsuki et al. 2022 extracted high-molecular-weight genomic DNA from activated\nT lymphocytes of 333 individuals (111 parent-offspring trios) from the Tohoku\nMedical Megabank (ToMMo) BirThree cohort and performed Oxford Nanopore\nwhole-genome sequencing on PromethION instruments with R9.4.1 flow cells\n(SQK-LSK109 libraries, Guppy v4.2.2 high-accuracy base-calling). After QC,\nmedian per-sample sequencing coverage was 22.2x with a read N50 of 25.8 kb.\nReads were aligned to GRCh38 with LRA, SVs were called per sample with\nCuteSV\nv1.0.9 (-min_sv_length 50), and per-sample calls were merged with\nSURVIVOR\nv1.0.6 (1000 bp distance, type-match, no length-match) into a nonredundant\npanel of 74,201 autosomal SVs (37,981 deletions and 36,220 insertions).\nOver 95% of the SVs were concordant with Mendelian inheritance in the 111\ntrio families; allele frequencies in this track are computed from the 222\nunrelated parents to avoid double-counting.\n
\n\nThe site-only VCF tommo-JSV1-20211208-GRCh38-without-genotype-count.vcf.gz\nwas downloaded from the jMorp JSV1 download page,\n\ntommo-jsv1-20211208-af.\n
\n\nThe step-by-step build commands (download, format conversion, bigBed build)\nare recorded in the UCSC makeDoc for this track container:\n\ndoc/hg38/lrSv.txt. The conversion scripts and autoSql schemas live in\n\nmakeDb/scripts/lrSv, and the track configuration is in trackDb/human/lrSv.ra.\n
\n\n\nSource data is available from the\ntommo-jsv1-20211208-af download page on the jMorp\nportal (ToMMo Japanese Multi Omics Reference Panel).\n
\n\n\nThe information in the ToMMo jMorp database is provided only to persons\nwho agree to jMorp's\n\nConditions of Use. By using these data, you are deemed to have read\nand understood those conditions and to agree to the following obligations:\n
\nThanks to the Tohoku Medical Megabank Organization for making their structural\nvariant calls publicly available through the jMorp data portal.\n
\n\n\nOtsuki A, Okamura Y, Ishida N, Tadaka S, Takayama J, Kumada K, Kawashima J, Taguchi K, Minegishi N,\nKuriyama S et al.\n\nConstruction of a trio-based structural variation panel utilizing activated T lymphocytes and long-\nread sequencing technology.\nCommun Biol. 2022 Sep 20;5(1):991.\nPMID: 36127505; PMC:
\nThis track displays structural variants (SVs), at least 50 bp long\n(deletions, insertions, and complex substitutions), from the Arab Pangenome\nReference (APR), a pangenome graph built from 53 UAE-resident Arab\nindividuals drawn from eight countries (UAE, Saudi Arabia, Oman, Jordan,\nEgypt, Morocco, Syria, Yemen). Each bubble in the graph that contains an\nSV-sized alternative allele is shown as a single variant site, with allele\ncounts aggregated across the 53 samples (the GRCh38 reference haplotype,\npresent as an extra sample column in the source VCF, is excluded from the\naggregation).
\n\n\nThe APR pangenome was built on the T2T-CHM13v2 reference. Variants are\nshown natively on the hs1 browser and lifted to hg38 using\nthe UCSC hs1ToHg38.over.chain.gz chain; variants that do not lift\ncleanly (often in T2T-added euchromatic sequence) are omitted from the\nhg38 version of the track.
\n\nItems are colored by SV type:
\nEach item spans from the start of REF to its end on the reference.\nThe name field is the graph snarl ID (e.g. <951452<1012008),\nwhich identifies the variant site in the APR pangenome graph.
\n\n\nThe source VCF is multi-allelic: a single graph snarl appears as one row\nwith a comma-separated ALT list. For this track, each ALT is classified\nindividually using the 50 bp threshold, and the row is emitted as a single\nbed item with:
\nRows whose alts are all smaller than 50 bp are not shown.
\n\n\nNassir et al. 2025 built the Arab Pangenome Reference (APR) from 53\nUAE-resident Arab individuals drawn from eight countries, sequenced with\n~35x PacBio HiFi on Sequel IIe/Revio (30-h movies), ~54x Oxford Nanopore\nultralong reads on R10.4.1 PromethION flow cells (96-h runs), and ~65x\nHi-C (Illumina NovaSeq 6000). Haplotype-phased de novo assemblies were\nproduced with hifiasm v0.19.5 (primary) and Verkko v1.3.1 (for\ncomparison), with a median N50 of 124 Mb. The pangenome graph was built\nwith Minigraph-Cactus seeded on T2T-CHM13v2 and augmented with GRCh38,\nand SVs were extracted by graph deconstruction. The released decomposed\nVCF (apr_review_v1_2902_chm13.vcf.gz) contains ~21 million\nvariants on CHM13v2 contigs; after filtering to alt alleles with ≥50 bp\nlength difference and collapsing the alts of each snarl into a single\nsite, the APR SV track is obtained. Variants are shown natively on hs1\nand lifted to hg38 with the UCSC hs1ToHg38.over.chain.gz chain\n(variants not lifting cleanly are omitted from the hg38 version).
\n\n\nThe source APR VCF was downloaded from the Mohammed Bin Rashid\nUniversity SharePoint page,\n\nmbru.ac.ae/the-arab-pangenome-reference; the accompanying project\nsource code is at\n\ngithub.com/muddinmbru/arab_pangenome_reference.
\n\n\nThe step-by-step build commands (download, graph-VCF conversion, liftOver,\nbigBed build) are recorded in the UCSC makeDoc for this track container:\n\ndoc/hg38/lrSv.txt. The conversion scripts and autoSql schemas live in\n\nmakeDb/scripts/lrSv, and the track configuration is in trackDb/human/lrSv.ra.\n
\n\nThe data can be explored interactively with the\nTable Browser or\nData Integrator, and accessed from\nscripts via our API\n(track=aprSv).
\n\nFor automated download, the bigBed files are at\n\nhttp://hgdownload.soe.ucsc.edu/gbdb/hs1/lrSv/apr.bb (native) and\n\nhttp://hgdownload.soe.ucsc.edu/gbdb/hg38/lrSv/apr.bb (lifted).
\n\n\nThe original APR pangenome VCF and assemblies can be downloaded from\n\nhttps://www.mbru.ac.ae/the-arab-pangenome-reference/,\nand the project source code is at\n\nhttps://github.com/muddinmbru/arab_pangenome_reference.
\n\nThanks to the Arab Pangenome Reference team at Mohammed Bin Rashid\nUniversity (Dubai), led by Mohammed Uddin, for producing and releasing\nthe pangenome and its variant calls.
\n\n\nNassir N, Almarri MA, Kumail M, Mohamed N, Balan B, Hanif S, AlObathani M, Jamalalail B, Elsokary H,\nKondaramage D et al.\n\nA draft UAE-based Arab pangenome reference.\nNat Commun. 2025 Jul 24;16(1):6747.\nPMID: 40707445; PMC:
\nThis track shows structural variants (SVs) identified by PacBio HiFi long-read\nsequencing of probands and their families enrolled in the Genomic Answers for\nKids (GA4K) program at Children's Mercy Research Institute. GA4K is a\nlongitudinal pediatric genomics initiative that aims to enroll 30,000 children\nwith suspected rare genetic disorders, together with their parents, to build\na large-scale resource of clinical and genomic data.\n
\n\nThe callset contains 115,554 SVs (52,564 deletions, 58,219 insertions, 4,408\nduplications, 363 inversions) from 502 sequenced samples. Variants are\nsite-level (no per-sample genotypes) and each SV has been replicated, meaning\nthat it was either observed in two or more unrelated GA4K individuals, or\nmatched an SV from an external long-read reference set (Decode or the Human\nPangenome Reference Consortium).\n
\n\n\nItems are colored by SV type:\n
\nInsertions are placed at the insertion site with a width of 1 bp; deletions,\nduplications and inversions span the affected interval. Filters are available\nfor SV type, SV length, carrier-sample count and allele frequency. The detail\npage also shows the total number of samples genotyped at each site.\n
\n\n\nThe Genomic Answers for Kids (GA4K) program at Children's Mercy Research\nInstitute is a longitudinal pediatric rare-disease initiative described in\nCohen et al. 2022. GA4K probands and their families are sequenced with\nPacBio HiFi long reads (Revio and Sequel II), and the 502-sample GA4K\nPacBio SV release (pb_joint_merged.sv.vcf.gz) is produced by\nrunning \npbsv per sample and merging with\nJASMINE\nv1.1.4 (--output-genotypes). The merged site-level VCF is\nfiltered to SVs replicated in at least two independent observations\n(either matching a second unrelated CMH individual in the same Jasmine\ncluster, or matching an SV in the deCODE Icelandic or HPRC callsets via\n\nsvpack match). The released catalog contains 115,554 replicated SVs\n(52,564 deletions, 58,219 insertions, 4,408 duplications and 363\ninversions) with recomputed carrier counts (SVC), total sample counts\n(SVN) and allele frequencies (SVF = SVC/SVN).\n
\n\nThe source VCF was cloned from the Children's Mercy Research Institute\nGA4K GitHub repository,\n\ngithub.com/ChildrensMercyResearchInstitute/GA4K\n(pacbio_sv_vcf/pb_joint_merged.sv.vcf.gz).\n
\n\nThe step-by-step build commands (download, format conversion, bigBed build)\nare recorded in the UCSC makeDoc for this track container:\n\ndoc/hg38/lrSv.txt. The conversion scripts and autoSql schemas live in\n\nmakeDb/scripts/lrSv, and the track configuration is in trackDb/human/lrSv.ra.\n
\n\n\nThe data can be explored interactively in table format with the\nTable Browser or the\nData Integrator and exported from there\nto spreadsheet or tab-sep tables. From scripts, the data can be accessed\nthrough our API, track=ga4kSv.\n
\n\nFor automated download and analysis, the annotation is stored in a bigBed file\nthat can be downloaded from\nour\ndownload server. The file for this track is called ga4kSv.bb.\nIndividual regions or the whole annotation can be obtained using the\nbigBedToBed utility, available as a precompiled binary or from source\nas described on our\nutilities\npage.\nExample:\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/lrSv/ga4kSv.bb -chrom=chr21 -start=0 -end=100000000 stdout.\n
\n\nThe original VCF is available from the Children's Mercy Research Institute\nGA4K data release at\n\ngithub.com/ChildrensMercyResearchInstitute/GA4K.\n
\n\n\nThanks to the Children's Mercy Research Institute and the Genomic Answers\nfor Kids participants and their families for making this dataset publicly\navailable.\n
\n\n\nCohen ASA, Farrow EG, Abdelmoity AT, Alaimo JT, Amudhavalli SM, Anderson JT, Bansal L, Bartik L,\nBaybayan P, Belden B et al.\n\nGenomic answers for children: Dynamic analyses of >1000 pediatric rare disease genomes.\nGenet Med. 2022 Jun;24(6):1336-1348.\nPMID: 35305867\n
\n\n" } }, "description": "Structural Variants from 502 GA4K samples (Children's Mercy, pediatric rare disease; PacBio HiFi)", "category": [ "Variation and Repeats" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-ga4kSv-LinearBasicDisplay", "mouseover": "jexl:`Var: ${get(feature,'name')} (${get(feature,'svType')})\nThis track shows structural variants (SVs) identified by long-read\nwhole-genome sequencing of 101 individuals, released together with the\nGWAS SVatalog\nweb tool described in Chirmade et al. 2026. GWAS SVatalog computes and\nvisualizes linkage disequilibrium between these SVs and GWAS-associated\nSNPs so that investigators can assess whether a SNP association signal\nmay be tagging an underlying SV.\n
\n\nThe table contains 87,068 SVs (42,435 deletions, 41,619 insertions,\n1,394 duplications, 912 inversions, 708 complex events; byte-identical\nduplicate records have been removed). Each SV is\nannotated with gene overlaps, GC content, repeat context, ClinGen\nhaploinsufficiency / triplosensitivity scores, gnomAD per-gene constraint\nmetrics (pLI, LOEUF, missense O/E), OMIM phenotype associations, ClinVar\nvariant IDs, and overlaps with DGV, Decipher and ClinGen regional\nannotations.\n
\n\n\nItems are colored by SV type:\n
\nFilters are available for SV type, SV length and the number of overlapping\ngenes. The detail page shows the full annotation row: gene-level constraint\nscores (per overlapping gene), ClinGen / Decipher / ClinVar region matches,\nOMIM phenotype annotations and gnomAD SV frequencies at >=90% reciprocal\noverlap. Because most genomic regions carry no clinical annotation, many\ncolumns will be blank for an arbitrary SV.\n
\n\n\nChirmade et al. 2026 called SVs from 101 whole-genome sequenced individuals\nenrolled in the CF Canada-SickKids Program in Individualized Therapy\n(CFIT), a predominantly-European cohort of people with cystic fibrosis.\nEach sample was sequenced with two long-read / linked-read technologies:\nPacBio continuous long reads on Sequel I (34 samples, 50x) or Sequel II\n(67 samples, 76x), and 10X Genomics linked reads on Illumina HiSeq X at\n~30x. SVs were called per sample with pbsv v2.2.2 (pbmm2 alignments) and\nSniffles v1.0.11 (NGMLR alignments) on the PacBio CLR data, and with Long\nRanger, CNVnator v0.4, ERDS v1.1 and Manta v1.6.0 on the 10XG data.\nPer-platform and cross-platform calls were merged in three steps using a\n50% reciprocal overlap rule (pbsv anchored, tagged by Sniffles on PacBio;\nManta anchored, augmented by CNVnator, ERDS and Long Ranger deletions on\n10XG; then a cross-platform merge with PacBio coordinates preferred), and\nSV records present in fewer than three participants were dropped. The\nreleased catalog contains 87,183 SVs (42,435 deletions, 41,734 insertions,\n1,394 duplications, 912 inversions and 708 complex events); the\npre-computed GWAS SVatalog LD analyses use a common-SV subset of 35,732\nsites against 116,870 GWAS-Catalog SNPs.\n
\n\nThe annotation TSV sv_annotations.tsv was downloaded from the\nZenodo companion record,\n\nzenodo.org/records/13367574. Coordinates in the TSV are 1-based closed\nand were converted to 0-based half-open BED for this track.\n
\n\nThe step-by-step build commands (download, coordinate shift, format\nconversion, bigBed build) are recorded in the UCSC makeDoc for this track\ncontainer:\n\ndoc/hg38/lrSv.txt. The conversion scripts and autoSql schemas live in\n\nmakeDb/scripts/lrSv, and the track configuration is in trackDb/human/lrSv.ra.\n
\n\n\nThe data can be explored interactively in table format with the\nTable Browser or the\nData Integrator, and accessed\nprogrammatically through our API,\ntrack=chirmade101Sv.\n
\n\nThe bigBed is available from\nour\ndownload server as chirmade101.bb. Example:\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/lrSv/chirmade101.bb -chrom=chr21 -start=0 -end=100000000 stdout.\n
\n\nThe original annotation table is available on Zenodo:\nzenodo.org/records/13367574.\nThe GWAS SVatalog web tool itself is at\nsvatalog.research.sickkids.ca.\n
\n\n\nThanks to Chirmade, Strug and colleagues at The Hospital for Sick Children\nand the University of Toronto for releasing this annotated long-read SV\ncallset alongside the GWAS SVatalog tool.\n
\n\n\nChirmade S, Wang Z, Mastromatteo S, Sanders E, Thiruvahindrapuram B, Nalpathamkalam T, Pellecchia G,\nLin F, Keenan K, Patel RV et al.\n\nGWAS SVatalog: a visualization tool to aid fine-mapping of GWAS loci with structural variations.\nHeredity (Edinb). 2026 Mar;135(3):199-210.\nPMID: 41203876; PMC:
\nThis track shows allele statistics for 361,362 variable number tandem repeat (VNTR)\nloci genotyped from Oxford Nanopore long-read whole-genome sequencing of 1,019 samples\nfrom the\n1000 Genomes\nONT Vienna project. VNTR genotyping was performed with\nVAMOS,\na tool that determines the motif composition of VNTR alleles from long reads.\nThis is version 1.1 of the dataset.\n
\n\n\nUnlike the other STR tracks in this collection which are based on short-read sequencing\nand limited to short tandem repeats (motifs of 1-6 bp), this track is derived from\nlong-read sequencing data, which can span much longer repeat regions. The VNTR loci\nin this track have average motif lengths ranging from a few base pairs to over 100 bp,\nand allele lengths up to several kilobases.\n
\n\n\nFor each locus, the track shows the average repeat unit length, the number of unique\nalleles observed, the range and median of repeat unit counts, and the range and median\nof allele lengths in base pairs. The 1000 Genomes Vienna ONT project also produced\nstructural variant calls available in the\nLong-Read Structural Variants track.\n
\n\n\nItems are colored by expected heterozygosity, computed as\nhet = 1 − ∑pi2 from allele frequencies\nacross the 1,019 samples:
\n\nThe 1000 Genomes Vienna ONT project sequenced 1,019 samples from the 1000 Genomes\ncollection using Oxford Nanopore Technologies long-read sequencing. VNTR genotyping\nwas performed using\nVAMOS,\nwhich determines the motif composition of VNTR alleles by aligning long reads to\na catalog of known VNTR sites.\nThe analysis pipeline is available at\nGitHub.\n
\n\nAt UCSC, the summary statistics file (vamos-summary.tsv) was converted\nto bigBed format using a\ncustom Python script.\nLoci with coordinates exceeding chromosome boundaries were excluded.\n
\n\n\nThe raw data can be explored interactively with the\nTable Browser or the\nData Integrator.\nThe data can be accessed from scripts through our\nAPI, the track name is viennaVntr.\n
\n\n\nFor automated download and analysis, the genome annotation is stored in a bigBed\nfile that can be downloaded from\nour download server.\nThe file for this track is called viennaVntr.bb.\nIndividual regions or the whole genome annotation can be obtained using our tool\nbigBedToBed, which can be compiled from the source code or downloaded as a\nprecompiled binary for your system. Instructions for downloading source code and\nbinaries can be found\nhere.\nThe tool can also be used to obtain features within a given range, e.g.\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/strVar/viennaVntr.bb\n-chrom=chr21 -start=0 -end=100000000 stdout\n
\n\n\nThe original data (multisample VCF and summary statistics) can be downloaded from\nthe 1000 Genomes FTP server.\nThe VNTR site list used for genotyping is available from\nZenodo.\n
\n\n\nThanks to the 1000 Genomes ONT Vienna consortium and the Marschall Lab at\nHeinrich Heine University Düsseldorf for making this data publicly available.\n
\n\n\nDe Coster W, Condon DE, De Baets G, Tsui A, Saeed F, Harerimana J, Amiraghdam F, Yaari R,\nDe Vos L, Mahfouz A et al.\n\nSequencing and variant calling of 1019 samples from the\n1000 Genomes Project using Oxford Nanopore Technology.\nbioRxiv. 2024 Dec 23;.\n
\n" } }, "description": "1000 Genomes Vienna ONT VNTR Allele Statistics (VAMOS, 1,019 samples, long-read)", "category": [ "Variation and Repeats" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-viennaVntr-LinearBasicDisplay", "mouseover": "jexl:`Avg motif: ${get(feature,'ruLenAvg')} bp\nAbSplice is a method that predicts aberrant splicing across human tissues, as described in Wagner, \nÇelik et al., 2023. This track displays precomputed AbSplice scores for all possible\nsingle-nucleotide variants genome-wide. The scores represent the probability that a given variant\ncauses aberrant splicing in a given tissue.\nAbSplice scores\ncan be computed from VCF files and are based on quantitative tissue-specific splice site annotations\n(SpliceMaps).\nWhile SpliceMaps can be generated for any tissue of interest from a cohort of RNA-seq samples, this \ntrack includes 49 tissues available from the \nGenotype-Tissue\nExpression (GTEx) dataset. \n
\n\n\nThe AbSplice score is a probability estimate of how likely aberrant splicing of some sort takes \nplace in a given tissue. The authors suggest three cutoffs which are represented by color in the track.\n
\n\n\nMouseover on items shows the gene name, maximum score, and tissues that had this score. Clicking on\nany item brings up a table with scores for all 49 GTEX tissues.\n
\n\n\nThe raw data can be explored interactively with the\nTable Browser, or the\nData Integrator. \nFor automated analysis, the data may be queried from our\nREST API.\nPlease refer to our\nmailing list archives \nfor questions, or our\nData Access FAQ \nfor more information.\n
Precomputed AbSplice-DNA scores in all 49 GTEx tissues are available at\n\nZenodo. \n\n
\nData was converted from the files (AbSplice_DNA_ hg38 _snvs_high_scores.zip) provided by the authors\nat zenodo.org. Files in the\nscore_cutoff=0.01 directory were concatenated. To convert the data to bigBed format, scores and\ntheir tissues were selected from the AbSplice_DNA fields and maximum scores, and then calculated\nusing a custom Python script, which can be found in the\n\nmakeDoc from our GitHub repository.
\n\n\nThanks to Nils Wagner for helpful comments and suggestions.
\n\n\nWagner N, Çelik MH, Hölzlwimmer FR, Mertes C, Prokisch H, Yépez VA, Gagneur J.\n\nAberrant splicing prediction across human tissues.\nNat Genet. 2023 May;55(5):861-870.\nPMID: 37142848\n
\n", "itemRgb": "on", "longLabel": "Aberrant Splicing Prediction Scores", "mouseOver": "change: $name\nThis track combines the structural-variant (SV) callsets from the individual\nsubtracks of the Long-read SVs supertrack into a\nsingle, position-merged overview. Each item is an SV locus seen in one or more\nof the contributing long-read databases. For every merged locus the track\nrecords which databases report it, the summed allele count across those\ndatabases, and the range of allele frequencies observed, making it useful for\nquickly seeing how widely an SV has been reported across cohorts.\n
\n\nThis is a summary view. For cohort-specific genotypes, per-population allele\nfrequencies, and dataset-specific annotations, use the individual subtracks of\nthe supertrack. The merge includes the released long-read callsets only;\npreliminary or unpublished subtracks (e.g. the Kim PD brain, 1000 Genomes\nlinear, and HPRC Jasmine sets) are not part of this merged track.\n
\n\n\nItems are colored by SV type, matching the individual subtracks:\n
\nThe mouseover shows the variant name, SV type, reference and insertion lengths,\nthe list of contributing source databases, the allele-frequency range across\nthose databases, and the total allele count. Filters are available for the\nsource database, SV type, SV length, insertion\nlength, total allele count, minimum and maximum allele\nfrequency, and the number of source databases reporting each locus.\nThe detail page lists the per-database allele counts.\n
\n\n\nThe merged track is built by the lrSvMergeAll.py script, which reads\nthe bigBed of each contributing subtrack (configured in\ndatabases.tsv) and groups records that share an identical\n(chromosome, start, end) position and SV type. For each merged locus\nthe script records the set of contributing databases (sources), the\nnumber of those databases (sourceCount), the sum of their allele counts\n(AC), and the minimum and maximum allele frequency across databases\nthat report one (minAF, maxAF). The per-database allele counts\nare carried as additional columns.\n
\n\nThe step-by-step build commands are recorded in the UCSC makeDoc for this track\ncollection:\n\ndoc/hg38/lrSv.txt. The merge script and autoSql schema live in\n\nmakeDb/scripts/lrSv, and the track configuration is in trackDb/human/lrSvAll.ra.\n
\n\n\nThe data can be explored interactively with the\nTable Browser or the\nData Integrator, and accessed\nprogrammatically through our API,\ntrack=lrSvAll.\n
\n\nThe bigBed is available from\nour\ndownload server as lrSvAll.bb. Example:\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/lrSv/lrSvAll.bb -chrom=chr21 -start=0 -end=100000000 stdout.\n
\n\n\nThis merged view is derived entirely from the contributing long-read SV\ncallsets; please see the individual subtrack description pages for the data\nproducers and citations for each cohort.\n
\n" } }, "description": "All long-read SVs merged across subtracks by exact position, with per-database AC", "category": [ "Variation and Repeats" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-lrSvAll-LinearBasicDisplay", "mouseover": "jexl:`Var: ${get(feature,'name')} (${get(feature,'svType')})The tracks that are listed here contain genetic variants and links to scientific publications that \nmention them.
\n\nFor additional information please click on the hyperlink of the respective track above.\n
\nBy default, each variant is labeled with the nucleotide change. Hover over the\nfeature to see more information, explained on the track details page of the particular track\nor when clicking onto the feature.
\n\nFor data provenance, access and descriptions, please click the documentation via the link above.\n
\n", "longLabel": "Avada Variants extracted from full text publications", "mouseOver": "Variant: $variant\nThis track shows a multiple alignment of 447 mammalian genomes made with Cactus and constraint scores derived from it.\nTo build this track, the Zoonomia 241 alignment was used as a starting point, all primates and a few outdated\nassemblies were removed and an alignment between 233 newly sequenced primates was added. See the Methods section below for details, and \nalso the publications by Kuderna et al. 2023 in the Reference section.\nAll alignments and operations on them were performed using the Cactus toolkit.\n
\n\n\nThis track shows four phyloP conservation score subtracks computed from the\n447-way Cactus alignment (and a primates subset of it):\n
\nThe SSREV substitution model is strand-symmetric, which avoids\nstrand-dependent bias in single-base conservation scores (Pollard\net al. 2010, supplementary section 2.4) -- relevant when analyzing\ntranscript-related nucleotides such as splice sites, miRNA seed regions, or\nother strand-specific sequence features. The REV model is the standard\nphyloP model and is appropriate for general genome-wide conservation\nanalysis. The primates subset tracks restrict scoring to the 233 primate\ngenomes included in the alignment, useful when conservation across\nnon-primate mammals would dilute primate-specific signal.\n
\n\n\nDownloads for data in this track are available from the directory:\n
\nIn full and pack display modes, conservation scores are displayed as a\nwiggle track (histogram) in which the height reflects the\nsize of the score.\nThe conservation wiggles can be configured in a variety of ways to\nhighlight different aspects of the displayed information.\nClick the Graph configuration help link for an explanation\nof the configuration options.
\n\nPairwise alignments of each species to the human genome are\ndisplayed below the conservation histogram as a grayscale density plot (in\npack mode) or as a wiggle (in full mode) that indicates alignment quality.\nIn dense display mode, conservation is shown in grayscale using\ndarker values to indicate higher levels of overall conservation\nas scored by phastCons.
\n\nCheckboxes on the track configuration page allow selection of the\nspecies to include in the pairwise display.\nNote that excluding species from the pairwise display does not alter the\nconservation score display.
\n\nTo view detailed information about the alignments at a specific\nposition, zoom the display in to 30,000 or fewer bases, then click on\nthe alignment.
\n\n\nThe Display chains between alignments configuration option\nenables display of gaps between alignment blocks in the pairwise alignments in\na manner similar to the Chain track display. Missing sequence in any\nassembly is highlighted in the track display by regions of yellow when zoomed\nout and by Ns when displayed at base level. The following conventions are used:\n
\nDiscontinuities in the genomic context (chromosome, scaffold or region) of the\naligned DNA in the aligning species are shown as follows:\n
\nWhen zoomed-in to the base-level display, the track shows the base\ncomposition of each alignment. The numbers and symbols on the Gaps\nline indicate the lengths of gaps in the human sequence at those\nalignment positions relative to the longest non-human sequence.\nIf there is sufficient space in the display, the size of the gap is shown.\nIf the space is insufficient and the gap size is a multiple of 3, a\n"*" is displayed; other gap sizes are indicated by "+".
\n\nCodon translation is available in base-level display mode if the\ndisplayed region is identified as a coding segment. To display this annotation,\nselect the species for translation from the pull-down menu in the Codon\nTranslation configuration section at the top of the page. Then, select one of\nthe following modes:\n
\nCodon translation uses the following gene tracks as the basis for translation:\n
\n\n\n
\nTable 2. Gene tracks used for codon translation.\n\n Gene Track Species \n RefSeq Genes Bos mutus, Canis lupus familiaris, Carlito syrichta, Cercocebus atys, Chinchilla lanigera, Colobus angolensis, Condylura cristata, Dipodomys ordii, Elephantulus edwardii, Eptesicus fuscus, Felis catus, Felis catus fca126, Fukomys damarensis, Homo sapiens, Ictidomys tridecemlineatus, Macaca mulatta, Macaca nemestrina, Marmota marmota, Microtus ochrogaster, Miniopterus natalensis, Mus musculus, Mus pahari, Myotis brandtii, Myotis davidii, Myotis lucifugus, Odobenus rosmarus, Orcinus orca, Otolemur garnettii, Peromyscus maniculatus, Piliocolobus tephrosceles, Propithecus coquerelli, Pteropus alecto, Pteropus vampyrus, Rattus norvegicus, Rhinopithecus roxellana, Saimiri boliviensis, Sorex araneus, Sus scrofa, Theropithecus gelada, Tupaia chinensis \n Ensembl Genes Cavia aperea \n Augustus Genes Eidolon helvum, Pteronotus parnellii \n no annotation Acinonyx jubatus, Acomys cahirinus, Ailuropoda melanoleuca, Ailurus fulgens, Allactaga bullata, Allenopithecus nigroviridis, Allochrocebus lhoesti, Allochrocebus preussi, Allochrocebus solatus, Alouatta belzebul, Alouatta caraya, Alouatta discolor, Alouatta juara, Alouatta macconnelli, Alouatta nigerrima, Alouatta palliata, Alouatta puruensis, Alouatta seniculus, Ammotragus lervia, Anoura caudifer, Antilocapra americana, Aotus azarae, Aotus griseimembra, Aotus nancymaae, Aotus trivirgatus, Aotus vociferans, Aplodontia rufa, Arctocebus calabarensis, Artibeus jamaicensis, Ateles geoffroyi_a, Ateles geoffroyi_b, Ateles belzebuth, Ateles chamek, Ateles marginatus, Ateles paniscus, Avahi laniger, Avahi peyrierasi, Balaenoptera acutorostrata, Balaenoptera bonaerensis, Beatragus hunteri, Bison bison, Bos indicus, Bos taurus, Bubalus bubalis, Cacajao ayresi, Cacajao calvus, Cacajao hosomi, Cacajao melanocephalus, Callibella humilis, Callimico goeldii, Callithrix geoffroyi, Callithrix jacchus, Callithrix kuhlii, Camelus bactrianus, Camelus dromedarius, Camelus ferus, Canis lupus VD, Canis lupus dingo, Canis lupus orion, Capra aegagrus, Capra hircus, Capromys pilorides, Carollia perspicillata, Castor canadensis, Catagonus wagneri, Cavia porcellus, Cavia tschudii, Cebuella niveiventris, Cebuella pygmaea, Cebus albifrons, Cebus olivaceus, Cebus unicolor, Cephalopachus bancanus, Ceratotherium simum, Ceratotherium simum cottoni, Cercocebus chrysogaster, Cercocebus lunulatus, Cercocebus torquatus, Cercopithecus ascanius, Cercopithecus cephus, Cercopithecus diana, Cercopithecus hamlyni, Cercopithecus lowei, Cercopithecus albogularis, Cercopithecus mona, Cercopithecus neglectus, Cercopithecus nictitans, Cercopithecus petaurista, Cercopithecus pogonias, Cercopithecus roloway, Chaetophractus vellerosus, Cheirogaleus major, Cheirogaleus medius, Cheracebus lucifer, Cheracebus lugens, Cheracebus regulus, Cheracebus torquatus, Chiropotes albinasus, Chiropotes israelita, Chiropotes sagulatus, Chlorocebus aethiops, Chlorocebus pygerythrus, Chlorocebus sabaeus, Choloepus didactylus, Choloepus hoffmanni, Chrysochloris asiatica, Colobus guereza, Colobus polykomos, Craseonycteris thonglongyai, Cricetomys gambianus, Cricetulus griseus, Crocidura indochinensis, Cryptoprocta ferox, Ctenodactylus gundi, Ctenomys sociabilis, Cuniculus paca, Dasyprocta punctata, Dasypus novemcinctus, Daubentonia madagascariensis, Delphinapterus leucas, Desmodus rotundus, Dicerorhinus sumatrensis, Diceros bicornis, Dinomys branickii, Dipodomys stephensi, Dolichotis patagonum, Echinops telfairi, Elaphurus davidianus, Ellobius lutescens, Ellobius talpinus, Enhydra lutris, Equus asinus, Equus caballus, Equus przewalskii, Erinaceus europaeus, Erythrocebus patas, Eschrichtius robustus, Eubalaena japonica, Eulemur albifrons, Eulemur collaris, Eulemur coronatus, Eulemur flavifrons, Eulemur fulvus, Eulemur macaco, Eulemur mongoz, Eulemur rubriventer, Eulemur rufus, Eulemur sanfordi, Felis nigripes, Galago moholi, Galago senegalensis, Galagoides demidoff, Galeopterus variegatus, Giraffa tippelskirchi, Glis glis, Gorilla beringei, Gorilla gorilla, Graphiurus murinus, Hapalemur alaotrensis, Hapalemur gilberti, Hapalemur griseus, Hapalemur meridionalis, Hapalemur occidentalis, Helogale parvula, Hemitragus hylocrius, Heterocephalus glaber, Heterohyrax brucei, Hippopotamus amphibius, Hipposideros armiger, Hipposideros galeritus, Hoolock leuconedys, Hyaena hyaena, Hydrochoerus hydrochaeris, Hylobates abbotti, Hylobates agilis, Hylobates klossii, Hylobates pileatus, Hylobates muelleri, Hylobates pileatus, Hystrix cristata, Indri indri, Inia geoffrensis, Jaculus jaculus, Kogia breviceps, Lagothrix lagothricha, Lasiurus borealis, Lemur catta, Leontocebus fuscicollis, Leontocebus illigeri, Leontocebus nigricollis, Leontopithecus chrysomelas, Leontopithecus rosalia, Lepilemur ankaranensis, Lepilemur dorsalis, Lepilemur ruficaudatus, Lepilemur septentrionalis, Leptonychotes weddellii, Lepus americanus, Lipotes vexillifer, Lophocebus aterrimus, Loris lydekkerianus, Loris tardigradus, Loxodonta africana, Lycaon pictus, Macaca arctoides, Macaca assamensis, Macaca cyclopis, Macaca fascicularis, Macaca fuscata, Macaca leonina, Macaca maura, Macaca nigra, Macaca radiata, Macaca siberu, Macaca silenus, Macaca thibetana, Macaca tonkeana, Macroglossus sobrinus, Mandrillus leucophaeus, Mandrillus sphinx, Manis javanica, Manis pentadactyla, Megaderma lyra, Mellivora capensis, Meriones unguiculatus, Mesocricetus auratus, Mesoplodon bidens, Mico argentatus, Mico humeralifer, Mico schneideri, Microcebus murinus, Microgale talazaci, Micronycteris hirsuta, Miniopterus schreibersii, Miopithecus ogouensis, Mirounga angustirostris, Mirza zaza, Monodon monoceros, Mormoops blainvillei, Moschus moschiferus, Mungos mungo, Murina feae, Mus caroli, Mus spretus, Muscardinus avellanarius, Mustela putorius, Myocastor coypus, Myotis myotis, Myrmecophaga tridactyla, Nannospalax galili, Nasalis larvatus, Neomonachus schauinslandi, Neophocaena asiaeorientalis, Noctilio leporinus, Nomascus annamensis, Nomascus concolor, Nomascus gabriellae, Nomascus siki_a, Nomascus siki_b, Nyctereutes procyonoides, Nycticebus bengalensis, Nycticebus coucang, Nycticebus pygmaeus, Ochotona princeps, Octodon degus, Odocoileus virginianus, Okapia johnstoni, Ondatra zibethicus, Onychomys torridus, Orycteropus afer, Oryctolagus cuniculus, Otocyon megalotis, Otolemur crassicaudatus, Ovis aries, Ovis canadensis, Pan paniscus, Pan troglodytes, Panthera onca, Panthera pardus, Panthera tigris, Pantholops hodgsonii, Papio anubis, Papio cynocephalus, Papio hamadryas, Papio kindae, Papio papio, Papio ursinus, Paradoxurus hermaphroditus, Perodicticus ibeanus, Perodicticus potto, Perognathus longimembris, Petromus typicus, Phocoena phocoena, Piliocolobus badius, Piliocolobus gordonorum, Piliocolobus kirkii, Pipistrellus pipistrellus, Pithecia albicans, Pithecia chrysocephala, Pithecia hirsuta, Pithecia mittermeieri, Pithecia pissinattii, Pithecia pithecia, Pithecia vanzolinii, Platanista gangetica, Plecturocebus bernhardi, Plecturocebus brunneus, Plecturocebus caligatus, Plecturocebus cinerascens, Plecturocebus cupreus, Plecturocebus dubius, Plecturocebus grovesi, Plecturocebus hoffmannsi, Plecturocebus miltoni, Plecturocebus moloch, Pongo abelii, Pongo pygmaeus, Presbytis comata, Presbytis mitrata, Procavia capensis, Prolemur simus, Propithecus coronatus, Propithecus diadema, Propithecus edwardsi, Propithecus perrieri, Propithecus tattersalli, Propithecus verreauxi, Psammomys obesus, Pteronura brasiliensis, Puma concolor, Pygathrix cinerea, Pygathrix nigripes, Pygathrix nigripes, Rangifer tarandus, Rhinolophus sinicus, Rhinopithecus bieti, Rhinopithecus strykeri, Rousettus aegyptiacus, Saguinus bicolor, Saguinus geoffroyi, Saguinus imperator, Saguinus inustus, Saguinus labiatus, Saguinus midas, Saguinus mystax, Saguinus oedipus, Saiga tatarica, Saimiri cassiquiarensis, Saimiri macrodon, Saimiri oerstedii, Saimiri sciureus, Saimiri ustus, Sapajus apella, Sapajus macrocephalus, Scalopus aquaticus, Semnopithecus entellus, Semnopithecus hypoleucos, Semnopithecus johnii, Semnopithecus priam, Semnopithecus schistaceus, Semnopithecus vetulus, Sigmodon hispidus, Solenodon paradoxus, Spermophilus dauricus, Spilogale gracilis, Suricata suricatta, Symphalangus syndactylus, Tadarida brasiliensis, Tamandua tetradactyla, Tapirus indicus, Tapirus terrestris, Tarsius lariang, Tarsius wallacei, Thryonomys swinderianus, Tolypeutes matacus, Tonatia saurophila, Trachypithecus auratus, Trachypithecus crepusculus, Trachypithecus cristatus, Trachypithecus francoisi, Trachypithecus geei, Trachypithecus germaini, Trachypithecus hatinhensis, Trachypithecus laotum, Trachypithecus leucocephalus, Trachypithecus melamera, Trachypithecus obscurus, Trachypithecus phayrei, Trachypithecus pileatus, Tragulus javanicus, Trichechus manatus, Tupaia tana, Tursiops truncatus, Uropsilus gracilis, Ursus maritimus, Varecia rubra, Varecia variegata, Vicugna pacos, Vulpes lagopus, Xerus inauris, Zalophus californianus, Zapus hudsonius, Ziphius cavirostris\n
\nThis alignment was created by making three edits (using Cactus) to the\n241-way mammalian Zoonomia Cactus alignment\n(\nhttps://cglgenomics.ucsc.edu/data/cactus/).\n
\nphyloP scores were computed from the Cactus 447-way alignment using the\nphyloP program from the\nPHAST package.\nPer-base scores were produced with options\n--method LRT --mode CONACC --wig-scores; positive scores\nindicate conservation under purifying selection, negative scores indicate\nacceleration relative to neutral evolution.\n
\nFor the all-species tracks, base-composition and substitution-rate\nparameters were estimated from 4-fold degenerate sites using\nphyloFit (PHAST, EM algorithm, medium precision) under either the\nREV or strand-symmetric reversible (SSREV) substitution model. Background\nbase frequencies were adjusted with modFreqs so that\ncomplementary bases (A/T and C/G) appear at equal expected frequencies,\nwhich is required for strand-symmetric scoring.\n
\nFor the primates-subset tracks, the alignment was restricted to the 233\nprimate species and an independent phyloFit / phyloP run was performed on\nthat sub-alignment using the SSREV model. All scores were encoded into\nwiggle format and loaded as either bigWig files (REV all-species,\nprimates LRT) or wig SQL tables backed by .wib data files\n(SSREV all-species, SSREV primates).\n
\nThe phylogenic tree was established by the research described\nin A global catalog of whole-genome diversity from 233 primate\nspecies.\n\n
\n
\n\n\n\n\n
\n count \ncommon \n
nameclade \nscientific name \n
(link to browser when existing)taxon id \n
link to NCBI\n 001 human primates catarrhini Homo sapiens/hg38
reference species9606 \n 002 western gorilla primates catarrhini Gorilla gorilla
GCA_900006655.3_Susie39593 \n 003 Sumatran orangutan primates catarrhini Pongo abelii
GCA_002880775.3_Susie_PABv29601 \n 004 Eastern Gorilla primates catarrhini Gorilla beringei 499232 \n 005 chimpanzee primates catarrhini Pan troglodytes
GCA_002880755.3_Clint_PTRv29598 \n 006 Bornean orangutan primates catarrhini Pongo pygmaeus 9600 \n 007 Rhesus monkey primates catarrhini Macaca mulatta
rheMac109544 \n 008 gelada primates catarrhini Theropithecus gelada
GCF_003255815.1_Tgel_1.09565 \n 009 stump-tailed macaque primates catarrhini Macaca arctoides 9540 \n 010 Northern Talapoin Monkey primates catarrhini Miopithecus ogouensis 100488 \n 011 crab-eating macaque primates catarrhini Macaca fascicularis 9541 \n 012 Allen's swamp monkey primates catarrhini Allenopithecus nigroviridis 54135 \n 013 siamang primates catarrhini Symphalangus syndactylus 9590 \n 014 black crested mangabey primates catarrhini Lophocebus aterrimus 75566 \n 015 drill primates catarrhini Mandrillus leucophaeus 9568 \n 016 Bonnet Macaque primates catarrhini Macaca radiata 9548 \n 017 Red-capped Mangabey primates catarrhini Cercocebus torquatus 9530 \n 018 Golden-bellied Mangabey primates catarrhini Cercocebus chrysogaster 75569 \n 019 Owl-faced Monkey primates catarrhini Cercopithecus hamlyni 9536 \n 020 Siberut Macaque primates catarrhini Macaca siberu 244255 \n 021 pig-tailed macaque primates catarrhini Macaca nemestrina
GCF_000956065.1_Mnem_1.09545 \n 022 White-naped Mangabey primates catarrhini Cercocebus lunulatus (Cercocebus atys lunulatus) 75570 \n 023 Tonkean Macaque primates catarrhini Macaca tonkeana 40843 \n 024 Diana Monkey primates catarrhini Cercopithecus diana 36224 \n 025 red guenon primates catarrhini Erythrocebus patas 9538 \n 026 Northern Pig-tailed Macaque primates catarrhini Macaca leonina 90387 \n 027 Moor Macaque primates catarrhini Macaca maura 90383 \n 028 Guinea Baboon primates catarrhini Papio papio 100937 \n 029 hamadryas baboon primates catarrhini Papio hamadryas 9557 \n 030 liontail macaque primates catarrhini Macaca silenus 54601 \n 031 olive baboon primates catarrhini Papio anubis
GCA_000264685.2_Panu_3.09555 \n 032 Roloway Monkey primates catarrhini Cercopithecus roloway 1137049 \n 033 Kinda Baboon primates catarrhini Papio kindae 208091 \n 034 Chacma Baboon primates catarrhini Papio ursinus 36229 \n 035 Sun-tailed Monkey primates catarrhini Allochrocebus solatus 147650 \n 036 golden snub-nosed monkey primates catarrhini Rhinopithecus roxellana
GCF_007565055.1_ASM756505v161622 \n 037 Vervet Monkey primates catarrhini Chlorocebus pygerythrus 60710 \n 038 sooty mangabey primates catarrhini Cercocebus atys
GCF_000955945.1_Caty_1.09531 \n 039 green monkey primates catarrhini Chlorocebus sabaeus
GCA_000409795.2_Chlorocebus_sabeus_1.160711 \n 040 De Brazza's monkey primates catarrhini Cercopithecus neglectus 36227 \n 041 Yellow Baboon primates catarrhini Papio cynocephalus 9556 \n 042 Celebes crested macaque primates catarrhini Macaca nigra 54600 \n 043 proboscis monkey primates catarrhini Nasalis larvatus 43780 \n 044 Preuss's Monkey primates catarrhini Allochrocebus preussi 147649 \n 045 Putty-nosed Monkey primates catarrhini Cercopithecus nictitans 36228 \n 046 Javan Surili primates catarrhini Presbytis comata 78452 \n 047 Sykes' Monkey primates catarrhini Cercopithecus albogularis 36225 \n 048 LHoests Monkey primates catarrhini Allochrocebus lhoesti 100224 \n 049 Crowned Monkey primates catarrhini Cercopithecus pogonias 102108 \n 050 Southern Mitered Langur primates catarrhini Presbytis mitrata (Presbytis melalophos mitrata) 272115 \n 051 Grey-shanked Douc Langur primates catarrhini Pygathrix cinerea 693712 \n 052 Mona monkey primates catarrhini Cercopithecus mona 36226 \n 053 Spot-nosed Monkey primates catarrhini Cercopithecus petaurista 100487 \n 054 grivet primates catarrhini Chlorocebus aethiops 9534 \n 055 Lowes Monkey primates catarrhini Cercopithecus lowei 304410 \n 056 Northern Yellow-cheeked Crested Gibbon primates catarrhini Nomascus annamensis 1616038 \n 057 Red-cheeked Gibbon primates catarrhini Nomascus gabriellae 61852 \n 058 Japanese macaque primates catarrhini Macaca fuscata 9542 \n 059 Western Red Colobus primates catarrhini Piliocolobus badius 164648 \n 060 southern white-cheeked gibbon primates catarrhini Nomascus siki_a 9586 \n 061 Taiwan macaque primates catarrhini Macaca cyclopis 78449 \n 062 black-shanked douc langur primates catarrhini Pygathrix nigripes 310352 \n 063 King Colobus primates catarrhini Colobus polykomos 9572 \n 064 Black Crested Gibbon primates catarrhini Nomascus concolor 29089 \n 065 Udzungwa Red Colobus primates catarrhini Piliocolobus gordonorum 591933 \n 066 Gee's Golden Langur primates catarrhini Trachypithecus geei 164650 \n 067 Kloss's Gibbon primates catarrhini Hylobates klossii 9587 \n 068 Spectacled Leaf Monkey primates catarrhini Trachypithecus obscurus 54181 \n 069 Zanzibar Red Colobus primates catarrhini Piliocolobus kirkii 591937 \n 070 Indochinese Silvered Langur primates catarrhini Trachypithecus germaini 271260 \n 071 Hatinh Langur primates catarrhini Trachypithecus hatinhensis 867383 \n 072 Moustached Monkey primates catarrhini Cercopithecus cephus 9535 \n 073 Laotian Langur primates catarrhini Trachypithecus laotum 465718 \n 074 Francois's langur primates catarrhini Trachypithecus francoisi 54180 \n 075 Purple-faced Langur primates catarrhini Semnopithecus vetulus (Trachypithecus vetulus) 54137 \n 076 Capped Langur primates catarrhini Trachypithecus pileatus 164651 \n 077 Ugandan red Colobus primates catarrhini Piliocolobus tephrosceles
GCF_002776525.2_ASM277652v2591936 \n 078 Spangled Ebony Langur primates catarrhini Trachypithecus auratus 222416 \n 079 Red-tailed Monkey primates catarrhini Cercopithecus ascanius 36223 \n 080 Silvery Lutung primates catarrhini Trachypithecus cristatus 122765 \n 081 Nilgiri Langur primates catarrhini Semnopithecus johnii (Trachypithecus johnii) 66063 \n 082 Indochinese grey langur primates catarrhini Trachypithecus crepusculus (Trachypithecus phayrei crepuscula) 272121 \n 083 White-headed langur primates catarrhini Trachypithecus leucocephalus (Trachypithecus poliocephalus) 465719 \n 084 pygmy chimpanzee primates catarrhini Pan paniscus
GCA_000258655.2_panpan1.19597 \n 085 northern white-cheeked gibbon primates catarrhini Nomascus siki_b 9586 \n 086 Agile Gibbon primates catarrhini Hylobates agilis 9579 \n 087 Phayre's Leaf-monkey primates catarrhini Trachypithecus melamera n/a \n 088 Nepal Gray Langur primates catarrhini Semnopithecus schistaceus 2804203 \n 089 Abbott's Gray Gibbon primates catarrhini Hylobates abbotti (Hylobates muelleri abbotti) 716694 \n 090 Bornean Gibbon primates catarrhini Hylobates muelleri 9588 \n 091 Tufted Gray Langur primates catarrhini Semnopithecus priam 1208733 \n 092 Black-footed Gray Langur primates catarrhini Semnopithecus hypoleucos 1208734 \n 093 mantled guereza primates catarrhini Colobus guereza 33548 \n 094 Hanuman langur primates catarrhini Semnopithecus entellus 88029 \n 095 pileated gibbon primates catarrhini Hylobates pileatus 9589 \n 096 black snub-nosed monkey primates catarrhini Rhinopithecus bieti 61621 \n 097 Burmese snub-nosed monkey primates catarrhini Rhinopithecus strykeri 1194336 \n 098 Angolan colobus primates catarrhini Colobus angolensis
colAng154131 \n 099 Pileated Gibbon primates catarrhini Hylobates pileatus 9589 \n 100 black-shanked douc langur primates catarrhini Pygathrix nigripes 310352 \n 101 Milne-edwards' Macaque primates catarrhini Macaca thibetana 54602 \n 102 Phayre's Leaf-monkey primates catarrhini Trachypithecus phayrei 61618 \n 103 Assam macaque primates catarrhini Macaca assamensis 9551 \n 104 Eastern hoolock gibbon primates catarrhini Hoolock leuconedys 61851 \n 105 mandrill primates catarrhini Mandrillus sphinx 9561 \n 106 White-faced Saki primates platyrrhini Pithecia chrysocephala 2946515 \n 107 Monk Saki primates platyrrhini Pithecia hirsuta 2946516 \n 108 white-faced saki primates platyrrhini Pithecia pithecia 43777 \n 109 Mittermeier's Tapajós saki primates platyrrhini Pithecia mittermeieri 2946517 \n 110 Buffy Saki primates platyrrhini Pithecia albicans 2946514 \n 111 Pissinatti's saki primates platyrrhini Pithecia pissinattii (Pithecia pissinatti) 2946518 \n 112 Vanzolini's Bald-faced Saki primates platyrrhini Pithecia vanzolinii 2946519 \n 113 Bald-headed Uacari primates platyrrhini Cacajao calvus 30596 \n 114 Ayres Black Uakari primates platyrrhini Cacajao ayresi 535896 \n 115 Black-headed Uacari primates platyrrhini Cacajao melanocephalus 70825 \n 116 Black-headed Uacari primates platyrrhini Cacajao hosomi 535897 \n 117 Reddish-brown bearded saki primates platyrrhini Chiropotes sagulatus (Chiropotes chiropotes) 658221 \n 118 brown-backed bearded saki primates platyrrhini Chiropotes israelita 280163 \n 119 Collared Titi Monkey primates platyrrhini Cheracebus lugens 210166 \n 120 Brown Titi Monkey primates platyrrhini Plecturocebus brunneus 1812042 \n 121 Hoffmanns's titi monkey primates platyrrhini Plecturocebus hoffmannsi 78255 \n 122 Milton's Titi Monkey primates platyrrhini Plecturocebus miltoni 1812038 \n 123 Widow Monkey primates platyrrhini Cheracebus torquatus 30592 \n 124 Ashy Black Titi Monkey primates platyrrhini Plecturocebus cinerascens 1812037 \n 125 Prince Bernhard's Titi Monkey primates platyrrhini Plecturocebus bernhardi 1812036 \n 126 Yellow-handed Titi Monkey primates platyrrhini Cheracebus lucifer 2487712 \n 127 Coppery Titi Monkey primates platyrrhini Plecturocebus cupreus 202457 \n 128 Chestnut-bellied Titi primates platyrrhini Plecturocebus caligatus 867332 \n 129 Hershkovitzs Titi primates platyrrhini Plecturocebus dubius 2946520 \n 130 Red-bellied Titi Monkey primates platyrrhini Plecturocebus moloch 9523 \n 131 Groves' Titi primates platyrrhini Plecturocebus grovesi 2488670 \n 132 black-handed spider monkey primates platyrrhini Ateles geoffroyi_a 9509 \n 133 Widow Monkey primates platyrrhini Cheracebus regulus 1812110 \n 134 Guiana Spider Monkey primates platyrrhini Ateles paniscus 9510 \n 135 Black-faced Black Spider Monkey primates platyrrhini Ateles chamek 118643 \n 136 White-cheeked Spider Monkey primates platyrrhini Ateles marginatus 1529884 \n 137 White-bellied Spider Monkey primates platyrrhini Ateles belzebuth 9507 \n 138 Common Woolly Monkey primates platyrrhini Lagothrix lagothricha (Lagothrix lagotricha) 9519 \n 139 large-headed capuchin primates platyrrhini Sapajus macrocephalus (Sapajus apella macrocephalus) 1547595 \n 140 Spixs White-fronted Capuchin primates platyrrhini Cebus unicolor 1985288 \n 141 Central American spider monkey primates platyrrhini Ateles geoffroyi_b 9509 \n 142 Guinan Weeper Capuchin primates platyrrhini Cebus olivaceus 37295 \n 143 mantled howler monkey primates platyrrhini Alouatta palliata 30589 \n 144 white-fronted capuchin primates platyrrhini Cebus albifrons 9514 \n 145 Northern Night Monkey primates platyrrhini Aotus trivirgatus 9505 \n 146 Grey-handed Night Monkey primates platyrrhini Aotus griseimembra 292213 \n 147 Black-and-gold Howler Monkey primates platyrrhini Alouatta caraya 9502 \n 148 Spixs Night Monkey primates platyrrhini Aotus vociferans 57176 \n 149 Red-handed Howler Monkey primates platyrrhini Alouatta belzebul 30590 \n 150 Red-handed Howler Monkey primates platyrrhini Alouatta discolor 2905217 \n 151 Azara's Night Monkey primates platyrrhini Aotus azarae (Aotus azarai) 30591 \n 152 Purús Red Howler Monkey primates platyrrhini Alouatta puruensis (Alouatta seniculus puruensis) 1347729 \n 153 Black Howler Monkey primates platyrrhini Alouatta nigerrima (Alouatta belzebul) 30590 \n 154 Guianan Red Howler Monkey primates platyrrhini Alouatta macconnelli 198115 \n 155 Colombian Red Howler Monkey primates platyrrhini Alouatta juara 2946512 \n 156 Colombian Red Howler Monkey primates platyrrhini Alouatta seniculus 9503 \n 157 tufted capuchin primates platyrrhini Sapajus apella 9515 \n 158 Ma's night monkey primates platyrrhini Aotus nancymaae
GCA_000952055.2_Anan_2.037293 \n 159 Bolivian squirrel monkey primates platyrrhini Saimiri boliviensis
GCF_016699345.1_BCM_Sbol_2.027679 \n 160 White-nosed Saki primates platyrrhini Chiropotes albinasus 198627 \n 161 Black Mantle Tamarin primates platyrrhini Leontocebus nigricollis 9489 \n 162 brown-mantled tamarin primates platyrrhini Leontocebus fuscicollis 9487 \n 163 Illiger's saddle-back tamarin primates platyrrhini Leontocebus illigeri (Leontocebus fuscicollis illigeri) 881947 \n 164 Cotton-headed Tamarin primates platyrrhini Saguinus oedipus 9490 \n 165 Pied Tamarin primates platyrrhini Saguinus bicolor 37588 \n 166 Geoffroy's Tamarin primates platyrrhini Saguinus geoffroyi 43778 \n 167 White-fronted Titi Monkey primates platyrrhini Saguinus inustus 1079039 \n 168 Moustached Tamarin primates platyrrhini Saguinus mystax 9488 \n 169 tamarin primates platyrrhini Saguinus imperator 9491 \n 170 Guianan Squirrel Monkey primates platyrrhini Saimiri sciureus 9521 \n 171 Red-chested Mustached Tamarin primates platyrrhini Saguinus labiatus 78454 \n 172 Goeldi's Monkey primates platyrrhini Callimico goeldii 9495 \n 173 Black-crowned Central American Squirrel Monkey primates platyrrhini Saimiri oerstedii 70928 \n 174 Golden-headed Lion Tamarin primates platyrrhini Leontopithecus chrysomelas 57374 \n 175 golden lion tamarin primates platyrrhini Leontopithecus rosalia 30588 \n 176 Humboldt's Squirrel Monkey primates platyrrhini Saimiri cassiquiarensis 2946521 \n 177 bare-eared squirrel monkey primates platyrrhini Saimiri ustus 66265 \n 178 Ecuadorian squirrel monkey primates platyrrhini Saimiri macrodon 2946522 \n 179 white-tufted-ear marmoset primates platyrrhini Callithrix jacchus 9483 \n 180 Eastern Pygmy Marmoset primates platyrrhini Cebuella niveiventris 2826950 \n 181 Western Pygmy Marmoset primates platyrrhini Cebuella pygmaea 9493 \n 182 Black And White Tassel-ear Marmoset primates platyrrhini Mico humeralifer 52232 \n 183 Black-crowned Dwarf Marmoset primates platyrrhini Callibella humilis (Mico humilis) 666519 \n 184 Mico schneideri primates platyrrhini Mico schneideri n/a \n 185 Silvery Marmoset primates platyrrhini Mico argentatus 9482 \n 186 Midas tamarin primates platyrrhini Saguinus midas 30586 \n 187 Wieds Marmoset primates platyrrhini Callithrix kuhlii 867363 \n 188 Geoffroy's Tufted-ear Marmoset primates platyrrhini Callithrix geoffroyi 52231 \n 189 Horsfield's tarsier primates tarsiidae Cephalopachus bancanus 9477 \n 190 Philippine tarsier primates tarsiidae Carlito syrichta
tarSyr21868482 \n 191 Lariang Tarsier primates tarsiidae Tarsius lariang 630277 \n 192 Wallace's Tarsier primates tarsiidae Tarsius wallacei 981131 \n 193 aye-aye primates strepsirrhini Daubentonia madagascariensis 31869 \n 194 Crowned Sifaka primates strepsirrhini Propithecus coronatus (Propithecus deckenii coronatus) 475619 \n 195 Perrier's Sifaka primates strepsirrhini Propithecus perrieri 989338 \n 196 ruffed lemur primates strepsirrhini Varecia variegata 9455 \n 197 Diademed Sifaka primates strepsirrhini Propithecus diadema 83281 \n 198 Milne-Edwards Sifaka primates strepsirrhini Propithecus edwardsi 543559 \n 199 babakoto primates strepsirrhini Indri indri 34827 \n 200 Golden-crowned Sifaka primates strepsirrhini Propithecus tattersalli 30601 \n 201 Eastern Woolly Lemur primates strepsirrhini Avahi laniger 122246 \n 202 Verreauxs Sifaka primates strepsirrhini Propithecus verreauxi 34825 \n 203 Peyrieras Woolly Lemur primates strepsirrhini Avahi peyrierasi 1313323 \n 204 Red Ruffed Lemur primates strepsirrhini Varecia rubra 554167 \n 205 greater bamboo lemur primates strepsirrhini Prolemur simus 1328070 \n 206 Red-bellied Lemur primates strepsirrhini Eulemur rubriventer 34829 \n 207 mongoose lemur primates strepsirrhini Eulemur mongoz 34828 \n 208 Geoffroys Dwarf Lemur primates strepsirrhini Cheirogaleus major 47177 \n 209 Crowned Lemur primates strepsirrhini Eulemur coronatus 13514 \n 210 black lemur primates strepsirrhini Eulemur macaco 30602 \n 211 lesser dwarf lemur primates strepsirrhini Cheirogaleus medius 9460 \n 212 Sclater's lemur primates strepsirrhini Eulemur flavifrons 87288 \n 213 Coquerel's sifaka primates strepsirrhini Propithecus coquerelli (Propithecus coquereli)
proCoq1379532 \n 214 Collared Brown Lemur primates strepsirrhini Eulemur collaris (Eulemur fulvus collaris) 47178 \n 215 Red-tailed Sportive Lemur primates strepsirrhini Lepilemur ruficaudatus 78866 \n 216 Red Brown Lemur primates strepsirrhini Eulemur rufus 859983 \n 217 Sanfords Brown Lemur primates strepsirrhini Eulemur sanfordi 122225 \n 218 White-fronted Lemur primates strepsirrhini Eulemur albifrons 1215604 \n 219 Gray's Sportive Lemur primates strepsirrhini Lepilemur dorsalis 78583 \n 220 brown lemur primates strepsirrhini Eulemur fulvus 13515 \n 221 Sahafary Sportive Lemur primates strepsirrhini Lepilemur septentrionalis 78584 \n 222 Sambirano Lesser Bamboo Lemur primates strepsirrhini Hapalemur occidentalis 867377 \n 223 Alaotra Reed Lemur primates strepsirrhini Hapalemur alaotrensis (Hapalemur griseus alaotrensis) 122220 \n 224 Eastern Lesser Bamboo Lemur primates strepsirrhini Hapalemur griseus 13557 \n 225 Ankarana Sportive Lemur primates strepsirrhini Lepilemur ankaranensis 342401 \n 226 ring-tailed lemur primates strepsirrhini Lemur catta 9447 \n 227 gray bamboo lemur primates strepsirrhini Hapalemur gilberti 3043110 \n 228 Rusty-gray Lesser Bamboo Lemur primates strepsirrhini Hapalemur meridionalis 3043112 \n 229 Demidoffs Dwarf Galago primates strepsirrhini Galagoides demidoff 89672 \n 230 northern giant mouse lemur primates strepsirrhini Mirza zaza 339999 \n 231 gray mouse lemur primates strepsirrhini Microcebus murinus
GCA_000165445.3_Mmur_3.030608 \n 232 small-eared galago primates strepsirrhini Otolemur garnettii
otoGar330611 \n 233 Northern Lesser Galago primates strepsirrhini Galago senegalensis 9465 \n 234 Thick-tailed Greater Galago primates strepsirrhini Otolemur crassicaudatus 9463 \n 235 Grey Slender Loris primates strepsirrhini Loris lydekkerianus 300163 \n 236 slender loris primates strepsirrhini Loris tardigradus 9468 \n 237 West African Potto primates strepsirrhini Perodicticus potto 9472 \n 238 East African Potto primates strepsirrhini Perodicticus ibeanus (Perodicticus potto ibeanus) 261737 \n 239 Moholi bushbaby primates strepsirrhini Galago moholi 30609 \n 240 Pygmy Slow Loris primates strepsirrhini Nycticebus pygmaeus (Xanthonycticebus pygmaeus) 101278 \n 241 Bengal slow loris primates strepsirrhini Nycticebus bengalensis 261741 \n 242 Calabar Angwantibo primates strepsirrhini Arctocebus calabarensis 261739 \n 243 slow loris primates strepsirrhini Nycticebus coucang 9470 \n 244 jaguar carnivora Panthera onca
GCA_004023805.1_PanOnc_v1_BIUU9690 \n 245 leopard carnivora Panthera pardus
GCA_001857705.1_PanPar1.09691 \n 246 giant panda carnivora Ailuropoda melanoleuca
GCA_002007445.1_ASM200744v19646 \n 247 Hawaiian monk seal carnivora Neomonachus schauinslandi
GCA_002201575.1_ASM220157v129088 \n 248 California sea lion carnivora Zalophus californianus
GCA_004024565.1_ZalCal_v1_BIUU9704 \n 249 Greenland wolf carnivora Canis lupus orion
GCA_905319855.2_mCanLor1.22605939 \n 250 Pacific walrus carnivora Odobenus rosmarus
odoRosDiv19707 \n 251 domestic cat (Fca126) carnivora Felis catus fca126 (Felis catus)
GCF_018350175.1_F.catus_Fca126_mat1.09685 \n 252 northern elephant seal carnivora Mirounga angustirostris
GCA_004023865.1_MirAng_v1_BIUU9716 \n 253 domestic cat carnivora Felis catus
felCat89685 \n 254 domestic dog (BS72/Village Dog) carnivora Canis lupus familiaris
GCA_004027395.1_CanFam_VD_v1_BIUU\n 255 German Shepherd dog (Mischka) carnivora Canis lupus familiaris (CanFam4) (Canis lupus familiaris)
canFam4\n 256 dingo carnivora Canis lupus dingo 286419 \n 257 raccoon dog carnivora Nyctereutes procyonoides 34880 \n 258 fossa carnivora Cryptoprocta ferox 94188 \n 259 polar bear carnivora Ursus maritimus
GCA_000687225.1_UrsMar_1.029073 \n 260 Asian palm civet carnivora Paradoxurus hermaphroditus
GCA_004024585.1_ParHer_v1_BIUU71117 \n 261 African hunting dog carnivora Lycaon pictus
GCA_001887905.1_LycPicSAfr1.09622 \n 262 Arctic fox carnivora Vulpes lagopus
GCA_004023825.1_VulLag_v1_BIUU494514 \n 263 dog carnivora Canis lupus familiaris
GCF_000002285.3_CanFam3.19615 \n 264 striped hyena carnivora Hyaena hyaena
GCA_004023945.1_HyaHya_v1_BIUU95912 \n 265 n/a carnivora Acinonyx jubatus
GCA_001443585.1_aciJub132536 \n 266 tiger carnivora Panthera tigris
GCA_000464555.1_PanTig1.09694 \n 267 Sea otter carnivora Enhydra lutris
GCA_002288905.2_ASM228890v234882 \n 268 giant otter carnivora Pteronura brasiliensis 9672 \n 269 bat-eared fox carnivora Otocyon megalotis 9624 \n 270 Weddell seal carnivora Leptonychotes weddellii
GCA_000349705.1_LepWed1.09713 \n 271 Lesser panda carnivora Ailurus fulgens
GCA_002007465.1_ASM200746v19649 \n 272 ratel carnivora Mellivora capensis
GCA_004024625.1_MelCap_v1_BIUU9664 \n 273 banded mongoose carnivora Mungos mungo
GCA_004023785.1_MunMun_v1_BIUU210652 \n 274 dwarf mongoose carnivora Helogale parvula
GCA_004023845.1_HelPar_v1_BIUU210647 \n 275 meerkat carnivora Suricata suricatta
GCA_004023905.1_SurSur_v1_BIUU37032 \n 276 puma carnivora Puma concolor
GCA_003327715.1_PumCon1.09696 \n 277 black-footed cat carnivora Felis nigripes
GCA_004023925.1_FelNig_v1_BIUU61379 \n 278 European polecat carnivora Mustela putorius
GCA_000239315.1_MusPutFurMale1.09668 \n 279 western spotted skunk carnivora Spilogale gracilis
GCA_004023965.1_SpiGra_v1_BIUU30551 \n 280 Sumatran rhinoceros laurasiatheria Dicerorhinus sumatrensis
GCA_002844835.1_ASM284483v189632 \n 281 black rhinoceros laurasiatheria Diceros bicornis
GCA_004027315.1_DicBicMic_v1_BIUU9805 \n 282 Asiatic tapir laurasiatheria Tapirus indicus
GCA_004024905.1_TapInd_v1_BIUU9802 \n 283 Brazilian tapir laurasiatheria Tapirus terrestris
GCA_004025025.1_TapTer_v1_BIUU9801 \n 284 northern white rhinoceros laurasiatheria Ceratotherium simum cottoni 310713 \n 285 ass laurasiatheria Equus asinus
GCA_001305755.1_ASM130575v19793 \n 286 Southern white rhinoceros laurasiatheria Ceratotherium simum
GCA_000283155.1_CerSimSim1.09807 \n 287 Przewalski's horse laurasiatheria Equus przewalskii
GCA_000696695.1_Burgud9798 \n 288 horse laurasiatheria Equus caballus
GCA_000002305.1_EquCab2.09796 \n 289 Malayan pangolin laurasiatheria Manis javanica
GCA_001685135.1_ManJav1.09974 \n 290 Chinese pangolin laurasiatheria Manis pentadactyla
GCA_000738955.1_M_pentadactyla-1.1.1143292 \n 291 Hispaniolan solenodon laurasiatheria Solenodon paradoxus 79805 \n 292 eastern mole laurasiatheria Scalopus aquaticus
GCA_004024925.1_ScaAqu_v1_BIUU71119 \n 293 gracile shrew mole laurasiatheria Uropsilus gracilis
GCA_004024945.1_UroGra_v1_BIUU182669 \n 294 star-nosed mole laurasiatheria Condylura cristata
GCF_000260355.1_ConCri1.0143302 \n 295 western European hedgehog laurasiatheria Erinaceus europaeus
GCA_000296755.1_EriEur2.09365 \n 296 European shrew laurasiatheria Sorex araneus
sorAra242254 \n 297 Indochinese shrew laurasiatheria Crocidura indochinensis
GCA_004027635.1_CroInd_v1_BIUU876679 \n 298 Hoffmann's two-fingered sloth xenarthra Choloepus hoffmanni
GCA_000164785.2_C_hoffmanni-2.0.19358 \n 299 nine-banded armadillo xenarthra Dasypus novemcinctus
GCA_000208655.2_Dasnov3.09361 \n 300 giant anteater xenarthra Myrmecophaga tridactyla
GCA_004026745.1_MyrTri_v1_BIUU71006 \n 301 southern tamandua xenarthra Tamandua tetradactyla
GCA_004025105.1_TamTet_v1_BIUU48850 \n 302 placentals xenarthra Tolypeutes matacus 183749 \n 303 southern two-toed sloth xenarthra Choloepus didactylus
GCA_004027855.1_ChoDid_v1_BIUU27675 \n 304 screaming hairy armadillo xenarthra Chaetophractus vellerosus
GCA_004027955.1_ChaVel_v1_BIUU340076 \n 305 North Pacific right whale artiodactyla Eubalaena japonica 302098 \n 306 grey whale artiodactyla Eschrichtius robustus 9764 \n 307 hippopotamus artiodactyla Hippopotamus amphibius
GCA_004027065.1_HipAmp_v1_BIUU9833 \n 308 Minke whale artiodactyla Balaenoptera acutorostrata
GCA_000493695.1_BalAcu1.09767 \n 309 beluga whale artiodactyla Delphinapterus leucas
GCA_002288925.2_ASM228892v29749 \n 310 Antarctic minke whale artiodactyla Balaenoptera bonaerensis
GCA_000978805.1_ASM97880v133556 \n 311 boutu artiodactyla Inia geoffrensis 9725 \n 312 harbor porpoise artiodactyla Phocoena phocoena 9742 \n 313 narwhal artiodactyla Monodon monoceros
GCA_004026685.1_MonMon_M_v1_BIUU40151 \n 314 Yangtze River dolphin artiodactyla Lipotes vexillifer
GCA_000442215.1_Lipotes_vexillifer_v1118797 \n 315 killer whale artiodactyla Orcinus orca
orcOrc19733 \n 316 Ganges River dolphin artiodactyla Platanista gangetica 118798 \n 317 Yangtze finless porpoise artiodactyla Neophocaena asiaeorientalis
GCA_003031525.1_Neophocaena_asiaeorientalis_V1189058 \n 318 Sowerby's beaked whale artiodactyla Mesoplodon bidens 48745 \n 319 alpaca artiodactyla Vicugna pacos
GCA_000767525.1_Vi_pacos_V1.030538 \n 320 Cuvier's beaked whale\" artiodactyla Ziphius cavirostris 9760 \n 321 Bactrian camel artiodactyla Camelus bactrianus
GCA_000767855.1_Ca_bactrianus_MBC_1.09837 \n 322 Arabian camel artiodactyla Camelus dromedarius
GCA_000767585.1_PRJNA234474_Ca_dromedarius_V1.09838 \n 323 wild Bactrian camel artiodactyla Camelus ferus
GCA_000311805.2_CB1419612 \n 324 pygmy sperm whale artiodactyla Kogia breviceps 27615 \n 325 Chacoan peccary artiodactyla Catagonus wagneri
GCA_004024745.1_CatWag_v1_BIUU51154 \n 326 reindeer artiodactyla Rangifer tarandus
GCA_004026565.1_RanTarSib_v1_BIUU9870 \n 327 Pere David's deer artiodactyla Elaphurus davidianus
GCA_002443075.1_Milu1.043332 \n 328 okapi artiodactyla Okapia johnstoni
GCA_001660835.1_ASM166083v186973 \n 329 Masai giraffe artiodactyla Giraffa tippelskirchi
GCA_001651235.1_ASM165123v1439328 \n 330 Siberian musk deer artiodactyla Moschus moschiferus
GCA_004024705.1_MosMos_v1_BIUU68415 \n 331 water buffalo artiodactyla Bubalus bubalis
GCA_000471725.1_UMD_CASPUR_WB_2.089462 \n 332 cow artiodactyla Bos taurus
GCA_000003205.6_Btau_5.0.19913 \n 333 pronghorn artiodactyla Antilocapra americana
GCA_004027515.1_AntAmePen_v1_BIUU9891 \n 334 white-tailed deer artiodactyla Odocoileus virginianus
GCA_002102435.1_Ovir.te_1.09874 \n 335 aoudad artiodactyla Ammotragus lervia
GCA_002201775.1_ALER1.09899 \n 336 bighorn sheep artiodactyla Ovis canadensis
GCA_004026945.1_OviCan_v1_BIUU37174 \n 337 goat artiodactyla Capra hircus
GCA_001704415.1_ARS19925 \n 338 Nilgiri tahr artiodactyla Hemitragus hylocrius
GCA_004026825.1_HemHyl_v1_BIUU330464 \n 339 hirola artiodactyla Beatragus hunteri
GCA_004027495.1_BeaHun_v1_BIUU59527 \n 340 wild yak artiodactyla Bos mutus
bosMut172004 \n 341 American bison artiodactyla Bison bison
GCA_000754665.1_Bison_UMD1.09901 \n 342 sheep artiodactyla Ovis aries
GCA_000298735.2_Oar_v4.09940 \n 343 chiru artiodactyla Pantholops hodgsonii
GCA_000400835.1_PHO1.059538 \n 344 wild goat artiodactyla Capra aegagrus
GCA_000978405.1_CapAeg_1.09923 \n 345 Java mouse-deer artiodactyla Tragulus javanicus
GCA_004024965.1_TraJav_v1_BIUU9849 \n 346 pig artiodactyla Sus scrofa
susScr39823 \n 347 zebu cattle artiodactyla Bos indicus
GCA_000247795.2_Bos_indicus_1.09915 \n 348 common bottlenose dolphin artiodactyla Tursiops truncatus
GCA_001922835.1_NIST_Tur_tru_v19739 \n 349 Saiga antelope artiodactyla Saiga tatarica
GCA_004024985.1_SaiTat_v1_BIUU34875 \n 350 Chinese rufous horseshoe bat chiroptera Rhinolophus sinicus
GCA_001888835.1_ASM188883v189399 \n 351 black flying fox chiroptera Pteropus alecto
pteAle19402 \n 352 Cantor's roundleaf bat chiroptera Hipposideros galeritus 58069 \n 353 Egyptian rousette chiroptera Rousettus aegyptiacus
GCA_004024865.1_RouAeg_v1_BIUU9407 \n 354 long-tongued fruit bat chiroptera Macroglossus sobrinus 326083 \n 355 large flying fox chiroptera Pteropus vampyrus
GCF_000151845.1_Pvam_2.0132908 \n 356 Brazilian free-tailed bat chiroptera Tadarida brasiliensis
GCA_004025005.1_TadBra_v1_BIUU9438 \n 357 great roundleaf bat chiroptera Hipposideros armiger
GCA_001890085.1_ASM189008v1186990 \n 358 straw-colored fruit bat chiroptera Eidolon helvum
eidHel177214 \n 359 Antillean ghost-faced bat chiroptera Mormoops blainvillei
GCA_004026545.1_MorMeg_v1_BIUU118852 \n 360 tailed tailless bat chiroptera Anoura caudifer
GCA_004027475.1_AnoCau_v1_BIUU27642 \n 361 common vampire bat chiroptera Desmodus rotundus
GCA_002940915.2_ASM294091v29430 \n 362 hairy big-eared bat chiroptera Micronycteris hirsuta
GCA_004026765.1_MicHir_v1_BIUU148065 \n 363 stripe-headed round-eared bat chiroptera Tonatia saurophila
GCA_004024845.1_TonSau_v1_BIUU171122 \n 364 Seba's short-tailed bat chiroptera Carollia perspicillata
GCA_004027735.1_CarPer_v1_BIUU40233 \n 365 Jamaican fruit-eating bat chiroptera Artibeus jamaicensis
GCA_004027435.1_ArtJam_v1_BIUU9417 \n 366 Indian false vampire chiroptera Megaderma lyra
GCA_004026885.1_MegLyr_v1_BIUU9413 \n 367 Schreibers' long-fingered bat chiroptera Miniopterus schreibersii
GCA_004026525.1_MinSch_v1_BIUU9433 \n 368 greater bulldog bat chiroptera Noctilio leporinus
GCA_004026585.1_NocLep_v1_BIUU94963 \n 369 Natal long-fingered bat chiroptera Miniopterus natalensis
GCF_001595765.1_Mnat.v1291302 \n 370 hog-nosed bat chiroptera Craseonycteris thonglongyai
GCA_004027555.1_CraTho_v1_BIUU208972 \n 371 Parnell's mustached bat chiroptera Pteronotus parnellii
ptePar159476 \n 372 greater mouse-eared bat chiroptera Myotis myotis
GCA_004026985.1_MyoMyo_v1_BIUU51298 \n 373 Ashy-gray tube-nosed bat chiroptera Murina feae (Murina aurata feae)
GCA_004026665.1_MurFea_v1_BIUU1453894 \n 374 David's myotis chiroptera Myotis davidii
myoDav1225400 \n 375 Brandt's bat chiroptera Myotis brandtii
myoBra1109478 \n 376 big brown bat chiroptera Eptesicus fuscus
GCF_000308155.1_EptFus1.029078 \n 377 red bat chiroptera Lasiurus borealis
GCA_004026805.1_LasBor_v1_BIUU258930 \n 378 little brown bat chiroptera Myotis lucifugus
myoLuc259463 \n 379 common pipistrelle chiroptera Pipistrellus pipistrellus
GCA_004026625.1_PipPip_v1_BIUU59474 \n 380 African savanna elephant afrotheria Loxodonta africana
GCA_000001905.1_Loxafr3.09785 \n 381 Florida manatee afrotheria Trichechus manatus
GCA_000243295.1_TriManLat1.09778 \n 382 yellow-spotted hyrax afrotheria Heterohyrax brucei
GCA_004026845.1_HetBruBak_v1_BIUU77598 \n 383 Cape rock hyrax afrotheria Procavia capensis
GCA_004026925.1_ProCapCap_v1_BIUU9813 \n 384 aardvark afrotheria Orycteropus afer 9818 \n 385 Cape golden mole afrotheria Chrysochloris asiatica
GCA_004027935.1_ChrAsi_v1_BIUU185453 \n 386 Cape elephant shrew afrotheria Elephantulus edwardii
eleEdw128737 \n 387 Talazac's shrew tenrec afrotheria Microgale talazaci (Nesogale talazaci)
GCA_004026705.1_MicTal_v1_BIUU2583312 \n 388 small Madagascar hedgehog afrotheria Echinops telfairi
GCA_000313985.1_EchTel2.09371 \n 389 Sunda flying lemur euarchontoglires Galeopterus variegatus
GCA_004027255.1_GalVar_v1_BIUU482537 \n 390 Chinese tree shrew euarchontoglires Tupaia chinensis
tupChi1246437 \n 391 South African ground squirrel euarchontoglires Xerus inauris
GCA_004024805.1_XerIna_v1_BIUU234690 \n 392 large tree shrew euarchontoglires Tupaia tana 70687 \n 393 mountain beaver euarchontoglires Aplodontia rufa
GCA_004027875.1_AplRuf_v1_BIUU51342 \n 394 Alpine marmot euarchontoglires Marmota marmota
GCF_001458135.1_marMar2.19993 \n 395 Daurian ground squirrel euarchontoglires Spermophilus dauricus
GCA_002406435.1_ASM240643v199837 \n 396 crested porcupine euarchontoglires Hystrix cristata
GCA_004026905.1_HysCri_v1_BIUU10137 \n 397 thirteen-lined ground squirrel euarchontoglires Ictidomys tridecemlineatus
speTri243179 \n 398 American beaver euarchontoglires Castor canadensis
GCA_004027675.1_CasCan_v1_BIUU51338 \n 399 long-tailed chinchilla euarchontoglires Chinchilla lanigera
chiLan134839 \n 400 punctate agouti euarchontoglires Dasyprocta punctata 34846 \n 401 pacarana euarchontoglires Dinomys branickii
GCA_004027595.1_DinBra_v1_BIUU108858 \n 402 fat dormouse euarchontoglires Glis glis
GCA_004027185.1_GliGli_v1_BIUU41261 \n 403 northern gundi euarchontoglires Ctenodactylus gundi
GCA_004027205.1_CteGun_v1_BIUU10166 \n 404 naked mole-rat euarchontoglires Heterocephalus glaber
GCA_000247695.1_HetGla_female_1.010181 \n 405 Patagonian cavy euarchontoglires Dolichotis patagonum
GCA_004027295.1_DolPat_v1_BIUU29091 \n 406 capybara euarchontoglires Hydrochoerus hydrochaeris
GCA_004027455.1_HydHyd_v1_BIUU10149 \n 407 Montane guinea pig euarchontoglires Cavia tschudii
GCA_004027695.1_CavTsc_v1_BIUU143287 \n 408 domestic guinea pig euarchontoglires Cavia porcellus
GCA_000151735.1_Cavpor3.010141 \n 409 degu euarchontoglires Octodon degus
GCA_000260255.1_OctDeg1.010160 \n 410 lowland paca euarchontoglires Cuniculus paca 108852 \n 411 social tuco-tuco euarchontoglires Ctenomys sociabilis
GCA_004027165.1_CteSoc_v1_BIUU43321 \n 412 Damara mole-rat euarchontoglires Fukomys damarensis
fukDam1885580 \n 413 woodland dormouse euarchontoglires Graphiurus murinus 51346 \n 414 Desmarest's hutia euarchontoglires Capromys pilorides
GCA_004027915.1_CapPil_v1_BIUU34842 \n 415 Upper Galilee mountains blind mole rat euarchontoglires Nannospalax galili
GCA_000622305.1_S.galili_v1.01026970 \n 416 nutria euarchontoglires Myocastor coypus
GCA_004027025.1_MyoCoy_v1_BIUU10157 \n 417 hazel dormouse euarchontoglires Muscardinus avellanarius
GCA_004027005.1_MusAve_v1_BIUU39082 \n 418 dassie-rat euarchontoglires Petromus typicus
GCA_004026965.1_PetTyp_v1_BIUU10183 \n 419 greater cane rat euarchontoglires Thryonomys swinderianus
GCA_004025085.1_ThrSwi_v1_BIUU10169 \n 420 snowshoe hare euarchontoglires Lepus americanus
GCA_004026855.1_LepAme_v1_BIUU48086 \n 421 Gambian giant pouched rat euarchontoglires Cricetomys gambianus
GCA_004027575.1_CriGam_v1_BIUU10085 \n 422 Prairie deer mouse euarchontoglires Peromyscus maniculatus
GCF_000500345.1_Pman_1.010042 \n 423 southern grasshopper mouse euarchontoglires Onychomys torridus
GCA_004026725.1_OnyTor_v1_BIUU38674 \n 424 rabbit euarchontoglires Oryctolagus cuniculus
GCA_000003625.1_OryCun2.09986 \n 425 muskrat euarchontoglires Ondatra zibethicus
GCA_004026605.1_OndZib_v1_BIUU10060 \n 426 northern mole vole euarchontoglires Ellobius talpinus
GCA_001685095.1_ETalpinus_0.1329620 \n 427 Mongolian gerbil euarchontoglires Meriones unguiculatus
GCA_004026785.1_MerUng_v1_BIUU10047 \n 428 fat sand rat euarchontoglires Psammomys obesus
GCA_002215935.1_ASM221593v148139 \n 429 house mouse euarchontoglires Mus musculus
mm1010090 \n 430 Chinese hamster euarchontoglires Cricetulus griseus
GCA_900186095.1_CHOK1S_HZDv110029 \n 431 Norway rat euarchontoglires Rattus norvegicus
GCF_000001895.5_Rnor_6.010116 \n 432 western wild mouse euarchontoglires Mus spretus
GCA_001624865.1_SPRET_EiJ_v110096 \n 433 meadow jumping mouse euarchontoglires Zapus hudsonius
GCA_004024765.1_ZapHud_v1_BIUU160400 \n 434 prairie vole euarchontoglires Microtus ochrogaster
micOch179684 \n 435 Ryukyu mouse euarchontoglires Mus caroli
GCA_900094665.2_CAROLI_EIJ_v1.110089 \n 436 Egyptian spiny mouse euarchontoglires Acomys cahirinus
GCA_004027535.1_AcoCah_v1_BIUU10068 \n 437 Gobi jerboa euarchontoglires Allactaga bullata (Orientallactaga bullata)
GCA_004027895.1_AllBul_v1_BIUU1041416 \n 438 shrew mouse euarchontoglires Mus pahari
GCF_900095145.1_PAHARI_EIJ_v1.110093 \n 439 Transcaucasian mole vole euarchontoglires Ellobius lutescens
GCA_001685075.1_ASM168507v139086 \n 440 hispid cotton rat euarchontoglires Sigmodon hispidus
GCA_004025045.1_SigHis_v1_BIUU42415 \n 441 lesser Egyptian jerboa euarchontoglires Jaculus jaculus
GCA_000280705.1_JacJac1.051337 \n 442 Brazilian guinea pig euarchontoglires Cavia aperea
cavApe137548 \n 443 golden hamster euarchontoglires Mesocricetus auratus
GCA_000349665.1_MesAur1.010036 \n 444 Stephens's kangaroo rat euarchontoglires Dipodomys stephensi
GCA_004024685.1_DipSte_v1_BIUU323379 \n 445 American pika euarchontoglires Ochotona princeps
GCA_000292845.1_OchPri3.09978 \n 446 Ord's kangaroo rat euarchontoglires Dipodomys ordii
dipOrd210020 \n 447 little pocket mouse euarchontoglires Perognathus longimembris 38669
\nTable 1. Genome assemblies included in the 447-way Conservation track.\n
\nPollard KS, Hubisz MJ, Rosenbloom KR, Siepel A.\n\nDetection of nonneutral substitution rates on mammalian phylogenies.\nGenome Res. 2010 Jan;20(1):110-21.\nPMID: 19858363;\nPMC: PMC2798823\n
\n\nKuderna LFK, Ulirsch JC, Rashid S, Ameen M, Sundaram L, Hickey G, Cox AJ, Gao H, Kumar A, Aguet F\net al.\n\nIdentification of constrained sequence elements across 239 primate genomes.\nNature. 2023 Nov 29;.\nDOI: 10.1038/s41586-023-06798-8; PMID:
\nKuderna LFK, Gao H, Janiak MC, Kuhlwilm M, Orkin JD, Bataillon T, Manu S, Valenzuela A, Bergman J,\nRousselle M et al.\n\nA global catalog of whole-genome diversity from 233 primate species.\nScience. 2023 Jun 2;380(6648):906-913.\nDOI: 10.1126/science.abn7829;\nPMID: 37262161\n
\n\nZoonomia Consortium.\n\nA comparative genomics multitool for scientific discovery and conservation.\nNature. 2020 Nov;587(7833):240-245.\nDOI: 10.1038/s41586-020-2876-6; PMID:
\nFeng S, Stiller J, Deng Y, Armstrong J, Fang Q, Reeve AH, Xie D, Chen G, Guo C, Faircloth BC et\nal.\n\nDense sampling of bird diversity increases power of comparative genomics.\nNature. 2020 Nov;587(7833):252-257.\nDOI: 10.1038/s41586-020-2873-9; PMID:
\nArmstrong J, Hickey G, Diekhans M, Fiddes IT, Novak AM, Deran A, Fang Q, Xie D, Feng S, Stiller J\net al.\n\nProgressive Cactus is a multiple-genome aligner for the thousand-genome era.\nNature. 2020 Nov;587(7833):246-251.\nDOI: 10.1038/s41586-020-2871-y; PMID:
This track collection shows Combined Annotation Dependent Depletion scores.\nCADD is a tool for scoring the deleteriousness of single nucleotide variants as\nwell as insertion/deletion variants in the human genome.
\n\n\nSome mutation annotations\ntend to exploit a single information type (e.g., phastCons or phyloP for\nconservation) and/or are restricted in scope (e.g., to missense changes). Thus,\na broadly applicable metric that objectively weights and integrates diverse\ninformation is needed. Combined Annotation Dependent Depletion (CADD) is a\nframework that integrates multiple annotations into one metric by contrasting\nvariants that survived natural selection with simulated mutations.\n
\n\n\nCADD scores strongly correlate with allelic diversity, pathogenicity of both\ncoding and non-coding variants, experimentally measured regulatory effects,\nand also rank causal variants within individual genome sequences with a higher\nvalue than non-causal variants. \nFinally, CADD scores of complex trait-associated variants from genome-wide\nassociation studies (GWAS) are significantly higher than matched controls and\ncorrelate with study sample size, likely reflecting the increased accuracy of\nlarger GWAS.\n
\n\n\nA CADD score represents a ranking not a prediction, and no threshold is defined\nfor a specific purpose. Higher scores are more likely to be deleterious: \nScores are \n\n
10 * -log of the rank\n\nso that variants with scores above 20 are \npredicted to be among the 1.0% most deleterious possible substitutions in \nthe human genome. We recommend thinking carefully about what threshold is \nappropriate for your application.\n\n\n
\nThere are six subtracks of this track: four for single-nucleotide mutations,\none for each base, showing all possible substitutions, \none for insertions and one for deletions. All subtracks show the CADD Phred\nscore on mouseover. Zooming in shows the exact score on mouseover, same\nbase = score 0.0.
\n\nPHRED-scaled scores are normalized to all potential ~9 billion SNVs, and\nthereby provide an externally comparable unit for analysis. For example, a\nscaled score of 10 or greater indicates a raw score in the top 10% of all\npossible reference genome SNVs, and a score of 20 or greater indicates a raw\nscore in the top 1%, regardless of the details of the annotation set, model\nparameters, etc.\n
\n\nThe four single-nucleotide mutation tracks have a default viewing range of\nscore 10 to 50. As explained in the paragraph above, that results in\nslightly less than 10% of the data displayed. The \ndeletion and insertion tracks have a default filter of 10-100, because they\ndisplay discrete items and not graphical data.\n
\n\n\nSingle nucleotide variants (SNV): For SNVs, at every\ngenome position, there are three values per position, one for every possible\nnucleotide mutation. The fourth value, "no mutation", representing \nthe reference allele, e.g., A to A, is always set to zero.\n
\n\nWhen using this track, zoom in until you can see every base at the\ntop of the display. Otherwise, there are several nucleotides per pixel under \nyour mouse cursor and instead of an actual score, the tooltip text will show\nthe average score of all nucleotides under the cursor. This is indicated by\nthe prefix "~" in the mouseover. Averages of scores are not useful for any\napplication of CADD.\n
\n\nInsertions and deletions: Scores are also shown on mouseover for a\nset of insertions and deletions. On hg38, the set has been obtained from\ngnomAD3. On hg19, the set of indels has been obtained from various sources\n(gnomAD2, ExAC, 1000 Genomes, ESP). If your insertion or deleletion of interest\nis not in the track, you will need to use CADD's\nonline scoring tool\nto obtain them.
\n\n\nCADD scores are freely available for all non-commercial applications from\nthe CADD website.\nFor commercial applications, see\nthe license instructions there.\n
\n\n\nThe CADD data on the UCSC Genome Browser can be explored interactively with the\nTable Browser or the\nData Integrator.\nFor automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed\nfiles that can be downloaded from\nour download server.\nThe files for this track are called a.bw, c.bw, g.bw, t.bw, ins.bb and del.bb. Individual\nregions or the whole genome annotation can be obtained using our tools bigWigToWig\nor bigBedToBed which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tools can also be used to obtain features confined to a given range, e.g.,\n
\nbigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd/a.bw stdout\n
\nor\n
\nbigBedToBed -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd/ins.bb stdout
\nData were converted from the files provided on\nthe CADD Downloads website,\nprovided by the Kircher lab, using\n\ncustom Python scripts, \ndocumented in our \nmakeDoc files.\n
\n\n\nThanks to the CADD development team for providing precomputed data as simple tab-separated files.\n
\n\n\nKircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J.\n\nA general framework for estimating the relative pathogenicity of human genetic variants.\nNat Genet. 2014 Mar;46(3):310-5.\nPMID: 24487276;\nPMC: PMC3992975\n
\n\n\nRentzsch P, Witten D, Cooper GM, Shendure J, Kircher M.\n\nCADD: predicting the deleteriousness of variants throughout the human genome.\nNucleic Acids Res. 2019 Jan 8;47(D1):D886-D894.\nPMID: 30371827;\nPMC: PMC6323892\n
\n", "longLabel": "CADD 1.6 Score: Deletions - label is length of deletion", "mouseOver": "Mutation: $change CADD Phred score: $phred", "parent": "caddSuper", "shortLabel": "CADD 1.6 Del", "track": "caddDel", "type": "bigBed 9 +", "visibility": "dense" } }, "description": "CADD 1.6 Score: Deletions - label is length of deletion", "category": [ "Phenotypes, Variants, and Literature" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-caddDel-LinearBasicDisplay", "mouseover": "jexl:`Mutation: ${get(feature,'change')} CADD Phred score: ${get(feature,'phred')}`", "jexlFilters": [ "get(feature,'gbkey')!='Src'", "get(feature,'score') >= 10" ] } ] }, { "trackId": "hg38-caddIns", "name": "CADD 1.6 - CADD 1.6 Ins", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd/ins.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/cadd/ins.bb", "filter.score": "10:100", "filterByRange.score": "on", "filterLabel.score": "Show only items with PHRED scale score of", "filterLimits.score": "0:100", "html": "This track collection shows Combined Annotation Dependent Depletion scores.\nCADD is a tool for scoring the deleteriousness of single nucleotide variants as\nwell as insertion/deletion variants in the human genome.
\n\n\nSome mutation annotations\ntend to exploit a single information type (e.g., phastCons or phyloP for\nconservation) and/or are restricted in scope (e.g., to missense changes). Thus,\na broadly applicable metric that objectively weights and integrates diverse\ninformation is needed. Combined Annotation Dependent Depletion (CADD) is a\nframework that integrates multiple annotations into one metric by contrasting\nvariants that survived natural selection with simulated mutations.\n
\n\n\nCADD scores strongly correlate with allelic diversity, pathogenicity of both\ncoding and non-coding variants, experimentally measured regulatory effects,\nand also rank causal variants within individual genome sequences with a higher\nvalue than non-causal variants. \nFinally, CADD scores of complex trait-associated variants from genome-wide\nassociation studies (GWAS) are significantly higher than matched controls and\ncorrelate with study sample size, likely reflecting the increased accuracy of\nlarger GWAS.\n
\n\n\nA CADD score represents a ranking not a prediction, and no threshold is defined\nfor a specific purpose. Higher scores are more likely to be deleterious: \nScores are \n\n
10 * -log of the rank\n\nso that variants with scores above 20 are \npredicted to be among the 1.0% most deleterious possible substitutions in \nthe human genome. We recommend thinking carefully about what threshold is \nappropriate for your application.\n\n\n
\nThere are six subtracks of this track: four for single-nucleotide mutations,\none for each base, showing all possible substitutions, \none for insertions and one for deletions. All subtracks show the CADD Phred\nscore on mouseover. Zooming in shows the exact score on mouseover, same\nbase = score 0.0.
\n\nPHRED-scaled scores are normalized to all potential ~9 billion SNVs, and\nthereby provide an externally comparable unit for analysis. For example, a\nscaled score of 10 or greater indicates a raw score in the top 10% of all\npossible reference genome SNVs, and a score of 20 or greater indicates a raw\nscore in the top 1%, regardless of the details of the annotation set, model\nparameters, etc.\n
\n\nThe four single-nucleotide mutation tracks have a default viewing range of\nscore 10 to 50. As explained in the paragraph above, that results in\nslightly less than 10% of the data displayed. The \ndeletion and insertion tracks have a default filter of 10-100, because they\ndisplay discrete items and not graphical data.\n
\n\n\nSingle nucleotide variants (SNV): For SNVs, at every\ngenome position, there are three values per position, one for every possible\nnucleotide mutation. The fourth value, "no mutation", representing \nthe reference allele, e.g., A to A, is always set to zero.\n
\n\nWhen using this track, zoom in until you can see every base at the\ntop of the display. Otherwise, there are several nucleotides per pixel under \nyour mouse cursor and instead of an actual score, the tooltip text will show\nthe average score of all nucleotides under the cursor. This is indicated by\nthe prefix "~" in the mouseover. Averages of scores are not useful for any\napplication of CADD.\n
\n\nInsertions and deletions: Scores are also shown on mouseover for a\nset of insertions and deletions. On hg38, the set has been obtained from\ngnomAD3. On hg19, the set of indels has been obtained from various sources\n(gnomAD2, ExAC, 1000 Genomes, ESP). If your insertion or deleletion of interest\nis not in the track, you will need to use CADD's\nonline scoring tool\nto obtain them.
\n\n\nCADD scores are freely available for all non-commercial applications from\nthe CADD website.\nFor commercial applications, see\nthe license instructions there.\n
\n\n\nThe CADD data on the UCSC Genome Browser can be explored interactively with the\nTable Browser or the\nData Integrator.\nFor automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed\nfiles that can be downloaded from\nour download server.\nThe files for this track are called a.bw, c.bw, g.bw, t.bw, ins.bb and del.bb. Individual\nregions or the whole genome annotation can be obtained using our tools bigWigToWig\nor bigBedToBed which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tools can also be used to obtain features confined to a given range, e.g.,\n
\nbigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd/a.bw stdout\n
\nor\n
\nbigBedToBed -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd/ins.bb stdout
\nData were converted from the files provided on\nthe CADD Downloads website,\nprovided by the Kircher lab, using\n\ncustom Python scripts, \ndocumented in our \nmakeDoc files.\n
\n\n\nThanks to the CADD development team for providing precomputed data as simple tab-separated files.\n
\n\n\nKircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J.\n\nA general framework for estimating the relative pathogenicity of human genetic variants.\nNat Genet. 2014 Mar;46(3):310-5.\nPMID: 24487276;\nPMC: PMC3992975\n
\n\n\nRentzsch P, Witten D, Cooper GM, Shendure J, Kircher M.\n\nCADD: predicting the deleteriousness of variants throughout the human genome.\nNucleic Acids Res. 2019 Jan 8;47(D1):D886-D894.\nPMID: 30371827;\nPMC: PMC6323892\n
\n", "longLabel": "CADD 1.6 Score: Insertions - label is length of insertion", "mouseOver": "Mutation: $change CADD Phred score: $phred", "parent": "caddSuper", "shortLabel": "CADD 1.6 Ins", "track": "caddIns", "type": "bigBed 9 +", "visibility": "dense" } }, "description": "CADD 1.6 Score: Insertions - label is length of insertion", "category": [ "Phenotypes, Variants, and Literature" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-caddIns-LinearBasicDisplay", "mouseover": "jexl:`Mutation: ${get(feature,'change')} CADD Phred score: ${get(feature,'phred')}`", "jexlFilters": [ "get(feature,'gbkey')!='Src'", "get(feature,'score') >= 10" ] } ] }, { "trackId": "hg38-cadd1_7_Del", "name": "CADD 1.7 - CADD 1.7 Del", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd1.7/del.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/cadd1.7/del.bb", "filter.score": "10:100", "filterByRange.score": "on", "filterLabel.score": "Show only items with PHRED scale score of", "filterLimits.score": "0:100", "html": "This track collection shows Combined Annotation Dependent Depletion scores.\nCADD is a tool for scoring the deleteriousness of single nucleotide variants as\nwell as insertion/deletion variants in the human genome.
\n\n\nSome mutation annotations\ntend to exploit a single information type (e.g., phastCons or phyloP for\nconservation) and/or are restricted in scope (e.g., to missense changes). Thus,\na broadly applicable metric that objectively weights and integrates diverse\ninformation is needed. Combined Annotation Dependent Depletion (CADD) is a\nframework that integrates multiple annotations into one metric by contrasting\nvariants that survived natural selection with simulated mutations.\n
\n\n\nCADD scores strongly correlate with allelic diversity, pathogenicity of both\ncoding and non-coding variants, experimentally measured regulatory effects,\nand also rank causal variants within individual genome sequences with a higher\nvalue than non-causal variants. \nFinally, CADD scores of complex trait-associated variants from genome-wide\nassociation studies (GWAS) are significantly higher than matched controls and\ncorrelate with study sample size, likely reflecting the increased accuracy of\nlarger GWAS.\n
\n\n\nA CADD score represents a ranking not a prediction, and no threshold is defined\nfor a specific purpose. Higher scores are more likely to be deleterious: \nScores are \n\n
10 * -log of the rank\n\nso that variants with scores above 20 are \npredicted to be among the 1.0% most deleterious possible substitutions in \nthe human genome. We recommend thinking carefully about what threshold is \nappropriate for your application.\n\n\n
\nThere are six subtracks of this track: four for single-nucleotide mutations,\none for each base, showing all possible substitutions, \none for insertions and one for deletions. All subtracks show the CADD Phred\nscore on mouseover. Zooming in shows the exact score on mouseover, same\nbase = score 0.0.
\n\nPHRED-scaled scores are normalized to all potential ~9 billion SNVs, and\nthereby provide an externally comparable unit for analysis. For example, a\nscaled score of 10 or greater indicates a raw score in the top 10% of all\npossible reference genome SNVs, and a score of 20 or greater indicates a raw\nscore in the top 1%, regardless of the details of the annotation set, model\nparameters, etc.\n
\n\nThe four single-nucleotide mutation tracks have a default viewing range of\nscore 10 to 50. As explained in the paragraph above, that results in\nslightly less than 10% of the data displayed. The \ndeletion and insertion tracks have a default filter of 10-100, because they\ndisplay discrete items and not graphical data.\n
\n\n\nSingle nucleotide variants (SNV): For SNVs, at every\ngenome position, there are three values per position, one for every possible\nnucleotide mutation. The fourth value, "no mutation", representing \nthe reference allele, e.g., A to A, is always set to zero.\n
\n\nWhen using this track, zoom in until you can see every base at the\ntop of the display. Otherwise, there are several nucleotides per pixel under \nyour mouse cursor and instead of an actual score, the tooltip text will show\nthe average score of all nucleotides under the cursor. This is indicated by\nthe prefix "~" in the mouseover. Averages of scores are not useful for any\napplication of CADD.\n
\n\nInsertions and deletions: Scores are also shown on mouseover for a\nset of insertions and deletions. On hg38, the set has been obtained from\ngnomAD3. On hg19, the set of indels has been obtained from various sources\n(gnomAD2, ExAC, 1000 Genomes, ESP). If your insertion or deleletion of interest\nis not in the track, you will need to use CADD's\nonline scoring tool\nto obtain them.
\n\nTrack colors
\n\nThis track is colored according to Table 2 in Vikas et al. The colors represent the recommended ACMG/AMP score cutoffs. \n\n
| Range | \nClassification | \n
|---|---|
| ≥ 25.3 | \nPathogenic | \n
| 25.2 - 22.6 | \nNeutral | \n
| ≤ 22.7 | \nBenign | \n
\nIn CADD version 1.7, new features have been added to improve CADD scores for certain variant\neffects, boosting the overall performance of CADD and bringing new developments to the community.\nCADD v1.7 integrates annotations from recent efforts to assess variant effects, along with new\nconservation and mutation scores.
\n\nCADD v1.7 supports only the major chromosomes of the hg38/GRCh38 reference genome (chromosomes 1-22,\nX, and Y) and may be the last version to support the hg19/GRCh37 human reference genome.
\n\nThis version includes scores derived from Evolutionary Scale Modeling (ESM) for assessing variants\nin protein-coding regions, along with scores from a convolutional neural network (CNN) trained on\nopen chromatin sequences, used as a proxy for regulatory regions in the genome. The previously\nincluded conservation scores have been updated with data from the Zoonomia project. New annotations\nhave also been added for 3' Untranslated Regions (3' UTRs), along with models of genome-wide\nmutational rates. The gene and transcript models have been updated by advancing from Ensembl version\n95 to version 110, and the Ensembl Variant Effect Predictor (VEP) has been upgraded accordingly.
\n\nThe models in CADD v1.7 have been trained similarly to the version 1.6 release. The logistic\nregression uses an L2 penalty with C = 1, and training was completed after thirteen L-BFGS\niterations using the sklearn library The new models exhibit a high degree of similarity to the\nprevious release, with a Spearman correlation of 0.946 for CADD scores calculated for 100,000\nrandomly selected variants between CADD GRCh38-v1.6 and CADD GRCh38-v1.7. The v1.7 models perform\ncomparably to earlier versions in distinguishing known pathogenic variants (ClinVar) from common\nvariants (gnomAD) across the genome. Improvements in CADD v1.7 are particularly evident when\nfocusing on specific variant categories, such as missense or 3' UTR variants, where the latest\nrelease includes updated annotations.
\n\nMore information can be found at the\nCADD site\nand the Schubach et al., Nucleic Acids Res, 2024 publication.\n\n\nData were converted from the files provided on\nthe CADD Downloads website,\nprovided by the Kircher lab, using\n\ncustom Python scripts,\ndocumented in our \nmakeDoc files.\n
\n\n\n\nCADD scores are freely available for all non-commercial applications from\nthe CADD website.\nFor commercial applications, see\nthe license instructions there.\n
\n\n\nThe CADD data on the UCSC Genome Browser can be explored interactively with the\nTable Browser or the\nData Integrator.\nFor automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed\nfiles that can be downloaded from\nour download server.\nThe files for this track are called a.bw, c.bw, g.bw, t.bw, ins.bb and del.bb. Individual\nregions or the whole genome annotation can be obtained using our tools bigWigToWig\nor bigBedToBed which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tools can also be used to obtain features confined to a given range, e.g.,\n
\nbigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd1.7/a.bw stdout\n
\nor\n
\nbigBedToBed -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd1.7/ins.bb stdout
\nThanks to the CADD development team for providing precomputed data as simple tab-separated files.\n
\n\n\nKircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J.\n\nA general framework for estimating the relative pathogenicity of human genetic variants.\nNat Genet. 2014 Mar;46(3):310-5.\nPMID: 24487276;\nPMC: PMC3992975\n
\n\n\nRentzsch P, Witten D, Cooper GM, Shendure J, Kircher M.\n\nCADD: predicting the deleteriousness of variants throughout the human genome.\nNucleic Acids Res. 2019 Jan 8;47(D1):D886-D894.\nPMID: 30371827;\nPMC: PMC6323892\n
\n\n\nSchubach M, Maass T, Nazaretyan L, Röner S, Kircher M.\n\nCADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to\nimprove genome-wide variant predictions.\nNucleic Acids Res. 2024 Jan 5;52(D1):D1143-D1154.\nPMID: 38183205; PMC:
This track collection shows Combined Annotation Dependent Depletion scores.\nCADD is a tool for scoring the deleteriousness of single nucleotide variants as\nwell as insertion/deletion variants in the human genome.
\n\n\nSome mutation annotations\ntend to exploit a single information type (e.g., phastCons or phyloP for\nconservation) and/or are restricted in scope (e.g., to missense changes). Thus,\na broadly applicable metric that objectively weights and integrates diverse\ninformation is needed. Combined Annotation Dependent Depletion (CADD) is a\nframework that integrates multiple annotations into one metric by contrasting\nvariants that survived natural selection with simulated mutations.\n
\n\n\nCADD scores strongly correlate with allelic diversity, pathogenicity of both\ncoding and non-coding variants, experimentally measured regulatory effects,\nand also rank causal variants within individual genome sequences with a higher\nvalue than non-causal variants. \nFinally, CADD scores of complex trait-associated variants from genome-wide\nassociation studies (GWAS) are significantly higher than matched controls and\ncorrelate with study sample size, likely reflecting the increased accuracy of\nlarger GWAS.\n
\n\n\nA CADD score represents a ranking not a prediction, and no threshold is defined\nfor a specific purpose. Higher scores are more likely to be deleterious: \nScores are \n\n
10 * -log of the rank\n\nso that variants with scores above 20 are \npredicted to be among the 1.0% most deleterious possible substitutions in \nthe human genome. We recommend thinking carefully about what threshold is \nappropriate for your application.\n\n\n
\nThere are six subtracks of this track: four for single-nucleotide mutations,\none for each base, showing all possible substitutions, \none for insertions and one for deletions. All subtracks show the CADD Phred\nscore on mouseover. Zooming in shows the exact score on mouseover, same\nbase = score 0.0.
\n\nPHRED-scaled scores are normalized to all potential ~9 billion SNVs, and\nthereby provide an externally comparable unit for analysis. For example, a\nscaled score of 10 or greater indicates a raw score in the top 10% of all\npossible reference genome SNVs, and a score of 20 or greater indicates a raw\nscore in the top 1%, regardless of the details of the annotation set, model\nparameters, etc.\n
\n\nThe four single-nucleotide mutation tracks have a default viewing range of\nscore 10 to 50. As explained in the paragraph above, that results in\nslightly less than 10% of the data displayed. The \ndeletion and insertion tracks have a default filter of 10-100, because they\ndisplay discrete items and not graphical data.\n
\n\n\nSingle nucleotide variants (SNV): For SNVs, at every\ngenome position, there are three values per position, one for every possible\nnucleotide mutation. The fourth value, "no mutation", representing \nthe reference allele, e.g., A to A, is always set to zero.\n
\n\nWhen using this track, zoom in until you can see every base at the\ntop of the display. Otherwise, there are several nucleotides per pixel under \nyour mouse cursor and instead of an actual score, the tooltip text will show\nthe average score of all nucleotides under the cursor. This is indicated by\nthe prefix "~" in the mouseover. Averages of scores are not useful for any\napplication of CADD.\n
\n\nInsertions and deletions: Scores are also shown on mouseover for a\nset of insertions and deletions. On hg38, the set has been obtained from\ngnomAD3. On hg19, the set of indels has been obtained from various sources\n(gnomAD2, ExAC, 1000 Genomes, ESP). If your insertion or deleletion of interest\nis not in the track, you will need to use CADD's\nonline scoring tool\nto obtain them.
\n\nTrack colors
\n\nThis track is colored according to Table 2 in Vikas et al. The colors represent the recommended ACMG/AMP score cutoffs. \n\n
| Range | \nClassification | \n
|---|---|
| ≥ 25.3 | \nPathogenic | \n
| 25.2 - 22.6 | \nNeutral | \n
| ≤ 22.7 | \nBenign | \n
\nIn CADD version 1.7, new features have been added to improve CADD scores for certain variant\neffects, boosting the overall performance of CADD and bringing new developments to the community.\nCADD v1.7 integrates annotations from recent efforts to assess variant effects, along with new\nconservation and mutation scores.
\n\nCADD v1.7 supports only the major chromosomes of the hg38/GRCh38 reference genome (chromosomes 1-22,\nX, and Y) and may be the last version to support the hg19/GRCh37 human reference genome.
\n\nThis version includes scores derived from Evolutionary Scale Modeling (ESM) for assessing variants\nin protein-coding regions, along with scores from a convolutional neural network (CNN) trained on\nopen chromatin sequences, used as a proxy for regulatory regions in the genome. The previously\nincluded conservation scores have been updated with data from the Zoonomia project. New annotations\nhave also been added for 3' Untranslated Regions (3' UTRs), along with models of genome-wide\nmutational rates. The gene and transcript models have been updated by advancing from Ensembl version\n95 to version 110, and the Ensembl Variant Effect Predictor (VEP) has been upgraded accordingly.
\n\nThe models in CADD v1.7 have been trained similarly to the version 1.6 release. The logistic\nregression uses an L2 penalty with C = 1, and training was completed after thirteen L-BFGS\niterations using the sklearn library The new models exhibit a high degree of similarity to the\nprevious release, with a Spearman correlation of 0.946 for CADD scores calculated for 100,000\nrandomly selected variants between CADD GRCh38-v1.6 and CADD GRCh38-v1.7. The v1.7 models perform\ncomparably to earlier versions in distinguishing known pathogenic variants (ClinVar) from common\nvariants (gnomAD) across the genome. Improvements in CADD v1.7 are particularly evident when\nfocusing on specific variant categories, such as missense or 3' UTR variants, where the latest\nrelease includes updated annotations.
\n\nMore information can be found at the\nCADD site\nand the Schubach et al., Nucleic Acids Res, 2024 publication.\n\n\nData were converted from the files provided on\nthe CADD Downloads website,\nprovided by the Kircher lab, using\n\ncustom Python scripts,\ndocumented in our \nmakeDoc files.\n
\n\n\n\nCADD scores are freely available for all non-commercial applications from\nthe CADD website.\nFor commercial applications, see\nthe license instructions there.\n
\n\n\nThe CADD data on the UCSC Genome Browser can be explored interactively with the\nTable Browser or the\nData Integrator.\nFor automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed\nfiles that can be downloaded from\nour download server.\nThe files for this track are called a.bw, c.bw, g.bw, t.bw, ins.bb and del.bb. Individual\nregions or the whole genome annotation can be obtained using our tools bigWigToWig\nor bigBedToBed which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tools can also be used to obtain features confined to a given range, e.g.,\n
\nbigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd1.7/a.bw stdout\n
\nor\n
\nbigBedToBed -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/cadd1.7/ins.bb stdout
\nThanks to the CADD development team for providing precomputed data as simple tab-separated files.\n
\n\n\nKircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J.\n\nA general framework for estimating the relative pathogenicity of human genetic variants.\nNat Genet. 2014 Mar;46(3):310-5.\nPMID: 24487276;\nPMC: PMC3992975\n
\n\n\nRentzsch P, Witten D, Cooper GM, Shendure J, Kircher M.\n\nCADD: predicting the deleteriousness of variants throughout the human genome.\nNucleic Acids Res. 2019 Jan 8;47(D1):D886-D894.\nPMID: 30371827;\nPMC: PMC6323892\n
\n\n\nSchubach M, Maass T, Nazaretyan L, Röner S, Kircher M.\n\nCADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to\nimprove genome-wide variant predictions.\nNucleic Acids Res. 2024 Jan 5;52(D1):D1143-D1154.\nPMID: 38183205; PMC:
\nThis track shows genomic locations for variants in the\nCIViC (Clinical\nInterpretation of Variants in Cancer) database. These clinically\nrelevant variant interpretations are expert and crowd-sourced from\npeer-reviewed literature, clinical trials, and some conference\nabstracts.\n
\n\n\nEach variant's interpretation is in the context of a broader molecular\nprofile: one or more variants grouped together. For example, clinical\nevidence may be relevant to a KRAS G12 mutation on its own, but other\nclinical evidence may relevant for cases with either a mutation in\nKRAS G12 or G13.\n
\n\n\nThe primary points of data from the scientific literature are curated\nas Clinical Evidence, which connects to a molecular profile, which in\nturn connects to the variants shown in this track. Groups of evidence\ncan become curator Assertions about the relevence of a molecular\nprofile.\n
\n\n\nThe detail for a feature will list diseases and therapies that have\nbeen associated with a genomic variant. Visiting the CIViC page for a\nvariant will allow browsing the Molecular Profiles associated with\nthat variant, and in turn each Molecular Profile shows the Clinical\nEvidence and Assertions for various diseases and therapies.\n
\n\n\n\nThere are three types of variant feature types in CIViC: gene, fusion, and\nfactor, of which only the gene and fusion fetaures have a genomic location.\n
\n\n\nGene variants are shown as a single item, with a name indicating the\nvariant's mode: sequence change, gene expression, gene deletion,\netc.\n
\n\n\nFusion variants connect two genes via a structural DNA rearrangement,\ntypically in the introns or promotors of genes. For CIViC fusions that\nhave an annotated transcript and exon, the exon will be shown as a\nthick bar. If there is an intron associated with the fusion, it will\nbe annotated as a thin bar on the feature.\n
\n\n\nThis track reflects the monthly data summaries published by CIViC. The\nlatest information is always available directly on the CIViC website\nor by its API.\n
\n\n\nThe raw data can be explored interactively with the Table Browser\nor the Data Integrator. The data can be\naccessed from scripts through our API, via the track name\n"civic".\n
\n\n\nThe monthly CIViC Variant Summaries were reformatted at UCSC\nto bigBed format. The\ndata is updated every month, the week after CIViC data summary\nrelease. The diseases and therapies associated with a variant are\ncollected from the corresponding TSV files from CIViC, using the\nmolecular profile summaries as a mapping.\n
\n\n\nThanks to the CIViC contributors and organizers for curating the\ndatabase and making the data available for download.\n
\n\n\nGriffith M, Spies NC, Krysiak K, McMichael JF, Coffman AC, Danos AM, Ainscough BJ, Ramirez CA,\nRieke DT, Kujan L et al. CIViC is a community knowledgebase for expert crowdsourcing the clinical\ninterpretation of variants in cancer. Nat Genet. 2017Jan31;49(2):170-174. PMID:\n28138153; PMC:\nPMC5367263\n
\n\n" } }, "description": "CIViC - Expert & crowd-sourced cancer variant interpretation", "category": [ "Phenotypes, Variants, and Literature" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-civic-LinearBasicDisplay", "mouseover": "jexl:get(feature,'mouseOverHTML')" } ] }, { "trackId": "hg38-clinGenCspec", "name": "ClinGen - ClinGen VCEP Specifications", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/clinGen/clinGenCspec.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/bbi/clinGen/clinGenCspec.bb", "longLabel": "Clingen CSpec Variant Interpretation VCEP Specifications", "mouseOver": "Disease: $diseaseNOTE:
VarChat is an open platform \npowered by enGenome, and registration is free of charge. VarChat is intended for research\nuse and may provide inaccurate answers. It is advisable to verify critical information independently.
\nVarChat is an open platform that leverages \nthe power of generative artificial intelligence to support the genomic variant interpretation process \nby searching the available scientific literature for each variant and condensing it into a brief\nyet informative text. Each query quickly scans the latest scientific literature to provide\nup-to-date variant information.\n
\n\n\nVarChat is a generative AI-based system and each answer is generated live, so you may obtain slightly \ndifferent answers at each iteration. A literature search will be performed and the total number of \nidentified publications will be shown. Only a subset of them will be reported and used to generate your answer.\n
\n\n\nIf you would like to stay updated on the latest developments, you may register for updates on the\nVarChat website. For data questions, VarChat\ncan be contacted at varchat@engenome.com.
\n\n\nGenomic locations of variants are labeled with the nucleotide change.\nMousing over the items will show how many papers the variant was observed in, its gene,\nits HGVS nomenclature, and dbSNP rsID.\nClicking on any item will provide a link directly to VarChat with additional information.
\n\n\nThe items are colored based on the amount of literature support as described on the table below:\n
\n\n\n
| Color | \nLevel of literature support | \n
|---|---|
| \n | High: at least 25 papers mention the variant | \n
| \n | Medium: between 10 and 24 papers mention the variant | \n
| \n | Low: fewer than 10 papers mention the variant | \n
\nVarChat software is powered by enGenome.
\nenGenome, an accredited spin-off from the University of Pavia founded in 2016, combines bioinformatics, biotechnology, and software development expertise to enhance genetic disease diagnosis and treatment through advanced AI and bioinformatics tools, supported by a multidisciplinary team of engineers, biotechnologists, and developers.\n
\nFor every queried variant, VarChat produces concise and coherent summaries through an LLM model.\nRelevant references are identified through a modified BM25 ranking algorithm. More weight is\ngiven to papers that cite the variant in the abstract and were published in the last two years,\nwhile papers that report the variant only in the supplementary are penalized.
\n\n\nThe raw data can be explored interactively with the Table Browser\nor the Data Integrator. The data can be accessed from scripts through our\nAPI, the track name is "varChat".
\n\n\nFor automated download and analysis, the genome annotation is stored in a bigBed file that\ncan be downloaded from\nour download server.\nThe file for this track is called varChat.bb. Individual\nregions or the whole genome annotation can be obtained using our tool bigBedToBed\nwhich can be compiled from the source code or downloaded as a precompiled\nbinary for your system.
\n\nInstructions for downloading source code and binaries can be found\nhere.\nThe tool\ncan also be used to obtain only features within a given range, e.g.\n
\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/varChat.bb -chrom=chr21 -start=0 -end=10000000 stdout
\nDe Paoli F, Berardelli S, Limongelli I, Rizzo E, Zucca S.
\nThis track displays detailed, evidence-based gene-disease models, curated from the literature by\nexperts. The track can be used to filter genomic sequencing data from individuals with genetic\ndisorders to identify likely causative variants and accelerate diagnosis. More information about\nthe G2P project can be found on the\nGene2Phenotype\nwebsite.\n
\n\n\nFor each track, items are colored according to the likelihood that the gene-disease\nassociation is true:
\nEach mouseover tooltip provides the following information:
\n\nExpert-curated gene disease models released by the Gene2Phenotype project were imported and\nprocessed to create a BED-based track annotating genomic regions reported to be associated with\ndisease in the literature. Standard genome assembly coordinates and gene annotations were used to\nmap entries to the browser.\n
\n\n\nFor more information on the Gene2Phenotype project, please contact \n\ng2p-help@ebi.\nac.\nuk\n\n
\n\n\nSource data for these tracks are available directly from\nGene2Phenotype. \n
\n\n\nThormann A, Halachev M, McLaren W, Moore DJ, Svinti V, Campbell A, Kerr SM, Tischkowitz M, Hunt SE,\nDunlop MG et al.\n\nFlexible and scalable diagnostic filtering of genomic variants using G2P with Ensembl VEP.\nNat Commun. 2019 May 30;10(1):2373.\nDOI: 10.1038/s41467-019-10016-3; PMID:
\nYates TM, Ansari M, Thompson L, Hunt SE, Uhalte EC, Hobson RJ, Marsh JA, Wright CF, Firth HV.\n\nCurating genomic disease-gene relationships with Gene2Phenotype (G2P).\nGenome Med. 2024 Nov 6;16(1):127.\nDOI: 10.1186/s13073-024-01398-1; PMID:
\nThe GC percent track shows the percentage of G (guanine) and C (cytosine) bases\nin 5-base windows. High GC content is typically associated with\ngene-rich areas.\n
\n\nThis track may be configured in a variety of ways to highlight different\napsects of the displayed information. Click the\n"Graph configuration help"\nlink for an explanation of the configuration options.\n\n
The data and presentation of this graph were prepared by\nHiram Clawson.\n
\n\n", "longLabel": "GC Percent in 5-Base Windows", "maxHeightPixels": "128:36:16", "shortLabel": "GC Percent", "track": "gc5BaseBw", "type": "bigWig 0 100", "viewLimits": "30:70", "visibility": "hide", "windowingFunction": "Mean" } }, "description": "GC Percent in 5-Base Windows", "category": [ "Mapping and Sequencing" ] }, { "trackId": "hg38-genCC", "name": "GenCC", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/genCC.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/bbi/genCC.bb", "filterLabel.classification_title": "evidence classification", "filterValues.classification_title": "Supportive,Strong,Definitive,Limited,Moderate,No Known Disease Relationship,Disputed Evidence,Refuted Evidence", "group": "phenDis", "itemRgb": "on", "longLabel": "GenCC: The Gene Curation Coalition Annotations", "mouseOver": "Disease title: $disease_title\nThis track shows annotations from The Gene Curation Coalition (GenCC).\nThe GenCC provides information pertaining to the validity of gene-disease relationships, \nwith a current focus on Mendelian diseases. Curated gene-disease relationships are submitted \nby GenCC member organizations that currently provide online resources (e.g. ClinGen, DECIPHER, \nOrphanet, etc.), as well as diagnostic laboratories that have committed to sharing their internal \ncurated gene-level knowledge (e.g. Ambry Genetics, Illumina, Invitae, etc.).
\n\nThe GenCC aims to clarify overlap between gene curation efforts and develop\nconsistent terminology for validity, allelic requirement and mechanism\nof disease. Each item on this track corresponds with a gene, and contains\na large number of information such as associated disease, evidence classification,\nspecific submission notes and identifiers from different databases. In cases where\nmultiple annotations exist for the same gene, multiple items are displayed.
\n\n\nEach item displayed represents a submission to the GenCC database. The displayed \nname is a combination of the gene symbol and the disease's original submission ID. \nThis submission ID is either the OMIM#, MONDO# or Orphanet#. Clicking\non any item will display the complete meta data for that item, including\nlinkouts to the GenCC, NCBI, Ensembl, HGNC, GeneCards, Pombase (MONDO),\nand Human Phenotype Ontology (HPO). Mousing over any item will display the\nassociated disease title, the classification title, and the mode of inheritance\ntitle.
\n\n\nItems are colored based on the GenCC classification, or validation, of the\nevidence in the color scheme seen in the table below. \nFor more information on this process, see the GenCC\nvalidity terms FAQ. A filter for the track is also available\nto display a subset of the items based on their classification.
\n\n\n
| Color | \nEvidence classification | \n
|---|---|
| Definitive | |
| Strong | |
| Moderate | |
| Supportive | |
| Limited | |
| Disputed Evidence | |
| Refuted Evidence | |
| No Known Disease Relationship |
\nLimitations: Most entries include both NM_ accessions as well as ENST and ENSG identifiers.\nFrom the original file, which contains no coordinates, two genes were not mapped\nto the hg38 genome, SLCO1B7 and ATXN8. This means that the hg38 track has 2 fewer items\nthan what can be found in the GenCC download file. For hg19, one additional\ngene was not mapped, KCNJ18. In addition to this, the GenCC data in the Genome\nBrowser does not include OMIM data due to licensing restrictions. For more\ninformation, see the Methods section below.
\n\n\nThe source data can be explored in \nGenCC database. The source files can also be found on the GenCC downloads page.
\n\n\nThe GenCC data on the UCSC Genome Browser can be explored interactively with the\nTable Browser or the\nData Integrator.\nFor automated download and analysis, the genome annotation is stored at UCSC in bigBed\nfiles that can be downloaded from\nour download server.\nThe data may also be explored interactively using our\nREST API.
\n\n\nThe file for this track may also be locally explored using our tools bigBedToBed \nwhich can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tools can also be used to obtain features confined to a given range, e.g.,\n
\nbigBedToBed -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/genCC.bb stdout
\nThe data were downloaded from the GenCC downloads page in tsv format. Manual\ncuration was performed on the file to remove newline characters and tab characters present in \nthe submission notes, in total fewer than 20 manual edits were made.
\n\nThe track was first built on hg38 by associating the gene symbols with the NCBI MANE 1.0 \nrelease transcripts. These coordinates were added to the items as well as the NM_ accession,\nENST ID and ENSG ID. For items where there was no gene symbol match in MANE (~130), the gene\nsymbols were queried against GENCODEv40 comprehensive set release. In places where multiple\ntranscript matches were found, the earliest transcription start and latest end site was used\nfrom among the transcripts to encompass the entire gene coordinates. Two genes were not able\nto be mapped for hg38, SLCO1B7 and ATXN8, resulting in two missing submissions in the Genome\nBrowser when compared to the raw file. Lastly, the items were colored according to their\nevidence classification as seen on the GenCC database.
\n\nFor hg19, the hg38 NM_ accessions were used to convert the item coordinates according to the\nlatest hg19 refseq release. For items that failed to convert, the gene symbols were queried\nusing the GENCODEv40 hg19 lift comprehensive set. One additional gene symbol failed to map in\nhg19, KCNJ18, leading to 3 fewer items on this track when compared to the raw file.
\n\nFor both assemblies, GenCC OMIM data is excluded do to data restrictions.\nFor complete documentation of the processing of these tracks, read the\n\nGenCC MakeDoc.
\n\n\nThanks to the entire GenCC\ncommittee for creating these annotations and making them available.
\n\n\nDiStefano MT, Goehringer S, Babb L, Alkuraya FS, Amberger J, Amin M, Austin-Tse C, Balzotti M, Berg\nJS, Birney E et al.\n\nThe Gene Curation Coalition: A global effort to harmonize gene-disease evidence resources.\nGenet Med. 2022 May 4;.\nPMID: 35507016\n
\n" } }, "description": "GenCC: The Gene Curation Coalition Annotations", "category": [ "Phenotypes, Variants, and Literature" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-genCC-LinearBasicDisplay", "mouseover": "jexl:`Disease title: ${get(feature,'disease_title')}\nThe three Gencode ncORF tracks in the non-canonical ORF track container show \nnon-canonical translated open reading frames (ncORFs) identified\nfrom ribosome profiling (Ribo-seq) data and mapped to the GENCODE annotation by the\nGENCODE / TransCODE consortium.\nThe data is available in two phases:\n
\n\n\nThe Phase I catalog contains 7,264 unique human ncORFs called from Ribo-seq data\nacross seven publications and mapped to GENCODE v35. Only translations of 16 codons or above\nand initiating from ATG start codons were incorporated. Redundant sense-overlapping ORFs were\nmerged. Of these, 3,085 ORFs were found by more than one publication, providing independent\nreplication evidence. This catalog was developed as part of an effort to standardize the\nannotation of translated ORFs across reference databases including Ensembl/GENCODE, HGNC,\nUniProtKB, and PeptideAtlas.\n
\n\n\nThe Phase II catalog nearly quadruples the Phase I set, defining 28,359 ncORFs in the\nComprehensive set, mapped to GENCODE v45. Compared to Phase I, additional published\nRibo-seq datasets were incorporated and the restrictions on ORF size and initiation codon\nwere lifted.\n
\n\n\nTwo subsets are provided for the Phase II data:\n
\n\nAll three GENCODE ncORF tracks are displayed in bigGenePred format and labeled with their\nORF identifier. The default color scheme and available filter controls differ by track.\n
\n\n\nThe Phase I track colors items by Kozak consensus strength by default.\nTwo alternative color schemes can be selected from the track controls page\n(Color by dropdown): Evidence type and HLA class (see below).\n
\n\n| \n | Golden amber — Strong Kozak context. Both position −3 (A/G) and\n position +4 (G) match the consensus. | \n
|---|---|
| \n | Steel blue — Moderate Kozak context. One of the two positions matches. | \n
| \n | Gray — Weak Kozak context. Neither position matches. | \n
| \n | Black — Non-ATG start codon (Kozak rule does not apply) or context\n unavailable. | \n
\nSelect Color by: Evidence type to highlight peptide evidence from\nDeutsch et al. (see References). ORFs with no mass spectrometry evidence are gray.\n
\n\n| \n | Gold — TransCODE peptidein (628 ORFs). Confirmed as\n confidently translated by PeptideAtlas; candidate for peptidein annotation in\n reference databases. | \n
|---|---|
| \n | Steel blue — HLA immunopeptidomics evidence only (1,373 ORFs). | \n
| \n | Forest green — Non-HLA (whole-cell tryptic) evidence only (66 ORFs). | \n
| \n | Orange — Both HLA and non-HLA evidence (35 ORFs). | \n
| \n | Gray — No peptide evidence (5,114 ORFs) or in the peptidein set\n based on binding predictions only with no direct MS sequences (48 ORFs). | \n
\nSelect Color by: HLA class to color items by the HLA class in which peptides were\ndetected:\n
\n\n| \n | Steel blue — Class I only (1,632 ORFs). | \n
|---|---|
| \n | Crimson — Class II only (10 ORFs). | \n
| \n | Orange — Both class I and class II (143 ORFs). | \n
| \n | Gray — No HLA data (5,479 ORFs). | \n
\nThe Phase I track can be filtered by: start codon, Kozak strength, Kozak TE, replicated\nstatus, and — using the peptide evidence fields — peptidein status\n(isPeptidein), HLA class (hlaClass), HLA evidence tier\n(hlaFinalTier), HPP guideline category (hlaHppCategory), and Ribo-seq\nquality (riboseqQuality).\n
\n\n\nMouseover for Phase I shows ORF name, host gene, Kozak strength and TE,\nreplicated status, peptidein flag, HLA evidence tier, and HLA peptide count.\n
\n\n\nThe Phase II Primary and Comprehensive tracks color items by Kozak strength using the\nsame scheme as Phase I. Peptide evidence fields are not included in the Phase II tracks.\nCommon filters: start codon, Kozak strength, Kozak TE.\n
\n\n\nEach Phase I item carries the following peptide evidence fields from Deutsch et al.\n(2026), accessible via the details page and Table Browser:\n
\n\n| Field | Description |
|---|---|
| isPeptidein | yes/no: ORF is in the PeptideAtlas peptidein set (Table S12) |
| hlaClass | HLA class(es) detected: I, II, or Both |
| hlaFinalTier | HLA evidence tier (Tier 1B = numerous peptides; Tier 2B = one peptide) |
| hlaHppCategory | HPP guideline category (HPP+, 1PepCandidate, Insufficient) |
| hlaNPeptides | Number of distinct HLA peptide sequences detected |
| riboseqQuality | Manual quality of Ribo-seq evidence (Excellent/Sufficient/Insufficient) |
| hlaIPeptides | HLA class I peptide sequences (comma-separated) |
| hlaIIPeptides | HLA class II peptide sequences (comma-separated) |
| nonHlaFinalTier | Non-HLA (tryptic proteomics) evidence tier |
| nonHlaHppCategory | Non-HLA HPP guideline category |
| nonHlaNPeptides | Number of distinct non-HLA peptide sequences |
| nonHlaPeptides | Non-HLA tryptic peptide sequences (comma-separated) |
\nThe raw data can be explored interactively with the\nTable Browser or the\nData Integrator. The data can be accessed from\nscripts through our API; the track names are\n"gencNcOrfs" (Phase I), "gencNcOrfsPrimary" (Phase II Primary),\nand "gencNcOrfsComprehensive" (Phase II Comprehensive).\n
\n\n\nFor automated download and analysis, the genome annotations are stored in bigBed files that\ncan be downloaded from\nour download server.\nIndividual regions or the whole genome annotation can be obtained using our tool\nbigBedToBed, which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\n
\n\n\nMudge et al. (2022, see References) consolidated translation evidence from seven published\nribosome profiling datasets that used harringtonine or lactimidomycin treatment to enrich\nfor translation initiation sites. Ribo-seq reads were mapped to the GENCODE v35 annotation\non GRCh38. Only ATG-initiated ORFs of at least 16 codons were retained, and redundant\nsense-overlapping ORFs were merged by taking the longest representative, yielding 7,264\nncORFs across five biotype classes: upstream ORFs (uORFs), downstream ORFs (dORFs),\nintronic ORFs (intORFs), pseudogenic translations (PT), and lncRNA-embedded ORFs.\nThe catalog was developed as part of a reference-database coordination effort involving\nEnsembl/GENCODE, HGNC, UniProtKB, and PeptideAtlas.\n
\n\n\nChothani et al. (2025, see References) expanded the catalog by incorporating additional\nRibo-seq datasets across more cell types and tissues and mapping to GENCODE v45. The\nATG-start codon and 16-codon length restrictions were lifted to capture near-cognate\ninitiations and micropeptides. A data-driven scoring framework using ribosome occupancy\nuniformity and P-site in-frame fraction identified a Primary subset of 10,127 ncORFs with\ntranslation signatures comparable to canonical coding genes; the Comprehensive set contains\nall 28,359 mapped ORFs.\n
\n\n\nEach ORF was annotated with its Kozak consensus strength by fetching the 11-base genomic\ncontext around the start codon from hg38.2bit and classifying positions −3 and +4\nrelative to the A of the start codon: both matching (A/G at −3 and G at +4) =\nStrong; one matching = Moderate; neither = Weak; non-ATG start = non-ATG. A numeric\ntranslational efficiency (TE) score was also assigned by looking up the 11-base context in\nthe Noderer 2014 TE table (Mol Syst Biol 10:748, PMID 25170020).\n
\n\n\nDeutsch et al. (2026, see References) queried the 7,264 Phase I ORFs against two independent\nPeptideAtlas mass spectrometry repositories. The HLA immunopeptidomics build was constructed\nfrom HLA-I and HLA-II peptidomes across more than 100 HLA-typed donors spanning multiple\ntissue types and cancer cell lines; peptides were enriched by affinity purification and\nidentified by tandem mass spectrometry. The whole-cell tryptic proteomics build used\nconventional shotgun proteomics from a broad range of cell lines and tissues. Spectra\nwere manually reviewed and classified according to the Prensner et al. tier system (Tier 1B =\nnumerous HPP-quality HLA peptides; Tier 2B = a single qualifying HLA peptide; Tier 1A/2A =\nadditional non-HLA evidence). The study introduced the term peptidein for a\ntranslation product detectable by mass spectrometry but not yet annotatable as a protein due\nto absent functional evidence. Of the 7,264 Phase I ORFs, 628 passed PeptideAtlas curation\nas peptideins (Table S12); a further 1,522 have HLA or tryptic peptide evidence below the\npeptidein threshold.\n
\n\n\nThe supplementary data tables (Tables S2, S3, S6, S7, and S12) from Deutsch et al. were\ndownloaded from the paper's supplementary materials at\n\nhttps://doi.org/10.1038/s41586-026-10459-x.\nEach table was joined to the Phase I bigGenePred by the short ORF identifier (e.g.,\nc14riboseqorf80) using the script\naddPeptideEvidence.py.\nThe script appended 14 new fields to all 7,264 Phase I items; the 5,114 ORFs without peptide\nevidence receive default empty values so they remain visible in the track and filterable on\nisPeptidein and related fields. Non-HLA peptides that map to known proteins or\nare too short to be informative were excluded (Tables S2, exclude column).\nThe complete build procedure is documented in\nncOrfs.txt.\n
\nThanks to Jonathan Mudge, Jorge Ruiz-Orera, John Prensner, Sebastiaan van Heesch, and the\nGENCODE / TransCODE consortium for creating and maintaining these annotations.\n
\n\n\nDeutsch EW, Kok LW, Mudge JM, Valls CF, Jungreis I, Ruiz-Orera J, Sun Z, Kusebauch U, Fierro-Monti\nI, Abelin JG et al.\n\nExpanding the human proteome with microproteins and peptideins.\nNature. 2026 May 6;.\nPMID: 42092140\n
\n\n\nChothani S, Ruiz-Orera J, Tierney JAS, Clauwaert J, Deutsch EW, Alba MM, Aspden JL, Baranov PV,\nBazzini AA, Bruford EA et al.\n\nAn expanded reference catalog of translated open reading frames for biomedical research.\nbioRxiv. 2025 Jul 7;.\nPMID: 40672165; PMC:
\nMudge JM, Ruiz-Orera J, Prensner JR, Brunet MA, Calvet F, Jungreis I, Gonzalez JM, Magrane M,\nMartinez TF, Schulz JF et al.\n\nStandardized annotation of translated open reading frames.\nNat Biotechnol. 2022 Jul;40(7):994-999.\nPMID: 35831657; PMC:
\nThe three Gencode ncORF tracks in the non-canonical ORF track container show \nnon-canonical translated open reading frames (ncORFs) identified\nfrom ribosome profiling (Ribo-seq) data and mapped to the GENCODE annotation by the\nGENCODE / TransCODE consortium.\nThe data is available in two phases:\n
\n\n\nThe Phase I catalog contains 7,264 unique human ncORFs called from Ribo-seq data\nacross seven publications and mapped to GENCODE v35. Only translations of 16 codons or above\nand initiating from ATG start codons were incorporated. Redundant sense-overlapping ORFs were\nmerged. Of these, 3,085 ORFs were found by more than one publication, providing independent\nreplication evidence. This catalog was developed as part of an effort to standardize the\nannotation of translated ORFs across reference databases including Ensembl/GENCODE, HGNC,\nUniProtKB, and PeptideAtlas.\n
\n\n\nThe Phase II catalog nearly quadruples the Phase I set, defining 28,359 ncORFs in the\nComprehensive set, mapped to GENCODE v45. Compared to Phase I, additional published\nRibo-seq datasets were incorporated and the restrictions on ORF size and initiation codon\nwere lifted.\n
\n\n\nTwo subsets are provided for the Phase II data:\n
\n\nAll three GENCODE ncORF tracks are displayed in bigGenePred format and labeled with their\nORF identifier. The default color scheme and available filter controls differ by track.\n
\n\n\nThe Phase I track colors items by Kozak consensus strength by default.\nTwo alternative color schemes can be selected from the track controls page\n(Color by dropdown): Evidence type and HLA class (see below).\n
\n\n| \n | Golden amber — Strong Kozak context. Both position −3 (A/G) and\n position +4 (G) match the consensus. | \n
|---|---|
| \n | Steel blue — Moderate Kozak context. One of the two positions matches. | \n
| \n | Gray — Weak Kozak context. Neither position matches. | \n
| \n | Black — Non-ATG start codon (Kozak rule does not apply) or context\n unavailable. | \n
\nSelect Color by: Evidence type to highlight peptide evidence from\nDeutsch et al. (see References). ORFs with no mass spectrometry evidence are gray.\n
\n\n| \n | Gold — TransCODE peptidein (628 ORFs). Confirmed as\n confidently translated by PeptideAtlas; candidate for peptidein annotation in\n reference databases. | \n
|---|---|
| \n | Steel blue — HLA immunopeptidomics evidence only (1,373 ORFs). | \n
| \n | Forest green — Non-HLA (whole-cell tryptic) evidence only (66 ORFs). | \n
| \n | Orange — Both HLA and non-HLA evidence (35 ORFs). | \n
| \n | Gray — No peptide evidence (5,114 ORFs) or in the peptidein set\n based on binding predictions only with no direct MS sequences (48 ORFs). | \n
\nSelect Color by: HLA class to color items by the HLA class in which peptides were\ndetected:\n
\n\n| \n | Steel blue — Class I only (1,632 ORFs). | \n
|---|---|
| \n | Crimson — Class II only (10 ORFs). | \n
| \n | Orange — Both class I and class II (143 ORFs). | \n
| \n | Gray — No HLA data (5,479 ORFs). | \n
\nThe Phase I track can be filtered by: start codon, Kozak strength, Kozak TE, replicated\nstatus, and — using the peptide evidence fields — peptidein status\n(isPeptidein), HLA class (hlaClass), HLA evidence tier\n(hlaFinalTier), HPP guideline category (hlaHppCategory), and Ribo-seq\nquality (riboseqQuality).\n
\n\n\nMouseover for Phase I shows ORF name, host gene, Kozak strength and TE,\nreplicated status, peptidein flag, HLA evidence tier, and HLA peptide count.\n
\n\n\nThe Phase II Primary and Comprehensive tracks color items by Kozak strength using the\nsame scheme as Phase I. Peptide evidence fields are not included in the Phase II tracks.\nCommon filters: start codon, Kozak strength, Kozak TE.\n
\n\n\nEach Phase I item carries the following peptide evidence fields from Deutsch et al.\n(2026), accessible via the details page and Table Browser:\n
\n\n| Field | Description |
|---|---|
| isPeptidein | yes/no: ORF is in the PeptideAtlas peptidein set (Table S12) |
| hlaClass | HLA class(es) detected: I, II, or Both |
| hlaFinalTier | HLA evidence tier (Tier 1B = numerous peptides; Tier 2B = one peptide) |
| hlaHppCategory | HPP guideline category (HPP+, 1PepCandidate, Insufficient) |
| hlaNPeptides | Number of distinct HLA peptide sequences detected |
| riboseqQuality | Manual quality of Ribo-seq evidence (Excellent/Sufficient/Insufficient) |
| hlaIPeptides | HLA class I peptide sequences (comma-separated) |
| hlaIIPeptides | HLA class II peptide sequences (comma-separated) |
| nonHlaFinalTier | Non-HLA (tryptic proteomics) evidence tier |
| nonHlaHppCategory | Non-HLA HPP guideline category |
| nonHlaNPeptides | Number of distinct non-HLA peptide sequences |
| nonHlaPeptides | Non-HLA tryptic peptide sequences (comma-separated) |
\nThe raw data can be explored interactively with the\nTable Browser or the\nData Integrator. The data can be accessed from\nscripts through our API; the track names are\n"gencNcOrfs" (Phase I), "gencNcOrfsPrimary" (Phase II Primary),\nand "gencNcOrfsComprehensive" (Phase II Comprehensive).\n
\n\n\nFor automated download and analysis, the genome annotations are stored in bigBed files that\ncan be downloaded from\nour download server.\nIndividual regions or the whole genome annotation can be obtained using our tool\nbigBedToBed, which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\n
\n\n\nMudge et al. (2022, see References) consolidated translation evidence from seven published\nribosome profiling datasets that used harringtonine or lactimidomycin treatment to enrich\nfor translation initiation sites. Ribo-seq reads were mapped to the GENCODE v35 annotation\non GRCh38. Only ATG-initiated ORFs of at least 16 codons were retained, and redundant\nsense-overlapping ORFs were merged by taking the longest representative, yielding 7,264\nncORFs across five biotype classes: upstream ORFs (uORFs), downstream ORFs (dORFs),\nintronic ORFs (intORFs), pseudogenic translations (PT), and lncRNA-embedded ORFs.\nThe catalog was developed as part of a reference-database coordination effort involving\nEnsembl/GENCODE, HGNC, UniProtKB, and PeptideAtlas.\n
\n\n\nChothani et al. (2025, see References) expanded the catalog by incorporating additional\nRibo-seq datasets across more cell types and tissues and mapping to GENCODE v45. The\nATG-start codon and 16-codon length restrictions were lifted to capture near-cognate\ninitiations and micropeptides. A data-driven scoring framework using ribosome occupancy\nuniformity and P-site in-frame fraction identified a Primary subset of 10,127 ncORFs with\ntranslation signatures comparable to canonical coding genes; the Comprehensive set contains\nall 28,359 mapped ORFs.\n
\n\n\nEach ORF was annotated with its Kozak consensus strength by fetching the 11-base genomic\ncontext around the start codon from hg38.2bit and classifying positions −3 and +4\nrelative to the A of the start codon: both matching (A/G at −3 and G at +4) =\nStrong; one matching = Moderate; neither = Weak; non-ATG start = non-ATG. A numeric\ntranslational efficiency (TE) score was also assigned by looking up the 11-base context in\nthe Noderer 2014 TE table (Mol Syst Biol 10:748, PMID 25170020).\n
\n\n\nDeutsch et al. (2026, see References) queried the 7,264 Phase I ORFs against two independent\nPeptideAtlas mass spectrometry repositories. The HLA immunopeptidomics build was constructed\nfrom HLA-I and HLA-II peptidomes across more than 100 HLA-typed donors spanning multiple\ntissue types and cancer cell lines; peptides were enriched by affinity purification and\nidentified by tandem mass spectrometry. The whole-cell tryptic proteomics build used\nconventional shotgun proteomics from a broad range of cell lines and tissues. Spectra\nwere manually reviewed and classified according to the Prensner et al. tier system (Tier 1B =\nnumerous HPP-quality HLA peptides; Tier 2B = a single qualifying HLA peptide; Tier 1A/2A =\nadditional non-HLA evidence). The study introduced the term peptidein for a\ntranslation product detectable by mass spectrometry but not yet annotatable as a protein due\nto absent functional evidence. Of the 7,264 Phase I ORFs, 628 passed PeptideAtlas curation\nas peptideins (Table S12); a further 1,522 have HLA or tryptic peptide evidence below the\npeptidein threshold.\n
\n\n\nThe supplementary data tables (Tables S2, S3, S6, S7, and S12) from Deutsch et al. were\ndownloaded from the paper's supplementary materials at\n\nhttps://doi.org/10.1038/s41586-026-10459-x.\nEach table was joined to the Phase I bigGenePred by the short ORF identifier (e.g.,\nc14riboseqorf80) using the script\naddPeptideEvidence.py.\nThe script appended 14 new fields to all 7,264 Phase I items; the 5,114 ORFs without peptide\nevidence receive default empty values so they remain visible in the track and filterable on\nisPeptidein and related fields. Non-HLA peptides that map to known proteins or\nare too short to be informative were excluded (Tables S2, exclude column).\nThe complete build procedure is documented in\nncOrfs.txt.\n
\nThanks to Jonathan Mudge, Jorge Ruiz-Orera, John Prensner, Sebastiaan van Heesch, and the\nGENCODE / TransCODE consortium for creating and maintaining these annotations.\n
\n\n\nDeutsch EW, Kok LW, Mudge JM, Valls CF, Jungreis I, Ruiz-Orera J, Sun Z, Kusebauch U, Fierro-Monti\nI, Abelin JG et al.\n\nExpanding the human proteome with microproteins and peptideins.\nNature. 2026 May 6;.\nPMID: 42092140\n
\n\n\nChothani S, Ruiz-Orera J, Tierney JAS, Clauwaert J, Deutsch EW, Alba MM, Aspden JL, Baranov PV,\nBazzini AA, Bruford EA et al.\n\nAn expanded reference catalog of translated open reading frames for biomedical research.\nbioRxiv. 2025 Jul 7;.\nPMID: 40672165; PMC:
\nMudge JM, Ruiz-Orera J, Prensner JR, Brunet MA, Calvet F, Jungreis I, Gonzalez JM, Magrane M,\nMartinez TF, Schulz JF et al.\n\nStandardized annotation of translated open reading frames.\nNat Biotechnol. 2022 Jul;40(7):994-999.\nPMID: 35831657; PMC:
\nThe Pathways and Gene Interactions track shows a summary of gene interaction and pathway data\ncollected from two sources: curated pathway/protein-interaction databases and interactions found\nthrough text mining of PubMed abstracts.
\n\n\nThe track features a single item for each gene loci in the genome. On the item itself, the gene\nsymbol for the loci is displayed followed by the top gene interactions noted by their gene symbol.\nClicking an item will take you a\ngene interaction graph\nthat includes detailed information on the support for the various interactions.
\n\n\nItems are colored based on the number of documents supporting the interactions of a\nparticular gene. Genes with >100 supporting documents are colored\nblack, genes with >10 but <100\nsupporting documents are colored dark blue, and\nthose with >10 supporting documents are colored\nlight blue.
\n\n\nSee the\nhelp documentation\naccompanying this gene interaction graph for more information on its configuration.
\n\n\nThe pathways and gene interactions were imported from a number of databases and mined from\nmillions of PubMed abstracts. More information can be found in the\n"Data Sources\nand Methods"\nsection of the help page for the gene interaction graph.
\n\n\nThe underlying data for this track can be accessed interactively through the\nTable Browser or\nData Integrator. \nThe data for this track is spread across a number of relational tables. The best way to \nexport or analyze the data is using our public MySQL server.\nThe list of tables and how they are linked together are described in the \ndocumentation \nlinked at the bottom of the gene interaction viewer.\n
\n\n\nThe genome annotation is just a summary of the actual interactions database and therefore often not \nof interest to most users. It is stored in a bigBed file that can be obtained\nfrom the\ndownload server.\n\nThe data underlying the\ngraphical display is in bigBed\nformatted file named interactions.bb. Individual regions or the whole genome annotation\ncan be obtained using our tool bigBedToBed. Instructions\nfor downloading source code and precompiled binaries can be found\nhere. The tool can also\nbe used to obtain only features within a given range, for example:\n
\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/interactions.bb\n-chrom=chr6 -start=0 -end=1000000 stdout\n
\n\n\nThe text-mined data for the gene interactions and pathways were generated by Chris Quirk and\nHoifung Poon as part of\nMicrosoft Research, Project\nHanover.
\n\n\nPathway data was provided by the databases listed under\n"Data Sources\nand Methods"\nsection of the help page for the gene interaction graph.\nIn particular, thank you to Ian Donaldson from IRef for his\nunique collection of interaction databases.
\n\n\nThe short gene descriptions are a merge of the HPRD\nand PantherDB gene/molecule classifications. Thanks to Arun Patil from\nHPRD for making them available as a download.
\n\n\nThe track display and gene interaction graph\nwere developed at the UCSC Genome Browser by Max Haeussler.
\n\n\nPoon H, Quirk C, DeZiel C, Heckerman D.\nLiterome: PubMed-scale genomic knowledge base in the cloud\nBioinformatics. 2014 Oct;30(19):2840-2.\nPMID: 24939151
\n" } }, "description": "Protein Interactions from Curated Databases and Text-Mining", "category": [ "Phenotypes, Variants, and Literature" ] }, { "trackId": "hg38-geneReviews", "name": "GeneReviews", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/geneReviews/geneReviews.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/geneReviews/geneReviews.bb", "color": "0, 80, 0", "group": "phenDis", "html": "\nGeneReviews is an online collection of expert-authored, peer-reviewed\narticles that describe specific gene-related diseases. GeneReviews articles are\nsearchable by disease name, gene symbol, protein name, author, or title. GeneReviews\nis supported by the National Institutes of Health, hosted at NCBI as part of the\n\nGenetic Testing Registry (GTR). The GeneReviews data underlying this track will be updated frequently. \n
\n\nThe GeneReviews track allows the user to locate the NCBI GeneReviews resource\nquickly from the Genome Browser. Hovering the mouse on track items shows the gene symbol and \nassociated diseases. A condensed version of the GeneReviews article\nname and its related diseases are displayed on the item details page as links. Similar\ninformation, when available, is provided in the details page of items from the UCSC Genes,\nRefSeq Genes, and OMIM Genes tracks.\n
\n\n\nThe raw data for the GeneReviews track can be explored interactively with the\nTable Browser. Cross-referencing can be done with\nData Integrator. The complete source file,\nin bigBed format, \ncan be downloaded from our\ndownloads directory.\nFor automated analysis,\nthe data may be queried from our\nREST API.\n
\n\n\nPrevious versions of this track can be found on our archive download server.\n
\n\n\nPagon RA, Adam MP, Bird TD, et al., editors. GeneReviews® [Internet]. Seattle (WA): University of Washington, Seattle; 1993-2014. Available from: \n\nhttps://www.ncbi.nlm.nih.gov/books/NBK1116.\n
\n\n", "longLabel": "GeneReviews", "mouseOver": "Gene: $name\nThe Genome Aggregation Database (gnomAD) - Rare CNV variants (<1% overall site frequency) v4.1 track set shows rare autosomal coding copy number variants (CNVs) with an overall\nsite frequency of less than 1%. These variants were identified from exome sequencing (ES) data of\n464,297 individuals. The data can also be explored via the\ngnomAD browser.\n\n
\nItems are colored by the type of variant:\n
| Variant Type | \n|
|---|---|
| Deletion (DEL) | \n31939 | \n
| Duplication (DUP) | \n36760 | \n
Mouseover on an item will display the position, size of variant, genes impacted by\nvariant (>=10% CDS overlap by deletion or >=75% CDS overlap by duplication), and site\nfrequency of non-neuro control samples. Item description pages include a linkout to\nthe gnomAD browser showing additional genetic ancestry group information.
\n\n\n\nTo identify rare coding CNVs from the ES data of 464,297 individuals in gnomAD v4, the GATK-gCNV\nmethod was employed, as described in Babadi et al., Nat Genet, 2023.
\n\nThe CNV discovery process started with collecting the number of reads mapped to 363,301 autosomal\ntarget intervals derived from protein-coding exons (Fig. 1a, b; Babadi et al.). These read counts\nwere used to capture sample-level technical variability, such as differences in exome capture kits\nor sequencing centers, and generated 1,045 different batches of samples for parallel processing\n(Fig. 1c). For each of these batches, 200 random samples were selected for training GATK-gCNV in\ncohort mode,which can be thought of as the creation of a "panel of normals" (PoN). The resulting\nPoN models were then used to efficiently delineate CNV events on all of the samples of their\nrespective cohorts using the GATK-gCNV case mode (Fig. 1d,e).\n
\n\n\nThe raw, individual-level CNV calls produced by GATK-gCNV for all samples were then collated,\nand variants observed in multiple individuals were clustered using single-linkage clustering.\nQuality filtering followed the procedures outlined in Babadi et al., filtering CNVs based on\nsample-level (number of events per individual) and call-level (frequency, size, quality score) metrics\nDue to the significant increase in cohort size and heterogeneity compared to the datasets reported\nin Babadi et al., additional filters were applied. Samples with more than five chromosomes harboring\nrare CNVs, as well as those containing more than three rare terminal CNVs, were excluded. 1,049\nsites producing noisy normalized read-depth signals were masked. The final retained CNVs and sites\nwere subsequently annotated for impacted genes and frequencies.
\n\n\nMore information can be found at the\n\ngnomAD site.
\n\n\nThe bed files was obtained from the gnomAD Google Storage bucket:
\n\n\nhttps://storage.googleapis.com/gcp-public-data--gnomad/release/4.1/exome_cnv/gnomad.v4.1.cnv.all.bed\n\n\nThe data was then transformed into a bigBed track. For the full list of commands used to make this\ntrack please see the "gnomAD CNVs v4.1" section of the\nmakedoc.\n\n\n
\nThe raw data can be explored interactively with the Table Browser, or\nthe Data Integrator. For automated access, this track, like all \nothers, is available via our API. However, for bulk \nprocessing, it is recommended to download the dataset. The genome annotation is stored in a bigBed \nfile that can be downloaded from the\ndownload server.\nThe exact filenames can be found in the track configuration file. Annotations can be converted to\nASCII text by our tool bigBedToBed which can be compiled from the source code or\ndownloaded as a precompiled binary for your system. Instructions for downloading source code and\nbinaries can be found\nhere. The tool can\nalso be used to obtain only features within a given range, for example:
\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/gnomAD/v4/cnv/gnomad.v4.1.cnv.all.bb -chrom=chr6 -start=0 -end=1000000 stdout\n\n\n
\nPlease refer to our\nmailing list archives\nfor questions and example queries, or our\nData Access FAQ\nfor more information.
\n\n\nMore information about using and understanding the gnomAD data can be found in the\ngnomAD FAQ site.\n
\n\n\nThanks to the Genome Aggregation\nDatabase Consortium for making these data available. The data are released under the
\nBabadi M, Fu JM, Lee SK, Smirnov AN, Gauthier LD, Walker M, Benjamin DI, Zhao X, Karczewski KJ, Wong\nI et al.\n\nGATK-gCNV enables the discovery of rare copy number variants from exome sequencing data.\nNat Genet. 2023 Sep;55(9):1589-1597.\nPMID: 37604963; PMC:
\nCollins RL, Brand H, Karczewski KJ, Zhao X, Alföldi J, Francioli LC, Khera AV, Lowther C,\nGauthier LD, Wang H et al.\n\nA structural variation reference for medical and population genetics.\nNature. 2020 May;581(7809):444-451.\nPMID: 32461652; PMC:
\nCummings BB, Karczewski KJ, Kosmicki JA, Seaby EG, Watts NA, Singer-Berk M, Mudge JM, Karjalainen J,\nSatterstrom FK, O'Donnell-Luria AH et al.\n\nTranscript expression-aware annotation improves rare variant interpretation.\nNature. 2020 May;581(7809):452-458.\nPMID: 32461655; PMC:
\nKarczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, Collins RL, Laricchia KM,\nGanna A, Birnbaum DP et al.\n\nThe mutational constraint spectrum quantified from variation in 141,456 humans.\nNature. 2020 May;581(7809):434-443.\nPMID: 32461654; PMC:
\nLek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill\nAJ, Cummings BB et al.\n\nAnalysis of protein-coding genetic variation in 60,706 humans.\nNature. 2016 Aug 18;536(7616):285-91.\nPMID: 27535533; PMC:
\nThe gnomAD STR track displays short tandem repeat (STR) genotypes at 87\ndisease-associated loci from the\nGenome Aggregation\nDatabase (gnomAD) v3.1.3. The data include individual-level STR genotypes from\n18,511 whole-genome sequenced samples across 10 populations, aggregated\ninto per-locus allele frequency distributions.
\n\n\nThese loci were selected because tandem repeat expansions at these sites have been\nreported to cause human genetic diseases, including Huntington disease (HTT),\nfragile X syndrome (FMR1), Friedreich ataxia (FXN), various\nspinocerebellar ataxias, myotonic dystrophies, and other neurological and\nneuromuscular disorders. Most loci (56) have motifs between 3–6 bp, while\nadditional loci have longer motifs of 10–24 bp.
\n\n\nThe genotypes were generated using\nExpansionHunter\nv5 on gnomAD v3.1 whole-genome sequencing data (150 bp read lengths). Of the\nsamples, 64% were PCR-free, 13% PCR-plus, and 23% had unknown PCR protocol.\nExpansionHunter was selected because it had the best accuracy among existing tools\nfor detecting expansions at disease-associated loci. Results were generated without\noff-target regions to minimize overestimation of repeat sizes.\nFor each locus, the data show the distribution of repeat allele sizes observed\nacross the gnomAD population, providing a reference for normal and expanded allele\nranges. For more details on the methods, see the\ngnomAD blog post on STR calls.
\n\n\nItems are colored by the length of the repeat motif:
\n\nEach item is labeled by the gene name. Hovering shows the repeat motif,\ngene, total sample count, and number passing quality filters. Clicking an item\nlinks to the corresponding gnomAD STR locus page with interactive allele\nfrequency histograms and detailed population breakdowns.
\n\n\nThe detail page for each locus shows:
\n\nThe gnomAD STR genotype data file\n(gnomAD_STR_genotypes__2025_03_17.tsv.gz) was downloaded from the\ngnomAD downloads page. This file contains individual-level\nSTR genotypes at 87 disease-associated loci generated using\nExpansionHunter\non gnomAD v3.1.3 whole-genome sequencing data.
\nFor the UCSC Genome Browser track, the individual genotype records (~1.4 million rows)\nwere aggregated per locus to produce summary statistics: total sample count,\nPASS-filter count, allele size frequency distributions, and per-population sample counts.\nCoordinates were used as provided (0-based). Some loci include genotypes for multiple\nmotif patterns (e.g., complex repeat structures) and for adjacent repeats; these are\nrepresented as separate records.
\n\n\nThe 10 populations represented are: African/African American (afr),\nAdmixed American/Latino (amr), Amish (ami), Ashkenazi Jewish (asj),\nEast Asian (eas), Finnish (fin), Middle Eastern (mid), Non-Finnish European (nfe),\nSouth Asian (sas), and Other (oth).
\n\n\nThe raw data can be explored interactively with the\nTable Browser or the\nData Integrator. For automated\nanalysis, the data may be queried from our\nREST API. The underlying bigBed\nfile can be downloaded from our\ndownload\nserver.
\n\n\nThe complete gnomAD STR dataset, including individual-level genotypes, is available\nfrom the gnomAD downloads page. Interactive locus-level views with\nallele frequency histograms are available at the\ngnomAD STR browser.
\n\n\nThanks to the gnomAD\nproduction team at the Broad Institute for generating and distributing this data.
\n\n\nChen S, Francioli LC, Goodrich JK, Collins RL, Kanai M, Wang Q,\nAlföldi J, Watts NA, Vittal C, Gauthier LD et al.\n\nA genomic mutational constraint map using variation in 76,156 human\ngenomes.\nNature. 2024 Jan;625(7993):92-100.\nPMID: 38057664; PMC:
\nDolzhenko E, Deshpande V, Schlesinger F, Krusche P, Petrovski R,\nChen S, Emig-Agius D, Gross A, Narzisi G, Bowman B\net al.\n\nExpansionHunter: a sequence-graph-based tool to analyze variation\nin short tandem repeat regions.\nBioinformatics. 2019 Nov 1;35(22):4754-4756.\nPMID: 31134279; PMC:
NOTE: Only variants that have passed\n the quality filter are displayed by default.
\n
\nThe Genome Aggregation Database (gnomAD) - Structural Variants v4.1 track set shows structural variants calls (>=50 nucleotides) from the gnomAD v4.1\nrelease on 63,046 unrelated genomes. It mostly (but not entirely) overlaps with the genome set used\nfor the gnomAD short variant release. For more information see the following blog post, \n\nStructural variants in gnomAD.
\n\n\nItems are shaded according to variant type, mouseover on items indicates affected\nprotein-coding genes, size of the variant (which may differ from the chromosomal coordinates in\ncases like insertions), variant type (insertion, duplication, etc), allele count, allele number,\nand allele frequency. When more than 2 genes are affected by a variant, the full list can be\nobtained by clicking on the item and reading the details page. A short summary is available in the\nbelow table:
\n\n| Variant Type | \nAll SV's | \n
|---|---|
| Breakend (BND) | \n356035 | \n
| Complex (CPX) | \n15189 | \n
| Translocation (CTX) | \n99 | \n
| Deletion (DEL) | \n1206278 | \n
| Duplication (DUP) | \n269326 | \n
| Insertion (INS) | \n304645 | \n
| Inversion (INV) | \n2193 | \n
| Copy number variants (CNV) | \n721 | \n
\nDetailed information on the CNV color code is described \nhere. All tracks can be \nfiltered according to the size of the variant and variant type, using the track Configure\noptions.\n
\n\n\nThree filters are available for this track:\n
\n\nThe bed files was obtained from the gnomAD Google Storage bucket:\n\n
\nhttps://storage.googleapis.com/gcp-public-data--gnomad/release/4.1/genome_sv/gnomad.v4.1.sv.non_neuro_controls.sites.bed.gz\n\n\nThe data was then transformed into a bigBed track. For the full list of commands used to make this\ntrack please see the "gnomAD Structural Variants v4" section of the\nmakedoc.\n\n\n
\nThe raw data can be explored interactively with the Table Browser, or\nthe Data Integrator. For automated access, this track, like all \nothers, is available via our API. However, for bulk \nprocessing, it is recommended to download the dataset. The genome annotation is stored in a bigBed \nfile that can be downloaded from the\ndownload server.\nThe exact filenames can be found in the track configuration file. Annotations can be converted to\nASCII text by our tool bigBedToBed which can be compiled from the source code or\ndownloaded as a precompiled binary for your system. Instructions for downloading source code and\nbinaries can be found\nhere. The tool can\nalso be used to obtain only features within a given range, for example:
\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/gnomAD/v4/structuralVariants/gnomad.v4.1.sv.non_neuro_controls.sites.bb -chrom=chr6 -start=0 -end=1000000 stdout\n\n\n
\nPlease refer to our\nmailing list archives\nfor questions and example queries, or our\nData Access FAQ\nfor more information.
\n\n\nMore information about using and understanding the gnomAD data can be found in the\ngnomAD FAQ site.\n
\n\n\nThanks to the Genome Aggregation\nDatabase Consortium for making these data available. The data are released under the
\nLek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill\nAJ, Cummings BB et al.\n\nAnalysis of protein-coding genetic variation in 60,706 humans.\nNature. 2016 Aug 18;536(7616):285-91.\nPMID: 27535533; PMC:
\nKarczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, Collins RL, Laricchia KM,\nGanna A, Birnbaum DP et al.\n\nThe mutational constraint spectrum quantified from variation in 141,456 humans.\nNature. 2020 May;581(7809):434-443.\nPMID: 32461654; PMC:
\nCollins RL, Brand H, Karczewski KJ, Zhao X, Alföldi J, Francioli LC, Khera AV, Lowther C,\nGauthier LD, Wang H et al.\n\nA structural variation reference for medical and population genetics.\nNature. 2020 May;581(7809):444-451.\nPMID: 32461652; PMC:
\nCummings BB, Karczewski KJ, Kosmicki JA, Seaby EG, Watts NA, Singer-Berk M, Mudge JM, Karjalainen J,\nSatterstrom FK, O'Donnell-Luria AH et al.\n\nTranscript expression-aware annotation improves rare variant interpretation.\nNature. 2020 May;581(7809):452-458.\nPMID: 32461655; PMC:
\nThis track shows locations in the human assembly where assembly\nproblems have been noted or resolved, as reported by the\nGenome Reference Consortium (GRC). \n
\n\nIf you would like to report an assembly problem, please use the GRC\nissue reporting system.\n
\n\n\nData for this track are extracted from the GRC\nincident database from the specific species *_issues.gff3 file.\nThe track is synchronized once daily to incorporate new updates. \n
\n\nThe data and presentation of this track were prepared by\nHiram Clawson.\n
\n" } }, "description": "GRC Incident Database", "category": [ "Mapping and Sequencing" ] }, { "trackId": "hg38-gwipsvizRiboseq", "name": "GWIPS-viz Riboseq", "type": "QuantitativeTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigWigAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/gwipsvizRiboseq/gwipsvizRiboseq.bw" }, "metadata": { "ucsc": { "autoScale": "off", "group": "expression", "html": "\nRibosome profiling (ribo-seq) is a technique that takes advantage of NGS\ntechnology to sequence ribosome-protected mRNA fragments and consequently\nallows the locations of translating ribosomes to be determined at the entire\ntranscriptome level (Ingolia et al., 2009).\n
\n\n\nFor a more detailed description of the protocol, see Ingolia et al.\n(2012). For reviews on this technique and its applications, please refer to\nIngolia (2014) and Michel et al. (2013).\n
\n\n\nThis track displays cumulative ribo-seq data obtained from human cells under\ndifferent conditions and can be used for the exploration of human genomic loci\nthat are being translated. The values on the y-axis represent the number of\nribosome footprint sequence reads at a given position. As of February\n2016, the track contains data from 9 studies (see References section for\ndetails). Further details about the aggregated track and additional ribo-seq\ndata from these and other studies including data obtained from other organisms\ncan be found at the specialized ribo-seq browser\nGWIPS-viz.\n
\n\n\nFor each study used to generate this track, raw fastq files were downloaded from\na repository (e.g., NCBI GEO datasets).\nCutadapt\nwas used to trim the relevant adapter sequence from the reads, after which reads\nbelow 25 nt in length were discarded. The trimmed reads were aligned to\nribosomal RNA using\nBowtie\nand aligning reads were discarded. The remaining reads were then aligned to the\nhg38 (GRCh38) genome assembly using Bowtie. An offset of 15 nt (to infer the\nposition of the A-site) was added to the most 5' nucleotide coordinate of each\nuniquely-mapped read.\n
\n\n\nThe alignment files from each of the included studies were merged to generate\nthis aggregate track.\n
\n\n\nSee individual studies at\nGWIPS-viz for a full\ndescription of the methods of data acquisition and processing.\n
\n\n\nThanks to Audrey Michel, Stephen Kiniry and GWIPS-viz for providing the data for\nthis track. If you wish to cite this track, please reference:\n
\n\n\nMichel AM, Fox G, M Kiran A, De Bo C, O'Connor PB, Heaphy SM, Mullan JP, Donohue CA, Higgins DG,\nBaranov PV.\nGWIPS-viz: development of a ribo-seq genome browser.\nNucleic Acids Res. 2014 Jan;42(Database issue):D859-64.\nPMID: 24185699; PMC:
\nBattle A, Khan Z, Wang SH, Mitrano A, Ford MJ, Pritchard JK, Gilad Y.\n\nImpact of regulatory variation from RNA to protein.\nScience. 2015 Feb 6;347(6222):664-7.\nPMID: 25657249;\nPMC: PMC4507520\n
\n\n\nCenik C, Cenik ES, Byeon GW, Grubert F, Candille SI, Spacek D, Alsallakh B, Tilgner H, Araya CL, Tang H et al.\n\nIntegrative analysis of RNA, translation and protein levels reveals distinct regulatory variation across humans.\nGenome Res. 2015 Nov;25(11):1610-21.\nPMID: 26297486;\nPMC: PMC4617958\n
\n\n\n\nElkon R, Loayza-Puch F, Korkmaz G, Lopes R, van Breugel PC, Bleijerveld OB, Altelaar AM, Wolf E, Lorenzin F, Eilers M et al.\n\nMyc coordinates transcription and translation to enhance transformation and suppress invasiveness.\nEMBO Rep. 2015 Dec;16(12):1723-36.\nPMID: 26538417;\nPMC: PMC4687422\n
\n\n\nJang C, Lahens NF, Hogenesch JB, Sehgal A.\n\nRibosome profiling reveals an important role for translational control in circadian gene expression.\nGenome Res 2015 Dec;25(12):1836-47.\nPMID: 26338483;\nPMC: PMC4665005\n
\n\n\nJi Z, Song R, Regev A, Struhl K.\n\nMany lncRNAs, 5'UTRs, and pseudogenes are translated and some are likely to express functional proteins.\nElife. 2015 Dec 19;4.\nPMID: 26687005;\nPMC: PMC4739776\n
\n\n\nSidrauski C, McGeachy AM, Ingolia NT, Walter P.\n\nThe small molecule ISRIB reverses the effects of eIF2α phosphorylation on translation and stress granule assembly.\nElife. 2015 Feb 26;4.\nPMID: 25719440;\nPMC: PMC4341466\n
\n\n\nTanenbaum ME, Stern-Ginossar N, Weissman JS, Vale RD.\n\nRegulation of mRNA translation during mitosis.\nElife. 2015 Aug 25;4.\nPMID: 26305499;\nPMC: PMC4548207\n
\n\n\nTirosh O, Cohen Y, Shitrit A, Shani O, Le-Trilling VT, Trilling M, Friedlander G, Tanenbaum M, Stern-Ginossar N.\n\nThe transcription and translation landscapes during human cytomegalovirus infection reveal novel host-pathogen interactions.\nPLoS Pathog. 2015 Nov 24;11(11):e1005288.\nPMID: 26599541;\nPMC: PMC4658056\n
\n\n\nWerner A, Iwasaki S, McGourty CA, Medina-Ruiz S, Teerikorpi N, Fedrigo I, Ingolia NT, Rape M.\n\nCell fate determination by ubiquitin-dependent regulation of translation.\nNature. 2015 Sep 24;525(7570):523-7.\nPMID: 26399832;\nPMC: PMC4602398\n
\n\n\nIngolia NT.\n\nRibosome profiling: new views of translation, from single codons to genome scale.\nNat Rev Genet. 2014 Mar;15(3):205-13.\nPMID: 24468696\n
\n\n\nIngolia NT, Brar GA, Rouskin S, McGeachy AM, Weissman JS.\n\nThe ribosome profiling strategy for monitoring translation in vivo by deep sequencing of ribosome-\nprotected mRNA fragments.\nNat Protoc. 2012 Jul 26;7(8):1534-50.\nPMID: 22836135; PMC:
\nIngolia NT, Ghaemmaghami S, Newman JR, Weissman JS.\n\nGenome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling.\nScience. 2009 Apr 10;324(5924):218-23.\nPMID: 19213877; PMC:
\nMichel AM, Baranov PV.\n\nRibosome profiling: a Hi-Def monitor for protein synthesis at the genome-wide scale.\nWiley Interdiscip Rev RNA. 2013 Sep-Oct;4(5):473-90.\nPMID: 23696005; PMC:
\nThe HGNC is \nresponsible for approving unique symbols and names for human loci, including protein \ncoding genes, ncRNA genes and pseudogenes, to allow unambiguous scientific communication.\n
\nFor each known human gene, the HGNC approves a gene name and symbol (short-form abbreviation).\nAll approved symbols are stored in the HGNC database, www.genenames.org, a curated online repository of HGNC-approved gene \nnomenclature, gene groups and associated resources including links to genomic, proteomic, \nand phenotypic information. Each symbol is unique and we ensure that each gene is only \ngiven one approved gene symbol. It is necessary to provide a unique symbol for each gene \nso that we and others can talk about them, and this also facilitates electronic data \nretrieval from publications and databases. In preference, each symbol maintains \nparallel construction in different members of a gene family and can also be \nused in other species, especially other vertebrates including mouse.\n\n
\nThe raw data can be explored interactively with the Table Browser, or the Data Integrator. For computational analysis, genome annotations are stored in\na bigBigFile file that can be downloaded from the\ndownload\nserver. Regional or genome-wide annotations can be converted from binary data to human readable\ntext using our command line utility bigBedToBed which can be compiled from source code or\ndownloaded as a precompiled binary for your system. Files and instructions can be found in the\nutilities directory.\n\nThe utility can be used to obtain features within a given range, for example:
\nbigBedToBed -chrom=chr6 -start=0 -end=1000000 http://hgdownload.soe.ucsc.edu/gbdb/hg38/hgnc/hgnc.bb stdout\n\n\n\n
\nPlease refer to our Data Access FAQ\nfor more information or our mailing list for archived user questions.
\n\n\nHGNC Database, HUGO Gene Nomenclature Committee (HGNC), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom www.genenames.org.\n\n
\nTweedie S, Braschi B, Gray KA, Jones TEM, Seal RL, Yates B, Bruford EA. Genenames.org: the HGNC and VGNC resources in 2021. Nucleic Acids Res. PMID: 33152070 PMCID: PMC7779007 DOI: 10.1093/nar/gkaa980\n
\n" } }, "description": "HUGO Gene Nomenclature", "category": [ "Genes and Gene Predictions" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-hgnc-LinearBasicDisplay", "labels": { "name": "jexl:get(feature,'symbol')" }, "mouseover": "jexl:`Symbol: ${get(feature,'symbol')}\nLocus Reference Genomic (LRG)\nsequences are manually curated, stable DNA sequences that surround a\nlocus (typically a gene) and provide an unchanging coordinate system\nfor reporting sequence variants. They are not necessarily identical\nto the corresponding sequence in a particular reference genome\nassembly (such as Dec. 2013 (GRCh38/hg38)), but can be mapped to each version of a\nreference genome assembly in order to convert between the stable LRG\nvariant coordinates and the various assembly coordinates.\n
\n\n\nWe import the data from the LRG database at the EBI. \nThe NCBI RefSeqGene database is almost identical to LRG, \nbut it may contain a few more sequences. See the NCBI documentation.\n
\n\n\nEach LRG record also includes at least one stable transcript\non which variants may be reported. These transcripts\nappear in the LRG Transcripts track in the Gene and Gene Predictions\ntrack section.
\n\n\nLRG sequences are suggested by the community studying a locus (for example,\nLocus-Specific Database curators, research laboratories, mutation consortia).\nLRG curators then examine the submitted transcript as well as other known\ntranscripts at the locus, in the context of alignment and public expression\ndata.\nFor more information on the selection and annotation process, see the \nLRG FAQ,\n(Dalgleish, et al.) and (MacArthur, et al.).\n
\n\n\nThis track was produced at UCSC using\nLRG XML files.\nThanks to\nLRG collaborators\nfor making these data available.\n
\n\n\nDalgleish R, Flicek P, Cunningham F, Astashyn A, Tully RE, Proctor G, Chen Y, McLaren WM, Larsson P,\nVaughan BW et al.\n \nLocus Reference Genomic sequences: an improved basis for describing human DNA variants.\nGenome Med. 2010 Apr 15;2(4):24.\nPMID: 20398331; PMC:
\nMacArthur JA, Morales J, Tully RE, Astashyn A, Gil L, Bruford EA, Larsson P, Flicek P, Dalgleish R,\nMaglott DR et al.\n\nLocus Reference Genomic: reference sequences for the reporting of clinically relevant sequence\nvariants.\nNucleic Acids Res. 2014 Jan;42(Database issue):D873-8.\nPMID: 24285302; PMC:
\nThe Matched Annotation from\nNCBI and EMBL-EBI (MANE) project aims to produce a matched set of \nhigh-confidence transcripts that are identically annotated between RefSeq (NCBI) and \nEnsembl/GENCODE (led by EMBL-EBI). Transcripts for MANE are chosen by a combination of \nautomated and manual methods based on conservation, expression levels, clinical significance, \nand other factors. Transcripts are matched between the NCBI RefSeq and Ensembl/GENCODE annotations\nbased on the GRCh38 genome assembly, with precise 5' and 3' ends defined by high-throughput\nsequencing or other available data.
\n\nThis track is automatically updated, see the source data version above for the current\nversion number. MANE includes almost all human protein-coding genes and genes of clinical relevance,\nincluding genes in the\nAmerican\nCollege of Medical Genetics and Genomics (ACMG) Secondary Findings list (SF) v3.0. It includes \nboth MANE Select and MANE Plus Clinical transcripts. MANE\nPlus Clinical items are colored red.\n
\nFor more information on the different gene tracks, including MANE vs GENCODE or RefSeq,\nsee our Genes FAQ.
\n\n\nThe raw data can be explored interactively with the Table Browser, or the Data Integrator. For computational analysis, genome annotations are stored in\na bigGenePred file that can be downloaded from the\ndownload\nserver. Regional or genome-wide annotations can be converted from binary data to human readable\ntext using our command line utility bigBedToBed which can be compiled from source code or\ndownloaded as a precompiled binary for your system. Files and instructions can be found in the\nutilities directory.\n\nThe utility can be used to obtain features within a given range, for example:
\nbigBedToBed -chrom=chr6 -start=0 -end=1000000 http://hgdownload.soe.ucsc.edu/gbdb/hg38/mane/mane.bb stdout\n\n\nDownload links for MANE:\nftp://ftp.ncbi.nlm.nih.gov/refseq/MANE\n
\n\n\nPrevious MANE versions are also available on our download archive.
\n\n\nPlease refer to our Data Access FAQ\nfor more information or our mailing list for archived user questions.
\n\n\nThank you to the RefSeq project at NCBI and the Ensembl/GENCODE project at EMBL-EBI.\nYou can contact the authors directly at \nMANE-help@ncbi.nlm.nih.gov\nor \nmane-help@ebi.ac.uk.
\n\n\nMorales J, Pujar S, Loveland JE, Astashyn A, Bennett R, Berry A, Cox E, Davidson C, Ermolaeva O,\nFarrell CM et al.\n\nA joint NCBI and EMBL-EBI transcript set for clinical genomics and research.\nNature. 2022 Apr;604(7905):310-315.\nPMID: 35388217; PMC:
\nThis track displays 664,558 unique small open reading frames (sORFs) in the human\ngenome from MetamORF, a\nmeta-database that consolidates sORF data identified by both experimental and computational\napproaches. sORFs are defined as ORFs encoding fewer than 100 amino acids (excluding stop\ncodons and introns).\n
\n\n\nMetamORF was built by gathering publicly available sORF data from multiple sources,\nnormalizing it, and removing redundancy. From 2,594,154 source ORFs across human and mouse,\nMetamORF identified 1,162,675 unique ORFs (664,771 human, 497,904 mouse) associated with\n153,553 unique transcripts. The database enables comparison of sORFs across distinct original\ndata sources at the ORF, transcript, and gene levels. For full documentation, see the\nMetamORF documentation page.\n
\n\n\nThe human sORFs in MetamORF were compiled from seven primary data sources and 46 individual\nribosome profiling datasets from\nsORFs.org.\nThe primary sources are:\n
\n\n| Source | \nDescription | \nReference | \n
|---|---|---|
| Erhard et al. 2018 | \nUnion of ORFs detected by PRICE, RP-BP, ORF-RATER, or annotated in Ensembl v75 | \nNat Methods 2018 | \n
| Johnstone et al. 2016 | \nLocation and translation data for analyzed transcripts and ORFs | \nEMBO J 2016 | \n
| Laumont et al. 2016 | \nCryptic MAPs (minor ORF-encoded peptides) with genomic and proteomic features | \nNat Commun 2016 | \n
| Mackowiak et al. 2015 | \nSystematic identification of sORFs across vertebrate genomes | \nGenome Biol 2015 | \n
| Samandi et al. 2017 | \nAlternative protein predictions based on RefSeq GRCh38 | \neLife 2017 | \n
| sORFs.org | \nRepository of sORFs from 46 individual ribosome profiling experiments | \nOlexiouk et al., Nucleic Acids Res 2018 | \n
\nORFs were identified using three main approaches: bioinformatic predictions, ribosome profiling\nexperiments, and mass spectrometry (proteomics, peptidomics, and proteogenomics).\n
\n\n\nMetamORF classifies ORFs by their position relative to annotated coding sequences:\n
\n\nORFs are also classified by the biotype of their host RNA: intergenic, ncRNA, pseudogene,\nNMD (nonsense-mediated decay), or readthrough transcripts.\n
\n\n\nItems are displayed in bigGenePred format. Each item is labeled with its MetamORF ORF\nID. Color reflects the categorical Kozak consensus strength:\n
\n\n Strong – A/G at position −3 and G at position +4
\n Moderate – only one of those positions matches
\n Weak – neither position matches
\n non-ATG – near-cognate start codon; the Kozak rule does not apply
\n no context – chromosome edge or context unavailable\n
\nMouseover shows the ORF ID, ORF annotation, start codon, Kozak strength and TE,\nhost transcripts, and the cell types where the ORF was reported.\n
\n\nAvailable filters: start codon, Kozak strength, Kozak TE.
\n\n\nThe raw data can be explored interactively with the\nTable Browser or the\nData Integrator. The data can be accessed from\nscripts through our API; the track name is\n"metamorf".\n
\n\n\nFor automated download and analysis, the genome annotation is stored in a bigBed file that\ncan be downloaded from\nour download server.\nIndividual regions or the whole genome annotation can be obtained using our tool\nbigBedToBed, which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tool can also be used to obtain only features within a given range, e.g.\n
\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/ncOrfs/metamorf/MetamORF.kozak.bb -chrom=chr21 -start=0 -end=100000000 stdout\n\n\nThe original data and additional downloads are available from the\nMetamORF website.\nSource code is available on\nGitHub.\n
\n\n\nThe MetamORF BED 12 data was obtained from the MetamORF\ntrack hub\nand converted to bigBed format at UCSC. Coordinates are on the GRCh38/hg38 assembly\n(based on Ensembl release 90).\n
\n\n\nThanks to the MetamORF team at the TAGC (Theories and Approaches of Genomic Complexity)\nlaboratory, Aix-Marseille University, for creating this resource and making it publicly\navailable.\n
\n\n\nErhard F, Halenius A, Zimmermann C, L'Hernault A, Kowalewski DJ, Weekes MP, Stevanovic S,\nZimmer R, Dölken L.\n\nImproved Ribo-seq enables identification of cryptic translation events.\nNat Methods. 2018 May;15(5):363-366.\nPMID: 29529017; PMC:
\nJohnstone TG, Bazzini AA, Giraldez AJ.\n\nUpstream ORFs are prevalent translational repressors in vertebrates.\nEMBO J. 2016 Apr 1;35(7):706-23.\nPMID: 26896445; PMC:
\nLaumont CM, Daouda T, Laverdure JP, Bonneil É, Caron-Lizotte O, Hardy MP, Granados DP, Durette C,\nLemieux S, Thibault P et al.\n\nGlobal proteogenomic analysis of human MHC class I-associated peptides derived from non-canonical\nreading frames.\nNat Commun. 2016 Jan 5;7:10238.\nPMID: 26728094; PMC:
\nMackowiak SD, Zauber H, Bielow C, Thiel D, Kutz K, Calviello L, Mastrobuoni G, Rajewsky N, Kempa S,\nSelbach M et al.\n\nExtensive identification and analysis of conserved small ORFs in animals.\nGenome Biol. 2015 Sep 14;16:179.\nPMID: 26364619; PMC:
\nOlexiouk V, Van Criekinge W, Menschaert G.\n\nAn update on sORFs.org: a repository of small ORFs identified by ribosome profiling.\nNucleic Acids Res. 2018 Jan 4;46(D1):D497-D502.\nPMID: 29140531; PMC:
\nSamandi S, Roy AV, Delcourt V, Lucier JF, Gagnon J, Beaudoin MC, Vanderperre B, Breton MA, Motard J,\nJacques JF et al.\n\nDeep transcriptome annotation enables the discovery and functional characterization of cryptic small\nproteins.\nElife. 2017 Oct 30;6.\nPMID: 29083303; PMC:
\nThis track displays 229,251 non-canonical open reading frames (ORFs) from\nnuORFdb v1.2\n(novel unannotated ORF database), a database of ORFs with evidence of translation detected by\nribosome profiling (Ribo-seq). nuORFdb was developed at the Broad Institute of MIT and Harvard as a resource for\nidentifying non-canonical peptides in immunopeptidomic mass spectrometry datasets.\n
\n\n\nThe ORFs were predicted using a hierarchical pipeline that aggregates ribosome profiling signal\nacross 29 primary healthy and cancer tissue samples and cell lines. The pipeline operates at\nmultiple levels—individual samples, tissues, and combined across all samples—to predict\nlowly translated ORFs while maintaining sensitivity for tissue-specific variants.\nAll ORFs have a minimum length of 8 amino acids.\n
\n\n\nItems are displayed in bigGenePred format. Each item is labeled with the nuORFdb ORF\nidentifier, which encodes the source Ensembl transcript and ORF number (e.g.\nENST00000488147.1_1_1). Color reflects the categorical\nKozak consensus strength:\n
\n\n Strong – A/G at position −3 and G at position +4
\n Moderate – only one of those positions matches
\n Weak – neither position matches
\n non-ATG – near-cognate start codon; the Kozak rule does not apply
\n no context – chromosome edge or context unavailable\n
\nMouseover shows the ORF ID in its host gene, gene biotype, start codon, Kozak\nstrength and TE, predictor type, and the simplified plotType category.\n
\n\n\nAvailable filters: start codon, Kozak strength, Kozak TE, ORF category\n(plotType: 8 broad classes; or type: 25 finer\ncategories).\n
\n\n\nThe track includes the following ORF categories (by type):\n
\n\nEach item also includes the predicted protein sequence and additional classification fields\n(predictorType, plotType, geneType) from the nuORFdb annotations.\n
\n\n\nThe raw data can be explored interactively with the\nTable Browser or the\nData Integrator. The data can be accessed from\nscripts through our API; the track name is\n"nuorfdb".\n
\n\n\nFor automated download and analysis, the genome annotation is stored in a bigBed file that\ncan be downloaded from\nour download server.\nIndividual regions or the whole genome annotation can be obtained using our tool\nbigBedToBed, which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tool can also be used to obtain only features within a given range, e.g.\n
\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/ncOrfs/nuorfdb/nuorfdb.kozak.bb -chrom=chr21 -start=0 -end=100000000 stdout\n\n\nThe original data files can be downloaded from the\nnuORFdb website\nat the Broad Institute.\n
\n\n\nThe nuORFdb v1.2 data files (BED12 coordinates, Excel annotations, and protein FASTA sequences)\nwere downloaded from the Broad Institute. The BED12 file was combined with the annotation\nspreadsheet (keyed on ORF_ID_hg38) and protein FASTA (keyed on sequence header ID) to\nproduce a bigGenePred+ format file with 23 fields (12 standard BED fields, 8 bigGenePred fields,\nand 3 extended fields: predictorType, plotType, and proteinSequence).\n
\n\n\nA small number of entries (176 out of 229,251) used non-standard chromosome names\n(e.g. chrGL000008.2, chrMT) which were mapped to UCSC standard names\n(e.g. chr4_GL000008v2_random, chrM).\n
\n\n\nThanks to Tamara Ouspenskaia, Travis Law, Karl Clauser, and colleagues at the Broad Institute\nof MIT and Harvard for creating nuORFdb and making the data publicly available.\nThanks to Eric Malekos, UCSC, for suggesting this database.\n
\n\n\nOuspenskaia T, Law T, Clauser KR, Klaeger S, Sarkizova S, Aguet F, Li B, Christian E, Knisbacher BA,\nLe PM et al.\n\nUnannotated proteins expand the MHC-I-restricted immunopeptidome in cancer.\nNat Biotechnol. 2022 Feb;40(2):209-217.\nDOI: 10.1038/s41587-021-01021-3; PMID:
\nThis track displays 921,170 protein-coding ORFs from\nOpenProt v2.2, a database that\nprovides a comprehensive annotation of all possible protein-coding ORFs in the human genome.\nIn addition to currently annotated coding sequences (CDSs) and their reference proteins\n(RefProts), OpenProt predicts alternative ORFs (AltORFs) and their corresponding alternative\nproteins (AltProts) that are hidden within transcripts previously considered to encode only\na single protein.\n
\n\n\nA pre-filtered subtrack (OpenProt MS>=2) is also available, containing only the\n377,916 ORFs with at least 2 unique mass spectrometry peptides detected across studies,\nmatching the MS-evidence threshold used by OpenProt for their curated downloads.\n
\n\n\nOpenProt classifies proteins into three types:\n
\n\nAltORFs are further classified by their localization relative to the annotated CDS:\n
\n\nItems are displayed in bigGenePred format. Items are labeled with the protein accession\nnumber: IDs starting with IP_ are predicted AltProts, II_ are novel\nisoforms, and other IDs (e.g. NP_, ENSP) are RefProts from existing\nannotations. Color reflects the categorical Kozak consensus strength:\n
\n\n Strong – A/G at position −3 and G at position +4
\n Moderate – only one of those positions matches
\n Weak – neither position matches
\n non-ATG – near-cognate start codon; the Kozak rule does not apply
\n no context – chromosome edge or context unavailable\n
\nMouseover shows the protein accession in its host gene, protein type and ORF\nlocalization, start codon, Kozak strength and TE, MS score, TE score, and InterPro\ndomain count.\n
\n\nThe track includes the following filter options:
\n\nThe raw data can be explored interactively with the\nTable Browser or the\nData Integrator. The data can be accessed from\nscripts through our API; the track name is\n"openprot" (all ORFs) or "openprotMs" (MS-filtered).\n
\n\n\nFor automated download and analysis, the genome annotations are stored in bigBed files that\ncan be downloaded from\nour download server.\nIndividual regions or the whole genome annotation can be obtained using our tool\nbigBedToBed, which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tool can also be used to obtain only features within a given range, e.g.\n
\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/ncOrfs/openprot/openprot.kozak.bb -chrom=chr21 -start=0 -end=100000000 stdout\n\n\nThe original data files can be downloaded from the\nOpenProt download page.\n
\n\n\nThe OpenProt v2.2 BED12 and TSV annotation files were downloaded from the OpenProt API.\nThe BED file (2,846,289 rows) contains genomic coordinates for all predicted ORFs; since the\nsame protein can be mapped through multiple transcripts to identical genomic coordinates,\ndeduplication reduced this to 921,170 unique genomic features (3 entries with overlapping\nBED blocks were excluded).\n
\n\n\nEach BED entry was annotated with metadata from the TSV file by joining on protein accession.\nFor proteins with multiple transcript entries in the TSV, the annotation with the highest\nMS score was retained. Extended fields include protein type (AltProt/RefProt/Isoform),\nORF localization, MS score, TE (Translation Event) score, Kozak motif status, InterPro domain\ncount, and reading frame.\n
\n\n\nThe annotation is based on GRCh38.p13, Ensembl release 106, and UniProt release 2022_06_01.\n
\n\n\nThanks to Xavier Roucou and the OpenProt team at the Université de Sherbrooke for\ncreating OpenProt and making the data publicly available.\n
\n\n\nBrunet MA, Brunelle M, Lucier JF, Delcourt V, Levesque M, Grenier F, Samandi S, Leblanc S, Aguilar\nJD, Dufour P et al.\n\nOpenProt: a more comprehensive guide to explore eukaryotic coding potential and proteomes.\nNucleic Acids Res. 2019 Jan 8;47(D1):D403-D410.\nPMID: 30299502; PMC:
\nBrunet MA, Lucier JF, Levesque M, Leblanc S, Jacques JF, Al-Saedi HRH, Guilloy N, Grenier F, Avino\nM, Fournier I et al.\n\nOpenProt 2021: deeper functional annotation of the coding potential of eukaryotic genomes.\nNucleic Acids Res. 2021 Jan 8;49(D1):D380-D388.\nPMID: 33179748; PMC:
\nThis track displays 921,170 protein-coding ORFs from\nOpenProt v2.2, a database that\nprovides a comprehensive annotation of all possible protein-coding ORFs in the human genome.\nIn addition to currently annotated coding sequences (CDSs) and their reference proteins\n(RefProts), OpenProt predicts alternative ORFs (AltORFs) and their corresponding alternative\nproteins (AltProts) that are hidden within transcripts previously considered to encode only\na single protein.\n
\n\n\nA pre-filtered subtrack (OpenProt MS>=2) is also available, containing only the\n377,916 ORFs with at least 2 unique mass spectrometry peptides detected across studies,\nmatching the MS-evidence threshold used by OpenProt for their curated downloads.\n
\n\n\nOpenProt classifies proteins into three types:\n
\n\nAltORFs are further classified by their localization relative to the annotated CDS:\n
\n\nItems are displayed in bigGenePred format. Items are labeled with the protein accession\nnumber: IDs starting with IP_ are predicted AltProts, II_ are novel\nisoforms, and other IDs (e.g. NP_, ENSP) are RefProts from existing\nannotations. Color reflects the categorical Kozak consensus strength:\n
\n\n Strong – A/G at position −3 and G at position +4
\n Moderate – only one of those positions matches
\n Weak – neither position matches
\n non-ATG – near-cognate start codon; the Kozak rule does not apply
\n no context – chromosome edge or context unavailable\n
\nMouseover shows the protein accession in its host gene, protein type and ORF\nlocalization, start codon, Kozak strength and TE, MS score, TE score, and InterPro\ndomain count.\n
\n\nThe track includes the following filter options:
\n\nThe raw data can be explored interactively with the\nTable Browser or the\nData Integrator. The data can be accessed from\nscripts through our API; the track name is\n"openprot" (all ORFs) or "openprotMs" (MS-filtered).\n
\n\n\nFor automated download and analysis, the genome annotations are stored in bigBed files that\ncan be downloaded from\nour download server.\nIndividual regions or the whole genome annotation can be obtained using our tool\nbigBedToBed, which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tool can also be used to obtain only features within a given range, e.g.\n
\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/ncOrfs/openprot/openprot.kozak.bb -chrom=chr21 -start=0 -end=100000000 stdout\n\n\nThe original data files can be downloaded from the\nOpenProt download page.\n
\n\n\nThe OpenProt v2.2 BED12 and TSV annotation files were downloaded from the OpenProt API.\nThe BED file (2,846,289 rows) contains genomic coordinates for all predicted ORFs; since the\nsame protein can be mapped through multiple transcripts to identical genomic coordinates,\ndeduplication reduced this to 921,170 unique genomic features (3 entries with overlapping\nBED blocks were excluded).\n
\n\n\nEach BED entry was annotated with metadata from the TSV file by joining on protein accession.\nFor proteins with multiple transcript entries in the TSV, the annotation with the highest\nMS score was retained. Extended fields include protein type (AltProt/RefProt/Isoform),\nORF localization, MS score, TE (Translation Event) score, Kozak motif status, InterPro domain\ncount, and reading frame.\n
\n\n\nThe annotation is based on GRCh38.p13, Ensembl release 106, and UniProt release 2022_06_01.\n
\n\n\nThanks to Xavier Roucou and the OpenProt team at the Université de Sherbrooke for\ncreating OpenProt and making the data publicly available.\n
\n\n\nBrunet MA, Brunelle M, Lucier JF, Delcourt V, Levesque M, Grenier F, Samandi S, Leblanc S, Aguilar\nJD, Dufour P et al.\n\nOpenProt: a more comprehensive guide to explore eukaryotic coding potential and proteomes.\nNucleic Acids Res. 2019 Jan 8;47(D1):D403-D410.\nPMID: 30299502; PMC:
\nBrunet MA, Lucier JF, Levesque M, Leblanc S, Jacques JF, Al-Saedi HRH, Guilloy N, Grenier F, Avino\nM, Fournier I et al.\n\nOpenProt 2021: deeper functional annotation of the coding potential of eukaryotic genomes.\nNucleic Acids Res. 2021 Jan 8;49(D1):D380-D388.\nPMID: 33179748; PMC:
\n The Orphadata: Aggregated data from Orphanet (Orphanet) track shows genomic positions \n of genes and their association to human disorders, related epidemiological data, and phenotypic\n annotations. As a consortium of 40 countries throughout the world, \n Orphanet\n gathers and improves knowledge regarding rare diseases and maintains the Orphanet rare disease \n nomenclature (ORPHAcode), essential in improving the visibility of rare diseases in health and\n research information systems. The data is updated monthly by Orphanet and updated monthly \n on the UCSC Genome Browser.\n
\n\nMouseover on items shows the gene name, disorder name, modes of inheritance(s) (if available), \nand age(s) of onset (if available). Tracks can be filtered according to gene-disorder association \ntypes, modes of inheritance, and ages of onset. Clicking an item from the browser will return \nthe complete entry, including gene linkouts to Ensembl, OMIM, and HGNC, as well as phenotype information \nusing HPO (human phenotype ontology) terms.\n\nFor more information on the use of this data, see \nthe Orphadata FAQs.
\n\nThe raw data can be explored interactively with the Table Browser, \nor the Data Integrator. \nFor automated analysis, the data may be queried from our REST API. \nPlease refer to our mailing list archives \nfor questions, or our Data Access FAQ \nfor more information.\n\n
Data is also freely available through \nOrphadata datasets.
\n\nOrphadata files were reformatted at UCSC to the \nbigBed format.
\n\nThank you to the Orphanet and Orphadata team and to Tiana Pereira, Christopher Lee, \nDaniel Schmelter, and Anna Benet-Pages of the Genome Browser team.
\n\n\nPavan S, Rommel K, Mateo Marquina ME, Höhn S, Lanneau V, Rath A.\n\nClinical Practice Guidelines for Rare Diseases: The Orphanet Database.\nPLoS One. 2017;12(1):e0170365.\nPMID: 28099516; PMC:
\nNguengang Wakap S, Lambert DM, Olry A, Rodwell C, Gueydan C, Lanneau V, Murphy D, Le Cam Y, Rath A.\n\nEstimating cumulative point prevalence of rare diseases: analysis of the Orphanet database.\nEur J Hum Genet. 2020 Feb;28(2):165-173.\nPMID: 31527858; PMC:
\nThis container track helps call out sections of the genome that often cause problems or\nconfusion when working with the genome. The hg19 genome has a track with the same name, but with\nmore subtracks, as the GeT-RM and Genome-in-a-Bottle artifact variants do not exist \nfor hg38.\n\n
\nThe Problematic Regions track contains the following subtracks:\n
\nThe Highly Reproducible Regions track highlights regions and variants\nfrom eight samples that can be used to assess variant detection pipelines. The\n"Highly Reproducible Regions" subtrack comprises the intersection of the reproducible\nregions across all eight samples, while the "Variants" subtracks contain the reproducible\nvariants from each assayed sample. Both tracks contain data from the following samples:\n
\nThe Genome in a Bottle (GIAB) Problematic Regions tracks provide stratifications of the\ngenome to evaluate variant calls in complex regions. It is designed for use with Global Alliance\nfor Genomic Health (GA4GH) benchmarking tools like\nhap.py\nand includes regions with low complexity, segmental duplications, functional regions,\nand difficult-to-sequence areas. Developed in collaboration with GA4GH, the\nGenome in a Bottle (GIAB) consortium, and the\nTelomere-to-Telomere Consortium (T2T), the dataset aims to standardize the\nanalysis of genetic variation by offering pre-defined BED files for stratifying true and false\npositives in genomic studies, facilitating accurate assessments in complex areas of the genome.
\n\n\nThe creation of the GIAB Problematic Regions tracks involves using a pipeline and configuration to\ngenerate stratification BED files that categorize genomic regions based on specific challenges,\nsuch as low complexity or difficult mapping, to facilitate accurate benchmarking of variant calls.\nFor more information on the pipeline and configuration used, please visit the following webpage:\n\nhttps://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/genome-stratifications/v3.5/README.md.\nIf you have questions or comments, please write to Justin Zook (jzook@nist.gov).
\n\n\nThe Panmask Easy 151b Regions subtrack contains a set of sample-agnostic easy regions where\nshort-read variant calling reaches high accuracy. Easy regions are derived for variant filtration\nagnostic to individual samples. They are genomic intervals where general variant callers achieve\nhigh accuracy without sophisticated filtering.
\n\nA set of easy regions for ancient DNA variant filtering was generated by selecting 35-mers that\ncould not be mapped elsewhere within one mismatch or gap. Read alignments from multiple samples\nwere inspected to exclude regions with excessively high or low coverage or those enriched with\nlow mapping quality alignments. The easy regions generated through this k-mer uniqueness procedure\nare referred to as pm151:lenient, where "pm" stands for panmask. In addition, low\ncomplexity regions identified by SDUST were removed.
\nThe pm151 regions are used to filter spurious variant calls in centromeres, long repeats, and\nother genomic regions where short-read mapping is often problematic. They cover 88.2% of hg38,\n92.2% of coding regions, and 96.3% of ClinVar pathogenic variants. The track can be used to filter\nvariant calls for clinical or research human samples. Like the HighRepro track in this container\n(see above), it shows regions that are easy to sequence, not those that are problematic. The data\nwas derived from the HPRC assemblies, and this track presents the 151b-easy panmask set.
\n\n\nEach track contains a set of regions of varying length with no special configuration options. \nThe UCSC Unusual Regions track has a mouse-over description, all other tracks have at most\na name field, which can be shown in pack mode. The tracks are usually kept in dense mode.\n
\n\n\nThe Hide empty subtracks control hides subtracks with no data in the browser window.\nChanging the browser window by zooming or scrolling may result in the display of a different\nselection of tracks.\n
\n\n\nThe raw data can be explored interactively with the Table Browser\nor the Data Integrator.\n\n
\nFor automated download and analysis, the genome annotation is stored in bigBed files that\ncan be downloaded from\nour download server.\nIndividual\nregions or the whole genome annotation can be obtained using our tool bigBedToBed\nwhich can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tool\ncan also be used to obtain only features within a given range, e.g. \n
\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/problematic/comments.bb -chrom=chr21 -start=0 -end=100000000 stdout
\n
\nFiles were downloaded from the respective databases and converted to bigBed format.\nThe procedure is documented in our\nhg38 makeDoc file.\n
\n\n\nThanks to Anna Benet-Pagès, Max Haeussler, Angie Hinrichs, Daniel Schmelter, and Jairo\nNavarro at the UCSC Genome Browser for planning, building, and testing these tracks. The\nunderlying data comes from the\nENCODE Blacklist and some parts were copied manually from the HGNC and NCBI\nRefSeq tracks.\n
\n\n\nAmemiya HM, Kundaje A, Boyle AP.\n\nThe ENCODE Blacklist: Identification of Problematic Regions of the Genome.\nSci Rep. 2019 Jun 27;9(1):9354.\nPMID: 31249361; PMC:
\nDwarshuis N, Kalra D, McDaniel J, Sanio P, Alvarez Jerez P, Jadhav B, Huang WE, Mondal R, Busby B,\nOlson ND et al.\n\nThe GIAB genomic stratifications resource for human reference genomes.\nNat Commun. 2024 Oct 19;15(1):9029.\nPMID: 39424793; PMC:
\nKrusche P, Trigg L, Boutros PC, Mason CE, De La Vega FM, Moore BL, Gonzalez-Porta M, Eberle MA,\nTezak Z, Lababidi S et al.\n\nBest practices for benchmarking germline small-variant calls in human genomes.\nNat Biotechnol. 2019 May;37(5):555-560.\nPMID: 30858580; PMC:
\nLi H.\n\nFinding easy regions for short-read variant calling from pangenome data.\nArXiv. 2025 Aug 8;.\nPMID: 40799803; PMC:
\nPan B, Ren L, Onuchic V, Guan M, Kusko R, Bruinsma S, Trigg L, Scherer A, Ning B, Zhang C et\nal.\n\nAssessing reproducibility of inherited variants detected with short-read whole genome\nsequencing.\nGenome Biol. 2022 Jan 3;23(1):2.\nPMID: 34980216; PMC:
The tracks that are listed here contain genetic variants and links to scientific publications that \nmention them.
\n\nFor additional information please click on the hyperlink of the respective track above.\n
\nBy default, each variant is labeled with the nucleotide change. Hover over the\nfeature to see more information, explained on the track details page of the particular track\nor when clicking onto the feature.
\n\nFor data provenance, access and descriptions, please click the documentation via the link above.\n
\n", "itemRgb": "on", "longLabel": "dbSNP variants and other genetic variants grounded to dbSNP by tmVar; collected by PubTator3", "mouseOver": "$name found in ${numPubmedIds} PubMed articles", "noScoreFilter": "on", "parent": "varsInPubs pack", "shortLabel": "PubTator Variants", "track": "pubtator", "type": "bigBed 9 +", "urls": "pubmedIds=\"https://www.ncbi.nlm.nih.gov/pubmed/$$\"", "visibility": "dense" } }, "description": "dbSNP variants and other genetic variants grounded to dbSNP by tmVar; collected by PubTator3", "category": [ "Phenotypes, Variants, and Literature" ], "displays": [ { "type": "LinearBasicDisplay", "displayId": "hg38-pubtator-LinearBasicDisplay", "mouseover": "jexl:`${get(feature,'name')} found in ${get(feature,'numPubmedIds')} PubMed articles`" } ] }, { "trackId": "hg38-refSeqFuncElems", "name": "RefSeq Func Elems", "type": "FeatureTrack", "assemblyNames": [ "hg38" ], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/ncbiRefSeq/refSeqFuncElems.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/hg38/ncbiRefSeq/refSeqFuncElems.bb", "group": "regulation", "itemRgb": "on", "longLabel": "NCBI RefSeq Functional Elements", "mouseOverField": "_mouseOver", "noScoreFilter": ".", "shortLabel": "RefSeq Func Elems", "track": "refSeqFuncElems", "type": "bigBed 9 +", "urls": "geneIds=https://www.ncbi.nlm.nih.gov/gene?cmd=Retrieve&dopt=full_report&list_uids=$$ pubMedIds=https://www.ncbi.nlm.nih.gov/pubmed/$$ soTerm=http://www.sequenceontology.org/browser/obob.cgi?rm=term_list&release=current_svn&obo_query=$$", "html": "\nNCBI recently announced a new release of\n functional regulatory elements.\n\nNCBI is now providing \nRefSeq and \nGene\nrecords for non-genic functional elements that have been described in the literature and are \nexperimentally validated. Elements in scope include experimentally-verified gene regulatory \nregions (e.g., enhancers, silencers, locus control regions), known structural elements\n(e.g., insulators, DNase I hypersensitive sites, matrix/scaffold-associated regions), \nwell-characterized DNA replication origins, and clinically-significant sites of DNA recombination\nand genomic instability. Priority is given to genomic regions that are implicated in human disease \nor are otherwise of significant interest to the research community. Currently, the scope of this \nproject is restricted to human and mouse. The current scope does not include functional elements\npredicted from large-scale epigenomic mapping studies, nor elements based on disease-associated \nvariation.
\n\n\nFunctional elements are colored by Sequence Ontology (SO) term\nusing the same scheme as NCBI's Genome Data Viewer:\n
\nNCBI manually curated features in accordance with International Nucleotide \nSequence Database Collaboration (INSDC) standards. Features that are supported by direct \nexperimental evidence include at least one experiment qualifier with an evidence code (ECO ID) \nfrom the Evidence and Conclusion Ontology, and at least one citation from PubMed. Currently\n971 distinct PubMed citations are included in this track. \n
\n\n\nThis track was made with assistance from\nTerence Murphy at NCBI.
\n\n\nThe raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be \nqueried from our REST API,\nand the genome annotations are stored in files that can be downloaded from our \ndownload server, with more information available on\nour blog.
\n\n\nSeveral new enhancements to the RefSeq Functional Elements dataset are available as a Public Hub.\nThe hub can be found on the Public Hub page.\nThe track hub was prepared by Dr. Catherine M. Farrell, NCBI/NLM/NIH with further insights discussed\nin a related NCBI blog post.
\n\n\nPruitt KD, Brown GR, Hiatt SM, Thibaud-Nissen F, Astashyn A, Ermolaeva O, Farrell CM, Hart J,\nLandrum MJ, McGarvey KM et al.\nRefSeq: an update on mammalian reference sequences.\nNucleic Acids Res. 2014 Jan;42(Database issue):D756-63.\nPMID: 24259432; PMC:
\nPruitt KD, Tatusova T, Maglott DR.\nNCBI Reference Sequence (RefSeq): a curated non-redundant\nsequence database of genomes, transcripts and proteins.\nNucleic Acids Res. 2005 Jan 1;33(Database issue):D501-4.\nPMID: 15608248; PMC:
\nThe \"Splicing Impact\" container track contains tracks showing the predicted or validated effect of variants\nclose to splice sites.\n
\n\nAbSplice is a method that predicts aberrant splicing across human tissues, as described in Wagner,\nÇelik et al., 2023. This track displays precomputed AbSplice scores for all possible\nsingle-nucleotide variants genome-wide. The scores represent the probability that a given variant\ncauses aberrant splicing in a given tissue.\nAbSplice scores\ncan be computed from VCF files and are based on quantitative tissue-specific splice site annotations\n(SpliceMaps).\nWhile SpliceMaps can be generated for any tissue of interest from a cohort of RNA-seq samples, this\ntrack includes 49 tissues available from the\nGenotype-Tissue\nExpression (GTEx) dataset.\n
\n\nSpliceAI is an open-source deep\nlearning splicing prediction algorithm that can predict splicing alterations caused by DNA variations.\nTo score variants, the spliceAI algorithm is run on the genome sequence itself and scores each\nnucleotide for the probability that it is a donor or acceptor site, on both the\nforward and the reverse strand. Then variants are added to the sequence and the new sequence is\nscored. Variants may activate nearby cryptic splice sites, leading to abnormal transcript isoforms.\nSpliceAI was developed at Illumina; a\nlookup tool\nis provided by the Broad institute. \n
\n\n\nThis SpliceAI "Wildtype" container track shows the scores for the genome sequence itself,\nwithout variants, from predicted splice donor (5' intron boundaries) and splice acceptor\n(3' intron boundaries) sites. Predictions are strand-specific, with separate subtracks for the\nplus and minus strands. These tracks are useful in combination with the variants track for\nevaluating new transcript models. They can be used to assess potential exon boundaries or\npossible splice acceptor sites.
\n\nWhy are some variants not scored by SpliceAI?\n\nSpliceAI only annotates variants within genes defined by the gene\nannotation file. Additionally, SpliceAI does not annotate variants if they are close to chromosome\nends (5kb on either side), deletions of length greater than twice the input parameter -D, or\ninconsistent with the reference fasta file.\n
\n\nWhat are the differences between masked and unmasked tracks?\n\nThe unmasked tracks include splicing changes corresponding to strengthening annotated splice sites\nand weakening unannotated splice sites, which are typically much less pathogenic than weakening\nannotated splice sites and strengthening unannotated splice sites. The delta scores of such splicing\nchanges are set to 0 in the masked files. We recommend using the unmasked tracks for alternative\nsplicing analysis and masked tracks for variant interpretation.\n
\n\nSpliceVarDB is an online database consolidating over 50,000 variants assayed\nfor their effects on splicing in over 8,000 human genes. The authors evaluated\nover 500 published data sources and established a spliceogenicity scale to\nstandardize, harmonize, and consolidate variant validation data generated by a\nrange of experimental protocols. Genes and variant locations were obtained using\nGENCODE v44. Splice regions were calculated as specific distances from the closest\ncanonical exon, including 5' and 3' untranslated regions (UTRs). The\ndatabase is available at\nsplicevardb.org.
\n\nThe AbSplice score is a probability estimate of how likely aberrant splicing of some sort takes\nplace in a given tissue. The authors suggest three cutoffs which are represented by color in the track.\n
\n\n\nMouseover on items shows the gene name, maximum score, and tissues that had this score. Clicking on\nany item brings up a table with scores for all 49 GTEX tissues.\n
\n\n\nVariants are colored according to Walker et al. 2023 splicing impact:\n
\n\nThe scores range from 0 to 1 and can be interpreted as the\nprobability of the variant being splice-altering. In the paper, a detailed characterization is\nprovided for 0.2 (high recall), 0.5 (recommended), and 0.8 (high precision) cutoffs.
\n\n\nThese tracks are in bigWig format. The signal height represents the SpliceAI probability score.\nThis track may be configured in a variety of ways to highlight different aspects of the displayed\ninformation. Click the "Graph configuration help" link for an explanation of configuration\noptions.
\n\nAccording to the strength of their supporting\nevidence, variants were classified as "splice-altering" (~25%), "not\nsplice-altering" (~25%), and "low-frequency splice-altering" (~50%), which\ncorrespond to weak or indeterminate evidence of spliceogenicity. 55% of the\nsplice-altering variants in SpliceVarDB are outside the canonical splice sites\n(5.6% are deep intronic). The data is shown as lollipop plots that can be clicked, \nthe details page then shows a link to SpliceVarDB with full details.\n
\n\nThe classification thresholds primarily follow those established by the original study.\nHowever, most studies only defined criteria for splice-altering variants and did not define\ncriteria for variants that resulted in normal splicing. The authors implemented stringent\nthresholds to define the normal category and ensure a high-quality set of control variants.\nVariants that did not meet these criteria were classified as low-frequency splice-altering\nvariants with a wide range of sub-optimal scores. Variants that fell between the normal and\nsplice-altering classifications were placed into a low-frequency splice-altering category.\nIn situations where a variant was validated multiple times, if at least one validation\nreturned splice-altering and another returned normal, the "conflicting" category\nwas applied.\n
\n\n\nThe lollipop plots are color-coded based on the score value, which corresponds\nto the following classifications:\n
Data was converted from the files (AbSplice_DNA_ hg38 _snvs_high_scores.zip) provided by the authors\nat zenodo.org. Files in the\nscore_cutoff=0.01 directory were concatenated. To convert the data to bigBed format, scores and\ntheir tissues were selected from the AbSplice_DNA fields and maximum scores, and then calculated\nusing a custom Python script, which can be found in the\n\nmakeDoc from our GitHub repository.
\n\n\nThe data were downloaded from \nSince most of the values are 0 or almost 0, we selected only those variants\nwith a score equal to or greater than 0.02.\n \nThe complete processing of this track can be found in the \nmakedoc.\n Data was provided by the Michael Hiller lab. SpliceAI was run on the entire genome reference\nchromosomes. Since the algorithm does not know where transcripts start or end, the scores\ncan differ from those on other websites, especially for splice sites before the last exon or\naround the first exon. The data was converted by Patricia Sullivan from SpliceVarDB to\nbigLolly format, and the UCSC\nBrowser staff downloaded it for display.\n Precomputed AbSplice-DNA scores in all 49 GTEx tissues are available at\n\nZenodo. \nThe SpliceAI data is not available for download from the Genome Browser.\nThe raw data can be found directly on\nIllumina.\nFOR ACADEMIC AND NOT-FOR-PROFIT RESEARCH USE ONLY. The SpliceAI scores are\nmade available by Illumina only for academic or not-for-profit research only.\nBy accessing the SpliceAI data, you acknowledge and agree that you may only\nuse this data for your own personal academic or not-for-profit research only,\nand not for any other purposes. You may not use this data for any for-profit,\nclinical, or other commercial purpose without obtaining a commercial license\nfrom Illumina, Inc.\n \nThe raw data can be explored interactively with the Table Browser\nor the Data Integrator. For automated analysis, the data may\nbe queried from our REST API. \nFor automated download and analysis, the genome annotation is stored in a bigBed or a bigWig file\nthat can be downloaded from\nour download server.\nIndividual regions or the whole genome annotation can be obtained using our tools, e.g.\n Thanks to Illumina for making SpliceAI available, both the model and the precomputed data files. Thanks to Francois Lecoquierre from the University of Oxford, Jean-Madeleine de Sainte Agathe\nfrom Institut Pasteur Paris, and Michael Hiller from the Senckenberg Museum Frankfurt for\nsuggesting and then creating the SpliceAI Wildtype annotations. Thanks to Nils Wagner for helpful comments and suggestions for the AbSplice track. Thanks to the SpliceVarDB team for converting the data into our data formats. \nJaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, Darbandi SF, Knowles D, Li YI, Kosmicki JA,\nArbelaez J, Cui W, Schwartz GB et al.\n\nPredicting Splicing from Primary Sequence with Deep Learning.\nCell. 2019 Jan 24;176(3):535-548.e24.\nPMID: 30661751\n \nSullivan PJ, Quinn JMW, Wu W, Pinese M, Cowley MJ.\n\n SpliceVarDB: A comprehensive database of experimentally validated human splicing variants.\nAm J Hum Genet. 2024 Oct 3;111(10):2164-2175.\nPMID: 39226898; PMC: \nWagner N, Çelik MH, Hölzlwimmer FR, Mertes C, Prokisch H, Yépez VA, Gagneur J.\n\nAberrant splicing prediction across human tissues.\nNat Genet. 2023 May;55(5):861-870.\nPMID: 37142848\n \nWalker LC, Hoya M, Wiggins GAR, Lindy A, Vincent LM, Parsons MT, Canson DM, Bis-Brewer D, Cass A,\nTchourbanov A et al.\n\nUsing the ACMG/AMP framework to capture evidence related to predicted and observed impact on\nsplicing: Recommendations from the ClinGen SVI Splicing Subgroup.\nAm J Hum Genet. 2023 Jul 6;110(7):1046-1067.\nPMID: 37352859; PMC: \nThe STRchive track displays 75 disease-associated short tandem repeat (STR) loci\ncurated by the STRchive project.\nSTRchive is a dynamic, community-driven resource that compiles population-level and\nlocus-specific data for tandem repeat loci implicated in human genetic diseases. \nTandem repeat expansion disorders are caused by the expansion of short repetitive DNA\nsequences beyond a pathogenic threshold. These expansions can cause a wide range of\nneurological, neuromuscular, and developmental disorders, including Huntington disease,\nfragile X syndrome, Friedreich ataxia, and many forms of spinocerebellar ataxia. \nThis track shows the genomic positions of disease-associated STR loci from the STRchive\ncatalog, along with the reference and pathogenic repeat motifs, minimum pathogenic repeat\ncount thresholds, mode of inheritance, and associated diseases. The data are based on\nthe GRCh38/hg38 reference assembly. \nItems are colored by mode of inheritance: \nEach item is labeled by its STRchive locus ID, which combines the disease abbreviation\nand gene symbol (e.g., "HD_HTT" for Huntington disease at the HTT\ngene). Hovering over an item shows the repeat motif, gene, pathogenic threshold,\nand inheritance mode. Clicking an item links to the corresponding\nSTRchive locus page with detailed\nclinical and population-level information. \nThe STRchive disease locus catalog was downloaded from the\nSTRchive GitHub\nrepository (file \nFor each locus, the catalog provides: \nThe BED file was converted to bigBed format for display in the Genome Browser. Coordinates\nwere used as provided (0-based half-open BED format). \nThe raw data can be explored interactively with the\nTable Browser or the\nData Integrator. For automated\nanalysis, the data may be queried from our\nREST API. The underlying bigBed\nfile can be downloaded from our\ndownload\nserver. \nThe complete STRchive dataset, including additional annotations not shown in this track,\nis available from strchive.org and\nthe STRchive GitHub\nrepository. The data are released under a\nCC BY 4.0\nlicense. \nThanks to Harriet Dashnow (University of Colorado), Laurel Hiatt (University of Utah),\nBen Weisburd (Broad Institute), and the STRchive team for creating and maintaining this\nresource. \nHiatt L, Weisburd B, Dolzhenko E, Rubinetti V, Avvaru AK,\nVanNoy GE, Kurtas NE, Rehm HL, Quinlan AR, Dashnow H.\n\nSTRchive: a dynamic resource detailing population-level and\nlocus-specific insights at tandem repeat disease loci.\nGenome Med. 2025 Mar 26;17(1):29.\nPMID: 40140942; PMC: \nThis track shows allele count distributions for 174,300 short tandem repeat (STR)\nloci genotyped across 61,000 Japanese individuals by the\nTohoku Medical Megabank\nOrganization (ToMMo). STR genotyping was performed with\nExpansion Hunter,\nwhich estimates repeat copy numbers from short-read whole-genome sequencing data.\n \nFor each locus, the track provides the repeat motif, the reference copy number, the\nmean and median copy number across the cohort, and a histogram of allele counts\nby repeat size. Click on any locus to see the allele count distribution as a\nbar chart.\n \nItems are colored by expected heterozygosity, computed as\nhet = 1 − ∑pi2 from allele counts\nacross the 61,000 Japanese individuals:\n \nThe allele count histogram on the detail page shows the number of alleles observed\nat each repeat copy number. The reference allele count is computed as AN minus the\nsum of all alternate allele counts.\n \nGenomic DNA was obtained from peripheral blood, saliva, or cord blood samples\nfrom participants in the Tohoku Medical Megabank Project. Whole-genome sequencing\nwas performed on multiple Illumina and MGI platforms (HiSeq 2500, NovaSeq 6000,\nDNBSeq-T7). STR genotyping was performed with\nExpansion Hunter,\nwhich uses paired-end reads and read pairs spanning, flanking, and fully contained\nwithin repeat regions to estimate repeat copy numbers.\n \nAt UCSC, the Expansion Hunter VCF was converted to bigBed format using a\ncustom Python script.\nFor each STR locus, the <STRn> symbolic alleles in the VCF ALT field encode\nthe repeat copy number, and the INFO/AC field provides the allele count for each.\nThe reference allele count was computed as AN minus the sum of all alternate AC values.\nThese were assembled into a histogram of copies=count pairs for display.\n \nThe raw data can be explored interactively with the\nTable Browser or the\nData Integrator.\nThe data can be accessed from scripts through our\nAPI, the track name is tommoStr.\n \nFor automated download and analysis, the genome annotation is stored in a bigBed\nfile that can be downloaded from\nour download server.\nThe file for this track is called tommoStr.bb.\nIndividual regions or the whole genome annotation can be obtained using our tool\nbigBedToBed, which can be compiled from the source code or downloaded as a\nprecompiled binary for your system. Instructions for downloading source code and\nbinaries can be found\nhere.\nThe tool can also be used to obtain features within a given range, e.g.\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/strVar/tommoStr.bb\n-chrom=chr21 -start=0 -end=100000000 stdout\n \nThe original data can be downloaded from the\njMorp 61KJPN-STR Downloads page.\nUse of the data requires agreement to the\nToMMo conditions of use.\n \nThanks to the Tohoku Medical Megabank Organization and the participants of the\nToMMo cohort study for making this data publicly available.\n \nTadaka S, Hishinuma E, Komaki S, Motoike IN, Kawashima J,\nSaigusa D, Inoue J, Takayama J, Okamura Y, Aoki Y\net al.\n\njMorp updates in 2020: large enhancement of multi-omics data\nresources on the general Japanese population.\nNucleic Acids Res. 2021 Jan 8;49(D1):D536-D544.\nPMID: 33179747; PMC: \nTadaka S, Kawashima J, Hishinuma E, Saito S, Okamura Y,\nOtsuki A, Kojima K, Komaki S, Aoki Y, Kanno T et al.\n\njMorp: Japanese Multi-Omics Reference Panel update report\n2023.\nNucleic Acids Res. 2024 Jan 5;52(D1):D622-D632.\nPMID: 37930845; PMC: \nThe TRExplorer track displays 5,599,658 tandem repeat (TR) loci from the\nTRExplorer\ncatalog. Tandem repeats are adjacent copies of a short DNA sequence motif; they include\nshort tandem repeats (STRs, motifs of 1–6 bp) and variable number tandem repeats\n(VNTRs, longer motifs). TRs are among the most polymorphic and mutationally active loci\nin the human genome, contributing to gene expression variation, complex disease risk,\nand over 60 known Mendelian disorders. \nThe catalog integrates loci from multiple sources, including perfect repeats in the\nreference genome, polymorphic TRs discovered in T2T assemblies and the Illumina 174k\ncohort, HipSTR catalog loci, and curated disease-associated repeat expansions. Each\nlocus is annotated with repeat purity, gene context, disease associations, and\npopulation allele frequency data from up to three cohorts. \nItems are colored by expected heterozygosity, computed as\nhet = 1 − ∑pi2 from allele counts\npooled across the TenK10K and HPRC256 cohorts: \nItems are labeled by the repeat motif sequence (truncated with “..” for\nmotifs longer than 25 characters). The BED score reflects repeat purity (0–1000).\nHovering over an item shows the full motif, motif size, number of reference copies,\nrepeat purity, gene annotation, and data source. \nClicking an item opens the details page, which includes a link to the corresponding\nTRExplorer locus\npage with interactive allele frequency visualizations. \nAllele frequency histograms are available for two cohorts where genotyping was\nperformed: \nFor each cohort, two parallel fields store allele sizes (in repeat copy numbers) and\ntheir corresponding counts, preserving the original order for histogram visualization.\nSummary allele counts are also available for the AoU1027\ncohort (1,027 HiFi PacBio samples from the All of Us Research Program\ngenotyped using TRGT-LPS). \nLoci in this catalog were compiled from multiple sources: \nThe TRExplorer catalog was built by merging tandem repeat annotations from multiple\nreference-based and population-based discovery approaches. For each locus, the repeat\nmotif, copy number, and purity were determined from the GRCh38 reference sequence.\nGene annotations were derived from MANE Select transcripts (with fallback to Gencode).\nPopulation allele frequencies were obtained by genotyping large cohorts using\nExpansionHunter and other TR genotyping tools. \nFor the UCSC Genome Browser track, the source catalog (TSV format) was converted to\nbigBed format. Coordinates in the source data are already 0-based half-open (BED\nconvention). Allele frequency histograms were split into parallel size and count fields\nto facilitate visualization. Items are colored by expected heterozygosity. \nThe raw data can be explored interactively with the\nTable Browser or the\nData Integrator. For automated\nanalysis, the data may be queried from our\nREST API. The underlying bigBed\nfile can be downloaded from our\ndownload\nserver. \nThe complete TRExplorer dataset and interactive tools are available from the\nTRExplorer web\nportal at the Broad Institute. Thanks to Ben Weisburd, Egor Dolzhenko, and the TRExplorer team\nfor making these data available. \nWeisburd B, Dolzhenko E, Bennett MF, Danzi MC, Xu IRL,\nTanudisastro H, Gu B, English A, Hiatt L, Mokveld T\net al.\n\nTRExplorer: A comprehensive catalog of tandem repeat variation in the human genome.\nbioRxiv. 2024.\ndoi: 10.1101/2024.10.04.615514\n NOTE: \nThis track shows the genomic positions of natural and artifical amino acid variants\nin the UniProt/SwissProt database.\nThe data has been curated from scientific publications by the UniProt staff.\n \nGenomic locations of UniProt/SwissProt variants are labeled with the amino acid\nchange at a given position and, if known, the abbreviated disease name. A\n"?" is used if there is no disease annotated at this location, but the\nprotein is described as being linked to only a single disease in UniProt.\n \nMouse over a mutation to see the UniProt comments.\n \nArtificially-introduced mutations are colored green and naturally-occurring variants are colored\nred. For full information about a particular variant, click the "UniProt variant" linkout. \nThe "UniProt record" linkout lists all variants of a particular protein sequence.\nThe "Source articles" linkout lists the articles in PubMed that originally described\nthe variant(s) and were used as evidence by the UniProt curators.\n \nUniProt sequences were aligned to RefSeq sequences first with BLAT, then lifted\nto genome positions with pslMap. UniProt variants were parsed from the UniProt\nXML file. The variants were then mapped to the genome through the alignment\nusing the pslMap program. This mapping approach\ndraws heavily on the LS-SNP pipeline by Mark Diekhans. The complete script is\npart of the kent source tree and is located in src/hg/utils/uniprotMutations. \n \nThe raw data can be explored interactively with the\nTable Browser, or the\nData Integrator.\nFor automated analysis, the genome annotation is stored in a bigBed file that\ncan be downloaded from the\ndownload server.\nThe underlying data file for this track is called spMut.bb. Individual \nregions or the whole genome annotation can be obtained using our tool bigBedToBed \nwhich can be compiled from the source code or downloaded as a precompiled binary\nfor your system. Instructions for downloading source code and binaries can be found\nhere. \nThe tool can also be used to obtain only features within a given range, for example:\n \nThis track was created by Maximilian Haeussler, with advice from Mark Diekhans and Brian Raney.\n \nUniProt Consortium.\n\nReorganizing the protein space at the Universal Protein Resource (UniProt).\nNucleic Acids Res. 2012 Jan;40(Database issue):D71-5.\nPMID: 22102590; PMC: \nYip YL, Scheib H, Diemand AV, Gattiker A, Famiglietti LM, Gasteiger E, Bairoch A.\n\nThe Swiss-Prot variant page and the ModSNP database: a resource for sequence and structure\ninformation on human protein variants.\nHum Mutat. 2004 May;23(5):464-70.\nPMID: 15108278\n \nThis track shows 44k upstream open reading frames (uORFs) in 5' UTRs of human genes,\ncurated from ribosome profiling data by the\nUTRannotator\nproject, annotated by UCSC with the Kozak strength and translational efficiency.\n \nuORFs are small open reading frames located in the 5' UTR of mRNAs, upstream of the main\nprotein-coding sequence. They play an important role in translational regulation: ribosomes\nscanning from the 5' cap may translate a uORF first, which can reduce translation of the\ndownstream main ORF. Genetic variants that create or disrupt uORFs can therefore alter\nprotein expression and contribute to disease.\n \nUTRannotator is a plugin for the\nEnsembl\nVariant Effect Predictor (VEP) that annotates 5' UTR variants with respect to uORFs.\nIt detects five types of uORF-perturbing events (AUG gained/lost, stop lost/gained, frameshift).\nThis plugin needs a database of uORFs to annotate, so the authors compiled a\ncurated reference set of translated small ORFs in human 5' UTRs, derived from\nribosome profiling data in the\nsorfs.org database. This reference set\nis what is displayed in this track. Almost all of these ORFs are annotated as 5' uORFs, only \na tiny fraction, 270 of them, are annotated as 5'UTR+3'UTR uORF, when transcripts overlap.\n \nItems are displayed in bigGenePred format. Each item is labeled with the gene symbol of\nthe host transcript. Color reflects the categorical Kozak consensus strength:\n \n Strong – A/G at position −3 and G at position +4 \nThe UTRannotator source data has no exon/intron structure, so each uORF is projected\nonto a same-strand host transcript whose coordinates overlap the uORF range. The host's\nexons are clipped to the uORF range, so any host intron inside the overlap becomes an\nintron of the displayed feature; a uORF that extends past either end of the host gets a\nsingle bridging block for the orphan portion. The primary donor pool is the\nMANE Select / MANE Plus Clinical set;\nif every MANE candidate is rejected (e.g. the original UTRannotator transcript had a\ndifferent UTR exon boundary), the full GENCODE comprehensive set is consulted as a\nfallback. The chosen donor transcript ID is stored in intronsSource\n(none if no host was found in either pool).\n \nMouseover shows the gene symbol, uORF type, start codon, Kozak strength and\ntranslational efficiency, and the host transcript whose exons supplied the intron\nstructure.\n \nThe track offers the following filters: start codon, Kozak strength, Kozak TE (range),\nuORF type (5'UTR-only vs spans into 3'UTR).\n \nThe raw data can be explored interactively with the\nTable Browser or the\nData Integrator. The data can be accessed from\nscripts through our API; the track name is\n"utrAnnotUorfs".\n \nFor automated download and analysis, the genome annotation is stored in a bigBed file that\ncan be downloaded from\nour download server.\nIndividual regions or the whole genome annotation can be obtained using our tool\nbigBedToBed, which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tool can also be used to obtain only features within a given range, e.g.\n \nThe uORF reference data was downloaded from the\nUTRannotator\nGitHub repository (file uORF_5UTR_GRCh38_PUBLIC.txt) and converted to bigBed format\nat UCSC. Coordinates for reverse-strand uORFs were swapped to genomic orientation. Four entries\nwith invalid coordinates were excluded. Host transcripts were annotated as described above. \n \nThanks to Xiaolei Zhang, Nicola Whiffin, and the UTRannotator team at the Imperial College London\nCardiovascular Genetics group for making this data publicly available.\n \nWhiffin N, Karczewski KJ, Zhang X, Chothani S, Smith MJ, Evans DG, Roberts AM, Quaife NM, Schafer S,\nRackham O et al.\n\nCharacterising the loss-of-function impact of 5' untranslated region variants in 15,708\nindividuals.\nNat Commun. 2020 May 27;11(1):2523.\nPMID: 32461616; PMC: \nZhang X, Wakeling M, Ware J, Whiffin N.\n\nAnnotating high-impact 5'untranslated region variants with the UTRannotator.\nBioinformatics. 2021 May 23;37(8):1171-1173.\nPMID: 32926138; PMC: NOTE: \nVaraico\n(Variation Research Advancing Insight in Complex\nOrganisms) was created using\nliterature mining, similar to AVADA. Varaico variants are generated by an automated process that\nextracts purely factual information about genes from scientific papers (by matching strings against\ngene names) and HGVS variant descriptions (using regular expressions). Varaico aims to reduce\nfalse-positive gene and variant mentions and link them together appropriately, but nonetheless, many\nvariants displayed are not mapped to the genomic position intended by the authors.\n Varaico Variants (suppl) contains variants extracted from supplementary data files\nusing similar methods as in the Varaico track. \nFor data questions, Varaico can be contacted at\n\njbirgmei@gmail.com\n\n \nGenomic locations of variants are labeled with the HGNC gene symbol and the variant change.\nMouse over the variants to show the gene, variant, latest author/year/title, number of publications\nmentioning the variant, and variant effect. \nClicking on an item will provide a link directly to\nVaraico to view all publications mentioning this variant. \nThe items are colored based on the amount of literature support and are a gradient from the\ncolors described on the table below:\n \nSpliceAI=G|OR4F5|0.01|0.00|0.00|0.00|-32|49|-40|-31
\nHere, the pipe-separated fields contain\n\n
\nSpliceAI Wildtype
\nSpliceVarDB
\nData Access
\n\n
\n
\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg19/splicevardb/SVADB.bb\n -chrom=chr21 -start=0 -end=100000000 stdout\n
\nbigWigToBedGraph -chrom=chr1 -start=100000 -end=100500\n http://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/spliceAi/wildtype/spliceAiAcceptorMinus.bw\n stdout\n
\n
\nThese tools can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.Credits
\n\nReferences
\n
Motif: $referenceMotif
Minimum pathogenic repeat: $pathogenicMin
Mode of inheritance: $inheritance
Associated disease(s): $disease",
"searchIndex": "name",
"shortLabel": "STRchive",
"superTrack": "strVar pack",
"track": "strchive",
"type": "bigBed 9 +",
"url": "https://strchive.org/loci/$$",
"urlLabel": "STRchive locus page",
"visibility": "pack",
"html": "Description
\nDisplay Conventions
\n\n
\n\nMethods
\nSTRchive-disease-loci.hg38.general.bed). The catalog is\nmanually curated by the STRchive team from published literature and contains loci where\ntandem repeat expansions have been reported to cause or be associated with human disease.\n
\n\nData Access
\nCredits
\nReferences
\n
Motif: ${get(feature,'referenceMotif')}
Minimum pathogenic repeat: ${get(feature,'pathogenicMin')}
Mode of inheritance: ${get(feature,'inheritance')}
Associated disease(s): ${get(feature,'disease')}`"
}
]
},
{
"trackId": "hg38-tommoStr",
"name": "ToMMo 61k STR",
"type": "FeatureTrack",
"assemblyNames": [
"hg38"
],
"adapter": {
"type": "BigBedAdapter",
"uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/strVar/tommoStr.bb"
},
"metadata": {
"ucsc": {
"bigDataUrl": "/gbdb/hg38/strVar/tommoStr.bb",
"detailsScript.histogram.alleleHist": "{\"title\":\"Allele Count Distribution (61K Japanese)\",\"xLabel\":\"Allele size (repeat copies)\"}",
"filter.het": "0:1",
"filterByRange.het": "on",
"filterLimits.het": "0:1",
"itemRgb": "on",
"longLabel": "ToMMo 61KJPN Short Tandem Repeat Allele Counts (Expansion Hunter)",
"mouseOver": "Motif: $motif ($period bp)
Ref copies: $numCopies
Mean: $mean, Median: $median
Heterozygosity: $het",
"scoreFilter": "0",
"searchIndex": "name",
"shortLabel": "ToMMo 61k STR",
"superTrack": "strVar dense",
"track": "tommoStr",
"type": "bigBed 9 +",
"visibility": "dense",
"html": "Description
\nDisplay Conventions
\n\n
\n\nMethods
\nData Access
\nCredits
\nReferences
\n
Ref copies: ${get(feature,'numCopies')}
Mean: ${get(feature,'mean')}, Median: ${get(feature,'median')}
Heterozygosity: ${get(feature,'het')}`"
}
]
},
{
"trackId": "hg38-trexplorer",
"name": "TRExplorer",
"type": "FeatureTrack",
"assemblyNames": [
"hg38"
],
"adapter": {
"type": "BigBedAdapter",
"uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/strVar/trexplorer.bb"
},
"metadata": {
"ucsc": {
"bigDataUrl": "/gbdb/hg38/strVar/trexplorer.bb",
"detailsScript.histogram.hprcAlleleHist": "{\"title\":\"HPRC256 Allele Distribution\",\"xLabel\":\"Allele size (repeat copies)\"}",
"detailsScript.histogram.tenKAlleleHist": "{\"title\":\"TenK10K Allele Distribution\",\"xLabel\":\"Allele size (repeat copies)\"}",
"filter.het": "0:1",
"filterByRange.het": "on",
"filterLimits.het": "0:1",
"itemRgb": "on",
"longLabel": "TRExplorer V2 Tandem Repeat Catalog",
"mouseOver": "Motif: $referenceMotif ($motifSize bp)
Copies in ref: $numRepeats
Purity: $repeatPurity
Heterozygosity: $het
Gene: $geneName ($geneRegion)",
"searchIndex": "name",
"shortLabel": "TRExplorer",
"superTrack": "strVar dense",
"track": "trexplorer",
"type": "bigBed 9 +",
"urlLabel": "TRExplorer locus page",
"urls": "locusId=\"https://trexplorer.broadinstitute.org/index.html?#showRs=1&q=$$\"",
"visibility": "dense",
"html": "Description
\nDisplay Conventions
\n\n
\n\nPopulation Frequency Data
\n\n
\nData Sources
\n\n
\n\nMethods
\nData Access
\nCredits
\nReferences
\n
Copies in ref: ${get(feature,'numRepeats')}
Purity: ${get(feature,'repeatPurity')}
Heterozygosity: ${get(feature,'het')}
Gene: ${get(feature,'geneName')} (${get(feature,'geneRegion')})`"
}
]
},
{
"trackId": "hg38-spMut",
"name": "UniProt Variants",
"type": "FeatureTrack",
"assemblyNames": [
"hg38"
],
"adapter": {
"type": "BigBedAdapter",
"uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/uniprot/unipMut.bb"
},
"metadata": {
"ucsc": {
"bigDataUrl": "/gbdb/hg38/uniprot/unipMut.bb",
"exonNumbers": "off",
"group": "phenDis",
"itemRgb": "on",
"longLabel": "UniProt/SwissProt Amino Acid Substitutions",
"maxWindowCoverage": "10000000",
"mouseOverField": "comments",
"noScoreFilter": "on",
"shortLabel": "UniProt Variants",
"track": "spMut",
"type": "bigBed 12 +",
"urls": "variationId=\"http://www.uniprot.org/uniprot/$$\" uniProtId=\"http://www.uniprot.org/uniprot/$$\" pmids=\"https://www.ncbi.nlm.nih.gov/pubmed/$$\"",
"visibility": "hide",
"html": "Description
\n\n\n\n\n
\nThis track is intended for use primarily by physicians and other\nprofessionals concerned with genetic disorders, by genetics researchers, and\nby advanced students in science and medicine. While the genome browser database\nis open to the public, users seeking information about a personal medical or\ngenetic condition are urged to consult with a qualified physician for\ndiagnosis and for answers to personal questions.Display Conventions and Configuration
\n\nMethods
\n\nData Access
\n\n
\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/uniprot/spMut.bb -chrom=chr6 -start=0 -end=1000000 stdout \n
\nPlease refer to our\nmailing list archives\nfor questions, or our\nData Access FAQ\nfor more information. \nCredits
\n\nReferences
\n\n
Start codon: $startCodon
Kozak: $kozakStrength (TE $kozakTE)
Host transcript: $intronsSource",
"parent": "ncOrfs",
"shortLabel": "UTRannotator uORFs",
"track": "utrAnnotUorfs",
"type": "bigGenePred",
"visibility": "pack",
"html": "Description
\n\nDisplay Conventions and Configuration
\n\n
\n Moderate – only one of those positions matches
\n Weak – neither position matches
\n non-ATG – near-cognate start codon; the Kozak rule does not apply
\n no context – chromosome edge or context unavailable\nData Access
\n\nMethods
\n\nCredits
\n\nReferences
\n\n
Start codon: ${get(feature,'startCodon')}
Kozak: ${get(feature,'kozakStrength')} (TE ${get(feature,'kozakTE')})
Host transcript: ${get(feature,'intronsSource')}`"
}
]
},
{
"trackId": "hg38-varaico",
"name": "Variants in Papers - Varaico Variants",
"type": "FeatureTrack",
"assemblyNames": [
"hg38"
],
"adapter": {
"type": "BigBedAdapter",
"uri": "https://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/varaico.bb"
},
"metadata": {
"ucsc": {
"bigDataUrl": "/gbdb/hg38/bbi/varaico.bb",
"dataVersion": "release 3 (20260527)",
"exonNumbers": "off",
"html": "Description
\n\n\n\n
\nSome rights reserved. This work permits non-commercial use, distribution and reproduction in any\nmedium, provided the original author and source are credited.\n
\nLicense and legal information can be found on the Varaico website.Display Conventions and Configuration
\n\n\n \n
\n\n \n \n Color \n Level of literature support \n \n \n \n ≥20 papers mention the variant \n \n \n \n 15 papers mention the variant \n \n \n \n 10 papers mention the variant \n \n \n \n 5 papers mention the variant \n \n \n\n 1 paper mentions the variant \n
\nThe raw data can be explored interactively with the Table Browser\nor the Data Integrator. The data can be accessed from scripts through our\nAPI, the track name is "varaico".
\n\n\nFor automated download and analysis, the genome annotation is stored in a bigBed file that\ncan be downloaded from\nour download server.\nThe file for this track is called varaico.bb. Individual\nregions or the whole genome annotation can be obtained using our tool bigBedToBed,\nwhich can be compiled from the source code or downloaded as a precompiled\nbinary for your system.
\n\nThe previous Varaico Variants version is also available in our\ndownload archive.
\n\nInstructions for downloading source code and binaries can be found\nhere.\nThe tool\ncan also be used to obtain only features within a given range, e.g.\n
\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/varaico.bb -chrom=chr21 -start=0 -end=10000000 stdout
NOTE:
\nSome rights reserved. This work permits non-commercial use, distribution and reproduction in any\nmedium, provided the original author and source are credited.\n
\nLicense and legal information can be found on the Varaico website.
\nVaraico\n(Variation Research Advancing Insight in Complex\nOrganisms) was created using\nliterature mining, similar to AVADA. Varaico variants are generated by an automated process that\nextracts purely factual information about genes from scientific papers (by matching strings against\ngene names) and HGVS variant descriptions (using regular expressions). Varaico aims to reduce\nfalse-positive gene and variant mentions and link them together appropriately, but nonetheless, many\nvariants displayed are not mapped to the genomic position intended by the authors.\n
\n\nVaraico Variants (suppl) contains variants extracted from supplementary data files\nusing similar methods as in the Varaico track.
\n\n\nFor data questions, Varaico can be contacted at\n\njbirgmei@gmail.com\n\n
\n\n\nGenomic locations of variants are labeled with the HGNC gene symbol and the variant change.\nMouse over the variants to show the gene, variant, latest author/year/title, number of publications\nmentioning the variant, and variant effect.
\n\n\nClicking on an item will provide a link directly to\nVaraico to view all publications mentioning this variant.
\n\n\nThe items are colored based on the amount of literature support and are a gradient from the\ncolors described on the table below:\n
\n\n\n
| Color | \nLevel of literature support | \n
|---|---|
| \n | ≥20 papers mention the variant | \n
| \n | 15 papers mention the variant | \n
| \n | 10 papers mention the variant | \n
| \n | 5 papers mention the variant | \n
| \n | 1 paper mentions the variant | \n
\nThe raw data can be explored interactively with the Table Browser\nor the Data Integrator. The data can be accessed from scripts through our\nAPI, the track name is "varaico".
\n\n\nFor automated download and analysis, the genome annotation is stored in a bigBed file that\ncan be downloaded from\nour download server.\nThe file for this track is called varaico.bb. Individual\nregions or the whole genome annotation can be obtained using our tool bigBedToBed,\nwhich can be compiled from the source code or downloaded as a precompiled\nbinary for your system.
\n\nThe previous Varaico Variants version is also available in our\ndownload archive.
\n\nInstructions for downloading source code and binaries can be found\nhere.\nThe tool\ncan also be used to obtain only features within a given range, e.g.\n
\nbigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/bbi/varaico.bb -chrom=chr21 -start=0 -end=10000000 stdout
This track shows potential enhancers whose activity was experimentally validated in transgenic\nmice. Most of these noncoding elements were selected for testing based on their extreme conservation\nin other vertebrates or epigenomic evidence (ChIP-Seq) of putative enhancer marks. More information\ncan be found on the VISTA Enhancer Browser\npage.\n
\n\nItems appearing in blue (positive) indicate that a\nreproducible pattern was observed in the in vivo enhancer assay under at least one of the\ntested conditions. Items appearing in gray (negative) indicate\nthat NO reproducible pattern was observed in the in vivo enhancer assay under any of the tested\nconditions. This does not exclude the possibility that this region is a reproducible enhancer active\nunder different conditions, for example at an earlier or later timepoint in development.
\n\nExcerpted from the Vista Enhancer Mouse Enhancer Screen Handbook and Methods page at the Lawrence Berkeley\nNational Laboratory (LBNL) website:\n
Most enhancer candidate sequences are identified by extreme evolutionary sequence conservation or\nby ChIP-seq. Detailed information related to enhancer identification by extreme evolutionary\nconservation can be found in the following publications:\n
\nDetailed information related to enhancer identification by ChIP-seq can be found in the\nfollowing publications:
\nSee the Transgenic Mouse Assay section for experimental procedures that were used to perform the\ntransgenic assays: Mouse Enhancer Screen Handbook and Methods\n\n
UCSC converted the\nvista-data bed files for\nhg38 and mm10 into bigBed format using the bedToBigBed utility. The data for mm39 was lifted over\nfrom mm10. The data for hg19 was lifted over from hg38.
\n\n\nVISTA Enhancers data can be explored interactively with the\nTable Browser and cross-referenced with the\nData Integrator. For programmatic access, the track can be\naccessed using the Genome Browser's REST API. ReMap\nannotations can be downloaded from the Genome Browser's\ndownload server\nas a bigBed file. This compressed binary format can be remotely queried through\ncommand line utilities. Please note that some of the download files can be quite large.
\n\nThanks to the Lawrence Berkeley National Laboratory for providing this data.
\n\n\n\nKosicki M, Baltoumas FA, Kelman G, Boverhof J, Ong Y, Cook LE, Dickel DE, Pavlopoulos GA, Pennacchio\nLA, Visel A.\n\nVISTA Enhancer browser: an updated database of tissue-specific developmental enhancers.\nNucleic Acids Res. 2025 Jan 6;53(D1):D324-D330.\nPMID: 39470740; PMC:
\nVisel A, Minovitsky S, Dubchak I, Pennacchio LA.\n\nVISTA Enhancer Browser--a database of tissue-specific human enhancers.\nNucleic Acids Res. 2007 Jan;35(Database issue):D88-92.\nPMID: 17130149; PMC:
\nThe WebSTR track displays 1,710,833 short tandem repeat (STR) loci across the\nhuman genome from the\nWebSTR database.
\n\n\nThis track is based on the EnsembleTR panel for the GRCh38/hg38 assembly,\nwhich represents a combined set of tandem repeats genotyped by four separate methods\n(HipSTR, GangSTR, ExpansionHunter, and AdVNTR) on data from the\n1000 Genomes Project.\nEnsembleTR\nwas applied to jointly genotype all 3,550 samples, producing consensus calls at\nover 1.7 million autosomal tandem repeat loci.
\n\n\nThe track includes allele frequency distributions for five 1000 Genomes continental\npopulations:
\n\nFor each population, allele frequencies are defined as the number of copies of each allele\ndivided by the total number of alleles in that population. Alleles are represented as\nthe number of repeat unit copies.
\n\n\nItems are colored by expected heterozygosity, computed as\nhet = 1 − ∑pi2 from allele frequencies\npooled across all five 1000 Genomes populations weighted by sample count:
\n\nEach item is labeled by its repeat motif and copy count. Hovering over an item shows the repeat\nmotif, number of reference copies, and heterozygosity. Clicking an item links to the\ncorresponding\nWebSTR locus page, which provides\ninteractive allele frequency histograms and additional annotations.
\n\n\nThe EnsembleTR reference panel was constructed as follows:
\n\nFor the UCSC Genome Browser track, the source data were converted from CSV to bigBed\nformat. Per-population allele frequency distributions are stored as extra bigBed fields.
\n\n\nThe raw data can be explored interactively with the\nTable Browser or the\nData Integrator. For automated\nanalysis, the data may be queried from our\nREST API. The underlying bigBed\nfile can be downloaded from our\ndownload\nserver.
\n\n\nThe complete WebSTR dataset, including additional cohorts and data types not included in\nthis track, is available from the\nWebSTR web portal. Programmatic\naccess to the full WebSTR database is available through the\nWebSTR REST API.
\n\n\nThanks to Melissa Gymrek (UC San Diego) and the WebSTR team for\nproviding the data for this track.
\n\n\nLundström OS, Adriaan Verbiest M, Xia F, Jam HZ, Zlobec I,\nAnisimova M, Gymrek M.\n\nWebSTR: A Population-wide Database of Short Tandem Repeat Variation\nin Humans.\nJ Mol Biol. 2023 Oct 15;435(20):168260.\nPMID: 37678708\n
\n\n\nZiaei Jam H, Li Y, DeVito R, Mousavi N, Ma N, Lujumba I, Adam Y,\nMaksimov M, Huang B, Dolzhenko E et al.\n\nA deep population reference panel of tandem repeat variation.\nNat Commun. 2023 Oct 23;14(1):6711.\nPMID: 37872149; PMC:
\nTrack indicating the location of the centromere sequences.\nCentromeres are specialized chromatin structures that are required for cell division. These\ngenomic regions are normally defined by long tracts of tandem repeats, or satellite DNA, that\ncontain a limited number of sequence differences to distinguish the linear order of repeat copies.\nThe size and repetitive nature of these regions mean they are typically not represented in\nreference assemblies. Unlike all previous versions of the human reference assembly, where the\ncentromere regions have been represented by a multi-megabase gap, GRCh38 incorporates centromere\nreference models that provide an initial genomic description derived from chromosome-assigned whole\ngenome shotgun (WGS) read libraries of alpha satellite.\n
\n\n\nEach reference model provides an approximation of the true array sequence organization.\nAlthough the long-range repeat ordering is not expected to represent the true organization,\nthe submissions are expected to provide a biologically rich description of array variants and\nlocal-monomer organization as observed in the initial WGS read dataset. As a result, these\nsequences serve as a useful mapping target to extend sequence-based studies to sites previously\nomitted from the human reference genome.\n
\n\n\nThe sequences are generated based on second-order Markov models of monomer\nvariants, and graphical models of larger scale higher order repeats.\nThe graphical models are based on an analysis of Sanger reads from the\nHuRef sequencing project (Assembly\nGCA_000002125.1; BioProject\nPRJNA19621),\nand their local-ordering is supported by observed same-read monomer\nadjacencies. The Markov models are generated by the program linearSat, which\nwas written for this project and that also generates a linear representation\nof monomer order. The software linearSat generates a second-order Markov\nchain to the size of a given array provided by sequence coverage normalization\nestimates. The sequence definitions of transposable element insertions are\nlimited to the sequences directly adjacent to alpha satellite within the read\ndatabase, and incomplete representations are noted with an adjacent\n100 bp gap. In total, these sequences provide a more complete reference\nof sequence composition and higher order repeat variation inherent to a\ngiven alpha satellite array, used to assemble centromeric regions of the\nhuman chromosomes.\n
\n\n\nThe data for this track was supplied by\nKaren Miga.\n
\n\n\nMiga KH, Newton Y, Jain M, Altemose N, Willard HF, Kent WJ.\n\nCentromere reference models for human chromosomes X and Y satellite arrays.\nGenome Res. 2014 Apr;24(4):697-707.\nPMID: 24501022; PMC:
\nThe Coriell Cell Line Copy Number Variants track displays\ncopy-number variants (CNVs) in chromosomal aberration and inherited disorder\ncell lines in the NIGMS Human Genetic Cell Repository. The Repository,\nsponsored by the National Institute of General Medical Sciences, provides\nscientists around the world with resources for cell and genetic research.\nThe samples include highly characterized cell lines and high quality DNA.\nNIGMS Repository samples represent a variety of disease states, chromosomal\nabnormalities, apparently healthy individuals and many distinct human\npopulations.\n
\n\n\nApproximately 1000 samples from the Chromosomal Aberrations and Heritable\nDiseases collections of the NIGMS Repository were genotyped on the Affymetrix\nGenome-Wide Human SNP 6.0 Array and analyzed for CNVs at the Coriell Institute\nfor Medical Research. Genotyping data for many of these samples is available\nthrough dbGaP.\n
\n\n\nThe genotyped samples represent a diverse set of copy-number variants. The\nselection was weighted to over-sample commonly manifested types of aberrations.\nKaryotyping was performed on all NIGMS Repository cell lines that were\nsubmitted with reported chromosome abnormalities. When available, the ISCN\ndescription of the sample, based on G-banding and FISH analysis, is included\nin the phenotypic data. Karyotypes for these cells can be viewed in the\nonline Repository catalog.\n
\n\n\nField definitions for an item description:\n
\nCN State item coloring:\n
\nWe thank Dorit Berlin and Zhenya Tang of the NIGMS Human Genetic Cell\nRepository at the\nCoriell Institute for Medical\nResearch for these data.\n
\n\n\nNCBI dbGaP:\n\nGenotyping NIGMS Chromosomal Aberration and Inherited Disorder Samples.\n\n
\nNIGMS Human Genetic Cell Repository\nonline catalog at the Coriell Institute for Medical Research.\n\n
CpG islands are associated with genes, particularly housekeeping\ngenes, in vertebrates. CpG islands are typically common near\ntranscription start sites and may be associated with promoter\nregions. Normally a C (cytosine) base followed immediately by a \nG (guanine) base (a CpG) is rare in\nvertebrate DNA because the Cs in such an arrangement tend to be\nmethylated. This methylation helps distinguish the newly synthesized\nDNA strand from the parent strand, which aids in the final stages of\nDNA proofreading after duplication. However, over evolutionary time,\nmethylated Cs tend to turn into Ts because of spontaneous\ndeamination. The result is that CpGs are relatively rare unless\nthere is selective pressure to keep them or a region is not methylated\nfor some other reason, perhaps having to do with the regulation of gene\nexpression. CpG islands are regions where CpGs are present at\nsignificantly higher levels than is typical for the genome as a whole.
\n\n\nThe unmasked version of the track displays potential CpG islands\nthat exist in repeat regions and would otherwise not be visible\nin the repeat masked version.\n
\n\n\nBy default, only the masked version of the track is displayed. To view the\nunmasked version, change the visibility settings in the track controls at\nthe top of this page.\n
\n\nCpG islands were predicted by searching the sequence one base at a\ntime, scoring each dinucleotide (+17 for CG and -1 for others) and\nidentifying maximally scoring segments. Each segment was then\nevaluated for the following criteria:\n\n
\nThe entire genome sequence, masking areas included, was\nused for the construction of the track Unmasked CpG.\nThe track CpG Islands is constructed on the sequence after\nall masked sequence is removed.\n
\n\nThe CpG count is the number of CG dinucleotides in the island. \nThe Percentage CpG is the ratio of CpG nucleotide bases\n(twice the CpG count) to the length. The ratio of observed to expected \nCpG is calculated according to the formula (cited in \nGardiner-Garden et al. (1987)):\n\n
Obs/Exp CpG = Number of CpG * N / (Number of C * Number of G)\n\nwhere N = length of sequence.\n
\nThe calculation of the track data is performed by the following command sequence:\n
\ntwoBitToFa assembly.2bit stdout | maskOutFa stdin hard stdout \n | cpg_lh /dev/stdin 2> cpg_lh.err \n | awk '{$2 = $2 - 1; width = $3 - $2; printf(\"%s t%d t%s t%s %s t%s t%s t%0.0f t%0.1f t%s t%s n\", $1, $2, $3, $5, $6, width, $6, width*$7*0.01, 100.0*2*$6/width, $7, $9);}' \n | sort -k1,1 -k2,2n > cpgIsland.bed\n\nThe unmasked track data is constructed from\ntwoBitToFa -noMask output for the twoBitToFa command.\n\n\n\nCpG islands and its associated tables can be explored interactively using the\nREST API, the\nTable Browser or the\nData Integrator.\nAll the tables can also be queried directly from our public MySQL\nservers, with more information available on our\nhelp page as well as on\nour blog.
\n\nThe source for the cpg_lh program can be obtained from\nsrc/utils/cpgIslandExt/.\nThe cpg_lh program binary can be obtained from: http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/cpg_lh (choose \"save file\")\n
\n\nThis track was generated using a modification of a program developed by G. Micklem and L. Hillier \n(unpublished).
\n\n\nGardiner-Garden M, Frommer M.\n\nCpG islands in vertebrate genomes.\nJ Mol Biol. 1987 Jul 20;196(2):261-82.\nPMID: 3656447\n
\n", "longLabel": "CpG Islands (Islands < 300 Bases are Light Green)", "parent": "cpgIslandSuper pack", "priority": "1", "shortLabel": "CpG Islands", "track": "cpgIslandExt" } }, "description": "CpG Islands (Islands < 300 Bases are Light Green)", "category": [ "Regulation" ] }, { "type": "FeatureTrack", "trackId": "hg38-cpgIslandExtUnmasked", "name": "CpG Islands - Unmasked CpG", "assemblyNames": [ "hg38" ], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "cpgIslandExtUnmasked.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "cpgIslandExtUnmasked.bed.gz.csi" } } }, "metadata": { "ucsc": { "html": "CpG islands are associated with genes, particularly housekeeping\ngenes, in vertebrates. CpG islands are typically common near\ntranscription start sites and may be associated with promoter\nregions. Normally a C (cytosine) base followed immediately by a \nG (guanine) base (a CpG) is rare in\nvertebrate DNA because the Cs in such an arrangement tend to be\nmethylated. This methylation helps distinguish the newly synthesized\nDNA strand from the parent strand, which aids in the final stages of\nDNA proofreading after duplication. However, over evolutionary time,\nmethylated Cs tend to turn into Ts because of spontaneous\ndeamination. The result is that CpGs are relatively rare unless\nthere is selective pressure to keep them or a region is not methylated\nfor some other reason, perhaps having to do with the regulation of gene\nexpression. CpG islands are regions where CpGs are present at\nsignificantly higher levels than is typical for the genome as a whole.
\n\n\nThe unmasked version of the track displays potential CpG islands\nthat exist in repeat regions and would otherwise not be visible\nin the repeat masked version.\n
\n\n\nBy default, only the masked version of the track is displayed. To view the\nunmasked version, change the visibility settings in the track controls at\nthe top of this page.\n
\n\nCpG islands were predicted by searching the sequence one base at a\ntime, scoring each dinucleotide (+17 for CG and -1 for others) and\nidentifying maximally scoring segments. Each segment was then\nevaluated for the following criteria:\n\n
\nThe entire genome sequence, masking areas included, was\nused for the construction of the track Unmasked CpG.\nThe track CpG Islands is constructed on the sequence after\nall masked sequence is removed.\n
\n\nThe CpG count is the number of CG dinucleotides in the island. \nThe Percentage CpG is the ratio of CpG nucleotide bases\n(twice the CpG count) to the length. The ratio of observed to expected \nCpG is calculated according to the formula (cited in \nGardiner-Garden et al. (1987)):\n\n
Obs/Exp CpG = Number of CpG * N / (Number of C * Number of G)\n\nwhere N = length of sequence.\n
\nThe calculation of the track data is performed by the following command sequence:\n
\ntwoBitToFa assembly.2bit stdout | maskOutFa stdin hard stdout \n | cpg_lh /dev/stdin 2> cpg_lh.err \n | awk '{$2 = $2 - 1; width = $3 - $2; printf(\"%s t%d t%s t%s %s t%s t%s t%0.0f t%0.1f t%s t%s n\", $1, $2, $3, $5, $6, width, $6, width*$7*0.01, 100.0*2*$6/width, $7, $9);}' \n | sort -k1,1 -k2,2n > cpgIsland.bed\n\nThe unmasked track data is constructed from\ntwoBitToFa -noMask output for the twoBitToFa command.\n\n\n\nCpG islands and its associated tables can be explored interactively using the\nREST API, the\nTable Browser or the\nData Integrator.\nAll the tables can also be queried directly from our public MySQL\nservers, with more information available on our\nhelp page as well as on\nour blog.
\n\nThe source for the cpg_lh program can be obtained from\nsrc/utils/cpgIslandExt/.\nThe cpg_lh program binary can be obtained from: http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/cpg_lh (choose \"save file\")\n
\n\nThis track was generated using a modification of a program developed by G. Micklem and L. Hillier \n(unpublished).
\n\n\nGardiner-Garden M, Frommer M.\n\nCpG islands in vertebrate genomes.\nJ Mol Biol. 1987 Jul 20;196(2):261-82.\nPMID: 3656447\n
\n", "longLabel": "CpG Islands on All Sequence (Islands < 300 Bases are Light Green)", "parent": "cpgIslandSuper hide", "priority": "2", "shortLabel": "Unmasked CpG", "track": "cpgIslandExtUnmasked" } }, "description": "CpG Islands on All Sequence (Islands < 300 Bases are Light Green)", "category": [ "Regulation" ] }, { "type": "FeatureTrack", "trackId": "hg38-cytoBand", "name": "Chromosome Band", "assemblyNames": [ "hg38" ], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "cytoBand.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "cytoBand.bed.gz.csi" } } }, "metadata": { "ucsc": { "group": "map", "longLabel": "Chromosome Bands Localized by FISH Mapping Clones", "shortLabel": "Chromosome Band", "track": "cytoBand", "type": "bed 4 +", "visibility": "hide", "html": "\nThe chromosome band track represents the approximate \nlocation of bands seen on Giemsa-stained chromosomes.\nChromosomes are displayed in the browser with the short arm first. \nCytologically identified bands on the chromosome are numbered outward \nfrom the centromere on the short (p) and long (q) arms. At low resolution, \nbands are classified using the nomenclature \n[chromosome][arm][band], where band is a \nsingle digit. Examples of bands on chromosome 3 include 3p2, 3p1, cen, 3q1, \nand 3q2. At a finer resolution, some of the bands are subdivided into \nsub-bands, adding a second digit to the band number, e.g. 3p26. This \nresolution produces about 500 bands. A final subdivision into a \ntotal of 862 sub-bands is made by adding a period and another digit to the \nband, resulting in 3p26.3, 3p26.2, etc.
\n\n\nChromosome band information was downloaded from NCBI\nusing the ideogram.gz file for the respective assembly. These data were then \ntransformed into our visualization format. See our \nassembly creation documentation for the organism of interest\nto see the specific steps taken to transform these data.\nBand lengths are typically estimated based on FISH or other\nmolecular markers interpreted via microscopy.
\n\nFor some of our older assemblies, greater than 10 years old, the tracks were\ncreated as detailed below and in Furey and Haussler, 2003.
\n\nBarbara Trask, Vivian Cheung, Norma Nowak and others in the BAC Resource\nConsortium used fluorescent in-situ hybridization (FISH) to determine a \ncytogenetic location for large genomic clones on the chromosomes.\nThe results from these experiments are the primary source of information used\nin estimating the chromosome band locations.\nFor more information about the process, see the paper, Cheung,\net al., 2001. and the accompanying web site,\nHuman BAC Resource.
\n\nBAC clone placements in the human sequence are determined at UCSC using a \ncombination of full BAC clone sequence, BAC end sequence, and STS marker \ninformation.
\n\n\nWe would like to thank all the labs that have contributed to this resource:\n
\nCheung VG, Nowak N, Jang W, Kirsch IR, Zhao S, Chen XN, Furey TS, Kim UJ, Kuo WL, Olivier M et\nal.\n\nIntegration of cytogenetic landmarks into the draft sequence of the human genome.\nNature. 2001 Feb 15;409(6822):953-8.\nPMID: 11237021\n
\n\n\nFurey TS, Haussler D.\n\nIntegration of the cytogenetic map with the draft human genome sequence.\nHum Mol Genet. 2003 May 1;12(9):1037-44.\nPMID: 12700172\n
\n\n" } }, "description": "Chromosome Bands Localized by FISH Mapping Clones", "category": [ "Mapping and Sequencing" ] }, { "type": "FeatureTrack", "trackId": "hg38-cytoBandIdeo", "name": "Chromosome Band (Ideogram)", "assemblyNames": [ "hg38" ], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "cytoBandIdeo.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "cytoBandIdeo.bed.gz.csi" } } }, "metadata": { "ucsc": { "group": "map", "longLabel": "Chromosome Bands Localized by FISH Mapping Clones (for Ideogram)", "shortLabel": "Chromosome Band (Ideogram)", "track": "cytoBandIdeo", "type": "bed 4 +", "visibility": "dense", "html": "" } }, "description": "Chromosome Bands Localized by FISH Mapping Clones (for Ideogram)", "category": [ "Mapping and Sequencing" ] }, { "type": "FeatureTrack", "trackId": "hg38-fishClones", "name": "FISH Clones (hg18)", "assemblyNames": [ "hg38" ], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "fishClones.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "fishClones.bed.gz.csi" } } }, "metadata": { "ucsc": { "color": "0,150,0,", "group": "map", "longLabel": "Clones Placed on Cytogenetic Map Using FISH", "origAssembly": "hg18", "pennantIcon": "18.jpg ../goldenPath/help/liftOver.html \"lifted from hg18\"", "shortLabel": "FISH Clones", "superTrack": "assemblyContainer pack", "track": "fishClones", "type": "bed 5 +", "visibility": "hide", "html": "\nThis track shows the location of fluorescent in situ hybridization \n(FISH)-mapped clones along the assembly sequence. The locations of\nthese clones were obtained from the NCBI Human BAC Resource\nhere. Earlier versions of this track obtained this\ninformation directly from the paper Cheung, et al. (2001).\n
\n\n\nMore information about the BAC clones, including how they may be obtained, \ncan be found at the \nHuman BAC Resource and the \nClone Registry web sites hosted by \nNCBI.\nTo view Clone Registry information for a clone, click on the clone name at \nthe top of the details page for that item.
\n\n\nThis track has a filter that can be used to change the color or \ninclude/exclude the display of a dataset from an individual lab. This is \nhelpful when many items are shown in the track display, especially when only \nsome are relevant to the current task. The filter is located at the top of \nthe track description page, which is accessed via the small button to the \nleft of the track's graphical display or through the link on the track's \ncontrol menu. To use the filter:\n
\nWhen you have finished configuring the filter, click the Submit \nbutton.
\n\n\nWe would like to thank all of the labs that have contributed to this resource:\n
\nCheung VG, Nowak N, Jang W, Kirsch IR, Zhao S, Chen XN, Furey TS, Kim UJ, Kuo WL, Olivier M et\nal.\n\nIntegration of cytogenetic landmarks into the draft sequence of the human genome.\nNature. 2001 Feb 15;409(6822):953-8.\nPMID: 11237021\n
\n" } }, "description": "Clones Placed on Cytogenetic Map Using FISH", "category": [ "Mapping and Sequencing" ] }, { "type": "FeatureTrack", "trackId": "hg38-gap", "name": "Gap", "assemblyNames": [ "hg38" ], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "gap.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "gap.bed.gz.csi" } } }, "metadata": { "ucsc": { "group": "map", "html": "\nThis track shows the gaps in the GRCh38 (hg38) genome assembly defined in the\nAGP file delivered with the sequence. These gaps are being closed during the \nfinishing process on the human genome. For information on the AGP file format, see the NCBI \nAGP Specification. The NCBI website also provides an \noverview of genome assembly procedures, as well as \nspecific information about the hg38 assembly.\n
\n\nGaps are represented as black boxes in this track.\nIf the relative order and orientation of the contigs on either side\nof the gap is supported by read pair data, \nit is a bridged gap and a white line is drawn \nthrough the black box representing the gap. \n
\nThis assembly contains the following principal types of gaps:\n
\nThis track shows regions detected as putative genomic duplications within the\ngolden path. The following display conventions are used to distinguish\nlevels of similarity:\n
\nSegmental duplications play an important role in both genomic disease \nand gene evolution. This track displays an analysis of the global \norganization of these long-range segments of identity in genomic sequence.\n
\n\nLarge recent duplications (>= 1 kb and >= 90% identity) were detected\nby identifying high-copy repeats, removing these repeats from the genomic \nsequence ("fuguization") and searching all sequence for similarity. The\nrepeats were then reinserted into the pairwise alignments, the ends of \nalignments trimmed, and global alignments were generated.\nFor a full description of the "fuguization" detection method, see Bailey\net al., 2001. This method has become\nknown as WGAC (whole-genome assembly comparison); for example, see Bailey \net al., 2002.\n\n
\nThese data were provided by Ginger Cheng, Xinwei She,\nArchana Raja,\nTin Louie and\nEvan Eichler \nat the University of Washington.
\n\n\nBailey JA, Gu Z, Clark RA, Reinert K, Samonte RV, Schwartz S, Adams MD, \nMyers EW, Li PW, Eichler EE.\nRecent segmental duplications in the human genome.\nScience. 2002 Aug 9;297(5583):1003-7.\nPMID: 12169732\n
\n\n\nBailey JA, Yavor AM, Massa HF, Trask BJ, Eichler EE.\nSegmental duplications: organization and impact within the \ncurrent human genome project assembly.\nGenome Res. 2001 Jun;11(6):1005-17.\nPMID: 11381028; PMC:
\nThis track shows the contigs used to construct the GRCh38 (hg38) genome assembly, as defined in the\nAGP file delivered with the sequence. \nFor information on the AGP file format, see the NCBI \nAGP Specification. The NCBI website also provides an \noverview of genome assembly procedures, as well as \nspecific information about the hg38 assembly.\n
\n\nIn dense mode, this track depicts the contigs that make up the \ncurrently viewed scaffold. \nContig boundaries are distinguished by the use of alternating gold and brown \ncoloration. Where gaps\nexist between contigs, spaces are shown between the gold and brown\nblocks. The relative order and orientation of the contigs\nwithin a scaffold is always known; therefore, a line is drawn in the graphical\ndisplay to bridge the blocks.
\n\nComponent types found in this track (with counts of that type in parenthesis):\n
\nIn addition to the standard nucleotide codes, the raw sequence files from NCBI also include\nIUPAC ambiguity codes for bases that could not be positively identified as A, C, G or T (see\nWikipedia's IUPAC notation article for more information). As part of the UCSC\nassembly creation process, all IUPAC ambiguity characters are converted to Ns. The FASTA files\navailable for download from UCSC reflect this. The raw data files containing the original IUPAC\ncharacters can be downloaded from the NCBI\nFTP site.\n
\n\n\nThe following table lists the counts by chromosome of the various IUPAC ambiguity characters\nin the original NCBI data files:\n
\n\n\n
| chromosome | \n | |||||||||||||||||
| \n | \n | 1 | \n2 | \n3 | \n6 | \n7 | \n9 | \n10 | \n12 | \n13 | \n16 | \n17 | \n21 | \n22 | \nX | \nY | \n\n | Total | \n
| code | \n | |||||||||||||||||
| B | \n\n | \n | \n | 1 | \n\n | \n | \n | 1 | \n\n | \n | \n | \n | \n | \n | \n | \n | \n | 2 | \n
| K | \n\n | \n | 1 | \n\n | \n | \n | \n | 4 | \n\n | 1 | \n\n | 2 | \n\n | \n | \n | \n | \n | 8 | \n
| M | \n\n | 1 | \n1 | \n\n | \n | \n | \n | 3 | \n1 | \n\n | \n | \n | 2 | \n\n | \n | \n | \n | 8 | \n
| R | \n\n | 1 | \n1 | \n1 | \n\n | 1 | \n1 | \n13 | \n\n | \n | 1 | \n3 | \n1 | \n2 | \n1 | \n1 | \n\n | 27 | \n
| S | \n\n | \n | \n | \n | \n | 1 | \n\n | 1 | \n\n | \n | \n | 1 | \n\n | \n | 1 | \n1 | \n\n | 5 | \n
| W | \n\n | \n | 2 | \n2 | \n\n | \n | \n | 6 | \n\n | \n | \n | 1 | \n\n | 1 | \n1 | \n1 | \n\n | 14 | \n
| Y | \n\n | \n | 4 | \n3 | \n1 | \n2 | \n2 | \n8 | \n2 | \n2 | \n\n | 5 | \n\n | 2 | \n2 | \n2 | \n\n | 35 | \n
| \n | ||||||||||||||||||
| Total | \n\n | 2 | \n9 | \n7 | \n1 | \n4 | \n3 | \n36 | \n3 | \n3 | \n1 | \n12 | \n3 | \n5 | \n5 | \n5 | \n\n | 99 | \n
\nThis track displays single nucleotide polymorphisms (SNPs) identified by published \nGenome-Wide Association Studies (GWAS), collected in the \nNHGRI-EBI GWAS Catalog\npublished jointly by the National\nHuman Genome Research Institute (NHGRI) and the European Bioinformatics Institute (EMBL-EBI).\nSome abbreviations\nare used above.\n
\n\nFrom http://www.ebi.ac.uk/gwas/docs/about:\n
\nThe Catalog is a quality controlled, manually curated, literature-derived\ncollection of all published genome-wide association studies assaying at least\n100,000 SNPs and all SNP-trait associations with p-values < 1.0 x\n10-5 (Hindorff et al., 2009). For more details about the Catalog\ncuration process and data extraction procedures, please refer to the\nMethods page.\n\n\n\n
\nFrom http://www.ebi.ac.uk/gwas/docs/methods:\n
\nThe GWAS Catalog data is extracted from the literature. Extracted information\nincludes publication information, study cohort information such as cohort size,\ncountry of recruitment and subject ethnicity, and SNP-disease association\ninformation including SNP identifier (i.e. RSID), p-value, gene and risk\nallele. Each study is also assigned a trait that best represents the phenotype\nunder investigation. When multiple traits are analysed in the same study either\nmultiple entries are created, or individual SNPs are annotated with their\nspecific traits. Traits are used both to query and visualise the data in the\nCatalog's web form and diagram-based query interfaces.\n\n\n\n
\nData extraction and curation for the GWAS Catalog is an expert activity; each\nstep is performed by scientists supported by a web-based tracking and data\nentry system which allows multiple curators to search, annotate, verify and\npublish the Catalog data. Papers that qualify for inclusion in the Catalog are\nidentified through weekly PubMed searches. They then undergo two levels of\ncuration. First all data, including association information for SNPs, traits\nand general information about the study, are extracted by one curator. A second\ncurator then performs an additional round of curation to double-check the\naccuracy and consistency of all the information. Finally, an automated pipeline\nperforms validation of the extracted data, see the\nQuality control and SNP mapping section below for more\ndetails. This information is then used for queries and in the production of the\ndiagram.\n
\nPrevious versions of this track can be found on our archive download server.\n
\n\n\nHindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA.\n\nPotential etiologic and functional implications of genome-wide association loci for human diseases\nand traits.\nProc Natl Acad Sci U S A. 2009 Jun 9;106(23):9362-7.\nPMID: 19474294; PMC:
\nThis track shows the differences between the GRCh38 (hg38) and previous GRCh37 (hg19)\nhuman genome assemblies, indicating contigs (or portions of contigs) that are new\nto the hg38 assembly.\n
\n\n\nThe following color/score key is used:\n
\n
\n
| color | score | change from hg19 to hg38 |
|---|---|---|
| 0 | New contig added to\nhg38 to update sequence or fill gaps present in hg19 | |
| 500 | Different portions\nof this same contig used in the construction of hg38 and hg19 assemblies | |
| 1000 | Updated version of\nan hg19 contig in which sequence errors have been corrected |
\nUse the score filter to select which categories to show in the display.\n
\n\n\nThe contig coordinates were extracted from the AGP files for both assemblies.\nContigs that matched the same name, same version, and the same specific\nportion of sequence in both assemblies were considered identical between the two\nassemblies and were excluded from this data set. The remaining contigs are shown\nin this track.\n
\n\n\nThe data and presentation of this track were prepared by\nHiram Clawson, UCSC Genome\nBrowser engineering.\n
\n" } }, "description": "Contigs New to GRCh38/(hg38), Not Carried Forward from GRCh37/(hg19)", "category": [ "Mapping and Sequencing" ] }, { "type": "FeatureTrack", "trackId": "hg38-iscaBenignGainCum", "name": "ClinGen CNVs - Benign Gain", "assemblyNames": [ "hg38" ], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "iscaBenignGainCum.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "iscaBenignGainCum.bed.gz.csi" } } }, "metadata": { "ucsc": { "color": "0,0,200", "longLabel": "ClinGen CNVs: Benign Gain Coverage", "parent": "iscaViewTotal", "shortLabel": "Benign Gain", "subGroups": "view=cov class=ben level=sub", "track": "iscaBenignGainCum", "html": "" } }, "description": "ClinGen CNVs: Benign Gain Coverage", "category": [ "Phenotypes, Variants, and Literature" ] }, { "type": "FeatureTrack", "trackId": "hg38-iscaBenignLossCum", "name": "ClinGen CNVs - Benign Loss", "assemblyNames": [ "hg38" ], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "iscaBenignLossCum.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "iscaBenignLossCum.bed.gz.csi" } } }, "metadata": { "ucsc": { "color": "200,0,0", "longLabel": "ClinGen CNVs: Benign Loss Coverage", "parent": "iscaViewTotal", "shortLabel": "Benign Loss", "subGroups": "view=cov class=ben level=sub", "track": "iscaBenignLossCum", "html": "" } }, "description": "ClinGen CNVs: Benign Loss Coverage", "category": [ "Phenotypes, Variants, and Literature" ] }, { "type": "FeatureTrack", "trackId": "hg38-iscaPathGainCum", "name": "ClinGen CNVs - Pathogenic Gain", "assemblyNames": [ "hg38" ], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "iscaPathGainCum.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "iscaPathGainCum.bed.gz.csi" } } }, "metadata": { "ucsc": { "color": "0,0,200", "longLabel": "ClinGen CNVs: Pathogenic Gain Coverage", "parent": "iscaViewTotal", "shortLabel": "Path Gain", "subGroups": "view=cov class=path level=sub", "track": "iscaPathGainCum", "html": "" } }, "description": "ClinGen CNVs: Pathogenic Gain Coverage", "category": [ "Phenotypes, Variants, and Literature" ] }, { "type": "FeatureTrack", "trackId": "hg38-iscaPathLossCum", "name": "ClinGen CNVs - Pathogenic Loss", "assemblyNames": [ "hg38" ], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "iscaPathLossCum.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "iscaPathLossCum.bed.gz.csi" } } }, "metadata": { "ucsc": { "color": "200,0,0", "longLabel": "ClinGen CNVs: Pathogenic Loss Coverage", "parent": "iscaViewTotal", "shortLabel": "Path Loss", "subGroups": "view=cov class=path level=sub", "track": "iscaPathLossCum", "html": "" } }, "description": "ClinGen CNVs: Pathogenic Loss Coverage", "category": [ "Phenotypes, Variants, and Literature" ] }, { "type": "FeatureTrack", "trackId": "hg38-knownAlt", "name": "UCSC Alt Events", "assemblyNames": [ "hg38" ], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "knownAlt.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "knownAlt.bed.gz.csi" } } }, "metadata": { "ucsc": { "color": "90,0,150", "group": "genes", "longLabel": "Alternative Splicing, Alternative Promoter and Similar Events in UCSC Genes", "noScoreFilter": ".", "shortLabel": "UCSC Alt Events", "track": "knownAlt", "type": "bed 6 .", "visibility": "hide", "html": "This track shows various types of alternative splicing and other\nevents that result in more than a single transcript from the same\ngene. The label by an item describes the type of event. The events are:
\nThis track is based on an analysis by the txgAnalyse program of splicing graphs\nproduced by the txGraph program. Both of these programs were written by Jim\nKent at UCSC.
\n" } }, "description": "Alternative Splicing, Alternative Promoter and Similar Events in UCSC Genes", "category": [ "Genes and Gene Predictions" ] }, { "type": "FeatureTrack", "trackId": "hg38-microsat", "name": "Microsatellite", "assemblyNames": [ "hg38" ], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "microsat.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "microsat.bed.gz.csi" } } }, "metadata": { "ucsc": { "group": "rep", "longLabel": "Microsatellites - Di-nucleotide and Tri-nucleotide Repeats", "priority": "3", "shortLabel": "Microsatellite", "track": "microsat", "type": "bed 4", "visibility": "hide", "html": "\nThis track displays regions that are likely to be useful as microsatellite\nmarkers. These are sequences of at least 15 perfect di-nucleotide and \ntri-nucleotide repeats and tend to be highly polymorphic in the\npopulation.\n
\n\n\nThe data shown in this track are a subset of the Simple Repeats track, \nselecting only those \nrepeats of period 2 and 3, with 100% identity and no indels and with\nat least 15 copies of the repeat. The Simple Repeats track is\ncreated using the \nTandem Repeats Finder. For more information about this \nprogram, see Benson (1999).
\n\n\nTandem Repeats Finder was written by \nGary Benson.
\n\n\nBenson G.\n\nTandem repeats finder: a program to analyze DNA sequences.\nNucleic Acids Res. 1999 Jan 15;27(2):573-80.\nPMID: 9862982; PMC:
\nThis track shows joined fragments of interrupted repeats extracted\nfrom the output of the \nRepeatMasker program which screens DNA sequences\nfor interspersed repeats and low complexity DNA sequences using the\n\nRepbase Update library of repeats from the\nGenetic\nInformation Research Institute (GIRI). Repbase Update is described in\nJurka (2000) in the References section below.\n
\n\n\nThe detailed annotations from RepeatMasker are in the RepeatMasker track. This\ntrack shows fragments of original repeat insertions which have been interrupted\nby insertions of younger repeats or through local rearrangements. The fragments\nare joined using the ID column of RepeatMasker output.\n
\n\n\nIn pack or full mode, each interrupted repeat is displayed as boxes\n(fragments) joined by horizontal lines, labeled with the repeat name.\nIf all fragments are on the same strand, arrows are added to the\nhorizontal line to indicate the strand. In dense or squish mode, labels\nand arrows are omitted and in dense mode, all items are collapsed to\nfit on a single row.\n
\n\n\nItems are shaded according to the average identity score of their\nfragments. Usually, the shade of an item is similar to the shades of\nits fragments unless some fragments are much more diverged than\nothers. The score displayed above is the average identity score,\nclipped to a range of 50% - 100% and then mapped to the range\n0 - 1000 for shading in the browser.\n
\n\n\nUCSC has used the most current versions of the RepeatMasker software\nand repeat libraries available to generate these data. Note that these\nversions may be newer than those that are publicly available on the Internet.\n
\n\n\nData are generated using the RepeatMasker -s flag. Additional flags\nmay be used for certain organisms. See the\nFAQ for more information.\n
\n\n\nThanks to Arian Smit, Robert Hubley and GIRI for providing the tools and\nrepeat libraries used to generate this track.\n
\n\n\nSmit AFA, Hubley R, Green P.\nRepeatMasker Open-3.0.\n\nhttps://www.repeatmasker.org/. 1996-2010.\n
\n\n\nRepbase Update is described in:\n
\n\n\nJurka J.\n\nRepbase Update: a database and an electronic journal of repetitive elements.\nTrends Genet. 2000 Sep;16(9):418-420.\nPMID: 10973072\n
\n\n\nFor a discussion of repeats in mammalian genomes, see:\n
\n\n\nSmit AF.\n\nInterspersed repeats and other mementos of transposable elements in mammalian genomes.\nCurr Opin Genet Dev. 1999 Dec;9(6):657-63.\nPMID: 10607616\n
\n\n\nSmit AF.\n\nThe origin of interspersed repeats in the human genome.\nCurr Opin Genet Dev. 1996 Dec;6(6):743-8.\nPMID: 8994846\n
\n" } }, "description": "Fragments of Interrupted Repeats Joined by RepeatMasker ID", "category": [ "Repeats" ] }, { "type": "FeatureTrack", "trackId": "hg38-oreganno", "name": "ORegAnno", "assemblyNames": [ "hg38" ], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "oreganno.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "oreganno.bed.gz.csi" } } }, "metadata": { "ucsc": { "color": "102,102,0", "group": "regulation", "longLabel": "Regulatory elements from ORegAnno", "shortLabel": "ORegAnno", "track": "oreganno", "type": "bed 4 +", "visibility": "hide", "html": "\nThis track displays literature-curated regulatory regions, transcription\nfactor binding sites, and regulatory polymorphisms from\nORegAnno (Open Regulatory Annotation). For more detailed\ninformation on a particular regulatory element, follow the link to ORegAnno\nfrom the details page. \n\n
\n\nThe display may be filtered to show only selected region types, such as:
\n\nTo exclude a region type, uncheck the appropriate box in the list at the top of \nthe Track Settings page.
\n\n\nAn ORegAnno record describes an experimentally proven and published regulatory\nregion (promoter, enhancer, etc.), transcription factor binding site, or\nregulatory polymorphism. Each annotation must have the following attributes:\n
\nORegAnno core team and principal contacts: Stephen Montgomery, Obi Griffith, \nand Steven Jones from Canada's Michael Smith Genome Sciences Centre, Vancouver, \nBritish Columbia, Canada.
\n\nThe ORegAnno community (please see individual citations for various\nfeatures): ORegAnno Citation.\n\n
\nLesurf R, Cotto KC, Wang G, Griffith M, Kasaian K, Jones SJ, Montgomery SB, Griffith OL, Open\nRegulatory Annotation Consortium..\n\nORegAnno 3.0: a community-driven resource for curated regulatory annotation.\nNucleic Acids Res. 2016 Jan 4;44(D1):D126-32.\nPMID: 26578589; PMC:
\nGriffith OL, Montgomery SB, Bernier B, Chu B, Kasaian K, Aerts S, Mahony S, Sleumer MC, Bilenky M,\nHaeussler M et al.\n\nORegAnno: an open-access community-driven resource for regulatory annotation.\nNucleic Acids Res. 2008 Jan;36(Database issue):D107-13.\nPMID: 18006570; PMC:
\nMontgomery SB, Griffith OL, Sleumer MC, Bergman CM, Bilenky M, Pleasance ED, \nPrychyna Y, Zhang X, Jones SJ. \nORegAnno: an open access database and curation system for \nliterature-derived promoters, transcription factor binding sites and regulatory variation.\nBioinformatics. 2006 Mar 1;22(5):637-40.\nPMID: 16397004\n
\n\n" } }, "description": "Regulatory elements from ORegAnno", "category": [ "Regulation" ] }, { "type": "FeatureTrack", "trackId": "hg38-phastConsElements100way", "name": "Conserved Elements - 100 Vert. El", "assemblyNames": [ "hg38" ], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "phastConsElements100way.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "phastConsElements100way.bed.gz.csi" } } }, "metadata": { "ucsc": { "color": "110,10,40", "longLabel": "100 vertebrates Conserved Elements", "noInherit": "on", "parent": "cons100wayViewelements off", "priority": "23", "shortLabel": "100 Vert. El", "subGroups": "view=elements", "track": "phastConsElements100way", "type": "bed 5 .", "html": "" } }, "description": "100 vertebrates Conserved Elements", "category": [ "Comparative Genomics" ] }, { "type": "FeatureTrack", "trackId": "hg38-phastConsElements30way", "name": "Conserved Elements - 30-way El", "assemblyNames": [ "hg38" ], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "phastConsElements30way.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "phastConsElements30way.bed.gz.csi" } } }, "metadata": { "ucsc": { "color": "110,10,40", "longLabel": "30 mammals Conserved Elements (27 primates)", "noInherit": "on", "parent": "cons30wayViewelements on", "priority": "23", "shortLabel": "30-way El", "subGroups": "view=elements", "track": "phastConsElements30way", "type": "bed 5 .", "html": "" } }, "description": "30 mammals Conserved Elements (27 primates)", "category": [ "Comparative Genomics" ] }, { "type": "FeatureTrack", "trackId": "hg38-rmsk", "name": "RepeatMasker", "assemblyNames": [ "hg38" ], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "rmsk.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "rmsk.bed.gz.csi" } } }, "metadata": { "ucsc": { "canPack": "off", "group": "rep", "html": "\nThis track was created by using Arian Smit's\nRepeatMasker\nprogram, which screens DNA sequences\nfor interspersed repeats and low complexity DNA sequences. The program\noutputs a detailed annotation of the repeats that are present in the\nquery sequence (represented by this track), as well as a modified version\nof the query sequence in which all the annotated repeats have been masked\n(generally available on the\nDownloads page). RepeatMasker uses the\nRepbase Update library of repeats from the\nGenetic \nInformation Research Institute (GIRI).\nRepbase Update is described in Jurka (2000) in the References section below.
\n\nThis track and the masking information in our \n hg38 genome download FASTA files was created in 2010 with the original RepBase library from 2010-03-02 and RepeatMasker 3.0.1.\nSince April 2019, RepBase is under a commercial license, we cannot distribute\nit or update the track using the RepBase library without a license. Therefore, and for\ncompatibility with past results, given how central the masking is for many other\nannotations, we decided to not update the repeatmasking of hg38. However, you can show the\nsmall differences between the RepeatMasker 3/RepBase from 2010 and RepeatMasker 4/DFAM\nfrom 2020 using the track \"RepeatMasker Viz\" in the same track group. It\ncontains two subtracks, one with the old and one with the new data. Also, these\ntracks have many more visualization options than the original RepeatMasker\ntrack.\n
\n\nHowever, the last track update time of this track at UCSC is not 2010, because we had to add\nrepeatmasking annotations to the rarely used _alt and _fix \"patch\" sequences of\nthe hg38 genome. The repeatmasking annotations of the main chromosomes were unaffected\nand have not changed since 2010.\nFor more information on genome patches, see our blog post.\n
\n\n\nIn full display mode, this track displays up to ten different classes of repeats:\n
\nThe level of color shading in the graphical display reflects the amount of\nbase mismatch, base deletion, and base insertion associated with a repeat\nelement. The higher the combined number of these, the lighter the shading.\n
\n\n\nA "?" at the end of the "Family" or "Class" (for example, DNA?) signifies that\nthe curator was unsure of the classification. At some point in the future,\neither the "?" will be removed or the classification will be changed.
\n\n\nData are generated using the RepeatMasker -s flag. Additional flags\nmay be used for certain organisms. Repeats are soft-masked. Alignments may\nextend through repeats, but are not permitted to initiate in them.\nSee the FAQ for more information.\n
\n\n\nThanks to Arian Smit, Robert Hubley and GIRI for providing the tools and\nrepeat libraries used to generate this track.\n
\n\n\nSmit AFA, Hubley R, Green P. RepeatMasker Open-3.0.\n\nhttps://www.repeatmasker.org/. 1996-2010.\n
\n\n\nRepbase Update is described in:\n
\n\n\nJurka J.\n\nRepbase Update: a database and an electronic journal of repetitive elements.\nTrends Genet. 2000 Sep;16(9):418-420.\nPMID: 10973072\n
\n\n\nFor a discussion of repeats in mammalian genomes, see:\n
\n\n\nSmit AF.\n\nInterspersed repeats and other mementos of transposable elements in mammalian genomes.\nCurr Opin Genet Dev. 1999 Dec;9(6):657-63.\nPMID: 10607616\n
\n\n\nSmit AF.\n\nThe origin of interspersed repeats in the human genome.\nCurr Opin Genet Dev. 1996 Dec;6(6):743-8.\nPMID: 8994846\n
\n", "longLabel": "Repeating Elements by RepeatMasker", "maxWindowToDraw": "10000000", "priority": "1", "shortLabel": "RepeatMasker", "spectrum": "on", "track": "rmsk", "type": "rmsk", "visibility": "dense" } }, "description": "Repeating Elements by RepeatMasker", "category": [ "Repeats" ] }, { "type": "FeatureTrack", "trackId": "hg38-rmskJoinedBaseline", "name": "RepeatMasker Viz. - v3.0.1 db20100302 : Browser Baseline Dataset", "assemblyNames": [ "hg38" ], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "rmskJoinedBaseline.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "rmskJoinedBaseline.bed.gz.csi" } } }, "metadata": { "ucsc": { "group": "rep", "longLabel": "RepeatMasker v3.0.1 db20100302 : Browser Baseline Dataset", "parent": "joinedRmsk on", "priority": "4", "shortLabel": "RepeatMasker Viz.", "track": "rmskJoinedBaseline", "visibility": "hide", "html": "" } }, "description": "RepeatMasker v3.0.1 db20100302 : Browser Baseline Dataset", "category": [ "Repeats" ] }, { "type": "FeatureTrack", "trackId": "hg38-rmskJoinedCurrent", "name": "RepeatMasker Viz. - v4.0.7 Dfam_2.0 : Current Dataset", "assemblyNames": [ "hg38" ], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "rmskJoinedCurrent.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "rmskJoinedCurrent.bed.gz.csi" } } }, "metadata": { "ucsc": { "longLabel": "RepeatMasker v4.0.7 Dfam_2.0 : Current Dataset", "parent": "joinedRmsk on", "priority": "2", "shortLabel": "RepeatMasker Viz.", "track": "rmskJoinedCurrent", "html": "" } }, "description": "RepeatMasker v4.0.7 Dfam_2.0 : Current Dataset", "category": [ "Repeats" ] }, { "type": "FeatureTrack", "trackId": "hg38-scaffolds", "name": "Scaffolds", "assemblyNames": [ "hg38" ], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "scaffolds.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "scaffolds.bed.gz.csi" } } }, "metadata": { "ucsc": { "color": "0,0,0", "longLabel": "GRCh38 Defined Scaffold Identifiers", "shortLabel": "Scaffolds", "superTrack": "assemblyContainer pack", "track": "scaffolds", "type": "bed 4 .", "html": "\nThis track shows the Genome Reference Consortium (GRC) names for the \nscaffolds in the GRCh38 (hg38) assembly, downloaded from the GRCh38\nacc2name file in GenBank. \n
\n" } }, "description": "GRCh38 Defined Scaffold Identifiers", "category": [ "Mapping and Sequencing" ] }, { "type": "FeatureTrack", "trackId": "hg38-simpleRepeat", "name": "Simple Repeats", "assemblyNames": [ "hg38" ], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "simpleRepeat.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "simpleRepeat.bed.gz.csi" } } }, "metadata": { "ucsc": { "group": "rep", "longLabel": "Simple Tandem Repeats by TRF", "priority": "7", "shortLabel": "Simple Repeats", "track": "simpleRepeat", "type": "bed 4 +", "visibility": "hide", "html": "\nThis track displays simple tandem repeats (possibly imperfect repeats) located\nby Tandem Repeats\nFinder (TRF) which is specialized for this purpose. These repeats can\noccur within coding regions of genes and may be quite\npolymorphic. Repeat expansions are sometimes associated with specific\ndiseases.
\n\n\nFor more information about the TRF program, see Benson (1999).\n
\n\n\nTRF was written by \nGary Benson.
\n\n\nBenson G.\n\nTandem repeats finder: a program to analyze DNA sequences.\nNucleic Acids Res. 1999 Jan 15;27(2):573-80.\nPMID: 9862982; PMC:
This track shows locations of Sequence Tagged Site (STS) markers\nalong the draft assembly. These markers have been mapped using either\ngenetic mapping (Genethon, Marshfield, and deCODE maps), radiation\nhybridization mapping (Stanford, Whitehead RH, and GeneMap99 maps) or\nYAC mapping (the Whitehead YAC map) techniques. Since August 2001,\nthis track no longer displays fluorescent in situ hybridization (FISH)\nclones, which are now displayed in a separate track.
\n\nGenetic map markers are shown in blue; radiation hybrid map markers\nare shown in black. When a marker maps to multiple positions in the\ngenome, it is shown in a lighter color.
\n\nPositions of STS markers are determined using both full sequences\nand primer information. Full sequences are aligned using The track filter can be used to change the color or include/exclude\na set of map data within the track. This is helpful when many items\nare shown in the track display, especially when only some are relevant\nto the current task. To use the filter: \nUsing the Filter
\n \n
When you have finished configuring the filter, click the\nSubmit button.
\n\nThis track was designed and implemented by Terry Furey. Many\nthanks to the researchers who worked on these maps, and to Greg\nSchuler, Arek Kasprzyk, Wonhee Jang, and Sanja Rogic for helping\nprocess the data. Additional data on the individual maps can be found\nat the following links:\n
\nThis track displays tRNA genes predicted by using \ntRNAscan-SE v.1.23. \n
\n\ntRNAscan-SE is an integrated program that uses tRNAscan (Fichant) and an A/B box motif detection \nalgorithm (Pavesi) as pre-filters to obtain an initial list of tRNA candidates. \nThe program then filters these candidates with a covariance model-based \nsearch program \nCOVE (Eddy) to obtain a highly specific set of primary sequence \nand secondary structure predictions that represent 99-100% of true tRNAs \nwith a false positive rate of fewer than 1 per 15 gigabases.
\n\nDetailed tRNA annotations for eukaryotes, bacteria, and archaea are available at\nGenomic tRNA Database (GtRNAdb). \n
\n\nWhat does the tRNAscan-SE score mean? Anything with a score above 20 bits is likely to be\nderived from a tRNA, although this does not indicate whether the tRNA gene still encodes a \nfunctional tRNA molecule (i.e. tRNA-derived SINES probably do not function in the ribosome in translation).\nVertebrate tRNAs with scores of >60.0 (bits) are likely to encode functional tRNA genes, and \nthose with scores below ~45 have sequence or structural features that indicate they probably are\nno longer involved in translation. tRNAs with scores between 45-60 bits are in the "grey" zone, and may\nor may not have all the required features to be functional. In these cases, tRNAs should be inspected\ncarefully for loss of specific primary or secondary structure features (usually in alignments with other\ngenes of the same isotype), in order to make a better educated guess. These rough score range guides \nare not exact, nor are they based on specific biochemical studies of atypical tRNA features,\nso please treat them accordingly.\n
\n\nPlease note that tRNA genes marked as "Pseudo" are low scoring predictions that are mostly pseudogenes or \ntRNA-derived elements. These genes do not usually fold into a typical cloverleaf tRNA secondary \nstructure and the provided images of the predicted secondary structures may appear rotated.\n
\n\n\nBoth tRNAscan-SE and GtRNAdb are maintained by the\nLowe Lab at UCSC.\n
\n\nCove-predicted tRNA secondary structures were rendered by NAVIEW (c) 1988 Robert E. Bruccoleri.\n
\n\n\nWhen making use of these data, please cite the following articles:
\n\nChan PP, Lowe TM. \nGtRNAdb: a database of transfer RNA genes detected in genomic sequence.\nNucleic Acids Res. 2009 Jan;37(Database issue):D93-7.\nPMID: 18984615; PMC:
\nEddy SR, Durbin R. \n\nRNA sequence analysis using covariance models.\nNucleic Acids Res. 1994 Jun 11;22(11):2079-88.\nPMID: 8029015; PMC:
\nFichant GA, Burks C. \n\nIdentifying potential tRNA genes in genomic DNA sequences.\nJ Mol Biol. 1991 Aug 5;220(3):659-71.\nPMID: 1870126\n
\n\n\nLowe TM, Eddy SR. \n\ntRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence.\nNucleic Acids Res. 1997 Mar 1;25(5):955-64.\nPMID: 9023104; PMC:
\nPavesi A, Conterio F, Bolchi A, Dieci G, Ottonello S.\n\nIdentification of new eukaryotic tRNA genes in genomic DNA databases by a multistep weight matrix\nanalysis of transcriptional control regions.\nNucleic Acids Res. 1994 Apr 11;22(7):1247-56.\nPMID: 8165140; PMC:
\nMost proteins are composed of one or more conserved functional regions called\ndomains. This track shows the high-quality, manually-curated\n\nPfam-A\ndomains found in transcripts located in the GENCODE Genes track by the software HMMER3.\n
\n\n\nThis track follows the display conventions for\ngene\ntracks.\n
\n\n\nThe sequences from the knownGenePep table (see \nGENCODE Genes description page)\nare submitted to the set of Pfam-A HMMs which annotate regions within the\npredicted peptide that are recognizable as Pfam protein domains. These regions\nare then mapped to the transcripts themselves using the\n\npslMap utility. A complete shell script log for every version of UCSC genes can be found in \nour GitHub repository under \n\nhg/makeDb/doc/ucscGenes, e.g. \n\nmm10.knownGenes17.csh is for the database mm10 and version 17 of UCSC known genes.\n
\n\n\nOf the several options for filtering out false positives, the "Trusted cutoff (TC)" \nthreshold method is used in this track to determine significance. For more information regarding \nthresholds and scores, see the HMMER \ndocumentation and\nresults interpretation pages.\n
\n\n\nNote: There is currently an undocumented but known HMMER problem which results in lessened \nsensitivity and possible missed searches for some zinc finger domains. Until a fix is released for \nHMMER /PFAM thresholds, please also consult the "UniProt Domains" subtrack of the UniProt\ntrack for more comprehensive zinc finger annotations.\n
\n\n\npslMap was written by Mark Diekhans at UCSC.\n
\n\n\nFinn RD, Mistry J, Tate J, Coggill P, Heger A, Pollington JE, Gavin OL, Gunasekaran P, Ceric G,\nForslund K et al.\nThe Pfam protein families database.\nNucleic Acids Res. 2010 Jan;38(Database issue):D211-22.\nPMID: 19920124; PMC:
\nThis track associates UCSC Genome Browser chromosome names to accession\nnames from the International Nucleotide Sequence Database Collaboration (INSDC).\n
\n\n\nThe data were downloaded from the NCBI assembly database.\n
\n\nThe data for this track was prepared by\nHiram Clawson.\n\n" } }, "description": "Accession at INSDC - International Nucleotide Sequence Database Collaboration", "category": [ "Mapping and Sequencing" ] }, { "type": "FeatureTrack", "trackId": "hg38-ucscToRefSeq", "name": "RefSeq Acc", "assemblyNames": [ "hg38" ], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "ucscToRefSeq.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "ucscToRefSeq.bed.gz.csi" } } }, "metadata": { "ucsc": { "group": "map", "longLabel": "RefSeq Accession", "shortLabel": "RefSeq Acc", "track": "ucscToRefSeq", "type": "bed 4", "url": "https://www.ncbi.nlm.nih.gov/nuccore/$$", "urlLabel": "RefSeq accession:", "visibility": "hide", "html": "
\nThis track associates UCSC Genome Browser chromosome names to accession\nidentifiers from the NCBI Reference Sequence Database (RefSeq).\n
\n\n\nThe data were downloaded from the NCBI assembly database.\n
\n\nThe data for this track was prepared by\nHiram Clawson.\n" } }, "description": "RefSeq Accession", "category": [ "Mapping and Sequencing" ] }, { "type": "FeatureTrack", "trackId": "hg38-wgRna", "name": "sno/miRNA", "assemblyNames": [ "hg38" ], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "wgRna.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "wgRna.bed.gz.csi" } } }, "metadata": { "ucsc": { "color": "200,80,0", "dataVersion": "miRBase Release 22 (March 2018) and snoRNABase Version 3 (lifted from hg19)", "group": "genes", "longLabel": "C/D and H/ACA Box snoRNAs, scaRNAs, and microRNAs from snoRNABase and miRBase", "noScoreFilter": ".", "shortLabel": "sno/miRNA", "superTrack": "nonCodingRNAs pack", "track": "wgRna", "type": "bed 8 +", "url": "http://www-snorna.biotoul.fr/plus.php?id=$$", "url2": "http://www.mirbase.org/cgi-bin/query.pl?terms=$$", "url2Label": "miRBase:", "urlLabel": "Laboratoire de Biologie Moleculaire Eucaryote:", "visibility": "hide", "html": "
\nThis track displays positions of four different types of RNA in the human \ngenome: \n
\nC/D box and H/ACA box snoRNAs are guides for the 2'O-ribose methylation and \nthe pseudouridilation, respectively, of rRNAs and snRNAs, although many of \nthem have no documented target RNA. The scaRNAs guide modifications of the\nspliceosomal snRNAs transcribed by RNA polymerase II, and often contain both \nC/D and H/ACA domains.
\n\n\nThis track follows the general display conventions for \ngene prediction \ntracks.
\n\nThe miRNA precursor forms (pre-miRNA) are represented by red blocks.
\n\nC/D box snoRNAs, H/ACA box snoRNAs and scaRNAs are represented by blue, \ngreen and magenta blocks, respectively. At a zoomed-in resolution, arrows \nsuperimposed on the blocks indicate the sense orientation of the snoRNAs.
\n\n\nPrecursor miRNA genomic locations from\n\nmiRBase\nwere calculated using wublastn for sequence alignment with the requirement of\n100% identity. \nThe extents of the precursor sequences were not generally known and were\npredicted based on base-paired hairpin structure. miRBase is\ndescribed in Griffiths-Jones, S. (2004) and Weber, M.J. (2005) in the \nReferences section below.
\n\nThe snoRNAs and scaRNAs from the snoRNABase were aligned against the \nhuman genome using blat. \n
\n\n\n\n
\nWhen making use of these data, please cite the folowing articles in addition to\nthe primary sources of the miRNA sequences:
\n\nGriffiths-Jones S, Saini HK, van Dongen S, Enright AJ.\nmiRBase: tools for microRNA genomics.\nNucleic Acids Res. 2008 Jan 1;36(Database issue):D154-8.
\n\nGriffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ.\nmiRBase: microRNA sequences, targets and gene nomenclature.\nNucleic Acids Res. 2006 Jan 1;34(Database issue):D140-4.
\n\nGriffiths-Jones S.\nThe microRNA Registry.\nNucleic Acids Res. 2004 Jan 1;32(Database issue):D109-11.
\n\nWeber MJ.\nNew human and mouse microRNA genes found by homology search.\n
\nYou may also want to cite The Wellcome Trust Sanger Institute \nmiRBase and The Laboratoire de Biologie Moleculaire \nEucaryote snoRNABase.
\n\nThe following publication provides guidelines on miRNA annotation:\nAmbros V. et al., \nA uniform system for microRNA annotation. \nRNA. 2003;9(3):277-9.
\n\n" } }, "description": "C/D and H/ACA Box snoRNAs, scaRNAs, and microRNAs from snoRNABase and miRBase", "category": [ "Genes and Gene Predictions" ] }, { "type": "FeatureTrack", "trackId": "hg38-windowmaskerSdust", "name": "WM + SDust", "assemblyNames": [ "hg38" ], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "windowmaskerSdust.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "windowmaskerSdust.bed.gz.csi" } } }, "metadata": { "ucsc": { "group": "rep", "longLabel": "Genomic Intervals Masked by WindowMasker + SDust", "priority": "8", "shortLabel": "WM + SDust", "track": "windowmaskerSdust", "type": "bed 3", "visibility": "hide", "html": "
\nThis track depicts masked sequence as determined by\nWindowMasker. The\nWindowMasker tool is included in the NCBI C++ toolkit. The source code\nfor the entire toolkit is available from the NCBI\n\nFTP site.\n
\n\n\nTo create this track, WindowMasker was run with the following parameters:\n
\nwindowmasker -mk_counts true -input hg38.fa -output wm_counts\nwindowmasker -ustat wm_counts -sdust true -input hg38.fa -output repeats.bed\n\nThe repeats.bed (BED3) file was loaded into the "windowmaskerSdust" table for\nthis track.\n\n\n
\nMorgulis A, Gertz EM, Schäffer AA, Agarwala R.\nWindowMasker: window-based masker for sequenced genomes.\nBioinformatics. 2006 Jan 15;22(2):134-41.\nPMID: 16287941\n
\n" } }, "description": "Genomic Intervals Masked by WindowMasker + SDust", "category": [ "Repeats" ] }, { "type": "FeatureTrack", "trackId": "hg38-ccdsGene", "name": "CCDS", "assemblyNames": [ "hg38" ], "adapter": { "type": "Gff3TabixAdapter", "gffGzLocation": { "uri": "ccdsGene.gff.gz" }, "index": { "indexType": "CSI", "location": { "uri": "ccdsGene.gff.gz.csi" } } }, "metadata": { "ucsc": { "baseColorDefault": "genomicCodons", "baseColorUseCds": "given", "color": "12,120,12", "group": "genes", "longLabel": "Consensus CDS", "shortLabel": "CCDS", "track": "ccdsGene", "type": "genePred", "visibility": "hide", "html": "\nThis track shows human genome high-confidence gene annotations from the\nConsensus \nCoding Sequence (CCDS) project. This project is a collaborative effort \nto identify a core set of \nhuman protein-coding regions that are consistently annotated and of high \nquality. The long-term goal is to support convergence towards a standard set \nof gene annotations on the human genome.\n
\nCollaborators include:\n
\nFor more information on the different gene tracks, see our Genes FAQ.
\n\n\nCDS annotations of the human genome were obtained from two sources:\nNCBI \nRefSeq and a union of the gene annotations from \nEnsembl and \nVega, collectively known \nas Hinxton.
\n\nGenes with identical CDS genomic coordinates in both sets become CCDS \ncandidates. The genes undergo a quality evaluation, which must be approved by \nall collaborators. The following criteria are currently used to assess each\ngene: \n
\nA unique CCDS ID is assigned to the CCDS, which links together all gene \nannotations with the same CDS. CCDS gene annotations are under continuous\nreview, with periodic updates to this track.\n
\n\n\nThis track was produced at UCSC from data downloaded from the\nCCDS project \nweb site.\n
\n\n\nHubbard T, Barker D, Birney E, Cameron G, Chen Y, Clark L, Cox T, Cuff J, Curwen V, Down T et\nal.\nThe Ensembl genome database project.\nNucleic Acids Res. 2002 Jan 1;30(1):38-41.\nPMID: 11752248; PMC:
\nPruitt KD, Harrow J, Harte RA, Wallin C, Diekhans M, Maglott DR, Searle S, Farrell CM, Loveland JE,\nRuef BJ et al.\n\nThe consensus coding sequence (CCDS) project: Identifying a common protein-coding gene set for the\nhuman and mouse genomes.\nGenome Res. 2009 Jul;19(7):1316-23.\nPMID: 19498102; PMC:
\nPruitt KD, Tatusova T, Maglott DR.\n\nNCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts\nand proteins.\nNucleic Acids Res. 2005 Jan 1;33(Database issue):D501-4.\nPMID: 15608248; PMC:
\nThe RefSeq Genes track shows known human protein-coding and\nnon-protein-coding genes taken from the NCBI RNA reference sequences\ncollection (RefSeq). The data underlying this track are updated weekly.
\n\n\nPlease visit the Feedback for Gene and Reference Sequences (RefSeq) page to\nmake suggestions, submit additions and corrections, or ask for help concerning\nRefSeq records.\n
\n\n\nFor more information on the different gene tracks, see our Genes FAQ.
\n\n\nThis track follows the display conventions for\n\ngene prediction tracks.\nThe color shading indicates the level of review the RefSeq record has\nundergone: predicted (light), provisional (medium), reviewed (dark).\n
\n\n\nThe item labels and display colors of features within this track can be\nconfigured through the controls at the top of the track description page.\n
\nRefSeq RNAs were aligned against the human genome using BLAT. Those\nwith an alignment of less than 15% were discarded. When a single RNA\naligned in multiple places, the alignment having the highest base identity\nwas identified. Only alignments having a base identity level within 0.1% of\nthe best and at least 96% base identity with the genomic sequence were kept.\n
\n\n\nThis track was produced at UCSC from RNA sequence data generated by scientists\nworldwide and curated by the NCBI\nRefSeq project.\n
\n\n\nKent WJ.\n\nBLAT - the BLAST-like alignment tool.\nGenome Res. 2002 Apr;12(4):656-64.\nPMID: 11932250; PMC:
\nPruitt KD, Brown GR, Hiatt SM, Thibaud-Nissen F, Astashyn A, Ermolaeva O, Farrell CM, Hart J,\nLandrum MJ, McGarvey KM et al.\n\nRefSeq: an update on mammalian reference sequences.\nNucleic Acids Res. 2014 Jan;42(Database issue):D756-63.\nPMID: 24259432; PMC:
\nPruitt KD, Tatusova T, Maglott DR.\n\nNCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins.\nNucleic Acids Res. 2005 Jan 1;33(Database issue):D501-4.\nPMID: 15608248; PMC:
\nThis track shows known protein-coding and non-protein-coding genes \nfor organisms other than human, taken from the NCBI RNA reference \nsequences collection (RefSeq). The data underlying this track are \nupdated weekly.
\n\n\nThis track follows the display conventions for \ngene prediction \ntracks.\nThe color shading indicates the level of review the RefSeq record has \nundergone: predicted (light), provisional (medium), reviewed (dark).
\n\nThe item labels and display colors of features within this track can be\nconfigured through the controls at the top of the track description page. \n
\nThe RNAs were aligned against the human genome using blat; those\nwith an alignment of less than 15% were discarded. When a single RNA aligned \nin multiple places, the alignment having the highest base identity was \nidentified. Only alignments having a base identity level within 0.5% of \nthe best and at least 25% base identity with the genomic sequence were kept.\n
\n\n\nThis track was produced at UCSC from RNA sequence data\ngenerated by scientists worldwide and curated by the \nNCBI RefSeq project.
\n\n\nKent WJ.\n\nBLAT--the BLAST-like alignment tool.\nGenome Res. 2002 Apr;12(4):656-64.\nPMID: 11932250; PMC:
\nPruitt KD, Brown GR, Hiatt SM, Thibaud-Nissen F, Astashyn A, Ermolaeva O, Farrell CM, Hart J,\nLandrum MJ, McGarvey KM et al.\n\nRefSeq: an update on mammalian reference sequences.\nNucleic Acids Res. 2014 Jan;42(Database issue):D756-63.\nPMID: 24259432; PMC:
\nPruitt KD, Tatusova T, Maglott DR.\n\nNCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins.\nNucleic Acids Res. 2005 Jan 1;33(Database issue):D501-4.\nPMID: 15608248; PMC: