38bafc856320cf5360e0482faeee72b78f2ea963 lrnassar Tue May 5 14:13:30 2026 -0700 QA pass on varFreqs per-subtrack description pages: encode 3 plain emails, add target=_blank to 15 boilerplate REST API links, and add missing References sections (and Data Access on varFreqsAll). refs #36642 Mechanical fixes across 18 per-subtrack description pages: - Encoded 3 plain author/contact emails: pfeliciano@simonsfoundation.org (sfariSparkExomes), m.hobbs@garvan.org.au (mgrb), contact_npco@a-star.edu.sg (npm). - Added target="_blank" to 15 occurrences of the boilerplate "<a href=https://api.genome.ucsc.edu>REST API</a>" link across allofus, topmed, sfariSparkExomes, tommo60kjpn, alfaVcf, gasp, abraom, indigenomes, hrc, saudi, schema, sgdpFreq, gregor, hgdp1kFreq, colorsDbSnv. Added missing References sections: - allofus.html: All of Us Research Program 2024 Nature. - topmed.html: Taliun 2021 Nature. - alfaVcf.html: NCBI ALFA documentation citation (no peer-reviewed paper yet). - gregor.html: GREGoR R04 Methods document + consortium website (no flagship publication yet). - varFreqsAll.html: pointer to the supertrack's References section, plus tool citations (bcftools csq, Ensembl VEP). Added missing Data Access section on varFreqsAll.html explaining that the merged callset is not downloadable due to mixed source-data licensing, but can be reconstructed from the per-subtrack VCFs using the conversion scripts on GitHub. All 25 unique varFreqs description pages now have Description, Methods, Data Access, References. No non-ASCII characters and no inline event handlers across the set. diff --git src/hg/makeDb/trackDb/human/varFreqsAll.html src/hg/makeDb/trackDb/human/varFreqsAll.html index d1203549bd7..75b48c59f13 100644 --- src/hg/makeDb/trackDb/human/varFreqsAll.html +++ src/hg/makeDb/trackDb/human/varFreqsAll.html @@ -1,155 +1,198 @@ <h2>Description</h2> <p> This track merges variants from all individual variant frequency databases into a single bigBed file with predicted protein consequences and cross-database filtering. It contains over 1.1 billion variants from 20 population databases worldwide. For a summary of all available databases, see the <a href="hgTrackUi?g=varFreqs">Variant Frequencies</a> supertrack page. </p> <p> Each variant is annotated with its predicted consequence on protein-coding genes (using <a href="https://samtools.github.io/bcftools/howtos/csq-calling.html" target="_blank">bcftools csq</a> with <a href="https://www.ensembl.org/info/data/ftp/index.html" target="_blank">Ensembl</a> gene models), and colored by severity. Allele counts and frequencies are shown for each source database and, where available, broken down by ancestry or population group. </p> <h2>Display Conventions</h2> <h3>Color by Consequence</h3> <p>Variants are colored by their most severe predicted consequence:</p> <table class="stdTbl"> <tr><th>Color</th><th>Consequence class</th><th>Examples</th></tr> <tr> <td style="background-color: rgb(255,0,0); color: white; text-align: center;"><b>Red</b></td> <td>Protein-truncating / Loss-of-function</td> <td>stop_gained, frameshift, splice_donor, splice_acceptor, stop_lost, start_lost</td> </tr> <tr> <td style="background-color: rgb(31,119,180); color: white; text-align: center;"><b>Blue</b></td> <td>Missense / In-frame</td> <td>missense, inframe_insertion, inframe_deletion, protein_altering</td> </tr> <tr> <td style="background-color: rgb(0,128,0); color: white; text-align: center;"><b>Green</b></td> <td>Synonymous</td> <td>synonymous, stop_retained</td> </tr> <tr> <td style="background-color: rgb(128,128,128); color: white; text-align: center;"><b>Grey</b></td> <td>Non-coding / Intergenic</td> <td>intron, non_coding, intergenic, UTR</td> </tr> </table> <h3>Amino Acid Change Notation</h3> <p> The "AA change" field uses bcftools csq notation: <b>23I>23V</b> means position 23 changed from Isoleucine (I) to Valine (V) (missense). <b>23I</b> alone (no arrow) means position 23 is Isoleucine and unchanged (synonymous). A "*" indicates a stop codon (e.g. 45R>45* is a stop_gained). </p> <h2>Filters</h2> <p> This track supports extensive filtering via the track settings page. Click on the track title or use the "Configure" button to access filters: </p> <h3>Variant Type and Consequence</h3> <ul> <li><b>Variant Type</b>: Filter by SNV, Insertion, Deletion, or MNV.</li> <li><b>Consequence</b>: Filter by predicted consequence (Missense, Synonymous, Stop Gained, Frameshift, Splice Donor, Splice Acceptor, Intron, Intergenic).</li> </ul> <p><b>How to find protein-truncating variants:</b> Set the Consequence filter to include only "Stop Gained", "Frameshift", "Splice Donor", and "Splice Acceptor". These will appear as red items in the track display.</p> <h3>Frequency and Count Filters</h3> <ul> <li><b>Max Allele Frequency</b>: Filter by the maximum allele frequency observed across all databases. Useful for finding rare variants (e.g., set max to 0.01 for variants with AF < 1% in all databases).</li> <li><b>Total Allele Count</b>: Filter by the sum of allele counts across all databases. Useful for excluding singletons (e.g., set minimum to 2 to remove AC=1 variants that may be sequencing errors).</li> <li><b>Per-database AF and AC</b>: Filter by allele frequency or allele count in any specific database. For example, filter to variants with TOPMed AF > 0.001.</li> </ul> <h3>Source Database</h3> <p> The <b>Source Database</b> filter lets you restrict to variants present in specific databases. For example, select only "GREGoR" to see variants found in the rare disease cohort. This filter uses OR logic: selecting multiple databases shows variants found in <em>any</em> of the selected databases. </p> <h3>Population-Specific Filters</h3> <p> Several databases provide ancestry-specific allele frequencies: </p> <ul> <li><b>AllOfUs</b>: African, Indigenous American, East Asian, European, Oceanian, South Asian (from local ancestry inference)</li> <li><b>GenomeAsia</b>: Northeast Asian, Southeast Asian, South Asian</li> <li><b>gnomAD HGDP+1kG</b>: African, Amish, Latino, Ashkenazi Jewish, East Asian, Finnish, Middle Eastern, European, Other, South Asian</li> <li><b>GREGoR</b>: Affected, Unaffected, Unknown (disease status, not ancestry)</li> </ul> <h3>Length Filters</h3> <ul> <li><b>Reference/Alternate Length</b>: Filter by the length of the reference or alternate allele.</li> <li><b>Length Change</b>: Filter by the size difference between alternate and reference (positive = insertion, negative = deletion, zero = SNV or MNV).</li> </ul> <h2>Methods</h2> <p> Variant frequency VCF files from 20 databases were stripped of their INFO fields (to reduce size), normalized with <code>bcftools norm</code> (splitting multi-allelic sites), and merged with <code>bcftools merge</code>. The merged VCF was then annotated with predicted protein consequences using <code>bcftools csq</code> with the <a href="https://www.ensembl.org/info/data/ftp/index.html" target="_blank">Ensembl</a> GRCh38 release 115 gene annotation (GFF3). </p> <p> The annotated VCF was converted to bigBed format using a custom Python script (<code>vcfToBigBed.py</code>) that reads frequency data from each source VCF in parallel, matches variants by position/ref/alt, and writes a BED file with consequence coloring, per-database allele counts and frequencies, and population breakdowns. The database configuration (which VCFs to include, field mappings, and population definitions) is stored in two TSV files (<a href="https://github.com/ucscGenomeBrowser/kent/tree/master/src/hg/makeDb/scripts/varFreqs/databases.tsv" target="_blank">databases.tsv</a> and <a href="https://github.com/ucscGenomeBrowser/kent/tree/master/src/hg/makeDb/scripts/varFreqs/populations.tsv" target="_blank">populations.tsv</a>) to make future updates easy. </p> <p> We provide documentation that indicates how all source files of the varFreqs track were converted in the <a href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/doc/hg38/varFreqs.txt" target="_blank">makeDoc file</a> of the track. Scripts are available from <a href="https://github.com/ucscGenomeBrowser/kent/tree/master/src/hg/makeDb/scripts/varFreqs" target="_blank">Github</a>. </p> +<h2>Data Access</h2> +<p> +The data can be explored interactively with the +<a href="../cgi-bin/hgTables">Table Browser</a> or the +<a href="../cgi-bin/hgIntegrator">Data Integrator</a>. +For programmatic access, our <a href="https://api.genome.ucsc.edu" target="_blank">REST API</a> +can be used; the track name is <em>varFreqsAll</em>. +</p> +<p> +Because the merged callset includes data from multiple sources whose redistribution +licenses differ, the combined bigBed is <b>not available for download</b> from our +download server. The combined track can be reconstructed from the individual source VCFs +using the +<a href="https://github.com/ucscGenomeBrowser/kent/tree/master/src/hg/makeDb/scripts/varFreqs" +target="_blank">conversion scripts on GitHub</a> together with the +<a href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/doc/hg38/varFreqs.txt" +target="_blank">build documentation</a>. Where individual source data is downloadable from UCSC, +the per-subtrack description page indicates the path on our download server. +</p> + <h2>Credits</h2> <p> This track is only possible thanks to the data from millions of volunteers around the world, who donated blood, signed consent forms and provided health information about themselves and sometimes their families. Click on any of the individual tracks in the <a href="hgTrackUi?g=varFreqs">Variant Frequencies</a> supertrack to see the specific credits for each project. Thanks to Alex Ioannidis, UCSC, for the motivation for this track and to Andreas Lahner, MGZ, for feedback. </p> + +<h2>References</h2> +<p> +For primary citations of each source dataset, see the References section on the +<a href="hgTrackUi?g=varFreqs">Variant Frequencies</a> supertrack page. The merged-track +build itself uses the following tools: +</p> +<p> +Danecek P, McCarthy SA. +<a href="https://doi.org/10.1093/bioinformatics/btx100" target="_blank"> +BCFtools/csq: haplotype-aware variant consequences</a>. +<em>Bioinformatics</em>. 2017 Jul 1;33(13):2037-2039. +PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/28205675" target="_blank">28205675</a>; PMC: <a +href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5870570/" target="_blank">PMC5870570</a> +</p> +<p> +McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, Flicek P, Cunningham F. +<a href="https://doi.org/10.1186/s13059-016-0974-4" target="_blank"> +The Ensembl Variant Effect Predictor</a>. +<em>Genome Biol</em>. 2016 Jun 6;17(1):122. +PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/27268795" target="_blank">27268795</a>; PMC: <a +href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4893825/" target="_blank">PMC4893825</a> +</p>