9b06248769177714d1104ed0a8915e3641bd38cd gperez2 Wed Oct 9 15:00:21 2024 -0700 Adding and updating the CADD 1.7 track wrangled by Jeltje, refs #33940 diff --git src/hg/makeDb/trackDb/human/caddSuper.html src/hg/makeDb/trackDb/human/caddSuper.html index 5a2a856..cfe7fdb 100644 --- src/hg/makeDb/trackDb/human/caddSuper.html +++ src/hg/makeDb/trackDb/human/caddSuper.html @@ -1,151 +1,194 @@ <h2>Description</h2> <p> This track collection shows <a href="https://cadd.gs.washington.edu/" target="_blank">Combined Annotation Dependent Depletion</a> scores. CADD is a tool for scoring the deleteriousness of single nucleotide variants as well as insertion/deletion variants in the human genome.</p> <p> Some mutation annotations tend to exploit a single information type (e.g., phastCons or phyloP for conservation) and/or are restricted in scope (e.g., to missense changes). Thus, a broadly applicable metric that objectively weights and integrates diverse information is needed. Combined Annotation Dependent Depletion (CADD) is a framework that integrates multiple annotations into one metric by contrasting variants that survived natural selection with simulated mutations. </p> <p> CADD scores strongly correlate with allelic diversity, pathogenicity of both coding and non-coding variants, experimentally measured regulatory effects, and also rank causal variants within individual genome sequences with a higher value than non-causal variants. Finally, CADD scores of complex trait-associated variants from genome-wide association studies (GWAS) are significantly higher than matched controls and correlate with study sample size, likely reflecting the increased accuracy of larger GWAS. </p> <p> A CADD score represents a ranking not a prediction, and no threshold is defined for a specific purpose. Higher scores are more likely to be deleterious: Scores are <pre> 10 * -log of the rank</pre> so that variants with scores above 20 are predicted to be among the 1.0% most deleterious possible substitutions in the human genome. We recommend thinking carefully about what threshold is appropriate for your application. </p> <h2>Display Conventions and Configuration</h2> <p> There are six subtracks of this track: four for single-nucleotide mutations, one for each base, showing all possible substitutions, one for insertions and one for deletions. All subtracks show the CADD Phred score on mouseover. Zooming in shows the exact score on mouseover, same basepair = score 0.0.</p> <p> PHRED-scaled scores are normalized to all potential ~9 billion SNVs, and thereby provide an externally comparable unit for analysis. For example, a scaled score of 10 or greater indicates a raw score in the top 10% of all possible reference genome SNVs, and a score of 20 or greater indicates a raw score in the top 1%, regardless of the details of the annotation set, model parameters, etc. </p> <p> The four single-nucleotide mutation tracks have a default viewing range of score 10 to 50. As explained in the paragraph above, that results in slightly less than 10% of the data displayed. The deletion and insertion tracks have a default filter of 10-100, because they display discrete items and not graphical data. </p> <p> <b>Single nucleotide variants (SNV):</b> For SNVs, at every genome position, there are three values per position, one for every possible nucleotide mutation. The fourth value, "no mutation", representing the reference allele, e.g., A to A, is always set to zero. </p> <p> When using this track, zoom in until you can see every basepair at the top of the display. Otherwise, there are several nucleotides per pixel under your mouse cursor and instead of an actual score, the tooltip text will show the average score of all nucleotides under the cursor. This is indicated by the prefix "~" in the mouseover. Averages of scores are not useful for any application of CADD. </p> <p><b>Insertions and deletions:</b> Scores are also shown on mouseover for a set of insertions and deletions. On hg38, the set has been obtained from gnomAD3. On hg19, the set of indels has been obtained from various sources (gnomAD2, ExAC, 1000 Genomes, ESP). If your insertion or deleletion of interest is not in the track, you will need to use CADD's <a target="_blank" href="https://cadd.gs.washington.edu/score">online scoring tool</a> to obtain them.</p> +<h2>Methods</h2> + +<p> +In CADD version 1.7, new features have been added to improve CADD scores for certain variant +effects, boosting the overall performance of CADD and bringing new developments to the community. +CADD v1.7 integrates annotations from recent efforts to assess variant effects, along with new +conservation and mutation scores.</p> +<p> +CADD v1.7 supports only the major chromosomes of the hg38/GRCh38 reference genome (chromosomes 1-22, +X, and Y) and may be the last version to support the hg19/GRCh37 human reference genome.</p> +<p> +This version includes scores derived from Evolutionary Scale Modeling (ESM) for assessing variants +in protein-coding regions, along with scores from a convolutional neural network (CNN) trained on +open chromatin sequences, used as a proxy for regulatory regions in the genome. The previously +included conservation scores have been updated with data from the Zoonomia project. New annotations +have also been added for 3' Untranslated Regions (3' UTRs), along with models of genome-wide +mutational rates. The gene and transcript models have been updated by advancing from Ensembl version +95 to version 110, and the Ensembl Variant Effect Predictor (VEP) has been upgraded accordingly.</p> +<p> +The models in CADD v1.7 have been trained similarly to the version 1.6 release. The logistic +regression uses an L2 penalty with C = 1, and training was completed after thirteen L-BFGS +iterations using the sklearn library The new models exhibit a high degree of similarity to the +previous release, with a Spearman correlation of 0.946 for CADD scores calculated for 100,000 +randomly selected variants between CADD GRCh38-v1.6 and CADD GRCh38-v1.7. The v1.7 models perform +comparably to earlier versions in distinguishing known pathogenic variants (ClinVar) from common +variants (gnomAD) across the genome. Improvements in CADD v1.7 are particularly evident when +focusing on specific variant categories, such as missense or 3' UTR variants, where the latest +release includes updated annotations.</p> +<p> +More information can be found at the +<a href="https://cadd.bihealth.org/download" target="_blank">CADD site</a> +and the Schubach et al., Nucleic Acids Res, 2024 publication. + + +Data were converted from the files provided on +<a href="https://cadd.bihealth.org/download" target="_blank">the CADD Downloads website</a>, +provided by the Kircher lab, using +<a href="https://github.com/ucscGenomeBrowser/kent/tree/master/src/hg/makeDb/cadd" target="_blank"> +custom Python scripts</a>, +documented in our <a target="_blank" +href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/doc/hg38/cadd.txt"> +makeDoc</a> files. +</p> + + <h2>Data access</h2> <p> CADD scores are freely available for all non-commercial applications from <a target="_blank" href="https://cadd.gs.washington.edu/download">the CADD website</a>. For commercial applications, see <a target="_blank" href="https://cadd.gs.washington.edu/contact">the license instructions</a> there. </p> <p> The CADD data on the UCSC Genome Browser 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 automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed files that can be downloaded from -<a href="http://hgdownload.soe.ucsc.edu/gbdb/$db/cadd/" target="_blank">our download server</a>. +<a href="http://hgdownload.soe.ucsc.edu/gbdb/$db/cadd1.7/" target="_blank">our download server</a>. The files for this track are called <tt>a.bw, c.bw, g.bw, t.bw, ins.bb and del.bb</tt>. Individual regions or the whole genome annotation can be obtained using our tools <tt>bigWigToWig</tt> or <tt>bigBedToBed</tt> which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found <a href="http://hgdownload.soe.ucsc.edu/downloads.html#utilities_downloads">here</a>. The tools can also be used to obtain features confined to a given range, e.g., <br> -<tt>bigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/$db/cadd/a.bw stdout</tt> +<tt>bigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/$db/cadd1.7/a.bw stdout</tt> <br> or <br> -<tt>bigBedToBed -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/$db/cadd/ins.bb stdout</tt></p> - -<h2>Methods</h2> +<tt>bigBedToBed -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/$db/cadd1.7/ins.bb stdout</tt></p> -<p> -Data were converted from the files provided on -<a href="https://cadd.gs.washington.edu/download" target="_blank">the CADD Downloads website</a>, -provided by the Kircher lab, using -<a href="https://github.com/ucscGenomeBrowser/kent/tree/master/src/hg/makeDb/cadd" target="_blank"> -custom Python scripts</a>, -documented in our <a target="_blank" -href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/doc/hg38/cadd.txt"> -makeDoc</a> files. -</p> <h2>Credits</h2> <p> Thanks to the CADD development team for providing precomputed data as simple tab-separated files. </p> <h2>References</h2> <p> Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J. <a href="https://www.nature.com/articles/ng.2892" target="_blank"> A general framework for estimating the relative pathogenicity of human genetic variants</a>. <em>Nat Genet</em>. 2014 Mar;46(3):310-5. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/24487276" target="_blank">24487276</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3992975/" target="_blank">PMC3992975</a> </p> <p> Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. <a href="https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gky1016" target="_blank"> CADD: predicting the deleteriousness of variants throughout the human genome</a>. <em>Nucleic Acids Res</em>. 2019 Jan 8;47(D1):D886-D894. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/30371827" target="_blank">30371827</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323892/" target="_blank">PMC6323892</a> </p> + +<p> +Schubach M, Maass T, Nazaretyan L, Röner S, Kircher M. +<a href="https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkad989" target="_blank"> +CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to +improve genome-wide variant predictions</a>. +<em>Nucleic Acids Res</em>. 2024 Jan 5;52(D1):D1143-D1154. +PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/38183205" target="_blank">38183205</a>; PMC: <a +href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10767851/" target="_blank">PMC10767851</a> +</p>