b9328bcc8d11dacf366183711dd8a481da74664b max Tue Jun 7 01:42:07 2022 -0700 tiny changes to constraint super track docs page after email exchange with Lou, refs #29043 diff --git src/hg/makeDb/trackDb/human/constraintSuper.html src/hg/makeDb/trackDb/human/constraintSuper.html index bfdb885..50d2270 100644 --- src/hg/makeDb/trackDb/human/constraintSuper.html +++ src/hg/makeDb/trackDb/human/constraintSuper.html @@ -1,38 +1,42 @@ <h2>Description</h2> <p> The "Constraint scores" container track includes several subtracks showing the results of constraint prediction algorithms. These try to find regions of negative selection, where variations likely have functional impact. The algorithms do not use multi-species alignments to derive evolutionary constraint, but use primarily human variation, usually from variants collected by gnomAD (see the gnomAD V2 or V3 tracks on hg19 and hg38) or TOPMED (contained in our dbSNP -tracks and available as a filter). Another constraint score, gnomAD +tracks and available as a filter). +</p> + +<p>Note that another important constraint score, gnomAD constraint, is not part of this container but can be found in the hg38 gnomAD track. +</p> -The algorithms covered here are: +The algorithms included in this track are: <ol> <li><b><a href="https://github.com/astrazeneca-cgr-publications/jarvis" target="_blank"> JARVIS - "Junk" Annotation genome-wide Residual Variation Intolerance Score</a></b>: - First scan the entire genome with a + This algorithm first scans the entire genome with a sliding-window approach (using a 1-nucleotide step), recording the number of all TOPMED variants and common variants, irrespective of their predicted effect, within each window, to eventually calculate a single-nucleotide resolution - genome-wide residual variation intolerance score (gwRVIS). Then combine - gwRVIS, primary genomic sequence context, and additional genomic + genome-wide residual variation intolerance score (gwRVIS). In a second step, it combines + this gwRVIS score, primary genomic sequence context, and additional genomic annotations with a multi-module deep learning framework to infer pathogenicity of noncoding regions that still remains naive to existing phylogenetic conservation metrics. The higher the score, the more deleterious is the prediction. <li><b><a href="https://www.cardiodb.org/hmc/" target="_blank"> HMC - Homologous Missense Constraint</a></b>: Homologous Missense Constraint (HMC) is a amino acid level measure of genetic intolerance of missense variants within human populations. For all assessable amino-acid positions in Pfam domains, the number of missense substitutions directly observed in gnomAD (Observed) was counted and compared to the expected value under a neutral evolution model (Expected). The upper limit of a 95% confidence interval for the Observed/Expected ratio is defined as the HMC score. Missense variants disrupting the amino-acid positions with HMC<0.8 are predicted to be @@ -40,56 +44,58 @@ <li><b><a href="http://biosig.unimelb.edu.au/mtr-viewer/" target="_blank"> MTR - Missense Tolerance Ratio</a> (hg19 only)</b>: Missense Tolerance Ratio (MTR) scores aim to quantify the amount of purifying selection acting specifically on missense variants in a given window of protein-coding sequence. It is estimated across sliding windows of 31 codons (default) and uses observed standing variation data from the WES component of gnomAD / the Exome Aggregation Consortium Database (ExAC), version 2.0. Scores were computed using Ensembl v95 release </ol> <h2>Display Conventions and Configuration</h2> <h3>JARVIS</h3> <p> -JARVIS scores are the scores as a signal ("wiggle") track, with one score per genome position. +JARVIS scores are shown as a signal ("wiggle") track, with one score per genome position. Mousing over the bars displays the exact values. The scores were downloaded and converted to a single bigWig file. See <a href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/doc/hg19.txt" target=_blank>hg19 makeDoc</a> and <a href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/doc/hg38/jarvis.txt" target=_blank>hg38 makeDoc</a>.</p> <h3>HMC</h3> <p> HMC scores are displayed as a signal ("wiggle") track, with one score per genome position. Mousing over the bars displays the exact values. The highly-constrained cutoff of 0.8 is indicated with a line.</p> <p> The HMC scores were downloaded and converted to .bedGraph files with a custom Python script. The bedGraph files were then converted to bigWig files, as documented in our <a href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/doc/hg19.txt" target=_blank>makeDoc</a> hg19 build log.</p> <h3>MTR</h3> <p> MTR data can be found on two tracks, <b>MTR All data</b> and <b>MTR Scores</b>. In the <b>MTR Scores</b> track the data has been converted into 4 separate signal tracks -representing each base pair mutation, with the lowest possible score represented when -multiple transcripts overlap. A horizontal line is drawn on the 0.8 score line +representing each base pair mutation, with the lowest possible score shown when +multiple transcripts overlap at a position. Overlaps can happen since this score +is derived from transcripts and multiple transcripts can overlap. +A horizontal line is drawn on the 0.8 score line to roughly represent the 25th percentile, meaning the items below may be of particular interest. It is recommended that the data be explored using -this version of the track, as it condenses the information substatially while +this version of the track, as it condenses the information substantially while retaining the magnitude of the data.</p> <p>Any specific point mutations of interest can then be researched in the <b> MTR All data</b> track. This track contains all of the information from <a href="http://biosig.unimelb.edu.au/mtr-viewer/downloads" target="_blank"> MTRV2</a> including more than 3 possible scores per base when transcripts overlap. A mouse-over on this track shows the ref and alt allele, as well as the MTR score and the MTR score percentile. Filters are available for MTR score, False Discovery Rate (FDR), MTR percentile, and variant consequence. By default, only items in the bottom 25 percentile are shown. Items in the track are colored according to their MTR percentile:</p> <ul> <li><b><font color=green>Green items</font></b> MTR percentiles over 75 <li><b><font color=black>Black items</font></b> MTR percentiles between 25 and 75 <li><b><font color=red>Red items</font></b> MTR percentiles below 25 @@ -123,32 +129,33 @@ target=_blank>hg38 makeDoc</a></p> <h3>MTR</h3> <p> <a href="http://biosig.unimelb.edu.au/mtr-viewer/downloads" target="_blank">V2 file</a> was downloaded and columns were reshuffled as well as itemRgb added for the <b>MTR All data</b> track. For the <b>MTR Scores</b> track the file was parsed with a python script to pull out the highest possible MTR score for each of the 3 possible mutations at each base pair and 4 tracks built out of these values representing each mutation.</p> <p> See the <a href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg /makeDb/doc/hg19.txt" target=_blank>hg19 makeDoc</a> entry on MTR for more info.</p> <h2>Credits</h2> <p> -Thanks 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 for helping clean up the hg38 Jarvis files. -</p> +Thanks 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 for helping clean up the hg38 Jarvis files. </p> <h2>References</h2> <p> Vitsios D, Dhindsa RS, Middleton L, Gussow AB, Petrovski S. <a href="https://www.ncbi.nlm.nih.gov/pubmed/33686085" target="_blank"> Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning</a>. <em>Nat Commun</em>. 2021 Mar 8;12(1):1504. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/33686085" target="_blank">33686085</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7940646/" target="_blank">PMC7940646</a> </p> <p> Xiaolei Zhang, Pantazis I. Theotokis, Nicholas Li, the SHaRe Investigators, Caroline F. Wright, Kaitlin E. Samocha, Nicola Whiffin, James S. Ware