6180cce0bc1c3644813d2c0a5a9b5aa2fa82244b max Wed May 11 03:00:22 2022 -0700 fixing merge conflict, refs #29152 diff --git src/hg/makeDb/trackDb/human/constraintSuper.html src/hg/makeDb/trackDb/human/constraintSuper.html index 8104597..902daed 100644 --- src/hg/makeDb/trackDb/human/constraintSuper.html +++ src/hg/makeDb/trackDb/human/constraintSuper.html @@ -26,69 +26,135 @@ 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 likely deleterious + <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> <p> Shown are the scores as a signal ("wiggle") track, with one score per genome position. Mouse over the bars to see the exact values. </p> <p> For HMC, the highly-constrained cutoff 0.8 is indicated with a line. </p> -<h2>Methods</h2> +<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 highest possible score represented when +multiple transcripts overlap. It is recommended that the data be explored using +this version of the track, as it condenses the information substatially 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. Items in the track are colored according +to their MTR percentile:</p> +<ul> +<li><b><font color=red>Red items</font></b> MTR percentiles over 75 +<li><b><font color=black>Black items</font></b> MTR percentiles between 50 and 75 +<li><b><font color=green>Green items</font></b> MTR percentiles below 25 +<li><b><font color=blue>Blue items</font></b> No MTR score +</ul> <p> +<<<<<<< HEAD <b>Jarvis:</b> 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>. <br> <b>HMC:</b> 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> +======= +By default, only scores with an MTR percentile over 75 are shown.</p> + +<h2>Methods</h2> + +<h3>HMC</h3> +<p> +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>Jarvis</h3> +<p> +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>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> <h2>References</h2> <p> Xiaolei Zhang, Pantazis I. Theotokis, Nicholas Li, the SHaRe Investigators, Caroline F. Wright, Kaitlin E. Samocha, Nicola Whiffin, James S. Ware <a href="https://doi.org/10.1101/2022.02.16.22271023" target="_blank"> Genetic constraint at single amino acid resolution improves missense variant prioritisation and gene discovery</a>. <em>Medrxiv</em> 2022.02.16.22271023 </p> + <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> +Silk M, Petrovski S, Ascher DB. +<a href="https://www.ncbi.nlm.nih.gov/pubmed/31170280" target="_blank"> +MTR-Viewer: identifying regions within genes under purifying selection</a>. +<em>Nucleic Acids Res</em>. 2019 Jul 2;47(W1):W121-W126. +PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/31170280" target="_blank">31170280</a>; PMC: <a +href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6602522/" target="_blank">PMC6602522</a> +</p>