1e9aa1fd4296d98295a43350379e96e37dc7cbe4
Merge parents b9328bc 4418945
max
  Tue Jun 7 01:54:49 2022 -0700
Lou was quicker than I. :-) Merge branch 'master' of hgwdev.gi.ucsc.edu:/data/git/kent

Conflicts:
src/hg/makeDb/trackDb/human/constraintSuper.html

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  <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>: 
-     This algorithm first scans the entire genome with a
 -    JARVIS scores were creating by first scanning the entire genome with a
++    JARVIS scores were created by first scanning 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). In a second step, it combines
-     this gwRVIS score, primary genomic sequence context, and additional genomic
+     genome-wide residual variation intolerance score (gwRVIS). That score, gwRVIS
+     was then combined with 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.
+     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&lt;0.8 are predicted to be
-     likely deleterious
+     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 
+     were computed using Ensembl v95 release.
  </ol>
  
  <h2>Display Conventions and Configuration</h2>
  
  <h3>JARVIS</h3>
  <p>
 -JARVIS scores are displayed as a signal ("wiggle") track, with one score per genome position.
 -Mousing over the bars displays the exact values. A horizontal line exists at the <b>0.733</b>
 +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.
++Move the mouse over the bars to display the exact values. A horizontal line is shown at the <b>0.733</b>
+ value which signifies the 90th percentile.</p>
 +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>
+ <p>
+ <b>Interpretation:</b> The authors offer a suggested guideline of <b> > 0.9998</b> for identifying
+ higher confidence calls and minimizing false positives. In addition to that strict threshold, the 
+ following two more relaxed cutoffs can be used to explore additional hits. Note that these
+ thresholds are offered as guidelines and are not necessarily representative of pathogenicity.</p>
+ 
+ <p>
+ <table class="stdTbl">
+     <tr align=left>
+         <th>Percentile</th><th>JARVIS score threshold</th></tr>
+     <tr align=left>
+         <td>99th</td><td>0.9998</td></tr>
+     <tr align=left>
+         <td>95th</td><td>0.9826</td></tr>
+     <tr align=left>
+         <td>90th</td><td>0.7338</td></tr>
+ </table>
+ </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>