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small addition to hmc docs page, refs #

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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
 constraint, is not part of this container but can be found in the hg38 gnomAD
 track.
 
 The algorithms covered here are:
 <ol>
-    <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.
-
     <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
     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
     annotations with a multi-module deep learning framework to infer
     pathogenicity of noncoding regions that still remains naïve to existing
     phylogenetic conservation metrics
 
+    <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
+
 </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>
 
 <p>
 <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.<br>
 <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>
 
 </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>