680c0cb830ff738f2e9e8fe559a0b2cb894945a6
jnavarr5
  Fri May 30 15:42:30 2025 -0700
Adding a hypen for 'non-coding', and add a link to the references section on the track description page for the linsight track, refs #35730

diff --git src/hg/makeDb/trackDb/human/constraintSuper.html src/hg/makeDb/trackDb/human/constraintSuper.html
index 19a058b1431..106f16aed8d 100644
--- src/hg/makeDb/trackDb/human/constraintSuper.html
+++ src/hg/makeDb/trackDb/human/constraintSuper.html
@@ -57,37 +57,37 @@
     <li><b><a href="https://biosig.lab.uq.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 version 2.0. Scores
     were computed using Ensembl v95 release. The number of gnomAD 2 exomes used here
     is higher than the number of gnomAD 3 samples (125 exoms versus 76k full genomes), 
     and this score only covers coding regions so gnomAD 2 was more appropriate.
 
     <li><b><a href="https://github.com/CshlSiepelLab/LINSIGHT" target="_blank">
     LINSIGHT</a> (hg19 only)</b>:
     LINSIGHT is a statistical model for estimating negative selection on
     noncoding sequences in the human genome. The LINSIGHT score measures the
-    probability of negative selection on noncoding sites which can be used to
+    probability of negative selection on non-coding sites which can be used to
     prioritize SNVs associated with genetic diseases or quantify evolutionary
     constraint on regulatory sequences, e.g., enhancers or promoters. More
-    specifically, if a noncoding site is under negative selection, it will be
+    specifically, if a non-coding site is under negative selection, it will be
     less likely to have a substitution or SNV in the human lineage. In
     addition, even if we see a SNV at the site, it will tend to segregate at
-    low frequency because of selection. See (Huang et al, Nat Genet 2017).
+    low frequency because of selection. See (<a href="#references">Huang et al, Nat Genet 2017</a>).
 
     <li><b><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9329122/" target="_blank">
     UK Biobank depletion rank score</a> (hg38 only)</b>:
     Halldorsson et al. tabulated the number of UK Biobank variants in each
     500bp window of the genome and compared this number to an expected number
     given the heptamer nucleotide composition of the window and the fraction of
     heptamers with a sequence variant across the genome and their mutational
     classes. A variant depletion score was computed for every overlapping set
     of 500-bp windows in the genome with a 50-bp step size.  They then assigned
     a rank (depletion rank (DR)) from 0 (most depletion) to 100 (least
     depletion) for each 500-bp window. Since the windows are overlapping, we
     plot the value only in the central 50bp of the 500bp window, following
     advice from the author of the score,
     Hakon Jonsson, deCODE Genetics. He suggested that the value of the central
     window, rather than the worst possible score of all overlapping windows, is
@@ -274,30 +274,31 @@
 
 <p>
 Please refer to our
 <a HREF="../FAQ/FAQdownloads.html#download36" target=_blank>Data Access FAQ</a>
 for more information.
 </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 and Slave Petrovski for helping clean up the hg38 JARVIS files for providing guidance on interpretation. Additional
 thanks to Laurens van de Wiel for providing the MetaDome data as well as guidance on the track development and interpretation. 
 </p>
 
+<a name="references"></a>
 <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
 <a href="https://doi.org/10.1101/2022.02.16.22271023" target="_blank">