e226ca32f92c500fb9294cd84eff5f7938ba45cb
lrnassar
  Tue Aug 11 14:19:27 2026 -0700
Alphabetize popEVE references and credits by author per QA. refs #37791

List Frazer (2021) before Orenbuch (2025) in References, and order the Credits names by
surname, per QA feedback on the description page.

diff --git src/hg/makeDb/trackDb/human/popEve.html src/hg/makeDb/trackDb/human/popEve.html
index 7b648d708fd..c4ac516171e 100644
--- src/hg/makeDb/trackDb/human/popEve.html
+++ src/hg/makeDb/trackDb/human/popEve.html
@@ -147,38 +147,38 @@
 that can be downloaded from
 <a href="http://hgdownload.soe.ucsc.edu/gbdb/$db/popEve" target="_blank">our download
 server</a>. The file for this track is called <tt>popEve.bb</tt>. Individual regions or the
 whole genome annotation can be obtained using our tool <tt>bigBedToBed</tt>, which can be
 compiled from the source code or downloaded as a precompiled binary for your system.
 Instructions for downloading source code and binaries can be found
 <a href="http://hgdownload.soe.ucsc.edu/downloads.html#utilities_downloads" target="_blank">here</a>.
 The tool can also be used to obtain features within a given range, e.g.
 <tt>bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/$db/popEve/popEve.bb -chrom=chr17
 -start=43000000 -end=43200000 stdout</tt></p>
 <p>The original annotation source data can be downloaded from
 <a href="https://pop.evemodel.org" target="_blank">https://pop.evemodel.org</a>.</p>
 
 <h2>Credits</h2>
 <p>
-Thanks to Rose Orenbuch, Mafalda Dias, Jonathan Frazer, Debora S. Marks, and colleagues at
+Thanks to Mafalda Dias, Jonathan Frazer, Debora S. Marks, Rose Orenbuch, and colleagues at
 Harvard Medical School, the Centre for Genomic Regulation, and collaborating institutions for
 developing popEVE and making the scores publicly available at
 <a href="https://pop.evemodel.org" target="_blank">pop.evemodel.org</a>.
 </p>
 
 <h2>References</h2>
+<p>
+Frazer J, Notin P, Dias M, Gomez A, Min JK, Brock K, Gal Y, Marks DS.
+<a href="https://doi.org/10.1038/s41586-021-04043-8" target="_blank">
+Disease variant prediction with deep generative models of evolutionary data</a>.
+<em>Nature</em>. 2021 Nov;599(7883):91-95.
+PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/34707284" target="_blank">34707284</a>
+</p>
+
 <p>
 Orenbuch R, Shearer CA, Kollasch AW, Spinner AD, Hopf TA, van Niekerk L, Franceschi D,
 Dias M, Frazer J, Marks DS.
 <a href="https://doi.org/10.1038/s41588-025-02400-1" target="_blank">
 Proteome-wide model for human disease genetics</a>.
 <em>Nat Genet</em>. 2025 Dec;57(12):3165-3174.
 PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/41286104" target="_blank">41286104</a>
 </p>
-
-<p>
-Frazer J, Notin P, Dias M, Gomez A, Min JK, Brock K, Gal Y, Marks DS.
-<a href="https://doi.org/10.1038/s41586-021-04043-8" target="_blank">
-Disease variant prediction with deep generative models of evolutionary data</a>.
-<em>Nature</em>. 2021 Nov;599(7883):91-95.
-PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/34707284" target="_blank">34707284</a>
-</p>