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 our download server. The file for this track is called popEve.bb. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed, 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 here. The tool can also be used to obtain features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/$db/popEve/popEve.bb -chrom=chr17 -start=43000000 -end=43200000 stdout

The original annotation source data can be downloaded from https://pop.evemodel.org.

Credits

-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 pop.evemodel.org.

References

+

+Frazer J, Notin P, Dias M, Gomez A, Min JK, Brock K, Gal Y, Marks DS. + +Disease variant prediction with deep generative models of evolutionary data. +Nature. 2021 Nov;599(7883):91-95. +PMID: 34707284 +

+

Orenbuch R, Shearer CA, Kollasch AW, Spinner AD, Hopf TA, van Niekerk L, Franceschi D, Dias M, Frazer J, Marks DS. Proteome-wide model for human disease genetics. Nat Genet. 2025 Dec;57(12):3165-3174. PMID: 41286104

- -

-Frazer J, Notin P, Dias M, Gomez A, Min JK, Brock K, Gal Y, Marks DS. - -Disease variant prediction with deep generative models of evolutionary data. -Nature. 2021 Nov;599(7883):91-95. -PMID: 34707284 -