50466766840ded6cb8bd5cb868bdf2ff3f613bc0 lrnassar Tue Apr 21 11:17:15 2026 -0700 QA fixes for PrimateAI-3D track. Config (primateAi.ra): - Fix broken Ensembl transcript linkout: urls $S expanded to chromosome name; switch to the Ensembl transcript page with $$ - Add numeric filters on percentile and raw score (label notes the paper's 0.821 clinical threshold) - Add maxWindowToDraw 2000000 Data (primateAiToBigBed.py): - Change hardcoded strand '+' to '.': the source file has no strand column - Accept input/output paths as CLI args (previously hardcoded the hg38 input path) - Handle variable field count: ~2.4M rows in the hg19 source are missing the refseq column Description (primateAi.html): - Fix two broken hgTrackUi&... internal links to the Zoonomia 447-way track - Regenerate the first reference via getTrackReferences (wrong article number and wrong PMC ID in the previous text) - Fix the GitHub URL for the conversion script in Methods - Move the Zoonomia 447-way mention out of Description; rephrase the license note to describe precisely what is disabled relatedTracks.ra: - Add reciprocal cross-links for primateAi <-> alphaMissense (hg38), primateAi <-> revel (hg38 + hg19), and primateAi <-> promoterAi (hg38). Also includes promoterAi <-> alphaMissense cross-links. refs #37274 #37279 diff --git src/hg/makeDb/trackDb/human/primateAi.html src/hg/makeDb/trackDb/human/primateAi.html index a3ac78c3b1f..afd3c3ed330 100644 --- src/hg/makeDb/trackDb/human/primateAi.html +++ src/hg/makeDb/trackDb/human/primateAi.html @@ -1,86 +1,86 @@ <h2>Description</h2> <p> <a href="https://primateai3d.basespace.illumina.com/" target="_blank">PrimateAI-3D</a> is a semi-supervised 3D convolutional neural network that predicts the pathogenicity of all possible missense variants in the human genome. It was trained on 4.5 million benign missense variants: 4.3 million common variants from 809 non-human primate individuals across 233 species, plus common human variants (>0.1% allele frequency) from gnomAD, TOPMed, and UK Biobank. These represent about 6% of all possible human missense variants. -Activate the <a href="hgTrackUi&db=hg38&g=cons447way">Zoonomia 447 way Mammal/Primate</a> alignment -track to show these variants. </p> <p> The model operates on voxelized protein structures at 2 Å resolution (from AlphaFold or homology models) combined with multiple sequence alignments from 592 species. It uses three complementary loss functions: benign variant classification, 3D fill-in-the-blank prediction on masked amino acids, and a language model ranking component. This track shows 70.7 million scored variants across all protein-coding genes. </p> <h2>Display Conventions</h2> <p> Each variant is colored <span style="color:blue">blue (benign)</span> or <span style="color:red">red (pathogenic)</span> based on the raw score. The score field (0-1000) represents the percentile rank of the raw PrimateAI-3D score, where higher values indicate greater predicted pathogenicity. Mouseover shows the nucleotide change, amino acid change, raw score, percentile, and prediction. Items can be filtered by prediction (benign/pathogenic) and by percentile score. </p> <p> Score interpretation: raw scores range from 0 to 1, with higher values indicating greater predicted pathogenicity. The authors suggest a clinical threshold of 0.821 for distinguishing pathogenic from benign missense variants. The percentile field shows where a variant's score ranks relative to all other scored variants. 75% of variants are classified as benign, 25% as pathogenic. </p> <h2>Data Access</h2> <p> -Due to the data license, this track is not available for bulk download from UCSC and the API, the Table Browser -and the "Download track data" button do not work. However, the source data can be downloaded from the +Due to the data license, the Table Browser, Data Integrator, and the REST API's +<code>getData</code> endpoint are disabled for this track. The source data can be +downloaded from the <a href="https://primateai3d.basespace.illumina.com/" target="_blank">PrimateAI-3D website</a> (requires registration). The primate variant database is available at <a href="https://primad.basespace.illumina.com/" target="_blank">PrimAD</a>. -Note that our <a href="hgTrackUi&db=hg38&g=cons447way">Zoonomia 447 way</a> alignment -track includes the primate variants. +Our <a href="hgTrackUi?db=hg38&g=cons447way">Zoonomia 447-way Mammal/Primate</a> alignment +track displays the primate variants used in training PrimateAI-3D. </p> <h2>Methods</h2> <p> The PrimateAI-3D hg38 site list was downloaded from the Illumina BaseSpace website. The tab-separated file contains pre-computed scores for all possible single nucleotide missense variants. Positions were formatted as bigBed. The percentile score was put into the track score field (scaled to 0-1000). No filtering was applied; all 70.7 million scored variants are included. A conversion script is available from -<a href="https://github.com/ucscGenomeBrowser/kent/tree/master/src/hg/makeDb/scripts/primateAiToBigBed.py" +<a href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/scripts/primateai/primateAiToBigBed.py" target="_blank">our Github</a>. </p> <h2>Credits</h2> <p> Thanks to Illumina, in particular Gao Hong, for making PrimateAI-3D predictions publicly available. </p> <h2>References</h2> <p> -Gao H, Hamp T, Ede J, Schraiber JG, McRae J, Singer-Berk M, Yang Y, Dietrich ASD, -Fiziev PP, Kuderna LFK <em>et al</em>. -<a href="https://doi.org/10.1126/science.abn8197" target="_blank"> +Gao H, Hamp T, Ede J, Schraiber JG, McRae J, Singer-Berk M, Yang Y, Dietrich ASD, Fiziev PP, Kuderna +LFK <em>et al</em>. +<a href="https://www.science.org/doi/10.1126/science.abn8197?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed" +target="_blank"> The landscape of tolerated genetic variation in humans and primates</a>. -<em>Science</em>. 2023 Jun 2;380(6648):eabn8197. +<em>Science</em>. 2023 Jun 2;380(6648):eabn8153. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/37262156" target="_blank">37262156</a>; PMC: <a -href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10187174/" target="_blank">PMC10187174</a> +href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10713091/" target="_blank">PMC10713091</a> </p> <p> -Sundaram L, Gao H, Padigepati SR, McRae JF, Li Y, Kosmicki JA, Fritzilas N, Hakenberg J, -Dutta A, Shon J <em>et al</em>. +Sundaram L, Gao H, Padigepati SR, McRae JF, Li Y, Kosmicki JA, Fritzilas N, Hakenberg J, Dutta A, +Shon J <em>et al</em>. <a href="https://doi.org/10.1038/s41588-018-0167-z" target="_blank"> Predicting the clinical impact of human mutation with deep neural networks</a>. <em>Nat Genet</em>. 2018 Aug;50(8):1161-1170. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/30038395" target="_blank">30038395</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6237276/" target="_blank">PMC6237276</a> </p>