e82f973dc7a5a814f0fd23999aa27222fa1260da max Fri Mar 20 09:56:15 2026 -0700 primateAI track, refs #37274 diff --git src/hg/makeDb/trackDb/human/primateAi.html src/hg/makeDb/trackDb/human/primateAi.html new file mode 100644 index 00000000000..a3ac78c3b1f --- /dev/null +++ src/hg/makeDb/trackDb/human/primateAi.html @@ -0,0 +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 +<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. +</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" +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"> +The landscape of tolerated genetic variation in humans and primates</a>. +<em>Science</em>. 2023 Jun 2;380(6648):eabn8197. +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> +</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>. +<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>