EVE missense variant effect scores for the HGF locus (chr7, +GRCh38/hg38). Each row is an amino acid substitution; each column is a protein position. Hovering +over a cell shows the substitution and EVE score.
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+ ++We are pleased to announce the release of two new proteome-wide missense variant effect +prediction tracks for hg38: +popEVE and +EVE. +Both are deep generative models that predict variant pathogenicity by learning patterns of +natural sequence variation across species, without relying on clinical labels. +
+ ++EVE (Evolutionary model of Variant Effect) is a +deep generative model that predicts the pathogenicity of missense variants by learning patterns of +natural sequence variation across thousands of species, without relying on clinical labels. +Pre-computed scores are provided for all possible missense substitutions across 2,949 +disease-associated human proteins. +
+ ++Scores range from 0 to 1, with higher values indicating a greater predicted likelihood that a +variant is disease-relevant. As with any pathogenicity prediction score, EVE is intended as +supporting evidence rather than a stand-alone classifier. +
+ ++Each track entry spans one protein at its genomic locus. The heatmap columns correspond to +individual amino acid positions, placed at the codon's genomic coordinate, and the rows correspond +to the 20 standard amino acids, ordered by amino acid class. Each cell shows the EVE score for +substituting the wildtype amino acid at that position with the row amino acid. Empty cells +indicate the wildtype amino acid at a given position, or positions for which no score is +available. +
+ +EVE missense variant effect scores for the HGF locus (chr7, +GRCh38/hg38). Each row is an amino acid substitution; each column is a protein position. Hovering +over a cell shows the substitution and EVE score.
++Items in this track are colored according to score: +
++Note: Zoom in to base level to see the full amino-acid heatmap with per-substitution scores +on mouseover. At wider zoom levels, the track switches to displaying a density graph. +
+ ++popEVE builds on EVE by combining its +evolutionary signal with predictions from the ESM-1v protein language model and human population +variation from the UK Biobank. A key difference is that EVE scores are calibrated separately +within each protein, whereas popEVE scores are calibrated across the entire proteome. That means a +popEVE score in one gene can be compared directly to a popEVE score in a completely different +gene. This is useful when prioritizing which of several candidate variants across different genes +is most likely to be damaging. The track provides popEVE scores for missense substitutions across +roughly 18,000 human proteins. +
+ ++popEVE scores are continuous, with lower, more negative values indicating greater predicted +deleteriousness. The authors define approximately −5.056 as a severe, high-confidence +deleterious threshold and −4.617 as a moderate threshold. Because the scores are calibrated +across the proteome, they can be compared directly between genes. +
+ ++Each track item represents a protein at its genomic locus and is displayed as a heatmap. Columns +correspond to amino acid positions, while rows represent possible amino acid substitutions. Each +cell shows the popEVE score for that substitution. +
+ ++Items are colored according to score: +
++Colors are interpolated between these values on a single proteome-wide gradient. As with other +computational variant-effect predictors, popEVE is intended to provide supporting evidence for +variant interpretation rather than serve as a stand-alone clinical classifier. +
+ +popEVE missense deleteriousness scores for all possible amino acid +substitutions in the SCN10A gene at chr3, GRCh38/hg38, where colors indicate predicted severity +from severe (red) to tolerated (blue).
++We would like to thank 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, and Jonathan Frazer, +Pascal Notin, Mafalda Dias, and Debora S. Marks at Harvard Medical School, along with Yarin Gal at +the University of Oxford, for making the EVE scores publicly available at +evemodel.org. Lou Nassar and Max Haeussler +developed these tracks, with QA by Jairo Navarro, Barali Kitiyakara, and Eliza Alde. +
+We are excited to announce the updated Genome Aggregation Database (gnomAD) v4.1.1 tracks for human assembly hg38/GRCh38, and new gnomAD Missense Deleteriousness Prediction by Constraint (MPC) tracks, found in the gnomAD superTrack. The tracks are: