+Missense mutations change a single amino acid in a protein and make up a large fraction +of the variants observed in human populations, yet most have no established clinical +significance. popEVE places missense +variants on a single, proteome-wide spectrum of +deleteriousness, so that variants in different genes can be compared directly. It is a deep +generative model that combines cross-species evolutionary scores with human population +variation: scores from EVE (an evolutionary variational autoencoder) and the ESM-1v protein +language model are calibrated against allele observations in the UK Biobank using a Gaussian +process, yielding a continuous, human-specific measure of variant impact. This track shows +popEVE scores for single-nucleotide missense substitutions across roughly 18,000 human proteins. +
+ ++Each entry spans one protein at its genomic locus. The heatmap columns correspond to +individual amino acid positions in the protein, placed at the codon's genomic coordinate. +The rows correspond to the 20 standard amino acids (A–Y, alphabetical). Each cell +shows the popEVE score for substituting the wildtype amino acid at that position with the +row amino acid. Because popEVE is distributed as a list of genomic single-nucleotide +variants, only amino acid substitutions reachable by a single-nucleotide change are scored; +cells for substitutions requiring more than one nucleotide change, for the wildtype amino +acid, or for positions without a score (for example start codons) are left empty. +
+ ++Unlike per-gene scores, popEVE is calibrated across the whole proteome, so cells are colored +on a single global gradient keyed to the raw popEVE score (lower, more negative scores are +more deleterious). The color is interpolated between the five anchors below: the published +severe and moderate thresholds are fixed anchors, and the extremes saturate at the 0.5th and +99.5th percentiles of the proteome-wide score distribution. +
+| Color | +popEVE score | +Interpretation |
|---|---|---|
| + | ≤ −5.74 | +Most deleterious (color saturates here) |
| + | ≈ −5.056 | +Severe threshold: high-confidence deleterious (99.99% likelihood of falling in the more deleterious distribution) |
| + | ≈ −4.617 | +Moderate threshold |
| + | ≈ −3.5 | +Near the proteome-wide median |
| + | ≥ −2.29 | +Most tolerated (color saturates here) |
+Note: popEVE ranks deleteriousness to organismal fitness, weighted toward severe, +often early-onset phenotypes, rather than classic clinical pathogenicity. Some well-known +disease genes whose variants act mainly through loss of function or cause adult-onset +conditions (for example BRCA1) may therefore show few or no cells in the severe range. +
+ ++Hovering over a cell shows a summary of that substitution and the scores behind it, for +example: +
+
+G1042→A
+popEVE: -3.652 (tolerated)
+EVE: 6.75
+ESM1v: -8.779
+popAdj: EVE -3.537 / ESM1v -3.767
+gap: 0.277
+
| Field | +Meaning |
|---|---|
| G1042→A | +The substitution: the wildtype amino acid (G) at the protein position (1042) changed to the variant amino acid (A) represented by this row. |
| popEVE | +The popEVE score and its severity class (severe / moderate / tolerated). This is the calibrated, proteome-wide score — comparable across genes, with lower (more negative) values more deleterious — and it determines the cell color. |
| EVE | +The raw EVE evolutionary index used as an input to popEVE (a log-likelihood-ratio scale on which higher values are more deleterious). This is not the same as the 0–1 score shown in the separate EVE track. |
| ESM1v | +The raw ESM-1v protein language model score used as the other input to popEVE, on which more negative values are more deleterious. |
| popAdj | +The EVE and ESM-1v scores after popEVE's population-based recalibration onto the proteome-wide scale; the popEVE score is the combination of these two. |
| gap | +The fraction of sequences with a gap at this position in the multiple sequence alignment (a per-position value; higher means sparser alignment coverage and a weaker evolutionary signal). |
+For reverse-strand genes, protein positions are displayed left to right in genomic +order (C-terminus to N-terminus on the screen), consistent with the standard genome +browser orientation. +
+ ++popEVE scores are computed per protein isoform, so a gene with more than one RefSeq isoform +appears as several overlapping entries (one per protein accession) that may stack in the +display. The track item name is the gene symbol; the specific RefSeq protein accession is +available as a field in the Table Browser and details page. +
+ ++popEVE is built on top of two unsupervised variant effect models: EVE, a Bayesian +variational autoencoder trained per protein on multiple sequence alignments of evolutionary +variation, and ESM-1v, a protein language model trained on the UniRef database. For each +protein, the evolutionary scores from these models are treated as a prior, and a Gaussian +process is used to model how the probability of observing a variant in the human population +(the UK Biobank, ~500,000 exomes) depends on its evolutionary score. Population data are +used only as a coarse "seen / not seen" signal rather than by allele frequency, which lets +the resulting score rank the deleteriousness of variants across different proteins while +leaving the within-gene ranking essentially unchanged. The inferred function rescales the +evolutionary score per protein to account for the degree of constraint acting on that +protein in the population, producing a calibrated, proteome-wide score. Low (more negative) +scores indicate greater predicted deleteriousness. Following the authors, a high-confidence +severe threshold is set at −5.056 and a moderate threshold at −4.617, each +derived from a two-component Gaussian mixture fit. See Orenbuch et al. 2025 for full details. +
+ ++The data were downloaded as a complete GRCh38 VCF (UKBB-trained popEVE, version 1.1) from +https://pop.evemodel.org. +The VCF lists, for every possible missense substitution, its genomic coordinates, the RefSeq +protein accession and gene symbol, the popEVE score and component scores. Multiple codon +changes encoding the same amino acid substitution carry identical popEVE scores and were +deduplicated to one record per substitution. Records were converted to heatmap bigBed format +with a custom Python script; full processing instructions are in the +makedoc file, and the conversion scripts are available in +our GitHub repository. The strand of each protein was taken from NCBI +RefSeq. The global color gradient saturates at the 0.5th and 99.5th percentiles of the +proteome-wide score distribution. +
+ +The data can be explored interactively in table format with the +Table Browser or the +Data Integrator and exported from there to +spreadsheet or tab-separated tables. From scripts, the data can be accessed through our +API, track=popEve.
+For automated download and analysis, the genome annotation is stored in a bigBed file +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.
+ ++Thanks to Rose Orenbuch, Mafalda Dias, Jonathan Frazer, Debora S. Marks, 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. +
+ ++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 +