Depletion of high-scoring splice-proximal SNVs in gnomAD, TOPMed and UK Biobank, high-pLI genes (SpliceAI2 paper, Spearman)
Spearman correlation between per-variant model score and combined multi-cohort allele count for splice-proximal SNVs in high-pLI genes; more negative means stronger depletion of high-scoring variants.
Overview
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4 recorded evaluations, 4 metric rows. A comparison chart has not yet been validated for these results. The table retains the individual findings and their sources.
Results
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All evaluations
4 evaluations · 4 results. Different protocols are not a single leaderboard.
Protocol: Depletion of high-scoring splice-proximal SNVs in gnomAD, TOPMed and UK Biobank, high-pLI genes (SpliceAI2 paper, Spearman) · Dataset: gnomAD, TOPMed and UK Biobank splice-proximal SNV depletion set (Jaganathan et al. 2026)
Sorted by Spearman (with multi-cohort raw allele count in high-pLI genes; more negative is better) (lower is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.
| Tested configuration | Spearman (with multi-cohort raw allele count in high-pLI genes; more negative is better) |
|---|---|
| SpliceAI2 two-model multispecies ensemble (Jaganathan et al. 2026)Evaluation | -0.122 (best in column) |
| Pangolin default settings, maximum absolute gain or loss score, masking off (Jaganathan et al. 2026)Evaluation | -0.11 |
| AlphaGenome raw composite splice score, maximum aggregation (Jaganathan et al. 2026)Evaluation | -0.105 |
| SpliceAI 1.3.1 standard delta scores, masking off (Jaganathan et al. 2026)Evaluation | -0.096 |
All 4 result rows with coverage, uncertainty and sources
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Methods and evaluation design
Procedure, tasks and evaluated configurations
Recorded evaluations
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- AlphaGenome on splice-proximal SNV depletion in high-pLI genes (Spearman, SpliceAI2 paper)
- Pangolin on splice-proximal SNV depletion in high-pLI genes (Spearman, SpliceAI2 paper)
- SpliceAI on splice-proximal SNV depletion in high-pLI genes (Spearman, SpliceAI2 paper)
- SpliceAI2 on splice-proximal SNV depletion in high-pLI genes (Spearman, SpliceAI2 paper)
Baseline coverage
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- Author-reported evaluations
- 4
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Protocol coverage CSV (gzip) · Model evaluation matrix (gzip) · Source table (gzip) · Release and checksums (gzip)
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Strengths, limitations and unresolved questions
Evidence
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Evidence table
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Sources and history
Release 2026-10-22-1b7cf027e4f6 · Record review: source checked
1 source record and release history
- A unified framework for quantitative splicing and transcript prediction (Jaganathan et al., bioRxiv 2026) · Original source · bioRxiv preprint version 1, posted 2026-10-11; full-text PDF (57 pages)
Technical metadata and extraction receipts
Stable ID: rna-splicing-20261013-spliceai2-protocol-population-constraint
- areas
- dna-genomes
- contexts
- research
- protocol
- Protein-coding SNVs within 30 bp of annotated splice sites, absent and common variants trinucleotide-matched; Spearman between score and raw multi-cohort allele count in genes with pLI > 0.8.
- version
- bioRxiv v1 Fig. 3c
- metric
- spearman-correlation
- metric direction
- lower
- source locator
- Methods 'Population genetics analysis'; Fig. 3c
- limitations
- Proxy endpoint: purifying selection, not a measured splicing outcome.; SpliceAI and Pangolin use their standard scores here, not the position-resolved scores of the GT/AG and sQTL benchmarks.; Developer-reported: the SpliceAI2 authors (Illumina) built the benchmark, chose comparator settings and ran every model; AlphaGenome was scored by co-authors at the University of Oxford.; Training overlap: SpliceAI2 was trained on all human genes except those on chromosomes 21 and 22, and the benchmark is genome-wide, so most variants lie in genes seen in training.; Essential splice variants are excluded, so results do not cover canonical GT/AG disruption.; No confidence intervals or resampling uncertainty are printed.; Preprint (bioRxiv v1), not peer reviewed.
Related records
- uses data: gnomAD, TOPMed and UK Biobank splice-proximal SNV depletion set (Jaganathan et al. 2026)
- assessment: AlphaGenome on splice-proximal SNV depletion in high-pLI genes (Spearman, SpliceAI2 paper)
- assessment: Pangolin on splice-proximal SNV depletion in high-pLI genes (Spearman, SpliceAI2 paper)
- assessment: SpliceAI on splice-proximal SNV depletion in high-pLI genes (Spearman, SpliceAI2 paper)
- assessment: SpliceAI2 on splice-proximal SNV depletion in high-pLI genes (Spearman, SpliceAI2 paper)