SpliceAI2
Deep learning model that predicts splice site usage, splice junction usage and complete transcripts from about 200 kb of genomic sequence, and scores variant effects on splicing.
Results are recorded on linked configurations and versions of this model. Their settings and evaluations are kept separate below.
Results on linked records
This model has no results of its own in this release. 4 linked records have results. They are not assigned to the underlying model: each record keeps its own settings and evaluations, and its scores are on its own page.
- SpliceAI2 fine-tuned on myotonic dystrophy type 1 muscle RNA-seq (Jaganathan et al. 2026) Configuration · configuration · 1 evaluation · 1 result
- SpliceAI2 two-model multispecies ensemble (Jaganathan et al. 2026) Configuration · configuration · 10 evaluations · 23 results
- SpliceAI2 fine-tuned on SF3B1-mutant and wild-type uveal melanoma (Jaganathan et al. 2026) Configuration · configuration · 1 evaluation · 1 result
- SpliceAI2 tissue-specific fine-tune with a 147-RBP expression signature, 48 GTEx v8 tissues (Jaganathan et al. 2026) Configuration · configuration · 1 evaluation · 3 results
Overview
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Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.
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How it works, versions and access
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Evidence
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Evidence table
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Sources and history
Release 2026-10-22-1b7cf027e4f6 · Record review: source checked
4 source records 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)
- Supplementary Table 1: training RNA-seq samples and junctions per species (Jaganathan et al., bioRxiv 2026) · Original source · bioRxiv version 1 supplement DC1 (757673_file02.tsv)
- SpliceAI2 README at commit 1dcd2a1 (Illumina/SpliceAI2) · Original source · Commit 1dcd2a1882a8223095e7529982ae70bcb658340a (main, 2026-10-11); two README-only commits after tag v2.0 (aa634374bc8700414b1a5388729faed7c4f7ba07)
- SpliceAI2 Model Terms of Use (LICENSE at commit 1dcd2a1, Illumina/SpliceAI2) · Original source · Commit 1dcd2a1882a8223095e7529982ae70bcb658340a; file unchanged since tag v2.0
Technical metadata and extraction receipts
Stable ID: rna-splicing-20261013-spliceai2-model-spliceai2
- areas
- dna-genomes
- contexts
- research
- method types
- supervised_machine_learning
- reported name
- SpliceAI2
- entity level
- family
- training data
- Splice site and junction usage from 314,745 short-read RNA-seq samples (67,493,026 junctions) in ten mammalian species, human and mouse from recount3, plus transcript usage from 330 ENCODE long-read samples (189 human, 141 mouse). Human genes on chromosomes 21 and 22 held out for validation.
- licence
- SpliceAI2 Model Terms of Use: non-commercial organisations only; research use only, not for diagnostic procedures
- access
- Source code on GitHub (Illumina/SpliceAI2, tag v2.0); weights and precomputed scores on Hugging Face (illumina-ai/SpliceAI2) behind a gated licence acceptance; precomputed scores also through DRAGEN Annotation (README)
- source locator
- bioRxiv v1: Main 'Hierarchical model of RNA splicing'; Methods 'Training datasets' and 'SpliceAI2 model'; README 'SpliceAI2'; LICENSE sections 2, 3 and 13
- missing metadata
- version: reason: inapplicable; note: Family record; the evaluated setup is on each configuration
Related records
- subject: SpliceAI2 licence and intended use
- subject: SpliceAI2 top-transcript reconstruction
- subject: SpliceAI2 training data
- subject: SpliceAI2 training split and benchmark overlap
- configuration of: SpliceAI2 fine-tuned on myotonic dystrophy type 1 muscle RNA-seq (Jaganathan et al. 2026)
- configuration of: SpliceAI2 two-model multispecies ensemble (Jaganathan et al. 2026)
- configuration of: SpliceAI2 fine-tuned on SF3B1-mutant and wild-type uveal melanoma (Jaganathan et al. 2026)
- configuration of: SpliceAI2 tissue-specific fine-tune with a 147-RBP expression signature, 48 GTEx v8 tissues (Jaganathan et al. 2026)