Evidence claim

Evidence summary: patient RNA splicing validation

Summary of Select a patient-RNA splicing-variant validation workflow

The one recovery result already on this page is FRASER on the 119-sample Kremer fibroblast cohort, reported by its developers: a mean 85% (11 of 13) of known pathogenic splicing events recovered at a 30-sample subsample. Six published comparisons add evidence. Segarra-Casas et al. (2025), independent of the tool developers, recorded which callers reported 16 known pathogenic splicing events in clinical muscle RNA-seq from 98 samples: FRASER with |dPSI| > 0.1 reported 13, FRASER with |dPSI| > 0.3 reported 9, FRASER2 10 (and one more only with the full cohort), rMATS-turbo 7 and LeafCutterMD 4 (Table 2); the events were established partly with these callers. Segers et al. (2026), the saseR developers, ranked 12 reported disease genes in the Kremer cohort: the gene ranked within the top 10 in 9 of 12 samples for saseR-junctions, in 6 of 12 for FRASER 2.0 with the autoencoder and in 7 of 12 for FRASER 2.0 with PCA (Table 2); the 12 genes were reported in earlier analyses of the same cohort, partly with OUTRIDER and FRASER, and several are expression rather than splicing defects. Silverstein et al. (2026, preprint), a clinical research group that did not develop the splicing callers, scored them on 97 previously diagnosed neuromuscular RNA-seq samples and also counted outlier calls in true-negative samples (Table S9). On known pathogenic splicing events in muscle and fibroblast RNA-seq, rMATS-turbo detected 15 of 23 and MINTIE 8 of 23; on muscle, fibroblast and cycloheximide-treated fibroblast RNA-seq, FRASER detected 21 of 68 and LeafcutterMD none of 67; FRASER2.0, run on muscle only, detected 2 of 16. rMATS-turbo and MINTIE reported outliers in all 9 true-negative samples they were run on, FRASER and LeafcutterMD in all 21, and FRASER2.0 in 2 of 6. Denominators differ by tool, a negative sample with any outlier counts as one false positive, and the known events were chosen because they were clinically interpretable and visible in the RNA-seq, which the authors say favours clearly defined events. Drost et al. (2025), a diagnostic laboratory independent of the tool developers, scored 243 clinically ascertained variants, each tested for a splicing effect in patient cells or by exon trapping, with four DNA-based predictors. On all 243, AUROC was 0.895 for SpliceAI, 0.885 for Pangolin, 0.830 for SPiP and 0.806 for SQUIRLS (Data S1 Table S3); at literature thresholds sensitivity was 0.871, 0.858, 0.832 and 0.800 and specificity 0.750, 0.773, 0.761 and 0.591 (Table S4), and the authors' rule of requiring at least two of the four tools gave sensitivity 0.916 and specificity 0.727. Most in-house variants were sent for testing because another predictor (Alamut) flagged them, so negatives are mostly predicted-but-unconfirmed variants. Two further DNA-based comparisons add proxy evidence. Fortier et al. (2026), independent of the SpliceAI, CI-SpliceAI and OpenSpliceAI developers, report balanced accuracy on functional splice-assay variants in ABCA4 and MYBPC3 of 0.836 for SpliceAI, 0.794 for CI-SpliceAI and 0.746 for OpenSpliceAI, and on 242 deep intronic pathogenic variants with 242 common controls of 0.940, 0.890 and 0.841 (Tables 2 and 4); both sets of thresholds were tuned on the same data, so these are upper-bound estimates, and CI-SpliceAI was trained on all chromosomes. Alavi (2026, preprint) applied thresholds tuned on OPA1 variants to 76 functionally validated canonical splice-site variants and 107 ClinVar benign intronic variants from 65 genes: sensitivity and specificity were 88.2% and 99.1% for SpliceAI, 85.5% and 86.9% for pretrained NTv3 650M, 71.1% and 100% for Evo2 40B with a human identifier, 56.6% and 80.4% for AlphaGenome (either module) and 27.6% and 85.0% for Genos 10B (Table IV). Jaganathan et al. (2026, preprint), the SpliceAI2 developers at Illumina, scored four DNA-based predictors on rare variants that create a GT or AG dinucleotide in 5,435 Genomics England rare-disease participants with blood RNA-seq, against new junctions seen in the carriers' RNA (3,065 cryptic gains and 1,775,800 nulls): auPRC was 0.59 for SpliceAI2, 0.48 for SpliceAI, 0.40 for Pangolin and 0.36 for AlphaGenome, or 0.45 with AlphaGenome's raw splice site score (Fig. 4g; Methods). Only splicing visible in blood can be confirmed, so some nulls may alter splicing in other tissues, and SpliceAI2 was trained on all human genes except those on chromosomes 21 and 22. Of 23 rare variants in phenotype-matched panel genes that SpliceAI2 prioritised, 17 showed the predicted splicing change in RNA-seq; no other tool was put through this step. The paper compares no rule-based splice-site scorer. All values are transcribed from the publications, not reproduced; none is a diagnostic-yield estimate, and results from different sources measure different endpoints and must not be pooled. This summary describes evidence and does not recommend a clinical workflow.

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Claims, original sources and review scope · Release 2026-10-22-1b7cf027e4f6
Property and statement	Original source and location	Review and provenance
Claimed field summary Context-only references	Detection of aberrant splicing events in RNA-seq data using FRASER Original source ↗ amp-oncology-rna-20261007-source-pmc7822922: FRASER results; rna-splicing-20261009-source-segarracasas2025: Table 2, Results paragraph 3; rna-splicing-20261009-source-segers2026: Table 2; rna-splicing-20261009-source-drost2025-data-s1: Data S1 Tables S3 and S4; rna-splicing-20261009-source-drost2025: Methods; rna-splicing-20261011-silverstein2026-source-tables-s1-s10: Table S9 rows 2 to 6; rna-splicing-20261011-fortier2026-source-article: Tables 2 and 4; rna-splicing-20261011-alavi2026-source-article: Table IV; rna-splicing-20261013-spliceai2-source-article: Fig. 4g, Methods 'Evaluation of competing methods', Main 'Contribution to rare genetic disease' paragraph 4 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Europe PMC fullTextXML retrieved 2026-10-07T12:23:57.289375+00:00 Retrieved: 2026-10-07T12:23:57.289375+00:00	not individually reviewed No individual claim review recorded Audit details Field: `attributes.field` Source artifact SHA-256: `1f1f028cddd9145bd342422ec768ecdca6ecfeb6f9bc2e114ecfab061b5e93a6` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Claimed field summary Context-only references	Routine RNA-based analysis of potential splicing variants facilitates genomic diagnostics and reveals limitations of in silico prediction tools Original source ↗ amp-oncology-rna-20261007-source-pmc7822922: FRASER results; rna-splicing-20261009-source-segarracasas2025: Table 2, Results paragraph 3; rna-splicing-20261009-source-segers2026: Table 2; rna-splicing-20261009-source-drost2025-data-s1: Data S1 Tables S3 and S4; rna-splicing-20261009-source-drost2025: Methods; rna-splicing-20261011-silverstein2026-source-tables-s1-s10: Table S9 rows 2 to 6; rna-splicing-20261011-fortier2026-source-article: Tables 2 and 4; rna-splicing-20261011-alavi2026-source-article: Table IV; rna-splicing-20261013-spliceai2-source-article: Fig. 4g, Methods 'Evaluation of competing methods', Main 'Contribution to rare genetic disease' paragraph 4 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: HGG Advances 7(1):100521, published online 2025-09-22; PMC12547740 full-text XML Retrieved: 2026-10-09T20:30:41Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.field` Source artifact SHA-256: `2a2e970526e4348d505d26356b830d96da9e32688c4de84841ac8137b62d5d32` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Claimed field summary Context-only references	Drost et al. 2025, Data S1 (Tables S1-S6) Original source ↗ amp-oncology-rna-20261007-source-pmc7822922: FRASER results; rna-splicing-20261009-source-segarracasas2025: Table 2, Results paragraph 3; rna-splicing-20261009-source-segers2026: Table 2; rna-splicing-20261009-source-drost2025-data-s1: Data S1 Tables S3 and S4; rna-splicing-20261009-source-drost2025: Methods; rna-splicing-20261011-silverstein2026-source-tables-s1-s10: Table S9 rows 2 to 6; rna-splicing-20261011-fortier2026-source-article: Tables 2 and 4; rna-splicing-20261011-alavi2026-source-article: Table IV; rna-splicing-20261013-spliceai2-source-article: Fig. 4g, Methods 'Evaluation of competing methods', Main 'Contribution to rare genetic disease' paragraph 4 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: mmc2.xlsx (Data S1. Tables S1-S6) inside the Europe PMC supplementaryFiles zip for PMC12547740 Retrieved: 2026-10-09T20:31:37Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.field` Source artifact SHA-256: `a3a69202b8f0d9ecb7fa22a16991d5e4d583b5ae72fd598206ea5c2b4c5c14ca` Hash scope: SHA-256 of mmc2.xlsx as extracted from the supplementaryFiles zip (zip SHA-256 1c89a7ebc0bf686c6087d0fee86ba6f364a6d754d5bc7eb9c446b7db017f1ebc; the zip is re-built by Europe PMC on each request, so only the member hash is stable). Inspected artifact
Claimed field summary Context-only references	Translating Muscle RNAseq Into the Clinic for the Diagnosis of Muscle Diseases Original source ↗ amp-oncology-rna-20261007-source-pmc7822922: FRASER results; rna-splicing-20261009-source-segarracasas2025: Table 2, Results paragraph 3; rna-splicing-20261009-source-segers2026: Table 2; rna-splicing-20261009-source-drost2025-data-s1: Data S1 Tables S3 and S4; rna-splicing-20261009-source-drost2025: Methods; rna-splicing-20261011-silverstein2026-source-tables-s1-s10: Table S9 rows 2 to 6; rna-splicing-20261011-fortier2026-source-article: Tables 2 and 4; rna-splicing-20261011-alavi2026-source-article: Table IV; rna-splicing-20261013-spliceai2-source-article: Fig. 4g, Methods 'Evaluation of competing methods', Main 'Contribution to rare genetic disease' paragraph 4 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Annals of Clinical and Translational Neurology 12(7):1465, published online 2025-05-25; PMC12257123 full-text XML Retrieved: 2026-10-09T20:33:50Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.field` Source artifact SHA-256: `6e61a917c80d08357fe7316ad84f5cb84ac457fcce4cb1d3b5bf866046e2095d` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Claimed field summary Context-only references	saseR: juggling offsets unlocks RNA-seq tools for fast and scalable differential usage, aberrant splicing and expression retrieval Original source ↗ amp-oncology-rna-20261007-source-pmc7822922: FRASER results; rna-splicing-20261009-source-segarracasas2025: Table 2, Results paragraph 3; rna-splicing-20261009-source-segers2026: Table 2; rna-splicing-20261009-source-drost2025-data-s1: Data S1 Tables S3 and S4; rna-splicing-20261009-source-drost2025: Methods; rna-splicing-20261011-silverstein2026-source-tables-s1-s10: Table S9 rows 2 to 6; rna-splicing-20261011-fortier2026-source-article: Tables 2 and 4; rna-splicing-20261011-alavi2026-source-article: Table IV; rna-splicing-20261013-spliceai2-source-article: Fig. 4g, Methods 'Evaluation of competing methods', Main 'Contribution to rare genetic disease' paragraph 4 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Genome Biology 27:103, published online 2026-02-18; PMC13019952 full-text XML Retrieved: 2026-10-09T20:30:42Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.field` Source artifact SHA-256: `af11b79beec80f945a14f13ce487193ad733d740cace922eba0bd6d63b9de560` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Claimed field summary Context-only references	Probing Genomic Foundation Models with Splice-Variant Perturbations (Alavi, bioRxiv 2026) Original source ↗ amp-oncology-rna-20261007-source-pmc7822922: FRASER results; rna-splicing-20261009-source-segarracasas2025: Table 2, Results paragraph 3; rna-splicing-20261009-source-segers2026: Table 2; rna-splicing-20261009-source-drost2025-data-s1: Data S1 Tables S3 and S4; rna-splicing-20261009-source-drost2025: Methods; rna-splicing-20261011-silverstein2026-source-tables-s1-s10: Table S9 rows 2 to 6; rna-splicing-20261011-fortier2026-source-article: Tables 2 and 4; rna-splicing-20261011-alavi2026-source-article: Table IV; rna-splicing-20261013-spliceai2-source-article: Fig. 4g, Methods 'Evaluation of competing methods', Main 'Contribution to rare genetic disease' paragraph 4 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: bioRxiv preprint version 1, posted 2026-10-09; full-text PDF (11 pages) Retrieved: 2026-10-11T21:42:49Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.field` Source artifact SHA-256: `e11f4bd8818992a831cb2829d934287c2587b108881c9785c2ef5c8cdc730004` Hash scope: SHA-256 of one retrieval of the bioRxiv PDF. bioRxiv stamps the PDF when serving it (ModDate and the second document ID), so later retrievals differ. A reviewer retrieval on 2026-10-12 of the same length (1,823,372 bytes) had SHA-256 e22491e217c7c158528668b38bcb8b9855b89617b807c60b740795a204c84b0a and ModDate D:20261011221204-07'00'. All 168 Table III and IV cells in that copy match the stored results. Inspected artifact
Claimed field summary Context-only references	Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Original source ↗ amp-oncology-rna-20261007-source-pmc7822922: FRASER results; rna-splicing-20261009-source-segarracasas2025: Table 2, Results paragraph 3; rna-splicing-20261009-source-segers2026: Table 2; rna-splicing-20261009-source-drost2025-data-s1: Data S1 Tables S3 and S4; rna-splicing-20261009-source-drost2025: Methods; rna-splicing-20261011-silverstein2026-source-tables-s1-s10: Table S9 rows 2 to 6; rna-splicing-20261011-fortier2026-source-article: Tables 2 and 4; rna-splicing-20261011-alavi2026-source-article: Table IV; rna-splicing-20261013-spliceai2-source-article: Fig. 4g, Methods 'Evaluation of competing methods', Main 'Contribution to rare genetic disease' paragraph 4 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: PLOS One 21(5):e0348885, published 2026-05-13; PMC13170886 full-text XML Retrieved: 2026-10-11T21:37:04Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.field` Source artifact SHA-256: `dffe74cee7782cbdfdbc9423cf00f0a846b3d720b90b10e9732d6f768c485176` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Claimed field summary Context-only references	Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026) Original source ↗ amp-oncology-rna-20261007-source-pmc7822922: FRASER results; rna-splicing-20261009-source-segarracasas2025: Table 2, Results paragraph 3; rna-splicing-20261009-source-segers2026: Table 2; rna-splicing-20261009-source-drost2025-data-s1: Data S1 Tables S3 and S4; rna-splicing-20261009-source-drost2025: Methods; rna-splicing-20261011-silverstein2026-source-tables-s1-s10: Table S9 rows 2 to 6; rna-splicing-20261011-fortier2026-source-article: Tables 2 and 4; rna-splicing-20261011-alavi2026-source-article: Table IV; rna-splicing-20261013-spliceai2-source-article: Fig. 4g, Methods 'Evaluation of competing methods', Main 'Contribution to rare genetic disease' paragraph 4 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: medRxiv preprint version 1, posted 2026-01-28; JATS source XML Retrieved: 2026-10-11T21:38:32Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.field` Source artifact SHA-256: `8b987ac49f086adc0a191522436529a70f243505f7dda9926786004e15b4601c` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Claimed field summary Context-only references	Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) Original source ↗ amp-oncology-rna-20261007-source-pmc7822922: FRASER results; rna-splicing-20261009-source-segarracasas2025: Table 2, Results paragraph 3; rna-splicing-20261009-source-segers2026: Table 2; rna-splicing-20261009-source-drost2025-data-s1: Data S1 Tables S3 and S4; rna-splicing-20261009-source-drost2025: Methods; rna-splicing-20261011-silverstein2026-source-tables-s1-s10: Table S9 rows 2 to 6; rna-splicing-20261011-fortier2026-source-article: Tables 2 and 4; rna-splicing-20261011-alavi2026-source-article: Table IV; rna-splicing-20261013-spliceai2-source-article: Fig. 4g, Methods 'Evaluation of competing methods', Main 'Contribution to rare genetic disease' paragraph 4 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: medRxiv preprint version 1 supplement DC3 (file 344940_file09.xlsx in the JATS XML) Retrieved: 2026-10-11T21:38:52Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.field` Source artifact SHA-256: `3d137444b13225cf1223ad73758fdfcb4b5f772263e0aafd827d8ca57ca00120` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Claimed field summary Context-only references	A unified framework for quantitative splicing and transcript prediction (Jaganathan et al., bioRxiv 2026) Original source ↗ amp-oncology-rna-20261007-source-pmc7822922: FRASER results; rna-splicing-20261009-source-segarracasas2025: Table 2, Results paragraph 3; rna-splicing-20261009-source-segers2026: Table 2; rna-splicing-20261009-source-drost2025-data-s1: Data S1 Tables S3 and S4; rna-splicing-20261009-source-drost2025: Methods; rna-splicing-20261011-silverstein2026-source-tables-s1-s10: Table S9 rows 2 to 6; rna-splicing-20261011-fortier2026-source-article: Tables 2 and 4; rna-splicing-20261011-alavi2026-source-article: Table IV; rna-splicing-20261013-spliceai2-source-article: Fig. 4g, Methods 'Evaluation of competing methods', Main 'Contribution to rare genetic disease' paragraph 4 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: bioRxiv preprint version 1, posted 2026-10-11; full-text PDF (57 pages) Retrieved: 2026-10-13T12:40:34Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.field` Source artifact SHA-256: `f8f3db2c3943d4827b670bc2e962e0cec8fbe96e0b1acf9d4a9b9f0c7a78c06b` Hash scope: SHA-256 of one retrieval of the bioRxiv PDF. bioRxiv stamps the PDF when serving it (XMP ModifyDate, MetadataDate, DocumentID, InstanceID, Info ModDate and the second trailer ID), so later retrievals differ. A reviewer retrieval on 2026-10-13 of the same length (2,679,664 bytes) had SHA-256 407f5884427dda0226e0ce1f022e4479d3ff2b243da18bcc691eb345b0e04a6a and ModDate D:20261013063115-07'00'; its bytes differ only in those fields and its pdftotext text layer is identical to the pinned copy. Inspected artifact

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Release 2026-10-22-1b7cf027e4f6 · Record review: source checked

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Detection of aberrant splicing events in RNA-seq data using FRASER · Original source · Europe PMC fullTextXML retrieved 2026-10-07T12:23:57.289375+00:00
Translating Muscle RNAseq Into the Clinic for the Diagnosis of Muscle Diseases · Original source · Annals of Clinical and Translational Neurology 12(7):1465, published online 2025-05-25; PMC12257123 full-text XML
saseR: juggling offsets unlocks RNA-seq tools for fast and scalable differential usage, aberrant splicing and expression retrieval · Original source · Genome Biology 27:103, published online 2026-02-18; PMC13019952 full-text XML
Routine RNA-based analysis of potential splicing variants facilitates genomic diagnostics and reveals limitations of in silico prediction tools · Original source · HGG Advances 7(1):100521, published online 2025-09-22; PMC12547740 full-text XML
Drost et al. 2025, Data S1 (Tables S1-S6) · Original source · mmc2.xlsx (Data S1. Tables S1-S6) inside the Europe PMC supplementaryFiles zip for PMC12547740
Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026) · Original source · medRxiv preprint version 1, posted 2026-01-28; JATS source XML
Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Original source · medRxiv preprint version 1 supplement DC3 (file 344940_file09.xlsx in the JATS XML)
Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) · Original source · PLOS One 21(5):e0348885, published 2026-05-13; PMC13170886 full-text XML
Probing Genomic Foundation Models with Splice-Variant Perturbations (Alavi, bioRxiv 2026) · Original source · bioRxiv preprint version 1, posted 2026-10-09; full-text PDF (11 pages)
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)

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Stable ID: use-case-summary-patient-rna-splicing-validation

field: summary
value: The one recovery result already on this page is FRASER on the 119-sample Kremer fibroblast cohort, reported by its developers: a mean 85% (11 of 13) of known pathogenic splicing events recovered at a 30-sample subsample. Six published comparisons add evidence. Segarra-Casas et al. (2025), independent of the tool developers, recorded which callers reported 16 known pathogenic splicing events in clinical muscle RNA-seq from 98 samples: FRASER with |dPSI| > 0.1 reported 13, FRASER with |dPSI| > 0.3 reported 9, FRASER2 10 (and one more only with the full cohort), rMATS-turbo 7 and LeafCutterMD 4 (Table 2); the events were established partly with these callers. Segers et al. (2026), the saseR developers, ranked 12 reported disease genes in the Kremer cohort: the gene ranked within the top 10 in 9 of 12 samples for saseR-junctions, in 6 of 12 for FRASER 2.0 with the autoencoder and in 7 of 12 for FRASER 2.0 with PCA (Table 2); the 12 genes were reported in earlier analyses of the same cohort, partly with OUTRIDER and FRASER, and several are expression rather than splicing defects. Silverstein et al. (2026, preprint), a clinical research group that did not develop the splicing callers, scored them on 97 previously diagnosed neuromuscular RNA-seq samples and also counted outlier calls in true-negative samples (Table S9). On known pathogenic splicing events in muscle and fibroblast RNA-seq, rMATS-turbo detected 15 of 23 and MINTIE 8 of 23; on muscle, fibroblast and cycloheximide-treated fibroblast RNA-seq, FRASER detected 21 of 68 and LeafcutterMD none of 67; FRASER2.0, run on muscle only, detected 2 of 16. rMATS-turbo and MINTIE reported outliers in all 9 true-negative samples they were run on, FRASER and LeafcutterMD in all 21, and FRASER2.0 in 2 of 6. Denominators differ by tool, a negative sample with any outlier counts as one false positive, and the known events were chosen because they were clinically interpretable and visible in the RNA-seq, which the authors say favours clearly defined events. Drost et al. (2025), a diagnostic laboratory independent of the tool developers, scored 243 clinically ascertained variants, each tested for a splicing effect in patient cells or by exon trapping, with four DNA-based predictors. On all 243, AUROC was 0.895 for SpliceAI, 0.885 for Pangolin, 0.830 for SPiP and 0.806 for SQUIRLS (Data S1 Table S3); at literature thresholds sensitivity was 0.871, 0.858, 0.832 and 0.800 and specificity 0.750, 0.773, 0.761 and 0.591 (Table S4), and the authors' rule of requiring at least two of the four tools gave sensitivity 0.916 and specificity 0.727. Most in-house variants were sent for testing because another predictor (Alamut) flagged them, so negatives are mostly predicted-but-unconfirmed variants. Two further DNA-based comparisons add proxy evidence. Fortier et al. (2026), independent of the SpliceAI, CI-SpliceAI and OpenSpliceAI developers, report balanced accuracy on functional splice-assay variants in ABCA4 and MYBPC3 of 0.836 for SpliceAI, 0.794 for CI-SpliceAI and 0.746 for OpenSpliceAI, and on 242 deep intronic pathogenic variants with 242 common controls of 0.940, 0.890 and 0.841 (Tables 2 and 4); both sets of thresholds were tuned on the same data, so these are upper-bound estimates, and CI-SpliceAI was trained on all chromosomes. Alavi (2026, preprint) applied thresholds tuned on OPA1 variants to 76 functionally validated canonical splice-site variants and 107 ClinVar benign intronic variants from 65 genes: sensitivity and specificity were 88.2% and 99.1% for SpliceAI, 85.5% and 86.9% for pretrained NTv3 650M, 71.1% and 100% for Evo2 40B with a human identifier, 56.6% and 80.4% for AlphaGenome (either module) and 27.6% and 85.0% for Genos 10B (Table IV). Jaganathan et al. (2026, preprint), the SpliceAI2 developers at Illumina, scored four DNA-based predictors on rare variants that create a GT or AG dinucleotide in 5,435 Genomics England rare-disease participants with blood RNA-seq, against new junctions seen in the carriers' RNA (3,065 cryptic gains and 1,775,800 nulls): auPRC was 0.59 for SpliceAI2, 0.48 for SpliceAI, 0.40 for Pangolin and 0.36 for AlphaGenome, or 0.45 with AlphaGenome's raw splice site score (Fig. 4g; Methods). Only splicing visible in blood can be confirmed, so some nulls may alter splicing in other tissues, and SpliceAI2 was trained on all human genes except those on chromosomes 21 and 22. Of 23 rare variants in phenotype-matched panel genes that SpliceAI2 prioritised, 17 showed the predicted splicing change in RNA-seq; no other tool was put through this step. The paper compares no rule-based splice-site scorer. All values are transcribed from the publications, not reproduced; none is a diagnostic-yield estimate, and results from different sources measure different endpoints and must not be pooled. This summary describes evidence and does not recommend a clinical workflow.
source locator: amp-oncology-rna-20261007-source-pmc7822922: FRASER results; rna-splicing-20261009-source-segarracasas2025: Table 2, Results paragraph 3; rna-splicing-20261009-source-segers2026: Table 2; rna-splicing-20261009-source-drost2025-data-s1: Data S1 Tables S3 and S4; rna-splicing-20261009-source-drost2025: Methods; rna-splicing-20261011-silverstein2026-source-tables-s1-s10: Table S9 rows 2 to 6; rna-splicing-20261011-fortier2026-source-article: Tables 2 and 4; rna-splicing-20261011-alavi2026-source-article: Table IV; rna-splicing-20261013-spliceai2-source-article: Fig. 4g, Methods 'Evaluation of competing methods', Main 'Contribution to rare genetic disease' paragraph 4
review: method: independent-cell-check; scope-review; automated-source-review; reviewer: claude; reviewer note: Claude (Opus 5.5) research agent, independent reviewer of the SpliceAI2 batch; did not extract it; wrote the added sentences; no human review claimed; reviewed at: 2026-10-13T00:00:00Z; note: Adds the SpliceAI2 paper (Jaganathan et al. 2026, bioRxiv v1). Every added number traced to a source_checked result in data/omics/use-case-coverage-rna-splicing-20261013-spliceai2; earlier text unchanged. See docs/reviews/use-cases/spliceai2-2026-10-13.md. Previous review note: Drafted by the collector (data/omics/lit-search-20261011-uc-splicing-cnv/report.md) and rewritten by the reviewer; every number traced to stored or batch results. Descriptive summary only; no execution, human scientific review or clinical validation. See docs/reviews/use-cases/splicing-cnv-use-cases-2026-10-12.md.
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