Evidence claim

Relevance of Splice-altering variant classification, Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants) (Fortier et al. Table 4) to "Select a patient-RNA splicing-variant validation workflow"

Use case: Select a patient-RNA splicing-variant validation workflow
Assessed by: Splice-altering variant classification, Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants) (Fortier et al. Table 4)
Relevance: Proxy evidence
Endpoint: Sensitivity, specificity, F1 and balanced accuracy of SpliceAI, CI-SpliceAI, OpenSpliceAI and a legacy N-of-4 vote on the Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants)

Proxy: DNA-based splice predictors scored against experimentally established splicing outcomes, the sequence-classification kind of evidence the use case keeps separate from patient-RNA recovery; inputs are variants, not patient RNA-seq.

4 reviewed evaluations

Evidence

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Evidence table

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19 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-10-22-1b7cf027e4f6
Property and statement	Original source and location	Review and provenance
Citation locations 1 values source id rna-splicing-20261011-fortier2026-source-article locator Methods 'Barbosa deep intronic benchmark dataset'; Results 'Barbosa deep intronic benchmark results'; Table 4 Context-only references	Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Original source ↗ rna-splicing-20261011-fortier2026-source-article: Methods 'Barbosa deep intronic benchmark dataset'; Results 'Barbosa deep intronic benchmark results'; Table 4 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.citation_locators` Source artifact SHA-256: `dffe74cee7782cbdfdbc9423cf00f0a846b3d720b90b10e9732d6f768c485176` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison group fortier2026-spliceai-reimplementations Context-only references	Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Original source ↗ rna-splicing-20261011-fortier2026-source-article: Methods 'Barbosa deep intronic benchmark dataset'; Results 'Barbosa deep intronic benchmark results'; Table 4 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.comparison_group` Source artifact SHA-256: `dffe74cee7782cbdfdbc9423cf00f0a846b3d720b90b10e9732d6f768c485176` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison title SpliceAI and its open-source reimplementations on six splice benchmarks (Fortier et al. 2026) Context-only references	Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Original source ↗ rna-splicing-20261011-fortier2026-source-article: Methods 'Barbosa deep intronic benchmark dataset'; Results 'Barbosa deep intronic benchmark results'; Table 4 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.comparison_title` Source artifact SHA-256: `dffe74cee7782cbdfdbc9423cf00f0a846b3d720b90b10e9732d6f768c485176` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Constraints 2 values Inspect every linked evaluation's source locator before citing a result. Do not combine this mapping's evaluations with any other protocol's results; variant sets, labels and thresholds differ. Context-only references	Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Original source ↗ rna-splicing-20261011-fortier2026-source-article: Methods 'Barbosa deep intronic benchmark dataset'; Results 'Barbosa deep intronic benchmark results'; Table 4 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.constraints` Source artifact SHA-256: `dffe74cee7782cbdfdbc9423cf00f0a846b3d720b90b10e9732d6f768c485176` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Endpoint Sensitivity, specificity, F1 and balanced accuracy of SpliceAI, CI-SpliceAI, OpenSpliceAI and a legacy N-of-4 vote on the Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants) Context-only references	Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Original source ↗ rna-splicing-20261011-fortier2026-source-article: Methods 'Barbosa deep intronic benchmark dataset'; Results 'Barbosa deep intronic benchmark results'; Table 4 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.endpoint` Source artifact SHA-256: `dffe74cee7782cbdfdbc9423cf00f0a846b3d720b90b10e9732d6f768c485176` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Claimed field links:assessed_by:rna-splicing-20261011-fortier2026-protocol-barbosa-deep-intronic Context-only references	Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Original source ↗ rna-splicing-20261011-fortier2026-source-article: Methods 'Barbosa deep intronic benchmark dataset'; Results 'Barbosa deep intronic benchmark results'; Table 4 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
Headline metric balanced-accuracy Context-only references	Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Original source ↗ rna-splicing-20261011-fortier2026-source-article: Methods 'Barbosa deep intronic benchmark dataset'; Results 'Barbosa deep intronic benchmark results'; Table 4 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.headline_metric` Source artifact SHA-256: `dffe74cee7782cbdfdbc9423cf00f0a846b3d720b90b10e9732d6f768c485176` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Limitations 5 values Thresholds optimised on the same data for this benchmark, which may overestimate real-world performance (Discussion 'Study limitations'). Labels and variant sets are reused from earlier publications. CI-SpliceAI was trained on all human chromosomes, so no benchmark variant is held out from its training; ABCA4 lies on a test chromosome held out by both SpliceAI and OpenSpliceAI (Methods 'Technical overview of deep learning methods'; Discussion). No uncertainty intervals are printed; significance is from two-proportion Z-tests on accuracy in the Results. Independent of the SpliceAI, CI-SpliceAI and OpenSpliceAI developers; the authors' employer supplies the Legacy Ensemble software, so those rows are author_reported. Variant-level splicing effect, not pathogenicity or diagnostic yield; tissue context is not modelled. Context-only references	Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Original source ↗ rna-splicing-20261011-fortier2026-source-article: Methods 'Barbosa deep intronic benchmark dataset'; Results 'Barbosa deep intronic benchmark results'; Table 4 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.limitations` Source artifact SHA-256: `dffe74cee7782cbdfdbc9423cf00f0a846b3d720b90b10e9732d6f768c485176` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
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attributes.output attributes.exclusions attributes.setting attributes.clinical_scope attributes.intended_users sha256 b85cad56e15fbfad66a2e70c3b193ed56c464e5e44b62a19b12f391e409ec897 Context-only references	Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Original source ↗ rna-splicing-20261011-fortier2026-source-article: Methods 'Barbosa deep intronic benchmark dataset'; Results 'Barbosa deep intronic benchmark results'; Table 4 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.pins` Source artifact SHA-256: `dffe74cee7782cbdfdbc9423cf00f0a846b3d720b90b10e9732d6f768c485176` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Rationale Proxy: DNA-based splice predictors scored against experimentally established splicing outcomes, the sequence-classification kind of evidence the use case keeps separate from patient-RNA recovery; inputs are variants, not patient RNA-seq. Context-only references	Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Original source ↗ rna-splicing-20261011-fortier2026-source-article: Methods 'Barbosa deep intronic benchmark dataset'; Results 'Barbosa deep intronic benchmark results'; Table 4 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.rationale` Source artifact SHA-256: `dffe74cee7782cbdfdbc9423cf00f0a846b3d720b90b10e9732d6f768c485176` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

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

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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

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Technical metadata and extraction receipts

Stable ID: use-case-mapping-rna-splicing-20261011-fortier2026-barbosa-deep-intronic

field: links:assessed_by:rna-splicing-20261011-fortier2026-protocol-barbosa-deep-intronic
value: rna-splicing-20261011-fortier2026-protocol-barbosa-deep-intronic
relevance: proxy
endpoint: Sensitivity, specificity, F1 and balanced accuracy of SpliceAI, CI-SpliceAI, OpenSpliceAI and a legacy N-of-4 vote on the Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants)
rationale: Proxy: DNA-based splice predictors scored against experimentally established splicing outcomes, the sequence-classification kind of evidence the use case keeps separate from patient-RNA recovery; inputs are variants, not patient RNA-seq.
constraints: Inspect every linked evaluation's source locator before citing a result.; Do not combine this mapping's evaluations with any other protocol's results; variant sets, labels and thresholds differ.
limitations: Thresholds optimised on the same data for this benchmark, which may overestimate real-world performance (Discussion 'Study limitations').; Labels and variant sets are reused from earlier publications. CI-SpliceAI was trained on all human chromosomes, so no benchmark variant is held out from its training; ABCA4 lies on a test chromosome held out by both SpliceAI and OpenSpliceAI (Methods 'Technical overview of deep learning methods'; Discussion).; No uncertainty intervals are printed; significance is from two-proportion Z-tests on accuracy in the Results.; Independent of the SpliceAI, CI-SpliceAI and OpenSpliceAI developers; the authors' employer supplies the Legacy Ensemble software, so those rows are author_reported.; Variant-level splicing effect, not pathogenicity or diagnostic yield; tissue context is not modelled.
citation locators: source id: rna-splicing-20261011-fortier2026-source-article; locator: Methods 'Barbosa deep intronic benchmark dataset'; Results 'Barbosa deep intronic benchmark results'; Table 4
source locator: rna-splicing-20261011-fortier2026-source-article: Methods 'Barbosa deep intronic benchmark dataset'; Results 'Barbosa deep intronic benchmark results'; Table 4
headline metric: balanced-accuracy
comparison group: fortier2026-spliceai-reimplementations
comparison title: SpliceAI and its open-source reimplementations on six splice benchmarks (Fortier et al. 2026)
stratum label: Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants)
stratum order: 4
reason: Recorded from the 2026-10-11 use-case literature pass (data/omics/use-case-coverage-rna-splicing-20261011-fortier2026). Independently reviewed in docs/reviews/use-cases/splicing-cnv-use-cases-2026-10-12.md.
review: method: source-hash-verification; independent-cell-check; automated-source-review; scope-review; reviewer: claude; reviewer note: Independent Claude review agent reviewer-scnv; did not extract this batch; no human review claimed; date: 2026-10-12; artifact sha256: dffe74cee7782cbdfdbc9423cf00f0a846b3d720b90b10e9732d6f768c485176; retrieval url: https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13170886/fullTextXML; method note: Drafted by the collector agent uc-splicing-cnv from the pinned source.; note: Checked by reviewer-scnv against the pinned source; verdict in docs/reviews/use-cases/splicing-cnv-use-cases-2026-10-12.md and in this batch's review.md.
reviewed evaluations: rna-splicing-20261011-fortier2026-eval-barbosa-deep-intronic-ci-spliceai; rna-splicing-20261011-fortier2026-eval-barbosa-deep-intronic-legacy-ensemble; rna-splicing-20261011-fortier2026-eval-barbosa-deep-intronic-openspliceai; rna-splicing-20261011-fortier2026-eval-barbosa-deep-intronic-spliceai
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subject: Select a patient-RNA splicing-variant validation workflow

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