rewirebio.iobenchmarks
← Browse all sources
Source

Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx)

medRxiv preprint version 1 supplement DC3 (file 344940_file09.xlsx in the JATS XML) · Preprint

DOI: 10.64898/2026.01.27.26344940

What this source contributed

Records in this release that cite this source. A record can cite several sources.

Results

Values as the source printed them. Compare results only under compatible protocols, datasets and metrics.

ValueMetricModel or configurationDatasetEvaluation
0.46153846153846156 fractionAccuracy (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))ANEVA-H (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)ANEVA-H on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance)
0.5882352941176471 fractionF1 (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))ANEVA-H (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)ANEVA-H on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance)
3 countFalse negatives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))ANEVA-H (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)ANEVA-H on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance)
4 countFalse positives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))ANEVA-H (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)ANEVA-H on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance)
-0.18446619684315546 unitlessMCC (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))ANEVA-H (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)ANEVA-H on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance)
0.5555555555555556 fractionPrecision (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))ANEVA-H (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)ANEVA-H on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance)
0.625 fractionRecall (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))ANEVA-H (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)ANEVA-H on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance)
1 countTrue negatives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))ANEVA-H (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)ANEVA-H on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance)
5 countTrue positives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))ANEVA-H (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)ANEVA-H on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance)
0.23595505617977527 fractionAccuracy (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))FRASER (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)FRASER on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
0.38181818181818183 fractionF1 (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))FRASER (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)FRASER on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
47 countFalse negatives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))FRASER (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)FRASER on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
21 countFalse positives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))FRASER (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)FRASER on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
-0.5878675320972554 unitlessMCC (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))FRASER (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)FRASER on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
0.5 fractionPrecision (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))FRASER (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)FRASER on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
0.3088235294117647 fractionRecall (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))FRASER (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)FRASER on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
0 countTrue negatives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))FRASER (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)FRASER on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
21 countTrue positives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))FRASER (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)FRASER on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
0.2727272727272727 fractionAccuracy (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))FRASER2.0 (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)FRASER2.0 on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
0.2 fractionF1 (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))FRASER2.0 (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)FRASER2.0 on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
14 countFalse negatives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))FRASER2.0 (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)FRASER2.0 on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
2 countFalse positives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))FRASER2.0 (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)FRASER2.0 on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
-0.24056261216234406 unitlessMCC (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))FRASER2.0 (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)FRASER2.0 on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
0.5 fractionPrecision (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))FRASER2.0 (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)FRASER2.0 on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
0.125 fractionRecall (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))FRASER2.0 (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)FRASER2.0 on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
4 countTrue negatives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))FRASER2.0 (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)FRASER2.0 on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
2 countTrue positives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))FRASER2.0 (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)FRASER2.0 on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
0 fractionAccuracy (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))LeafcutterMD (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)LeafcutterMD on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
0 fractionF1 (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))LeafcutterMD (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)LeafcutterMD on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
67 countFalse negatives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))LeafcutterMD (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)LeafcutterMD on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
21 countFalse positives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))LeafcutterMD (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)LeafcutterMD on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
-1 unitlessMCC (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))LeafcutterMD (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)LeafcutterMD on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
0 fractionPrecision (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))LeafcutterMD (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)LeafcutterMD on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
0 fractionRecall (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))LeafcutterMD (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)LeafcutterMD on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
0 countTrue negatives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))LeafcutterMD (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)LeafcutterMD on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
0 countTrue positives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))LeafcutterMD (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)LeafcutterMD on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
0.38461538461538464 fractionAccuracy (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))MAE (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)MAE on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance)
0.5555555555555556 fractionF1 (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))MAE (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)MAE on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance)
3 countFalse negatives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))MAE (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)MAE on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance)
5 countFalse positives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))MAE (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)MAE on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance)
-0.4330127018922193 unitlessMCC (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))MAE (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)MAE on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance)
0.5 fractionPrecision (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))MAE (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)MAE on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance)
0.625 fractionRecall (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))MAE (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)MAE on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance)
0 countTrue negatives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))MAE (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)MAE on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance)
5 countTrue positives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))MAE (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)MAE on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance)
0.25 fractionAccuracy (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))MINTIE (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)MINTIE on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
0.39999999999999997 fractionF1 (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))MINTIE (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)MINTIE on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
15 countFalse negatives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))MINTIE (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)MINTIE on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
9 countFalse positives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))MINTIE (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)MINTIE on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
-0.5875955600576714 unitlessMCC (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))MINTIE (Silverstein et al. 2026)NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)MINTIE on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)

Claims

Statements this source supports, each with where in the source it was checked.

  • Evidence summary: patient RNA splicing validation: Summary 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. (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)
  • Relevance of Recovery of known allelic-imbalance genes and outlier calls in true-negative samples, patient muscle RNA-seq (Silverstein et al. Table S9, ANEVA-H and MAE) to "Select a patient-RNA splicing-variant validation workflow": Assessed by Recovery of known allelic-imbalance genes and outlier calls in true-negative samples, patient muscle RNA-seq (Silverstein et al. Table S9, ANEVA-H and MAE) (rna-splicing-20261011-silverstein2026-source-tables-s1-s10: Table S9 rows 9 and 10; rna-splicing-20261011-silverstein2026-source-article: Methods 'Allelic Imbalance Tools' and 'True Positive and Negative Detection'; Table S8)
  • Relevance of Recovery of known expression-outlier genes and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, OUTRIDER) to "Select a patient-RNA splicing-variant validation workflow": Assessed by Recovery of known expression-outlier genes and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, OUTRIDER) (rna-splicing-20261011-silverstein2026-source-tables-s1-s10: Table S9 rows 7 and 8; rna-splicing-20261011-silverstein2026-source-article: Methods 'Expression Tools' and 'True Positive and Negative Detection'; Table S7)
  • Relevance of Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) to "Select a patient-RNA splicing-variant validation workflow": Assessed by Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) (rna-splicing-20261011-silverstein2026-source-tables-s1-s10: Table S9 rows 2 to 6; rna-splicing-20261011-silverstein2026-source-article: Methods 'One vs All Splice Tools', 'Case vs Control Splice Tools', 'True Positive and Negative Detection' and 'Performance Metrics')
  • Silverstein et al.: tools detected 28 of 68 known diagnoses: Known diagnoses detected 28 of 68 known diagnoses with aberrant RNA events (41%) detected across all tool analyses; splicing tools 24 of 68; allelic imbalance found 4 not found by other tools (Abstract Results; Results 'Poor predictive performance across tools' paragraph 1; Discussion paragraph 2; Results 'Recall rate of allelic imbalance highlights ANEVA-H' paragraph 1)

Models, benchmarks and other records

Methods (3)

Configurations (9)

Protocols (3)

Datasets (1)

Evaluations (9)

Evidence

Source checking verifies the cited claim or transcription. It does not establish independent reproduction.

Evidence table

Inspect claims, sources and review details

Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.

One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.

18 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-10-22-1b7cf027e4f6
Property and statementOriginal source and locationReview and provenance
attributes.artifact_sha256
3d137444b13225cf1223ad73758fdfcb4b5f772263e0aafd827d8ca57ca00120
Source metadata		Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx)

Original source ↗

No field-specific location recorded

Version: medRxiv preprint version 1 supplement DC3 (file 344940_file09.xlsx in the JATS XML)
Retrieved: 2026-10-11T21:38:52Z

catalogued

No individual claim review recorded

Audit details

Field: attributes.artifact_sha256

Source artifact SHA-256: 3d137444b13225cf1223ad73758fdfcb4b5f772263e0aafd827d8ca57ca00120

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Artifact url
https://www.medrxiv.org/content/medrxiv/early/2026/01/28/2026.01.27.26344940/DC3/embed/media-3.xlsx?download=true
Source metadata		Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx)

Original source ↗

No field-specific location recorded

Version: medRxiv preprint version 1 supplement DC3 (file 344940_file09.xlsx in the JATS XML)
Retrieved: 2026-10-11T21:38:52Z

catalogued

No individual claim review recorded

Audit details

Field: attributes.artifact_url

Source artifact SHA-256: 3d137444b13225cf1223ad73758fdfcb4b5f772263e0aafd827d8ca57ca00120

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Doi
10.64898/2026.01.27.26344940
Source metadata		Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx)

Original source ↗

No field-specific location recorded

Version: medRxiv preprint version 1 supplement DC3 (file 344940_file09.xlsx in the JATS XML)
Retrieved: 2026-10-11T21:38:52Z

catalogued

No individual claim review recorded

Audit details

Field: attributes.doi

Source artifact SHA-256: 3d137444b13225cf1223ad73758fdfcb4b5f772263e0aafd827d8ca57ca00120

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Licence
CC0-1.0
Source metadata		Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx)

Original source ↗

No field-specific location recorded

Version: medRxiv preprint version 1 supplement DC3 (file 344940_file09.xlsx in the JATS XML)
Retrieved: 2026-10-11T21:38:52Z

catalogued

No individual claim review recorded

Audit details

Field: attributes.licence

Source artifact SHA-256: 3d137444b13225cf1223ad73758fdfcb4b5f772263e0aafd827d8ca57ca00120

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Limitations
6 values
  • Table S9 values are spreadsheet formulas over the TP, FN, TN and FP cells of the same row; the stored results are recorded.
  • Tables S3 to S5 list 22 true-negative rows (6 muscle, 3 fibroblast, 13 cycloheximide-treated fibroblast); Results and Table S9 use 21 true-negative samples.
  • Results ('True negative analysis reveals high false positive rates') give an OUTRIDER + GTEx false-positive rate of 83%; Table S9 row 8 (TN 1, FP 4) implies 4 of 5 (80%).
  • Table S9 gives LeafcutterMD 67 positives (TP 0 + FN 67) while FRASER, run on the same three tissue sets, has 68 (TP 21 + FN 47).
  • Cell I6 (LeafcutterMD F1) holds a typed 0 rather than the row formula; with precision and recall both 0 the F1 formula would be undefined.
  • Table S7 true-negative rows do not reproduce Table S9 rows 7 and 8: read as in Tables S3, S4 and S8 ('YES' = no outlier), Table S7 gives OUTRIDER 8 of 9 negative samples without outliers (Table S9: 4) and OUTRIDER + GTEx 1 of 6 (Table S9: 1 of 5). The prose rates (56% and 83%) match Table S9 for OUTRIDER and Table S7 for OUTRIDER + GTEx.
Source metadata		Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx)

Original source ↗

No field-specific location recorded

Version: medRxiv preprint version 1 supplement DC3 (file 344940_file09.xlsx in the JATS XML)
Retrieved: 2026-10-11T21:38:52Z

catalogued

No individual claim review recorded

Audit details

Field: attributes.limitations

Source artifact SHA-256: 3d137444b13225cf1223ad73758fdfcb4b5f772263e0aafd827d8ca57ca00120

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Media type
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
Source metadata		Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx)

Original source ↗

No field-specific location recorded

Version: medRxiv preprint version 1 supplement DC3 (file 344940_file09.xlsx in the JATS XML)
Retrieved: 2026-10-11T21:38:52Z

catalogued

No individual claim review recorded

Audit details

Field: attributes.media_type

Source artifact SHA-256: 3d137444b13225cf1223ad73758fdfcb4b5f772263e0aafd827d8ca57ca00120

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Publication status
preprint
Source metadata		Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx)

Original source ↗

No field-specific location recorded

Version: medRxiv preprint version 1 supplement DC3 (file 344940_file09.xlsx in the JATS XML)
Retrieved: 2026-10-11T21:38:52Z

catalogued

No individual claim review recorded

Audit details

Field: attributes.publication_status

Source artifact SHA-256: 3d137444b13225cf1223ad73758fdfcb4b5f772263e0aafd827d8ca57ca00120

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Retrieved at
2026-10-11T21:38:52Z
Source metadata		Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx)

Original source ↗

No field-specific location recorded

Version: medRxiv preprint version 1 supplement DC3 (file 344940_file09.xlsx in the JATS XML)
Retrieved: 2026-10-11T21:38:52Z

catalogued

No individual claim review recorded

Audit details

Field: attributes.retrieved_at

Source artifact SHA-256: 3d137444b13225cf1223ad73758fdfcb4b5f772263e0aafd827d8ca57ca00120

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Url
https://doi.org/10.64898/2026.01.27.26344940
Source metadata		Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx)

Original source ↗

No field-specific location recorded

Version: medRxiv preprint version 1 supplement DC3 (file 344940_file09.xlsx in the JATS XML)
Retrieved: 2026-10-11T21:38:52Z

catalogued

No individual claim review recorded

Audit details

Field: attributes.url

Source artifact SHA-256: 3d137444b13225cf1223ad73758fdfcb4b5f772263e0aafd827d8ca57ca00120

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Version
medRxiv preprint version 1 supplement DC3 (file 344940_file09.xlsx in the JATS XML)
Source metadata		Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx)

Original source ↗

No field-specific location recorded

Version: medRxiv preprint version 1 supplement DC3 (file 344940_file09.xlsx in the JATS XML)
Retrieved: 2026-10-11T21:38:52Z

catalogued

No individual claim review recorded

Audit details

Field: attributes.version

Source artifact SHA-256: 3d137444b13225cf1223ad73758fdfcb4b5f772263e0aafd827d8ca57ca00120

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

How to read the evidence

Sources and history

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

Primary source retrieved and hashed for the 2026-10-11 use-case literature pass.

Release history

No supporting source is linked yet.

Read original source

Download this release (gzip)
Technical metadata and extraction receipts

Stable ID: rna-splicing-20261011-silverstein2026-source-tables-s1-s10

areas
rna-transcriptomes
contexts
clinical_research
url
https://doi.org/10.64898/2026.01.27.26344940
artifact url
https://www.medrxiv.org/content/medrxiv/early/2026/01/28/2026.01.27.26344940/DC3/embed/media-3.xlsx?download=true
version
medRxiv preprint version 1 supplement DC3 (file 344940_file09.xlsx in the JATS XML)
retrieved at
2026-10-11T21:38:52Z
artifact sha256
3d137444b13225cf1223ad73758fdfcb4b5f772263e0aafd827d8ca57ca00120
doi
10.64898/2026.01.27.26344940
publication status
preprint
licence
CC0-1.0
media type
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
limitations
Table S9 values are spreadsheet formulas over the TP, FN, TN and FP cells of the same row; the stored results are recorded.; Tables S3 to S5 list 22 true-negative rows (6 muscle, 3 fibroblast, 13 cycloheximide-treated fibroblast); Results and Table S9 use 21 true-negative samples.; Results ('True negative analysis reveals high false positive rates') give an OUTRIDER + GTEx false-positive rate of 83%; Table S9 row 8 (TN 1, FP 4) implies 4 of 5 (80%).; Table S9 gives LeafcutterMD 67 positives (TP 0 + FN 67) while FRASER, run on the same three tissue sets, has 68 (TP 21 + FN 47).; Cell I6 (LeafcutterMD F1) holds a typed 0 rather than the row formula; with precision and recall both 0 the F1 formula would be undefined.; Table S7 true-negative rows do not reproduce Table S9 rows 7 and 8: read as in Tables S3, S4 and S8 ('YES' = no outlier), Table S7 gives OUTRIDER 8 of 9 negative samples without outliers (Table S9: 4) and OUTRIDER + GTEx 1 of 6 (Table S9: 1 of 5). The prose rates (56% and 83%) match Table S9 for OUTRIDER and Table S7 for OUTRIDER + GTEx.
Related records

    Suggest a correction