Protocol

Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools)

Each aberrant-splicing caller is scored on known pathogenic splicing events (exact coordinate match) and on true-negative samples (any outlier is a false positive), aggregated across the tissue sets the tool was run on.

5 evaluations · 45 results

Overview

An explanatory profile has not yet been reviewed for this record.

Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.

5 recorded evaluations, 45 metric rows. A comparison chart has not yet been validated for these results. The table retains the individual findings and their sources.

View coverage and remaining gaps across all benchmarks

Results

Results are available, but no reviewed comparison panel is linked in this release.

All evaluations

5 evaluations · 45 results. Different protocols are not a single leaderboard.

Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) · Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)

Sorted by Accuracy (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) (higher 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	Accuracy (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))	F1 (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))	False negatives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))	False positives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))	MCC (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))	Precision (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))	Recall (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))	True negatives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))	True positives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9))
rMATS (Silverstein et al. 2026)Evaluation	0.469 (best in column)	0.638 (best in column)	8 (best in column)	9	-0.361	0.625 (best in column)	0.652 (best in column)	0	15
FRASER2.0 (Silverstein et al. 2026)Evaluation	0.273	0.2	14	2 (best in column)	-0.241 (best in column)	0.5	0.125	4 (best in column)	2
MINTIE (Silverstein et al. 2026)Evaluation	0.25	0.4	15	9	-0.588	0.471	0.348	0	8
FRASER (Silverstein et al. 2026)Evaluation	0.236	0.382	47	21	-0.588	0.5	0.309	0	21 (best in column)
LeafcutterMD (Silverstein et al. 2026)Evaluation	0	0	67	21	-1	0	0	0	0

All 45 result rows with coverage, uncertainty and sources

Filter evaluations

Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

Applied filters: All linked evaluations

Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: FRASER (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	0.236 Accuracy (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source FRASER on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell F4, row 'FRASER', column 'Accuracy'
Configuration: FRASER (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	0.382 F1 (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source FRASER on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell I4, row 'FRASER', column 'F1'
Configuration: FRASER (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	47 False negatives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) count · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source FRASER on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell C4, row 'FRASER', column 'False Negatives'
Configuration: FRASER (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	21 False positives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) count · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source FRASER on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell E4, row 'FRASER', column 'False Positives'
Configuration: FRASER (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	-0.588 MCC (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source FRASER on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell J4, row 'FRASER', column 'MCC'
Configuration: FRASER (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	0.5 Precision (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source FRASER on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell G4, row 'FRASER', column 'Precision'
Configuration: FRASER (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	0.309 Recall (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source FRASER on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell H4, row 'FRASER', column 'Recall'
Configuration: FRASER (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	0 True negatives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source FRASER on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell D4, row 'FRASER', column 'True Negatives'
Configuration: FRASER (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	21 True positives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source FRASER on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell B4, row 'FRASER', column 'True Positives'
Configuration: FRASER2.0 (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	0.273 Accuracy (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source FRASER2.0 on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell F5, row 'FRASER2.0', column 'Accuracy'
Configuration: FRASER2.0 (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	0.2 F1 (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source FRASER2.0 on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell I5, row 'FRASER2.0', column 'F1'
Configuration: FRASER2.0 (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	14 False negatives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) count · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source FRASER2.0 on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell C5, row 'FRASER2.0', column 'False Negatives'
Configuration: FRASER2.0 (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	2 False positives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) count · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source FRASER2.0 on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell E5, row 'FRASER2.0', column 'False Positives'
Configuration: FRASER2.0 (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	-0.241 MCC (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source FRASER2.0 on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell J5, row 'FRASER2.0', column 'MCC'
Configuration: FRASER2.0 (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	0.5 Precision (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source FRASER2.0 on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell G5, row 'FRASER2.0', column 'Precision'
Configuration: FRASER2.0 (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	0.125 Recall (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source FRASER2.0 on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell H5, row 'FRASER2.0', column 'Recall'
Configuration: FRASER2.0 (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	4 True negatives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source FRASER2.0 on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell D5, row 'FRASER2.0', column 'True Negatives'
Configuration: FRASER2.0 (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	2 True positives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source FRASER2.0 on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell B5, row 'FRASER2.0', column 'True Positives'
Configuration: LeafcutterMD (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	0 Accuracy (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source LeafcutterMD on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell F6, row 'LeafcutterMD', column 'Accuracy'
Configuration: LeafcutterMD (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	0 F1 (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source LeafcutterMD on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell I6, row 'LeafcutterMD', column 'F1'
Configuration: LeafcutterMD (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	67 False negatives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) count · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source LeafcutterMD on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell C6, row 'LeafcutterMD', column 'False Negatives'
Configuration: LeafcutterMD (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	21 False positives (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) count · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source LeafcutterMD on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell E6, row 'LeafcutterMD', column 'False Positives'
Configuration: LeafcutterMD (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	-1 MCC (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source LeafcutterMD on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell J6, row 'LeafcutterMD', column 'MCC'
Configuration: LeafcutterMD (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	0 Precision (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source LeafcutterMD on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell G6, row 'LeafcutterMD', column 'Precision'
Configuration: LeafcutterMD (Silverstein et al. 2026)	Protocol: Recovery of known pathogenic splicing events and outlier calls in true-negative samples, patient RNA-seq (Silverstein et al. Table S9, splicing tools) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	0 Recall (positives per known event or gene, negatives per true-negative sample; aggregated across the tissue sets each tool was run on (Table S9)) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source LeafcutterMD on the NIH neuromuscular diagnosed RNA-seq truth set (splicing) rna-splicing-20261011-silverstein2026-protocol-splicing Aggregation: Not reported Benchmarking RNA-seq Tools for Real-World Diagnostic Applications (Silverstein et al., medRxiv 2026); Silverstein et al. 2026 Supplemental Tables S1-S10 (medRxiv media-3.xlsx) · Table S9 (sheet 'Table S9') cell H6, row 'LeafcutterMD', column 'Recall'

Source checking is not independent reproduction. Release 2026-10-22-1b7cf027e4f6.

Methods and evaluation design

Procedure, tasks and evaluated configurations

Recorded evaluations

Each evaluation records what was tested and under which conditions.

Baseline coverage

Reference methods help show what a model adds beyond simple controls. We track a null control and a conventional method for each protocol.

0 of 2 active baseline roles have published Rewire measurements in this release. Measurements on a selected protocol do not establish coverage of an entire suite.

No execution recipe linked to this protocol. Recipe availability does not establish a completed evaluation.

External evaluations: 5

Literature evidence is not a Rewire measurement. Executed but unpublished runs and private review status are not included.

Null control

Proposed control: requires review

Select a task-valid null control after reviewing inputs and metric

Protocol-specific applicability, permitted inputs, access, split, evaluator and execution requirements need review before implementation or execution.

This is a suggested selection rule, not a validated method or a measured score.

Conventional reference

Proposed control: requires review

Select an upstream conventional reference after reviewing the full protocol

Protocol-specific applicability, permitted inputs, access, split, evaluator and execution requirements need review before implementation or execution.

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2 source records and release history

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)

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Stable ID: rna-splicing-20261011-silverstein2026-protocol-splicing

areas: rna-transcriptomes
contexts: clinical_research
protocol: Run FRASER and LeafcutterMD (one versus all, by tissue cohort: muscle, fibroblast, cycloheximide-treated fibroblast), FRASER2.0 (muscle only) and rMATS-turbo and MINTIE (each patient against ten GTEx tissue-matched controls; muscle and fibroblast only), all at FDR 5%. A true positive is an exact coordinate match to the validated splice event; a true-negative sample is correct only if no outlier is reported. Table S9 aggregates true positives and negatives across tissue types and derives accuracy, precision, recall, F1 and MCC.
version: Table S9 (medRxiv v1 supplement)
source locator: Table S9 rows 2 to 6; Methods 'One vs All Splice Tools', 'Case vs Control Splice Tools', 'True Positive and Negative Detection' and 'Performance Metrics'
limitations: Denominators differ by tool: rMATS and MINTIE 23 positives (muscle and fibroblast), FRASER 68 and LeafcutterMD 67 (three tissue sets), FRASER2.0 16 (muscle only).; Splicing true positives require at least 3 split reads supporting the novel junction in manual IGV review, so events invisible in the RNA-seq data are not counted.; The authors did not develop rMATS-turbo, MINTIE, FRASER, LeafcutterMD, OUTRIDER or the DROP MAE module. ANEVA-h is the authors' own method: two of its authors (K. R. Ganapathy and S. Silverstein) are the first and third authors of reference 36, the ANEVA-h preprint, so the ANEVA-H row is recorded as author_reported.; Positives are counted per known event or gene, negatives per true-negative sample: a negative sample with any reported outlier counts as one false positive. Accuracy, precision and MCC therefore mix event-level and sample-level units (Methods 'True Positive and Negative Detection').; Variants with clinically interpretable mechanisms were selected; the authors state this favours clearly defined events and likely underestimates tool sensitivity, and that binary outlier scoring of negatives may overestimate false-positive rates and lowers MCC (Discussion, limitations paragraph).; Retrospective study of a single centre's neuromuscular cohort; no confidence intervals are printed.

Related records

uses data: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)
assessment: FRASER on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
assessment: FRASER2.0 on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
assessment: LeafcutterMD on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
assessment: MINTIE on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
assessment: rMATS on the NIH neuromuscular diagnosed RNA-seq truth set (splicing)
assessed by: Select a patient-RNA splicing-variant validation workflow

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