Protocol

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)

Gene-level ANEVA-H and variant-level DROP MAE, scored on known monoallelic loss-of-function events and on true-negative samples, muscle with matched DNA variant calls.

2 evaluations · 18 results

Overview

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2 recorded evaluations, 18 metric rows. A comparison chart has not yet been validated for these results. The table retains the individual findings and their sources.

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Results

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All evaluations

2 evaluations · 18 results. Different protocols are not a single leaderboard.

Protocol: 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) · 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))
ANEVA-H (Silverstein et al. 2026)Evaluation	0.462 (best in column)	0.588 (best in column)	3 (best in column)	4 (best in column)	-0.184 (best in column)	0.556 (best in column)	0.625 (best in column)	1 (best in column)	5 (best in column)
MAE (Silverstein et al. 2026)Evaluation	0.385	0.556	3 (best in column)	5	-0.433	0.5	0.625 (best in column)	0	5 (best in column)

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Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: ANEVA-H (Silverstein et al. 2026)	Protocol: 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) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	0.462 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	Author-reported evaluation · Source checked Methods, coverage and source ANEVA-H on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance) rna-splicing-20261011-silverstein2026-protocol-allelic-imbalance 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 F9, row 'ANEVA-H', column 'Accuracy'
Configuration: ANEVA-H (Silverstein et al. 2026)	Protocol: 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) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	0.588 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	Author-reported evaluation · Source checked Methods, coverage and source ANEVA-H on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance) rna-splicing-20261011-silverstein2026-protocol-allelic-imbalance 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 I9, row 'ANEVA-H', column 'F1'
Configuration: ANEVA-H (Silverstein et al. 2026)	Protocol: 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) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	3 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	Author-reported evaluation · Source checked Methods, coverage and source ANEVA-H on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance) rna-splicing-20261011-silverstein2026-protocol-allelic-imbalance 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 C9, row 'ANEVA-H', column 'False Negatives'
Configuration: ANEVA-H (Silverstein et al. 2026)	Protocol: 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) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	4 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	Author-reported evaluation · Source checked Methods, coverage and source ANEVA-H on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance) rna-splicing-20261011-silverstein2026-protocol-allelic-imbalance 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 E9, row 'ANEVA-H', column 'False Positives'
Configuration: ANEVA-H (Silverstein et al. 2026)	Protocol: 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) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	-0.184 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	Author-reported evaluation · Source checked Methods, coverage and source ANEVA-H on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance) rna-splicing-20261011-silverstein2026-protocol-allelic-imbalance 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 J9, row 'ANEVA-H', column 'MCC'
Configuration: ANEVA-H (Silverstein et al. 2026)	Protocol: 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) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	0.556 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	Author-reported evaluation · Source checked Methods, coverage and source ANEVA-H on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance) rna-splicing-20261011-silverstein2026-protocol-allelic-imbalance 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 G9, row 'ANEVA-H', column 'Precision'
Configuration: ANEVA-H (Silverstein et al. 2026)	Protocol: 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) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	0.625 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	Author-reported evaluation · Source checked Methods, coverage and source ANEVA-H on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance) rna-splicing-20261011-silverstein2026-protocol-allelic-imbalance 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 H9, row 'ANEVA-H', column 'Recall'
Configuration: ANEVA-H (Silverstein et al. 2026)	Protocol: 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) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	1 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	Author-reported evaluation · Source checked Methods, coverage and source ANEVA-H on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance) rna-splicing-20261011-silverstein2026-protocol-allelic-imbalance 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 D9, row 'ANEVA-H', column 'True Negatives'
Configuration: ANEVA-H (Silverstein et al. 2026)	Protocol: 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) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	5 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	Author-reported evaluation · Source checked Methods, coverage and source ANEVA-H on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance) rna-splicing-20261011-silverstein2026-protocol-allelic-imbalance 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 B9, row 'ANEVA-H', column 'True Positives'
Configuration: MAE (Silverstein et al. 2026)	Protocol: 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) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	0.385 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 MAE on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance) rna-splicing-20261011-silverstein2026-protocol-allelic-imbalance 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 F10, row 'MAE', column 'Accuracy'
Configuration: MAE (Silverstein et al. 2026)	Protocol: 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) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	0.556 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 MAE on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance) rna-splicing-20261011-silverstein2026-protocol-allelic-imbalance 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 I10, row 'MAE', column 'F1'
Configuration: MAE (Silverstein et al. 2026)	Protocol: 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) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	3 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 MAE on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance) rna-splicing-20261011-silverstein2026-protocol-allelic-imbalance 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 C10, row 'MAE', column 'False Negatives'
Configuration: MAE (Silverstein et al. 2026)	Protocol: 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) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	5 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 MAE on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance) rna-splicing-20261011-silverstein2026-protocol-allelic-imbalance 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 E10, row 'MAE', column 'False Positives'
Configuration: MAE (Silverstein et al. 2026)	Protocol: 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) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	-0.433 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 MAE on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance) rna-splicing-20261011-silverstein2026-protocol-allelic-imbalance 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 J10, row 'MAE', column 'MCC'
Configuration: MAE (Silverstein et al. 2026)	Protocol: 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) 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 MAE on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance) rna-splicing-20261011-silverstein2026-protocol-allelic-imbalance 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 G10, row 'MAE', column 'Precision'
Configuration: MAE (Silverstein et al. 2026)	Protocol: 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) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	0.625 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 MAE on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance) rna-splicing-20261011-silverstein2026-protocol-allelic-imbalance 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 H10, row 'MAE', column 'Recall'
Configuration: MAE (Silverstein et al. 2026)	Protocol: 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) 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 MAE on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance) rna-splicing-20261011-silverstein2026-protocol-allelic-imbalance 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 D10, row 'MAE', column 'True Negatives'
Configuration: MAE (Silverstein et al. 2026)	Protocol: 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) Dataset: NIH neuromuscular diagnosed RNA-seq truth set (Silverstein et al. 2026)	5 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 MAE on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance) rna-splicing-20261011-silverstein2026-protocol-allelic-imbalance 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 B10, row 'MAE', column 'True Positives'

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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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Technical metadata and extraction receipts

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

areas: rna-transcriptomes
contexts: clinical_research
protocol: Restrict to 59 muscle samples with exome or genome VCFs. Run the DROP MAE module (v1.4.0, allelic threshold 70) and ANEVA-h (phASER gene-level ASE, ANEVA-DOT test, GTEx v8 muscle reference, BH FDR < 0.05). A true positive requires the correct rare variant (MAE) or gene (ANEVA-H); a true-negative sample is correct only if no outlier is reported.
version: Table S9 (medRxiv v1 supplement)
source locator: Table S9 rows 9 and 10; Methods 'Allelic Imbalance Tools' and 'True Positive and Negative Detection'; Table S8
limitations: Eight true-positive genes and five or six true-negative samples; MAE reports TN 0 and FP 5, ANEVA-H TN 1 and FP 4.; 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: ANEVA-H on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance)
assessment: MAE on the NIH neuromuscular diagnosed RNA-seq truth set (allelic-imbalance)
assessed by: Select a patient-RNA splicing-variant validation workflow

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