Configuration

Alamut Consensus 3/4 (Riepe et al. 2021)

Alamut Consensus 3/4 (Riepe et al. 2021) as run in the cited comparison.

Overview

Alamut Consensus 3/4 (Riepe et al. 2021) as run in the cited comparison.

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

Evaluations and results

3 evaluations · 33 results. Different protocols are not a single leaderboard.

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: ABCA4 deep-intronic variants: classification against mini- or midigene splicing results (Riepe et al. Table 4) Dataset: ABCA4 deep-intronic variants (Riepe et al. benchmark set)	70% accuracy percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on ABCA4 deep-intronic variants splicing-follow-up-20261009-protocol-riepe2021-abca4-di Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 4 row 'Alamut Consensus 3/4', column 'Accuracy (%)'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: ABCA4 deep-intronic variants: classification against mini- or midigene splicing results (Riepe et al. Table 4) Dataset: ABCA4 deep-intronic variants (Riepe et al. benchmark set)	10 false-negative-count count · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on ABCA4 deep-intronic variants splicing-follow-up-20261009-protocol-riepe2021-abca4-di Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 4 row 'Alamut Consensus 3/4', column 'FN'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: ABCA4 deep-intronic variants: classification against mini- or midigene splicing results (Riepe et al. Table 4) Dataset: ABCA4 deep-intronic variants (Riepe et al. benchmark set)	14 false-positive-count count · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on ABCA4 deep-intronic variants splicing-follow-up-20261009-protocol-riepe2021-abca4-di Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 4 row 'Alamut Consensus 3/4', column 'FP'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: ABCA4 deep-intronic variants: classification against mini- or midigene splicing results (Riepe et al. Table 4) Dataset: ABCA4 deep-intronic variants (Riepe et al. benchmark set)	0.28 matthews-correlation-coefficient unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on ABCA4 deep-intronic variants splicing-follow-up-20261009-protocol-riepe2021-abca4-di Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 4 row 'Alamut Consensus 3/4', column 'MCC'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: ABCA4 deep-intronic variants: classification against mini- or midigene splicing results (Riepe et al. Table 4) Dataset: ABCA4 deep-intronic variants (Riepe et al. benchmark set)	0 count count · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on ABCA4 deep-intronic variants splicing-follow-up-20261009-protocol-riepe2021-abca4-di Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 4 row 'Alamut Consensus 3/4', column 'Missing values'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: ABCA4 deep-intronic variants: classification against mini- or midigene splicing results (Riepe et al. Table 4) Dataset: ABCA4 deep-intronic variants (Riepe et al. benchmark set)	82% negative-predictive-value percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on ABCA4 deep-intronic variants splicing-follow-up-20261009-protocol-riepe2021-abca4-di Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 4 row 'Alamut Consensus 3/4', column 'NPV (%)'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: ABCA4 deep-intronic variants: classification against mini- or midigene splicing results (Riepe et al. Table 4) Dataset: ABCA4 deep-intronic variants (Riepe et al. benchmark set)	44% precision percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on ABCA4 deep-intronic variants splicing-follow-up-20261009-protocol-riepe2021-abca4-di Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 4 row 'Alamut Consensus 3/4', column 'PPV (%)'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: ABCA4 deep-intronic variants: classification against mini- or midigene splicing results (Riepe et al. Table 4) Dataset: ABCA4 deep-intronic variants (Riepe et al. benchmark set)	52% recall percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on ABCA4 deep-intronic variants splicing-follow-up-20261009-protocol-riepe2021-abca4-di Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 4 row 'Alamut Consensus 3/4', column 'Sensitivity (%)'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: ABCA4 deep-intronic variants: classification against mini- or midigene splicing results (Riepe et al. Table 4) Dataset: ABCA4 deep-intronic variants (Riepe et al. benchmark set)	77% specificity percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on ABCA4 deep-intronic variants splicing-follow-up-20261009-protocol-riepe2021-abca4-di Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 4 row 'Alamut Consensus 3/4', column 'Specificity (%)'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: ABCA4 deep-intronic variants: classification against mini- or midigene splicing results (Riepe et al. Table 4) Dataset: ABCA4 deep-intronic variants (Riepe et al. benchmark set)	46 true-negative-count count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on ABCA4 deep-intronic variants splicing-follow-up-20261009-protocol-riepe2021-abca4-di Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 4 row 'Alamut Consensus 3/4', column 'TN'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: ABCA4 deep-intronic variants: classification against mini- or midigene splicing results (Riepe et al. Table 4) Dataset: ABCA4 deep-intronic variants (Riepe et al. benchmark set)	11 true-positive-count count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on ABCA4 deep-intronic variants splicing-follow-up-20261009-protocol-riepe2021-abca4-di Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 4 row 'Alamut Consensus 3/4', column 'TP'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: ABCA4 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 3) Dataset: ABCA4 noncanonical splice-site variants (Riepe et al. benchmark set)	56% accuracy percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on ABCA4 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-abca4-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 3 row 'Alamut Consensus 3/4', column 'Accuracy (%)'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: ABCA4 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 3) Dataset: ABCA4 noncanonical splice-site variants (Riepe et al. benchmark set)	29 false-negative-count count · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on ABCA4 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-abca4-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 3 row 'Alamut Consensus 3/4', column 'FN'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: ABCA4 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 3) Dataset: ABCA4 noncanonical splice-site variants (Riepe et al. benchmark set)	2 false-positive-count count · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on ABCA4 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-abca4-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 3 row 'Alamut Consensus 3/4', column 'FP'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: ABCA4 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 3) Dataset: ABCA4 noncanonical splice-site variants (Riepe et al. benchmark set)	0.16 matthews-correlation-coefficient unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on ABCA4 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-abca4-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 3 row 'Alamut Consensus 3/4', column 'MCC'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: ABCA4 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 3) Dataset: ABCA4 noncanonical splice-site variants (Riepe et al. benchmark set)	0 count count · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on ABCA4 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-abca4-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 3 row 'Alamut Consensus 3/4', column 'Missing values'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: ABCA4 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 3) Dataset: ABCA4 noncanonical splice-site variants (Riepe et al. benchmark set)	15% negative-predictive-value percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on ABCA4 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-abca4-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 3 row 'Alamut Consensus 3/4', column 'NPV (%)'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: ABCA4 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 3) Dataset: ABCA4 noncanonical splice-site variants (Riepe et al. benchmark set)	95% precision percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on ABCA4 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-abca4-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 3 row 'Alamut Consensus 3/4', column 'PPV (%)'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: ABCA4 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 3) Dataset: ABCA4 noncanonical splice-site variants (Riepe et al. benchmark set)	55% recall percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on ABCA4 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-abca4-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 3 row 'Alamut Consensus 3/4', column 'Sensitivity (%)'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: ABCA4 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 3) Dataset: ABCA4 noncanonical splice-site variants (Riepe et al. benchmark set)	71% specificity percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on ABCA4 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-abca4-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 3 row 'Alamut Consensus 3/4', column 'Specificity (%)'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: ABCA4 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 3) Dataset: ABCA4 noncanonical splice-site variants (Riepe et al. benchmark set)	5 true-negative-count count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on ABCA4 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-abca4-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 3 row 'Alamut Consensus 3/4', column 'TN'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: ABCA4 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 3) Dataset: ABCA4 noncanonical splice-site variants (Riepe et al. benchmark set)	35 true-positive-count count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on ABCA4 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-abca4-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 3 row 'Alamut Consensus 3/4', column 'TP'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: MYBPC3 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 5) Dataset: MYBPC3 noncanonical splice-site variants (Riepe et al. benchmark set)	75% accuracy percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on MYBPC3 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-mybpc3-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 5 row 'Alamut Consensus 3/4', column 'Accuracy (%)'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: MYBPC3 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 5) Dataset: MYBPC3 noncanonical splice-site variants (Riepe et al. benchmark set)	11 false-negative-count count · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on MYBPC3 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-mybpc3-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 5 row 'Alamut Consensus 3/4', column 'FN'
Configuration: Alamut Consensus 3/4 (Riepe et al. 2021)	Protocol: MYBPC3 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 5) Dataset: MYBPC3 noncanonical splice-site variants (Riepe et al. benchmark set)	4 false-positive-count count · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Alamut Consensus 3/4 on MYBPC3 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-mybpc3-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 5 row 'Alamut Consensus 3/4', column 'FP'

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Benchmarking deep learning splice prediction tools using functional splice assays · Original source · Human Mutation 42(7):799, published online 2021-05-20; PMC8360004 full-text XML

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Technical metadata and extraction receipts

Stable ID: splicing-follow-up-20261009-config-riepe2021-alamut-consensus

areas: dna-genomes
contexts: research
method types: conventional_pipeline
reported name: Alamut Consensus 3/4 (Riepe et al. 2021)
foundation model eligible: false
version: 3 of 4 tools in Alamut Visual 2.13
parameters: Classification cutoff per data set; Table 2 prints the ROC-optimal thresholds, and Tables 3-5 do not restate which cutoff was applied
source locator: Methods 'In silico splice prediction tools'; Table 2

Related records

configuration of: Alamut consensus 3/4
system: Alamut Consensus 3/4 on ABCA4 deep-intronic variants
system: Alamut Consensus 3/4 on ABCA4 noncanonical splice-site variants
system: Alamut Consensus 3/4 on MYBPC3 noncanonical splice-site variants

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