Evaluation

NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022)

Published somatic caller comparison; transcribed, not reproduced.

Evaluation results

1 evaluation · 48 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: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	62.9% F1 (SNVs, average over the 47 purity-coverage pairs as printed) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'Average', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	43.4% F1 (SNVs, tumour SPP_100x_10%T with normal SPP_100x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_100x_10%T vs SPP_100x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	96.5% F1 (SNVs, tumour SPP_100x_100%T with normal SPP_100x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_100x_100%T vs SPP_100x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	74.4% F1 (SNVs, tumour SPP_100x_20%T with normal SPP_100x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_100x_20%T vs SPP_100x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	19.2% F1 (SNVs, tumour SPP_100x_5%T with normal SPP_100x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_100x_5%T vs SPP_100x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	91.3% F1 (SNVs, tumour SPP_100x_50%T with normal SPP_100x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_100x_50%T vs SPP_100x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	94.5% F1 (SNVs, tumour SPP_100x_75%T with normal SPP_100x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_100x_75%T vs SPP_100x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	1.2% F1 (SNVs, tumour SPP_10x_10%T with normal SPP_10x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_10x_10%T vs SPP_10x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	57.2% F1 (SNVs, tumour SPP_10x_100%T with normal SPP_10x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_10x_100%T vs SPP_10x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	5.9% F1 (SNVs, tumour SPP_10x_20%T with normal SPP_10x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_10x_20%T vs SPP_10x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	0.2% F1 (SNVs, tumour SPP_10x_5%T with normal SPP_10x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_10x_5%T vs SPP_10x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	31.6% F1 (SNVs, tumour SPP_10x_50%T with normal SPP_10x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_10x_50%T vs SPP_10x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	48.6% F1 (SNVs, tumour SPP_10x_75%T with normal SPP_10x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_10x_75%T vs SPP_10x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	66.7% F1 (SNVs, tumour SPP_200x_10%T with normal SPP_200x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_200x_10%T vs SPP_200x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	97.7% F1 (SNVs, tumour SPP_200x_100%T with normal SPP_200x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_200x_100%T vs SPP_200x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	84% F1 (SNVs, tumour SPP_200x_20%T with normal SPP_200x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_200x_20%T vs SPP_200x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	34.3% F1 (SNVs, tumour SPP_200x_5%T with normal SPP_200x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_200x_5%T vs SPP_200x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	93.5% F1 (SNVs, tumour SPP_200x_50%T with normal SPP_200x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_200x_50%T vs SPP_200x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	96% F1 (SNVs, tumour SPP_200x_75%T with normal SPP_200x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_200x_75%T vs SPP_200x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	70.4% F1 (SNVs, tumour SPP_300x_10%T with normal SPP_300x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_300x_10%T vs SPP_300x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	98.1% F1 (SNVs, tumour SPP_300x_100%T with normal SPP_300x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_300x_100%T vs SPP_300x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	86.4% F1 (SNVs, tumour SPP_300x_20%T with normal SPP_300x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_300x_20%T vs SPP_300x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	43.2% F1 (SNVs, tumour SPP_300x_5%T with normal SPP_300x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_300x_5%T vs SPP_300x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	95% F1 (SNVs, tumour SPP_300x_50%T with normal SPP_300x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_300x_50%T vs SPP_300x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'
Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3) Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	96.9% F1 (SNVs, tumour SPP_300x_75%T with normal SPP_300x_100%N) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source NeuSomatic-S SEQC-WGS-GT-50 on SEQC2 tumour-normal titration, SNVs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv Aggregation: Not reported Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample; Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Additional file 2 Table S3, SNVs section, row 'SPP_300x_75%T vs SPP_300x_100%N', column 'NeuSomatic-S SEQC-WGS-GT-50'

Source checking is not independent reproduction. Release 2026-10-10-cbb3da59bc08.

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Readiness checks, gaps and artifacts

Release 2026-10-10-cbb3da59bc08 · Evidence verified: Not verified

Evidence incomplete

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Investigate discrepancies

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Missing or unresolved evidence

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Sample annotations are recorded: not yet verified
Dependence between samples is assessed: not yet verified

Verified: Not verified

Evidence incomplete

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Verified: Not verified

Evidence incomplete

Validate independently

Separate data and exposure records support an independent test.

Missing or unresolved evidence

No verified artifact manifest is linked to this exact record.
File checksums match the recorded files: not yet verified
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Verified: Not verified

Readiness describes the evidence in this release. Availability on your computer is checked separately when an investigation runs. Existing data exposure can prevent independent validation even when files are available.

Artifacts and reproduction

No verified artifact manifest is connected to this record yet. The gaps above identify what is needed before analysis can begin.

Evaluation procedure

SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)

Configuration: NeuSomatic-S SEQC-WGS-GT-50 model (Sahraeian et al. 2022)
Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)
Dataset: SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS

origin: Author-reported evaluation
configuration: Primary source as retrieved 2026-10-10
protocol id: somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv
dataset version: Not reported
split: Held-out 50% of the high-confidence genome
population: 42 purity-coverage pairs (5, 10, 20, 50, 75 and 100% tumour at 10x, 30x, 50x, 80x, 100x, 200x and 300x, pure normal) and 5 pairs at 80x with a 95% pure normal (10-100% tumour)
inputs: Tumour and matched normal WGS BAM files (BWA-MEM)
adaptation: Not reported
metric implementation: F1 (%) of PASS calls against the SEQC2 truth set, exact match
aggregation: Per purity-coverage pair, plus the printed average over 47
budget: Not reported

Metadata review: source checked. Unreported conditions prevent automatic comparisons.

Reproduction

Split: Held-out 50% of the high-confidence genome
Adaptation: Not reported
Scoring implementation: F1 (%) of PASS calls against the SEQC2 truth set, exact match

No execution recipe has been verified for this exact configuration and evaluation. A benchmark's general instructions may use different inputs, splits or model settings.

Reproducing this published result requires matching its model configuration, data, split and scorer. Source checking or a successful smoke test does not establish score reproduction.

Evidence

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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.

38 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-10-10-cbb3da59bc08
Property and statement	Original source and location	Review and provenance
Comparison: adaptation Not reported Context-only references	Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample Original source ↗ Additional file 2 Table S3, column 'NeuSomatic-S SEQC-WGS-GT-50', SNVs section Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Genome Biology 23:12, published 2022-01-07; PMC8740374 full-text XML Retrieved: 2026-10-10T06:04:11Z	missing or unspecified No individual claim review recorded author reported Audit details Field: `attributes.comparison.adaptation` Source artifact SHA-256: `78dbe540c2558585bc7537e0e6563706fdaa329b2708e5dc82b77074379b4a6e` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison: adaptation Not reported Context-only references	Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) Original source ↗ Additional file 2 Table S3, column 'NeuSomatic-S SEQC-WGS-GT-50', SNVs section Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 13059_2021_2592_MOESM2_ESM.pdf Retrieved: 2026-10-10T06:04:16Z	missing or unspecified No individual claim review recorded author reported Audit details Field: `attributes.comparison.adaptation` Source artifact SHA-256: `10a109d80f6f49446ea84bd9f7f6b31c20d634516c8666376ac8a8f51f3236d0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison: aggregation Per purity-coverage pair, plus the printed average over 47 Context-only references	Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample Original source ↗ Additional file 2 Table S3, column 'NeuSomatic-S SEQC-WGS-GT-50', SNVs section Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Genome Biology 23:12, published 2022-01-07; PMC8740374 full-text XML Retrieved: 2026-10-10T06:04:11Z	not individually reviewed No individual claim review recorded author reported Audit details Field: `attributes.comparison.aggregation` Source artifact SHA-256: `78dbe540c2558585bc7537e0e6563706fdaa329b2708e5dc82b77074379b4a6e` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison: aggregation Per purity-coverage pair, plus the printed average over 47 Context-only references	Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) Original source ↗ Additional file 2 Table S3, column 'NeuSomatic-S SEQC-WGS-GT-50', SNVs section Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 13059_2021_2592_MOESM2_ESM.pdf Retrieved: 2026-10-10T06:04:16Z	not individually reviewed No individual claim review recorded author reported Audit details Field: `attributes.comparison.aggregation` Source artifact SHA-256: `10a109d80f6f49446ea84bd9f7f6b31c20d634516c8666376ac8a8f51f3236d0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison: budget Not reported Context-only references	Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample Original source ↗ Additional file 2 Table S3, column 'NeuSomatic-S SEQC-WGS-GT-50', SNVs section Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Genome Biology 23:12, published 2022-01-07; PMC8740374 full-text XML Retrieved: 2026-10-10T06:04:11Z	missing or unspecified No individual claim review recorded author reported Audit details Field: `attributes.comparison.budget` Source artifact SHA-256: `78dbe540c2558585bc7537e0e6563706fdaa329b2708e5dc82b77074379b4a6e` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison: budget Not reported Context-only references	Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) Original source ↗ Additional file 2 Table S3, column 'NeuSomatic-S SEQC-WGS-GT-50', SNVs section Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 13059_2021_2592_MOESM2_ESM.pdf Retrieved: 2026-10-10T06:04:16Z	missing or unspecified No individual claim review recorded author reported Audit details Field: `attributes.comparison.budget` Source artifact SHA-256: `10a109d80f6f49446ea84bd9f7f6b31c20d634516c8666376ac8a8f51f3236d0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison: dataset version Not reported Context-only references	Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample Original source ↗ Additional file 2 Table S3, column 'NeuSomatic-S SEQC-WGS-GT-50', SNVs section Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Genome Biology 23:12, published 2022-01-07; PMC8740374 full-text XML Retrieved: 2026-10-10T06:04:11Z	missing or unspecified No individual claim review recorded author reported Audit details Field: `attributes.comparison.dataset_version` Source artifact SHA-256: `78dbe540c2558585bc7537e0e6563706fdaa329b2708e5dc82b77074379b4a6e` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison: dataset version Not reported Context-only references	Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) Original source ↗ Additional file 2 Table S3, column 'NeuSomatic-S SEQC-WGS-GT-50', SNVs section Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 13059_2021_2592_MOESM2_ESM.pdf Retrieved: 2026-10-10T06:04:16Z	missing or unspecified No individual claim review recorded author reported Audit details Field: `attributes.comparison.dataset_version` Source artifact SHA-256: `10a109d80f6f49446ea84bd9f7f6b31c20d634516c8666376ac8a8f51f3236d0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison: inputs Tumour and matched normal WGS BAM files (BWA-MEM) Context-only references	Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample Original source ↗ Additional file 2 Table S3, column 'NeuSomatic-S SEQC-WGS-GT-50', SNVs section Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Genome Biology 23:12, published 2022-01-07; PMC8740374 full-text XML Retrieved: 2026-10-10T06:04:11Z	not individually reviewed No individual claim review recorded author reported Audit details Field: `attributes.comparison.inputs` Source artifact SHA-256: `78dbe540c2558585bc7537e0e6563706fdaa329b2708e5dc82b77074379b4a6e` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison: inputs Tumour and matched normal WGS BAM files (BWA-MEM) Context-only references	Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) Original source ↗ Additional file 2 Table S3, column 'NeuSomatic-S SEQC-WGS-GT-50', SNVs section Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 13059_2021_2592_MOESM2_ESM.pdf Retrieved: 2026-10-10T06:04:16Z	not individually reviewed No individual claim review recorded author reported Audit details Field: `attributes.comparison.inputs` Source artifact SHA-256: `10a109d80f6f49446ea84bd9f7f6b31c20d634516c8666376ac8a8f51f3236d0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

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Sources and history

Release 2026-10-10-cbb3da59bc08 · Record review: source checked

2 source records and release history

Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample · Original source · Genome Biology 23:12, published 2022-01-07; PMC8740374 full-text XML
Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf) · Original source · 13059_2021_2592_MOESM2_ESM.pdf

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Technical metadata and extraction receipts

Stable ID: somatic-neusomatic-20261010-eval-sahraeian2022-neusomatic-s-seqc-wgs-gt-50-titration-snv

areas: dna-genomes
contexts: clinical_research
origin: author_reported
protocol: somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv
version: Primary source as retrieved 2026-10-10
comparison: protocol id: somatic-neusomatic-20261010-protocol-sahraeian2022-titration-snv; dataset version: Not reported; split: Held-out 50% of the high-confidence genome; population: 42 purity-coverage pairs (5, 10, 20, 50, 75 and 100% tumour at 10x, 30x, 50x, 80x, 100x, 200x and 300x, pure normal) and 5 pairs at 80x with a 95% pure normal (10-100% tumour); inputs: Tumour and matched normal WGS BAM files (BWA-MEM); adaptation: Not reported; metric implementation: F1 (%) of PASS calls against the SEQC2 truth set, exact match; aggregation: Per purity-coverage pair, plus the printed average over 47; budget: Not reported
source locator: Additional file 2 Table S3, column 'NeuSomatic-S SEQC-WGS-GT-50', SNVs section
missing metadata: comparison.dataset version: reason: unreported
evidence overlap: Trained on the test cell line: the SEQC-WGS-GT-50 model was trained on HCC1395/HCC1395BL WGS replicates with the SEQC2 truth set in the other 50% of the high-confidence genome (Methods). The scored region is held out, but the cell line, truth set and possibly the replicate pairs are the same. The titration libraries are not described as training data.

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