Evaluation

NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (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-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	54.6% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'Average', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	39.6% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_100x_10%T vs SPP_100x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	84.7% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_100x_100%T vs SPP_100x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	64.2% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_100x_20%T vs SPP_100x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	18.8% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_100x_5%T vs SPP_100x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	81.3% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_100x_50%T vs SPP_100x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	84.9% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_100x_75%T vs SPP_100x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	2.2% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_10x_10%T vs SPP_10x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	38% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_10x_100%T vs SPP_10x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	6.7% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_10x_20%T vs SPP_10x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_10x_5%T vs SPP_10x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	25.3% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_10x_50%T vs SPP_10x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	37.8% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_10x_75%T vs SPP_10x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	50.2% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_200x_10%T vs SPP_200x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	85% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_200x_100%T vs SPP_200x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	68.9% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_200x_20%T vs SPP_200x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	27.2% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_200x_5%T vs SPP_200x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	81.8% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_200x_50%T vs SPP_200x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	84.7% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_200x_75%T vs SPP_200x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	51.7% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_300x_10%T vs SPP_300x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	84.3% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_300x_100%T vs SPP_300x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	71.4% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_300x_20%T vs SPP_300x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	27.1% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_300x_5%T vs SPP_300x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	81.9% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_300x_50%T vs SPP_300x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'
Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)	Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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	85.5% F1 (INDELs, 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-GT50-SpikeWGS10 on SEQC2 tumour-normal titration, INDELs (Sahraeian et al. 2022) somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel 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, INDELs section, row 'SPP_300x_75%T vs SPP_300x_100%N', column 'NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10'

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

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

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Evaluation procedure

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

Configuration: NeuSomatic-S SEQC-WGS-GT50-SpikeWGS10 model (Sahraeian et al. 2022)
Protocol: SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel 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-indel
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

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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

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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-GT50-SpikeWGS10', INDELs 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-GT50-SpikeWGS10', INDELs 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-GT50-SpikeWGS10', INDELs 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-GT50-SpikeWGS10', INDELs 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-GT50-SpikeWGS10', INDELs 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-GT50-SpikeWGS10', INDELs 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-GT50-SpikeWGS10', INDELs 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-GT50-SpikeWGS10', INDELs 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-GT50-SpikeWGS10', INDELs 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-GT50-SpikeWGS10', INDELs 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-gt50-spikewgs10-titration-indel

areas: dna-genomes
contexts: clinical_research
origin: author_reported
protocol: somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel
version: Primary source as retrieved 2026-10-10
comparison: protocol id: somatic-neusomatic-20261010-protocol-sahraeian2022-titration-indel; 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-GT50-SpikeWGS10', INDELs section
missing metadata: comparison.dataset version: reason: unreported
evidence overlap: Trained on the test cell line: the SEQC-WGS-GT50-SpikeWGS10 model combines the SEQC-WGS-GT-50 training data (HCC1395 truth in the other half of the genome) with 10% of the SEQC-WGS-Spike data (HCC1395BL spike-ins across the genome) (Methods), and was chosen as the default model from these same evaluations. The titration libraries are not described as training data.

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