Contested result

Mutect2 and Octopus: Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample reverses the order

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Ordering reversal
Different tasks across studies

Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample ranks Octopus above Mutect2 in 154 of 154 comparisons; other studies rank Mutect2 above Octopus in 14 of 14.

An automatic lead, not a finding of error. The results below are shown as published, everywhere on the site, until a reviewer decides otherwise.

Which studies agree

ClairS-TO ranks Mutect2 above Octopus in all 14 of its comparisons. Achieving robust somatic mutation… ranks Octopus above Mutect2 in all 154 of its comparisons.

Rank Mutect2 higher (1): ClairS-TO
Rank Octopus higher (1): Achieving robust somatic mutation… (disagrees)
Mixed (0): none

Each paper sits where at least two thirds of its comparisons point; Mixed means neither model reaches two thirds. The larger, outlined dot is the paper that disagrees.

Mutect2 higherTieOctopus higherNumbers are counts of comparisons; the outlined bar is the paper that disagrees.

Papers compared (2)
Paper	Role	Ranks Mutect2 higher	Ranks Octopus higher	Ties
Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample and 1 more record · DOI	Disagrees	0	154	0
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling and 2 more records · DOI	Agrees	14	0	0

Comparisons in Achieving robust somatic mutation… (154)

Protocol	Dataset	Metric	Mutect2	Octopus	Higher
SEQC2 HCC1395 library preparation and DNA input, indel F1 (Sahraeian et al. 2022 Table S4)	SEQC2 HCC1395/HCC1395BL library-preparation replicates (TruSeq-Nano and Nextera Flex, 1-100 ng DNA)	F1 (INDELs, average over the 6 library pairs as printed), percent	42.2	66.3	Octopus
SEQC2 HCC1395 library preparation and DNA input, indel F1 (Sahraeian et al. 2022 Table S4)	SEQC2 HCC1395/HCC1395BL library-preparation replicates (TruSeq-Nano and Nextera Flex, 1-100 ng DNA)	F1 (INDELs, tumour LBP_Nextera_T_100ng with normal LBP_Nextera_N_100ng), percent	61.3	80.1	Octopus
SEQC2 HCC1395 library preparation and DNA input, indel F1 (Sahraeian et al. 2022 Table S4)	SEQC2 HCC1395/HCC1395BL library-preparation replicates (TruSeq-Nano and Nextera Flex, 1-100 ng DNA)	F1 (INDELs, tumour LBP_Nextera_T_10ng with normal LBP_Nextera_N_10ng), percent	38.6	74.2	Octopus
SEQC2 HCC1395 library preparation and DNA input, indel F1 (Sahraeian et al. 2022 Table S4)	SEQC2 HCC1395/HCC1395BL library-preparation replicates (TruSeq-Nano and Nextera Flex, 1-100 ng DNA)	F1 (INDELs, tumour LBP_Nextera_T_1ng with normal LBP_Nextera_N_1ng), percent	37.4	63.1	Octopus
SEQC2 HCC1395 library preparation and DNA input, indel F1 (Sahraeian et al. 2022 Table S4)	SEQC2 HCC1395/HCC1395BL library-preparation replicates (TruSeq-Nano and Nextera Flex, 1-100 ng DNA)	F1 (INDELs, tumour LBP_TruSeq_T_100ng with normal LBP_TruSeq_N_100ng), percent	56.1	75	Octopus
SEQC2 HCC1395 library preparation and DNA input, indel F1 (Sahraeian et al. 2022 Table S4)	SEQC2 HCC1395/HCC1395BL library-preparation replicates (TruSeq-Nano and Nextera Flex, 1-100 ng DNA)	F1 (INDELs, tumour LBP_TruSeq_T_10ng with normal LBP_TruSeq_N_10ng), percent	52.3	68	Octopus
SEQC2 HCC1395 library preparation and DNA input, indel F1 (Sahraeian et al. 2022 Table S4)	SEQC2 HCC1395/HCC1395BL library-preparation replicates (TruSeq-Nano and Nextera Flex, 1-100 ng DNA)	F1 (INDELs, tumour LBP_TruSeq_T_1ng with normal LBP_TruSeq_N_1ng), percent	7.7	37.2	Octopus
SEQC2 HCC1395 library preparation and DNA input, SNV F1 (Sahraeian et al. 2022 Table S4)	SEQC2 HCC1395/HCC1395BL library-preparation replicates (TruSeq-Nano and Nextera Flex, 1-100 ng DNA)	F1 (SNVs, average over the 6 library pairs as printed), percent	72.7	84.9	Octopus
SEQC2 HCC1395 library preparation and DNA input, SNV F1 (Sahraeian et al. 2022 Table S4)	SEQC2 HCC1395/HCC1395BL library-preparation replicates (TruSeq-Nano and Nextera Flex, 1-100 ng DNA)	F1 (SNVs, tumour LBP_Nextera_T_100ng with normal LBP_Nextera_N_100ng), percent	89.8	94.8	Octopus
SEQC2 HCC1395 library preparation and DNA input, SNV F1 (Sahraeian et al. 2022 Table S4)	SEQC2 HCC1395/HCC1395BL library-preparation replicates (TruSeq-Nano and Nextera Flex, 1-100 ng DNA)	F1 (SNVs, tumour LBP_Nextera_T_10ng with normal LBP_Nextera_N_10ng), percent	89.7	94.9	Octopus
SEQC2 HCC1395 library preparation and DNA input, SNV F1 (Sahraeian et al. 2022 Table S4)	SEQC2 HCC1395/HCC1395BL library-preparation replicates (TruSeq-Nano and Nextera Flex, 1-100 ng DNA)	F1 (SNVs, tumour LBP_Nextera_T_1ng with normal LBP_Nextera_N_1ng), percent	86	91.8	Octopus
SEQC2 HCC1395 library preparation and DNA input, SNV F1 (Sahraeian et al. 2022 Table S4)	SEQC2 HCC1395/HCC1395BL library-preparation replicates (TruSeq-Nano and Nextera Flex, 1-100 ng DNA)	F1 (SNVs, tumour LBP_TruSeq_T_100ng with normal LBP_TruSeq_N_100ng), percent	86.5	92.8	Octopus
SEQC2 HCC1395 library preparation and DNA input, SNV F1 (Sahraeian et al. 2022 Table S4)	SEQC2 HCC1395/HCC1395BL library-preparation replicates (TruSeq-Nano and Nextera Flex, 1-100 ng DNA)	F1 (SNVs, tumour LBP_TruSeq_T_10ng with normal LBP_TruSeq_N_10ng), percent	80.3	84.6	Octopus
SEQC2 HCC1395 library preparation and DNA input, SNV F1 (Sahraeian et al. 2022 Table S4)	SEQC2 HCC1395/HCC1395BL library-preparation replicates (TruSeq-Nano and Nextera Flex, 1-100 ng DNA)	F1 (SNVs, tumour LBP_TruSeq_T_1ng with normal LBP_TruSeq_N_1ng), percent	3.7	50.5	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, average over the 47 purity-coverage pairs as printed), percent	44.5	59.2	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_100x_10%T with normal SPP_100x_100%N), percent	28.9	49.9	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_100x_100%T with normal SPP_100x_100%N), percent	73.3	85.6	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_100x_20%T with normal SPP_100x_100%N), percent	66.3	75	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_100x_5%T with normal SPP_100x_100%N), percent	14.3	26.3	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_100x_50%T with normal SPP_100x_100%N), percent	68.1	83	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_100x_75%T with normal SPP_100x_100%N), percent	73.3	84.8	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_10x_10%T with normal SPP_10x_100%N), percent	0.9	3.6	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_10x_100%T with normal SPP_10x_100%N), percent	13	58.8	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_10x_20%T with normal SPP_10x_100%N), percent	5.5	13.8	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_10x_5%T with normal SPP_10x_100%N), percent	0.6	1.2	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_10x_50%T with normal SPP_10x_100%N), percent	9.9	41.3	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_10x_75%T with normal SPP_10x_100%N), percent	14.1	53.6	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_200x_10%T with normal SPP_200x_100%N), percent	57.3	75.5	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_200x_100%T with normal SPP_200x_100%N), percent	74.5	85.2	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_200x_20%T with normal SPP_200x_100%N), percent	68.4	80.6	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_200x_5%T with normal SPP_200x_100%N), percent	18.2	38.9	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_200x_50%T with normal SPP_200x_100%N), percent	73.2	83.1	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_200x_75%T with normal SPP_200x_100%N), percent	75.4	84.5	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_300x_10%T with normal SPP_300x_100%N), percent	52.7	78.1	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_300x_100%T with normal SPP_300x_100%N), percent	74.2	84.4	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_300x_20%T with normal SPP_300x_100%N), percent	68.1	81.2	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_300x_5%T with normal SPP_300x_100%N), percent	11	44.1	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_300x_50%T with normal SPP_300x_100%N), percent	70.9	82.9	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_300x_75%T with normal SPP_300x_100%N), percent	75.3	83.5	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_30x_10%T with normal SPP_30x_100%N), percent	14.7	21.2	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_30x_100%T with normal SPP_30x_100%N), percent	75.9	80.7	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_30x_20%T with normal SPP_30x_100%N), percent	41.5	48.6	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_30x_5%T with normal SPP_30x_100%N), percent	4.4	7.1	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_30x_50%T with normal SPP_30x_100%N), percent	60.5	74	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_30x_75%T with normal SPP_30x_100%N), percent	75.6	80	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_50x_10%T with normal SPP_50x_100%N), percent	20.8	34	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_50x_100%T with normal SPP_50x_100%N), percent	72.8	83.7	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_50x_20%T with normal SPP_50x_100%N), percent	48.9	61.3	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_50x_5%T with normal SPP_50x_100%N), percent	8.6	14.5	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_50x_50%T with normal SPP_50x_100%N), percent	71.2	80.4	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_50x_75%T with normal SPP_50x_100%N), percent	74.3	83.1	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_80x_10%T with normal SPP_80x_100%N), percent	26.6	46.3	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_80x_10%T with normal SPP_80x_95%N), percent	6.2	25.1	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_80x_100%T with normal SPP_80x_100%N), percent	73.2	85.6	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_80x_100%T with normal SPP_80x_95%N), percent	38.3	72.7	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_80x_20%T with normal SPP_80x_100%N), percent	50.4	68	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_80x_20%T with normal SPP_80x_95%N), percent	16.3	49.4	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_80x_5%T with normal SPP_80x_100%N), percent	12.5	22.1	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_80x_50%T with normal SPP_80x_100%N), percent	70.6	82.7	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_80x_50%T with normal SPP_80x_95%N), percent	33.4	70.9	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_80x_75%T with normal SPP_80x_100%N), percent	71.8	84.2	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (INDELs, tumour SPP_80x_75%T with normal SPP_80x_95%N), percent	36.8	71.4	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, average over the 47 purity-coverage pairs as printed), percent	57	71	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_100x_10%T with normal SPP_100x_100%N), percent	59.4	65.9	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_100x_100%T with normal SPP_100x_100%N), percent	89.1	97.3	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_100x_20%T with normal SPP_100x_100%N), percent	78.3	83.9	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_100x_5%T with normal SPP_100x_100%N), percent	33.1	36.3	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_100x_50%T with normal SPP_100x_100%N), percent	84.5	93.2	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_100x_75%T with normal SPP_100x_100%N), percent	87.6	96.1	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_10x_10%T with normal SPP_10x_100%N), percent	1	9.3	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_10x_100%T with normal SPP_10x_100%N), percent	7.1	74.8	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_10x_20%T with normal SPP_10x_100%N), percent	2.6	25.7	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_10x_5%T with normal SPP_10x_100%N), percent	0.3	3	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_10x_50%T with normal SPP_10x_100%N), percent	5.9	58	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_10x_75%T with normal SPP_10x_100%N), percent	6.9	69.3	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_200x_10%T with normal SPP_200x_100%N), percent	70.3	81.6	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_200x_100%T with normal SPP_200x_100%N), percent	92.4	98	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_200x_20%T with normal SPP_200x_100%N), percent	82.4	90.4	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_200x_5%T with normal SPP_200x_100%N), percent	51.9	56.1	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_200x_50%T with normal SPP_200x_100%N), percent	87.5	95.9	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_200x_75%T with normal SPP_200x_100%N), percent	91.1	97.2	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_300x_10%T with normal SPP_300x_100%N), percent	69.6	85.7	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_300x_100%T with normal SPP_300x_100%N), percent	93.3	97.9	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_300x_20%T with normal SPP_300x_100%N), percent	79.4	92.1	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_300x_5%T with normal SPP_300x_100%N), percent	54.2	63.4	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_300x_50%T with normal SPP_300x_100%N), percent	82.2	96.4	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_300x_75%T with normal SPP_300x_100%N), percent	92.9	97.5	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_30x_10%T with normal SPP_30x_100%N), percent	23	33.5	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_30x_100%T with normal SPP_30x_100%N), percent	84.9	90.9	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_30x_20%T with normal SPP_30x_100%N), percent	50.8	61	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_30x_5%T with normal SPP_30x_100%N), percent	7.3	14.3	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_30x_50%T with normal SPP_30x_100%N), percent	78.1	84.5	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_30x_75%T with normal SPP_30x_100%N), percent	83.1	88.8	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_50x_10%T with normal SPP_50x_100%N), percent	37.5	43.9	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_50x_100%T with normal SPP_50x_100%N), percent	86.4	94.4	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_50x_20%T with normal SPP_50x_100%N), percent	66.3	71.5	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_50x_5%T with normal SPP_50x_100%N), percent	13.5	20.1	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_50x_50%T with normal SPP_50x_100%N), percent	83.5	89.1	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_50x_75%T with normal SPP_50x_100%N), percent	85.8	93	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_80x_10%T with normal SPP_80x_100%N), percent	53.2	58.9	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_80x_10%T with normal SPP_80x_95%N), percent	13.6	40	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_80x_100%T with normal SPP_80x_100%N), percent	88.6	96.9	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_80x_100%T with normal SPP_80x_95%N), percent	43.8	85.6	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_80x_20%T with normal SPP_80x_100%N), percent	75.6	80.7	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_80x_20%T with normal SPP_80x_95%N), percent	26.5	62	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_80x_5%T with normal SPP_80x_100%N), percent	26.2	29.2	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_80x_50%T with normal SPP_80x_100%N), percent	84.5	92.6	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_80x_50%T with normal SPP_80x_95%N), percent	37.8	80.3	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_80x_75%T with normal SPP_80x_100%N), percent	86.5	95.2	Octopus
SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	SEQC2 tumour-normal titration: HCC1395 gDNA mixed with HCC1395BL gDNA at 5-100% tumour purity, 10x-300x WGS	F1 (SNVs, tumour SPP_80x_75%T with normal SPP_80x_95%N), percent	41.2	83.6	Octopus
SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (INDELs, average over the 21 replicate pairs as printed), percent	73.7	84.2	Octopus
SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (INDELs, tumour WGS_EA_T_1 with normal WGS_EA_N_1), percent	71	83.2	Octopus
SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (INDELs, tumour WGS_FD_T_1 with normal WGS_FD_N_1), percent	73.7	83	Octopus
SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (INDELs, tumour WGS_FD_T_2 with normal WGS_FD_N_2), percent	72.7	82.5	Octopus
SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (INDELs, tumour WGS_FD_T_3 with normal WGS_FD_N_3), percent	74.1	82.1	Octopus
SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (INDELs, tumour WGS_IL_T_1 with normal WGS_IL_N_1), percent	80.6	87.9	Octopus
SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (INDELs, tumour WGS_IL_T_2 with normal WGS_IL_N_2), percent	80.1	85.5	Octopus
SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (INDELs, tumour WGS_IL_T_3 with normal WGS_IL_N_3), percent	80.3	85.1	Octopus
SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (INDELs, tumour WGS_LL_T_1 with normal WGS_LL_N_1), percent	76	79.4	Octopus
SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (INDELs, tumour WGS_NC_T_1 with normal WGS_NC_N_1), percent	80	84.8	Octopus
SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (INDELs, tumour WGS_NS_T_1 with normal WGS_NS_N_1), percent	69.8	83.9	Octopus
SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (INDELs, tumour WGS_NS_T_2 with normal WGS_NS_N_2), percent	73.2	83.2	Octopus
SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (INDELs, tumour WGS_NS_T_3 with normal WGS_NS_N_3), percent	71	83.7	Octopus
SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (INDELs, tumour WGS_NS_T_4 with normal WGS_NS_N_4), percent	70.1	84	Octopus
SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (INDELs, tumour WGS_NS_T_5 with normal WGS_NS_N_5), percent	67.4	82.8	Octopus
SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (INDELs, tumour WGS_NS_T_6 with normal WGS_NS_N_6), percent	67.9	84	Octopus
SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (INDELs, tumour WGS_NS_T_7 with normal WGS_NS_N_7), percent	66.5	84.2	Octopus
SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (INDELs, tumour WGS_NS_T_8 with normal WGS_NS_N_8), percent	74.6	83.5	Octopus
SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (INDELs, tumour WGS_NS_T_9 with normal WGS_NS_N_9), percent	73.9	84.8	Octopus
SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (INDELs, tumour WGS_NV_T_1 with normal WGS_NV_N_1), percent	74.3	86.5	Octopus
SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (INDELs, tumour WGS_NV_T_2 with normal WGS_NV_N_2), percent	74.2	86.9	Octopus
SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (INDELs, tumour WGS_NV_T_3 with normal WGS_NV_N_3), percent	76	86.5	Octopus
SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (SNVs, average over the 21 replicate pairs as printed), percent	85.8	94.1	Octopus
SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (SNVs, tumour WGS_EA_T_1 with normal WGS_EA_N_1), percent	90.8	95.4	Octopus
SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (SNVs, tumour WGS_FD_T_1 with normal WGS_FD_N_1), percent	90.5	93.7	Octopus
SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (SNVs, tumour WGS_FD_T_2 with normal WGS_FD_N_2), percent	90.7	93.8	Octopus
SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (SNVs, tumour WGS_FD_T_3 with normal WGS_FD_N_3), percent	90.5	93.7	Octopus
SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (SNVs, tumour WGS_IL_T_1 with normal WGS_IL_N_1), percent	91.9	96.2	Octopus
SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (SNVs, tumour WGS_IL_T_2 with normal WGS_IL_N_2), percent	90.5	94.9	Octopus
SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (SNVs, tumour WGS_IL_T_3 with normal WGS_IL_N_3), percent	90.3	94.8	Octopus
SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (SNVs, tumour WGS_LL_T_1 with normal WGS_LL_N_1), percent	79	88.4	Octopus
SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (SNVs, tumour WGS_NC_T_1 with normal WGS_NC_N_1), percent	89.5	93.5	Octopus
SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (SNVs, tumour WGS_NS_T_1 with normal WGS_NS_N_1), percent	79.2	94.6	Octopus
SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (SNVs, tumour WGS_NS_T_2 with normal WGS_NS_N_2), percent	84.1	92.1	Octopus
SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (SNVs, tumour WGS_NS_T_3 with normal WGS_NS_N_3), percent	83.8	93.1	Octopus
SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (SNVs, tumour WGS_NS_T_4 with normal WGS_NS_N_4), percent	79	94.1	Octopus
SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (SNVs, tumour WGS_NS_T_5 with normal WGS_NS_N_5), percent	78.9	92.7	Octopus
SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (SNVs, tumour WGS_NS_T_6 with normal WGS_NS_N_6), percent	79	95	Octopus
SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (SNVs, tumour WGS_NS_T_7 with normal WGS_NS_N_7), percent	73.4	93.6	Octopus
SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (SNVs, tumour WGS_NS_T_8 with normal WGS_NS_N_8), percent	85.8	92.1	Octopus
SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (SNVs, tumour WGS_NS_T_9 with normal WGS_NS_N_9), percent	86.5	92.7	Octopus
SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (SNVs, tumour WGS_NV_T_1 with normal WGS_NV_N_1), percent	88.2	97	Octopus
SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (SNVs, tumour WGS_NV_T_2 with normal WGS_NV_N_2), percent	87.8	97	Octopus
SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	SEQC2 HCC1395/HCC1395BL WGS replicate pairs from multiple sequencing centres	F1 (SNVs, tumour WGS_NV_T_3 with normal WGS_NV_N_3), percent	92	97	Octopus

Comparisons in ClairS-TO (14)

Protocol	Dataset	Metric	Mutect2	Octopus	Higher
Illumina COLO829 tumour-only, 50x, somatic indels (Chen et al. 2025 Supplementary Table 7b)	COLO829 tumour Illumina NovaSeq 6000 WGS (ClairS-TO benchmark)	AUPRC (indel only, tumour-only, 50x tumour coverage), fraction	0.0223	0.0044	Mutect2
Illumina COLO829 tumour-only, 50x, somatic indels (Chen et al. 2025 Supplementary Table 7b)	COLO829 tumour Illumina NovaSeq 6000 WGS (ClairS-TO benchmark)	F1 (indel only, tumour-only, 50x tumour coverage, at the best-F1 threshold), fraction	0.0476	0.0096	Mutect2
Illumina COLO829 tumour-only, 50x, somatic indels (Chen et al. 2025 Supplementary Table 7b)	COLO829 tumour Illumina NovaSeq 6000 WGS (ClairS-TO benchmark)	False negatives (indel only, tumour-only, 50x tumour coverage, at the best-F1 threshold), count	514	609	Mutect2
Illumina COLO829 tumour-only, 50x, somatic indels (Chen et al. 2025 Supplementary Table 7b)	COLO829 tumour Illumina NovaSeq 6000 WGS (ClairS-TO benchmark)	False positives (indel only, tumour-only, 50x tumour coverage, at the best-F1 threshold), count	7490	20800	Mutect2
Illumina COLO829 tumour-only, 50x, somatic indels (Chen et al. 2025 Supplementary Table 7b)	COLO829 tumour Illumina NovaSeq 6000 WGS (ClairS-TO benchmark)	Precision (indel only, tumour-only, 50x tumour coverage, at the best-F1 threshold), fraction	0.026	0.005	Mutect2
Illumina COLO829 tumour-only, 50x, somatic indels (Chen et al. 2025 Supplementary Table 7b)	COLO829 tumour Illumina NovaSeq 6000 WGS (ClairS-TO benchmark)	Recall (indel only, tumour-only, 50x tumour coverage, at the best-F1 threshold), fraction	0.28	0.146	Mutect2
Illumina COLO829 tumour-only, 50x, somatic indels (Chen et al. 2025 Supplementary Table 7b)	COLO829 tumour Illumina NovaSeq 6000 WGS (ClairS-TO benchmark)	True positives (indel only, tumour-only, 50x tumour coverage, at the best-F1 threshold), count	200	104	Mutect2
Illumina COLO829 tumour-only, 50x, somatic SNVs (Chen et al. 2025 Supplementary Table 7b)	COLO829 tumour Illumina NovaSeq 6000 WGS (ClairS-TO benchmark)	AUPRC (SNV only, tumour-only, 50x tumour coverage), fraction	0.357	0.212	Mutect2
Illumina COLO829 tumour-only, 50x, somatic SNVs (Chen et al. 2025 Supplementary Table 7b)	COLO829 tumour Illumina NovaSeq 6000 WGS (ClairS-TO benchmark)	F1 (SNV only, tumour-only, 50x tumour coverage, at the best-F1 threshold), fraction	0.582	0.403	Mutect2
Illumina COLO829 tumour-only, 50x, somatic SNVs (Chen et al. 2025 Supplementary Table 7b)	COLO829 tumour Illumina NovaSeq 6000 WGS (ClairS-TO benchmark)	False negatives (SNV only, tumour-only, 50x tumour coverage, at the best-F1 threshold), count	8460	9790	Mutect2
Illumina COLO829 tumour-only, 50x, somatic SNVs (Chen et al. 2025 Supplementary Table 7b)	COLO829 tumour Illumina NovaSeq 6000 WGS (ClairS-TO benchmark)	False positives (SNV only, tumour-only, 50x tumour coverage, at the best-F1 threshold), count	37200	80400	Mutect2
Illumina COLO829 tumour-only, 50x, somatic SNVs (Chen et al. 2025 Supplementary Table 7b)	COLO829 tumour Illumina NovaSeq 6000 WGS (ClairS-TO benchmark)	Precision (SNV only, tumour-only, 50x tumour coverage, at the best-F1 threshold), fraction	0.46	0.275	Mutect2
Illumina COLO829 tumour-only, 50x, somatic SNVs (Chen et al. 2025 Supplementary Table 7b)	COLO829 tumour Illumina NovaSeq 6000 WGS (ClairS-TO benchmark)	Recall (SNV only, tumour-only, 50x tumour coverage, at the best-F1 threshold), fraction	0.79	0.757	Mutect2
Illumina COLO829 tumour-only, 50x, somatic SNVs (Chen et al. 2025 Supplementary Table 7b)	COLO829 tumour Illumina NovaSeq 6000 WGS (ClairS-TO benchmark)	True positives (SNV only, tumour-only, 50x tumour coverage, at the best-F1 threshold), count	31800	30400	Mutect2

What disagrees

Other studies: rank Mutect2 above Octopus in 14 of 14 comparisons
This study: ranks Octopus above Mutect2 in 154 of 154 comparisons

Compared: Mutect2, Octopus
Study that disagrees: 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)
Other studies: ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling, ClairS-TO Source Data workbook, ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines)
Rule: Ordering reversal; different tasks across studies
Model identity: Matched across studies partly through links not yet reviewed

The compared numbers

Each value links to its result record and source. Compare values within one protocol; the rule compares rankings, not raw values across protocols.

In the study that disagrees (308)
Study	Tool	Protocol or benchmark	Metric	Value
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, indel F1 (Sahraeian et al. 2022 Table S4)	F1 (INDELs, average over the 6 library pairs as printed)	42.2
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, indel F1 (Sahraeian et al. 2022 Table S4)	F1 (INDELs, tumour LBP_Nextera_T_100ng with normal LBP_Nextera_N_100ng)	61.3
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, indel F1 (Sahraeian et al. 2022 Table S4)	F1 (INDELs, tumour LBP_Nextera_T_10ng with normal LBP_Nextera_N_10ng)	38.6
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, indel F1 (Sahraeian et al. 2022 Table S4)	F1 (INDELs, tumour LBP_Nextera_T_1ng with normal LBP_Nextera_N_1ng)	37.4
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, indel F1 (Sahraeian et al. 2022 Table S4)	F1 (INDELs, tumour LBP_TruSeq_T_100ng with normal LBP_TruSeq_N_100ng)	56.1
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, indel F1 (Sahraeian et al. 2022 Table S4)	F1 (INDELs, tumour LBP_TruSeq_T_10ng with normal LBP_TruSeq_N_10ng)	52.3
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, indel F1 (Sahraeian et al. 2022 Table S4)	F1 (INDELs, tumour LBP_TruSeq_T_1ng with normal LBP_TruSeq_N_1ng)	7.7
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, SNV F1 (Sahraeian et al. 2022 Table S4)	F1 (SNVs, average over the 6 library pairs as printed)	72.7
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, SNV F1 (Sahraeian et al. 2022 Table S4)	F1 (SNVs, tumour LBP_Nextera_T_100ng with normal LBP_Nextera_N_100ng)	89.8
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, SNV F1 (Sahraeian et al. 2022 Table S4)	F1 (SNVs, tumour LBP_Nextera_T_10ng with normal LBP_Nextera_N_10ng)	89.7
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, SNV F1 (Sahraeian et al. 2022 Table S4)	F1 (SNVs, tumour LBP_Nextera_T_1ng with normal LBP_Nextera_N_1ng)	86
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, SNV F1 (Sahraeian et al. 2022 Table S4)	F1 (SNVs, tumour LBP_TruSeq_T_100ng with normal LBP_TruSeq_N_100ng)	86.5
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, SNV F1 (Sahraeian et al. 2022 Table S4)	F1 (SNVs, tumour LBP_TruSeq_T_10ng with normal LBP_TruSeq_N_10ng)	80.3
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, SNV F1 (Sahraeian et al. 2022 Table S4)	F1 (SNVs, tumour LBP_TruSeq_T_1ng with normal LBP_TruSeq_N_1ng)	3.7
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, average over the 47 purity-coverage pairs as printed)	44.5
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_100x_10%T with normal SPP_100x_100%N)	28.9
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_100x_100%T with normal SPP_100x_100%N)	73.3
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_100x_20%T with normal SPP_100x_100%N)	66.3
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_100x_5%T with normal SPP_100x_100%N)	14.3
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_100x_50%T with normal SPP_100x_100%N)	68.1
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_100x_75%T with normal SPP_100x_100%N)	73.3
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_10x_10%T with normal SPP_10x_100%N)	0.9
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_10x_100%T with normal SPP_10x_100%N)	13
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_10x_20%T with normal SPP_10x_100%N)	5.5
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_10x_5%T with normal SPP_10x_100%N)	0.6
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_10x_50%T with normal SPP_10x_100%N)	9.9
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_10x_75%T with normal SPP_10x_100%N)	14.1
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_200x_10%T with normal SPP_200x_100%N)	57.3
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_200x_100%T with normal SPP_200x_100%N)	74.5
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_200x_20%T with normal SPP_200x_100%N)	68.4
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_200x_5%T with normal SPP_200x_100%N)	18.2
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_200x_50%T with normal SPP_200x_100%N)	73.2
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_200x_75%T with normal SPP_200x_100%N)	75.4
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_300x_10%T with normal SPP_300x_100%N)	52.7
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_300x_100%T with normal SPP_300x_100%N)	74.2
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_300x_20%T with normal SPP_300x_100%N)	68.1
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_300x_5%T with normal SPP_300x_100%N)	11
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_300x_50%T with normal SPP_300x_100%N)	70.9
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_300x_75%T with normal SPP_300x_100%N)	75.3
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_30x_10%T with normal SPP_30x_100%N)	14.7
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_30x_100%T with normal SPP_30x_100%N)	75.9
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_30x_20%T with normal SPP_30x_100%N)	41.5
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_30x_5%T with normal SPP_30x_100%N)	4.4
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_30x_50%T with normal SPP_30x_100%N)	60.5
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_30x_75%T with normal SPP_30x_100%N)	75.6
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_50x_10%T with normal SPP_50x_100%N)	20.8
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_50x_100%T with normal SPP_50x_100%N)	72.8
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_50x_20%T with normal SPP_50x_100%N)	48.9
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_50x_5%T with normal SPP_50x_100%N)	8.6
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_50x_50%T with normal SPP_50x_100%N)	71.2
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_50x_75%T with normal SPP_50x_100%N)	74.3
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_80x_10%T with normal SPP_80x_100%N)	26.6
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_80x_10%T with normal SPP_80x_95%N)	6.2
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_80x_100%T with normal SPP_80x_100%N)	73.2
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_80x_100%T with normal SPP_80x_95%N)	38.3
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_80x_20%T with normal SPP_80x_100%N)	50.4
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_80x_20%T with normal SPP_80x_95%N)	16.3
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_80x_5%T with normal SPP_80x_100%N)	12.5
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_80x_50%T with normal SPP_80x_100%N)	70.6
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_80x_50%T with normal SPP_80x_95%N)	33.4
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_80x_75%T with normal SPP_80x_100%N)	71.8
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_80x_75%T with normal SPP_80x_95%N)	36.8
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, average over the 47 purity-coverage pairs as printed)	57
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_100x_10%T with normal SPP_100x_100%N)	59.4
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_100x_100%T with normal SPP_100x_100%N)	89.1
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_100x_20%T with normal SPP_100x_100%N)	78.3
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_100x_5%T with normal SPP_100x_100%N)	33.1
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_100x_50%T with normal SPP_100x_100%N)	84.5
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_100x_75%T with normal SPP_100x_100%N)	87.6
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_10x_10%T with normal SPP_10x_100%N)	1
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_10x_100%T with normal SPP_10x_100%N)	7.1
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_10x_20%T with normal SPP_10x_100%N)	2.6
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_10x_5%T with normal SPP_10x_100%N)	0.3
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_10x_50%T with normal SPP_10x_100%N)	5.9
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_10x_75%T with normal SPP_10x_100%N)	6.9
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_200x_10%T with normal SPP_200x_100%N)	70.3
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_200x_100%T with normal SPP_200x_100%N)	92.4
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_200x_20%T with normal SPP_200x_100%N)	82.4
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_200x_5%T with normal SPP_200x_100%N)	51.9
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_200x_50%T with normal SPP_200x_100%N)	87.5
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_200x_75%T with normal SPP_200x_100%N)	91.1
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_300x_10%T with normal SPP_300x_100%N)	69.6
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_300x_100%T with normal SPP_300x_100%N)	93.3
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_300x_20%T with normal SPP_300x_100%N)	79.4
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_300x_5%T with normal SPP_300x_100%N)	54.2
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_300x_50%T with normal SPP_300x_100%N)	82.2
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_300x_75%T with normal SPP_300x_100%N)	92.9
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_30x_10%T with normal SPP_30x_100%N)	23
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_30x_100%T with normal SPP_30x_100%N)	84.9
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_30x_20%T with normal SPP_30x_100%N)	50.8
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_30x_5%T with normal SPP_30x_100%N)	7.3
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_30x_50%T with normal SPP_30x_100%N)	78.1
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_30x_75%T with normal SPP_30x_100%N)	83.1
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_50x_10%T with normal SPP_50x_100%N)	37.5
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_50x_100%T with normal SPP_50x_100%N)	86.4
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_50x_20%T with normal SPP_50x_100%N)	66.3
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_50x_5%T with normal SPP_50x_100%N)	13.5
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_50x_50%T with normal SPP_50x_100%N)	83.5
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_50x_75%T with normal SPP_50x_100%N)	85.8
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_80x_10%T with normal SPP_80x_100%N)	53.2
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_80x_10%T with normal SPP_80x_95%N)	13.6
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_80x_100%T with normal SPP_80x_100%N)	88.6
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_80x_100%T with normal SPP_80x_95%N)	43.8
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_80x_20%T with normal SPP_80x_100%N)	75.6
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_80x_20%T with normal SPP_80x_95%N)	26.5
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_80x_5%T with normal SPP_80x_100%N)	26.2
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_80x_50%T with normal SPP_80x_100%N)	84.5
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_80x_50%T with normal SPP_80x_95%N)	37.8
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_80x_75%T with normal SPP_80x_100%N)	86.5
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_80x_75%T with normal SPP_80x_95%N)	41.2
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, average over the 21 replicate pairs as printed)	73.7
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_EA_T_1 with normal WGS_EA_N_1)	71
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_FD_T_1 with normal WGS_FD_N_1)	73.7
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_FD_T_2 with normal WGS_FD_N_2)	72.7
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_FD_T_3 with normal WGS_FD_N_3)	74.1
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_IL_T_1 with normal WGS_IL_N_1)	80.6
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_IL_T_2 with normal WGS_IL_N_2)	80.1
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_IL_T_3 with normal WGS_IL_N_3)	80.3
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_LL_T_1 with normal WGS_LL_N_1)	76
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NC_T_1 with normal WGS_NC_N_1)	80
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NS_T_1 with normal WGS_NS_N_1)	69.8
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NS_T_2 with normal WGS_NS_N_2)	73.2
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NS_T_3 with normal WGS_NS_N_3)	71
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NS_T_4 with normal WGS_NS_N_4)	70.1
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NS_T_5 with normal WGS_NS_N_5)	67.4
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NS_T_6 with normal WGS_NS_N_6)	67.9
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NS_T_7 with normal WGS_NS_N_7)	66.5
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NS_T_8 with normal WGS_NS_N_8)	74.6
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NS_T_9 with normal WGS_NS_N_9)	73.9
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NV_T_1 with normal WGS_NV_N_1)	74.3
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NV_T_2 with normal WGS_NV_N_2)	74.2
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NV_T_3 with normal WGS_NV_N_3)	76
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, average over the 21 replicate pairs as printed)	85.8
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_EA_T_1 with normal WGS_EA_N_1)	90.8
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_FD_T_1 with normal WGS_FD_N_1)	90.5
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_FD_T_2 with normal WGS_FD_N_2)	90.7
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_FD_T_3 with normal WGS_FD_N_3)	90.5
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_IL_T_1 with normal WGS_IL_N_1)	91.9
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_IL_T_2 with normal WGS_IL_N_2)	90.5
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_IL_T_3 with normal WGS_IL_N_3)	90.3
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_LL_T_1 with normal WGS_LL_N_1)	79
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NC_T_1 with normal WGS_NC_N_1)	89.5
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NS_T_1 with normal WGS_NS_N_1)	79.2
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NS_T_2 with normal WGS_NS_N_2)	84.1
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NS_T_3 with normal WGS_NS_N_3)	83.8
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NS_T_4 with normal WGS_NS_N_4)	79
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NS_T_5 with normal WGS_NS_N_5)	78.9
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NS_T_6 with normal WGS_NS_N_6)	79
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NS_T_7 with normal WGS_NS_N_7)	73.4
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NS_T_8 with normal WGS_NS_N_8)	85.8
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NS_T_9 with normal WGS_NS_N_9)	86.5
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NV_T_1 with normal WGS_NV_N_1)	88.2
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NV_T_2 with normal WGS_NV_N_2)	87.8
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)	MuTect2 4.beta.6 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NV_T_3 with normal WGS_NV_N_3)	92
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)	Octopus-hard v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_100x_20%T with normal SPP_100x_100%N)	75
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)	Octopus-hard v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_200x_10%T with normal SPP_200x_100%N)	75.5
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)	Octopus-hard v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_200x_20%T with normal SPP_200x_100%N)	80.6
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)	Octopus-hard v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_300x_10%T with normal SPP_300x_100%N)	78.1
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)	Octopus-hard v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_300x_20%T with normal SPP_300x_100%N)	81.2
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)	Octopus-hard v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_80x_50%T with normal SPP_80x_95%N)	70.9
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)	Octopus-hard v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_200x_10%T with normal SPP_200x_100%N)	81.6
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)	Octopus-hard v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_200x_20%T with normal SPP_200x_100%N)	90.4
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)	Octopus-hard v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_300x_10%T with normal SPP_300x_100%N)	85.7
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)	Octopus-hard v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_300x_20%T with normal SPP_300x_100%N)	92.1
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)	Octopus-hard v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_30x_10%T with normal SPP_30x_100%N)	33.5
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)	Octopus-hard v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_30x_5%T with normal SPP_30x_100%N)	14.3
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)	Octopus-hard v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_50x_5%T with normal SPP_50x_100%N)	20.1
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)	Octopus-hard v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_80x_10%T with normal SPP_80x_95%N)	40
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)	Octopus-hard v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_80x_50%T with normal SPP_80x_95%N)	80.3
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, indel F1 (Sahraeian et al. 2022 Table S4)	F1 (INDELs, average over the 6 library pairs as printed)	66.3
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, indel F1 (Sahraeian et al. 2022 Table S4)	F1 (INDELs, tumour LBP_Nextera_T_100ng with normal LBP_Nextera_N_100ng)	80.1
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, indel F1 (Sahraeian et al. 2022 Table S4)	F1 (INDELs, tumour LBP_Nextera_T_10ng with normal LBP_Nextera_N_10ng)	74.2
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, indel F1 (Sahraeian et al. 2022 Table S4)	F1 (INDELs, tumour LBP_Nextera_T_1ng with normal LBP_Nextera_N_1ng)	63.1
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, indel F1 (Sahraeian et al. 2022 Table S4)	F1 (INDELs, tumour LBP_TruSeq_T_100ng with normal LBP_TruSeq_N_100ng)	75
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, indel F1 (Sahraeian et al. 2022 Table S4)	F1 (INDELs, tumour LBP_TruSeq_T_10ng with normal LBP_TruSeq_N_10ng)	68
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, indel F1 (Sahraeian et al. 2022 Table S4)	F1 (INDELs, tumour LBP_TruSeq_T_1ng with normal LBP_TruSeq_N_1ng)	37.2
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, SNV F1 (Sahraeian et al. 2022 Table S4)	F1 (SNVs, average over the 6 library pairs as printed)	84.9
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, SNV F1 (Sahraeian et al. 2022 Table S4)	F1 (SNVs, tumour LBP_Nextera_T_100ng with normal LBP_Nextera_N_100ng)	94.8
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, SNV F1 (Sahraeian et al. 2022 Table S4)	F1 (SNVs, tumour LBP_Nextera_T_10ng with normal LBP_Nextera_N_10ng)	94.9
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, SNV F1 (Sahraeian et al. 2022 Table S4)	F1 (SNVs, tumour LBP_Nextera_T_1ng with normal LBP_Nextera_N_1ng)	91.8
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, SNV F1 (Sahraeian et al. 2022 Table S4)	F1 (SNVs, tumour LBP_TruSeq_T_100ng with normal LBP_TruSeq_N_100ng)	92.8
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, SNV F1 (Sahraeian et al. 2022 Table S4)	F1 (SNVs, tumour LBP_TruSeq_T_10ng with normal LBP_TruSeq_N_10ng)	84.6
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 library preparation and DNA input, SNV F1 (Sahraeian et al. 2022 Table S4)	F1 (SNVs, tumour LBP_TruSeq_T_1ng with normal LBP_TruSeq_N_1ng)	50.5
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, average over the 47 purity-coverage pairs as printed)	59.2
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_100x_10%T with normal SPP_100x_100%N)	49.9
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_100x_100%T with normal SPP_100x_100%N)	85.6
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_100x_5%T with normal SPP_100x_100%N)	26.3
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_100x_50%T with normal SPP_100x_100%N)	83
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_100x_75%T with normal SPP_100x_100%N)	84.8
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_10x_10%T with normal SPP_10x_100%N)	3.6
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_10x_100%T with normal SPP_10x_100%N)	58.8
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_10x_20%T with normal SPP_10x_100%N)	13.8
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_10x_5%T with normal SPP_10x_100%N)	1.2
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_10x_50%T with normal SPP_10x_100%N)	41.3
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_10x_75%T with normal SPP_10x_100%N)	53.6
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_200x_100%T with normal SPP_200x_100%N)	85.2
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_200x_5%T with normal SPP_200x_100%N)	38.9
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_200x_50%T with normal SPP_200x_100%N)	83.1
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_200x_75%T with normal SPP_200x_100%N)	84.5
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_300x_100%T with normal SPP_300x_100%N)	84.4
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_300x_5%T with normal SPP_300x_100%N)	44.1
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_300x_50%T with normal SPP_300x_100%N)	82.9
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_300x_75%T with normal SPP_300x_100%N)	83.5
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_30x_10%T with normal SPP_30x_100%N)	21.2
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_30x_100%T with normal SPP_30x_100%N)	80.7
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_30x_20%T with normal SPP_30x_100%N)	48.6
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_30x_5%T with normal SPP_30x_100%N)	7.1
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_30x_50%T with normal SPP_30x_100%N)	74
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_30x_75%T with normal SPP_30x_100%N)	80
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_50x_10%T with normal SPP_50x_100%N)	34
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_50x_100%T with normal SPP_50x_100%N)	83.7
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_50x_20%T with normal SPP_50x_100%N)	61.3
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_50x_5%T with normal SPP_50x_100%N)	14.5
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_50x_50%T with normal SPP_50x_100%N)	80.4
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_50x_75%T with normal SPP_50x_100%N)	83.1
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_80x_10%T with normal SPP_80x_100%N)	46.3
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_80x_10%T with normal SPP_80x_95%N)	25.1
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_80x_100%T with normal SPP_80x_100%N)	85.6
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_80x_100%T with normal SPP_80x_95%N)	72.7
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_80x_20%T with normal SPP_80x_100%N)	68
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_80x_20%T with normal SPP_80x_95%N)	49.4
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_80x_5%T with normal SPP_80x_100%N)	22.1
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_80x_50%T with normal SPP_80x_100%N)	82.7
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_80x_75%T with normal SPP_80x_100%N)	84.2
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, indel F1 (Sahraeian et al. 2022 Table S3)	F1 (INDELs, tumour SPP_80x_75%T with normal SPP_80x_95%N)	71.4
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, average over the 47 purity-coverage pairs as printed)	71
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_100x_10%T with normal SPP_100x_100%N)	65.9
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_100x_100%T with normal SPP_100x_100%N)	97.3
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_100x_20%T with normal SPP_100x_100%N)	83.9
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_100x_5%T with normal SPP_100x_100%N)	36.3
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_100x_50%T with normal SPP_100x_100%N)	93.2
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_100x_75%T with normal SPP_100x_100%N)	96.1
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_10x_10%T with normal SPP_10x_100%N)	9.3
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_10x_100%T with normal SPP_10x_100%N)	74.8
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_10x_20%T with normal SPP_10x_100%N)	25.7
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_10x_5%T with normal SPP_10x_100%N)	3
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_10x_50%T with normal SPP_10x_100%N)	58
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_10x_75%T with normal SPP_10x_100%N)	69.3
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_200x_100%T with normal SPP_200x_100%N)	98
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_200x_5%T with normal SPP_200x_100%N)	56.1
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_200x_50%T with normal SPP_200x_100%N)	95.9
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_200x_75%T with normal SPP_200x_100%N)	97.2
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_300x_100%T with normal SPP_300x_100%N)	97.9
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_300x_5%T with normal SPP_300x_100%N)	63.4
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_300x_50%T with normal SPP_300x_100%N)	96.4
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_300x_75%T with normal SPP_300x_100%N)	97.5
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_30x_100%T with normal SPP_30x_100%N)	90.9
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_30x_20%T with normal SPP_30x_100%N)	61
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_30x_50%T with normal SPP_30x_100%N)	84.5
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_30x_75%T with normal SPP_30x_100%N)	88.8
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_50x_10%T with normal SPP_50x_100%N)	43.9
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_50x_100%T with normal SPP_50x_100%N)	94.4
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_50x_20%T with normal SPP_50x_100%N)	71.5
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_50x_50%T with normal SPP_50x_100%N)	89.1
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_50x_75%T with normal SPP_50x_100%N)	93
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_80x_10%T with normal SPP_80x_100%N)	58.9
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_80x_100%T with normal SPP_80x_100%N)	96.9
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_80x_100%T with normal SPP_80x_95%N)	85.6
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_80x_20%T with normal SPP_80x_100%N)	80.7
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_80x_20%T with normal SPP_80x_95%N)	62
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_80x_5%T with normal SPP_80x_100%N)	29.2
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_80x_50%T with normal SPP_80x_100%N)	92.6
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_80x_75%T with normal SPP_80x_100%N)	95.2
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 tumour purity, coverage and normal contamination, SNV F1 (Sahraeian et al. 2022 Table S3)	F1 (SNVs, tumour SPP_80x_75%T with normal SPP_80x_95%N)	83.6
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, average over the 21 replicate pairs as printed)	84.2
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_EA_T_1 with normal WGS_EA_N_1)	83.2
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_FD_T_1 with normal WGS_FD_N_1)	83
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_FD_T_2 with normal WGS_FD_N_2)	82.5
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_FD_T_3 with normal WGS_FD_N_3)	82.1
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_IL_T_1 with normal WGS_IL_N_1)	87.9
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_IL_T_2 with normal WGS_IL_N_2)	85.5
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_IL_T_3 with normal WGS_IL_N_3)	85.1
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_LL_T_1 with normal WGS_LL_N_1)	79.4
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NC_T_1 with normal WGS_NC_N_1)	84.8
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NS_T_1 with normal WGS_NS_N_1)	83.9
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NS_T_2 with normal WGS_NS_N_2)	83.2
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NS_T_3 with normal WGS_NS_N_3)	83.7
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NS_T_4 with normal WGS_NS_N_4)	84
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NS_T_5 with normal WGS_NS_N_5)	82.8
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NS_T_6 with normal WGS_NS_N_6)	84
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NS_T_7 with normal WGS_NS_N_7)	84.2
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NS_T_8 with normal WGS_NS_N_8)	83.5
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NS_T_9 with normal WGS_NS_N_9)	84.8
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NV_T_1 with normal WGS_NV_N_1)	86.5
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NV_T_2 with normal WGS_NV_N_2)	86.9
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, indel F1 (Sahraeian et al. 2022 Table S2)	F1 (INDELs, tumour WGS_NV_T_3 with normal WGS_NV_N_3)	86.5
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, average over the 21 replicate pairs as printed)	94.1
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_EA_T_1 with normal WGS_EA_N_1)	95.4
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_FD_T_1 with normal WGS_FD_N_1)	93.7
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_FD_T_2 with normal WGS_FD_N_2)	93.8
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_FD_T_3 with normal WGS_FD_N_3)	93.7
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_IL_T_1 with normal WGS_IL_N_1)	96.2
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_IL_T_2 with normal WGS_IL_N_2)	94.9
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_IL_T_3 with normal WGS_IL_N_3)	94.8
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_LL_T_1 with normal WGS_LL_N_1)	88.4
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NC_T_1 with normal WGS_NC_N_1)	93.5
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NS_T_1 with normal WGS_NS_N_1)	94.6
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NS_T_2 with normal WGS_NS_N_2)	92.1
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NS_T_3 with normal WGS_NS_N_3)	93.1
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NS_T_4 with normal WGS_NS_N_4)	94.1
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NS_T_5 with normal WGS_NS_N_5)	92.7
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NS_T_6 with normal WGS_NS_N_6)	95
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NS_T_7 with normal WGS_NS_N_7)	93.6
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NS_T_8 with normal WGS_NS_N_8)	92.1
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NS_T_9 with normal WGS_NS_N_9)	92.7
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NV_T_1 with normal WGS_NV_N_1)	97
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NV_T_2 with normal WGS_NV_N_2)	97
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)	Octopus-RF v0.7.4 (Sahraeian et al. 2022)	SEQC2 HCC1395 sequencing centre and platform, SNV F1 (Sahraeian et al. 2022 Table S2)	F1 (SNVs, tumour WGS_NV_T_3 with normal WGS_NV_N_3)	97

In the other studies (28)
Study	Tool	Protocol or benchmark	Metric	Value
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Mutect2 GATK v4.2.6.1, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic indels (Chen et al. 2025 Supplementary Table 7b)	AUPRC (indel only, tumour-only, 50x tumour coverage)	0.0223
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Mutect2 GATK v4.2.6.1, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic indels (Chen et al. 2025 Supplementary Table 7b)	F1 (indel only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	0.0476
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Mutect2 GATK v4.2.6.1, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic indels (Chen et al. 2025 Supplementary Table 7b)	False negatives (indel only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	514
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Mutect2 GATK v4.2.6.1, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic indels (Chen et al. 2025 Supplementary Table 7b)	False positives (indel only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	7,492
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Mutect2 GATK v4.2.6.1, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic indels (Chen et al. 2025 Supplementary Table 7b)	Precision (indel only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	0.026
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Mutect2 GATK v4.2.6.1, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic indels (Chen et al. 2025 Supplementary Table 7b)	Recall (indel only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	0.28
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Mutect2 GATK v4.2.6.1, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic indels (Chen et al. 2025 Supplementary Table 7b)	True positives (indel only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	200
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Mutect2 GATK v4.2.6.1, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic SNVs (Chen et al. 2025 Supplementary Table 7b)	AUPRC (SNV only, tumour-only, 50x tumour coverage)	0.357
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Mutect2 GATK v4.2.6.1, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic SNVs (Chen et al. 2025 Supplementary Table 7b)	F1 (SNV only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	0.582
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Mutect2 GATK v4.2.6.1, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic SNVs (Chen et al. 2025 Supplementary Table 7b)	False negatives (SNV only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	8,464
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Mutect2 GATK v4.2.6.1, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic SNVs (Chen et al. 2025 Supplementary Table 7b)	False positives (SNV only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	37,243
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Mutect2 GATK v4.2.6.1, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic SNVs (Chen et al. 2025 Supplementary Table 7b)	Precision (SNV only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	0.46
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Mutect2 GATK v4.2.6.1, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic SNVs (Chen et al. 2025 Supplementary Table 7b)	Recall (SNV only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	0.79
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Mutect2 GATK v4.2.6.1, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic SNVs (Chen et al. 2025 Supplementary Table 7b)	True positives (SNV only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	31,768
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Octopus v0.7.4, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic indels (Chen et al. 2025 Supplementary Table 7b)	AUPRC (indel only, tumour-only, 50x tumour coverage)	0.0044
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Octopus v0.7.4, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic indels (Chen et al. 2025 Supplementary Table 7b)	F1 (indel only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	0.0096
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Octopus v0.7.4, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic indels (Chen et al. 2025 Supplementary Table 7b)	False negatives (indel only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	609
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Octopus v0.7.4, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic indels (Chen et al. 2025 Supplementary Table 7b)	False positives (indel only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	20,809
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Octopus v0.7.4, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic indels (Chen et al. 2025 Supplementary Table 7b)	Precision (indel only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	0.005
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Octopus v0.7.4, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic indels (Chen et al. 2025 Supplementary Table 7b)	Recall (indel only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	0.146
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Octopus v0.7.4, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic indels (Chen et al. 2025 Supplementary Table 7b)	True positives (indel only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	104
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Octopus v0.7.4, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic SNVs (Chen et al. 2025 Supplementary Table 7b)	AUPRC (SNV only, tumour-only, 50x tumour coverage)	0.212
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Octopus v0.7.4, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic SNVs (Chen et al. 2025 Supplementary Table 7b)	F1 (SNV only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	0.403
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Octopus v0.7.4, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic SNVs (Chen et al. 2025 Supplementary Table 7b)	False negatives (SNV only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	9,792
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Octopus v0.7.4, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic SNVs (Chen et al. 2025 Supplementary Table 7b)	False positives (SNV only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	80,410
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Octopus v0.7.4, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic SNVs (Chen et al. 2025 Supplementary Table 7b)	Precision (SNV only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	0.275
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Octopus v0.7.4, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic SNVs (Chen et al. 2025 Supplementary Table 7b)	Recall (SNV only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	0.757
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling; ClairS-TO Supplementary Information (Supplementary Tables 1-8 and command lines); ClairS-TO Source Data workbook	Octopus v0.7.4, tumour-only, Illumina (Chen et al. 2025)	Illumina COLO829 tumour-only, 50x, somatic SNVs (Chen et al. 2025 Supplementary Table 7b)	True positives (SNV only, tumour-only, 50x tumour coverage, at the best-F1 threshold)	30,440

Follow-up

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Finding discrepancy-4418e7326d072649 · All contested results · How findings are made and followed up