Contested result

Mutect2 and Octopus: ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling reverses the order

Medium severity
Not yet reviewed
Ordering reversal
Different tasks across studies

ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling ranks Mutect2 above Octopus in 14 of 14 comparisons; other studies rank Octopus above Mutect2 in 154 of 154.

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

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

Rank Octopus higher (1): Achieving robust somatic mutation…
Rank Mutect2 higher (1): ClairS-TO (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.

Octopus higherTieMutect2 higherNumbers are counts of comparisons; the outlined bar is the paper that disagrees.

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

Comparisons in ClairS-TO (14)

Protocol	Dataset	Metric	Octopus	Mutect2	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	0.0044	0.0223	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	0.0096	0.0476	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	609	514	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	20800	7490	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	0.005	0.026	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	0.146	0.28	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	104	200	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	0.212	0.357	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	0.403	0.582	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	9790	8460	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	80400	37200	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	0.275	0.46	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	0.757	0.79	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	30400	31800	Mutect2

Comparisons in Achieving robust somatic mutation… (154)

Protocol	Dataset	Metric	Octopus	Mutect2	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	66.3	42.2	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_100ng with normal LBP_Nextera_N_100ng), percent	80.1	61.3	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_10ng with normal LBP_Nextera_N_10ng), percent	74.2	38.6	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	63.1	37.4	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	75	56.1	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_10ng with normal LBP_TruSeq_N_10ng), percent	68	52.3	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_TruSeq_T_1ng with normal LBP_TruSeq_N_1ng), percent	37.2	7.7	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	84.9	72.7	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	94.8	89.8	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	94.9	89.7	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	91.8	86	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	92.8	86.5	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	84.6	80.3	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	50.5	3.7	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, average over the 47 purity-coverage pairs as printed), percent	59.2	44.5	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_100x_10%T with normal SPP_100x_100%N), percent	49.9	28.9	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	85.6	73.3	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_20%T with normal SPP_100x_100%N), percent	75	66.3	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_5%T with normal SPP_100x_100%N), percent	26.3	14.3	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	83	68.1	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_100x_75%T with normal SPP_100x_100%N), percent	84.8	73.3	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_10%T with normal SPP_10x_100%N), percent	3.6	0.9	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_10x_100%T with normal SPP_10x_100%N), percent	58.8	13	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	13.8	5.5	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_10x_5%T with normal SPP_10x_100%N), percent	1.2	0.6	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_50%T with normal SPP_10x_100%N), percent	41.3	9.9	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_10x_75%T with normal SPP_10x_100%N), percent	53.6	14.1	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_10%T with normal SPP_200x_100%N), percent	75.5	57.3	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_200x_100%T with normal SPP_200x_100%N), percent	85.2	74.5	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_20%T with normal SPP_200x_100%N), percent	80.6	68.4	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_200x_5%T with normal SPP_200x_100%N), percent	38.9	18.2	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_50%T with normal SPP_200x_100%N), percent	83.1	73.2	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_75%T with normal SPP_200x_100%N), percent	84.5	75.4	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_10%T with normal SPP_300x_100%N), percent	78.1	52.7	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_300x_100%T with normal SPP_300x_100%N), percent	84.4	74.2	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_20%T with normal SPP_300x_100%N), percent	81.2	68.1	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_5%T with normal SPP_300x_100%N), percent	44.1	11	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	82.9	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_300x_75%T with normal SPP_300x_100%N), percent	83.5	75.3	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_30x_10%T with normal SPP_30x_100%N), percent	21.2	14.7	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_100%T with normal SPP_30x_100%N), percent	80.7	75.9	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_30x_20%T with normal SPP_30x_100%N), percent	48.6	41.5	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_5%T with normal SPP_30x_100%N), percent	7.1	4.4	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_30x_50%T with normal SPP_30x_100%N), percent	74	60.5	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_75%T with normal SPP_30x_100%N), percent	80	75.6	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_50x_10%T with normal SPP_50x_100%N), percent	34	20.8	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_50x_100%T with normal SPP_50x_100%N), percent	83.7	72.8	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_50x_20%T with normal SPP_50x_100%N), percent	61.3	48.9	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_50x_5%T with normal SPP_50x_100%N), percent	14.5	8.6	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_50x_50%T with normal SPP_50x_100%N), percent	80.4	71.2	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_50x_75%T with normal SPP_50x_100%N), percent	83.1	74.3	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_100%N), percent	46.3	26.6	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_10%T with normal SPP_80x_95%N), percent	25.1	6.2	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_100%T with normal SPP_80x_100%N), percent	85.6	73.2	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_100%T with normal SPP_80x_95%N), percent	72.7	38.3	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_20%T with normal SPP_80x_100%N), percent	68	50.4	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_20%T with normal SPP_80x_95%N), percent	49.4	16.3	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_5%T with normal SPP_80x_100%N), percent	22.1	12.5	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_80x_50%T with normal SPP_80x_100%N), percent	82.7	70.6	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_50%T with normal SPP_80x_95%N), percent	70.9	33.4	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_75%T with normal SPP_80x_100%N), percent	84.2	71.8	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_80x_75%T with normal SPP_80x_95%N), percent	71.4	36.8	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, average over the 47 purity-coverage pairs as printed), percent	71	57	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	65.9	59.4	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_100x_100%T with normal SPP_100x_100%N), percent	97.3	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_100x_20%T with normal SPP_100x_100%N), percent	83.9	78.3	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_5%T with normal SPP_100x_100%N), percent	36.3	33.1	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_100x_50%T with normal SPP_100x_100%N), percent	93.2	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_100x_75%T with normal SPP_100x_100%N), percent	96.1	87.6	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_10x_10%T with normal SPP_10x_100%N), percent	9.3	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_100%T with normal SPP_10x_100%N), percent	74.8	7.1	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_20%T with normal SPP_10x_100%N), percent	25.7	2.6	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_10x_5%T with normal SPP_10x_100%N), percent	3	0.3	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	58	5.9	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_10x_75%T with normal SPP_10x_100%N), percent	69.3	6.9	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_10%T with normal SPP_200x_100%N), percent	81.6	70.3	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_100%T with normal SPP_200x_100%N), percent	98	92.4	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_20%T with normal SPP_200x_100%N), percent	90.4	82.4	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	56.1	51.9	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_50%T with normal SPP_200x_100%N), percent	95.9	87.5	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_200x_75%T with normal SPP_200x_100%N), percent	97.2	91.1	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_10%T with normal SPP_300x_100%N), percent	85.7	69.6	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_300x_100%T with normal SPP_300x_100%N), percent	97.9	93.3	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_300x_20%T with normal SPP_300x_100%N), percent	92.1	79.4	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_5%T with normal SPP_300x_100%N), percent	63.4	54.2	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_50%T with normal SPP_300x_100%N), percent	96.4	82.2	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_75%T with normal SPP_300x_100%N), percent	97.5	92.9	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_10%T with normal SPP_30x_100%N), percent	33.5	23	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	90.9	84.9	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	61	50.8	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_30x_5%T with normal SPP_30x_100%N), percent	14.3	7.3	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	84.5	78.1	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_30x_75%T with normal SPP_30x_100%N), percent	88.8	83.1	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_10%T with normal SPP_50x_100%N), percent	43.9	37.5	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_100%T with normal SPP_50x_100%N), percent	94.4	86.4	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	71.5	66.3	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_50x_5%T with normal SPP_50x_100%N), percent	20.1	13.5	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_50%T with normal SPP_50x_100%N), percent	89.1	83.5	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_75%T with normal SPP_50x_100%N), percent	93	85.8	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_80x_10%T with normal SPP_80x_100%N), percent	58.9	53.2	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_10%T with normal SPP_80x_95%N), percent	40	13.6	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_100%T with normal SPP_80x_100%N), percent	96.9	88.6	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_100%T with normal SPP_80x_95%N), percent	85.6	43.8	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_80x_20%T with normal SPP_80x_100%N), percent	80.7	75.6	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_95%N), percent	62	26.5	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_80x_5%T with normal SPP_80x_100%N), percent	29.2	26.2	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	92.6	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_80x_50%T with normal SPP_80x_95%N), percent	80.3	37.8	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_80x_75%T with normal SPP_80x_100%N), percent	95.2	86.5	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_80x_75%T with normal SPP_80x_95%N), percent	83.6	41.2	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, average over the 21 replicate pairs as printed), percent	84.2	73.7	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_EA_T_1 with normal WGS_EA_N_1), percent	83.2	71	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	83	73.7	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_FD_T_2 with normal WGS_FD_N_2), percent	82.5	72.7	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_FD_T_3 with normal WGS_FD_N_3), percent	82.1	74.1	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	87.9	80.6	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, tumour WGS_IL_T_2 with normal WGS_IL_N_2), percent	85.5	80.1	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_3 with normal WGS_IL_N_3), percent	85.1	80.3	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	79.4	76	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	84.8	80	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	83.9	69.8	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_2 with normal WGS_NS_N_2), percent	83.2	73.2	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	83.7	71	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	84	70.1	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_NS_T_5 with normal WGS_NS_N_5), percent	82.8	67.4	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_NS_T_6 with normal WGS_NS_N_6), percent	84	67.9	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_7 with normal WGS_NS_N_7), percent	84.2	66.5	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_8 with normal WGS_NS_N_8), percent	83.5	74.6	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, tumour WGS_NS_T_9 with normal WGS_NS_N_9), percent	84.8	73.9	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_1 with normal WGS_NV_N_1), percent	86.5	74.3	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	86.9	74.2	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_NV_T_3 with normal WGS_NV_N_3), percent	86.5	76	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	94.1	85.8	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_EA_T_1 with normal WGS_EA_N_1), percent	95.4	90.8	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_1 with normal WGS_FD_N_1), percent	93.7	90.5	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_FD_T_2 with normal WGS_FD_N_2), percent	93.8	90.7	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_3 with normal WGS_FD_N_3), percent	93.7	90.5	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_IL_T_1 with normal WGS_IL_N_1), percent	96.2	91.9	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_2 with normal WGS_IL_N_2), percent	94.9	90.5	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_IL_T_3 with normal WGS_IL_N_3), percent	94.8	90.3	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	88.4	79	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	93.5	89.5	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	94.6	79.2	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_NS_T_2 with normal WGS_NS_N_2), percent	92.1	84.1	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	93.1	83.8	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_NS_T_4 with normal WGS_NS_N_4), percent	94.1	79	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	92.7	78.9	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_NS_T_6 with normal WGS_NS_N_6), percent	95	79	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	93.6	73.4	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_NS_T_8 with normal WGS_NS_N_8), percent	92.1	85.8	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_NS_T_9 with normal WGS_NS_N_9), percent	92.7	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, tumour WGS_NV_T_1 with normal WGS_NV_N_1), percent	97	88.2	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_NV_T_2 with normal WGS_NV_N_2), percent	97	87.8	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_NV_T_3 with normal WGS_NV_N_3), percent	97	92	Octopus

What disagrees

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

Compared: Mutect2, Octopus
Study that disagrees: 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)
Other studies: Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample, Sahraeian et al. 2022, Additional file 2: Supplementary Tables S1-S10 (13059_2021_2592_MOESM2_ESM.pdf)
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 (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

In the other studies (308 results)

In the other studies
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

Follow-up

Current state: Not yet reviewed.

No one has reviewed this finding yet.

How to help resolve this

If you know why these studies disagree, for example a different population, metric or preprocessing step, or you have a corrected value, tell us through the contribution form. A reviewer checks every submission against its source before anything changes, and the step is added to the follow-up above.

Finding discrepancy-826efbc8dbe634da · All contested results · How findings are made and followed up