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Table 1 Methods for detecting somatic mutations

From: Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine

Objective Data Method Description
Somatic mutation detection SNV MuTect[22] Designed to detect low-frequency mutations in both whole-genome and exome data.
Strelka[23] Can be applied to both whole-genome and whole-exome data. Uses stringent post-call filtration.
VarScan 2[24] Demonstrates high sensitivity for detecting SNVs in relatively pure tumor samples from both whole-genome and exome data.
JointSNVMix[128] A probabilistic model that describes the observed allelic counts in both tumor and normal samples.
CNA or SV BIC-Seq[129] Detects CNAs from whole-genome data.
APOLLOH[130] Predicts loss of heterozygosity regions from whole-genome sequencing data.
CoNIFER[131] Detects CNAs from exome data.
BreakDancer[132] Cluster paired-end alignments to detect SVs. One version to detect large aberrations and another to detect smaller indels.
VariationHunter-CommonLaw[133], HYDRA[70] Cluster paired-reads, including reads with multiple possible alignments. Support simultaneous analysis of multiple samples.
GASV/GASVPro[134, 135], PeSV-Fisher[136] Combine paired-read and read-depth analysis to detect SVs.
Meerkat[130] Combines paired-end split-read and multiple alignment information to detect structural aberrations.
Delly[137], Break-Pointer[138] Combines paired-end and split-read signals to detect structural aberrations.
Tumor purity estimation SNV ABSOLUTE[28] Originally designed for SNP array data, but may be adapted for whole-genome sequencing data. Handles subclonal populations as outliers.
ASCAT[29] Designed for SNP array data, but may be adapted for whole-genome sequencing data. Only considers a single tumor population.
CNA THetA[30] Able to consider multiple subclonal tumor populations, but only if they differ by large CNAs. Designed for whole-genome sequencing data.
   SomatiCA[31] Only uses aberrations that are identified as clonal to estimate tumor purity.
  1. CNA, copy number aberration; SNV, single-nucleotide variant; SV, structural variant.
  2. A representative list of software available for the detection of somatic mutations from high-throughput sequencing data of cancer genomes. Some methods detect more than one type of mutation but are listed only once for clarity.