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

Benjamin J Raphael; Jason R Dobson; Layla Oesper; Fabio Vandin

doi:10.1186/gm524

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.
	SNV	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.

CNA, copy number aberration; SNV, single-nucleotide variant; SV, structural variant.
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.

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