Table 1 Methods for detecting somatic mutations
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. | ||
Cluster paired-reads, including reads with multiple possible alignments. Support simultaneous analysis of multiple samples. | |||
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. | ||
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. | |
| Â | Â | SomatiCA[31] | Only uses aberrations that are identified as clonal to estimate tumor purity. |