|
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.
|
