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