detect somatic mutations using SomaticSeq

SomaticSeq is an accurate somatic mutation detection pipeline implementing a stochastic boosting algorithm to produce highly accurate somatic mutation calls for both single nucleotide variants and small insertions and deletions. The workflow currently incorporates five state-of-the-art somatic mutation callers, and extracts over 70 individual genomic and sequencing features for each candidate site. A training set is provided to an adaptively boosted decision tree learner to create a classifier for predicting mutation statuses. We validate our results with both synthetic and real data. We report that SomaticSeq is able to achieve better overall accuracy than any individual tool incorporated. Background Cancers are diseases of the genome. Somatic single nucleotide variants (SNVs) and small insertions and dele-tions (indels) are common drivers of carcinogenesis. Therefore, accurately detecting somatic mutations is a key analysis in cancer research. The challenge and com-plexity of cancer sequencing analysis lie in the hetero-geneous nature of tumor samples, in addition to the cross-contamination between tumor andmatched normal samples. A somatic tool that performs well for one tumor may perform poorly for another, as reported in a number of comparative studies [1, 2]. For instance, MuTect is a somatic SNV caller that applies a Bayesian classifier to detect somatic mutations [3]. It is sensitive in detect-ing low variant allele frequency (VAF) somatic variants. It also incorporates a series of filters to penalize can-didate variants that have characteristics corresponding to sequencing artifacts to increase precision. However, MuTect applies severe penalties to somatic variant candi-dates if the variant reads are also found in the matched normal. While this approach filters out most germline variant false positives, it adversely affects sensitivity i