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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.

Software type:
Command line interface
Restrictions to use:
Operating system:
Programming languages:
Python, Shell (Bash)
Computer skills:
Python3 and regex package, R and ada package, GATK, snpEFF and snpSIFT, dbSNP, COSMIC file
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  • Hugo Y.K. Lam <li_tai.fang at bina.roche.com>


Bina Technologies, Roche Sequencing, Redwood City 94065, CA, USA

  • (Fang et al., 2015) An ensemble approach to accurately detect somatic mutations using SomaticSeq. Genome biology.
    PMID: 26381235
  • (Spencer et al., 2014) Performance of common analysis methods for detecting low-frequency single nucleotide variants in targeted next-generation sequence data. The Journal of molecular diagnostics.
    PMID: 24211364
  • (Stead et al., 2013) Accurately identifying low-allelic fraction variants in single samples with next-generation sequencing: applications in tumor subclone resolution. Human mutation.
    PMID: 23766071
  • (Roberts et al., 2013) A comparative analysis of algorithms for somatic SNV detection in cancer. Bioinformatics.
    PMID: 23842810
  • (Wang et al., 2013) Detecting somatic point mutations in cancer genome sequencing data: a comparison of mutation callers. Genome medicine.
    PMID: 24112718

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