Cancer NGS Data Analysis

Detecting somatic mutations in cancer research

The Bina Genome Analysis Platform includes a cancer pipeline that implements an integrative approach to identify and rank the most clinically important mutations based on a combination of different algorithms, sequencing features, and prior knowledge. This resource guide provides information and resources for NGS Cancer Analysis, including:

  • The Bina cancer analysis tools & pipeline
  • Speed and parallelization to provide multi-tool
    analysis with reduced overall run-time
  • OncoRank: a proprietary computational method developed
    by Bina bioinformatics that ranks mutations found in tumor-normal studies
  • Cancer databases (e.g. dbSNP, COSMIC, and Cancer Gene Census)
  • Sample analysis results for the breast cancer cell line HCC1143
  • Detecting low allele frequency variants in titration experiments
  • Resolving leukemia (AML) data sets with heavy cross-contamination

REFERENCES

  • Xi et al., Copy number variation detection in whole-genome sequencing data using the Bayesian information criterion, PNAS, 2011, PMID 22065754
  • Alkodsi et al., Comparative analysis of methods for identifying somatic copy number alterations from deep sequencing data, Brief Bioinformatics, 2014, PMID 24599115
  • Wang et al., Detecting somatic point mutations in cancer genome sequencing data: a comparison of mutation callers, Genome Medicine, 2013, PMID 24112718
  • Roberts et al., A comparative analysis of algorithms for somatic SNV detection in cancer, Bioinformatics, 2013, PMID 23842810
  • Goode et al., A simple consensus approach improves somatic mutation prediction accuracy. Genome Medicine, 2013, PMID 24073752