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  3. Whole-genome sequencing
  4. Somatic SNV detection
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A variant calling algorithm that uses a hierarchical Bayesian model to estimate allele frequency and call variants in heterogeneous samples. RVD2 improves upon current classifiers and has higher sensitivity and specificity over a wide range of median read depth and minor allele frequency.

Software type:
Package
Interface:
Command line interface
Restrictions to use:
None
Operating system:
Unix/Linux
Programming languages:
Python
Computer skills:
Advanced
Stability:
Stable
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Maintainer

  • Patrick Flaherty <pjflaherty at wpi.edu>

Institution(s)

Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA

  • (He et al., 2015) RVD2: An ultra-sensitive variant detection model for low-depth heterogeneous next-generation sequencing data. Bioinformatics.
    PMID: 25931517
  • (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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