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Estimates 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 fraction. It is able to use multiple cores in parallel, which can significantly improve time efficiency. The tool does not address identification of indels, structural variants (SV) or copy number variants (CNV). Those mutations typically require specific data analysis models and tests that are different than those for single nucleotide variants

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RVD classification

RVD specifications

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RVD distribution


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Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester and Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA, USA

Funding source(s)

Supported by seed funding from Worcester Polytechnic Institute and by PhRMA Foundation Informatics Grant 2013080079.

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