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Article
RVD2: an ultra-sensitive variant detection model for low-depth heterogeneous next-generation sequencing data
Bioinformatics (2015)
  • Patrick Flaherty
Abstract
MOTIVATION:
Next-generation sequencing technology is increasingly being used for clinical diagnostic tests. Clinical samples are often genomically heterogeneous due to low sample purity or the presence of genetic subpopulations. Therefore, a variant calling algorithm for calling low-frequency polymorphisms in heterogeneous samples is needed.
RESULTS:
We present a novel variant calling algorithm that uses a hierarchical Bayesian model to estimate allele frequency and call variants in heterogeneous samples. We show that our algorithm improves upon current classifiers and has higher sensitivity and specificity over a wide range of median read depth and minor allele fraction. We apply our model and identify 15 mutated loci in the PAXP1 gene in a matched clinical breast ductal carcinoma tumor sample; two of which are likely loss-of-heterozygosity events.
Publication Date
Spring April 29, 2015
DOI
10.1093/bioinformatics/btv275
Citation Information
Patrick Flaherty. "RVD2: an ultra-sensitive variant detection model for low-depth heterogeneous next-generation sequencing data" Bioinformatics Vol. 31 Iss. 17 (2015) p. 2785 - 2793
Available at: http://works.bepress.com/patrick-flaherty/12/