Publication Date
2015
Journal or Book Title
Bioinformatics
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.
DOI
https://doi.org/10.1093/bioinformatics/btv275
Pages
2785-2793
Volume
31
Issue
17
Recommended Citation
He, Yuting; Zhang, Fan; and Flaherty, Patrick, "RVD2: an ultra-sensitive variant detection model for low-depth heterogeneous next-generation sequencing data" (2015). Bioinformatics. 1278.
https://doi.org/10.1093/bioinformatics/btv275