RVD2: an ultra-sensitive variant detection model for low-depth heterogeneous next-generation sequencing data. (29th April 2015)

• Record Type: Journal Article
• Authors: He, Yuting; Zhang, Fan; Flaherty, Patrick
• Is Part Of: Bioinformatics. Volume 31:Number 17(2015)
• Page Start: 2785
• Page End: 2793
• Publication Date: 2015-04-29
• Subjects: Bioinformatics -- Periodicals; Genomics -- Data processing -- Periodicals; Computational biology -- Periodicals; 572.80285
• DOI: 10.1093/bioinformatics/btv275
• Languages: English
• ISSNs: 1367-4803

Abstract

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. Availability and implementation: http://genomics.wpi.edu/rvd2/ . Contact: [e-mail] Supplementary information : Supplementary data are available at Bioinformatics online.