Dataset Information
RVD2: an ultra-sensitive variant detection model for low-depth heterogeneous next-generation sequencing data.
Ontology highlight
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
pjflaherty@wpi.eduSupplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: He Y
PROVIDER: S-EPMC4547613 | biostudies-literature | 2015 Sep
REPOSITORIES: biostudies-literature
Publications
RVD2: an ultra-sensitive variant detection model for low-depth heterogeneous next-generation sequencing data.
Bioinformatics (Oxford, England) 20150429 17
<h4>Motivation</h4>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.<h4>Results</h4>We present a novel variant calling algorithm that uses a hierarchical Bayesian model to estimate allele frequency and call variants in ...[more]