RVD2: an ultra-sensitive variant detection model for low-depth heterogeneous next-generation sequencing data.

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.

SUBMITTER: He Y 

PROVIDER: S-EPMC4547613 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature