Format

Send to

Choose Destination
Bioinformatics. 2015 Sep 1;31(17):2785-93. doi: 10.1093/bioinformatics/btv275. Epub 2015 Apr 29.

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

Author information

1
Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester and.
2
Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester and Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA, USA.

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.edu

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
25931517
PMCID:
PMC4547613
DOI:
10.1093/bioinformatics/btv275
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Silverchair Information Systems Icon for PubMed Central
Loading ...
Support Center