General Information
| Description | An ultra-sensitive variant detection model for low-depth heterogeneous next-generation sequencing data |
| Homepage | http://genomics.wpi.edu/rvd2/ |
Publications
RVD2: an ultra-sensitive variant detection model for low-depth heterogeneous next-generation sequencing data
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.
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