Title

A Model-Based Clustering Method for Genomic Structural Variant Prediction and Genotyping Using Paired-End Sequencing Data

Funding Source

National Institutes of Health/NLM (LM008991) GAANN fellowship to MH.

Grant Number

LM008991

Department

Department of Physics and Computer Science - Dual Degree Engineering

Document Type

Article

Publication Date

12-27-2012

Abstract

Structural variation (SV) has been reported to be associated with numerous diseases such as cancer. With the advent of next generation sequencing (NGS) technologies, various types of SV can be potentially identified. We propose a model based clustering approach utilizing a set of features defined for each type of SV events. Our method, termed SVMiner, not only provides a probability score for each candidate, but also predicts the heterozygosity of genomic deletions. Extensive experiments on genome-wide deep sequencing data have demonstrated that SVMiner is robust against the variability of a single cluster feature, and it significantly outperforms several commonly used SV detection programs. SVMiner can be downloaded from http://cbc.case.edu/svminer/.

Comments

DOI: 10.1371/journal.pone.0052881

PubMed ID: 23300804

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