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

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  • Matthew Hayes
  • Yoon Soo Pyon
  • Jing Li

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/.

Suggested Citation

  • Matthew Hayes & Yoon Soo Pyon & Jing Li, 2012. "A Model-Based Clustering Method for Genomic Structural Variant Prediction and Genotyping Using Paired-End Sequencing Data," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-13, December.
    • Handle: RePEc:plo:pone00:0052881
    DOI: 10.1371/journal.pone.0052881
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    Cited by:

    1. Jing Xiao & Qiongqiong Xu & Chuanli Wu & Yuexia Gao & Tianqi Hua & Chenwu Xu, 2016. "Performance Evaluation of Missing-Value Imputation Clustering Based on a Multivariate Gaussian Mixture Model," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-14, August.

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