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Model based clustering approach for identifying structural variation using next generation sequencing data

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3 Author(s)
Yoon Soo Pyon ; Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA ; Matthew Hayes ; Jing Li

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 event. Our method, termed SVMiner, not only provides a probability score for each candidate, but also predicts the heterozygosity of genomic deletions. Experiments on genome-wide deep sequencing data have demonstrated that SVMiner is robust against the variability of a single cluster feature, and it performs well when classifying validated SV events with accentuated features.

Published in:

Computational Advances in Bio and Medical Sciences (ICCABS), 2011 IEEE 1st International Conference on

Date of Conference:

3-5 Feb. 2011