Copy Number Variation Detection Using Next Generation Sequencing Read Counts

Overview

Journal BMC Bioinformatics

Publisher Biomed Central

Specialty Biology

Date 2014 Apr 16

PMID 24731174

Citations 33

Authors

Heng Wang

Dan Nettleton

Kai Ying

Affiliations

Soon will be listed here.

Abstract

Background: A copy number variation (CNV) is a difference between genotypes in the number of copies of a genomic region. Next generation sequencing (NGS) technologies provide sensitive and accurate tools for detecting genomic variations that include CNVs. However, statistical approaches for CNV identification using NGS are limited. We propose a new methodology for detecting CNVs using NGS data. This method (henceforth denoted by m-HMM) is based on a hidden Markov model with emission probabilities that are governed by mixture distributions. We use the Expectation-Maximization (EM) algorithm to estimate the parameters in the model.

Results: A simulation study demonstrates that our proposed m-HMM approach has greater power for detecting copy number gains and losses relative to existing methods. Furthermore, application of our m-HMM to DNA sequencing data from the two maize inbred lines B73 and Mo17 to identify CNVs that may play a role in creating phenotypic differences between these inbred lines provides results concordant with previous array-based efforts to identify CNVs.

Conclusions: The new m-HMM method is a powerful and practical approach for identifying CNVs from NGS data.

Citing Articles

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