MSeq-CNV: accurate detection of Copy Number Variation from Sequencing of Multiple samples.

ABSTRACT: Currently a few tools are capable of detecting genome-wide Copy Number Variations (CNVs) based on sequencing of multiple samples. Although aberrations in mate pair insertion sizes provide additional hints for the CNV detection based on multiple samples, the majority of the current tools rely only on the depth of coverage. Here, we propose a new algorithm (MSeq-CNV) which allows detecting common CNVs across multiple samples. MSeq-CNV applies a mixture density for modeling aberrations in depth of coverage and abnormalities in the mate pair insertion sizes. Each component in this mixture density applies a Binomial distribution for modeling the number of mate pairs with aberration in the insertion size and also a Poisson distribution for emitting the read counts, in each genomic position. MSeq

SUBMITTER: Malekpour SA 

PROVIDER: S-EPMC5838159 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature