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-CNV is applied on simulated data and also on real data of six HapMap individuals with high-coverage sequencing, in 1000 Genomes Project. These individuals include a CEU trio of European ancestry and a YRI trio of Nigerian ethnicity. Ancestry of these individuals is studied by clustering the identified CNVs. MSeq-CNV is also applied for detecting CNVs in two samples with low-coverage sequencing in 1000 Genomes Project and six samples form the Simons Genome Diversity Project.
Highlights
Copy Number Variation (CNV) and balanced rearrangements such as inversions and translocations are types of the large structural variations in the human genome and other organisms
There are common CNVs which are shared by complex diseases[36] and can be detected from sequencing of multiple samples
In this article we proposed MSeq-CNV as a new tool for detecting genome-wide deletions and duplications from sequencing of multiple samples
Summary
Copy Number Variation (CNV) and balanced rearrangements such as inversions and translocations are types of the large structural variations in the human genome and other organisms. CNV detection methods which rely on a low-coverage sequencing data are more relevant in the future studies[37,38]. A major drawback of these tools is relying only on read depth data which results in suffering from a low power or a high false positive rate, due to the large noise in read depth signals. These tools do not take the observed aberrations in the mate pair reads into account. Besides read depth, mate pair insertion sizes provide another source of information for the genome-wide CNV detection with an increased resolution
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