Identification of copy number variants from exome sequence data.

ABSTRACT:

Background

With advances in next generation sequencing technologies and genomic capture techniques, exome sequencing has become a cost-effective approach for mutation detection in genetic diseases. However, computational prediction of copy number variants (CNVs) from exome sequence data is a challenging task. Whilst numerous programs are available, they have different sensitivities, and have low sensitivity to detect smaller CNVs (1-4 exons). Additionally, exonic CNV discovery using standard aCGH has limitations due to the low probe density over exonic regions. The goal of our study was to develop a protocol to detect exonic CNVs (including shorter CNVs that cover 1-4 exons), combining computational prediction algorithms and a high-resolution custom CGH array.

Results

We use

SUBMITTER: Samarakoon PS 

PROVIDER: S-EPMC4132917 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature