Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives

TLDR

Copy number variation is a common genetic alteration involving abnormal copy numbers of large genomic regions, traditionally detected by arrayCGH or genotyping arrays, but increasingly identified using next‑generation sequencing, driving the development of many computational CNV detection tools. The article reviews recent computational methods for CNV detection from whole‑genome and whole‑exome sequencing, discusses their strengths and weaknesses, and proposes future directions. The review focuses on computational approaches applied to whole‑genome and whole‑exome sequencing data for CNV detection.

Abstract

Copy number variation (CNV) is a prevalent form of critical genetic variation that leads to an abnormal number of copies of large genomic regions in a cell. Microarray-based comparative genome hybridization (arrayCGH) or genotyping arrays have been standard technologies to detect large regions subject to copy number changes in genomes until most recently high-resolution sequence data can be analyzed by next-generation sequencing (NGS). During the last several years, NGS-based analysis has been widely applied to identify CNVs in both healthy and diseased individuals. Correspondingly, the strong demand for NGS-based CNV analyses has fuelled development of numerous computational methods and tools for CNV detection. In this article, we review the recent advances in computational methods pertaining to CNV detection using whole genome and whole exome sequencing data. Additionally, we discuss their strengths and weaknesses and suggest directions for future development.