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Front Genet. 2015 Apr 13;6:138. doi: 10.3389/fgene.2015.00138. eCollection 2015.

Whole-genome CNV analysis: advances in computational approaches.

Author information

1
Mood Disorders Center, Department of Psychiatry and Behavioral Sciences, School of Medicine, Johns Hopkins University Baltimore, MD, USA.
2
Mood Disorders Center, Department of Psychiatry and Behavioral Sciences, School of Medicine, Johns Hopkins University Baltimore, MD, USA ; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health Baltimore, MD, USA USA.

Abstract

Accumulating evidence indicates that DNA copy number variation (CNV) is likely to make a significant contribution to human diversity and also play an important role in disease susceptibility. Recent advances in genome sequencing technologies have enabled the characterization of a variety of genomic features, including CNVs. This has led to the development of several bioinformatics approaches to detect CNVs from next-generation sequencing data. Here, we review recent advances in CNV detection from whole genome sequencing. We discuss the informatics approaches and current computational tools that have been developed as well as their strengths and limitations. This review will assist researchers and analysts in choosing the most suitable tools for CNV analysis as well as provide suggestions for new directions in future development.

KEYWORDS:

CNVs; computational modeling; copy number variation; next generation sequencing; structural variation (SV); whole-genome sequencing

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