Abstract
Copy number variations have been linked to numerous genetic diseases including cancer, Parkinson’s disease, pancreatitis, and lupus. While current best practices for CNV detection often require using microarrays for detecting large CNVs or multiplex ligation-dependent probe amplification (MLPA) for gene-sized CNVs, new methods have been developed with the goal of replacing both of these specialized assays with bioinformatic analysis applied to next-generation sequencing (NGS) data. Because NGS is already used by clinical labs to detect small coding variants, this approach reduces associated costs, resources, and analysis time. This chapter provides an overview of the various approaches to CNV detection via NGS data, and examines VS-CNV, a commercial tool developed by Golden Helix, which provides robust CNV calling capabilities for both gene panel and exome data.
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Fortier, N., Rudy, G., Scherer, A. (2018). Detection of CNVs in NGS Data Using VS-CNV. In: Bickhart, D. (eds) Copy Number Variants. Methods in Molecular Biology, vol 1833. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8666-8_9
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DOI: https://doi.org/10.1007/978-1-4939-8666-8_9
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