Abstract
Intragenic copy number variations (CNVs) in the human genome are significant contributors to the inherited genetic disorders. Currently the most established methods to detect CNVs are array comparative genomic hybridization (aCGH) and MPLA. With the fast adaption of next generation sequencing (NGS) in the clinical sequencing, increasing interest has been attributed to the detection of CNV from NGS data. In this chapter, we describe an easy-to-implement strategy to detect and visualize exonic CNVs from captured NGS data, as well as the confirmation. We also discuss the specificity and sensitivity of this strategy.
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Feng, Y., Chen, D., Wong, LJ.C. (2017). Detection of Copy Number Variations (CNVs) Based on the Coverage Depth from the Next Generation Sequencing Data. In: Wong, LJ. (eds) Next Generation Sequencing Based Clinical Molecular Diagnosis of Human Genetic Disorders. Springer, Cham. https://doi.org/10.1007/978-3-319-56418-0_2
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DOI: https://doi.org/10.1007/978-3-319-56418-0_2
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