| Chapter title |
Detection of CNVs in NGS Data Using VS-CNV
|
|---|---|
| Chapter number | 9 |
| Book title |
Copy Number Variants
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-8666-8_9 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-8665-1, 978-1-4939-8666-8
|
| Authors |
Nathan Fortier, Gabe Rudy, Andreas Scherer, Fortier, Nathan, Rudy, Gabe, Scherer, Andreas |
| 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. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 26 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 23% |
| Student > Bachelor | 6 | 23% |
| Other | 3 | 12% |
| Researcher | 2 | 8% |
| Student > Master | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 8 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 11 | 42% |
| Medicine and Dentistry | 3 | 12% |
| Veterinary Science and Veterinary Medicine | 1 | 4% |
| Computer Science | 1 | 4% |
| Neuroscience | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 9 | 35% |