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Detection of CNVs in NGS Data Using VS-CNV

Part of the Methods in Molecular Biology book series (MIMB,volume 1833)

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.

Key words

  • Copy number variation
  • Next generation sequencing

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References

  1. Chance PF, Pleasure D (1993) Charcot-Marie-tooth syndrome. Arch Neurol 50:1180–1184

    CrossRef  CAS  PubMed  Google Scholar 

  2. Conrad DF, Pinto D, Redon R et al (2010) Origins and functional impact of copy number variation in the human genome. Nature 464:704

    CrossRef  CAS  PubMed  Google Scholar 

  3. Redon R, Ishikawa S, Fitch KR et al (2006) Global variation in copy number in the human genome. Nature 444:444–454

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  4. Lupski JR (2007) Genomic rearrangements and sporadic disease. Nat Genet 39:S43–S47

    CrossRef  CAS  PubMed  Google Scholar 

  5. Stankiewicz P, Lupski JR (2010) Structural variation in the human genome and its role in disease. Annu Rev Med 61:437–455

    CrossRef  CAS  PubMed  Google Scholar 

  6. Fromer M, Moran JL, Chambert K et al (2012) Discovery and statistical genotyping of copy-number variation from whole-exome sequencing depth. Am J Hum Genet 91:597–607

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  7. Yau C (2013) OncoSNP-SEQ: a statistical approach for the identification of somatic copy number alterations from next-generation sequencing of cancer genomes. Bioinformatics 29(19):2482–2484

    CrossRef  CAS  PubMed  Google Scholar 

  8. Boeva V, Popova T, Bleakley K et al (2012) Control-FREEC: a tool for assessing copy number; allelic content using next-generation sequencing data. Bioinformatics 28(3):423–425

    CrossRef  CAS  PubMed  Google Scholar 

  9. Mayrhofer M, DiLorenzo S, Isaksson A (2013) Patchwork: allele-specific copy number analysis of whole-genome sequenced tumor tissue. Genome Biol 14(3):1

    CrossRef  Google Scholar 

  10. Miller CA, Hampton O, Coarfa C et al (2011) ReadDepth: a parallel R package for detecting copy number alterations from short sequencing reads. PLoS One 6(1):e16327

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  11. Packer JS, Maxwell EK, O’Dushlaine C et al (2016) CLAMMS: a scalable algorithm for calling common and rare copy number variants from exome sequencing data. Bioinformatics 32(1):133–135

    PubMed  CAS  Google Scholar 

  12. Jiang Y, Oldridge DA, Diskin SJ et al (2015) CODEX: a normalization and copy number variation detection method for whole exome sequencing. Nucleic Acids Res 43(6):e39

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  13. Krumm N, Sudmant PH, Ko A et al (2012) Copy number variation detection and genotyping from exome sequence data. Genome Res 22(8):1525–1532

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  14. Johansson LF, Dijk F, Boer EN et al (2016) CoNVaDING: single exon variation detection in targeted NGS data. Hum Mutat 37(5):457–464

    CrossRef  CAS  PubMed  Google Scholar 

  15. Pugh TJ, Amr SS, Bowser MJ et al (2015) VisCap: inference and visualization of germ-line copy-number variants from targeted clinical sequencing data. Genet Med 18(7):712–719

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  16. Talevish E, Shain AH, Botton T et al (2016). CNVkit: Genome-Wide copy number detection and visualization from targeted DNA sequencing. PLoS Computational Biology 12(4):e1004873

    Google Scholar 

  17. Xi R, Luquette J, Hadjipanayis A et al (2010) BIC-seq: a fast algorithm for detection of copy number alterations based on high-throughput sequencing data. Genome Biol 11(1):1

    CrossRef  CAS  Google Scholar 

  18. Koller D, Friedman N (2009) Probabilistic graphical models: principles and techniques. MIT Press, Cambridge

    Google Scholar 

  19. Backenroth D, Homsy J, Murillo LR et al (2014) CANOES: detecting rare copy number variants from whole exome sequencing data. Nucleic Acids Res 42(12):e97

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  20. Olshen AB, Venkatraman E, Lucito R et al (2004) Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5(4):557–572

    CrossRef  PubMed  Google Scholar 

  21. Koboldt DC, Zhang Q, Larson DE et al (2012) VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res 22(3):568–576

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  22. Sathirapongsasuti JF, Lee H, Horst BA et al (2011) Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV. Bioinformatics 27(19):2648–2654

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  23. Iacocca MA, Wang J, Dron JS et al (2017) Use of next-generation sequencing to detect LDLR gene copy number variation in familial hypercholesterolemia. J Lipid Res 58(11):2202–2209

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  24. Zhang J, Baran J, Cros A et al (2011) International cancer genome consortium data portal—a one-stop shop for cancer genomics data. Database (Oxford). https://doi.org/10.1093/database/bar026

  25. Craig DW, Liang W, Venkata Y et al (2013) Interim analysis of the Mmrf Commpass trial, a longitudinal study in multiple myeloma relating clinical outcomes to genomic and immunophenotypic profiles. Blood 122(21):532

    Google Scholar 

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Correspondence to Andreas Scherer .

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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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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8665-1

  • Online ISBN: 978-1-4939-8666-8

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