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Mycobacterial DNA Extraction using Bead Beating in Custom Buffer Followed by NGS Workflow
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Detection of CNVs in NGS Data Using VS-CNV.

Nathan Fortier1, Gabe Rudy1, Andreas Scherer2

  • 1Golden Helix Inc., Bozeman, MT, USA.

Methods in Molecular Biology (Clifton, N.J.)
|July 25, 2018
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Copy number variations (CNVs) are linked to genetic diseases. New bioinformatic methods using next-generation sequencing (NGS) data offer a cost-effective alternative to traditional assays for CNV detection.

Keywords:
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Area of Science:

  • Genetics
  • Bioinformatics
  • Genomic Medicine

Background:

  • Copy number variations (CNVs) are significant contributors to various genetic diseases, including cancer, Parkinson's disease, pancreatitis, and lupus.
  • Current standard methods for CNV detection, such as microarrays for large CNVs and multiplex ligation-dependent probe amplification (MLPA) for gene-sized CNVs, are specialized and resource-intensive.

Purpose of the Study:

  • To provide an overview of bioinformatic approaches for CNV detection using next-generation sequencing (NGS) data.
  • To evaluate VS-CNV, a commercial tool for robust CNV calling from NGS data.

Main Methods:

  • Review of various bioinformatic strategies for CNV detection from NGS data.
  • Application and assessment of the VS-CNV software tool for analyzing gene panel and exome sequencing data.

Main Results:

  • NGS-based bioinformatic analysis presents a unified and potentially more efficient approach to CNV detection compared to traditional assays.
  • VS-CNV demonstrates robust capabilities for calling copy number variations across different NGS data types, including gene panels and exomes.

Conclusions:

  • NGS-based CNV detection methods, including tools like VS-CNV, can reduce costs, resources, and analysis time for clinical laboratories already utilizing NGS.
  • Bioinformatic analysis of NGS data is emerging as a powerful alternative to specialized assays for comprehensive CNV detection.