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Targeted DNA Methylation Analysis by Next-generation Sequencing
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Free-access copy-number variant detection tools for targeted next-generation sequencing data.

Iria Roca1, Lorena González-Castro2, Helena Fernández2

  • 1University of Santiago de Compostela, Spain; Genomes and Disease Group, Molecular Medicine and Chronic Diseases Centre (CiMUS), University of Santiago de Compostela, Spain; Unit of Diagnosis and Treatment of Congenital Metabolic Diseases. Fundación Instituto de Investigación Sanitaria de Santiago de Compostela (FIDIS), Spain.

Mutation Research. Reviews in Mutation Research
|May 18, 2019
PubMed
Summary
This summary is machine-generated.

Copy number variants (CNVs) are crucial in human disease but often missed. This study reviews depth of coverage (DoC) methods for CNV detection in targeted next-generation sequencing (NGS) panels, identifying top tools for diagnostic use.

Keywords:
CNV-detection toolsGene panelsSimulated dataTargeted next-generation sequencing

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Area of Science:

  • Genomics and Bioinformatics
  • Human Genetics
  • Molecular Diagnostics

Background:

  • Copy number variants (CNVs) are significant contributors to genetic disease burden.
  • Small CNVs are often underestimated and undetectable by traditional Sanger sequencing.
  • Next-generation sequencing (NGS) enables CNV detection via depth of coverage (DoC) comparisons.

Purpose of the Study:

  • To review existing DoC-based CNV detection methods.
  • To evaluate these methods using simulated targeted NGS (tg-NGS) data.
  • To assess the strengths and weaknesses of CNV detection tools for diagnostic workflows.

Main Methods:

  • Literature review of DoC-based CNV detection methods.
  • Performance assessment using simulated tg-NGS data.
  • Analysis of tool usability for diagnostic laboratory staff.

Main Results:

  • DECoN, ExomeDepth, and ExomeCNV are identified as the most effective CNV detection tools for tg-NGS panels.
  • Current CNV detection tools face challenges due to limited annotated data and simulation tools.
  • Existing programs require enhanced user-friendliness for adoption in diagnostic settings.

Conclusions:

  • Effective CNV detection in tg-NGS panels is achievable with current tools like DECoN, ExomeDepth, and ExomeCNV.
  • Improving the user-friendliness of these bioinformatics tools is essential for widespread adoption in clinical diagnostics.
  • Further development of simulation tools and annotated datasets is needed to advance CNV detection methodologies.