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Detection of Copy Number Alterations Using Single Cell Sequencing
Published on: February 17, 2017
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Detection of copy-number variations from NGS data using read depth information: a diagnostic performance evaluation.
Olivier Quenez1, Kevin Cassinari1, Sophie Coutant2
1Normandie Univ, UNIROUEN, Inserm U1245 and Rouen University Hospital, Department of Genetics and CNR-MAJ, Normandy Center for Genomic and Personalized Medicine, Rouen, France.
European Journal of Human Genetics : EJHG
|June 28, 2020
Summary
This study demonstrates a cost-effective next-generation sequencing (NGS) workflow for detecting copy-number variations (CNVs). The bioinformatics tool CANOES achieves high accuracy, offering a viable alternative to traditional methods for genetic analysis.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Diagnostics
Background:
- Copy-number variations (CNVs) detection from next-generation sequencing (NGS) data is underutilized, with chip-based or targeted techniques still prevalent.
- Existing methods for CNV detection may not fully leverage the potential of NGS data, necessitating improved bioinformatics workflows.
Purpose of the Study:
- To evaluate the performance of a bioinformatics workflow centered on the CANOES tool for CNV detection using NGS data.
- To compare the accuracy of this NGS-based workflow against established methods like quantitative multiplex PCR of short fluorescent fragments (QMSPF) and array comparative genomic hybridization (aCGH).
Main Methods:
- Applied a workflow utilizing the CANOES bioinformatics tool, which analyzes read depth information, to gene panel (GP) and whole-exome sequencing (WES) data.
- Validated CNV calls against QMSPF for GP data and aCGH for WES data, including targeted confirmation for additional WES samples.
Main Results:
- Achieved an 87.8% positive predictive value (PPV) for CNV detection from GP data of 3776 samples, with 100% sensitivity and specificity in a subset compared to QMSPF.
- From WES data, demonstrated 87.25% sensitivity compared to aCGH for comparable exonic CNVs and an overall PPV of 86.4% after targeted confirmation.
- The CANOES workflow on WES data detected single-exon CNVs, identifying variations missed by aCGH, indicating higher resolution.
Conclusions:
- The CANOES-centered NGS workflow provides a highly accurate and sensitive method for CNV detection, comparable or superior to traditional techniques.
- Transitioning to an NGS-only approach for CNV detection is potentially cost-effective, offering stable diagnostic yields and reduced overall costs.
- The developed bioinformatics pipeline enables high-resolution CNV detection, including single-exon events, enhancing diagnostic capabilities.
