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Detection of Copy Number Alterations Using Single Cell Sequencing
Published on: February 17, 2017
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Comparative study of whole exome sequencing-based copy number variation detection tools
Lanling Zhao1, Han Liu1, Xiguo Yuan2
1Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
BMC Bioinformatics
|March 7, 2020
Summary
This study evaluates four whole exome sequencing (WES) copy number variation (CNV) detection tools, providing a guideline for selecting the best tool based on specific project needs like CNV size and computational cost.
Area of Science:
- Genomics
- Bioinformatics
Background:
- Whole exome sequencing (WES) enables copy number variation (CNV) detection.
- Clinical application of WES-based CNV tools is hindered by a lack of usage guidelines.
Purpose of the Study:
- To evaluate the performance of four WES-based CNV detection tools.
- To establish a guideline for selecting appropriate CNV detection tools for clinical settings.
Main Methods:
- Selected four WES-based CNV detection tools: CoNIFER, cn.MOPS, CNVkit, and exomeCopy.
- Evaluated tools based on sensitivity, specificity, overlapping consistency, and computational costs.
Main Results:
- Sensitivity increases with coverage/CNV size, while specificity decreases.
- CoNIFER excels at detecting insertions; others favor deletions.
- CoNIFER, cn.MOPS, and CNVkit show reliable overlapping consistency.
- CoNIFER offers best space complexity; cn.MOPS offers best time complexity.
Conclusions:
- No single tool is optimal for all scenarios; tool performance varies.
- A guideline is provided to aid tool selection based on CNV size, type, and computational resources.
- This resource assists users, even those with limited computational expertise, in choosing the right CNV detection tool.
Keywords:
Computational costsCopy number variantsGuidelineNext generation sequencingOverlapping consistencyRecommendationSensitivitySpecificityWhole exome sequencingMore Related Videos
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