Comparative study of whole exome sequencing-based copy number variation detection tools

Background With the rapid development of whole exome sequencing (WES), an increasing number of tools are being proposed for copy number variation (CNV) detection based on this technique. However, no comprehensive guide is available for the use of these tools in clinical settings, which renders them...

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Bibliographic Details
Published in:BMC bioinformatics Vol. 21; no. 1; pp. 97 - 10
Main Authors: Zhao, Lanling, Liu, Han, Yuan, Xiguo, Gao, Kun, Duan, Junbo
Format: Journal Article
Language:English
Published: London BioMed Central 05.03.2020
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Accuracy
Algorithms
Bioinformatics
Biomedical and Life Sciences
Comparative literature
Comparative studies
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer science
Copy number
Copy number variants
DNA Copy Number Variations
DNA sequencing
Exome - genetics
Genes
Genetic variation
Genomes
Humans
Life Sciences
Medical research
Methods
Microarrays
Next generation sequencing
Observations
Overlapping consistency
Research Article
Researchers
Sensitivity
Sequences
Setting (Literature)
Software - economics
Specificity
Structural analysis
Studies
Time
Whole exome sequencing
Whole Exome Sequencing - methods
China
Sensitivity
Specificity
Computational costs
Whole exome sequencing
Overlapping consistency
Guideline
Next generation sequencing
Recommendation
Copy number variants
ISSN:1471-2105, 1471-2105
Online Access:Get full text
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Summary:Background With the rapid development of whole exome sequencing (WES), an increasing number of tools are being proposed for copy number variation (CNV) detection based on this technique. However, no comprehensive guide is available for the use of these tools in clinical settings, which renders them inapplicable in practice. To resolve this problem, in this study, we evaluated the performances of four WES-based CNV tools, and established a guideline for the recommendation of a suitable tool according to the application requirements. Results In this study, first, we selected four WES-based CNV detection tools: CoNIFER, cn.MOPS, CNVkit and exomeCopy. Then, we evaluated their performances in terms of three aspects: sensitivity and specificity, overlapping consistency and computational costs. From this evaluation, we obtained four main results: (1) The sensitivity increases and subsequently stabilizes as the coverage or CNV size increases, while the specificity decreases. (2) CoNIFER performs better for CNV insertions than for CNV deletions, while the remaining tools exhibit the opposite trend. (3) CoNIFER, cn.MOPS and CNVkit realize satisfactory overlapping consistency, which indicates their results are trustworthy. (4) CoNIFER has the best space complexity and cn.MOPS has the best time complexity among these four tools. Finally, we established a guideline for tools’ usage according to these results. Conclusion No available tool performs excellently under all conditions; however, some tools perform excellently in some scenarios. Users can obtain a CNV tool recommendation from our paper according to the targeted CNV size, the CNV type or computational costs of their projects, as presented in Table 1, which is helpful even for users with limited knowledge of computer science.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-020-3421-1