Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives

Copy number variation (CNV) is a prevalent form of critical genetic variation that leads to an abnormal number of copies of large genomic regions in a cell. Microarray-based comparative genome hybridization (arrayCGH) or genotyping arrays have been standard technologies to detect large regions subje...

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Veröffentlicht in:BMC bioinformatics Jg. 14; H. Suppl 11; S. S1
Hauptverfasser: Zhao, Min, Wang, Qingguo, Wang, Quan, Jia, Peilin, Zhao, Zhongming
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 13.09.2013
Springer Nature B.V
Schlagworte:
Algorithms
Bioinformatics
Biomedical and Life Sciences
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
DNA Copy Number Variations
Exons
Genetic diversity
Genome
Genomics - methods
Genotype
High-Throughput Nucleotide Sequencing - methods
Humans
Life Sciences
Microarrays
Whole Genome Sequencing Data
Copy Number Variation Region
Copy Number Variation
Circular Binary Segmentation
Whole Exome Sequencing
ISSN:1471-2105, 1471-2105
Online-Zugang:Volltext
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Zusammenfassung:Copy number variation (CNV) is a prevalent form of critical genetic variation that leads to an abnormal number of copies of large genomic regions in a cell. Microarray-based comparative genome hybridization (arrayCGH) or genotyping arrays have been standard technologies to detect large regions subject to copy number changes in genomes until most recently high-resolution sequence data can be analyzed by next-generation sequencing (NGS). During the last several years, NGS-based analysis has been widely applied to identify CNVs in both healthy and diseased individuals. Correspondingly, the strong demand for NGS-based CNV analyses has fuelled development of numerous computational methods and tools for CNV detection. In this article, we review the recent advances in computational methods pertaining to CNV detection using whole genome and whole exome sequencing data. Additionally, we discuss their strengths and weaknesses and suggest directions for future development.
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ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-14-S11-S1