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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| Published in | BMC bioinformatics Vol. 14; no. S11; p. S1 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
BioMed Central
13.09.2013
BioMed Central Ltd |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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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| AbstractList | 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. 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.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. Doc number: S1 Abstract: 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. |
| ArticleNumber | S1 |
| Author | Zhao, Min Zhao, Zhongming Jia, Peilin Wang, Qingguo Wang, Quan |
| AuthorAffiliation | 3 Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA 2 Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA 1 Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA |
| AuthorAffiliation_xml | – name: 1 Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA – name: 2 Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA – name: 3 Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA |
| Author_xml | – sequence: 1 givenname: Min surname: Zhao fullname: Zhao, Min – sequence: 2 givenname: Qingguo surname: Wang fullname: Wang, Qingguo – sequence: 3 givenname: Quan surname: Wang fullname: Wang, Quan – sequence: 4 givenname: Peilin surname: Jia fullname: Jia, Peilin – sequence: 5 givenname: Zhongming surname: Zhao fullname: Zhao, Zhongming |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24564169$$D View this record in MEDLINE/PubMed |
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| Copyright | 2013 Zhao et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright © 2013 Zhao et al; licensee BioMed Central Ltd. 2013 Zhao et al; licensee BioMed Central Ltd. |
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| Snippet | 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.... Doc number: S1 Abstract: Copy number variation (CNV) is a prevalent form of critical genetic variation that leads to an abnormal number of copies of large... |
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| SubjectTerms | DNA Copy Number Variations Exons Genetic diversity Genome Genomics - methods Genotype High-Throughput Nucleotide Sequencing - methods Humans |
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| Title | Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives |
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