Journal article
Computational tools for copy number variation (CNV) detection using next-generation sequencing data: Features and perspectives
BMC Bioinformatics, Vol.14(Supplement 11), S1
2nd Workshop on Data Mining of Next-Generation Sequencing in conjunction with the 2012 IEEE International Conference on Bioinformatics and Biomedicine, 2012 (Philadelphia, United States, 04-Oct-2012–07-Oct-2012)
2013
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. © 2013 Zhao et al; licensee BioMed Central Ltd.
Details
- Title
- Computational tools for copy number variation (CNV) detection using next-generation sequencing data: Features and perspectives
- Authors
- Min Zhao (Author) - Vanderbilt University School of Medicine, United StatesQ Wang (Author) - Vanderbilt University School of Medicine, United StatesQ Wang (Author) - Vanderbilt University School of Medicine, United StatesP Jia (Author) - Vanderbilt University School of Medicine, United StatesZ Zhao (Author) - Vanderbilt University School of Medicine, United States
- Publication details
- BMC Bioinformatics, Vol.14(Supplement 11), S1; 16
- Conference details
- 2nd Workshop on Data Mining of Next-Generation Sequencing in conjunction with the 2012 IEEE International Conference on Bioinformatics and Biomedicine, 2012 (Philadelphia, United States, 04-Oct-2012–07-Oct-2012)
- Publisher
- BioMed Central Ltd.
- Date published
- 2013
- DOI
- 10.1186/1471-2105-14-S11-S1
- ISSN
- 1471-2105
- Copyright note
- 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.
- Organisation Unit
- School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Centre for Bioinnovation
- Language
- English
- Record Identifier
- 99449054902621
- Output Type
- Journal article
Metrics
618 File views/ downloads
1405 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Web Of Science research areas
- Biochemical Research Methods
- Biotechnology & Applied Microbiology
- Mathematical & Computational Biology
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites