CNV detection method optimized for high-resolution arrayCGH by normality test
- Authors
- Ahn, Jaegyoon; Yoon, Youngmi; Park, Chihyun; Park, Sanghyun
- Issue Date
- Apr-2012
- Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
- Keywords
- Data mining; Copy number variation; High-resolution arrayCGH; Genome analysis; Normality test
- Citation
- COMPUTERS IN BIOLOGY AND MEDICINE, v.42, no.4, pp.468 - 473
- Journal Title
- COMPUTERS IN BIOLOGY AND MEDICINE
- Volume
- 42
- Number
- 4
- Start Page
- 468
- End Page
- 473
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/16488
- DOI
- 10.1016/j.compbiomed.2011.12.015
- ISSN
- 0010-4825
- Abstract
- High-resolution arrayCGH platform makes it possible to detect small gains and losses which previously could not be measured. However, current CNV detection tools fitted to early low-resolution data are not applicable to larger high-resolution data. When CNV detection tools are applied to high-resolution data, they suffer from high false-positives, which increases validation cost. Existing CNV detection tools also require optimal parameter values. In most cases, obtaining these values is a difficult task. This study developed a CNV detection algorithm that is optimized for high-resolution arrayCGH data. This tool operates up to 1500 times faster than existing tools on a high-resolution arrayCGH of whole human chromosomes which has 42 million probes whose average length is 50 bases, while preserving false positive/negative rates. The algorithm also uses a normality test, thereby removing the need for optimal parameters. To our knowledge, this is the first formulation for CNV detecting problems that results in a near-linear empirical overall complexity for real high-resolution data. (c) 2012 Elsevier Ltd. All rights reserved.
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