CNV detection method optimized for high-resolution arrayCGH by normality test

Jaegyoon Ahn, Youngmi Yoon, Chihyun Park, Sang Hyun Park

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)468-473
Number of pages6
JournalComputers in Biology and Medicine
Volume42
Issue number4
DOIs
Publication statusPublished - 2012 Apr 1

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All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics

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