A High-Throughput Computational Framework for Identifying Significant Copy Number Aberrations from Array Comparative Genomic Hybridisation Data. (13th September 2012)

• Record Type: Journal Article
• Authors: Roberts, Ian; Carter, Stephanie A.; Scarpini, Cinzia G.; Karagavriilidou, Konstantina; Barna, Jenny C. J.; Calleja, Mark; Coleman, Nicholas
• Other Names: Van de Peer Yves Academic Editor.
• Is Part Of: Advances in bioinformatics. Volume 2012(2012)
• Publication Date: 2012-09-13
• Subjects: Bioinformatics -- Periodicals; Bioinformatics; Computational Biology -- Periodicals; Periodicals; 570.285
• DOI: 10.1155/2012/876976
• Languages: English
• ISSNs: 1687-8027

Abstract

Abstract : Reliable identification of copy number aberrations (CNA) from comparative genomic hybridization data would be improved by the availability of a generalised method for processing large datasets. To this end, we developed swatCGH, a data analysis framework and region detection heuristic for computational grids. swatCGH analyses sequentially displaced (sliding) windows of neighbouring probes and applies adaptive thresholds of varying stringency to identify the 10% of each chromosome that contains the most frequently occurring CNAs. We used the method to analyse a published dataset, comparing data preprocessed using four different DNA segmentation algorithms, and two methods for prioritising the detected CNAs. The consolidated list of the most commonly detected aberrations confirmed the value of swatCGH as a simplified high-throughput method for identifying biologically significant CNA regions of interest.