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Advances in Bioinformatics
Volume 2012, Article ID 876976, 12 pages
Research Article

A High-Throughput Computational Framework for Identifying Significant Copy Number Aberrations from Array Comparative Genomic Hybridisation Data

1Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge CB2 1QP, UK
2Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1QW, UK
3The Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, UK

Received 14 March 2012; Revised 22 June 2012; Accepted 26 June 2012

Academic Editor: Yves Van de Peer

Copyright © 2012 Ian Roberts et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.