Research article

SIRAC: Supervised Identification of Regions of Aberration in aCGH datasets

Carmen Lai^1* , Hugo M Horlings^2* , Marc J van de Vijver² , Eric H van Beers² , Petra M Nederlof² , Lodewyk FA Wessels² and Marcel JT Reinders¹

¹  Bioinformatics group, Delft University, Delft, The Netherlands

²  The Netherlands Cancer Institute, Amsterdam, The Netherlands

author email corresponding author email* Contributed equally

BMC Bioinformatics 2007, 8:422doi:10.1186/1471-2105-8-422

Published:	30 October 2007

Abstract

Background

Array comparative genome hybridization (aCGH) provides information about genomic aberrations. Alterations in the DNA copy number may cause the cell to malfunction, leading to cancer. Therefore, the identification of DNA amplifications or deletions across tumors may reveal key genes involved in cancer and improve our understanding of the underlying biological processes associated with the disease.

Results

We propose a supervised algorithm for the analysis of aCGH data and the identification of regions of chromosomal alteration (SIRAC). We first determine the DNA-probes that are important to distinguish the classes of interest, and then evaluate in a systematic and robust scheme if these relevant DNA-probes are closely located, i.e. form a region of amplification/deletion. SIRAC does not need any preprocessing of the aCGH datasets, and requires only few, intuitive parameters.

Conclusion

We illustrate the features of the algorithm with the use of a simple artificial dataset. The results on two breast cancer datasets show promising outcomes that are in agreement with previous findings, but SIRAC better pinpoints the dissimilarities between the classes of interest.