Nucleic Acids Research Advance Access originally published online on August 18, 2008
Nucleic Acids Research 2008 36(18):e115; doi:10.1093/nar/gkn482
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Nucleic Acids Research, 2008, Vol. 36, No. 18 e115
© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Methods Online |
Prioritization of candidate cancer genes—an aid to oncogenomic studies
1Research Unit on Biomedical Informatics, Experimental and Health Science Department, Universitat Pompeu Fabra, Barcelona 08080, 2Intelligent Systems Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country, Donostia-San Sebastián 20018 and 3Department of Artificial Intelligence, Technical University of Madrid, Boadilla del Monte 28660, Spain
*To whom correspondence should be addressed: Tel: +34 93 3160507; Fax: +34 93 2240875; Email: nuria.lopez{at}upf.edu
Received February 5, 2008. Revised June 27, 2008. Accepted July 11, 2008.
The development of techniques for oncogenomic analyses such as array comparative genomic hybridization, messenger RNA expression arrays and mutational screens have come to the fore in modern cancer research. Studies utilizing these techniques are able to highlight panels of genes that are altered in cancer. However, these candidate cancer genes must then be scrutinized to reveal whether they contribute to oncogenesis or are coincidental and non-causative. We present a computational method for the prioritization of candidate (i) proto-oncogenes and (ii) tumour suppressor genes from oncogenomic experiments. We constructed computational classifiers using different combinations of sequence and functional data including sequence conservation, protein domains and interactions, and regulatory data. We found that these classifiers are able to distinguish between known cancer genes and other human genes. Furthermore, the classifiers also discriminate candidate cancer genes from a recent mutational screen from other human genes. We provide a web-based facility through which cancer biologists may access our results and we propose computational cancer gene classification as a useful method of prioritizing candidate cancer genes identified in oncogenomic studies.