Nucleic Acids Research Advance Access published online on October 8, 2008
Nucleic Acids Research, doi:10.1093/nar/gkn610
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Methods Online |
Gene expression module-based chemical function similarity search
1Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, 2Shanghai Center for Bioinformation Technology, F1.12, No.100, Qinzhou Road, Shanghai 200235, PR China, 3Institute of Immunology of Rostock University, Schillingallee 69, D-18055 Rostock, Germany, 4Cardiovascular Center, Department of Internal Medicine, University of Michigan Medical Center, Ann Arbor, MI 48109, USA and 5Drug Discovery and Design Centre, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, PR China
*To whom correspondence should be addressed. Tel: +86 21 54065060; Fax: +86 21 54065058; Email: yxli{at}sibs.ac.cn
Correspondence may also be addressed to Lei Liu. Email: liulei{at}scbit.org; Hualiang Jiang. Email: hljiang{at}mail.shcnc.ac.cn
Received May 20, 2008. Revised September 8, 2008. Accepted September 9, 2008.
Investigation of biological processes using selective chemical interventions is generally applied in biomedical research and drug discovery. Many studies of this kind make use of gene expression experiments to explore cellular responses to chemical interventions. Recently, some research groups constructed libraries of chemical related expression profiles, and introduced similarity comparison into chemical induced transcriptome analysis. Resembling sequence similarity alignment, expression pattern comparison among chemical intervention related expression profiles provides a new way for chemical function prediction and chemical–gene relation investigation. However, existing methods place more emphasis on comparing profile patterns globally, which ignore noises and marginal effects. At the same time, though the whole information of expression profiles has been used, it is difficult to uncover the underlying mechanisms that lead to the functional similarity between two molecules. Here a new approach is presented to perform biological effects similarity comparison within small biologically meaningful gene categories. Regarding gene categories as units, a reduced similarity matrix is generated for measuring the biological distances between query and profiles in library and pointing out in which modules do chemical pairs resemble. Through the modularization of expression patterns, this method reduces experimental noises and marginal effects and directly correlates chemical molecules with gene function modules.
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.