Bioinformatics Advance Access published online on December 2, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn625
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Gene expression trends and protein features effectively comple-ment each other in gene function prediction
1Department of Plant Systems Biology, VIB Technologiepark 927, 9052 Gent, Belgium
2Department of Molecular Genetics, Ghent University, Technologiepark 927, 9052 Gent, Belgium
3The Linnaeus Centre for Bioinformatics, Uppsala University, BMC Box 598, SE-751 24 Uppsala, Sweden
4Bioinformatics and Genomics Group, Center for Genomic Regulation(CRG) and Department of Applied Mathematics I, Polytechnic University of Catalonia, Barcelona, Spain
5Umeå Plant Science Centre, Department of Plant Physiology, Umeå University, SE-901 87 Umeå, Sweden
6Department of Biology, Norwegian University of Science and Technology, 7491 Trondheim, Norway
*To whom correspondence should be addressed. Krzysztof Wabnik, E-mail: krwab{at}psb.ugent.be
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Abstract |
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Motivation: Genome-scale ‘omics’ data constitutes a potentially rich source of information about biological systems and their function. There is a plethora of tools and methods available to mine omics data. However, the diversity and complexity of different omics data types is a stumbling block for multi-data integration, hence there is a dire need for additional methods to exploit potential synergy from integrated orthogonal data. Rough Sets provide an efficient means to use complex information in classification approaches. Here, we set out to explore the possibilities of Rough Sets to incorporate diverse information sources in a functional classification of unknown genes.
Results: We explored the use of Rough Sets for a novel data integration strategy where gene expression data, protein features, and GO annotations were combined to describe general and biologically relevant patterns represented by If-Then rules. The descriptive rules were used to predict the function of unknown genes in Arabidopsis thaliana and Schizosaccharomyces pombe. The If-Then rule models showed success rates of up to 0.89 (discriminative and predictive power for both modeled organisms) whereas models built solely of one data type (protein features or gene expression data) yielded success rates varying from 0.68 to 0.78. Our models were applied to generate classifications for many unknown genes, of which a sizeable number were confirmed either by PubMed literature reports or electronically interfered annotations. Finally, we studied cell cycle protein-protein interactions derived from both tandem affinity purification (TAP) experiments and in silico experiments in the BioGRID interactome database and found strong experimental evidence for the predictions generated by our models. The results show that our approach can be used to build very robust models that create synergy from integrating gene expression data and protein fea-tures.
Availability: The Rough Set-based method is implemented in the Rosetta toolkit kernel version 1.0.1 available at: http://rosetta.lcb.uu.se/
Contact: kuiper{at}nt.ntnu.no; krwab{at}psb.ugent.be
Supplementary information: Supplementary data are available at Bioinformatics online
Associate Editor: Dr. Joaquin Dopazo
Received on April 29, 2008; revised on October 9, 2008; accepted on November 30, 2008
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