Bioinformatics Advance Access originally published online on December 2, 2008
Bioinformatics 2009 25(3):322-330; doi:10.1093/bioinformatics/btn625
Gene expression trends and protein features effectively complement each other in gene function prediction
1Department of Plant Systems Biology, VIB Technologiepark 927, 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, 4Umeå Plant Science Centre, Department of Plant Physiology, Umeå University, SE-901 87 Umeå, Sweden, 5Bioinformatics and Genomics Group, Center for Genomic Regulation (CRG) and Department of Applied Mathematics I, Polytechnic University of Catalonia, Barcelona, Spain and 6Department of Biology, Norwegian University of Science and Technology, 7491 Trondheim, Norway
*To whom correspondence should be addressed.
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Abstract |
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Motivation: Genome-scale ‘omics’ data constitute 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 Gene Ontology (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 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 features.
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: Joaquin Dopazo
Received on April 29, 2008; revised on October 9, 2008; accepted on November 30, 2008
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