A transversal approach to predict gene product networks from ontology-based similarity
1 E.A 3888, Modélisation Conceptuelle des Connaissances Biomédicales, Faculté de Médecine, Université de Rennes 1, IFR 140, 35043 Rennes Cedex, France
2 CNRS UMR 6061 Génétique et Développement, Faculté de Médecine, Université de Rennes 1, IFR 140, 35043 Rennes Cedex, France
3 OUEST-genopole® transcriptomic platform, Faculté de Médecine, Université de Rennes 1, IFR 140, 35043 Rennes, France
BMC Bioinformatics 2007, 8:235 doi:10.1186/1471-2105-8-235Published: 2 July 2007
Interpretation of transcriptomic data is usually made through a "standard" approach which consists in clustering the genes according to their expression patterns and exploiting Gene Ontology (GO) annotations within each expression cluster. This approach makes it difficult to underline functional relationships between gene products that belong to different expression clusters. To address this issue, we propose a transversal analysis that aims to predict functional networks based on a combination of GO processes and data expression.
The transversal approach presented in this paper consists in computing the semantic similarity between gene products in a Vector Space Model. Through a weighting scheme over the annotations, we take into account the representativity of the terms that annotate a gene product. Comparing annotation vectors results in a matrix of gene product similarities. Combined with expression data, the matrix is displayed as a set of functional gene networks. The transversal approach was applied to 186 genes related to the enterocyte differentiation stages. This approach resulted in 18 functional networks proved to be biologically relevant. These results were compared with those obtained through a standard approach and with an approach based on information content similarity.
Complementary to the standard approach, the transversal approach offers new insight into the cellular mechanisms and reveals new research hypotheses by combining gene product networks based on semantic similarity, and data expression.