Inferring gene regulatory relationships by combining target-target pattern recognition and regulator-specific motif examination

Although microarray data have been successfully used for gene clustering and classification, the use of time series microarray data for constructing gene regulatory networks remains a particularly difficult task. The challenge lies in reliably inferring regulatory relationships from datasets that normally possess a large number of genes and a limited number of time points. In addition to the numerical challenge, the enormous complexity and dynamic properties of gene expression regulation also impede the progress of inferring gene regulatory relationships. Based on the accepted model of the relationship between regulator and target genes, we developed a new approach for inferring gene regulatory relationships by combining target-target pattern recognition and examination of regulator-specific binding sites in the promoter regions of putative target genes. Pattern recognition was accomplished in two steps: A first algorithm was used to search for the genes that share expression profile similarities with known target genes (KTGs) of each investigated regulator. The selected genes were further filtered by examining for the presence of regulator-specific binding sites in their promoter regions. As we implemented our approach to 18 yeast regulator genes and their known target genes, we discovered 267 new regulatory relationships, among which 15% are rediscovered, experimentally validated ones. Of the discovered target genes, 36.1% have the same or similar functions to a KTG of the regulator. An even larger number of inferred genes fall in the biological context and regulatory scope of their regulators. Since the regulatory relationships are inferred from pattern recognition between target-target genes, the method we present is especially suitable for inferring gene regulatory relationships in which there is a time delay between the expression of regulating and target genes.