A software framework integrating gene expression patterns, binding site analysis and gene ontology to hypothesize gene regulation relationships

One known challenge in analyzing gene expression data is to combine analysis outcomes obtained disparately by applying multiple, independent meta-analysis methods. Here we present an integrative computational system that narrows down biological hypotheses by integrating gene expression patterns, transcription factor (TF) binding site analysis outcomes, and Gene Ontology (GO) enrichment analysis outcomes. This system identifies regulated genes from microarray experiments through statistical processes, categorizes similarly behaving groups of genes and then carries out binding site analysis and gene function enrichment analysis based on some significant clusters. The output is an ordered set of "putative" pair-wise relationships between TFs and their potential target genes. The relationships are ranked based on their closeness to the experimental context. We demonstrate the effectiveness of our framework using two independent microarray data sets.