Hypothesis-driven approach to predict transcriptional units from gene expression data

MOTIVATION: A major issue in computational biology is the reconstruction of functional relationships among genes, for example the definition of regulatory or biochemical pathways. One step towards this aim is the elucidation of transcriptional units, which are characterized by co-responding changes in mRNA expression levels. These units of genes will allow the generation of hypotheses about respective functional interrelationships. Thus, the focus of analysis currently moves from well-established functional assignment through comparison of protein and DNA sequences towards analysis of transcriptional co-response. Tools that allow deducing common control of gene expression have the potential to complement and extend routine BLAST comparisons, because gene function may be inferred from common transcriptional control. RESULTS: We present a co-clustering strategy of genome sequence information and gene expression data, which was applied to identify transcriptional units within diverse compendia of expression profiles. The phenomenon of prokaryotic operons was selected as an ideal test case to generate well-founded hypotheses about transcriptional units. The existence of overlapping and ambiguous operon definitions allowed the investigation of constitutive and conditional expression of transcriptional units in independent gene expression experiments of Escherichia coli. Our approach allowed identification of operons with high accuracy. Furthermore, both constitutive mRNA co-response as well as conditional differences became apparent. Thus, we were able to generate insight into the possible biological relevance of gene co-response. We conclude that the suggested strategy will be amenable in general to the identification of transcriptional units beyond the chosen example of E.coli operons. AVAILABILITY: The analyses of E.coli transcript data presented here are available upon request or at http://csbdb.mpimp-golm.mpg.de/