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Background  The learning of global genetic regulatory networks from expression data is a severely under-constrained problem that is aided by reducing the dimensionality of the search space by means of clustering genes into putatively  co-regulated  groups, as opposed to those that are simply  co-expressed . Be cause genes may be co-regulated only across a subset of all observed experimental conditions,  biclustering  (clustering of genes  and  conditions) is more appropriate than standard clustering. Co-regulated genes are also often functionally (physically, spatially, genetically, and/or evolutionarily) associated, and such  a priori  known or pre-computed associations can provide support for appropriately grouping genes. One important association is the presence of one or more common cis-regulatory motifs. In organisms where these motifs are not known, their  de novo  detection, integrated into the clustering algorithm, can help to guide the process towards more biologically parsimonious solutions.    Results  We have developed an algorithm, cMonkey, that detects putative co-regulated gene groupings by integrating the biclustering of gene expression data and various functional associations with the  de novo  detection of sequence motifs.    Conclusion  We have applied this procedure to the archaeon  Halobacterium  NRC-1, as part of our efforts to decipher its regulatory network. In addition, we used cMonkey on public data for three organisms in the other two domains of life:  Helicobacter pylori, Saccharomyces cerevisiae , and  Escherichia coli . The biclusters detected by cMonkey both recapitulated known biology and enabled novel predictions (some for  Halobacterium  were subsequently confirmed in the laboratory). For example, it identified the  bacteriorhodopsin  regulon, assigned additional genes to this regulon with apparently unrelated function, and detected its known promoter motif. We have performed a thorough comparison of cMonkey results against other clustering methods, and find that cMonkey biclusters are more parsimonious with all available evidence for co-regulation.

Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks