ModuleFinder and CoReg: linking gene expression modules with promoter sequences motifs to uncover gene regulation mechanisms in plants

Background: Uncovering the key sequence elements in gene promoters that regulate theexpression of plant genomes is a huge task that will require a series of complementary methods forprediction, substantial innovations in experimental validation and a much greater understanding ofthe role of combinatorial control in the regulation of plant gene expression.Results: To add to this larger process and to provide alternatives to existing prediction methods,we have developed several tools in the statistical package R. ModuleFinder identifies sets of genesand treatments that we have found to form valuable sets for analysis of the mechanisms underlyinggene co-expression. CoReg then links the hierarchical clustering of these co-expressed sets withfrequency tables of promoter elements. These promoter elements can be drawn from knownelements or all possible combinations of nucleotides in an element of various lengths. These setsof promoter elements represent putative cis-acting regulatory elements common to sets of coexpressedgenes and can be prioritised for experimental testing. We have used these new tools toanalyze the response of transcripts for nuclear genes encoding mitochondrial proteins in Arabidopsisto a range of chemical stresses. ModuleFinder provided a subset of co-expressed gene modules thatare more logically related to biological functions than did subsets derived from traditionalhierarchical clustering techniques. Importantly ModuleFinder linked responses in transcripts forelectron transport chain components, carbon metabolism enzymes and solute transporterproteins. CoReg identified several promoter motifs that helped to explain the patterns of expressionobserved.Conclusion: ModuleFinder identifies sets of genes and treatments that form useful sets for analysisof the mechanisms behind co-expression. CoReg links the clustering tree of expression-basedrelationships in these sets with frequency tables of promoter elements. These sets of promoterelements represent putative cis-acting regulatory elements for sets of genes, and can then be testedexperimentally. We consider these tools, both built on an open source software product toprovide valuable, alternative tools for the prioritisation of promoter elements for experimentalanalysis.