Background: Uncovering the key sequence elements in gene promoters that regulate the expression of plant genomes is a huge task that will require a series of complementary methods for prediction, substantial innovations in experimental validation and a much greater understanding of the 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 genes and treatments that we have found to form valuable sets for analysis of the mechanisms underlying gene co-expression. CoReg then links the hierarchical clustering of these co-expressed sets with frequency tables of promoter elements. These promoter elements can be drawn from known elements or all possible combinations of nucleotides in an element of various lengths. These sets of promoter elements represent putative cis-acting regulatory elements common to sets of co-expressed genes and can be prioritised for experimental testing. We have used these new tools to analyze the response of transcripts for nuclear genes encoding mitochondrial proteins in Arabidopsis to a range of chemical stresses. ModuleFinder provided a subset of co-expressed gene modules that are more logically related to biological functions than did subsets derived from traditional hierarchical clustering techniques. Importantly ModuleFinder linked responses in transcripts for electron transport chain components, carbon metabolism enzymes and solute transporter proteins. CoReg identified several promoter motifs that helped to explain the patterns of expression observed.Conclusion: ModuleFinder identifies sets of genes and treatments that form useful sets for analysis of the mechanisms behind co-expression. CoReg links the clustering tree of expression-based relationships in these sets with frequency tables of promoter elements. These sets of promoter elements represent putative cis-acting regulatory elements for sets of genes, and can then be tested experimentally. We consider these tools, both built on an open source software product to provide valuable, alternative tools for the prioritisation of promoter elements for experimental analysis.