The identification of cis-regulatory elements and modules is an important step in understanding the regulation of genes. We have developed a pipeline capable of running multiple motif prediction methods on a whole genome scale.

Using gene expression datasets to identify coexpressed genes and the Ensembl Compara database orthologues, we assemble input sequence sets comprised of the upstream regions of a target gene, its orthologues and co-expressed genes on the premise that such genes will share promoters by evolution (orthologues) or share regulatory control mechanisms (co-expressed genes). Co-expressed genes are identified by an approach that combines Pearson distances from multiple gene expression datasets derived from multiple experimental approaches and calibrated against the GO database. Our pipeline runs a number of established motif detection algorithms with a range of parameter settings on the input dataset. We integrate the diverse result sets by scoring motifs with a method-independent function. For each target gene, we assign p-values to the motif score by running the discovery pipeline on multiple sets of input sequence containing the target gene, non-coexpressed genes and "fake" orthologues generated by neutral numerical evolution.

We have predicted 30,636 motif binding sites in human for 4,182 genes and an initial set of 472 motif binding sites in mouse for 92 genes with p < 0.001. The positive predictive value against a library of biologically confirmed regulatory sites approaches 0.4 at the highest p-value threshold. Predicted regulatory elements and other resources from the project are available at www.cisred.org.