Background: Deciphering cis-regulatory networks has become an attractive yet challenging task. This paper presents a simple method for cis-regulatory network discovery which aims to avoid some of the common problems of previous approaches.
Results: Using promoter sequences and gene expression profiles as input, rather than clustering the genes by the expression data, our method utilizes co-expression neighborhood information for each individual gene, thereby overcoming the disadvantages of current clustering based models which may miss specific information for individual genes. In addition, rather than using a motif database as an input, it implements a simple motif count table for each enumerated k-mer for each gene promoter sequence. Thus, it can be used for species where previous knowledge of cis-regulatory motifs is unknown and has the potential to discover new transcription factor binding sites. Applications on Saccharomyces cerevisiae and Arabidopsis have shown that our method has a good prediction accuracy and outperforms a phylogenetic footprinting approach. Furthermore, the top ranked gene-motif regulatory clusters are evidently functionally co-regulated, and the regulatory relationships between the motifs and the enriched biological functions can often be confirmed by literature.
Conclusions: Since this method is simple and gene-specific, it can be readily utilized for insufficiently studied species or flexibly used as an additional step or data source for previous transcription regulatory networks discovery models.