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.

• PMID: 23368633
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Journal Article | Research Support, N.I.H., Extramural | Research Support, Non-U.S. Gov't | Research Support, U.S. Gov't, Non-P.H.S.

Authors

Gao Z, Zhao R, Ruan J

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