Discovering novel cis-regulatory motifs using functional networks.

We combined functional information such as protein-protein interactions or metabolic networks with genome information in Saccharomyces cerevisiae to predict cis-regulatory motifs in the upstream region of genes. We developed a new scoring metric combining these two information sources and used this metric in motif discovery. To estimate the statistical significance of this metric, we used brute-force randomization, which shows a consistent well-behaved trend. In contrast, real data showed complex nonrandom behavior. With conservative parameters we were able to find 42 degenerate motifs (that touch 40% of yeast genes) based on 647 original patterns, five of which are well known. Some of these motifs also show limited spatial position in the promoter, indicative of a true motif. We also tested the metric on other known motifs and show that this metric is a good discriminator of real motifs. As well as a pragmatic motif discovery method, with many applications beyond this work, these results also show that interacting proteins are often coordinated at the level of transcription, even in the absence of obvious coregulation in gene expression data sets.