A Gibbs Sampling method to detect over-represented motifs in upstream regions of co-expressed genes

Microarray experiments can reveal useful information on the transcriptional regulation. We try to find regulatory elements in the region upstream of translation start of co-expressed genes. Here we present a modification to the original Gibbs Sampling algorithm. We introduce a probability distribution to estimate the number of copies of the motif in a sequence. The second modification is the incorporation of a higher-order background model. We have successfully tested our algorithm on several data sets. First we show results on two selected data set: sequences from plants containing the G-box motif and the upstream sequences from bacterial genes regulated by O2-responsive protein FNR. In both cases the motif sampler is able to find the expected motifs. Finally, the sampler is tested on 4 clusters of coexpressed genes from a wounding experiment in Arabidopsis thaliana. We find several putative motifs that are related to the pathways involved in the plant defense mechanism.\emph{Proc. of the 5th Annual International Conference on Computational Biology (RECOMB'2001)}, Montreal, Canada, Apr. 2001status: publishe