Abstract

Microarray experiments can reveal useful information on the transcriptional regulation. We try to find regulatory elements in the region upstream of translation start of coexpressed genes. Here we present a modification to the original Gibbs Sampling algorithm [12]. 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.

Keywords

GeneSequence motifComputational biologyMotif (music)ArabidopsisUpstream (networking)BiologyGibbs samplingMicroarray analysis techniquesMicroarrayGeneticsComputer scienceGene expressionArtificial intelligence

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Year
2001
Type
article
Pages
305-312
Citations
70
Access
Closed

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Gert Thijs, Kathleen Marchal, Magali Lescot et al. (2001). A Gibbs sampling method to detect over-represented motifs in the upstream regions of co-expressed genes. , 305-312. https://doi.org/10.1145/369133.369253

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DOI
10.1145/369133.369253