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Session S8 - Nucleic Acids II.
ORAL session, Wednesday afternoon, March 24
510A, Palais des Congres

[S8.008] A Gibbs sampler for motif detection in phylogenetically close sequences

Rahul Siddharthan (The Rockefeller University), Erik van Nimwegen (University of Basel), Eric Siggia (The Rockefeller University)

Genes are regulated by transcription factors that bind to DNA upstream of genes and recognize short conserved ``motifs'' in a random intergenic ``background''. Motif-finders such as the Gibbs sampler compare the probability of these short sequences being represented by ``weight matrices'' to the probability of their arising from the background ``null model'', and explore this space (analogous to a free-energy landscape). But closely related species may show conservation not because of functional sites but simply because they have not had sufficient time to diverge, so conventional methods will fail. We introduce a new Gibbs sampler algorithm that accounts for common ancestry when searching for motifs, while requiring minimal ``prior'' assumptions on the number and types of motifs, assessing the significance of detected motifs by ``tracking'' clusters that stay together. We apply this scheme to motif detection in sporulation-cycle genes in the yeast S. cerevisiae, using recent sequences of other closely-related Saccharomyces species.

Part S of program listing