Results: The proposed Tree-Gibbs sampling algorithm makes this approach possible. It is an extension of the existing site/motif Gibbs sampler, programmed in C. On simulated data, the Tree-Gibbs algorithm works in situations where the classic site/motif sampler fails, but scores worse in others. Based on simulation studies a combination of the site Gibbs sampler with the Tree-Gibbs sampler gives the best results. Biological data will probably resemble a mixture of the generated datasets which makes the Tree-Gibbs sampler a valuable technique for phylogenetic footprinting of co-expressed genes.
Availability: The executable of the Tree-Gibbs sampler is available: http://biomath.rug.ac.be/~stefan/treegibbs, http://www.vancriekinge.com/treegibbs. The source code will be available at a later date. Both the 20/5 75 homology datasets and the perl datagenerating script are available.
Keywords: Gibbs sampling; Motif; Co-expressed genes; Phylogenetic footprinting; Tree-Gibbs sampling.