The Motif Sampler tries to find over-represented motifs in the upstream region of a set of co-regulated genes. This motif finding algorithm uses Gibbs sampling to find the position probability matrix that represents the motif. In this implementation we focus on the use of higher-order background models to improve the robustness of the motif finding. At the moment the Motif Sampler comes with background models for several organisms (see pop up list further down the page). But the Motif Sampler is also suitable for other organisms since the background model can also be calculated from the input sequences.
This is the web interface to the latest version of our Motif Sampler algorithm. Newly added features are the possibility to include both strands in the analysis, more precompiled background models and some internal optimizations of the code to speed up the computations. This web interface and the motif finding algorithm are the topic of research and are therefore always under further development and improvement, but this version is ready to search for common motifs in a set of coregulated DNA sequences. Since we are still working on the interface and the data processing, the server may be down once in a while.

NEW  Since Aug. 1 2002, you can also download the command line version of the MotifSampler.

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If you like our software, you can show your appreciation by citing one of our publications:

• Thijs G., Lescot M., Marchal K., Rombauts S., De Moor B., Rouzé P., Moreau Y., 2001. A higher order background model improves the detection of regulatory elements by Gibbs Sampling, Bioinformatics, 17(12),1113-1122.
• Thijs G., Marchal K., Lescot M., Rombauts S., De Moor B., Rouzé P., Moreau Y., 2002. A Gibbs Sampling method to detect over-represented motifs in upstream regions of coexpressed genes, Journal of Computational Biology (special issue Recomb'2001), 9(2), 447-464.