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Detecting DNA regulatory motifs by incorporating positional trends in information content

Katherina J Kechris14*, Erik van Zwet15, Peter J Bickel1 and Michael B Eisen23

Author Affiliations

1 Department of Statistics, University of California, Berkeley, CA 94720, USA

2 Department of Genome Sciences, Life Sciences Division, Ernest Orlando Lawrence Berkeley National Lab, Cyclotron Road, Berkeley, CA 94720, USA

3 Center for Integrative Genomics, Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA

4 Current address: Department of Biochemistry and Biophysics, 600 16th Street 2240, University of California, San Francisco, CA 94143, USA

5 Current address: Mathematical Institute, University Leiden, 2300 RA Leiden, The Netherlands

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Genome Biology 2004, 5:R50  doi:10.1186/gb-2004-5-7-r50

Published: 24 June 2004


On the basis of the observation that conserved positions in transcription factor binding sites are often clustered together, we propose a simple extension to the model-based motif discovery methods. We assign position-specific prior distributions to the frequency parameters of the model, penalizing deviations from a specified conservation profile. Examples with both simulated and real data show that this extension helps discover motifs as the data become noisier or when there is a competing false motif.