title: Detecting DNA regulatory motifs by incorporating positional trends in information content.
abstract: 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.
authors.affiliation: Department of Statistics, University of California, Berkeley, CA 94720, USA. kechris@genome.ucsf.edu
authors.forename: Katherina J
authors.initials: KJ
authors.lastname: Kechris
authors.forename: Erik
authors.initials: E
authors.lastname: van Zwet
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Bickel
authors.forename: Michael B
authors.initials: MB
authors.lastname: Eisen
mesh_terms: D000465:Algorithms; D001482:Base Composition / Q000235:genetics*; D019295:Computational Biology / Q000706:statistics & numerical data; D003198:Computer Simulation; D004271:DNA, Fungal / Q000235:genetics*; D005800:Genes, Fungal / Q000235:genetics; D008957:Models, Genetic; D011328:Prions / Q000235:genetics; D012045:Regulatory Sequences, Nucleic Acid / Q000235:genetics*; D012441:Saccharomyces cerevisiae / Q000235:genetics; D017422:Sequence Analysis, DNA / Q000379:methods* / Q000706:statistics & numerical data
chemical_list: D004271:DNA, Fungal; D011328:Prions