Detecting DNA regulatory motifs by incorporating positional trends in information content.
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.
Kechris
Katherina J
KJ
Department of Statistics, University of California, Berkeley, CA 94720, USA. kechris@genome.ucsf.edu
van Zwet
Erik
E
Bickel
Peter J
PJ
Eisen
Michael B
MB
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
D004271:DNA, Fungal; D011328:Prions