title: Compound poisson approximation of the number of occurrences of a position frequency matrix (PFM) on both strands.
abstract: Transcription factors play a key role in gene regulation by interacting with specific binding sites or motifs. Therefore, enrichment of binding motifs is important for genome annotation and efficient computation of the statistical significance, the p-value, of the enrichment of motifs is crucial. We propose an efficient approximation to compute the significance. Due to the incorporation of both strands of the DNA molecules and explicit modeling of dependencies between overlapping hits, we achieve accurate results for any DNA motif based on its Position Frequency Matrix (PFM) representation. The accuracy of the p-value approximation is shown by comparison with the simulated count distribution. Furthermore, we compare the approach with a binomial approximation, (compound) Poisson approximation, and a normal approximation. In general, our approach outperforms these approximations or is equally good but significantly faster. An implementation of our approach is available at http://mosta.molgen.mpg.de.
authors.affiliation: Computational Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany. utz.pape@molgen.mpg.de
authors.forename: Utz J
authors.initials: UJ
authors.lastname: Pape
authors.forename: Sven
authors.initials: S
authors.lastname: Rahmann
authors.forename: Fengzhu
authors.initials: F
authors.lastname: Sun
authors.forename: Martin
authors.initials: M
authors.lastname: Vingron
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D004268:DNA-Binding Proteins / Q000378:metabolism*; D010363:Pattern Recognition, Automated; D016012:Poisson Distribution; D017422:Sequence Analysis, DNA / Q000379:methods; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D004268:DNA-Binding Proteins; D014157:Transcription Factors