title: Joint modeling of DNA sequence and physical properties to improve eukaryotic promoter recognition.
abstract: We present an approach to integrate physical properties of DNA, such as DNA bendability or GC content, into our probabilistic promoter recognition system McPROMOTER. In the new model, a promoter is represented as a sequence of consecutive segments represented by joint likelihoods for DNA sequence and profiles of physical properties. Sequence likelihoods are modeled with interpolated Markov chains, physical properties with Gaussian distributions. The background uses two joint sequence/profile models for coding and non-coding sequences, each consisting of a mixture of a sense and an anti-sense submodel. On a large Drosophila test set, we achieved a reduction of about 30% of false positives when compared with a model solely based on sequence likelihoods.
authors.affiliation: Lehrstuhl für Informatik 5 (Mustererkennung), Universität Erlangen-Nürnberg, Martensstr. 3, D-91058 Erlangen. Uwe.Ohler@informatik.uni-erlangen.de
authors.forename: U
authors.initials: U
authors.lastname: Ohler
authors.forename: H
authors.initials: H
authors.lastname: Niemann
authors.lastname: Liao Gc
authors.forename: G M
authors.initials: GM
authors.lastname: Rubin
mesh_terms: D000818:Animals; D055598:Chemical Phenomena; D002627:Chemistry, Physical; D019295:Computational Biology; D004247:DNA / Q000737:chemistry* / Q000235:genetics*; D030561:Databases, Nucleic Acid; D004330:Drosophila / Q000235:genetics; D016013:Likelihood Functions; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011401:Promoter Regions, Genetic; D013269:Stochastic Processes
chemical_list: D004247:DNA