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Joint modeling of DNA sequence and physical properties to improve eukaryotic promoter recognition

Item Type:Article
Title:Joint modeling of DNA sequence and physical properties to improve eukaryotic promoter recognition
Creators Name:Ohler, U. and Niemann, H and Liao, G.C. and Rubin, G.M.
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.
Keywords:Computational Biology, DNA, Genetic Models, Genetic Promoter Regions, Likelihood Functions, Markov Chains, Neural Networks, Nucleic Acid Databases, Physical Chemistry, Physicochemical Phenomena, Statistical Models, Stochastic Processes, Animals, Drosophila
Source:Bioinformatics
ISSN:1367-4803
Publisher:Oxford Univ. Press (U.K.)
Volume:17 Suppl 1
Page Range:S199-206
Date:2001
Official Publication:https://doi.org/10.1093/bioinformatics/17.suppl_1.S199
PubMed:View item in PubMed

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