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Published Online: 28 July 2004

Maximum Entropy Modeling of Short Sequence Motifs with Applications to RNA Splicing Signals

Publication: Journal of Computational Biology
Volume 11, Issue Number 2-3


We propose a framework for modeling sequence motifs based on the maximum entropy principle (MEP). We recommend approximating short sequence motif distributions with the maximum entropy distribution (MED) consistent with low-order marginal constraints estimated from available data, which may include dependencies between nonadjacent as well as adjacent positions. Many maximum entropy models (MEMs) are specified by simply changing the set of constraints. Such models can be utilized to discriminate between signals and decoys. Classification performance using different MEMs gives insight into the relative importance of dependencies between different positions. We apply our framework to large datasets of RNA splicing signals. Our best models out-perform previous probabilistic models in the discrimination of human 5′ (donor) and 3′ (acceptor) splice sites from decoys. Finally, we discuss mechanistically motivated ways of comparing models.

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cover image Journal of Computational Biology
Journal of Computational Biology
Volume 11Issue Number 2-3March 2004
Pages: 377 - 394
PubMed: 15285897


Published online: 28 July 2004
Published in print: March 2004


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    Gene Yeo
    Department of Biology, Massachusetts Institute of Technology, 77 Massachusetts Avenue Building 68-223, Cambridge, MA 02319
    Christopher B. Burge
    Department of Brain and Cognitive Sciences, Center for Biological and Computational Learning, MassachusettsInstitute of Technology, Cambridge, MA 02319

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