Research Article
No access
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

Abstract

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.

Get full access to this article

View all available purchase options and get full access to this article.

Information & Authors

Information

Published In

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

History

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

Permissions

Request permissions for this article.

Topics

    Authors

    Affiliations

    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

    Metrics & Citations

    Metrics

    Citations

    Export citation

    Select the format you want to export the citations of this publication.

    View Options

    Get Access

    Access content

    To read the fulltext, please use one of the options below to sign in or purchase access.

    Society Access

    If you are a member of a society that has access to this content please log in via your society website and then return to this publication.

    Restore your content access

    Enter your email address to restore your content access:

    Note: This functionality works only for purchases done as a guest. If you already have an account, log in to access the content to which you are entitled.

    View options

    PDF/EPUB

    View PDF/ePub

    Media

    Figures

    Other

    Tables

    Share

    Share

    Copy the content Link

    Share on social media

    Back to Top