Sciweavers

Share
RECOMB
2003
Springer

Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals

9 years 7 months ago
Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals
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. Key words: maximum entropy, splice sites, nonneighborin...
Gene W. Yeo, Christopher B. Burge
Added 03 Dec 2009
Updated 03 Dec 2009
Type Conference
Year 2003
Where RECOMB
Authors Gene W. Yeo, Christopher B. Burge
Comments (0)
books