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.
Department of Biology, Massachusetts Institute of Technology, 77 Massachusetts Avenue Building 68-223, Cambridge, MA 02319, USA.
Gene
G
Yeo
Christopher B
CB
Burge
D001483:Base Sequence; D019295:Computational Biology; D016384:Consensus Sequence; D007438:Introns; D008390:Markov Chains; D008957:Models, Genetic; D010802:Phylogeny; D022821:RNA Splice Sites; D012372:ROC Curve
D022821:RNA Splice Sites