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

Authors:
Gene Yeo;Christopher B. Burge
Affiliations:
Massachusetts Institute of Technology (MIT), Cambridge, MA;Massachusetts Institute of Technology (MIT), Cambridge, MA
Venue:
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Year:
2003

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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 non-adjacent as well as adjacent positions.Finally, we suggest mechanistically-motivated ways of comparing models.