Comparing Sequence and Expression for Predicting microRNA Targets Using GenMIR3

J. C. Huang, B. J. Frey, Q. D. Morris

1Probabilistic and Statistical Inference Group, University of Toronto, 10 King's College Rd., Toronto, ON, M5S 3G4, Canada;2Banting and Best Department of Medical Research, University of Toronto, 160 College Street, Toronto, ON, M5S 1E3, Canada
Email: jim,;

Pac Symp Biocomput. 2008;:52-63.


We present a new model and learning algorithm, GenMiR3, which takes into ac- count mRNA sequence features in addition to paired mRNA and miRNA expres- sion profiles when scoring candidate miRNA-mRNA interactions. We evaluate three candidate sequence features for predicting miRNA targets by assessing the expression support for the predictions of each feature and the consistency of Gene Ontology Biological Process annotation of their target sets. We consider as se- quence features the total energy of hybridization between the microRNA and tar- get, conservation of the target site and the context score which is a composite of five individual sequence features. We demonstrate that only the total energy of hybridization is predictive of paired miRNA and mRNA expression data and Gene Ontology enrichment but this feature adds little to the total accuracy of GenMiR3 predictions using for expression features alone.

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