Comparing sequence and expression for predicting microRNA targets using GenMiR3.
We present a new model and learning algorithm, GenMiR3, which takes into account mRNA sequence features in addition to paired mRNA and miRNA expression 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 sequence features the total energy of hybridization between the microRNA and target, 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.
Huang
J C
JC
Probabilistic and Statistical Inference Group, University of Toronto, 10 King's College Rd., Toronto, ON, M5S 3G4, Canada. jim@psi.toronto.edu
Frey
B J
BJ
Morris
Q D
QD
D020413:3' Untranslated Regions; D000465:Algorithms; D001499:Bayes Theorem; D001665:Binding Sites; D019295:Computational Biology; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D016013:Likelihood Functions; D035683:MicroRNAs / Q000235:genetics* / Q000378:metabolism*; D008957:Models, Genetic; D015233:Models, Statistical; D009690:Nucleic Acid Conformation; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D013816:Thermodynamics
D020413:3' Untranslated Regions; D035683:MicroRNAs; D012333:RNA, Messenger