title: miRTar2GO: a novel rule-based model learning method for cell line specific microRNA target prediction that integrates Ago2 CLIP-Seq and validated microRNA-target interaction data.
abstract: MicroRNAs (miRNAs) are ∼19-22 nucleotides (nt) long regulatory RNAs that regulate gene expression by recognizing and binding to complementary sequences on mRNAs. The key step in revealing the function of a miRNA, is the identification of miRNA target genes. Recent biochemical advances including PAR-CLIP and HITS-CLIP allow for improved miRNA target predictions and are widely used to validate miRNA targets. Here, we present miRTar2GO, which is a model, trained on the common rules of miRNA-target interactions, Argonaute (Ago) CLIP-Seq data and experimentally validated miRNA target interactions. miRTar2GO is designed to predict miRNA target sites using more relaxed miRNA-target binding characteristics. More importantly, miRTar2GO allows for the prediction of cell-type specific miRNA targets. We have evaluated miRTar2GO against other widely used miRNA target prediction algorithms and demonstrated that miRTar2GO produced significantly higher F1 and G scores. Target predictions, binding specifications, results of the pathway analysis and gene ontology enrichment of miRNA targets are freely available at http://www.mirtar2go.org.
authors.affiliation: Faculty of Engineering and Information Technology, School of Software, University of Technology Sydney, PO Box 123, Broadway, Sydney, NSW 2007, Australia.|Faculty of Engineering and Information Technology, Centre of Health Technologies, University of Technology Sydney, PO Box 123, Broadway, Sydney, NSW 2007, Australia.
authors.forename: Alireza
authors.initials: A
authors.lastname: Ahadi
authors.affiliation: Plant Functional Biology and Climate Change Cluster (C3), University of Technology Sydney, PO Box 123, Broadway, Sydney, NSW 2007, Australia.
authors.forename: Gaurav
authors.initials: G
authors.lastname: Sablok
authors.affiliation: Faculty of Engineering and Information Technology, Centre of Health Technologies, University of Technology Sydney, PO Box 123, Broadway, Sydney, NSW 2007, Australia.
authors.forename: Gyorgy
authors.initials: G
authors.lastname: Hutvagner
mesh_terms: D000465:Algorithms; D060565:Argonaute Proteins / Q000378:metabolism*; D001665:Binding Sites; D002460:Cell Line; D003198:Computer Simulation; D005786:Gene Expression Regulation; D006801:Humans; D047468:Immunoprecipitation; D000069550:Machine Learning; D035683:MicroRNAs / Q000737:chemistry / Q000378:metabolism*; D008957:Models, Genetic; D012333:RNA, Messenger / Q000737:chemistry / Q000378:metabolism; D017423:Sequence Analysis, RNA; D012984:Software
chemical_list: C466422:AGO2 protein, human; D060565:Argonaute Proteins; D035683:MicroRNAs; D012333:RNA, Messenger