title: Learning to Predict miRNA-mRNA Interactions from AGO CLIP Sequencing and CLASH Data.
abstract: Recent technologies like AGO CLIP sequencing and CLASH enable direct transcriptome-wide identification of AGO binding and miRNA target sites, but the most widely used miRNA target prediction algorithms do not exploit these data. Here we use discriminative learning on AGO CLIP and CLASH interactions to train a novel miRNA target prediction model. Our method combines two SVM classifiers, one to predict miRNA-mRNA duplexes and a second to learn a binding model of AGO's local UTR sequence preferences and positional bias in 3'UTR isoforms. The duplex SVM model enables the prediction of non-canonical target sites and more accurately resolves miRNA interactions from AGO CLIP data than previous methods. The binding model is trained using a multi-task strategy to learn context-specific and common AGO sequence preferences. The duplex and common AGO binding models together outperform existing miRNA target prediction algorithms on held-out binding data. Open source code is available at https://bitbucket.org/leslielab/chimiric.
authors.affiliation: Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Yuheng
authors.initials: Y
authors.lastname: Lu
authors.affiliation: Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Christina S
authors.initials: CS
authors.lastname: Leslie
mesh_terms: D000818:Animals; D060565:Argonaute Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D057809:HEK293 Cells; D006801:Humans; D047468:Immunoprecipitation; D051379:Mice; D035683:MicroRNAs / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D015233:Models, Statistical; D017423:Sequence Analysis, RNA / Q000379:methods*; D014466:Ultraviolet Rays
chemical_list: D060565:Argonaute Proteins; D035683:MicroRNAs