Learning to Predict miRNA-mRNA Interactions from AGO CLIP Sequencing and CLASH Data

Yuheng Lu and Christina S Leslie

PLOS Computational Biology, 2016, vol. 12, issue 7, 1-18

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.Author Summary: MicroRNAs (or miRNAs) are a family of small RNA molecules that guide Argonaute (AGO) to specific target sites within mRNAs and regulate numerous biological processes in normal cells and in disease. Despite years of research, the principles of miRNA targeting are incompletely understood, and computational miRNA target prediction methods still achieve only modest performance. Most previous target prediction work has been based on indirect measurements of miRNA regulation, such as mRNA expression changes upon miRNA perturbation, without mapping actual binding sites, which limits accuracy and precludes discovery of more subtle miRNA targeting rules. The recent introduction of CLIP (UV crosslinking followed by immunoprecipitation) sequencing technologies enables direct identification of interactions between miRNAs and mRNAs. However, the data generated from these assays has not been fully exploited in target prediction. Here, we present a model to predict miRNA-mRNA interactions solely based on their sequences, using new technologies to map AGO and miRNA binding interactions with machine learning techniques. Our algorithm produces more accurate predictions than state-of-the-art methods based on indirect measurements. Moreover, interpretation of the learned model reveals novel features of miRNA-mRNA interactions, including potential cooperativity with specific RNA-binding proteins.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1005026

DOI: 10.1371/journal.pcbi.1005026

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