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.
Highlights
Recent high-throughput technologies like AGO CLIP sequencing [1, 2] and CLASH enable direct biochemical identification of AGO binding and miRNA target sites transcriptome-wide
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
Summary
Recent high-throughput technologies like AGO CLIP sequencing [1, 2] and CLASH (crosslinking, ligation, and sequencing of miRNA-RNA hybrids [3]) enable direct biochemical identification of AGO binding and miRNA target sites transcriptome-wide. The miRNA field has a strong tradition of computationally leveraging transcriptome-wide data to improve target site prediction, but the leading miRNA target prediction methods today do not exploit these new biochemical data. We present a systematic approach to learn both the rules of miRNA-target site pairing and a binding model of AGO’s local sequence preferences and positional bias in alternative 3’UTR isoforms in order to accurately predict miRNA-target interactions. Before it became possible to map AGO-mRNA and miRNA-mRNA interactions directly, the major advance in miRNA target prediction came from restricting to predefined classes of miRNA seed matches in 3’UTRs and training a model to predict mRNA expression changes in miRNA overexpression experiments. Similar observations were encapsulated in the TargetRank method [5], and these studies established that rules of miRNA targeting could be statistically decoded from transcriptome-wide data
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