microCLIP super learning framework uncovers functional transcriptome-wide miRNA interactions

Maria D. Paraskevopoulou (), Dimitra Karagkouni, Ioannis S. Vlachos, Spyros Tastsoglou and Artemis G. Hatzigeorgiou ()
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Maria D. Paraskevopoulou: University of Thessaly
Dimitra Karagkouni: University of Thessaly
Ioannis S. Vlachos: University of Thessaly
Spyros Tastsoglou: University of Thessaly
Artemis G. Hatzigeorgiou: University of Thessaly

Nature Communications, 2018, vol. 9, issue 1, 1-16

Abstract: Abstract Argonaute crosslinking and immunoprecipitation (CLIP) experiments are the most widely used high-throughput methodologies for miRNA targetome characterization. The analysis of Photoactivatable Ribonucleoside-Enhanced (PAR) CLIP methodology focuses on sequence clusters containing T-to-C conversions. Here, we demonstrate for the first time that the non-T-to-C clusters, frequently observed in PAR-CLIP experiments, exhibit functional miRNA-binding events and strong RNA accessibility. This discovery is based on the analysis of an extensive compendium of bona fide miRNA-binding events, and is further supported by numerous miRNA perturbation experiments and structural sequencing data. The incorporation of these previously neglected clusters yields an average of 14% increase in miRNA-target interactions per PAR-CLIP library. Our findings are integrated in microCLIP ( www.microrna.gr/microCLIP ), a cutting-edge framework that combines deep learning classifiers under a super learning scheme. The increased performance of microCLIP in CLIP-Seq-guided detection of miRNA interactions, uncovers previously elusive regulatory events and miRNA-controlled pathways.

Date: 2018
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DOI: 10.1038/s41467-018-06046-y

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