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microCLIP super learning framework uncovers functional transcriptome-wide miRNA interactions.

Maria D ParaskevopoulouDimitra KaragkouniIoannis S VlachosSpyros TastsoglouArtemis G Hatzigeorgiou
Published in: Nature communications (2018)
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.
Keyphrases
  • high throughput
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  • rna seq
  • gene expression
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  • genome wide
  • dna binding
  • binding protein
  • electronic health record
  • label free
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