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A robust model for quantitative prediction of the silencing efficacy of wild-type and A-to-I edited miRNAs.

Shen TianGoro TeraiYoshiaki KobayashiYasuaki KimuraHiroshi AbeKiyoshi AsaiKumiko Ui-Tei
Published in: RNA biology (2019)
MicroRNAs (miRNAs) are small non-coding RNAs that play essential roles in the regulation of gene function by a mechanism known as RNA silencing. In a previous study, we revealed that miRNA-mediated silencing efficacy is correlated with the combinatorial thermodynamic properties of the miRNA seed-target mRNA duplex and the 5´-terminus of the miRNA duplex, which can be predicted using 'miScore'. In this study, a robust refined-miScore was developed by integrating the thermodynamic properties of various miRNA secondary structures and the latest thermodynamic parameters of wobble base-pairing, including newly established parameters for I:C base pairing. Through repeated random sampling and machine learning, refined-miScore models calculated with either melting temperature (Tm) or free energy change (ΔG) values were successfully built and validated in both wild-type and adenosine-to-inosine edited miRNAs. In addition to the previously reported contribution of the seed-target duplex and 5´-terminus region, the refined-miScore suggests that the central and 3´-terminus regions of the miRNA duplex also play a role in the thermodynamic regulation of miRNA-mediated silencing efficacy.
Keyphrases
  • wild type
  • machine learning
  • crispr cas
  • high resolution
  • artificial intelligence
  • dna methylation
  • gene expression
  • single cell
  • transcription factor
  • genome wide analysis