microRNAs (miRNAs) are small non-coding RNAs that act as guide molecules in gene silencing. Maturation of miRNAs is a tightly controlled process and is important for nearly all aspects of cellular homeostasis. In the first step of miRNA biogenesis, long primary miRNA (pri-miRNA) transcripts form a 70-90 nt hairpin that is recognized and processed by the nuclear RNase Drosha in complex with its partner protein DGCR8. There are more than 1 800 annotated pri-miRNAs with evolutionary support that have been annotated and collected in the online database miRBase, and thousands more sequences without evolutionary support have been described. While several sequence features have been described for pri-miRNAs, such as UGU motif in the apical loop, only a small number of pri-miRNAs contain all of these features. We hypothesize that RNA structure plays an essential role in pri-miRNA recognition and processing by Drosha. In order to build an RNA structure-centric model for pri-miRNA processing by Drosha, we developed a deep sequencing based biochemical assay to test all of the known pri-miRNA sequences in miRBase. We observed pri-miRNA processing efficienies that spanned several logs of activity with a number of previously annotated miRNA sequences not cutting at all. Strikingly, known sequences features are not critical for pri-miRNA maturation by Drosha. With these experimental observations, we are building a machine learning model that takes into account RNA structure.<br/>

RNA structure centric machine learning of primary microRNA biogenesis

256th National Meeting (2018)

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