Predict Motif
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Computational prediction of RNA structural motifs involved in post transcriptional regulatory processes

Michal Rabani, Michael Kertesz, Eran Segal

mRNA molecules are tightly regulated, mostly through interactions with proteins and other RNAs, but the mechanisms that confer the specificity of such interactions are poorly understood. It is clear however that this specificity is determined by both the nucleotide sequence and secondary structure of the mRNA. Here, we develop efficient computational tools for identifying structural elements within mRNAs that are involved in specifying post-transcriptional regulations. By analyzing experimental data on mRNA decay rates we identify common structural elements in fast-decaying and slow-decaying mRNAs, and link them with binding preferences of several RNA binding proteins. We also predict structural elements in sets of mRNAs with a common sub-cellular localization in mouse neurons and fly embryos. Finally, by analyzing pre-microRNA stem-loop structures, we identify differences between animals and plants in the structures of pre-microRNA, which provide insights into the mechanism of microRNA biogenesis. Together, our results reveal unexplored layers of post transcriptional regulations in groups of RNAs, and are therefore an important step towards a better understanding of the regulatory information conveyed within RNA molecules.