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Using New Software, Researchers Identify RNA Structural Motifs


Researchers at the University of Central Florida have devised a new software solution for studying the structural characteristics of RNA. Shaojie Zhang and his team designed a computational framework called RNAMotifScan-based Clustering, or RNAMSC, that clusters together potential motifs based on their structural similarities. This framework is based on RNAMotif-Scan, an RNA-structural-motif-identification software tool Zhang's team had previously developed. RNAMSC quickly scans RNA samples and identifies motifs in RNA's 3D, accordion-like structure.

"RNA structural motifs are the recurrent substructures formed by the non-canonical base pairs with conserved geometries and functionalities," Zhang says. "In our previous work, we have developed an RNA structural motif identification tool, RNAMotifScan. This tool can compare two RNA structural motifs to find if they share similar base-pairing patterns, such as isosteric base pairs and multi-pairings. In this work, we build a more accurate clustering framework by incorporating RNAMotifScan and use it to discover novel RNA structural motif families."

Zhang says that there were several considerable challenges in developing this new clustering framework for structural motif discovery. "First, base pair annotations from RNA 3D structures sometimes are not very accurate, this can significantly affect other clustering methods," he says. "Second, some non-canonical base pairs are more critical or informative than others. And finally, we need to build an automatic clustering method that is suitable for large dataset."

In a study published in Nucleic Acids Research in February, Zhang and his colleagues describe how they applied RNAMSC on a dataset containing 5S, 16S, and 23S rRNAs. The team not only rediscovered many known motifs including GNRA tetraloop, kink-turn, C-loop, sarcin-ricin, and reverse kink-turn with higher accuracy than the state-of-the-art clustering method, but also potentially novel instances of these motifs.

The structural analyses generated by RNAMSC will hopefully provide researchers with methods for developing treatments for disease. "The RNA structural motifs are critical for many cellular functions and the dysfunction of these RNA structural motifs is likely to result in many diseases," Zhang says. "The discovery of new RNA structural motifs will help us understand the mechanism behind such kinds of diseases and will provide us clues for possible treatments."

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