An Algorithm for Template-Based Prediction of Secondary Structures of Individual RNA Sequences

While understanding the structure of RNA molecules is vital for deciphering their functions, determining RNA structures experimentally is exceptionally hard. At the same time, extant approaches to computational RNA structure prediction have limited applicability and reliability. In this paper we pro...

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Bibliographic Details
Published inFrontiers in genetics Vol. 8; p. 147
Main Authors Pánek, Josef, Modrák, Martin, Schwarz, Marek
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 10.10.2017
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Summary:While understanding the structure of RNA molecules is vital for deciphering their functions, determining RNA structures experimentally is exceptionally hard. At the same time, extant approaches to computational RNA structure prediction have limited applicability and reliability. In this paper we provide a method to solve a simpler yet still biologically relevant problem: prediction of secondary RNA structure using structure of different molecules as a template. Our method identifies conserved and unconserved subsequences within an RNA molecule. For conserved subsequences, the template structure is directly transferred into the generated structure and combined with de-novo predicted structure for the unconserved subsequences with low evolutionary conservation. The method also determines, when the generated structure is unreliable. The method is validated using experimentally identified structures. The accuracy of the method exceeds that of classical prediction algorithms and constrained prediction methods. This is demonstrated by comparison using large number of heterogeneous RNAs. The presented method is fast and robust, and useful for various applications requiring knowledge of secondary structures of individual RNA sequences.
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This article was submitted to Bioinformatics and Computational Biology, a section of the journal Frontiers in Genetics
Reviewed by: Zhi-Ping Liu, Shandong University, China; Cuncong Zhong, University of Kansas, United States
Edited by: Alessandro Laganà, Icahn School of Medicine at Mount Sinai, United States
ISSN:1664-8021
1664-8021
DOI:10.3389/fgene.2017.00147