Modified RNAs and predictions with the ViennaRNA Package

Author:

Varenyk Yuliia12ORCID,Spicher Thomas13ORCID,Hofacker Ivo L14ORCID,Lorenz Ronny1ORCID

Affiliation:

1. Department of Theoretical Chemistry, University of Vienna , Vienna 1090, Austria

2. Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna , Vienna 1030, Austria

3. UniVie Doctoral School Computer Science (DoCS), University of Vienna , Vienna 1090, Austria

4. Research Group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna , Vienna 1090, Austria

Abstract

Abstract Motivation In living organisms, many RNA molecules are modified post-transcriptionally. This turns the widely known four-letter RNA alphabet ACGU into a much larger one with currently more than 300 known distinct modified bases. The roles for the majority of modified bases remain uncertain, but many are already well-known for their ability to influence the preferred structures that an RNA may adopt. In fact, tRNAs sometimes require certain modifications to fold into their cloverleaf shaped structure. However, predicting the structure of RNAs with base modifications is still difficult due to the lack of efficient algorithms that can deal with the extended sequence alphabet, as well as missing parameter sets that account for the changes in stability induced by the modified bases. Results We present an approach to include sparse energy parameter data for modified bases into the ViennaRNA Package. Our method does not require any changes to the underlying efficient algorithms but instead uses a set of plug-in constraints that adapt the predictions in terms of loop evaluation at runtime. These adaptations are efficient in the sense that they are only performed for loops where additional parameters are actually available for. In addition, our approach also facilitates the inclusion of more modified bases as soon as further parameters become available. Availability and implementation Source code and documentation are available at https://www.tbi.univie.ac.at/RNA.

Funder

Austrian Science Fund

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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