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ExpertRNA: A new framework for RNA structure prediction

Menghan Liu, Giulia Pedrielli, Erik Poppleton, View ORCID ProfilePetr Šulc, Dimitri P. Bertsekas
doi: https://doi.org/10.1101/2021.01.18.427087
Menghan Liu
1Arizona State University, School of Computing Informatics and Decision Systems Engineering
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Giulia Pedrielli
1Arizona State University, School of Computing Informatics and Decision Systems Engineering
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Erik Poppleton
2Arizona State University, School of Molecular Sciences and Center for Molecular Design and Biomimetics
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Petr Šulc
2Arizona State University, School of Molecular Sciences and Center for Molecular Design and Biomimetics
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  • For correspondence: psulc@asu.edu
Dimitri P. Bertsekas
3Arizona State University, School of Computing Informatics and Decision Systems Engineering Massachussets Institute of Technology, Electrical Engineering
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Abstract

Ribonucleic acid (RNA) is a fundamental biological molecule that is essential to all living organisms, performing a versatile array of cellular tasks. The function of many RNA molecules is strongly related to the structure it adopts. As a result, great effort is being dedicated to the design of efficient algorithms that solve the “folding problem”: given a sequence of nucleotides, return a probable list of base pairs, referred to as the secondary structure prediction. Early algorithms have largely relied on finding the structure with minimum free energy. However, the predictions rely on effective simplified free energy models that may not correctly identify the correct structure as the one with the lowest free energy. In light of this, new, data-driven approaches that not only consider free energy, but also use machine learning techniques to learn motifs have also been investigated, and have recently been shown to outperform free energy based algorithms on several experimental data sets.

In this work, we introduce the new ExpertRNA algorithm that provides a modular framework which can easily incorporate an arbitrary number of rewards (free energy or non-parametric/data driven) and secondary structure prediction algorithms. We argue that this capability of ExpertRNA has the potential to balance out different strengths and weaknesses of state-of-the-art folding tools. We test the ExpertRNA on several RNA sequence-structure data sets, and we compare the performance of ExpertRNA against a state-of-the-art folding algorithm. We find that ExpertRNA produces, on average, more accurate predictions than the structure prediction algorithm used, thus validating the promise of the approach.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted January 19, 2021.
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ExpertRNA: A new framework for RNA structure prediction
Menghan Liu, Giulia Pedrielli, Erik Poppleton, Petr Šulc, Dimitri P. Bertsekas
bioRxiv 2021.01.18.427087; doi: https://doi.org/10.1101/2021.01.18.427087
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ExpertRNA: A new framework for RNA structure prediction
Menghan Liu, Giulia Pedrielli, Erik Poppleton, Petr Šulc, Dimitri P. Bertsekas
bioRxiv 2021.01.18.427087; doi: https://doi.org/10.1101/2021.01.18.427087

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