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BiORSEO: A bi-objective method to predict RNA secondary structures with pseudoknots using RNA 3D modules

Abstract : MOTIVATION:RNA loops have been modelled and clustered from solved 3D structures into ordered collections of recurrent non-canonical interactions called" RNA modules", available in databases. This work explores what information from such modules can be used to improve secondary structure prediction. We propose a bi-objective method for predicting RNA secondary structures by minimizing both an energy-based and a knowledge-based potential. The tool, called BiORSEO, outputs secondary structures corresponding to the optimal solutions from the Pareto set.RESULTS:We compare several approaches to predict secondary structures using inserted RNA modules information: two module data sources, Rna3Dmotif and The RNA 3D Motif Atlas, and different ways to score the module insertions: module size, module complexity, or module probability according to models like JAR3D and BayesPairing. We benchmark them against a large set of known secondary structures, including some state-of-the-art tools, and comment on the usefulness of the half physics-based, half data-based approach.AVAILABILITY:The software is available for download on the EvryRNA website, as well as the datasets.
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https://hal.archives-ouvertes.fr/hal-02442640
Contributor : Frédéric Davesne <>
Submitted on : Thursday, January 16, 2020 - 3:51:30 PM
Last modification on : Wednesday, October 14, 2020 - 4:22:02 AM

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Louis Becquey, Eric Angel, Fariza Tahi. BiORSEO: A bi-objective method to predict RNA secondary structures with pseudoknots using RNA 3D modules. Bioinformatics, Oxford University Press (OUP), 2020, 36 (8), pp.2451--2457. ⟨10.1093/bioinformatics/btz962⟩. ⟨hal-02442640⟩

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