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• W2999081115 abstract "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 and implementation The software is available for download on the EvryRNA website, as well as the datasets. Supplementary information Supplementary data are available at Bioinformatics online." @default.
• W2999081115 created "2020-01-23" @default.
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• W2999081115 date "2020-01-08" @default.
• W2999081115 modified "2023-09-27" @default.
• W2999081115 title "BiORSEO: a bi-objective method to predict RNA secondary structures with pseudoknots using RNA 3D modules" @default.
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• W2999081115 doi "https://doi.org/10.1093/bioinformatics/btz962" @default.
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