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      Bi-objective integer programming for RNA secondary structure prediction with pseudoknots

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          Abstract

          Background

          RNA structure prediction is an important field in bioinformatics, and numerous methods and tools have been proposed. Pseudoknots are specific motifs of RNA secondary structures that are difficult to predict. Almost all existing methods are based on a single model and return one solution, often missing the real structure. An alternative approach would be to combine different models and return a (small) set of solutions, maximizing its quality and diversity in order to increase the probability that it contains the real structure.

          Results

          We propose here an original method for predicting RNA secondary structures with pseudoknots, based on integer programming. We developed a generic bi-objective integer programming algorithm allowing to return optimal and sub-optimal solutions optimizing simultaneously two models. This algorithm was then applied to the combination of two known models of RNA secondary structure prediction, namely MEA and MFE. The resulting tool, called BiokoP, is compared with the other methods in the literature. The results show that the best solution (structure with the highest F 1-score) is, in most cases, given by BiokoP. Moreover, the results of BiokoP are homogeneous, regardless of the pseudoknot type or the presence or not of pseudoknots. Indeed, the F 1-scores are always higher than 70% for any number of solutions returned.

          Conclusion

          The results obtained by BiokoP show that combining the MEA and the MFE models, as well as returning several optimal and several sub-optimal solutions, allow to improve the prediction of secondary structures. One perspective of our work is to combine better mono-criterion models, in particular to combine a model based on the comparative approach with the MEA and the MFE models. This leads to develop in the future a new multi-objective algorithm to combine more than two models. BiokoP is available on the EvryRNA platform: https://EvryRNA.ibisc.univ-evry.fr.

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          Most cited references33

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          The equilibrium partition function and base pair binding probabilities for RNA secondary structure.

          A novel application of dynamic programming to the folding problem for RNA enables one to calculate the full equilibrium partition function for secondary structure and the probabilities of various substructures. In particular, both the partition function and the probabilities of all base pairs are computed by a recursive scheme of polynomial order N3 in the sequence length N. The temperature dependence of the partition function gives information about melting behavior for the secondary structure. The pair binding probabilities, the computation of which depends on the partition function, are visually summarized in a "box matrix" display and this provides a useful tool for examining the full ensemble of probable alternative equilibrium structures. The calculation of this ensemble representation allows a proper application and assessment of the predictive power of the secondary structure method, and yields important information on alternatives and intermediates in addition to local information about base pair opening and slippage. The results are illustrated for representative tRNA, 5S RNA, and self-replicating and self-splicing RNA molecules, and allow a direct comparison with enzymatic structure probes. The effect of changes in the thermodynamic parameters on the equilibrium ensemble provides a further sensitivity check to the predictions.
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            Forna (force-directed RNA): Simple and effective online RNA secondary structure diagrams

            Motivation: The secondary structure of RNA is integral to the variety of functions it carries out in the cell and its depiction allows researchers to develop hypotheses about which nucleotides and base pairs are functionally relevant. Current approaches to visualizing secondary structure provide an adequate platform for the conversion of static text-based representations to 2D images, but are limited in their offer of interactivity as well as their ability to display larger structures, multiple structures and pseudoknotted structures. Results: In this article, we present forna, a web-based tool for displaying RNA secondary structure which allows users to easily convert sequences and secondary structures to clean, concise and customizable visualizations. It supports, among other features, the simultaneous visualization of multiple structures, the display of pseudoknotted structures, the interactive editing of the displayed structures, and the automatic generation of secondary structure diagrams from PDB files. It requires no software installation apart from a modern web browser. Availability and implementation: The web interface of forna is available at http://rna.tbi.univie.ac.at/forna while the source code is available on github at www.github.com/pkerpedjiev/forna. Contact: pkerp@tbi.univie.ac.at Supplementary information: Supplementary data are available at Bioinformatics online.
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              A dynamic programming algorithm for RNA structure prediction including pseudoknots.

              We describe a dynamic programming algorithm for predicting optimal RNA secondary structure, including pseudoknots. The algorithm has a worst case complexity of O(N6) in time and O(N4) in storage. The description of the algorithm is complex, which led us to adopt a useful graphical representation (Feynman diagrams) borrowed from quantum field theory. We present an implementation of the algorithm that generates the optimal minimum energy structure for a single RNA sequence, using standard RNA folding thermodynamic parameters augmented by a few parameters describing the thermodynamic stability of pseudoknots. We demonstrate the properties of the algorithm by using it to predict structures for several small pseudoknotted and non-pseudoknotted RNAs. Although the time and memory demands of the algorithm are steep, we believe this is the first algorithm to be able to fold optimal (minimum energy) pseudoknotted RNAs with the accepted RNA thermodynamic model. Copyright 1999 Academic Press.
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                Author and article information

                Contributors
                fariza.tahi@univ-evry.fr
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                15 January 2018
                15 January 2018
                2018
                : 19
                : 13
                Affiliations
                ISNI 0000 0004 4910 6535, GRID grid.460789.4, IBISC, Univ Evry, Université Paris-Saclay, ; Evry, 91025 France
                Article
                2007
                10.1186/s12859-018-2007-7
                5769536
                29334887
                c8c67806-1f4d-4d06-9331-840e4c76353b
                © The Author(s) 2018

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 4 July 2017
                : 2 January 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100007149, Genopole;
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2018

                Bioinformatics & Computational biology
                rna,secondary structure,pseudoknot,integer programming,bi-objective,optimal solutions,sub-optimal solutions

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