EvryRNA : BiORSEO

BiORSEO

Bi-Objective RNA Structure Efficient Optimizer

 

 

RNA structure prediction is an important field in Bioinformatics, and numerous methods and tools have been proposed. RNA secondary structure prediction aims to propose a stem-loop assembly structure as a starting point, before looking at 3D additional, so-called "non-canonical" contacts.
RNA modules are recurrent collections of ordered non-canonical interactions commonly found in RNA loops. They now have been well studied and clustered, and stored in databases.

It is now well-known that RNA strands do not fold in one unique conformation and can switch between several meta-stable conformation. The task to achieve becomes the prediction of the most probable structures in the ensemble fold, i.e. tools should return several solutions which are supposed to co-exist.

Here we propose an original tool, BiORSEO, for predicting optimal and sub-optimal RNA secondary structures with pseudoknots with one input sequence, using a database of RNA modules. It is based on a bi-objective integer programming algorithm allowing optimizing both expected accuracy of the structure and compatibility of loops with known RNA modules. The information of the modules is used to detect loops in the sequence, via pattern-matching of a module sequence, or more complex models like JAR3D[1] or BayesPairing[2].

To date, BiORSEO is compatible with databases of modules Rna3Dmotifs[3], The RNA 3D Motif Atlas[4], CaRNAval[5], or your custom dataset provided in JSON format.

 

The executable BiORSEO for Linux, and the datasets from the benchmark are downloadable from here:

 


BiORSEO Docker image
(Win, Mac & Linux)


BiORSEO executable
(Linux x86_64)


source code

You might also want to read the documentation or clone the Git repository from our  Gitlab forge.

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How to cite BiORSEO:

Louis Becquey, Eric Angel, Fariza Tahi, BiORSEO: a bi-objective method to predict RNA secondary structures with pseudoknots using RNA 3D modules, Bioinformatics , Volume 36, Issue 8, 15 April 2020, Pages 2451–2457, https://doi.org/10.1093/bioinformatics/btz962

References:
  • [1] Zirbel, C. L., Roll, J., Sweeney, B. A., Petrov, A. I., Pirrung, M., and Leontis, N. B.(2015). Identifying novel sequence variants of RNA 3d motifs. Nucleic Acids Research, 43(15), 7504–7520
  • [2] Sarrazin-Gendron, R., Reinharz, V., Oliver, C. G., Moitessier, N., and Waldispühl, J. (2019). Automated, customizable and efficient identification of 3D base pair modules with BayesPairing. Nucleic acids research.
  • [3] Djelloul, M. and Denise, A. (2008). Automated motif extraction and classification in RNA tertiary structures. RNA, 14(12), 2489–2497
  • [4] Petrov, A. I., Zirbel, C. L., and Leontis, N. B. (2013). Automated classification of RNA 3D motifs and the RNA 3D Motif Atlas. RNA, 19(10), 1327–1340.
  • [5] Reinharz V, Soulé A, Westhof E, Waldispühl J, Denise A. (2018). Mining for recurrent long-range interactions in RNA structures reveals embedded hierarchies in network families. Nucleic Acids Res. 46(8):3841-3851.
For any questions, comments or suggestions about Biorseo, please feel free to contact: fariza.tahi@ibisc.univ-evry.fr