EvryRNA : 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 two databases of modules: Rna3Dmotifs[3] and The RNA 3D Motif Atlas[4].

 

Graphical pipeline of the tool

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

 


BiORSEO for Linux
(Source & data)

You might also want to clone the Git repository from GitHub.

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How to cite BiORSEO:
  • Becquey, L., Angel, E., & Tahi, F. Biorseo: Benchmarking ways to use RNA modules leads to improved secondary structure prediction with pseudoknots. Not published yet.
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.
For any questions, comments or suggestions about Biorseo, please feel free to contact: fariza.tahi@ibisc.univ-evry.fr