EvryRNA : RNAdvisor

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RNA 3D structures assessment


  RNA adopts three-dimensional structures that play a crucial and direct role in its biological function. Assessing the real or predictive quality of a structure is at stake with the complex 3D possible conformations of RNAs.

  Here we propose RNAdvisor, a comprehensive automated software that integrates and enhances the accessibility of existing metrics and scoring functions

We hope this tool will help the automation of RNA 3D structures evaluation.


RNANet pipeline schema


Git repository

Git repository


  • RNAdvisor : A python wrapper code with Docker image to compute RNA 3D structures metrics and scoring functions.
  • RNAdvisor - results : Code to reproduce the results shown in the original paper.
RNAdvisor uses the following repositories:
  • RNA_Assessment: a python repository that computes RMSD, P-VALUE, INF, and DI.
  • MCQ4Structures: a java code that computes the MCQ score.
  • Voronota: a C++ code that computes the CAD-score.
  • Zhanglab: a complete website to compute multiple scores, such as the GDT-TS scores.
  • BaRNAba: an implementation of the eRMSD and eSCORE.
  • DFIRE: an implementation of the DFIRE energy function.
  • RASP: an implementation of the RASP energy function.
  • rsRNASP: a Python implementation of the rsRNASP score.
  • OpenStructure: a C++ and Python implementation for structure analysis. It is used to compute TM-score and lDDT metrics.


How to cite State-of-the-RNArt
  • Clément Bernard, Guillaume Postic, Sahar Ghannay, Fariza Tahi RNAdvisor: a comprehensive benchmarking tool for the measure and prediction of RNA structural model quality Briefings in Bioinformatics, Volume 25, Issue 2, March 2024, bbae064https://doi.org/10.1093/bib/bbae064
Additional references:
  • The metrics that are used in the tools:
  • Davis, I. W., Leaver-Fay, A., Chen, V. B., Block, J. N., Kapral, G. J., Wang, X., Murray, L. W., Arendall, W. B., Snoeyink, J., Richardson, J. S., & Richardson, D. C.(2007). MolProbity: all-atom contacts and structure validation for proteins and nucleic acids. Nucleic Acids Research, 35(Web Server), W375–W383. https://doi.org/10.1093/nar/gkm216
  • Hajdin, C. E., Ding, F., Dokholyan, N. v., & Weeks, K. M. (2010). On the significance of an RNA tertiary structure prediction. RNA, 16(7), 1340–1349. https://doi.org/10.1261/rna.1837410
  • Parisien, M., Cruz, J. A., Westhof, É., & Major, F. (2009). New metrics for comparing and assessing discrepancies between RNA 3D structures and models. RNA, 15(10), 1875–1885. https://doi.org/10.1261/rna.1700409
  • Zok, T., Popenda, M., & Szachniuk, M. (2014). MCQ4Structures to compute similarity of molecule structures. Central European Journal of Operations Research, 22(3), 457–473. https://doi.org/10.1007/s10100-013-0296-5
  • Kliment Olechnovic, Eleonora Kulberkyte and Ceslovas Venclovas (2013). CAD-score: a new contact area difference-based function for evaluation of protein structural models. Proteins, 81:149–162. https://doi.org/10.1002/prot.24172
  • Kliment Olechnovic and Ceslovas Venclovas (2014) The use of interatomic contact areas to quantify discrepancies between RNA 3D models and reference structures. Nucleic Acids Res, 42:5407-5415 https://doi.org/10.1093/nar/gku191
  • Zemla A, Venclovas C, Moult J, Fidelis K. 1999. Processing and analysis of CASP3 protein structure predictions. Proteins3:22–29 https://doi.org/10.1002/(SICI)1097-0134(1999)37:3+<22::AID-PROT5>3.0.CO;2-W
  • Miao, Z., & Westhof, E. (2017). RNA Structure: Advances and Assessment of 3D Structure Prediction. Annual Review of Biophysics, 46(1), 483–503. https://doi.org/10.1146/annurev-biophys-070816-034125
  • Mariani, V., Biasini, M., Barbato, A., & Schwede, T. (2013). lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests. Bioinformatics (Oxford, England), 29(21), 2722–2728. https://doi.org/10.1093/bioinformatics/btt473
  • The scoring functions that are used:
  • T. Zhang, G. Hu, Y. Yang, J. Wang, and Y. Zhou, “All-atom knowledge-based potential for RNA structure discrimination based on the distance-scaled finite ideal-gas reference state.”, J. Computational Biology, in press (2019).
  • Capriotti E, Norambuena T, Marti-Renom MA, Melo F. (2011) All-atom knowledge-based potential for RNA structure prediction and assessment. Bioinformatics 27(8):1086-93
  • Tan YL, Wang X, Shi YZ, Zhang W, Tan ZJ. 2022. rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation. Biophys J. 121: 142-156.
For any questions, comments or suggestions about RNAdvisor, please feel free to contact: fariza.tahi@ibisc.univ-evry.fr