RNA 3D motifs modeling and software development of an RNA structure prediction tool

Type de poste
Niveau d'étude minimal
Durée du poste
Contrat renouvelable
Contrat non renouvelable
Détails de renouvellement
Negociations on dates and duration are welcome.
Date de prise de fonction
Date de fin de validité de l'annonce

23 Boulevard de France
91000 Evry

Fariza Tahi
Louis Becquey
Email du/des contacts

Context : RNA structure prediction is one of the scientific research activities of the AROBAS team. To do so, a software using a bi-objective optimisation algorithm has been developped [5]. This algorithm proposes solutions to the RNA molecule folding problem which satisfy both a molecule stability objective (a low energy state) and the occurrence of known motives, previously observed in other RNAs. However, a RNA “motif” has no consensual definition and several models exist and require comparison. The current version of the software (BiORSEO, available online on the EvryRNA platform) applies the algorithm to two first motif models [1][2]. However, more recent and complete motif models exist in literature, with databases indexing these motifs [3][4].

Internship : The scientific goal is to study the different RNA 3D motifs models available in several public databases and to benchmark their relative performances when used with our algorithm. To do so, a software tool should be developped, able to efficiently apply the algorithm with motives from any database. APIs should therefore be written for every data source.
In addition to the scientific results, a technical reflexion has to be done on the software architecture, regarding data load into memory and parallel execution.
A containerization of the app is desired to ease its deployment. A C++/Python code base already exists and can be used, or reimplemented.

Required knowledge : C++ development, containerization (for example Docker)
Optional : Databases, graph algorithms, container deployment, infrastructure-on-demand. If desired, the software can be made in Rust instead of C++ if the intern wants to give Rust a try.

[1] Djelloul & Denise. Automated motif extraction and classification in RNA tertiary structures. RNA, (2008)
[2] Petrov, Zirbel, & Leontis. Automated classification of RNA 3D motifs and the RNA 3D Motif Atlas. RNA, (2013)
[6]Reinharz, Soulé, Westhof, Waldispühl & Denise. Mining for recurrent long-range interactions in RNA structures reveals embedded hierarchies in network families. Nucleic acids research (2018).
[7]Ge, Islam, Zhong & Zhang, De novo discovery of structural motifs in RNA 3D structures through clustering, Nucleic Acids Research (2018)
[8]Becquey, Angel & Tahi (2019), A review of different ways to insert known RNA modules into RNA secondary structures, JoBIM 2019 conference Acts, pages 31-38

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