Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189, CNRS - Centre National de la Recherche Scientifique
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France
3AMIB - Algorithms and Models for Integrative Biology (Algorithmes et modèles pour la Biologie Intégrative
Bâtiment Alan Turing - Campus de l'École Polytechnique - 1 rue Honoré d'Estienne d'Orves - 91120 Palaiseau - France)
Abstract : Motivation: Kinetics is key to understand many phenomena involving RNAs, such as co-transcriptional folding and riboswitches. Exact out-of-equilibrium studies induce extreme computational demands, leading state-of-the-art methods to rely on approximated kinetics landscapes, obtained using sampling strategies that strive to generate the key landmarks of the landscape topology. However, such methods are impeded by a large level of redundancy within sampled sets. Such a redundancy is uninformative, and obfuscates important intermediate states, leading to an incomplete vision of RNA dynamics.
Results: We introduce RNANR, a new set of algorithms for the exploration of RNA kinetics landscapes at the secondary structure level. RNANR considers locally optimal structures, a reduced set of RNA con-formations, in order to focus its sampling on basins in the kinetic landscape. Along with an exhaustive enumeration, RNANR implements a novel non-redundant stochastic sampling, and offers a rich array of structural parameters. Our tests on both real and random RNAs reveal that RNANR allows to generate more unique structures in a given time than its competitors, and allows a deeper exploration of kinetics landscapes. Availability:
RNANR is freely available at https://project.inria.fr/rnalands/rnanr
Juraj Michálik, Hélène Touzet, Yann Ponty. Efficient approximations of RNA kinetics landscape using non-redundant sampling. ISMB/ECCB - 25th Annual international conference on Intelligent Systems for Molecular Biology/16th European Conference on Computational Biology - 2017, Jul 2017, Prague, Czech Republic. pp.i283 - i292, ⟨10.1093/bioinformatics/btx269⟩. ⟨hal-01500115⟩
