Motivation:Kinetics is key to understand many phenomena involving RNAs, such as co-transcriptional folding and riboswitches. Exact out-of-equilibrium studies induce extreme computa-tional demands, leading state-of-the-art methods to rely on approximated kinetics landscapes, ob-tained using sampling strategies that strive to generate the key landmarks of the landscape top-ology. 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 anincomplete vision of RNA dynamics.
Results:We introduce RNANR, a new set of algorithms for the exploration of RNA kinetics land-scapes at the secondary structure level. RNANR considers locally optimal structures, a reduced setof RNA conformations, in order to focus its sampling on basins in the kinetic landscape. Along withan exhaustive enumeration, RNANR implements a novel non-redundant stochastic sampling, andoffers a rich array of structural parameters. Our tests on both real and random RNAs reveal thatRNANR allows to generate more unique structures in a given time than its competitors, and allowsa deeper exploration of kinetics landscapes.
Availability and implementation:RNANR is freely available at https://project.inria.fr/rnalands/rnanr.