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Efficient approximations of RNA kinetics landscape using non-redundant sampling
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
Cited literature [45 references]
https://hal.inria.fr/hal-01500115
Contributor : Yann Ponty <>
Submitted on : Sunday, April 2, 2017 - 12:38:13 PM
Last modification on : Thursday, March 5, 2020 - 6:34:38 PM
Document(s) archivé(s) le : Monday, July 3, 2017 - 1:15:31 PM
Contributor : Yann Ponty <>
Submitted on : Sunday, April 2, 2017 - 12:38:13 PM
Last modification on : Thursday, March 5, 2020 - 6:34:38 PM
Document(s) archivé(s) le : Monday, July 3, 2017 - 1:15:31 PM
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michalik-ISMB-2017.pdf
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Distributed under a Creative Commons Attribution - NonCommercial 4.0 International License