Efficient approximations of RNA kinetics landscape using non-redundant sampling.
Bioinformatics
; 33(14): i283-i292, 2017 Jul 15.
Article
em En
| MEDLINE
| ID: mdl-28882001
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 conformations, 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 AND IMPLEMENTATION RNANR is freely available at https//project.inria.fr/rnalands/rnanr . CONTACT yann.ponty@lix.polytechnique.fr.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Termodinâmica
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Software
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RNA
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Biologia Computacional
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Riboswitch
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Conformação de Ácido Nucleico
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2017
Tipo de documento:
Article
País de afiliação:
França