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McGenus: a Monte Carlo algorithm to predict RNA secondary structures with pseudoknots
We present McGenus, an algorithm to predict RNA secondary structures with pseudoknots. The method is based on a classification of RNA structures according to their topological genus. McGenus can treat sequences of up to 1000 bases and performs an advanced stochastic search of their minimum free ener...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3561945/ https://www.ncbi.nlm.nih.gov/pubmed/23248008 http://dx.doi.org/10.1093/nar/gks1204 |
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author | Bon, Michaël Micheletti, Cristian Orland, Henri |
author_facet | Bon, Michaël Micheletti, Cristian Orland, Henri |
author_sort | Bon, Michaël |
collection | PubMed |
description | We present McGenus, an algorithm to predict RNA secondary structures with pseudoknots. The method is based on a classification of RNA structures according to their topological genus. McGenus can treat sequences of up to 1000 bases and performs an advanced stochastic search of their minimum free energy structure allowing for non-trivial pseudoknot topologies. Specifically, McGenus uses a Monte Carlo algorithm with replica exchange for minimizing a general scoring function which includes not only free energy contributions for pair stacking, loop penalties, etc. but also a phenomenological penalty for the genus of the pairing graph. The good performance of the stochastic search strategy was successfully validated against TT2NE which uses the same free energy parametrization and performs exhaustive or partially exhaustive structure search, albeit for much shorter sequences (up to 200 bases). Next, the method was applied to other RNA sets, including an extensive tmRNA database, yielding results that are competitive with existing algorithms. Finally, it is shown that McGenus highlights possible limitations in the free energy scoring function. The algorithm is available as a web server at http://ipht.cea.fr/rna/mcgenus.php. |
format | Online Article Text |
id | pubmed-3561945 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-35619452013-02-01 McGenus: a Monte Carlo algorithm to predict RNA secondary structures with pseudoknots Bon, Michaël Micheletti, Cristian Orland, Henri Nucleic Acids Res RNA We present McGenus, an algorithm to predict RNA secondary structures with pseudoknots. The method is based on a classification of RNA structures according to their topological genus. McGenus can treat sequences of up to 1000 bases and performs an advanced stochastic search of their minimum free energy structure allowing for non-trivial pseudoknot topologies. Specifically, McGenus uses a Monte Carlo algorithm with replica exchange for minimizing a general scoring function which includes not only free energy contributions for pair stacking, loop penalties, etc. but also a phenomenological penalty for the genus of the pairing graph. The good performance of the stochastic search strategy was successfully validated against TT2NE which uses the same free energy parametrization and performs exhaustive or partially exhaustive structure search, albeit for much shorter sequences (up to 200 bases). Next, the method was applied to other RNA sets, including an extensive tmRNA database, yielding results that are competitive with existing algorithms. Finally, it is shown that McGenus highlights possible limitations in the free energy scoring function. The algorithm is available as a web server at http://ipht.cea.fr/rna/mcgenus.php. Oxford University Press 2013-02 2012-12-15 /pmc/articles/PMC3561945/ /pubmed/23248008 http://dx.doi.org/10.1093/nar/gks1204 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com. |
spellingShingle | RNA Bon, Michaël Micheletti, Cristian Orland, Henri McGenus: a Monte Carlo algorithm to predict RNA secondary structures with pseudoknots |
title | McGenus: a Monte Carlo algorithm to predict RNA secondary structures with pseudoknots |
title_full | McGenus: a Monte Carlo algorithm to predict RNA secondary structures with pseudoknots |
title_fullStr | McGenus: a Monte Carlo algorithm to predict RNA secondary structures with pseudoknots |
title_full_unstemmed | McGenus: a Monte Carlo algorithm to predict RNA secondary structures with pseudoknots |
title_short | McGenus: a Monte Carlo algorithm to predict RNA secondary structures with pseudoknots |
title_sort | mcgenus: a monte carlo algorithm to predict rna secondary structures with pseudoknots |
topic | RNA |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3561945/ https://www.ncbi.nlm.nih.gov/pubmed/23248008 http://dx.doi.org/10.1093/nar/gks1204 |
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