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GTfold: Enabling parallel RNA secondary structure prediction on multi-core desktops

BACKGROUND: Accurate and efficient RNA secondary structure prediction remains an important open problem in computational molecular biology. Historically, advances in computing technology have enabled faster and more accurate RNA secondary structure predictions. Previous parallelized prediction progr...

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Detalles Bibliográficos
Autores principales: Swenson, M Shel, Anderson, Joshua, Ash, Andrew, Gaurav, Prashant, Sükösd, Zsuzsanna, Bader, David A, Harvey, Stephen C, Heitsch, Christine E
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3748833/
https://www.ncbi.nlm.nih.gov/pubmed/22747589
http://dx.doi.org/10.1186/1756-0500-5-341
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author Swenson, M Shel
Anderson, Joshua
Ash, Andrew
Gaurav, Prashant
Sükösd, Zsuzsanna
Bader, David A
Harvey, Stephen C
Heitsch, Christine E
author_facet Swenson, M Shel
Anderson, Joshua
Ash, Andrew
Gaurav, Prashant
Sükösd, Zsuzsanna
Bader, David A
Harvey, Stephen C
Heitsch, Christine E
author_sort Swenson, M Shel
collection PubMed
description BACKGROUND: Accurate and efficient RNA secondary structure prediction remains an important open problem in computational molecular biology. Historically, advances in computing technology have enabled faster and more accurate RNA secondary structure predictions. Previous parallelized prediction programs achieved significant improvements in runtime, but their implementations were not portable from niche high-performance computers or easily accessible to most RNA researchers. With the increasing prevalence of multi-core desktop machines, a new parallel prediction program is needed to take full advantage of today’s computing technology. FINDINGS: We present here the first implementation of RNA secondary structure prediction by thermodynamic optimization for modern multi-core computers. We show that GTfold predicts secondary structure in less time than UNAfold and RNAfold, without sacrificing accuracy, on machines with four or more cores. CONCLUSIONS: GTfold supports advances in RNA structural biology by reducing the timescales for secondary structure prediction. The difference will be particularly valuable to researchers working with lengthy RNA sequences, such as RNA viral genomes.
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spelling pubmed-37488332013-08-22 GTfold: Enabling parallel RNA secondary structure prediction on multi-core desktops Swenson, M Shel Anderson, Joshua Ash, Andrew Gaurav, Prashant Sükösd, Zsuzsanna Bader, David A Harvey, Stephen C Heitsch, Christine E BMC Res Notes Technical Note BACKGROUND: Accurate and efficient RNA secondary structure prediction remains an important open problem in computational molecular biology. Historically, advances in computing technology have enabled faster and more accurate RNA secondary structure predictions. Previous parallelized prediction programs achieved significant improvements in runtime, but their implementations were not portable from niche high-performance computers or easily accessible to most RNA researchers. With the increasing prevalence of multi-core desktop machines, a new parallel prediction program is needed to take full advantage of today’s computing technology. FINDINGS: We present here the first implementation of RNA secondary structure prediction by thermodynamic optimization for modern multi-core computers. We show that GTfold predicts secondary structure in less time than UNAfold and RNAfold, without sacrificing accuracy, on machines with four or more cores. CONCLUSIONS: GTfold supports advances in RNA structural biology by reducing the timescales for secondary structure prediction. The difference will be particularly valuable to researchers working with lengthy RNA sequences, such as RNA viral genomes. BioMed Central 2012-07-02 /pmc/articles/PMC3748833/ /pubmed/22747589 http://dx.doi.org/10.1186/1756-0500-5-341 Text en Copyright © 2012 Swenson et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Technical Note
Swenson, M Shel
Anderson, Joshua
Ash, Andrew
Gaurav, Prashant
Sükösd, Zsuzsanna
Bader, David A
Harvey, Stephen C
Heitsch, Christine E
GTfold: Enabling parallel RNA secondary structure prediction on multi-core desktops
title GTfold: Enabling parallel RNA secondary structure prediction on multi-core desktops
title_full GTfold: Enabling parallel RNA secondary structure prediction on multi-core desktops
title_fullStr GTfold: Enabling parallel RNA secondary structure prediction on multi-core desktops
title_full_unstemmed GTfold: Enabling parallel RNA secondary structure prediction on multi-core desktops
title_short GTfold: Enabling parallel RNA secondary structure prediction on multi-core desktops
title_sort gtfold: enabling parallel rna secondary structure prediction on multi-core desktops
topic Technical Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3748833/
https://www.ncbi.nlm.nih.gov/pubmed/22747589
http://dx.doi.org/10.1186/1756-0500-5-341
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