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Parallelization of the MAFFT multiple sequence alignment program

Summary: Multiple sequence alignment (MSA) is an important step in comparative sequence analyses. Parallelization is a key technique for reducing the time required for large-scale sequence analyses. The three calculation stages, all-to-all comparison, progressive alignment and iterative refinement,...

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Detalles Bibliográficos
Autores principales: Katoh, Kazutaka, Toh, Hiroyuki
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2905546/
https://www.ncbi.nlm.nih.gov/pubmed/20427515
http://dx.doi.org/10.1093/bioinformatics/btq224
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author Katoh, Kazutaka
Toh, Hiroyuki
author_facet Katoh, Kazutaka
Toh, Hiroyuki
author_sort Katoh, Kazutaka
collection PubMed
description Summary: Multiple sequence alignment (MSA) is an important step in comparative sequence analyses. Parallelization is a key technique for reducing the time required for large-scale sequence analyses. The three calculation stages, all-to-all comparison, progressive alignment and iterative refinement, of the MAFFT MSA program were parallelized using the POSIX Threads library. Two natural parallelization strategies (best-first and simple hill-climbing) were implemented for the iterative refinement stage. Based on comparisons of the objective scores and benchmark scores between the two approaches, we selected a simple hill-climbing approach as the default. Availability: The parallelized version of MAFFT is available at http://mafft.cbrc.jp/alignment/software/. This version currently supports the Linux operating system only. Contact: kazutaka.katoh@aist.go.jp Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-29055462010-07-19 Parallelization of the MAFFT multiple sequence alignment program Katoh, Kazutaka Toh, Hiroyuki Bioinformatics Applications Note Summary: Multiple sequence alignment (MSA) is an important step in comparative sequence analyses. Parallelization is a key technique for reducing the time required for large-scale sequence analyses. The three calculation stages, all-to-all comparison, progressive alignment and iterative refinement, of the MAFFT MSA program were parallelized using the POSIX Threads library. Two natural parallelization strategies (best-first and simple hill-climbing) were implemented for the iterative refinement stage. Based on comparisons of the objective scores and benchmark scores between the two approaches, we selected a simple hill-climbing approach as the default. Availability: The parallelized version of MAFFT is available at http://mafft.cbrc.jp/alignment/software/. This version currently supports the Linux operating system only. Contact: kazutaka.katoh@aist.go.jp Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2010-08-01 2010-04-28 /pmc/articles/PMC2905546/ /pubmed/20427515 http://dx.doi.org/10.1093/bioinformatics/btq224 Text en © The Author(s) 2010. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Note
Katoh, Kazutaka
Toh, Hiroyuki
Parallelization of the MAFFT multiple sequence alignment program
title Parallelization of the MAFFT multiple sequence alignment program
title_full Parallelization of the MAFFT multiple sequence alignment program
title_fullStr Parallelization of the MAFFT multiple sequence alignment program
title_full_unstemmed Parallelization of the MAFFT multiple sequence alignment program
title_short Parallelization of the MAFFT multiple sequence alignment program
title_sort parallelization of the mafft multiple sequence alignment program
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2905546/
https://www.ncbi.nlm.nih.gov/pubmed/20427515
http://dx.doi.org/10.1093/bioinformatics/btq224
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