Recursive MAGUS: Scalable and accurate multiple sequence alignment.

Multiple sequence alignment tools struggle to keep pace with rapidly growing sequence data, as few methods can handle large datasets while maintaining alignment accuracy. We recently introduced MAGUS, a new state-of-the-art method for aligning large numbers of sequences. In this paper, we present a...

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Autor principal:	Vladimir Smirnov
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/1a480c7a4dfb430ea27f619c372f5ae6
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spelling	oai:doaj.org-article:1a480c7a4dfb430ea27f619c372f5ae62021-11-25T05:40:32ZRecursive MAGUS: Scalable and accurate multiple sequence alignment.1553-734X1553-735810.1371/journal.pcbi.1008950https://doaj.org/article/1a480c7a4dfb430ea27f619c372f5ae62021-10-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1008950https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Multiple sequence alignment tools struggle to keep pace with rapidly growing sequence data, as few methods can handle large datasets while maintaining alignment accuracy. We recently introduced MAGUS, a new state-of-the-art method for aligning large numbers of sequences. In this paper, we present a comprehensive set of enhancements that allow MAGUS to align vastly larger datasets with greater speed. We compare MAGUS to other leading alignment methods on datasets of up to one million sequences. Our results demonstrate the advantages of MAGUS over other alignment software in both accuracy and speed. MAGUS is freely available in open-source form at https://github.com/vlasmirnov/MAGUS.Vladimir SmirnovPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 10, p e1008950 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Vladimir Smirnov Recursive MAGUS: Scalable and accurate multiple sequence alignment.
description	Multiple sequence alignment tools struggle to keep pace with rapidly growing sequence data, as few methods can handle large datasets while maintaining alignment accuracy. We recently introduced MAGUS, a new state-of-the-art method for aligning large numbers of sequences. In this paper, we present a comprehensive set of enhancements that allow MAGUS to align vastly larger datasets with greater speed. We compare MAGUS to other leading alignment methods on datasets of up to one million sequences. Our results demonstrate the advantages of MAGUS over other alignment software in both accuracy and speed. MAGUS is freely available in open-source form at https://github.com/vlasmirnov/MAGUS.
format	article
author	Vladimir Smirnov
author_facet	Vladimir Smirnov
author_sort	Vladimir Smirnov
title	Recursive MAGUS: Scalable and accurate multiple sequence alignment.
title_short	Recursive MAGUS: Scalable and accurate multiple sequence alignment.
title_full	Recursive MAGUS: Scalable and accurate multiple sequence alignment.
title_fullStr	Recursive MAGUS: Scalable and accurate multiple sequence alignment.
title_full_unstemmed	Recursive MAGUS: Scalable and accurate multiple sequence alignment.
title_sort	recursive magus: scalable and accurate multiple sequence alignment.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/1a480c7a4dfb430ea27f619c372f5ae6
work_keys_str_mv	AT vladimirsmirnov recursivemagusscalableandaccuratemultiplesequencealignment
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Recursive MAGUS: Scalable and accurate multiple sequence alignment.

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