RAxML and FastTree: comparing two methods for large-scale maximum likelihood phylogeny estimation.

Statistical methods for phylogeny estimation, especially maximum likelihood (ML), offer high accuracy with excellent theoretical properties. However, RAxML, the current leading method for large-scale ML estimation, can require weeks or longer when used on datasets with thousands of molecular sequenc...

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Autores principales:	Kevin Liu, C Randal Linder, Tandy Warnow
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8f2d65a6ab3048e9a1d99f907dc77cec
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spelling	oai:doaj.org-article:8f2d65a6ab3048e9a1d99f907dc77cec2021-11-18T07:33:49ZRAxML and FastTree: comparing two methods for large-scale maximum likelihood phylogeny estimation.1932-620310.1371/journal.pone.0027731https://doaj.org/article/8f2d65a6ab3048e9a1d99f907dc77cec2011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22132132/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Statistical methods for phylogeny estimation, especially maximum likelihood (ML), offer high accuracy with excellent theoretical properties. However, RAxML, the current leading method for large-scale ML estimation, can require weeks or longer when used on datasets with thousands of molecular sequences. Faster methods for ML estimation, among them FastTree, have also been developed, but their relative performance to RAxML is not yet fully understood. In this study, we explore the performance with respect to ML score, running time, and topological accuracy, of FastTree and RAxML on thousands of alignments (based on both simulated and biological nucleotide datasets) with up to 27,634 sequences. We find that when RAxML and FastTree are constrained to the same running time, FastTree produces topologically much more accurate trees in almost all cases. We also find that when RAxML is allowed to run to completion, it provides an advantage over FastTree in terms of the ML score, but does not produce substantially more accurate tree topologies. Interestingly, the relative accuracy of trees computed using FastTree and RAxML depends in part on the accuracy of the sequence alignment and dataset size, so that FastTree can be more accurate than RAxML on large datasets with relatively inaccurate alignments. Finally, the running times of RAxML and FastTree are dramatically different, so that when run to completion, RAxML can take several orders of magnitude longer than FastTree to complete. Thus, our study shows that very large phylogenies can be estimated very quickly using FastTree, with little (and in some cases no) degradation in tree accuracy, as compared to RAxML.Kevin LiuC Randal LinderTandy WarnowPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 11, p e27731 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kevin Liu C Randal Linder Tandy Warnow RAxML and FastTree: comparing two methods for large-scale maximum likelihood phylogeny estimation.
description	Statistical methods for phylogeny estimation, especially maximum likelihood (ML), offer high accuracy with excellent theoretical properties. However, RAxML, the current leading method for large-scale ML estimation, can require weeks or longer when used on datasets with thousands of molecular sequences. Faster methods for ML estimation, among them FastTree, have also been developed, but their relative performance to RAxML is not yet fully understood. In this study, we explore the performance with respect to ML score, running time, and topological accuracy, of FastTree and RAxML on thousands of alignments (based on both simulated and biological nucleotide datasets) with up to 27,634 sequences. We find that when RAxML and FastTree are constrained to the same running time, FastTree produces topologically much more accurate trees in almost all cases. We also find that when RAxML is allowed to run to completion, it provides an advantage over FastTree in terms of the ML score, but does not produce substantially more accurate tree topologies. Interestingly, the relative accuracy of trees computed using FastTree and RAxML depends in part on the accuracy of the sequence alignment and dataset size, so that FastTree can be more accurate than RAxML on large datasets with relatively inaccurate alignments. Finally, the running times of RAxML and FastTree are dramatically different, so that when run to completion, RAxML can take several orders of magnitude longer than FastTree to complete. Thus, our study shows that very large phylogenies can be estimated very quickly using FastTree, with little (and in some cases no) degradation in tree accuracy, as compared to RAxML.
format	article
author	Kevin Liu C Randal Linder Tandy Warnow
author_facet	Kevin Liu C Randal Linder Tandy Warnow
author_sort	Kevin Liu
title	RAxML and FastTree: comparing two methods for large-scale maximum likelihood phylogeny estimation.
title_short	RAxML and FastTree: comparing two methods for large-scale maximum likelihood phylogeny estimation.
title_full	RAxML and FastTree: comparing two methods for large-scale maximum likelihood phylogeny estimation.
title_fullStr	RAxML and FastTree: comparing two methods for large-scale maximum likelihood phylogeny estimation.
title_full_unstemmed	RAxML and FastTree: comparing two methods for large-scale maximum likelihood phylogeny estimation.
title_sort	raxml and fasttree: comparing two methods for large-scale maximum likelihood phylogeny estimation.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/8f2d65a6ab3048e9a1d99f907dc77cec
work_keys_str_mv	AT kevinliu raxmlandfasttreecomparingtwomethodsforlargescalemaximumlikelihoodphylogenyestimation AT crandallinder raxmlandfasttreecomparingtwomethodsforlargescalemaximumlikelihoodphylogenyestimation AT tandywarnow raxmlandfasttreecomparingtwomethodsforlargescalemaximumlikelihoodphylogenyestimation
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RAxML and FastTree: comparing two methods for large-scale maximum likelihood phylogeny estimation.

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