SATé-II: Very Fast and Accurate Simultaneous Estimation of Multiple Sequence Alignments and Phylogenetic Trees (original) (raw)

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1Department of Computer Science, University of Texas at Austin, Austin, TX 78712, USA

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1Department of Computer Science, University of Texas at Austin, Austin, TX 78712, USA

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2Department of Ecology and Evolutionary Biology, University of Kansas at Lawrence, Lawrence, KS 66045, USA

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3Department of Computer Science, Calvin College, Grand Rapids, MI 49546, USA

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2Department of Ecology and Evolutionary Biology, University of Kansas at Lawrence, Lawrence, KS 66045, USA

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4Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, D-69118 Heidelberg, Germany

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5Section of Integrative Biology, School of Biological Sciences, University of Texas at Austin, Austin, TX, USA

*Correspondence to be sent to: The University of Texas at Austin, Austin, TX 78712, USA; E-mail: rlinder@mail.utexas.edu.

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Revision received:

09 November 2010

Published:

01 December 2011

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Kevin Liu, Tandy J. Warnow, Mark T. Holder, Serita M. Nelesen, Jiaye Yu, Alexandros P. Stamatakis, C. Randal Linder, SATé-II: Very Fast and Accurate Simultaneous Estimation of Multiple Sequence Alignments and Phylogenetic Trees, Systematic Biology, Volume 61, Issue 1, January 2012, Page 90, https://doi.org/10.1093/sysbio/syr095
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Abstract

Highly accurate estimation of phylogenetic trees for large data sets is difficult, in part because multiple sequence alignments must be accurate for phylogeny estimation methods to be accurate. Coestimation of alignments and trees has been attempted but currently only SATé estimates reasonably accurate trees and alignments for large data sets in practical time frames (Liu K., Raghavan S., Nelesen S., Linder C.R., Warnow T. 2009b. Rapid and accurate large-scale coestimation of sequence alignments and phylogenetic trees. Science. 324:1561–1564). Here, we present a modification to the original SATé algorithm that improves upon SATé (which we now call SATé-I) in terms of speed and of phylogenetic and alignment accuracy. SATé-II uses a different divide-and-conquer strategy than SATé-I and so produces smaller more closely related subsets than SATé-I; as a result, SATé-II produces more accurate alignments and trees, can analyze larger data sets, and runs more efficiently than SATé-I. Generally, SATé is a metamethod that takes an existing multiple sequence alignment method as an input parameter and boosts the quality of that alignment method. SATé-II-boosted alignment methods are significantly more accurate than their unboosted versions, and trees based upon these improved alignments are more accurate than trees based upon the original alignments. Because SATé-I used maximum likelihood (ML) methods that treat gaps as missing data to estimate trees and because we found a correlation between the quality of tree/alignment pairs and ML scores, we explored the degree to which SATé's performance depends on using ML with gaps treated as missing data to determine the best tree/alignment pair. We present two lines of evidence that using ML with gaps treated as missing data to optimize the alignment and tree produces very poor results. First, we show that the optimization problem where a set of unaligned DNA sequences is given and the output is the tree and alignment of those sequences that maximize likelihood under the Jukes–Cantor model is uninformative in the worst possible sense. For all inputs, all trees optimize the likelihood score. Second, we show that a greedy heuristic that uses GTR+Gamma ML to optimize the alignment and the tree can produce very poor alignments and trees. Therefore, the excellent performance of SATé-II and SATé-I is not because ML is used as an optimization criterion for choosing the best tree/alignment pair but rather due to the particular divide-and-conquer realignment techniques employed.

© The Author(s) 2011. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com

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