MPBoot: fast phylogenetic maximum parsimony tree inference and bootstrap approximation

被引:128
作者
Diep Thi Hoang [1 ]
Le Sy Vinh [1 ]
Flouri, Tomas [2 ]
Stamatakis, Alexandros [3 ,4 ]
von Haeseler, Arndt [5 ,6 ]
Bui Quang Minh [5 ]
机构
[1] Vietnam Natl Univ, Univ Engn & Technol, Hanoi, Vietnam
[2] UCL, Dept Genet Evolut & Environm, Gower St, London WC1E 6BT, England
[3] Heidelberg Inst Theoret Studies, Heidelberg, Germany
[4] Karlsruhe Inst Technol, Inst Theoret Informat, Karlsruhe, Germany
[5] Univ Vienna, Med Univ Vienna, Ctr Integrat Bioinformat Vienna, Max F Perutz Labs, Campus Vienna Bioctr 5, A-1030 Vienna, Austria
[6] Univ Vienna, Fac Comp Sci, Bioinformat & Computat Biol, Vienna, Austria
来源
BMC EVOLUTIONARY BIOLOGY | 2018年 / 18卷
基金
奥地利科学基金会;
关键词
Phylogenetic inference; Nonparametric bootstrap; Maximum parsimony; ALGORITHM; CONFIDENCE; EVOLUTION; PROTEIN;
D O I
10.1186/s12862-018-1131-3
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Background: The nonparametric bootstrap is widely used to measure the branch support of phylogenetic trees. However, bootstrapping is computationally expensive and remains a bottleneck in phylogenetic analyses. Recently, an ultrafast bootstrap approximation (UFBoot) approach was proposed for maximum likelihood analyses. However, such an approach is still missing for maximum parsimony. Results: To close this gap we present MPBoot, an adaptation and extension of UFBoot to compute branch supports under the maximum parsimony principle. MPBoot works for both uniform and non-uniform cost matrices. Our analyses on biological DNA and protein showed that under uniform cost matrices, MPBoot runs on average 4.7 (DNA) to 7 times (protein data) (range: 1.2-20.7) faster than the standard parsimony bootstrap implemented in PAUP*; but 1.6 (DNA) to 4.1 times (protein data) slower than the standard bootstrap with a fast search routine in TNT (fast-TNT). However, for non-uniform cost matrices MPBoot is 5 (DNA) to 13 times (protein data) (range: 0.3-63. 9) faster than fast-TNT. We note that MPBoot achieves better scores more frequently than PAUP* and fast-TNT. However, this effect is less pronounced if an intensive but slower search in TNT is invoked. Moreover, experiments on large-scale simulated data show that while both PAUP* and TNT bootstrap estimates are too conservative, MPBoot bootstrap estimates appear more unbiased. Conclusions: MPBoot provides an efficient alternative to the standard maximum parsimony bootstrap procedure. It shows favorable performance in terms of run time, the capability of finding a maximum parsimony tree, and high bootstrap accuracy on simulated as well as empirical data sets. MPBoot is easy-to-use, open-source and available at http://www.cibiv.at/software/mpboot.
引用
收藏
页数:11
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