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Fast phylogenetic maximum parsimony tree inference and bootstrap approximation

Hoang, Thi Diep and Le, Sy Vinh and Tomas, Flouri and Alexandros, Stamatakis and Arndt, von Haeseler and Minh, Bui (2018) Fast phylogenetic maximum parsimony tree inference and bootstrap approximation. BMC Evolutionary Biology, 18:11 (18). p. 11.

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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

Item Type: Article
Subjects: Information Technology (IT)
ISI-indexed journals
Divisions: Faculty of Information Technology (FIT)
Depositing User: Asso.Prof. Vinh Le
Date Deposited: 17 Dec 2018 03:07
Last Modified: 17 Dec 2018 03:07

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