cibiv.bib

@article{Minh2013,
abstract = {Nonparametric bootstrap has been a widely used tool in phylogenetic analysis to assess the clade support of phylogenetic trees. However, with the rapidly growing amount of data, this task remains a computational bottleneck. Recently, approximation methods such as the RAxML rapid bootstrap (RBS) and the Shimodaira-Hasegawa-like approximate likelihood ratio test have been introduced to speed up the bootstrap. Here, we suggest an ultrafast bootstrap approximation approach (UFBoot) to compute the support of phylogenetic groups in maximum likelihood (ML) based trees. To achieve this, we combine the resampling estimated log-likelihood method with a simple but effective collection scheme of candidate trees. We also propose a stopping rule that assesses the convergence of branch support values to automatically determine when to stop collecting candidate trees. UFBoot achieves a median speed up of 3.1 (range: 0.66-33.3) to 10.2 (range: 1.32-41.4) compared with RAxML RBS for real DNA and amino acid alignments, respectively. Moreover, our extensive simulations show that UFBoot is robust against moderate model violations and the support values obtained appear to be relatively unbiased compared with the conservative standard bootstrap. This provides a more direct interpretation of the bootstrap support. We offer an efficient and easy-to-use software (available at http://www.cibiv.at/software/iqtree) to perform the UFBoot analysis with ML tree inference.},
author = {Minh, Bui Quang and Nguyen, Minh Anh Thi and {Von Haeseler}, Arndt},
doi = {10.1093/molbev/mst024},
isbn = {1537-1719 (Electronic)$\backslash$r0737-4038 (Linking)},
issn = {07374038},
journal = {Molecular Biology and Evolution},
keywords = {maximum likelihood,nonparametric bootstrap,phylogenetic inference,tree reconstruction},
number = {5},
pages = {1188--1195},
pmid = {23418397},
title = {{Ultrafast approximation for phylogenetic bootstrap}},
volume = {30},
year = {2013}
}
@article{Nguyen2015,
abstract = {Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2{\%} and 87.1{\%} of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7{\%} and 47.1{\%} of the DNA alignments and 42.2{\%} and 100{\%} of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1{\%}. IQ-TREE is freely available at http://www.cibiv.at/software/iqtree.},
author = {Nguyen, Lam Tung and Schmidt, Heiko A. and {Von Haeseler}, Arndt and Minh, Bui Quang},
doi = {10.1093/molbev/msu300},
isbn = {0737-4038},
issn = {15371719},
journal = {Molecular Biology and Evolution},
keywords = {Maximum likelihood,Phylogenetic inference,Phylogeny,Stochastic algorithm},
number = {1},
pages = {268--274},
pmid = {25371430},
title = {{IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies}},
volume = {32},
year = {2015}
}