Parallel inference of a 10.000-taxon phylogeny with maximum likelihood. Inference of large phylogenetic trees with statistical methods is computationally intensive. We recently introduced simple heuristics which yield accurate trees for synthetic as well as real data and are implemented in a sequential program called RAxML. We have demonstrated that RAxML outperforms the currently fastest statistical phylogeny programs (MrBayes, PHYML) in terms of speed and likelihood values on real data. In this paper we present a non-deterministic parallel implementation of our algorithm which in some cases yields super-linear speedups for an analysis of 1.000 organisms on a LINUX cluster. In addition, we use RAxML to infer a 10.000-taxon phylogenetic tree containing representative organisms from the three domains: Eukarya, Bacteria and Archaea. Finally, we compare the sequential speed and accuracy of RAxML and PHYML on 8 synthetic alignments comprising 4.000 sequences.

References in zbMATH (referenced in 45 articles )

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  1. Pouryahya, Fatemeh; Sankoff, David: Peripheral structures in unlabelled trees and the accumulation of subgenomes in the evolution of polyploids (2022)
  2. Richards, A.; Kubatko, L.: Site pattern probabilities under the multispecies coalescent and a relaxed molecular clock: theory and applications (2022)
  3. Collienne, Lena; Gavryushkin, Alex: Computing nearest neighbour interchange distances between ranked phylogenetic trees (2021)
  4. Jaffe, Ariel; Amsel, Noah; Aizenbud, Yariv; Nadler, Boaz; Chang, Joseph T.; Kluger, Yuval: Spectral neighbor joining for reconstruction of latent tree models (2021)
  5. Richards, Andrew; Kubatko, Laura: Bayesian-weighted triplet and quartet methods for species tree inference (2021)
  6. Zhang, Cheng; Dinh, Vu; Matsen, Frederick A. IV: Nonbifurcating phylogenetic tree inference via the adaptive Lasso (2021)
  7. Altan-Bonnet, Grégoire; Mora, Thierry; Walczak, Aleksandra M.: Quantitative immunology for physicists (2020)
  8. Spade, David A.: An extended model for phylogenetic maximum likelihood based on discrete morphological characters (2020)
  9. Warnow, Tandy (ed.): Bioinformatics and phylogenetics. Seminal contributions of Bernard Moret (2019)
  10. Gawrychowski, Pawel; Landau, Gad M.; Sung, Wing-Kin; Weimann, Oren: A faster construction of greedy consensus trees (2018)
  11. Maria Luiza Mondelli, Thiago Magalhães, Guilherme Loss, Michael Wilde, Ian Foster, Marta Mattoso, Daniel S. Katz, Helio J. C. Barbosa, Ana Tereza R. Vasconcelos, Kary Ocaña, Luiz M. R. Gadelha Jr: BioWorkbench: A High-Performance Framework for Managing and Analyzing Bioinformatics Experiments (2018) arXiv
  12. Whidden, Chris; Matsen, Frederick A. IV: Efficiently inferring pairwise subtree prune-and-regraft adjacencies between phylogenetic trees (2018)
  13. Bordewich, Magnus; Linz, Simone; Semple, Charles: Lost in space? Generalising subtree prune and regraft to spaces of phylogenetic networks (2017)
  14. Keith, Jonathan M. (ed.): Bioinformatics. Volume I. Data, sequence analysis, and evolution (2017)
  15. Roch, Sebastien; Sly, Allan: Phase transition in the sample complexity of likelihood-based phylogeny inference (2017)
  16. Schrempf, Dominik; Minh, Bui Quang; De Maio, Nicola; von Haeseler, Arndt; Kosiol, Carolin: Reversible polymorphism-aware phylogenetic models and their application to tree inference (2016)
  17. Urheim, Ellen; Ford, Eric; St. John, Katherine: Characterizing local optima for maximum parsimony (2016)
  18. Guo, Guangbao; You, Wenjie; Qian, Guoqi; Shao, Wei: Parallel maximum likelihood estimator for multiple linear regression models (2015)
  19. Kobert, Kassian; Hauser, Jörg; Stamatakis, Alexandros: Is the protein model assignment problem under linked branch lengths NP-hard? (2014)
  20. Lopez, M. Graham; Horton, Mitchel D.: Batch matrix exponentiation (2014)

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Further publications can be found at: http://www.exelixis-lab.org/