RAxML

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 34 articles )

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  1. Altan-Bonnet, Grégoire; Mora, Thierry; Walczak, Aleksandra M.: Quantitative immunology for physicists (2020)
  2. Spade, David A.: An extended model for phylogenetic maximum likelihood based on discrete morphological characters (2020)
  3. Warnow, Tandy (ed.): Bioinformatics and phylogenetics. Seminal contributions of Bernard Moret (2019)
  4. Whidden, Chris; Iv, Frederick A. Matsen: Efficiently inferring pairwise subtree prune-and-regraft adjacencies between phylogenetic trees (2018)
  5. Bordewich, Magnus; Linz, Simone; Semple, Charles: Lost in space? Generalising subtree prune and regraft to spaces of phylogenetic networks (2017)
  6. Keith, Jonathan M. (ed.): Bioinformatics. Volume I. Data, sequence analysis, and evolution (2017)
  7. Roch, Sebastien; Sly, Allan: Phase transition in the sample complexity of likelihood-based phylogeny inference (2017)
  8. 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)
  9. Urheim, Ellen; Ford, Eric; St. John, Katherine: Characterizing local optima for maximum parsimony (2016)
  10. Guo, Guangbao; You, Wenjie; Qian, Guoqi; Shao, Wei: Parallel maximum likelihood estimator for multiple linear regression models (2015)
  11. Kobert, Kassian; Hauser, Jörg; Stamatakis, Alexandros: Is the protein model assignment problem under linked branch lengths NP-hard? (2014)
  12. Lopez, M. Graham; Horton, Mitchel D.: Batch matrix exponentiation (2014)
  13. Brinkmeyer, Malte; Griebel, Thasso; Böcker, Sebastian: \textscFlipCutsupertrees: towards matrix representation accuracy in polynomial time (2013)
  14. Holland, Barbara R.: The rise of statistical phylogenetics (2013)
  15. Pratas, Frederico; Trancoso, Pedro; Sousa, Leonel; Stamatakis, Alexandros; Shi, Guochun; Kindratenko, Volodymyr: Fine-grain parallelism using multi-core, cell/BE, and GPU systems (2012) ioport
  16. Alachiotis, Nikolaos; Stamatakis, Alexandros: A vector-like reconfigurable floating-point unit for the logarithm (2011) ioport
  17. Brinkmeyer, Malte; Griebel, Thasso; Böcker, Sebastian: Polynomial supertree methods revisited (2011) ioport
  18. Jones, Martin O.; Koutsovoulos, Georgios D.; Blaxter, Mark L.: Iphy: an integrated phylogenetic workbench for supermatrix analyses (2011) ioport
  19. Luecking, Robert K.; Hodkinson, Brendan P.; Stamatakis, Alexandros; Cartwright, Reed A.: PICS-ord: Unlimited coding of ambiguous regions by pairwise identity and cost scores ordination (2011) ioport
  20. Rafique, M. Mustafa; Butt, Ali R.; Nikolopoulos, Dimitrios S.: A capabilities-aware framework for using computational accelerators in data-intensive computing (2011) ioport

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