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Parallel Inference of a 10.000-Taxon Phylogeny with Maximum Likelihood

  • Alexandros Stamatakis
  • Thomas Ludwig
  • Harald Meier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3149)

Abstract

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.

Keywords

Parallel Implementation Parallel Inference Good Likelihood Bayesian Phylogenetic Inference Rearrangement Step 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Alexandros Stamatakis
    • 1
  • Thomas Ludwig
    • 2
  • Harald Meier
    • 1
  1. 1.Department of Computer ScienceTechnische Universität MünchenGarching b. MünchenGermany
  2. 2.Department of Computer ScienceRuprecht-Karls-UniversitätHeidelbergGermany

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