A fast program for maximum likelihood-based inference of large phylogenetic trees

Authors:
A. P. Stamatakis;T. Ludwig;H. Meier
Affiliations:
Technical University of Munich, München, Germany;University of Heidelberg, Heidelberg, Germany;Technical University of Munich, München, Germany
Venue:
Proceedings of the 2004 ACM symposium on Applied computing
Year:
2004

Citing 4
Cited 0

Parallel implementation and performance of fastDNAml: a program for maximum likelihood phylogenetic inference

Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Accelerating parallel maximum likelihood-based phylogenetic tree calculations using subtree equality vectors

Proceedings of the 2002 ACM/IEEE conference on Supercomputing
An Investigation of Phylogenetic Likelihood Methods

BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
Phylogenetic Tree Inference on PC Architectures with AxML/PAxML

IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing

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Abstract

The computation of large phylogenetic trees with maximum likelihood is computationally intensive. In previous work we have introduced and implemented algorithmic optimizations in PAxML. The program shows run time improvements 25% over parallel fastDNAml yielding exactly the same results. This paper is focusing on computations of large phylogenetic trees ( 100 organisms) with maximum likelihood. We propose a novel, partially randomized algorithm and new parsimony-based rearrangement heuristics, which are implemented in a sequential and parallel program called RAxML.We provide experimental results for real biological data containing 101 up to 1000 sequences and simulated data containing 150 to 500 sequences, which show run time improvements of factor 8 up to 31 over PAxML yielding equally good trees in terms of likelihood values and RF distance rates at the same time. Finally, we compare the performance of the sequential version of RAxML with a greater variety of available ML codes such as fastDNAml, AxML and MrBayes. RAxML is a freely available open source program.