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

@inproceedings{AFPFMLIOLP04,
	author	 = {Alexandros P. Stamatakis and Thomas Ludwig and Harald Meier},
	title	 = {{A Fast Program for Maximum Likelihood-based Inferrence of Large Phylogenetic Trees}},
	year	 = {2004},
	booktitle	 = {{Proceedings of the 2004 ACM symposium on Applied computing}},
	editor	 = {},
	publisher	 = {ACM},
	address	 = {New York, USA},
	pages	 = {197--201},
	conference	 = {SAC-04},
	organization	 = {University of Cyprus},
	location	 = {Nicosia, Cyprus},
	isbn	 = {1-58113-812-1},
	doi	 = {http://doi.acm.org/10.1145/967900.967940},
	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},
}