A Fast Program for Maximum Likelihood-based Inference of Large Phylogenetic Trees

摘要

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.