A fast program for maximum likelihood-based inference of large phylogenetic trees
Proceedings of the 2004 ACM symposium on Applied computing
An Efficient Program for Phylogenetic Inference Using Simulated Annealing
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 7 - Volume 08
DRAxML@home: a distributed program for computation of large phylogenetic trees
Future Generation Computer Systems - Special issue: Parallel computing technologies
PBPI: a high performance implementation of Bayesian phylogenetic inference
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
DRAxML@home: a distributed program for computation of large phylogenetic trees
Future Generation Computer Systems - Special issue: Parallel computing technologies
Initial experiences porting a bioinformatics application to a graphics processor
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Quartet-Based phylogenetic inference: a grid approach
ISPA'04 Proceedings of the Second international conference on Parallel and Distributed Processing and Applications
RAxML-OMP: an efficient program for phylogenetic inference on SMPs
PaCT'05 Proceedings of the 8th international conference on Parallel Computing Technologies
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We analyze the performance of likelihood-based approaches used to reconstruct phylogenetic trees. Unlike other techniques such as Neighbor-Joining (NJ) and Maximum Parsimony (MP), relatively little is known regarding the behavior of algorithms founded on theprinciple of likelihood. We study the accuracy, speed, and likelihood scores of our representative likelihood-based methods (fastDNAml, MrBayes, PAUP*-ML, and TREE-PUZZLE) that use either Maximum Likelihood (ML) or Bayesian inference to find the optimal tree. NJ is also studied to provide a baseline comparison. Our simulation study is based on random birth-death trees, which are deviated from ultrametricity, and uses the Kimura 2-parameter +Gamma model of sequence evolution. We find that MrBayes (a Bayesian inference approach) consistently outperforms the other methods in terms of accuracy andrunning time.