An Investigation of Phylogenetic Likelihood Methods

Abstract— We analyze the performance of likelihoodbased 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 the principle of likelihood. We study the accuracy, speed, and likelihood scores of four representative likelihood-based methods (fastDNAml, MrBayes, PAUP£-ML, and TREEPUZZLE) 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 2parameter + Gamma model of sequence evolution. We find that MrBayes (a Bayesian inference approach) consistently outperforms the other methods in terms of accuracy and running time.

• Type: Conference
• Year: 2003
• Where: BIBE
• Authors: Tiffani L. Williams, Bernard M. E. Moret
• Keywords: Bayesian Inference, BIBE 2003, Bioinformatics, Maximum Likelihood, Representative Likelihood-based Methods