Phylogenetic models of rate heterogeneity: a high performance computing perspective

Inference of phylogenetic trees using the maximum likelihood (ML) method is NP-hard. Furthermore, the computation of the likelihood function for huge trees of more than 1,000 organisms is computationally intensive due to a large amount of floating point operations and high memory consumption. Within this context, the present paper compares two competing mathematical models that account for evolutionary rate heterogeneity: the Γ and CAT models. The intention of this paper is to show that—from a purely empirical point of view—CAT can be used instead of Γ. The main advantage of CAT over Γ consists in significantly lower memory consumption and faster inference times. An experimental study using RAxML has been performed on 19 real-world datasets comprising 73 up to 1,663 DNA sequences. Results show that CAT is on average 5.5 times faster than Γ and—surprisingly enough—also yields trees with slightly superior Γ likelihood values. The usage of the CAT model decreases the amoun...

• Type: Conference
• Year: 2006
• Where: IPPS
• Authors: Alexandros Stamatakis
• Keywords: Cat Model, Distributed And Parallel Computing, IPPS 2006, Maximum Likelihood, Memory Consumption