The Performance of Iterative Search Strategies for Maximum-Likelihood Estimation of Phylogeny from DNA Sequences

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9974124Sullivan and Swofford The construction or estimation of phylogenetic branching structure (trees) from DNA sequence data, whether for organisms or their constituent genes, is increasingly important in a wide array of biological studies. Maximum-likelihood (ML) estimation methods are also increasingly being used to construct phylogenetic trees because of several desirable statistical properties of the methods, including the capacity to modify parameters of nucleotide substitution that are thought to influence the rates and patterns of DNA base changes. Realistic models describing the process of nucleotide substitution are now available for use in calculating likelihood values; for example, all six reversible nucleotide substitution types can be allowed to evolve at a unique relative rate, base frequencies can deviate from equality, and different sites (and different codon positions) can be allowed to evolve at different rates. The most complex time-reversible models currently in use thus incorporate the estimation of ten parameters to describe the process of nucleotide substitution, in addition to the calculation of branch lengths for the tree under examaination. The penalty in this complexity is the sheer computational time required to calculate the maximum likelihood score, which becomes prohibitive for nearly all researchers working with more than 20 species in an analysis. Yet, gene-sequencing and genomic sequencing projects are underway for vastly more species, and the analysis of these huge datasets requires improvement in maximum-likelihood search methods. Dr. Jack Sullivan at the University of Idaho, working with his colleague Dr. David Swofford, is exploring the option of parallel processing of ML searches using the widely available software package PAUP*, in order to conduct iterative searches on the huge numbers of possible trees from large datasets. A multi-step approach will start with published datasets and trees for phylogenies involving small numbers of taxa, then move to larger numbers of taxa, and then add simulation studies on the iterative-search strategy focused on a target dataset of 50 or so taxa. The general goal is to improve upon current ML methods for finding both the optimum tree structure and the optimal combination of topology, branch lengths, and parameters of the model for nucleotide substitution during evolutionary change of a group.