Systematic Exploration of the High Likelihood Set of Phylogenetic Tree Topologies.

ABSTRACT: Bayesian Markov chain Monte Carlo explores tree space slowly, in part because it frequently returns to the same tree topology. An alternative strategy would be to explore tree space systematically, and never return to the same topology. In this article, we present an efficient parallelized method to map out the high likelihood set of phylogenetic tree topologies via systematic search, which we show to be a good approximation of the high posterior set of tree topologies on the data sets analyzed. Here, "likelihood" of a topology refers to the tree likelihood for the corresponding tree with optimized branch lengths. We call this method "phylogenetic topographer" (PT). The PT strategy is very simple: starting in a number of local topology maxima (obtained by hill-climbing from random starting

SUBMITTER: Whidden C 

PROVIDER: S-EPMC7682726 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature