"Phylogenetic trees represent the evolutionary relationships among individuals, populations, or species. These trees also contain information on the evolutionary dynamics in the underlying population, and can be used in a wide range of applications, from studying the dynamics of an epidemic to analyzing the speciation and extinction dynamics of groups of species.
Reconstructing phylogenetic trees from sequence data requires specifying a ""tree prior"", i.e. a model that represents a prior idea about the evolutionary dynamics. The adequacy of the tree prior influences the quality of the phylogenetic reconstruction, and consequently the quality of the inferred evolutionary dynamics. Unfortunately, the currently used priors are over-simplistic; in particular, they assume that evolutionary rates are constant in time and identical across lineages. These homogeneous tree priors are most often inconsistent with the model subsequently used to infer evolutionary dynamics, which is statistically problematic and likely to bias inferences. Several factors contribute to this issue: i) the computational challenges of carrying out ""full phylogenetic inferences"", that is analyses that co-estimate trees and dynamics, ii) the under-recognized influence of tree priors on phylogenetic reconstruction and subsequent analyses of evolutionary dynamics and iii) the lack of empirical guidance on the use and interpretation of rate-heterogeneous tree priors.
PHYLOBD will address this issue and significantly improve full Bayesian phylogenetic inference by: i) providing efficient implementations of tree priors that account for rate heterogeneity across lineages ii) evaluating the importance of using such tree priors iii) providing tools for an accurate representation of rate-heterogeneous Bayesian posteriors, and iv) providing efficient implementations of full Bayesian phylogenetic inference when data are sampled through time."
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