Data from: Bayesian species delimitation can be robust to guide tree inference errors

负责人：

关键词：

species delimitation;guide tree;BPP;Simulation;MCMC

DOI：

doi:10.5061/dryad.m1r32

摘要：

in the reversible-jump Markov chain Monte Carlo (rjMCMC) algorithm (Green, 1995). It has been pointed out that the method tends to over-split if a random population

Data from: Coestimating reticulate phylogenies and gene trees from multilocus sequence data

负责人：

Nakhleh, Luay

关键词：

RJMCMC Multispecies network coalescent reticulation incomplete lineage sorting phylogenetic network Bayesian inference

DOI：

doi:10.5061/dryad.3h185

摘要：

The multispecies network coalescent (MSNC) is a stochastic process that captures how gene trees grow within the branches of a phylogenetic network. Coupling the MSNC with a stochastic mutational process that operates along the branches of the gene trees gives rise to a generative model of how multiple loci from within and across species evolve in the presence of both incomplete lineage sorting (ILS) and reticulation (e.g., hybridization). We report on a Bayesian method for sampling the parameters of this generative model, including the species phylogeny, gene trees, divergence times, and population sizes, from DNA sequences of multiple independent loci. We demonstrate the utility of our method by analyzing simulated data and reanalyzing an empirical data set. Our results demonstrate the significance of not only co-estimating species phylogenies and gene trees, but also accounting for reticulation and ILS simultaneously. In particular, we show that when gene flow occurs, our method accurately estimates the evolutionary histories, coalescence times, and divergence times. Tree inference methods, on the other hand, underestimate divergence times and overestimate coalescence times when the evolutionary history is reticulate. While the MSNC corresponds to an abstract model of ``intermixture," we study the performance of the model and method on simulated data generated under a gene flow model. We show that the method accurately infers the most recent time at which gene flow occurs. Finally, we demonstrate the application of the new method to a 106-locus yeast data set.