Variational Bayesian phylogenies through matrix representation of tree space.

In this article, we study the distance matrix as a representation of a phylogeny by way of hierarchical clustering. By defining a multivariate normal distribution on (a subset of) the entries in a matrix, this allows us to represent a distribution over rooted time trees. Here, we demonstrate tree distributions can be represented accurately this way for a number of published tree distributions.

MASCOT-Skyline integrates population and migration dynamics to enhance phylogeographic reconstructions.

The spread of infectious diseases is shaped by spatial and temporal aspects, such as host population structure or changes in the transmission rate or number of infected individuals over time. These spatiotemporal dynamics are imprinted in the genome of pathogens and can be recovered from those genomes using phylodynamics methods. However, phylodynamic methods typically quantify either the temporal or spatial transmission dynamics, which leads to unclear biases, as one can potentially not be inferred without the other.

An Efficient Coalescent Epoch Model for Bayesian Phylogenetic Inference.

We present a two-headed approach called Bayesian Integrated Coalescent Epoch PlotS (BICEPS) for efficient inference of coalescent epoch models. Firstly, we integrate out population size parameters, and secondly, we introduce a set of more powerful Markov chain Monte Carlo (MCMC) proposals for flexing and stretching trees. Even though population sizes are integrated out and not explicitly sampled through MCMC, we are still able to generate samples from the population size posteriors.

Adaptive Metropolis-coupled MCMC for BEAST 2.

With ever more complex models used to study evolutionary patterns, approaches that facilitate efficient inference under such models are needed. Metropolis-coupled Markov chain Monte Carlo (MCMC) has long been used to speed up phylogenetic analyses and to make use of multi-core CPUs. Metropolis-coupled MCMC essentially runs multiple MCMC chains in parallel.

OBAMA: OBAMA for Bayesian amino-acid model averaging.

Background: Bayesian analyses offer many benefits for phylogenetic, and have been popular for analysis of amino acid alignments. It is necessary to specify a substitution and site model for such analyses, and often an ad hoc, or likelihood based method is employed for choosing these models that are typically of no interest to the analysis overall.

Methods: We present a method called OBAMA that averages over substitution models and site models, thus letting the data inform model choices and taking model uncertainty into account.

Model Selection and Parameter Inference in Phylogenetics Using Nested Sampling.

Bayesian inference methods rely on numerical algorithms for both model selection and parameter inference. In general, these algorithms require a high computational effort to yield reliable estimates. One of the major challenges in phylogenetics is the estimation of the marginal likelihood.

The origin and expansion of Pama-Nyungan languages across Australia.

It remains a mystery how Pama-Nyungan, the world's largest hunter-gatherer language family, came to dominate the Australian continent. Some argue that social or technological advantages allowed rapid language replacement from the Gulf Plains region during the mid-Holocene. Others have proposed expansions from refugia linked to climatic changes after the last ice age or, more controversially, during the initial colonization of Australia.

StarBEAST2 Brings Faster Species Tree Inference and Accurate Estimates of Substitution Rates.

Fully Bayesian multispecies coalescent (MSC) methods like *BEAST estimate species trees from multiple sequence alignments. Today thousands of genes can be sequenced for a given study, but using that many genes with *BEAST is intractably slow. An alternative is to use heuristic methods which compromise accuracy or completeness in return for speed.

bModelTest: Bayesian phylogenetic site model averaging and model comparison.

Background: Reconstructing phylogenies through Bayesian methods has many benefits, which include providing a mathematically sound framework, providing realistic estimates of uncertainty and being able to incorporate different sources of information based on formal principles. Bayesian phylogenetic analyses are popular for interpreting nucleotide sequence data, however for such studies one needs to specify a site model and associated substitution model. Often, the parameters of the site model is of no interest and an ad-hoc or additional likelihood based analysis is used to select a single site model.

Species delimitation using genome-wide SNP data.

The multispecies coalescent has provided important progress for evolutionary inferences, including increasing the statistical rigor and objectivity of comparisons among competing species delimitation models. However, Bayesian species delimitation methods typically require brute force integration over gene trees via Markov chain Monte Carlo (MCMC), which introduces a large computation burden and precludes their application to genomic-scale data. Here we combine a recently introduced dynamic programming algorithm for estimating species trees that bypasses MCMC integration over gene trees with sophisticated methods for estimating marginal likelihoods, needed for Bayesian model selection, to provide a rigorous and computationally tractable technique for genome-wide species delimitation.

Looking for trees in the forest: summary tree from posterior samples.

Background: Bayesian phylogenetic analysis generates a set of trees which are often condensed into a single tree representing the whole set. Many methods exist for selecting a representative topology for a set of unrooted trees, few exist for assigning branch lengths to a fixed topology, and even fewer for simultaneously setting the topology and branch lengths. However, there is very little research into locating a good representative for a set of rooted time trees like the ones obtained from a BEAST analysis.

DensiTree: making sense of sets of phylogenetic trees.

Motivation: Bayesian analysis through programs like BEAST (Drummond and Rumbaut, 2007) and MrBayes (Huelsenbeck et al., 2001) provides a powerful method for reconstruction of evolutionary relationships. One of the benefits of Bayesian methods is that well-founded estimates of uncertainty in models can be made available.