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Bouckaert, R. R.

Publications and source records attributed to Bouckaert, R. R..

3 recordsLinked to original sources

Skeletons in the Forest: Using Entropy-Based Rogue Detection on Bayesian Phylogenetic Tree Distributions

In a phylogenetic analysis, rogue taxa and clades are those that, due to their uncertain placement, contribute disproportionally to the variance in a set of phylogenetics trees. They can obscure clear evolutionary relationships and complicate the interpretation of results. While existing rogue detection algorithms focus on improving the consensus tree for a set of trees, we introduce algorithms based on phylogenetic entropy to improve the analysis in a Bayesian framework. In particular, we introduce a tool that extracts a skeleton distribution of the posterior tree distribution that contains the core relationships by removing a minimal subset of rogues. Additionally, we enable detailed analyses of rogues at both the taxon and clade levels, including the visualisation of different rogue placements.

bioinformatics↗

An independent base composition of each rate class for improved likelihood-based phylogeny estimation: The 5rf model

The combination of a time reversible Markov process with a "hidden" mixture of gamma distributed relative site rates plus invariant sites have become the most favoured options for likelihood and other probabilistic models of nucleotide evolution (e.g., tr4gi which approximates a gamma with four rate classes). However, these models assume a homogeneous and stationary distribution of nucleotide (character or base) frequencies. Here, we explore the potential benefits and pitfalls of allowing each rate category (rate class) of a 4gi mixture model to have its own base frequencies. This is achieved by starting each of the five rate classes, at the trees root, with its own free choice of nucleotide frequencies to create a 4gi5rf model or a 5rf model in shorthand. We assess the practical identifiability of this approach with a BEAST 2 implementation, aiming to determine if it can accurately estimate credibility intervals and expected values for a wide range of plausible parameter values. Practical identifiability, as distinguished from mathematical identifiability, gauges the models ability to identify parameters in real-world scenarios, as opposed to theoretically with infinite data. One of the most common types of phylogenetic data is mitochondrial DNA (mtDNA) protein coding sequence. It is often assumed current models analyse robustly such data and that higher likelihood/posterior probability models do better. However, this abstract shows that vertebrate mtDNA remains a very difficult type of data to fully model, and that dramatically higher likelihoods do not mean a model is measurably more accurate with respect to recovering key parameters of biological interest (e.g., monophyletic groups, their support and their ages). The 4gi5rf model considerably improves marginal likelihoods and seems to reverse some apparent errors exacerbated by the 4gi model, while introducing others. Problems appear to be linked to non-stationary DNA repair processes that alter the mutation/substitution spectra across lineages and time. We also show such problems are not unique to mtDNA and are encountered in analysing nuclear sequences. Non-stationarity of DNA repair processes mutation/substitution spectra thus pose an active challenge to obtaining reliable inferences of relationships and divergence times near the root of placental mammals, for example. An open source implementation is available under the LGPL 3.0 license in the beastbooster package for BEAST 2, available from https://github.com/rbouckaert/beastbooster.

evolutionary biology↗

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. Here, we address this challenge by introducing a structured coalescent skyline approach, MASCOT-Skyline that allows us to jointly infer spatial and temporal transmission dynamics of infectious diseases using Markov chain Monte Carlo inference. To do so, we model the effective population size dynamics in different locations using a non-parametric function, allowing us to approximate a range of population size dynamics. We show, using a range of different viral outbreak datasets, potential issues with phylogeographic methods. We then use these viral datasets to motivate simulations of outbreaks that illuminate the nature of biases present in the different phylogeographic methods. We show that spatial and temporal dynamics should be modeled jointly even if one seeks to recover just one of the two. Further, we showcase conditions under which we can expect phylogeographic analyses to be biased, particularly different subsampling approaches, as well as provide recommendations of when we can expect them to perform well. We implemented MASCOT-Skyline as part of the open-source software package MASCOT for the Bayesian phylodynamics platform BEAST2.

evolutionary biology↗