Biology subjects

Bouckaert, R.

Publications and source records attributed to Bouckaert, R..

29 records · Page 2Linked to original sources

bioRxiv2022

Online Bayesian Analysis with BEAST 2

AO_SCPLOWBSTRACTC_SCPLOWThere are a growing number of areas, e.g. epidemiology and within-organism cancer evolution, where re-analysing all available data from scratch every time new data becomes available or old data is refined is no longer feasible. All these and related areas can benefit from online phylogenetic inference that can booster previous data analyses. Here, we make the case that adding/removing taxa from an analysis can have substantial non-local impact on the tree that is inferred, both in a model based setting, as well as for distance based methods. Consequently, online phylogenetic algorithms may require global updates of the trees and other parameters, a task that in general is highly non-trivial. Motivated by this observation, we designed an online algorithm that benefits from a parallelism in a Bayesian setting that is substantially more efficient than re-running the analysis from scratch. Furthermore, our algorithm is not sensitive to the number of sequences added, allowing the sequence data to grow/be refined iteratively. We show how this approach can be used in a maximum likelihood setting, and - apart from adding/removing new sequences - demonstrate a number of practical alternative use cases of our algorithm, including how to break up a single (offline) large analysis to get results faster. An open source implementation is available under GPL3 license as the online package for BEAST 2 at https://github.com/rbouckaert/online and a tutorial at https://github.com/rbouckaert/online-tutorial.

bioinformatics↗

bioRxiv2022

Quantitatively defining species boundaries with moreefficiency and more biological realism

We introduce a widely applicable species delimitation method based on the multispecies coalescent model that is more efficient and more biologically realistic than existing methods. We extend the phylogenetic tree collapse model to the Yule-skyline model, allowing the ancestral speciation rate to vary through time as a smooth piecewise function. Furthermore, we introduce the cutting-edge proposal kernels of StarBeast3 to this model, thus enabling rapid species delimitation on large molecular datasets and allowing the use of relaxed molecular clock models. We validate these methods with genomic sequence data and SNP data, and show they are more efficient than existing methods at achieving parameter convergence during Bayesian MCMC. Lastly, we apply these methods to two datasets and find inconsistencies with the published literature. Our methods are powerful for rapid quantitative testing of species boundaries in large multilocus datasets and are implemented as an open source BEAST 2 package called SPEEDEMON.

evolutionary biology↗

bioRxiv2021

StarBeast3: Adaptive Parallelised Bayesian Inference of the Multispecies Coalescent

As genomic sequence data becomes increasingly available, inferring the phylogeny of the species as that of concatenated genomic data can be enticing. However, this approach makes for a biased estimator of branch lengths and substitution rates and an inconsistent estimator of tree topology. Bayesian multispecies coalescent methods address these issues. This is achieved by embedding a set of gene trees within a species tree and jointly inferring both under a Bayesian framework. However, this approach comes at the cost of increased computational demand. Here, we introduce StarBeast3 - a software package for efficient Bayesian inference of the multispecies coalescent model via Markov chain Monte Carlo. We gain efficiency by introducing cutting-edge proposal kernels and adaptive operators, and StarBeast3 is particularly efficient when a relaxed clock model is applied. Furthermore, gene tree inference is parallelised, allowing the software to scale with the size of the problem. We validated our software and benchmarked its performance using three real and two synthetic datasets. Our results indicate that StarBeast3 is up to one-and-a-half orders of magnitude faster than StarBeast2, and therefore more than two orders faster than *BEAST, depending on the dataset and on the parameter, and is suitable for multispecies coalescent inference on large datasets (100+ genes). StarBeast3 is open-source and is easy to set up with a friendly graphical user interface.

genetics↗

bioRxiv2021

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. This allows demographic reconstruction through time and estimating the timing and magnitude of population bottlenecks and full population histories. Altogether, BICEPS can be considered a more muscular version of the popular Bayesian skyline model. We demonstrate its power and correctness by a well calibrated simulation study. Furthermore, we demonstrate with an application to SARS-CoV-2 genomic data that some analyses that have trouble converging with the traditional Bayesian skyline prior and standard MCMC proposals can do well with the BICEPS approach. BICEPS is available as open source package for BEAST 2 under GPL license and has a user friendly graphical user interface. Bayesian phylogenetics, coalescent model, BEAST 2, BICEPS

bioinformatics↗

bioRxiv2020

Adaptive dating and fast proposals: revisiting the phylogenetic relaxed clock model

Uncorrelated relaxed clock models enable estimation of molecular substitution rates across lineages and are widely used in phylogenetics for dating evolutionary divergence times. In this article we delved into the internal complexities of the relaxed clock model in order to develop efficient MCMC operators for Bayesian phylogenetic inference. We compared three substitution rate parameterisations, introduced an adaptive operator which learns the weights of other operators during MCMC, and we explored how relaxed clock model estimation can benefit from two cutting-edge proposal kernels: the AVMVN and Bactrian kernels. This work has produced an operator scheme that is up to 65 times more efficient at exploring continuous relaxed clock parameters compared with previous setups, depending on the dataset. Finally, we explored variants of the standard narrow exchange operator which are specifically designed for the relaxed clock model. In the most extreme case, this new operator traversed tree space 40% more efficiently than narrow exchange. The methodologies introduced are adaptive and highly effective on short as well as long alignments. The results are available via the open source optimised relaxed clock (ORC) package for BEAST 2 under a GNU licence (https://github.com/jordandouglas/ORC). Author summaryBiological sequences, such as DNA, accumulate mutations over generations. By comparing such sequences in a phylogenetic framework, the evolutionary tree of lifeforms can be inferred. With the overwhelming availability of biological sequence data, and the increasing affordability of collecting new data, the development of fast and efficient phylogenetic algorithms is more important than ever. In this article we focus on the relaxed clock model, which is very popular in phylogenetics. We explored how a range of optimisations can improve the statistical inference of the relaxed clock. This work has produced a phylogenetic setup which can infer parameters related to the relaxed clock up to 65 times faster than previous setups, depending on the dataset. The methods introduced adapt to the dataset during computation and are highly efficient when processing long biological sequences.

genetics↗

bioRxiv2020

Class I Aminoacyl-tRNA Synthetase Urzyme and CP1 Modules have Distinct Genetic Origins

The provenance of the aminoacyl-tRNA synthetases (aaRS) poses unusually challenging questions because of their role in the emergence and evolution of genetic coding. We investigate evidence about their ancestry from highly curated structure-based multiple sequence alignments of a small "scaffold" that is structurally invariant in all 10 canonical Class I aaRS. Statistically different values of two uncorrelated phylogenetic metrics--residue by residue conservation derived from Clustal and row-by-row cladistic congruence derived from BEAST2--suggest that the Class I scaffold is a mosaic assembled from distinct, successive genetic sources. These data are especially significant in light of: (i) experimental fragmentations of the Class I scaffold into three partitions that retain catalytic activities in proportion to their length; and (ii) multiple sources of evidence that two of these partitions arose from an ancestral Class I aaRS gene encoding a Class II ancestor in frame on the opposite strand. Two additional metrics output by BEAST2 vary in accordance with the presumed functionality endowed by the various modules. The new evidence supplements previous aaRS phylogenies. It identifies a previously characterized 46-residue Class I "protozyme" as preceding the adaptive radiation of the superfamily containing variations of the Rossmann dinucleotide binding fold related to amino acid discrimination, and thus as root of that molecular tree. Such a rooting is consistent with near simultaneous emergence of genetic coding and the origin of the proteome, resolving a conundrum posed by previous inferences that Class I aaRS evolved long after the genetic code had been implemented in an RNA world. Further, it establishes a timeline for the growth of coding from a binary amino acid alphabet by pinpointing discontinuous enhancements of aaRS fidelity. Author SummaryPhylogenetic analysis uncovers evolutionary connections between different protein superfamily members. We describe complementary, uncorrelated, phylogenetic metrics that support multiple evolutionary histories for different segments within members of the Class I aminoacyl-tRNA synthetase superfamily. Using a carefully curated 3D crystal structure superposition as the primary source of the multiple sequence alignment substantially reduced dependence of these metrics on empirical amino acid substitution matrices. Two metrics are derived from the amino acid distribution observed in each successive position. A third depends on how individual sequences distribute into phylogenetic tree branches for each of the ten amino acids activated by the superfamily. All metrics confirm that a segment previously identified as an inserted element is, indeed, a more recent acquisition, despite its structural conservation. The residue-by-residue conservation metrics reveal significant co-variation of mutational frequencies between a core segment that forms the amino acid binding site and a neighboring segment derived from the more recent insertion element. We attribute that covariation to the differentiation of superfamily members as evolutionary divergence enhanced amino acid specificity. Finally, evidence that the insertion element is a recent acquisition implies a new branching order for much of the proteome.

evolutionary biology↗

bioRxiv2019

Coupled MCMC in BEAST 2

With ever more complex models used to study evolutionary patterns, approaches that facilitate efficient inference under such models are needed. Parallel tempering has long been used to speed up phylogenetic analyses and to make use of multi-core CPUs. Parallel tempering essentially runs multiple MCMC chains in parallel. All chains are heated except for one cold chain that explores the posterior probability space like a regular MCMC chain. This heating allows chains to make bigger jumps in phylogenetic state space. The heated chains can then be used to propose new states for other chains, including the cold chain. One of the practical challenges using this approach, is to find optimal temperatures of the heated chains to efficiently explore state spaces. We here provide an adaptive parallel tempering scheme to Bayesian phylogenetics, where the temperature difference between heated chains is automatically tuned to achieve a target acceptance probability of states being exchanged between individual chains. We first show the validity of this approach by comparing inferences of adaptive parallel tempering to MCMC on several datasets. We then explore where parallel tempering provides benefits over MCMC. We implemented this adaptive parallel tempering approach as an open source package licensed under GPL 3.0 to the Bayesian phylogenetics software BEAST2, available from https://github.com/nicfel/CoupledMCMC.

evolutionary biology↗

bioRxiv2018

BEAST 2.5: An Advanced Software Platform for Bayesian Evolutionary Analysis

Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments.\n\nDeveloping such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release.\n\nAuthor summaryBayesian phylogenetic inference methods have undergone considerable development in recent years, and joint modelling of rich evolutionary data, including genomes, phenotypes and fossil occurrences is increasingly common. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing scientific software is increasingly crucial to advancement in many fields of biology. The challenges range from practical software development and engineering, distributed team coordination, conceptual development and statistical modelling, to validation and testing. BEAST 2 is one such computational software platform for phylogenetics, population genetics and phylodynamics, and was first announced over 4 years ago. Here we describe the full range of new tools and models available on the BEAST 2.5 platform, which expand joint evolutionary inference in many new directions, especially for joint inference over multiple data types, non-tree models and complex phylodynamics.

evolutionary biology↗

bioRxiv2018

Bayesian phylodynamics of avian influenza virus H9N2 in Asia with time-dependent predictors of migration

Model-based phylodynamic approaches recently employed generalized linear models (GLMs) to uncover potential predictors of viral spread. Very recently some of these models have allowed both the predictors and their coefficients to be time-dependent. However, these studies mainly focused on predictors that are assumed to be constant through time. Here we inferred the phylodynamics of H9N2 viruses isolated in 12 Asian countries and regions under both discrete trait analysis (DTA) and structured coalescent (MASCOT) approaches. Using MASCOT we applied a new time-dependent GLM to uncover the underlying factors behind H9N2 spread. We curated a rich set of time-series predictors including annual international live poultry trade and national poultry production figures. This time-dependent phylodynamic prediction model was compared to commonly employed time-independent alternatives. Additionally the time-dependent MASCOT model allowed for the estimation of viral effective sub-population sizes and their changes through time and these effective population dynamics within each country were predicted by a GLM. International annual poultry trade is a strongly supported predictor of virus migration rates. There was also strong support for geographic proximity as a predictor of migration rate in all GLMs investigated. In time-dependent MASCOT models, national poultry production was also identified as a predictor of virus genetic diversity through time and this signal was obvious in mainland China and Bangladesh. Our application of a recently introduced time-dependent GLM predictors integrated rich time-series data in Bayesian phylodynamic prediction. We demonstrated the contribution of poultry trade and geographic proximity (potentially unheralded wild bird movements) to avian influenza spread in Asia. To gain a better understanding of the drivers of H9N2 spread, we suggest increased surveillance of the H9N2 virus in countries that are currently under-sampled as well as in wild bird populations in the most affected countries.\n\nAuthor summaryWhat drives the geographic dispersal and genetic diversity of H9N2 avian influenza virus in Asia? We used two model-based approaches, DTA and MASCOT, to reconstruct the phylogeographic dynamics of the virus. Further, multiple potential predictors were used to inform the virus spread and population dynamics by GLMs. Here, we maximised the power of time-series predictors in Bayesian phylodynamic prediction. For the first time, we were able to quantify the contribution of both time-series and constant predictors to both migration rates and effective population sizes in a structured population. We identified a positive association of international poultry trade and national poultry production time-series with virus migration rates and effective population sizes respectively. We also identify geographic proximity as a strongly supported driver to virus migration rates and this points to the potential role of wild bird populations in virus dispersal across countries. Our study is a practical exemplar of the use of temporal information in predictors to model heterogeneous spatial diffusion and population dynamic processes and provides direction to H9N2 control efforts in Asia.

epidemiology↗

bioRxiv2018

Phylodynamic model adequacy using posterior predictive simulations

Rapidly evolving pathogens, such as viruses and bacteria, accumulate genetic change at a similar timescale over which their epidemiological processes occur, such that it is possible to make inferences about their infectious spread using phylogenetic time-trees. For this purpose it is necessary to choose a phylodynamic model. However, the resulting inferences are contingent on whether the model adequately describes key features of the data. Model adequacy methods allow formal rejection of a model if it cannot generate the main features of the data. We present TreeModelAdequacy (TMA), a package for the popular BEAST2 software, that allows assessing the adequacy of phylodynamic models. We illustrate its utility by analysing phylogenetic trees from two viral outbreaks of Ebola and H1N1 influenza. The main features of the Ebola data were adequately described by the coalescent exponential-growth model, whereas the H1N1 influenza data was best described by the birth-death SIR model.

bioinformatics↗

bioRxiv2017

Pseudo Dollo models for the evolution of binary characters along a tree

The stochastic Dollo model is a model for capturing evolution of features, for example cognate data in language evolution. However, it is rather sensitive to borrowing events, coding errors, semantic shift and other anomalies, so other models, in particular the covarion model, tends to have a better fit to the data. Here, we introduce the pseudo Dollo model, a model of character evolution along a tree that can be formulated as a three-state continuous time Markov chain (CTMC) model. The initial state represent absence of a feature, then a birth event allows the feature to be present. A death event can follow so that the feature becomes absent again. However, no new birth events are allowed after a death event has taken place.\n\nWe examine the model in a fully Bayesian setting, and demonstrate it can have a better fit than some of the popular alternative models on some real world datasets. Some variations on the pseudo Dollo model are introduced as well, including the multi-state pseudo Dollo model and pseudo Dollo covarion model.\n\nThe model is implemented in open source software Babel, a package to BEAST [2] licensed under LGPL. A user friendly way to set up an analysis is available through BEAUti, the graphical user interface of BEAST.

evolutionary biology↗