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Drummond, A. J.

Publications and source records attributed to Drummond, A. J..

13 recordsLinked to original sources

A hierarchical clock-mixture model for Bayesian phylogenetic dating

Conditioning the inference of a Bayesian phylogenetic time tree on a single molecular clock model treats clock choice as fixed, even when support among plausible clock families is uncertain. When that assumption is wrong, estimated timescales and their uncertainty can be distorted. Existing practice usually addresses this by fitting strict, uncorrelated lognormal relaxed (UCLN), and autocorrelated clocks separately and comparing their marginal likelihoods, but this requires multiple computationally expensive model selection analyses. Here we introduce a hierarchical clock-mixture framework, implemented as the open-source RelaxClockAveraging package for BEAST 2, that separates two questions that are often conflated in clock comparison: whether branch-specific rate variation is needed at all, and, if it is, whether that variation is better described as uncorrelated or autocorrelated. The method averages analytically between strict and relaxed clocks at the top level and then compares UCLN and autocorrelated models within the relaxed class on a shared branch-rate vector, returning posterior probabilities for all three clock families together with model-averaged summaries for parameters shared across them. In stratified simulations, the generating clock family was retained in the 95% posterior model set in all replicates, while model-averaged estimates of the overall substitution rate, root age, and tree length remained accurate. On a DENV-4 benchmark, the mixture reproduced the higher-effort nested-sampling ranking of clock families while avoiding the extreme run-to-run variability of independent marginal-likelihood estimates. On empirical benchmarks, the method recovered strong support for the autocorrelated clock on the classical 31-taxon chloroplast rbcL data set. On an RSV-A G-gene data set it concentrated virtually all posterior support on UCLN while preserving the established RSV-A timescale. On a 245-taxon structured-coalescent H3N2 dataset it assigned most posterior mass to the relaxed-clock class, with support within that class concentrated on the autocorrelated family, and the inferred timescale remained consistent with the published estimate. These results show that clock-model support and downstream timescale sensitivity are related but not identical. Single-clock dating analyses can still be adequate, but fixing a clock model should be justified by posterior support rather than treated as a default assumption.

evolutionary biology↗

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↗

How to validate a Bayesian evolutionary model

Biology has become a highly mathematical discipline in which probabilistic models play a central role. As a result, research in the biological sciences is now dependent on computational tools capable of carrying out complex analyses. These tools must be validated before they can be used, but what is understood as validation varies widely among methodological contributions. This may be a consequence of the still embryonic stage of the literature on statistical software validation for computational biology. Our manuscript aims to advance this literature. Here, we describe and illustrate good practices for assessing the correctness of a model implementation, with an emphasis on Bayesian methods. We also introduce a suite of functionalities for automating validation protocols. It is our hope that the guidelines presented here help sharpen the focus of discussions on (as well as elevate) expected standards of statistical software for biology.

evolutionary biology↗

LinguaPhylo: a probabilistic model specification language for reproducible phylogenetic analyses

Phylogenetic models have become increasingly complex, and phylogenetic data sets have expanded in both size and richness. However, current inference tools lack a model specification language that can concisely describe a complete phylogenetic analysis while remaining independent of implementation details. We introduce a new lightweight and concise model specification language, LPhy, which is designed to be both human and machine-readable. A graphical user interface accompanies LPhy, allowing users to build models, simulate data, and create natural language narratives describing the models. These narratives can serve as the foundation for manuscript method sections. Additionally, we present a command-line interface for converting LPhy-specified models into analysis specification files (in XML format) compatible with the BEAST2 software platform. Collectively, these tools aim to enhance the clarity of descriptions and reporting of probabilistic models in phylogenetic studies, ultimately promoting reproducibility of results.

evolutionary biology↗

Scalable total-evidence inference from molecular and continuous characters in a Bayesian framework

Time-scaled phylogenetic trees are an ultimate goal of evolutionary biology and a necessary ingredient in comparative studies. The accumulation of genomic data has resolved the tree of life to a great extent, yet timing evolutionary events remains challenging if not impossible without external information such as fossil ages and morphological characters. Methods for incorporating morphology in tree estimation have lagged behind their molecular counter-parts, especially in the case of continuous characters. Despite recent advances, such tools are still direly needed as we approach the limits of what molecules can teach us. Here, we implement a suite of state-of-the-art methods for leveraging continuous morphology in phylogenetics, and by conducting extensive simulation studies we thoroughly validate and explore our methods properties. While retaining model generality and scalability, we make it possible to estimate absolute and relative divergence times from multiple continuous characters while accounting for uncertainty. We compile and analyze one of the most data-type diverse data sets to date, comprised of contemporaneous and ancient molecular sequences, and discrete and continuous characters from living and extinct Carnivora taxa. We conclude by synthesizing lessons about our methods behavior, and suggest future research venues.

systems biology↗

Ignoring errors causes inaccurate timing of single-cell phylogenies

Single-cell sequencing provides a new way to explore the evolutionary history of cells. Compared to traditional bulk sequencing, where a population of heterogeneous cells is pooled to form a single observation, single-cell sequencing isolates and amplifies genetic material from individual cells, thereby preserving the information about the origin of the sequences. However, single-cell data is more error-prone than bulk sequencing data due to the limited genomic material available per cell. Here, we present error and mutation models for evolutionary inference of single-cell data within a mature and extensible Bayesian framework, BEAST2. Our framework enables integration with biologically informative models such as relaxed molecular clocks and population dynamic models. Our simulations show that modeling errors increase the accuracy of relative divergence times and substitution parameters. We reconstruct the phylogenetic history of a colorectal cancer patient and a healthy patient from single-cell DNA sequencing data. We find that the estimated times of terminal splitting events are shifted forward in time compared to models which ignore errors. We observed that not accounting for errors can overestimate the phylogenetic diversity in single-cell DNA sequencing data. We estimate that 30-50% of the apparent diversity can be attributed to error. Our work enables a full Bayesian approach capable of accounting for errors in the data within the integrative Bayesian software framework BEAST2.

evolutionary biology↗

Approximate Bayesian computation of transcriptional pausing mechanisms

At a transcriptional pause site, RNA polymerase (RNAP) takes significantly longer than average to transcribe the nucleotide before moving on to the next position. At the single-molecule level this process is stochastic, while at the ensemble level it plays a variety of important roles in biological systems. The pause signal is complex and invokes interplay between a range of mechanisms. Among these factors are: non-canonical transcription events - such as backtracking and hypertranslocation; the catalytically inactive intermediate state hypothesised to act as a precursor to backtracking; the energetic configuration of basepairing within the DNA/RNA hybrid and of those flanking the transcription bubble; and the structure of the nascent mRNA. There are a variety of plausible models and hypotheses but it is unclear which explanations are better.\n\nWe performed a systematic comparison of 128 kinetic models of transcription using approximate Bayesian computation. Under this Bayesian framework, models and their parameters were assessed by their ability to predict the locations of pause sites in the E. coli genome.\n\nThese results suggest that the structural parameters governing the transcription bubble, and the dynamics of the transcription bubble during translocation, play significant roles in pausing. This is consistent with a model where the relative Gibbs energies between the pre and posttranslocated positions, and the rate of translocation between the two, is the primary factor behind invoking transcriptional pausing. Whereas, hypertranslocation, backtracking, and the intermediate state are not required to predict the locations of transcriptional pause sites. Finally, we compared the predictive power of these kinetic models to that of a non-explanatory statistical model. The latter approach has significantly greater predictive power (AUC = 0.89 cf. 0.73), suggesting that, while current models of transcription contain a moderate degree of predictive power, a much greater quantitative understanding of transcriptional pausing is required to rival that of a sequence motif.\n\nAuthor summaryTranscription involves the copying of a DNA template into messenger RNA (mRNA). This reaction is implemented by RNA polymerase (RNAP) successively incorporating nucleotides onto the mRNA. At a transcriptional pause site, RNAP takes significantly longer than average to incorporate the nucleotide. A model which can not only predict the locations of pause sites in a DNA template, but also explain how or why they are pause sites, is sought after.\n\nTranscriptional pausing emerges from cooperation between several mechanisms. These mechanisms include non-canonical RNAP reactions; and the thermodynamic properties of DNA and mRNA. There are many hypotheses and kinetic models of transcription but it is unclear which hypotheses and models are required to predict and explain transcriptional pausing.\n\nWe have developed a rigorous statistical framework for inferring model parameters and comparing hypotheses. By applying this framework to published pause-site data, we compared 128 kinetic models of transcription with the aim of finding the best models for predicting the locations of pause sites. This analysis offered insights into mechanisms of transcriptional pausing. However, the predictive power of these models lacks compared with non-explanatory statistical models - suggesting the data contains more information than can be satisfied by current quantitative understandings of transcriptional pausing.

systems biology↗

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↗

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↗

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↗

Inferring Species Trees Using Integrative Models of Species Evolution

Evolutionary models account for either population- or species-level processes, but usually not both. We introduce a new model, the FBD-MSC, which makes it possible for the first time to integrate both the genealogical and fossilization phenomena, by means of the multispecies coalescent (MSC) and the fossilized birth-death (FBD) processes. Using this model, we reconstruct the phylogeny representing all extant and many fossil Caninae, recovering both the relative and absolute time of speciation events. We quantify known inaccuracy issues with divergence time estimates using the popular strategy of concatenating molecular alignments, and show that the FBD-MSC solves them. Our new integrative method and empirical results advance the paradigm and practice of probabilistic total evidence analyses in evolutionary biology.

evolutionary biology↗

Directly Estimating Epidemic Curves From Genomic Data

Modern phylodynamic methods interpret an inferred phylogenetic tree as a partial transmission chain providing information about the dynamic process of transmission and removal (where removal may be due to recovery, death or behaviour change). Birth-death and coalescent processes have been introduced to model the stochastic dynamics of epidemic spread under common epidemiological models such as the SIS and SIR models, and are successfully used to infer phylogenetic trees together with transmission (birth) and removal (death) rates. These methods either integrate analytically over past incidence and prevalence to infer rate parameters, and thus cannot explicitly infer past incidence or prevalence, or allow such inference only in the coalescent limit of large population size. Here we introduce a particle filtering framework to explicitly infer prevalence and incidence trajectories along with phylogenies and epidemiological model parameters from genomic sequences and case count data in a manner consistent with the underlying birth-death model. After demonstrating the accuracy of this method on simulated data, we use it to assess the prevalence through time of the early 2014 Ebola outbreak in Sierra Leone.

epidemiology↗

Bayesian Inference Of Species Networks From Multilocus Sequence Data

Reticulate species evolution, such as hybridization or introgression, is relatively common in nature. In the presence of reticulation, species relationships can be captured by a rooted phylogenetic network, and orthologous gene evolution can be modeled as bifurcating gene trees embedded in the species network. We present a Bayesian approach to jointly infer species networks and gene trees from multilocus sequence data. A novel birth-hybridization process is used as the prior for the species network. We assume a multispecies network coalescent (MSNC) prior for the embedded gene trees. We verify the ability of our method to correctly sample from the posterior distribution, and thus to infer a species network, through simulations. We reanalyze a large dataset of genes from closely related spruces, and verify the previously suggested homoploid hybridization event in this clade. Our method is available within the BEAST 2 add-on SpeciesNetwork, and thus provides a general framework for Bayesian inference of reticulate evolution.

evolutionary biology↗