Fast Bayesian inference of phylogenies from multiple continuous characters.

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 counterparts, 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 method's behavior, and suggest future research venues.

Genomic epidemiology of SARS-CoV-2 variants during the first two years of the pandemic in Colombia.

BACKGROUND: The emergence of highly transmissible SARS-CoV-2 variants has led to surges in cases and the need for global genomic surveillance. While some variants rapidly spread worldwide, other variants only persist nationally. There is a need for more fine-scale analysis to understand transmission dynamics at a country scale. For instance, the Mu variant of interest, also known as lineage B.1.621, was first detected in Colombia and was responsible for a large local wave but only a few sporadic cases elsewhere. METHODS: To better understand the epidemiology of SARS-Cov-2 variants in Colombia, we used 14,049 complete SARS-CoV-2 genomes from the 32 states of Colombia. We performed Bayesian phylodynamic analyses to estimate the time of variants' introduction, their respective effective reproductive number, and effective population size, and the impact of disease control measures. RESULTS: Here, we detect a total of 188 SARS-CoV-2 Pango lineages circulating in Colombia since the pandemic's start. We show that the effective reproduction number oscillated drastically throughout the first two years of the pandemic, with Mu showing the highest transmissibility (Re and growth rate estimation). CONCLUSIONS: Our results reinforce that genomic surveillance programs are essential for countries to make evidence-driven interventions toward the emergence and circulation of novel SARS-CoV-2 variants.

Colombia reported its first COVID-19 case on 6th March 2020. By April 2022, the country had reported over 6 million infections and over 135,000 deaths. Here, we aim to understand how SARS-CoV-2, the virus that causes COVID-19, spread through Colombia over this time and how the predominant version of the virus (variant) changed over time. We found that there were multiple introductions of different variants from other countries into Colombia during the first two years of the pandemic. The Gamma variant was dominant earlier in 2021 but was replaced by the Delta variant. The Mu variant had the highest potential to be transmitted. Our findings provide valuable insights into the pandemic in Colombia and highlight the importance of continued surveillance of the virus to guide the public health response.

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.

Accounting for Errors in Data Improves Divergence Time Estimates in Single-cell Cancer Evolution.

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 are 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.

Phylodynamics reveals the role of human travel and contact tracing in controlling the first wave of COVID-19 in four island nations.

New Zealand, Australia, Iceland, and Taiwan all saw success in controlling their first waves of Coronavirus Disease 2019 (COVID-19). As islands, they make excellent case studies for exploring the effects of international travel and human movement on the spread of COVID-19. We employed a range of robust phylodynamic methods and genome subsampling strategies to infer the epidemiological history of Severe acute respiratory syndrome coronavirus 2 in these four countries. We compared these results to transmission clusters identified by the New Zealand Ministry of Health by contact tracing strategies. We estimated the effective reproduction number of COVID-19 as 1-1.4 during early stages of the pandemic and show that it declined below 1 as human movement was restricted. We also showed that this disease was introduced many times into each country and that introductions slowed down markedly following the reduction of international travel in mid-March 2020. Finally, we confirmed that New Zealand transmission clusters identified via standard health surveillance strategies largely agree with those defined by genomic data. We have demonstrated how the use of genomic data and computational biology methods can assist health officials in characterising the epidemiology of viral epidemics and for contact tracing.

Novel Integrative Modeling of Molecules and Morphology across Evolutionary Timescales.

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.[Caninae; fossilized birth-death; molecular clock; multispecies coalescent; phylogenetics; species trees.].

Evolutionary history of cotranscriptional editing in the paramyxoviral phosphoprotein gene.

The phosphoprotein gene of the paramyxoviruses encodes multiple protein products. The P, V, and W proteins are generated by transcriptional slippage. This process results in the insertion of non-templated guanosine nucleosides into the mRNA at a conserved edit site. The P protein is an essential component of the viral RNA polymerase and is encoded by a faithful copy of the gene in the majority of paramyxoviruses. However, in some cases, the non-essential V protein is encoded by default and guanosines must be inserted into the mRNA in order to encode P. The number of guanosines inserted into the P gene can be described by a probability distribution, which varies between viruses. In this article, we review the nature of these distributions, which can be inferred from mRNA sequencing data, and reconstruct the evolutionary history of cotranscriptional editing in the paramyxovirus family. Our model suggests that, throughout known history of the family, the system has switched from a P default to a V default mode four times; complete loss of the editing system has occurred twice, the canonical zinc finger domain of the V protein has been deleted or heavily mutated a further two times, and the W protein has independently evolved a novel function three times. Finally, we review the physical mechanisms of cotranscriptional editing via slippage of the viral RNA polymerase.

Use of Genomics to Track Coronavirus Disease Outbreaks, New Zealand.

Real-time genomic sequencing has played a major role in tracking the global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), contributing greatly to disease mitigation strategies. In August 2020, after having eliminated the virus, New Zealand experienced a second outbreak. During that outbreak, New Zealand used genomic sequencing in a primary role, leading to a second elimination of the virus. We generated genomes from 78% of the laboratory-confirmed samples of SARS-CoV-2 from the second outbreak and compared them with the available global genomic data. Genomic sequencing rapidly identified that virus causing the second outbreak in New Zealand belonged to a single cluster, thus resulting from a single introduction. However, successful identification of the origin of this outbreak was impeded by substantial biases and gaps in global sequencing data. Access to a broader and more heterogenous sample of global genomic data would strengthen efforts to locate the source of any new outbreaks.

Genomic Evidence of In-Flight Transmission of SARS-CoV-2 Despite Predeparture Testing.

Since the first wave of coronavirus disease in March 2020, citizens and permanent residents returning to New Zealand have been required to undergo managed isolation and quarantine (MIQ) for 14 days and mandatory testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of October 20, 2020, of 62,698 arrivals, testing of persons in MIQ had identified 215 cases of SARS-CoV-2 infection. Among 86 passengers on a flight from Dubai, United Arab Emirates, that arrived in New Zealand on September 29, test results were positive for 7 persons in MIQ. These passengers originated from 5 different countries before a layover in Dubai; 5 had negative predeparture SARS-CoV-2 test results. To assess possible points of infection, we analyzed information about their journeys, disease progression, and virus genomic data. All 7 SARS-CoV-2 genomes were genetically identical, except for a single mutation in 1 sample. Despite predeparture testing, multiple instances of in-flight SARS-CoV-2 transmission are likely.

Genomic epidemiology reveals transmission patterns and dynamics of SARS-CoV-2 in Aotearoa New Zealand.

New Zealand, a geographically remote Pacific island with easily sealable borders, implemented a nationwide 'lockdown' of all non-essential services to curb the spread of COVID-19. Here, we generate 649 SARS-CoV-2 genome sequences from infected patients in New Zealand with samples collected during the 'first wave', representing 56% of all confirmed cases in this time period. Despite its remoteness, the viruses imported into New Zealand represented nearly all of the genomic diversity sequenced from the global virus population. These data helped to quantify the effectiveness of public health interventions. For example, the effective reproductive number, Re of New Zealand's largest cluster decreased from 7 to 0.2 within the first week of lockdown. Similarly, only 19% of virus introductions into New Zealand resulted in ongoing transmission of more than one additional case. Overall, these results demonstrate the utility of genomic pathogen surveillance to inform public health and disease mitigation.

Bayesian inference and comparison of stochastic transcription elongation models.

Transcription elongation can be modelled as a three step process, involving polymerase translocation, NTP binding, and nucleotide incorporation into the nascent mRNA. This cycle of events can be simulated at the single-molecule level as a continuous-time Markov process using parameters derived from single-molecule experiments. Previously developed models differ in the way they are parameterised, and in their incorporation of partial equilibrium approximations. We have formulated a hierarchical network comprised of 12 sequence-dependent transcription elongation models. The simplest model has two parameters and assumes that both translocation and NTP binding can be modelled as equilibrium processes. The most complex model has six parameters makes no partial equilibrium assumptions. We systematically compared the ability of these models to explain published force-velocity data, using approximate Bayesian computation. This analysis was performed using data for the RNA polymerase complexes of E. coli, S. cerevisiae and Bacteriophage T7. Our analysis indicates that the polymerases differ significantly in their translocation rates, with the rates in T7 pol being fast compared to E. coli RNAP and S. cerevisiae pol II. Different models are applicable in different cases. We also show that all three RNA polymerases have an energetic preference for the posttranslocated state over the pretranslocated state. A Bayesian inference and model selection framework, like the one presented in this publication, should be routinely applicable to the interrogation of single-molecule datasets.

Inferring host roles in bayesian phylodynamics of global avian influenza A virus H9N2.

Role of avian hosts in shaping persistence, evolution, and dispersal of global low pathogenic avian influenza virus (LPAIV) H9N2 remains uncertain. Under Bayesian Markov Chain Monte Carlo framework, we used the discrete trait analysis (DTA) to reconstruct host and location switches in the evolutionary history of global H9N2 given hemagglutinin gene sequences from 18 countries/regions between 1976 and 2018. We employed generalized linear models (GLMs) to inform virus migration rates by empirical predictors. Global H9N2 isolates were mostly sampled from domestic Phasianidae in low- and middle-income countries with poor bio-security. Anatidae was inferred as the ancestral source from which the virus spread to domestic waterfowl, and later to domestic Phasianidae who have become the dominant host to sustain the virus, especially in Asia. Poultry trade was a well-supported driver to H9N2 spread across countries/regions. Strict bio-security and separation between wild and domestic poultry can be used to mitigate virus spread.

Bayesian phylodynamics of avian influenza A 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 avian influenza A virus H9N2 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. 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.

Estimating Epidemic Incidence and Prevalence 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 behavior 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.

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. Developing 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.

SANTA-SIM: simulating viral sequence evolution dynamics under selection and recombination.

Simulations are widely used to provide expectations and predictive distributions under known conditions against which to compare empirical data. Such simulations are also invaluable for testing and comparing the behaviour and power of inference methods. We describe SANTA-SIM, a software package to simulate the evolution of a population of gene sequences forwards through time. It models the underlying biological processes as discrete components: replication, recombination, point mutations, insertion-deletions, and selection under various fitness models and population size dynamics. The software is designed to be intuitive to work with for a wide range of users and executable in a cross-platform manner.

Estimating the biodiversity of terrestrial invertebrates on a forested island using DNA barcodes and metabarcoding data.

Invertebrates are a major component of terrestrial ecosystems, however, estimating their biodiversity is challenging. We compiled an inventory of invertebrate biodiversity along an elevation gradient on the temperate forested island of Hauturu, New Zealand, by DNA barcoding of specimens obtained from leaf litter samples and pitfall traps. We compared the barcodes and biodiversity estimates from this data set with those from a parallel DNA metabarcoding analysis of soil from the same locations, and with pre-existing sequences in reference databases, before exploring the use of combined data sets as a basis for estimating total invertebrate biodiversity. We obtained 1,282 28S and 1,610 COI barcodes from a total of 1,947 invertebrate specimens, which were clustered into 247 (28S) and 366 (COI) OTUs, of which ≤ 10% were represented in GenBank. Coleoptera were most abundant (730 sequenced specimens), followed by Hymenoptera, Diptera, Lepidoptera, and Amphipoda. The most abundant OTU from both the 28S (153 sequences) and COI (140 sequences) data sets was an undescribed beetle from the family Salpingidae. Based on the occurrences of COI OTUs along the elevation gradient, we estimated there are ~1,000 arthropod species (excluding mites) on Hauturu, including 770 insects, of which 344 are beetles. A DNA metabarcoding analysis of soil DNA from the same sites resulted in the identification of similar numbers of OTUs in most invertebrate groups compared with the DNA barcoding, but less than 10% of the DNA barcoding COI OTUs were also detected by the metabarcoding analysis of soil DNA. A mark-recapture analysis based on the overlap between these data sets estimated the presence of approximately 6,800 arthropod species (excluding mites) on the island, including ~3,900 insects. Estimates of New Zealand-wide biodiversity for selected arthropod groups based on matching of the COI DNA barcodes with pre-existing reference sequences suggested over 13,200 insect species are present, including 4,000 Coleoptera, 2,200 Diptera, and 2,700 Hymenoptera species, and 1,000 arachnid species (excluding mites). These results confirm that metabarcoding analyses of soil DNA tends to recover different components of terrestrial invertebrate biodiversity compared to traditional invertebrate sampling, but the combined methods provide a novel basis for estimating invertebrate biodiversity.

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, a package for the popular BEAST2 software that allows assessing the adequacy of phylodynamic models. We illustrate its utility by analyzing 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 were best described by the birth-death susceptible-infected-recovered model.

Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10.

The Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package has become a primary tool for Bayesian phylogenetic and phylodynamic inference from genetic sequence data. BEAST unifies molecular phylogenetic reconstruction with complex discrete and continuous trait evolution, divergence-time dating, and coalescent demographic models in an efficient statistical inference engine using Markov chain Monte Carlo integration. A convenient, cross-platform, graphical user interface allows the flexible construction of complex evolutionary analyses.

Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7.

Bayesian inference of phylogeny using Markov chain Monte Carlo (MCMC) plays a central role in understanding evolutionary history from molecular sequence data. Visualizing and analyzing the MCMC-generated samples from the posterior distribution is a key step in any non-trivial Bayesian inference. We present the software package Tracer (version 1.7) for visualizing and analyzing the MCMC trace files generated through Bayesian phylogenetic inference. Tracer provides kernel density estimation, multivariate visualization, demographic trajectory reconstruction, conditional posterior distribution summary, and more. Tracer is open-source and available at http://beast.community/tracer.