Biology subjects

Moore, B. R.

Publications and source records attributed to Moore, B. R..

5 recordsLinked to original sources

bioRxiv2022

PrioriTree: a utility for improving phylodynamic analyses in BEAST

SummaryPhylodynamic methods are central to studies of the geographic and demographic history of disease outbreaks. Inference under discrete-geographic phylodynamic models--which involve many parameters that must be inferred from minimal information--is inherently sensitive to our prior beliefs about the model parameters. We present an interactive utility, PrioriTree, to help researchers identify and accommodate prior sensitivity in discrete-geographic inferences. Specifically, PrioriTree provides a suite of functions to generate input files for--and summarize output from--BEAST analyses for performing robust Bayesian inference, data-cloning analyses, and assessing the relative and absolute fit of candidate discrete-geographic (prior) models to empirical datasets. Availability and ImplementationPrioriTree is distributed as an R package available at https://github.com/jsigao/prioritree, with a comprehensive user manual provided at https://bookdown.org/jsigao/prioritree_manual/. Contactjsigao@ucdavis.edu

evolutionary biology↗

bioRxiv2021

Phylodynamic insights on the early spread of the COVID-19 pandemic and the efficacy of intervention measures

We performed phylodynamic analyses of all available SARS-CoV-2 genomes from the early phase of the COVID-19 pandemic--combined with a novel dataset on contemporary global air-travel volume--to assess the efficacy of public-health measures on viral geographic spread. Globally, viral dispersal rates are significantly correlated with air-travel volume, and widespread international air-travel bans imposed against China by early February coincide with a significant reduction in geographic viral spread. In North America, the efficacy of this travel ban was temporary, possibly due to the lack of both containment measures against other infected regions and domestic mitigation measures. By contrast, in China, domestic mitigation measures were correlated with a long-term reduction in viral spread, despite repeated international introductions. Our study supports a role for both targeted international containment and domestic mitigation measures as critical components of a more comprehensive public-health strategy to mitigate future outbreaks caused by the emergence of novel SARS-CoV-2 variants. One sentence summaryPhylodynamic analyses reveal that variation in rates of early geographic spread of COVID-19 are correlated with intervention measures.

evolutionary biology↗

bioRxiv2019

A hierarchical Bayesian mixture model for distinguishing active gene expression from transcriptional noise

Transcriptomes are key to understanding the relationship between genotype and phenotype. The ability to infer the expression state (active or inactive) of genes in the transcriptome offers unique benefits for addressing this issue. For example, qualitative changes in gene expression may underly the origin of novel phenotypes, and expression states are readily comparable between tissues and species. However, inferring the expression state of genes is a surprisingly difficult problem, owing to the complex biological and technical processes that give rise to observed transcriptomic datasets. Here, we develop a hierarchical Bayesian mixture model that describes this complex process, and allows us to infer expression state of genes from replicate transcriptomic libraries. We explore the statistical behavior of this method with analyses of simulated datasets--where we demonstrate its ability to correctly infer true (known) expression states--and empirical-benchmark datasets, where we demonstrate that the expression states inferred from RNA-seq datasets using our method are consistent with those based on independent evidence. The power of our method to correctly infer expression states is generally high and, remarkably, approaches the maximum possible power for this inference problem. We present an empirical analysis of primate-brain transcriptomes, which identifies genes that have a unique expression state in humans. Our method is implemented in the freely-available R package zigzag.\n\nSignificance StatementHow do the cells of an organism--each with an identical genome--give rise to tissues of incredible phenotypic diversity? Key to answering this question is the transcriptome: the set of genes expressed in a given tissue. We would clearly benefit from the ability to identify qualitative differences in expression (whether a gene is active or inactive in a given tissue/species). Inferring the expression state of genes is surprisingly difficult, owing to the complex biological processes that give rise to transcriptomes, and to the vagaries of techniques used to generate transcriptomic datasets. We develop a hierarchical Bayesian mixture model that--by describing those biological and technical processes--allows us to infer the expression state of genes from replicate transcriptomic datasets.

genomics↗

bioRxiv2019

A Bayesian Approach for Inferring the Impact of a Discrete Character on Rates of Continuous-Character Evolution in the Presence of Background Rate Variation

Understanding how and why rates of character evolution vary across the Tree of Life is central to many evolutionary questions; e.g., does the trophic apparatus (a set of continuous characters) evolve at a higher rate in fish lineages that dwell in reef versus non-reef habitats (a discrete character)? Existing approaches for inferring the relationship between a discrete character and rates of continuous-character evolution rely on comparing a null model (in which rates of continuous-character evolution are constant across lineages) to an alternative model (in which rates of continuous-character evolution depend on the state of the discrete character under consideration). However, these approaches are susceptible to a \"straw-man\" effect: the influence of the discrete character is inflated because the null model is extremely unrealistic. Here, we describe MuSSCRat, a Bayesian approach for inferring the impact of a discrete trait on rates of continuous-character evolution in the presence of alternative sources of rate variation (\"background-rate variation\"). We demonstrate by simulation that our method is able to reliably infer the degree of state-dependent rate variation, and show that ignoring background-rate variation leads to biased inferences regarding the degree of state-dependent rate variation in grunts (the fish group Haemulidae). [continuous-character evolution; discrete-character evolution; Bayesian phylogenetic comparative methods; data augmentation]

evolutionary biology↗

bioRxiv2019

A Bayesian Approach for Estimating Branch-Specific Speciation and Extinction Rates

Species richness varies considerably among the tree of life which can only be explained by heterogeneous rates of diversification (speciation and extinction). Previous approaches use phylogenetic trees to estimate branch-specific diversification rates. However, all previous approaches disregard diversification-rate shifts on extinct lineages although 99% of species that ever existed are now extinct. Here we describe a lineage-specific birth-death-shift process where lineages, both extant and extinct, may have heterogeneous rates of diversification. To facilitate probability computation we discretize the base distribution on speciation and extinction rates into k rate categories. The fixed number of rate categories allows us to extend the theory of state-dependent speciation and extinction models (e.g., BiSSE and MuSSE) to compute the probability of an observed phylogeny given the set of speciation and extinction rates. To estimate branch-specific diversification rates, we develop two independent and theoretically equivalent approaches: numerical integration with stochastic character mapping and data-augmentation with reversible-jump Markov chain Monte Carlo sampling. We validate the implementation of the two approaches in RevBayes using simulated data and an empirical example study of primates. In the empirical example, we show that estimates of the number of diversification-rate shifts are, unsurprisingly, very sensitive to the choice of prior distribution. Instead, branch-specific diversification rate estimates are less sensitive to the assumed prior distribution on the number of diversification-rate shifts and consistently infer an increased rate of diversification for Old World Monkeys. Additionally, we observe that as few as 10 diversification-rate categories are sufficient to approximate a continuous base distribution on diversification rates. In conclusion, our implementation of the lineage-specific birth-death-shift model in RevBayes provides biologists with a method to estimate branch-specific diversification rates under a mathematically consistent model.

evolutionary biology↗