Knowing phylogenetic relationships among species is fundamental for many studies in biology. An accurate phylogenetic tree underpins our understanding of the major transitions in evolution, such as the emergence of new body plans or metabolism, and is key to inferring the origin of new genes, detecting molecular adaptation, understanding morphological character evolution and reconstructing demographic changes in recently diverged species. Although data are ever more plentiful and powerful analysis methods are available, there remain many challenges to reliable tree building. Here, we discuss the major steps of phylogenetic analysis, including identification of orthologous genes or proteins, multiple sequence alignment, and choice of substitution models and inference methodologies. Understanding the different sources of errors and the strategies to mitigate them is essential for assembling an accurate tree of life.

/pdf/phylogenetic-tree-building-in-the-genomic-age-5ckrdkkdbh.pdf

Phylogenetic tree building in the genomic age.

Five decades have passed since the proposal of the molecular clock hypothesis, which states that the rate of evolution at the molecular level is constant through time and among species. This hypothesis has become a powerful tool in evolutionary biology, making it possible to use molecular sequences to estimate the geological ages of species divergence events. With recent advances in Bayesian clock dating methodology and the explosive accumulation of genetic sequence data, molecular clock dating has found widespread applications, from tracking virus pandemics and studying the macroevolutionary process of speciation and extinction to estimating a timescale for life on Earth.

/pdf/bayesian-molecular-clock-dating-of-species-divergences-in-22vmea3zdh.pdf

Bayesian molecular clock dating of species divergences in the genomics era

A substantial proportion of chronic disease patients do not respond to self-management interventions, which suggests that one size interventions do not fit all, demanding more tailored interventions. To compose more individualized strategies, we aim to increase our understanding of characteristics associated with patient activation for self-management and to evaluate whether these are disease-transcending. A cross-sectional survey study was conducted in primary and secondary care in patients with type-2 Diabetes Mellitus (DM-II), Chronic Obstructive Pulmonary Disease (COPD), Chronic Heart Failure (CHF) and Chronic Renal Disease (CRD). Using multiple linear regression analysis, we analyzed associations between self-management activation (13-item Patient Activation Measure; PAM-13) and a wide range of socio-demographic, clinical, and psychosocial determinants. Furthermore, we assessed whether the associations between the determinants and the PAM were disease-transcending by testing whether disease was an effect modifier. In addition, we identified determinants associated with low activation for self-management using logistic regression analysis. We included 1154 patients (53% response rate); 422 DM-II patients, 290 COPD patients, 223 HF patients and 219 CRD patients. Mean age was 69.6±10.9. Multiple linear regression analysis revealed 9 explanatory determinants of activation for self-management: age, BMI, educational level, financial distress, physical health status, depression, illness perception, social support and underlying disease, explaining a variance of 16.3%. All associations, except for social support, were disease transcending. This study explored factors associated with varying levels of activation for self-management. These results are a first step in supporting clinicians and researchers to identify subpopulations of chronic disease patients less likely to be engaged in self-management. Increased scientific efforts are needed to explain the greater part of the factors that contribute to the complex nature of patient activation for self-management.

/pdf/patient-and-disease-characteristics-associated-with-5dm4np84px.pdf

Patient and disease characteristics associated with activation for self-management in patients with diabetes, chronic obstructive pulmonary disease, chronic heart failure and chronic renal disease: a cross-sectional survey study.

Bayesian methods have become very popular in molecular phylogenetics due to the availability of user-friendly software for running sophisticated models of evolution. However, Bayesian phylogenetic models are complex, and analyses are often carried out using default settings, which may not be appropriate. Here we summarize the major features of Bayesian phylogenetic inference and discuss Bayesian computation using Markov chain Monte Carlo (MCMC) sampling, the diagnosis of an MCMC run, and ways of summarizing the MCMC sample. We discuss the specification of the prior, the choice of the substitution model and partitioning of the data. Finally, we provide a list of common Bayesian phylogenetic software packages and recommend appropriate applications.

A biologist’s guide to Bayesian phylogenetic analysis

Phylogenetic methods can use the sampling times of molecular sequence data to calibrate the molecular clock, enabling the estimation of evolutionary rates and timescales for rapidly evolving pathogens and data sets containing ancient DNA samples A key aspect of such calibrations is whether a sufficient amount of molecular evolution has occurred over the sampling time window, that is, whether the data can be treated as having come from a measurably evolving population Here, we investigate the performance of a fully Bayesian evaluation of temporal signal (BETS) in sequence data The method involves comparing the fit to the data of two models: a model in which the data are accompanied by the actual (heterochronous) sampling times, and a model in which the samples are constrained to be contemporaneous (isochronous) We conducted simulations under a wide range of conditions to demonstrate that BETS accurately classifies data sets according to whether they contain temporal signal or not, even when there is substantial among-lineage rate variation We explore the behavior of this classification in analyses of five empirical data sets: modern samples of A/H1N1 influenza virus, the bacterium Bordetella pertussis, coronaviruses from mammalian hosts, ancient DNA from Hepatitis B virus, and mitochondrial genomes of dog species Our results indicate that BETS is an effective alternative to other tests of temporal signal In particular, this method has the key advantage of allowing a coherent assessment of the entire model, including the molecular clock and tree prior which are essential aspects of Bayesian phylodynamic analyses

https://minerva-access.unimelb.edu.au/bitstream/handle/11343/268197/Bayesian Evaluation of Temporal Signal in Measurably Evolving Populations.pdf

Bayesian Evaluation of Temporal Signal in Measurably Evolving Populations

Bayesian Phylogenetics : Methods, Algorithms, and Applications

With the rapid reduction in sequencing costs of high-throughput genomic data, it has become commonplace to use hundreds of genes to infer phylogeny of any study system. While sampling a large number of genes has given us a tremendous opportunity to uncover previously unknown relationships and improve phylogenetic resolution, it also presents us with new challenges when the phylogenetic signal is confused by differences in the evolutionary histories of sampled genes. Given the incorporation of accurate marginal likelihood estimation methods into popular Bayesian software programs, it is natural to consider using the Bayes Factor (BF) to compare different partition models in which genes within any given partition subset share both tree topology and edge lengths. We explore using marginal likelihood to assess data subset combinability when data subsets have varying levels of phylogenetic discordance due to deep coalescence events among genes (simulated within a species tree), and compare the results with our recently described phylogenetic informational dissonance index (D) estimated for each data set. BF effectively detects phylogenetic incongruence and provides a way to assess the statistical significance of D values. We use BFs to assess data combinability using an empirical data set comprising 56 plastid genes from the green algal order Volvocales. We also discuss the potential need for calibrating BFs and demonstrate that BFs used in this study are correctly calibrated.

/pdf/assessing-combinability-of-phylogenomic-data-using-bayes-3sdkuq0ady.pdf

Assessing Combinability of Phylogenomic Data Using Bayes Factors.

This dissertation treats three topics which are all related to the mixtures of Dirichlet processes arising as posterior distributions in a Bayesian context.
Computation of Bayes estimates based on mixtures of Dirichlet processes is treated in Chapter I. Empirical Bayes problems are selected to illustrate this computation. Bayes estimators are derived from Dirichlet processes priors for general empirical Bayes problems. These estimators may be written as ratios of two multidimensional integrals, each of which may be decomposed into a weighted average of products of one-dimensional integrals. A Monte Carlo method is proposed to approximate numerator and denominator separately. A prior error bound for numerator and denominator separately and a posterior error bound for the ratio are developed to measure the efficiency of the Monte Carlo method. Two numerical examples are presented in the end of Chapter I.
The Bayesian bio-assay design problem is treated in Chapter II. It is assumed that the potency curve is a Dirichlet random distribution with parameter (alpha)(t) = Mt, 0 (LESSTHEQ) t (LESSTHEQ) 1, and that n(,1),...,n(,L) animals are treated at drug levels t(,1),...,t(,L) respectively. We find the optimal design levels t(,1),...,t(,L) which minimize the Bayes risk
(DIAGRAM, TABLE OR GRAPHIC OMITTED...PLEASE SEE DAI)
where F(t) is the Bayes estimate of F(t), in the following cases: (i) L = 1 and w(t) arbitrary, (ii) w(t) = t, and two animals are treated, (iii) w(t) = t, and L arbitrary, but with M (--->) 0. These results disprove a conjecture of Antoniak.
Linear approximation to the potency curve estimate in bio-assay is treated in Chapter III. A Bayes estimate, restricted to the linear space generated by the samples, is derived for the potency curve with squared error loss. In conclusion, some examples and optimal properties of the linear Bayes estimate are presented.

Computations and applications of mixtures of dirichlet processes

The Department of Statistics at the University of Connecticut was founded in 1962. As one of the major statistics departments in New England, it provides outstanding preparation for careers in academia, industry, or government. With a core faculty of 15 members whose teaching and research expertise span virtually all major specializations in statistical science, our department has both national and international reputation in undergraduate and graduate education, research, and service to the profession. The department offers BA/BS majors, BA/BS majors in mathematics and statistics jointly, as well as Masters and PhD degrees in statistics.

University of Connecticut Department of Statistics

We consider k (≥ 2) negative exponential populations having unknown locations θ1,...,θk, but unknown and equal scale parameter σ. We look at the problem of constructing fixed-width confidence intervals for having the confidence coefficient at least or approximately α which is preassigned. We present various two-stage, sequential and three-stage sampling techniques and discuss several exact and first- as well as second-order asymptotic properties.

Fixed-width interval estimation of the largest location of k negative exponential populations

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