5,901 research outputs found
Probabilistic Graphical Model Representation in Phylogenetics
Recent years have seen a rapid expansion of the model space explored in
statistical phylogenetics, emphasizing the need for new approaches to
statistical model representation and software development. Clear communication
and representation of the chosen model is crucial for: (1) reproducibility of
an analysis, (2) model development and (3) software design. Moreover, a
unified, clear and understandable framework for model representation lowers the
barrier for beginners and non-specialists to grasp complex phylogenetic models,
including their assumptions and parameter/variable dependencies.
Graphical modeling is a unifying framework that has gained in popularity in
the statistical literature in recent years. The core idea is to break complex
models into conditionally independent distributions. The strength lies in the
comprehensibility, flexibility, and adaptability of this formalism, and the
large body of computational work based on it. Graphical models are well-suited
to teach statistical models, to facilitate communication among phylogeneticists
and in the development of generic software for simulation and statistical
inference.
Here, we provide an introduction to graphical models for phylogeneticists and
extend the standard graphical model representation to the realm of
phylogenetics. We introduce a new graphical model component, tree plates, to
capture the changing structure of the subgraph corresponding to a phylogenetic
tree. We describe a range of phylogenetic models using the graphical model
framework and introduce modules to simplify the representation of standard
components in large and complex models. Phylogenetic model graphs can be
readily used in simulation, maximum likelihood inference, and Bayesian
inference using, for example, Metropolis-Hastings or Gibbs sampling of the
posterior distribution
RevBayes: Bayesian Phylogenetic Inference Using Graphical Models and an Interactive Model-Specification Language.
Programs for Bayesian inference of phylogeny currently implement a unique and ïŹxed suite of models. Consequently, users of these software packages are simultaneously forced to use a number of programs for a given study, while also lacking the freedom to explore models that have not been implemented by the developers of those programs. We developed a new open-source software package, RevBayes, to address these problems. RevBayes is entirely based on probabilistic graphical models, a powerful generic framework for specifying and analyzing statistical models. Phylogenetic-graphical models can be speciïŹed interactively in RevBayes, piece by piece, using a new succinct and intuitive language called Rev. Rev is similar to the R language and the BUGS model-speciïŹcation language, and should be easy to learn for most users. The strength of RevBayes is the simplicity with which one can design, specify, and implement new and complex models. Fortunately, this tremendous ïŹexibility does not come at the cost of slower computation; as we demonstrate, RevBayes outperforms competing software for several standard analyses. Compared with other programs, RevBayes has fewer black-box elements. Users need to explicitly specify each part of the model and analysis. Although this explicitness may initially be unfamiliar, we are convinced that this transparency will improve understanding of phylogenetic models in our ïŹeld. Moreover, it will motivate the search for improvements to existing methods by brazenly exposing the model choices that we make to critical scrutiny. RevBayes is freely available at http://www.RevBayes.com [Bayesian inference; Graphical models; MCMC; statistical phylogenetics.]
Assembling the Tree of Life in Europe (AToLE)
A network of scientists under the umbrella of 'Assembling the Tree of Life in Europe (AToLE)' seeks funding under the FP7-Theme: Cooperation - Environment (including Climate Change and Biodiversity Conservation) programme of the European Commission.

Practice-oriented controversies and borrowed epistemic credibility in current evolutionary biology: phylogeography as a case study
Although there is increasing recognition that theory and practice in science are intimately intertwined, philosophy of science perspectives on scientific controversies have been historically focused on theory rather than practice. As a step in the construction of frameworks for understanding controversies linked to scientific practices, here we introduce the notion of borrowed epistemic credibility (BEC), to describe the situation in which scientists, in order to garner support for their own stances, exploit similarities between tenets in their own field and accepted statements or positions properly developed within other areas of expertise. We illustrate the scope of application of our proposal with the analysis of a heavily methods-grounded, recent controversy in phylogeography, a biological subdiscipline concerned with the study of the historical causes of biogeographical variation through population genetics- and phylogenetics-based computer analyses of diversity in DNA sequences, both within species and between closely related taxa. Toward this end, we briefly summarize the arguments proposed by selected authors representing each side of the controversy: the ânested clade analysisâ school versus the âstatistical phylogeographyâ orientation. We claim that whereas both phylogeographic âresearch stylesâ borrow epistemic credibility from sources such as formal logic, the familiarity of results from other scientific areas, the authority of prominent scientists, or the presumed superiority of quantitative vs. verbal reasoning, âtheoryâ plays essentially no role as a foundation of the controversy. Besides underscoring the importance of strictly methodological and other non-theoretical aspects of controversies in current evolutionary biology, our analysis suggests a perspective with potential usefulness for the re-examination of more general philosophy of biology issues, such as the nature of historical inference, rationality, justification, and objectivity
Uncertainty in phylogenetic tree estimates
Estimating phylogenetic trees is an important problem in evolutionary
biology, environmental policy and medicine. Although trees are estimated, their
uncertainties are discarded by mathematicians working in tree space. Here we
explicitly model the multivariate uncertainty of tree estimates. We consider
both the cases where uncertainty information arises extrinsically (through
covariate information) and intrinsically (through the tree estimates
themselves). The importance of accounting for tree uncertainty in tree space is
demonstrated in two case studies. In the first instance, differences between
gene trees are small relative to their uncertainties, while in the second, the
differences are relatively large. Our main goal is visualization of tree
uncertainty, and we demonstrate advantages of our method with respect to
reproducibility, speed and preservation of topological differences compared to
visualization based on multidimensional scaling. The proposal highlights that
phylogenetic trees are estimated in an extremely high-dimensional space,
resulting in uncertainty information that cannot be discarded. Most
importantly, it is a method that allows biologists to diagnose whether
differences between gene trees are biologically meaningful, or due to
uncertainty in estimation.Comment: Final version accepted to Journal of Computational and Graphical
Statistic
- âŠ