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

Bayesian Inference of Species Trees from Multilocus Data

Until recently, it has been common practice for a phylogenetic analysis to use a single gene sequence from a single individual organism as a proxy for an entire species. With technological advances, it is now becoming more common to collect data sets containing multiple gene loci and multiple individuals per species. These data sets often reveal the need to directly model intraspecies polymorphism and incomplete lineage sorting in phylogenetic estimation procedures

Hot, Sour and Breathless – Ocean under Stress.

ISBN 978-0-9519618-6-

Optimal data partitioning, multispecies coalescent and Bayesian concordance analyses resolve early divergences of the grape family (Vitaceae)

Evolutionary rate heterogeneity and rapid radiations are common phenomena in organismal evolution and represent major challenges for reconstructing deep-level phylogenies. Here we detected substantial conflicts in and among data sets as well as uncertainty concerning relationships among lineages of Vitaceae from individual gene trees, supernetworks and tree certainty values. Congruent deep-level relationships of Vitaceae were retrieved by comprehensive comparisons of results from optimal partitioning analyses, multispecies coalescent approaches and the Bayesian concordance method. We found that partitioning schemes selected by PartitionFinder were preferred over those by gene or by codon position, and the unpartitioned model usually performed the worst. For a data set with conflicting signals, however, the unpartitioned model outperformed models that included more partitions, demonstrating some limitations to the effectiveness of concatenation for these data. For a transcriptome data set, fast coalescent methods (STAR and MP-EST) and a Bayesian concordance approach yielded congruent topologies with trees from the concatenated analyses and previous studies. Our results highlight that well-resolved gene trees are critical for the effectiveness of coalescent-based methods. Future efforts to improve the accuracy of phylogenomic analyses should emphasize the development of newmethods that can accommodate multiple biological processes and tolerate missing data while remaining computationally tractable. (C) The Willi Hennig Society 2017.National Natural Science Foundation of China [NNSF 31500179, 31590822, 31270268]; National Basic Research Program of China [2014CB954101]; National Science Foundation [DEB0743474]; Smithsonian Scholarly Studies Grant Program and the Endowment Grant Program; CAS/SAFEA International Partnership Program for Creative Research Teams; Laboratory of Analytical Biology of the National Museum of Natural History, Smithsonian Institution; Science and Technology Basic Work [2013FY112100]info:eu-repo/semantics/publishedVersio

AUGIST: inferring species trees while accommodating gene tree uncertainty

Summary: AUGIST (accomodating uncertainty in genealogies while inferring species tress) is a new software package for inferring species trees while accommodating uncertainty in gene genealogies. It is written for the Mesquite software system and provides sampling procedures to incorporate uncertainty in gene tree reconstruction while providing confidence estimates for inferred species trees

Long-Branch Attraction Bias and Inconsistency in Bayesian Phylogenetics

Bayesian inference (BI) of phylogenetic relationships uses the same probabilistic models of evolution as its precursor maximum likelihood (ML), so BI has generally been assumed to share ML's desirable statistical properties, such as largely unbiased inference of topology given an accurate model and increasingly reliable inferences as the amount of data increases. Here we show that BI, unlike ML, is biased in favor of topologies that group long branches together, even when the true model and prior distributions of evolutionary parameters over a group of phylogenies are known. Using experimental simulation studies and numerical and mathematical analyses, we show that this bias becomes more severe as more data are analyzed, causing BI to infer an incorrect tree as the maximum a posteriori phylogeny with asymptotically high support as sequence length approaches infinity. BI's long branch attraction bias is relatively weak when the true model is simple but becomes pronounced when sequence sites evolve heterogeneously, even when this complexity is incorporated in the model. This bias—which is apparent under both controlled simulation conditions and in analyses of empirical sequence data—also makes BI less efficient and less robust to the use of an incorrect evolutionary model than ML. Surprisingly, BI's bias is caused by one of the method's stated advantages—that it incorporates uncertainty about branch lengths by integrating over a distribution of possible values instead of estimating them from the data, as ML does. Our findings suggest that trees inferred using BI should be interpreted with caution and that ML may be a more reliable framework for modern phylogenetic analysis

Structural biology and phylogenetic estimation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62633/1/388527a0.pd

Evolution of genes and repeats in the Nimrod superfamily

The recently identified Nimrod superfamily is characterized by the presence of a special type of EGF repeat, the NIM repeat, located right after a typical CCXGY/W amino acid motif. On the basis of structural features, nimrod genes can be divided into three types. The proteins encoded by Draper-type genes have an EMI domain at the N-terminal part and only one copy of the NIM motif, followed by a variable number of EGF-like repeats. The products of Nimrod B-type and Nimrod C-type genes (including the eater gene) have different kinds of N-terminal domains, and lack EGF-like repeats but contain a variable number of NIM repeats. Draper and Nimrod C-type (but not Nimrod B-type) proteins carry a transmembrane domain. Several members of the superfamily were claimed to function as receptors in phagocytosis and/or binding of bacteria, which indicates an important role in the cellular immunity and the elimination of apoptotic cells. In this paper, the evolution of the Nimrod superfamily is studied with various methods on the level of genes and repeats. A hypothesis is presented in which the NIM repeat, along with the EMI domain, emerged by structural reorganizations at the end of an EGF-like repeat chain, suggesting a mechanism for the formation of novel types of repeats. The analyses revealed diverse evolutionary patterns in the sequences containing multiple NIM repeats. Although in the Nimrod B and Nimrod C proteins show characteristics of independent evolution, many internal NIM repeats in Eater sequences seem to have undergone concerted evolution. An analysis of the nimrod genes has been performed using phylogenetic and other methods and an evolutionary scenario of the origin and diversification of the Nimrod superfamily is proposed. Our study presents an intriguing example how the evolution of multigene families may contribute to the complexity of the innate immune response