BEAST: Bayesian evolutionary analysis by sampling trees

<p>Abstract</p> <p>Background</p> <p>The evolutionary analysis of molecular sequence variation is a statistical enterprise. This is reflected in the increased use of probabilistic models for phylogenetic inference, multiple sequence alignment, and molecular population genetics. Here we present BEAST: a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree. A large number of popular stochastic models of sequence evolution are provided and tree-based models suitable for both within- and between-species sequence data are implemented.</p> <p>Results</p> <p>BEAST version 1.4.6 consists of 81000 lines of Java source code, 779 classes and 81 packages. It provides models for DNA and protein sequence evolution, highly parametric coalescent analysis, relaxed clock phylogenetics, non-contemporaneous sequence data, statistical alignment and a wide range of options for prior distributions. BEAST source code is object-oriented, modular in design and freely available at <url>http://beast-mcmc.googlecode.com/</url> under the GNU LGPL license.</p> <p>Conclusion</p> <p>BEAST is a powerful and flexible evolutionary analysis package for molecular sequence variation. It also provides a resource for the further development of new models and statistical methods of evolutionary analysis.</p

πBUSS:a parallel BEAST/BEAGLE utility for sequence simulation under complex evolutionary scenarios

Background: Simulated nucleotide or amino acid sequences are frequently used to assess the performance of phylogenetic reconstruction methods. BEAST, a Bayesian statistical framework that focuses on reconstructing time-calibrated molecular evolutionary processes, supports a wide array of evolutionary models, but lacked matching machinery for simulation of character evolution along phylogenies. Results: We present a flexible Monte Carlo simulation tool, called piBUSS, that employs the BEAGLE high performance library for phylogenetic computations within BEAST to rapidly generate large sequence alignments under complex evolutionary models. piBUSS sports a user-friendly graphical user interface (GUI) that allows combining a rich array of models across an arbitrary number of partitions. A command-line interface mirrors the options available through the GUI and facilitates scripting in large-scale simulation studies. Analogous to BEAST model and analysis setup, more advanced simulation options are supported through an extensible markup language (XML) specification, which in addition to generating sequence output, also allows users to combine simulation and analysis in a single BEAST run. Conclusions: piBUSS offers a unique combination of flexibility and ease-of-use for sequence simulation under realistic evolutionary scenarios. Through different interfaces, piBUSS supports simulation studies ranging from modest endeavors for illustrative purposes to complex and large-scale assessments of evolutionary inference procedures. The software aims at implementing new models and data types that are continuously being developed as part of BEAST/BEAGLE.Comment: 13 pages, 2 figures, 1 tabl

Analysis of the medium (M) segment sequence of Guaroa virus and its comparison to other orthobunyaviruses

Guaroa virus (GROV), a segmented virus in the genus Orthobunyavirus, has been linked to the Bunyamwera serogroup (BUN) through cross-reactivity in complement fixation assays of S segment-encoded nucleocapsid protein determinants, and also to the California serogroup (CAL) through cross-reactivity in neutralization assays of M segment-encoded glycoprotein determinants. Phylogenetic analysis of the S-segment sequence supported a closer relationship to the BUN serogroup for this segment and it was hypothesized that the serological reaction may indicate genome-segment reassortment. Here, cloning and sequencing of the GROV M segment are reported. Sequence analysis indicates an organization similar to that of other orthobunyaviruses, with genes in the order GN–nsm–gC, and mature proteins generated by protease cleavage at one, and by signalase at possibly three, sites. A potential role of motifs that are more similar to CAL than to BUN virus sequences with respect to the serological reaction is discussed. No discernable evidence for reassortment was identified

SPREAD: spatial phylogenetic reconstruction of evolutionary dynamics

Summary: SPREAD is a user-friendly, cross-platform application to analyze and visualize Bayesian phylogeographic reconstructions incorporating spatial–temporal diffusion. The software maps phylogenies annotated with both discrete and continuous spatial information and can export high-dimensional posterior summaries to keyhole markup language (KML) for animation of the spatial diffusion through time in virtual globe software. In addition, SPREAD implements Bayes factor calculation to evaluate the support for hypotheses of historical diffusion among pairs of discrete locations based on Bayesian stochastic search variable selection estimates. SPREAD takes advantage of multicore architectures to process large joint posterior distributions of phylogenies and their spatial diffusion and produces visualizations as compelling and interpretable statistical summaries for the different spatial projections

Phylogenetic Analysis of Guinea 2014 EBOV Ebolavirus Outbreak

Members of the genus Ebolavirus have caused outbreaks of haemorrhagic fever in humans in Africa. The most recent outbreak in Guinea, which began in February of 2014, is still ongoing. Recently published analyses of sequences from this outbreak suggest that the outbreak in Guinea is caused by a divergent lineage of Zaire ebolavirus. We report evidence that points to the same Zaire ebolavirus lineage that has previously caused outbreaks in the Democratic Republic of Congo, the Republic of Congo and Gabon as the culprit behind the outbreak in Guinea

Adaptive MCMC in Bayesian phylogenetics: an application to analyzing partitioned data in BEAST

Advances in sequencing technology continue to deliver increasingly large molecular sequence datasets that are often heavily partitioned in order to accurately model the underlying evolutionary processes. In phylogenetic analyses, partitioning strategies involve estimating conditionally independent models of molecular evolution for different genes and different positions within those genes, requiring a large number of evolutionary parameters that have to be estimated, leading to an increased computational burden for such analyses. The past two decades have also seen the rise of multi-core processors, both in the central processing unit (CPU) and Graphics processing unit processor markets, enabling massively parallel computations that are not yet fully exploited by many software packages for multipartite analyses.status: publishe