Dataset Information

BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis.

ABSTRACT: Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release.

SUBMITTER: Bouckaert R

PROVIDER: S-EPMC6472827 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Publications

BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis.

Bouckaert Remco R Vaughan Timothy G TG Barido-Sottani Joëlle J Duchêne Sebastián S Fourment Mathieu M Gavryushkina Alexandra A Heled Joseph J Jones Graham G Kühnert Denise D De Maio Nicola N Matschiner Michael M Mendes Fábio K FK Müller Nicola F NF Ogilvie Huw A HA du Plessis Louis L Popinga Alex A Rambaut Andrew A Rasmussen David D Siveroni Igor I Suchard Marc A MA Wu Chieh-Hsi CH Xie Dong D Zhang Chi C Stadler Tanja T Drummond Alexei J AJ

PLoS computational biology 20190408 4

Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Inclu ...[more]

PMID: 30958812

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Project description:BackgroundDNA microarrays open up a new horizon for studying the genetic determinants of disease. The high throughput nature of these arrays creates an enormous wealth of information, but also poses a challenge to data analysis. Inferential problems become even more pronounced as experimental designs used to collect data become more complex. An important example is multigroup data collected over different experimental groups, such as data collected from distinct stages of a disease process. We have developed a method specifically addressing these issues termed Bayesian ANOVA for microarrays (BAM). The BAM approach uses a special inferential regularization known as spike-and-slab shrinkage that provides an optimal balance between total false detections and total false non-detections. This translates into more reproducible differential calls. Spike and slab shrinkage is a form of regularization achieved by using information across all genes and groups simultaneously.ResultsBAMarray is a graphically oriented Java-based software package that implements the BAM method for detecting differentially expressing genes in multigroup microarray experiments (up to 256 experimental groups can be analyzed). Drop-down menus allow the user to easily select between different models and to choose various run options. BAMarraycan also be operated in a fully automated mode with preselected run options. Tuning parameters have been preset at theoretically optimal values freeing the user from such specifications. BAMarray provides estimates for gene differential effects and automatically estimates data adaptive, optimal cutoff values for classifying genes into biological patterns of differential activity across experimental groups. A graphical suite is a core feature of the product and includes diagnostic plots for assessing model assumptions and interactive plots that enable tracking of prespecified gene lists to study such things as biological pathway perturbations. The user can zoom in and lasso genes of interest that can then be saved for downstream analyses.ConclusionBAMarray is user friendly platform independent software that effectively and efficiently implements the BAM methodology. Classifying patterns of differential activity is greatly facilitated by a data adaptive cutoff rule and a graphical suite. BAMarray is licensed software freely available to academic institutions. More information can be found at http://www.bamarray.com.

Dataset Information

BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis.

Publications

BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis.

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