SUMMARY: We report the second version of the R package "seraphim", a toolbox developed to process and analyze the output of spatially explicit phylogeographic reconstructions. This approach - also known as continuous phylogeographic inference - is commonly used in molecular epidemiology to reconstruct the dispersal history and spatiotemporal dynamics of rapidly evolving pathogens. The "seraphim" package now implements a broad range of features including (i) visualization of phylogeographic inferences, (ii) estimation of lineage dispersal metrics, (iii) several phylogeographic simulators, and (iv) hypothesis testing procedures to investigate the impact of environmental factors on variables such as diffusion velocity, dispersal location, and dispersal frequency of phylogenetic lineages. AVAILABILITY AND IMPLEMENTATION: The package is openly available (https://github.com/sdellicour/seraphim) along with a series of tutorials describing the different analytical procedures it implements.

SUMMARY: UGENE is a free, open-source, cross-platform bioinformatics software. UGENE deploys pre-defined pipelines and a flexible instrument to design new workflows and visually build multi-step analytics pipelines. The new UGENE v.1.31 release offers graphical, user-friendly wrapping of a number of popular command-line metagenomics classification programs (Kraken, CLARK, DIAMOND), combinable serially and in parallel through the workflow designer, with multiple, customizable reference databases. Ensemble classification voting is available through the WEVOTE algorithm, with augmented output in the form of detailed table reports. Pre-built workflows (which include all steps from data cleaning to summaries) are included with the installation and a tutorial is available on the UGENE website. Further expansion with multiple visualization tools for reports is planned. AVAILABILITY AND IMPLEMENTATION: UGENE is available at http://ugene.net/, implemented in C++ and Qt, and released under GNU General Public License (GPL) version 2.

SERAPHIM ("Studying Environmental Rasters and PHylogenetically Informed Movements") is a suite of computational methods developed to study phylogenetic reconstructions of spatial movement in an environmental context. SERAPHIM extracts the spatio-temporal information contained in estimated phylogenetic trees and uses this information to calculate summary statistics of spatial spread and to visualize dispersal history. Most importantly, SERAPHIM enables users to study the impact of customized environmental variables on the spread of the study organism. Specifically, given an environmental raster, SERAPHIM computes environmental "weights" for each phylogeny branch, which represent the degree to which the environmental variable impedes (or facilitates) lineage movement. Correlations between movement duration and these environmental weights are then assessed, and the statistical significances of these correlations are evaluated using null distributions generated by a randomization procedure. SERAPHIM can be applied to any phylogeny whose nodes are annotated with spatial and temporal information. At present, such phylogenies are most often found in the field of emerging infectious diseases, but will become increasingly common in other biological disciplines as population genomic data grows. AVAILABILITY AND IMPLEMENTATION: SERAPHIM 1.0 is freely available from http://evolve.zoo.ox.ac.uk/ R package, source code, example files, tutorials and a manual are also available from this website. CONTACT: [email protected] or [email protected] information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Phylogenetic analysis is now an important tool in the study of viral outbreaks. It can reconstruct epidemic history when surveillance epidemiology data are sparse, and can indicate transmission linkages among infections that may not otherwise be evident. However, a remaining challenge is to develop an analytical framework that can test hypotheses about the effect of environmental variables on pathogen spatial spread. Recent phylogeographic approaches can reconstruct the history of virus dispersal from sampled viral genomes and infer the locations of ancestral infections. Such methods provide a unique source of spatio-temporal information, and are exploited here. RESULTS: We present and apply a new statistical framework that combines genomic and geographic data to test the impact of environmental variables on the mode and tempo of pathogen dispersal during emerging epidemics. First, the spatial history of an emerging pathogen is estimated using standard phylogeographic methods. The inferred dispersal path for each phylogenetic lineage is then assigned a "weight" using environmental data (e.g. altitude, land cover). Next, tests measure the association between each environmental variable and lineage movement. A randomisation procedure is used to assess statistical confidence and we validate this approach using simulated data. We apply our new framework to a set of gene sequences from an epidemic of rabies virus in North American raccoons. We test the impact of six different environmental variables on this epidemic and demonstrate that elevation is associated with a slower rabies spread in a natural population. CONCLUSION: This study shows that it is possible to integrate genomic and environmental data in order to test hypotheses concerning the mode and tempo of virus dispersal during emerging epidemics.