ape is distributed through the Comprehensive R Archive Network: http://cran.r-project.org/package=apeFurther information may be found athttp://ape-package.ird.fr/.

Summary: is a package for phylogenetic reconstruction and analysis in the R language. Previously it was only possible to estimate phylogenetic trees with distance methods in R. , now offers the possibility of reconstructing phylogenies with distance based methods, maximum parsimony or maximum likelihood (ML) and performing Hadamard conjugation. Extending the general ML framework, this package provides the possibility of estimating mixture and partition models. Furthermore, offers several functions for comparing trees, phylogenetic models or splits, simulating character data and performing congruence analyses.Availability: can be obtained through the CRAN homepage http://cran.r-project.org/web/packages/phangorn/index.html. is licensed under GPL 2.Contact: klaus.kschliep@snv.jussieu.frSupplementary information: Supplementary data are available at Bioinformatics online.

Through various pathways of cell death, degradation, and regulated extrusion, partial or complete genomes of various origins (e.g., host cells, fetal cells, and infiltrating viruses and microbes) are continuously shed into human body fluids in the form of segmented cell-free DNA (cfDNA) molecules. While the genetic complexity of total cfDNA is vast, the development of progressively efficient extraction, high-throughput sequencing, characterization via bioinformatics procedures, and detection have resulted in increasingly accurate partitioning and profiling of cfDNA subtypes. Not surprisingly, cfDNA analysis is emerging as a powerful clinical tool in many branches of medicine. In addition, the low invasiveness of longitudinal cfDNA sampling provides unprecedented access to study temporal genomic changes in a variety of contexts. However, the genetic diversity of cfDNA is also a great source of ambiguity and poses significant experimental and analytical challenges. For example, the cfDNA population in the bloodstream is heterogeneous and also fluctuates dynamically, differs between individuals, and exhibits numerous overlapping features despite often originating from different sources and processes. Therefore, a deeper understanding of the determining variables that impact the properties of cfDNA is crucial, however, thus far, is largely lacking. In this work we review recent and historical research on active vs. passive release mechanisms and estimate the significance and extent of their contribution to the composition of cfDNA.

Vibrio cholerae represents both an environmental pathogen and a widely distributed microbial species comprised of closely related strains occurring in the tropical to temperate coastal ocean across the globe (Colwell RR, Science 274:2025–2031, 1996; Griffith DC, Kelly-Hope LA, Miller MA, Am. J. Trop. Med. Hyg. 75:973–977, 2006; Reidl J, Klose KE, FEMS Microbiol. Rev. 26:125–139, 2002). However, although this implies dispersal and growth across diverse environmental conditions, how locally successful populations assemble from a possibly global gene pool, relatively unhindered by geographic boundaries, remains poorly understood. Here, we show that environmental Vibrio cholerae possesses two, largely distinct gene pools: one is vertically inherited and globally well mixed, and the other is local and rapidly transferred across species boundaries to generate an endemic population structure. While phylogeographic analysis of isolates collected from Bangladesh and the U.S. east coast suggested strong panmixis for protein-coding genes, there was geographic structure in integrons, which are the only genomic islands present in all strains of V. cholerae (Chun J, et al., Proc. Natl. Acad. Sci. U. S. A. 106:15442–15447, 2009) and are capable of acquiring and expressing mobile gene cassettes. Geographic differentiation in integrons arises from high gene turnover, with acquisition from a locally cooccurring sister species being up to twice as likely as exchange with conspecific but geographically distant V. cholerae populations.

Summary1. The fields of phylogenetic tree and network inference have dramatically advanced in the past decade, but independently with few attempts to bridge them. 2. Here we provide a framework, implemented in the PHANGORN library in R, to transfer information between trees and networks. 3. This includes: (i) identifying and labelling equivalent tree branches and network edges, (ii) transferring tree branch support to network edges, and (iii) mapping bipartition support from a sample of trees (e.g. from bootstrapping or Bayesian inference) onto network edges. 4. The ability to readily combine tree and network information should lead to more comprehensive evolutionary comparisons and inferences.

The fields of phylogenetic tree and network inference have dramatically advanced in the last decade, but independently with few attempts to bridge them. Here we provide a framework, implemented in the PHANGORN library in R, to transfer information between trees and networks.This includes: 1) identifying and labelling equivalent tree branches and network edges, 2) transferring branch support to network edges, and 3) mapping bipartition support from a sample of trees (e.g. from bootstrapping or Bayesian inference) onto network edges. The ability to readily combine tree and network information should lead to more comprehensive evolutionary comparisons and conclusions.

Background: Host-associated microbiomes, the microorganisms occurring inside and on host surfaces, influence evolutionary, immunological, and ecological processes. Interactions between host and microbiome affect metabolism and contribute to host adaptation to changing environments. Meta-analyses of hostassociated bacterial communities have the potential to elucidate global-scale patterns of microbial community structure and function. It is possible that host surface-associated (external) microbiomes respond more strongly to variations in environmental factors, whereas internal microbiomes are more tightly linked to host factors. Results: Here, we use the dataset from the Earth Microbiome Project and accumulate data from 50 additional studies totaling 654 host species and over 15,000 samples to examine global-scale patterns of bacterial diversity and function. We analyze microbiomes from non-captive hosts sampled from natural habitats and find patterns with bioclimate and geophysical factors, as well as land use, host phylogeny, and trophic level/ diet. Specifically, external microbiomes are best explained by variations in mean daily temperature range and precipitation seasonality. In contrast, internal microbiomes are best explained by host factors such as phylogeny/immune complexity and trophic level/diet, plus climate. Conclusions: Internal microbiomes are predominantly associated with top-down effects, while climatic factors are stronger determinants of microbiomes on host external surfaces. Host immunity may act on microbiome diversity through top-down regulation analogous to predators in non-microbial ecosystems. Noting gaps in geographic and host sampling, this combined dataset represents a global baseline available for interrogation by future microbial ecology studies.

In biogeography, there is growing interest in the analysis of datasets of ever-increasing size and complexity to explain biodiversity patterns and underlying processes. A common approach is biogeographical regionalization, the grouping of organisms based on shared features and how they respond to past or current physical and biological de