Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.

/pdf/phyloseq-an-r-package-for-reproducible-interactive-analysis-1ez1xx3dlr.pdf

phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data.

Summary
1. Here, I present a new, multifunctional phylogenetics package, phytools, for the R statistical computing environment.

2. The focus of the package is on methods for phylogenetic comparative biology; however, it also includes tools for tree inference, phylogeny input/output, plotting, manipulation and several other tasks.

3. I describe and tabulate the major methods implemented in phytools, and in addition provide some demonstration of its use in the form of two illustrative examples.

4. Finally, I conclude by briefly describing an active web-log that I use to document present and future developments for phytools. I also note other web resources for phylogenetics in the R computational environment.

/pdf/phytools-an-r-package-for-phylogenetic-comparative-biology-yli0omoidd.pdf

phytools: an R package for phylogenetic comparative biology (and other things)

Summary: While the R software is becoming a standard for the analysis of genetic data, classical population genetics tools are being challenged by the increasing availability of genomic sequences. Dedicated tools are needed for harnessing the large amount of information generated by next-generation sequencing technologies. We introduce new tools implemented in the adegenet 1.3-1 package for handling and analyzing genome-wide single nucleotide polymorphism (SNP) data. Using a bit-level coding scheme for SNP data and parallelized computation, adegenet enables the analysis of large genome-wide SNPs datasets using standard personal computers.

Availability:adegenet 1.3-1 is available from CRAN: http://cran.r-project.org/web/packages/adegenet/. Information and support including a dedicated forum of discussion can be found on the adegenet website: http://adegenet.r-forge.r-project.org/. adegenet is released with a manual and four tutorials totalling over 300 pages of documentation, and distributed under the GNU General Public Licence (≥2).

Contact: t.jombart@imperial.ac.uk

Supplementary Information:Supplementary data are available at Bioinformatics online.

adegenet 1.3-1

Many microbial, fungal, or oomcyete populations violate assumptions for population genetic analysis because these populations are clonal, admixed, partially clonal, and/or sexual. Furthermore, few tools exist that are specifically designed for analyzing data from clonal populations, making analysis difficult and haphazard. We developed the R package poppr providing unique tools for analysis of data from admixed, clonal, mixed, and/or sexual populations. Currently, poppr can be used for dominant/codominant and haploid/diploid genetic data. Data can be imported from several formats including GenAlEx formatted text files and can be analyzed on a user-defined hierarchy that includes unlimited levels of subpopulation structure and clone censoring. New functions include calculation of Bruvo’s distance for microsatellites, batch-analysis of the index of association with several indices of genotypic diversity, and graphing including dendrograms with bootstrap support and minimum spanning networks. While functions for genotypic diversity and clone censoring are specific for clonal populations, several functions found in poppr are also valuable to analysis of any populations. A manual with documentation and examples is provided. Poppr is open source and major releases are available on CRAN: http://cran.r-project.org/package=poppr. More supporting documentation and tutorials can be found under ‘resources’ at: http://grunwaldlab.cgrb.oregonstate.edu/.

/pdf/poppr-an-r-package-for-genetic-analysis-of-populations-with-24k9jtq97d.pdf

Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction.

An ordered draft sequence of the 17-gigabase hexaploid bread wheat (Triticum aestivum) genome has been produced by sequencing isolated chromosome arms. We have annotated 124,201 gene loci distributed nearly evenly across the homeologous chromosomes and subgenomes. Comparative gene analysis of wheat subgenomes and extant diploid and tetraploid wheat relatives showed that high sequence similarity and structural conservation are retained, with limited gene loss, after polyploidization. However, across the genomes there was evidence of dynamic gene gain, loss, and duplication since the divergence of the wheat lineages. A high degree of transcriptional autonomy and no global dominance was found for the subgenomes. These insights into the genome biology of a polyploid crop provide a springboard for faster gene isolation, rapid genetic marker development, and precise breeding to meet the needs of increasing food demand worldwide.

A chromosome-based draft sequence of the hexaploid bread wheat (Triticum aestivum) genome

Summary After more than fifteen years of existence, the R package ape has continuously grown its contents, and has been used by a growing community of users The release of version 50 has marked a leap towards a modern software for evolutionary analyses Efforts have been put to improve efficiency, flexibility, support for 'big data' (R's long vectors), ease of use and quality check before a new release These changes will hopefully make ape a useful software for the study of biodiversity and evolution in a context of increasing data quantity Availability and implementation ape is distributed through the Comprehensive R Archive Network: http://cranr-projectorg/package=ape Further information may be found at http://ape-packageirdfr/

/pdf/ape-5-0-an-environment-for-modern-phylogenetics-and-mon2ib4h2c.pdf

ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R.

Summary: phangorn 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. phangorn, 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, phangorn offers several functions for comparing trees, phylogenetic models or splits, simulating character data and performing congruence analyses.

Availability: phangorn can be obtained through the CRAN homepage http://cran.r-project.org/web/packages/phangorn/index.html. phangorn is licensed under GPL 2.

Contact: rf.ueissuj.vns@peilhcsk.sualk

Supplementary information: Supplementary data are available at Bioinformatics online.

/pdf/phangorn-phylogenetic-analysis-in-r-4pzxsv43go.pdf

phangorn: Phylogenetic analysis in R

In the field of statistical discrimination k-nearest neighbor classification is a well-known, easy and successful method. In this paper we present an extended version of this technique, where the distances of the nearest neighbors can be taken into account. In this sense there is a close connection to LOESS, a local regression technique. In addition we show possibilities to use nearest neighbor for classification in the case of an ordinal class structure. Empirical studies show the advantages of the new techniques.

/pdf/weighted-k-nearest-neighbor-techniques-and-ordinal-2t902o4set.pdf

Weighted k-Nearest-Neighbor Techniques and Ordinal Classification

Summary
The fields of phylogenetic tree and network inference have dramatically advanced in the past 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: (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.
The ability to readily combine tree and network information should lead to more comprehensive evolutionary comparisons and inferences.

https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.12760

Intertwining phylogenetic trees and networks

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 co-occurring sister species being up to twice as likely as exchange with conspecific but geographically distant V. cholerae populations. IMPORTANCE Functional predictions of integron genes show the predominance of secondary metabolism and cell surface modification, which is consistent with a role in competition and predation defense. We suggest that the integron gene pool's distinctness and tempo of sharing are adaptive in allowing rapid conversion of genomes to reflect local ecological constraints. Because the integron is frequently the main element differentiating clinical strains (Chun J, et al., Proc. Natl. Acad. Sci. U. S. A. 106:15442-15447, 2009) and its recombinogenic activity is directly stimulated by environmental stresses (Guerin E, et al., Science 324:1034, 2009), these observations are relevant for local emergence and subsequent dispersal.

https://journals.asm.org/doi/pdf/10.1128/mBio.00335-10

Klaus Schliep

Papers

ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R.

phangorn: Phylogenetic analysis in R

Weighted k-Nearest-Neighbor Techniques and Ordinal Classification

Intertwining phylogenetic trees and networks

Local Mobile Gene Pools Rapidly Cross Species Boundaries To Create Endemicity within Global Vibrio cholerae Populations