An extensive and rapidly growing collection of richly annotated phylogenetics data is now available in the NeXML format. NeXML relies on state-of-the-art data exchange technology to provide a format that can be both validated and extended, providing a data quality assurance and adaptability to the future that is lacking in other formats. See Vos et al 2012 for further details on the NeXML format.
RNeXML has been published in the following article:
Boettiger C, Chamberlain S, Vos R and Lapp H (2016). “RNeXML: A Package for Reading and Writing Richly Annotated Phylogenetic, Character, and Trait Data in R.” Methods in Ecology and Evolution, 7, pp. 352-357. doi:10.1111/2041-210X.12469
Although the published version of the article is paywalled, the source of the manuscript, and a much better rendered PDF, are included in this package (in the manuscripts folder). You can also find it freely available on arXiv.
The latest stable release of RNeXML is on CRAN, and can be installed with the usual install.packages("RNeXML") command. Some of the more specialized functionality described in the Vignettes (such as RDF manipulation) requires additional packages which can be installed using:
install.packages("RNeXML", deps=TRUE, repos=c("https://cran.rstudio.com", "http://packages.ropensci.org"))which will also install the development version of the RNeXML package. For most common tasks such as shown here, those additional packages are not required. The development version of RNeXML is also available on Github. With the devtools package installed on your system, RNeXML can be installed using:
library(devtools)
install_github("ropensci/RNeXML")
library(RNeXML)#> Loading required package: apeRead in a nexml file into the ape::phylo format:
f <- system.file("examples", "comp_analysis.xml", package="RNeXML")
nexml <- nexml_read(f)
tr <- get_trees(nexml) # or: as(nexml, "phylo")
plot(tr)
Write an ape::phylo tree into the nexml format:
data(bird.orders)
nexml_write(bird.orders, "test.xml")
#> [1] "test.xml"A key feature of NeXML is the ability to formally validate the construction of the data file against the standard (the lack of such a feature in nexus files had lead to inconsistencies across different software platforms, and some files that cannot be read at all). While it is difficult to make an invalid NeXML file from RNeXML, it never hurts to validate just to be sure:
nexml_validate("test.xml")
#> [1] TRUEExtract metadata from the NeXML file:
birds <- nexml_read("test.xml")
get_taxa(birds)
#> otu label about xsi.type otus
#> 1 ou1 Struthioniformes #ou1 NA os1
#> 2 ou2 Tinamiformes #ou2 NA os1
#> 3 ou3 Craciformes #ou3 NA os1
#> 4 ou4 Galliformes #ou4 NA os1
#> 5 ou5 Anseriformes #ou5 NA os1
#> 6 ou6 Turniciformes #ou6 NA os1
#> 7 ou7 Piciformes #ou7 NA os1
#> 8 ou8 Galbuliformes #ou8 NA os1
#> 9 ou9 Bucerotiformes #ou9 NA os1
#> 10 ou10 Upupiformes #ou10 NA os1
#> 11 ou11 Trogoniformes #ou11 NA os1
#> 12 ou12 Coraciiformes #ou12 NA os1
#> 13 ou13 Coliiformes #ou13 NA os1
#> 14 ou14 Cuculiformes #ou14 NA os1
#> 15 ou15 Psittaciformes #ou15 NA os1
#> 16 ou16 Apodiformes #ou16 NA os1
#> 17 ou17 Trochiliformes #ou17 NA os1
#> 18 ou18 Musophagiformes #ou18 NA os1
#> 19 ou19 Strigiformes #ou19 NA os1
#> 20 ou20 Columbiformes #ou20 NA os1
#> 21 ou21 Gruiformes #ou21 NA os1
#> 22 ou22 Ciconiiformes #ou22 NA os1
#> 23 ou23 Passeriformes #ou23 NA os1
get_metadata(birds)
#> LiteralMeta property datatype content
#> 1 m1 dc:creator xsd:string cboettig
#> 2 <NA> <NA> <NA> <NA>
#> 3 m3 dcterms:bibliographicCitation xsd:string <NA>
#> xsi.type ResourceMeta rel
#> 1 LiteralMeta <NA> <NA>
#> 2 ResourceMeta m2 cc:license
#> 3 LiteralMeta <NA> <NA>
#> href
#> 1 <NA>
#> 2 http://creativecommons.org/publicdomain/zero/1.0/
#> 3 <NA>Add basic additional metadata:
nexml_write(bird.orders, file="meta_example.xml",
title = "My test title",
description = "A description of my test",
creator = "Carl Boettiger <cboettig@gmail.com>",
publisher = "unpublished data",
pubdate = "2012-04-01")
#> [1] "meta_example.xml"By default, RNeXML adds certain metadata, including the NCBI taxon id numbers for all named taxa. This acts a check on the spelling and definitions of the taxa as well as providing a link to additional metadata about each taxonomic unit described in the dataset.
We can also add arbitrary metadata to a NeXML tree by define meta objects:
modified <- meta(property = "prism:modificationDate",
content = "2013-10-04")Advanced use requires specifying the namespace used. Metadata follows the RDFa conventions. Here we indicate the modification date using the prism vocabulary. This namespace is included by default, as it is used for some of the basic metadata shown in the previous example. We can see from this list:
RNeXML:::nexml_namespaces
#> nex
#> "http://www.nexml.org/2009"
#> xsi
#> "http://www.w3.org/2001/XMLSchema-instance"
#> xml
#> "http://www.w3.org/XML/1998/namespace"
#> cdao
#> "http://purl.obolibrary.org/obo/cdao.owl"
#> xsd
#> "http://www.w3.org/2001/XMLSchema#"
#> dc
#> "http://purl.org/dc/elements/1.1/"
#> dcterms
#> "http://purl.org/dc/terms/"
#> ter
#> "http://purl.org/dc/terms/"
#> prism
#> "http://prismstandard.org/namespaces/1.2/basic/"
#> cc
#> "http://creativecommons.org/ns#"
#> ncbi
#> "http://www.ncbi.nlm.nih.gov/taxonomy#"
#> tc
#> "http://rs.tdwg.org/ontology/voc/TaxonConcept#"This next block defines a resource (link), described by the rel attribute as a homepage, a term in the foaf vocabulalry. Becuase foaf is not a default namespace, we will have to provide its URL in the full definition below.
website <- meta(href = "http://carlboettiger.info",
rel = "foaf:homepage")Here we create a history node using the skos namespace. We can also add id values to any metadata element to make the element easier to reference externally:
history <- meta(property = "skos:historyNote",
content = "Mapped from the bird.orders data in the ape package using RNeXML",
id = "meta123")For this kind of richer annotation, it is best to build up our NeXML object sequentially. Frist we will add bird.orders phylogeny to a new phylogenetic object, and then we will add the metadata elements created above to this object. Finally, we will write the object out as an XML file:
birds <- add_trees(bird.orders)
birds <- add_meta(meta = list(history, modified, website),
namespaces = c(skos = "http://www.w3.org/2004/02/skos/core#",
foaf = "http://xmlns.com/foaf/0.1/"),
nexml=birds)
nexml_write(birds,
file = "example.xml")
#> [1] "example.xml"Add taxonomic identifier metadata to the OTU elements:
nex <- add_trees(bird.orders)
nex <- taxize_nexml(nex)NeXML also provides a standard exchange format for handling character data. The R platform is particularly popular in the context of phylogenetic comparative methods, which consider both a given phylogeny and a set of traits. NeXML provides an ideal tool for handling this metadata.
We can load the library, parse the NeXML file and extract both the characters and the phylogeny.
library(RNeXML)
nexml <- read.nexml(system.file("examples", "comp_analysis.xml", package="RNeXML"))
traits <- get_characters(nexml)
tree <- get_trees(nexml)(Note that get_characters would return both discrete and continuous characters together in the same data.frame, but we use get_characters_list to get separate data.frames for the continuous characters block and the discrete characters block).
We can then fire up geiger and fit, say, a Brownian motion model the continuous data and a Markov transition matrix to the discrete states:
library(geiger)
fitContinuous(tree, traits[1], ncores=1)
#> GEIGER-fitted comparative model of continuous data
#> fitted 'BM' model parameters:
#> sigsq = 1.166011
#> z0 = 0.255591
#>
#> model summary:
#> log-likelihood = -20.501183
#> AIC = 45.002367
#> AICc = 46.716652
#> free parameters = 2
#>
#> Convergence diagnostics:
#> optimization iterations = 100
#> failed iterations = 0
#> frequency of best fit = 1.00
#>
#> object summary:
#> 'lik' -- likelihood function
#> 'bnd' -- bounds for likelihood search
#> 'res' -- optimization iteration summary
#> 'opt' -- maximum likelihood parameter estimates
fitDiscrete(tree, traits[2], ncores=1)
#> GEIGER-fitted comparative model of discrete data
#> fitted Q matrix:
#> 0 1
#> 0 -0.07308302 0.07308302
#> 1 0.07308302 -0.07308302
#>
#> model summary:
#> log-likelihood = -4.574133
#> AIC = 11.148266
#> AICc = 11.648266
#> free parameters = 1
#>
#> Convergence diagnostics:
#> optimization iterations = 100
#> failed iterations = 0
#> frequency of best fit = 1.00
#>
#> object summary:
#> 'lik' -- likelihood function
#> 'bnd' -- bounds for likelihood search
#> 'res' -- optimization iteration summary
#> 'opt' -- maximum likelihood parameter estimates