Selected Publications

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.
In Methods in Ecology and Evolution, 2017.

Media coverage

Creators of computer programs that underpin experiments don’t always get their due — so the website Depsy is trying to track the impact of research code.
In Nature, 2016.

Recent Publications

More Publications

Comparison of the Serum Tumor Markers S100 and Melanoma-inhibitory Activity (MIA) in the Monitoring of Patients with Metastatic Melanoma Receiving Vaccination Immunotherapy with Dendritic Cells

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Intertwining phylogenetic trees and networks

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Linking genomics and population genetics with R

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apex: phylogenetics with multiple genes

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Life history linked to immune investment in developing amphibians

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Recent Posts

If data are not sequence alignment an phyDat object then there are generic functions as.phyDat() in phangorn to transform a matrices and data.frames into phyDat objects. For example you can read in your data with read.table() or read.csv(), but you might need to transpose your data. For matrices as.phyDat() assumes that the entries each row belongs to one individual (taxa), but for data.frame each column. For binary data you can transform these with a command like (depending how you coded them):

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As you may have seen a version of ape and phangorn have been released. ape jumped to version 4.0 and you can see the changes here. Some of the nicest new features are that many useful functions for transforming phylo objects have been made generic. This includes functions like is.rooted, unroot, reorder is.binary, is.ultrametric or di2multi and these now work also on multiPhylo objects. In practice this means that instead of typing

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phangorn is a package for R. To use it, you first need to download and install R. RStudio provides a nice user interface for R. You can install the latest release version from CRAN install.packages("phangorn") or install the latest development version using the install_github function in the devtools package from github. library(devtools) install_github("KlausVigo/phangorn") For devtools to work on windows you need addionally to have installed Rtools and on mac you need Xcode to compile some C code.

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Projects

phangorn

Phylogenetic analysis in R

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