Intertwining phylogenetic trees and networks
Author and article information
Abstract
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.
Cite this as
2016. Intertwining phylogenetic trees and networks. PeerJ Preprints 4:e2054v1 https://doi.org/10.7287/peerj.preprints.2054v1Author comment
This manuscript deals with a major update to the phangorn R library which focuses on objects and functions to transfer information between phylogenetic tree and network analyses. It has been submitted to a peer reviewed journal.
Sections
Additional Information
Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Klaus Schliep analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.
Alastair Alastair Potts conceived and designed the experiments, analyzed the data, wrote the paper, reviewed drafts of the paper.
David A Morrison wrote the paper, reviewed drafts of the paper.
Guido W Grimm conceived and designed the experiments, analyzed the data, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.
Data Deposition
The following information was supplied regarding data availability:
The new methods are available in the phangorn library in R.
Thus it is available from https://cran.r-project.org/ and phangorn is also managed on github (https://github.com/KlausVigo/phangorn).
Funding
This work was supported in part by a grant from the National Science Foundation (DEB 1350474 to KS); the Austrian Science Fund FWF (M1751-B16 to GWG); and the National Research Foundation (RCA13091944022 to AJP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
