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Gene tRee Ordination Visualization for ExplorationS

🌲 groves 🌳 is an R package for visualizing a set of gene-level phylogenies.

If you are studying microbial evolution, you will often make a phylogenomic tree (typically using concatenation of gene-level alignments) to summarize the evolutionary relationships between a set of genomes. However, genes that are subject to different evolutionary pressures can have distinct evolutionary histories. For this reason, it can be useful to investigate gene-level phylogenetic trees in addition to genome-level phylogenomic trees.

This package provides a visualization method to compare a set of gene-level trees and a phylogenomic tree. Specifically, it will produce a two-dimensional scatterplot, in which each point represents a tree, that preserves as much variation between the trees as possible. This can be used to identify genes with outlying evolutionary histories, compare sets of genes, or even highlight anomalies in the phylogenomic preprocessing process.

groves is available through GitHub and can be installed with the following.

if (!require("remotes", quietly = TRUE))
    install.packages("remotes") # check that remotes is installed
remotes::install_github("statdivlab/groves") 
library(groves)

This R package primarily produces visualizations of sets of phylogenetic trees. These visualizations can be produced from functions in this R package (see this vignette for an example analysis) or from a shiny app (run the function groves::run_shiny()).

Because many analyses don’t use gene-level trees, you may not be familiar with methods to generate them. See this vignette for a workflow to go from genome fasta files to a refined set of gene trees in which all retained genes are included in all retained genomes.

If you use groves in your work, please cite our paper:

Sarah Teichman, Michael D. Lee, and Amy D. Willis. (2023). Analyzing microbial evolution through gene and genome phylogenies. Biostatistics, https://doi.org/10.1093/biostatistics/kxad025.

An open-acess preprint is available on bioRxiv.

If you have any issues using our software or further questions please submit an issue here.