An R package for visualizing and annotating phylogenetic trees and other tree-like structures using the grammar of graphics.
ggtree is an R package designed for the visualization and annotation of phylogenetic trees and other tree-like structures. It extends the ggplot2 plotting system to handle hierarchical data, enabling researchers to map associated metadata onto tree branches and nodes. The package solves the problem of integrating complex tree data with annotation datasets for clear, publication-ready visualizations in evolutionary biology and genomics.
Bioinformaticians, evolutionary biologists, genomics researchers, and data scientists working with phylogenetic trees or hierarchical data who need to create annotated visualizations for analysis and publication.
Developers choose ggtree because it combines the familiar and flexible grammar of graphics from ggplot2 with specialized tools for tree data, offering a unified and extensible framework for phylogenetic visualization that is both powerful and accessible within the R/Bioconductor ecosystem.
:christmas_tree:Visualization and annotation of phylogenetic trees
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Leverages the familiar ggplot2 grammar of graphics, allowing for highly customizable plots using standard R plotting syntax, as highlighted in the 'Grammar of Graphics Integration' feature.
Supports mapping of traits, sequences, and metadata directly onto tree branches and nodes, enabling detailed data integration as described in the 'Tree Annotation' feature.
Seamlessly integrates with other Bioconductor packages for end-to-end phylogenetic analysis workflows, mentioned under 'Extensible Design'.
Generates high-resolution figures suitable for scientific publications, ensuring professional visual results as noted in 'Publication-Ready Outputs'.
Exclusively available in R, making it inaccessible for teams standardized on other languages, limiting cross-platform adoption and requiring R expertise.
Produces only static plots with no built-in interactivity, which hinders dynamic data exploration and web-based applications without additional tools.
Requires familiarity with R, ggplot2, and the Bioconductor ecosystem, adding overhead for beginners or researchers from non-computational backgrounds.