Bioconductor Ggtree logo

Bioconductor Ggtree

Organization
bioMate-AI
bioconductor-ggtree

'ggtree' extends the 'ggplot2' plotting system which implemented the grammar of graphics. 'ggtree' is designed for visualization and annotation of phylogenetic trees and other tree-like structures with their annotation data.

Overview

PublisherbioMate-AI
Repositorybiomate-bioconductor-kb
Skill namebioconductor-ggtree
Stars
804
Forks
66
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by bioMate-AI on GitHub. Read the source before you install it.

Installation

Install the Bioconductor Ggtree AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/bioMate-AI/biomate-bioconductor-kb/tree/main/skills/genomics/ggtree
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/bioMate-AI/biomate-bioconductor-kb.git /tmp/biomate-bioconductor-kb
mkdir -p .claude/skills
cp -r /tmp/biomate-bioconductor-kb/skills/genomics/ggtree .claude/skills/bioconductor-ggtree
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bioconductor Ggtree in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Bioconductor Ggtree on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Bioconductor Ggtree is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

ggtree

ggtree extends the ggplot2 plotting system to implement the grammar of graphics for phylogenetic trees. It is designed for the visualization, manipulation, and annotation of phylogenetic trees and other tree-like structures with their associated multi-omics and clinical metadata.

Dependencies & Environment

Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

  • Version: 4.2.0 · Bioconductor: 3.23 · R: ≥ 4.6
  • Imports: ape, aplot, cli, dplyr, ggfun, ggiraph, ggplot2, magrittr, purrr, rlang, scales, tidyr, tidytree, treeio, yulab.utils
  • Install: BiocManager::install("ggtree")

When to Use

  • Visualizing complex phylogenetic trees (e.g., from RAxML, IQ-TREE, or BEAST) integrated with multi-omics metadata (such as microbiome abundance, genomic features, or clinical traits).
  • Annotating tree nodes, clades, and leaves with custom shapes, colors, images, or subplots (e.g., barplots, pie charts) using the grammar of graphics.
  • Displaying circular, radial, rectangular, or slanted tree layouts for large-scale evolutionary studies containing hundreds to thousands of taxa.

When NOT to Use

  • For interactive, web-based tree exploration, use phylocanvas or iTOL instead because ggtree produces static vector graphics.
  • For simple, quick tree plotting without complex annotations, use ape::plot.phylo instead because it has zero overhead and requires no ggplot2 syntax.
  • For highly customized interactive network visualizations that are not strictly hierarchical, use ggnetwork or tidygraph instead because ggtree is optimized specifically for tree topologies.

Data Requirements

  • Input format: Tree objects of class phylo (from ape), treedata (from treeio), or standard R dendrograms.
  • Metadata: A data frame containing a column matching the tip labels of the tree.
  • Scale: Supports trees from 10 to 10,000+ tips, though rendering times scale with taxon count.

Key Parameters

  • layout ("rectangular"): Layout of the tree; options include "circular", "slanted", "fan", "radial", "unrooted", "equal_angle", or "daylight".
  • ladderize (TRUE): Logical; whether to sort the branches to make the tree look more structured.
  • branch.length ("branch.length"): Variable to determine branch lengths; set to "none" for cladograms with equal branch lengths.
  • as.Date (FALSE): Logical; whether to parse the x-axis as dates for time-resolved (chronogram) trees.
  • yscale (NULL): Variable used to scale the y-axis, useful for custom vertical spacing.

Best Practices

  • Use the treeio package to import trees from diverse software formats (e.g., BEAST, MrBayes, NHX) to preserve node-level annotations.
  • Attach external metadata using the %<+% operator to seamlessly map sample traits to tree tips or internal nodes.
  • For large trees, use layout = "circular" or layout = "fan" to maximize space efficiency and prevent text overlap.
  • Adjust label offsets and text sizes dynamically using geom_tiplab(offset = ...) to prevent labels from clipping outside the plot boundaries.

Common Pitfalls

  • Tip labels overlapping or cut off: Occurs when tree margins are too small or labels are long; fix this by adding hexpand() or xlim() to extend the plot canvas.
  • Metadata mapping failure: Occurs when the ID column in the metadata does not match the tip labels exactly; fix this by ensuring row.names or a specific ID column matches tree$tip.label.
  • Slow rendering of large trees: Occurs when adding too many individual geom layers to a tree with >10,000 tips; fix this by collapsing clades using collapse() before plotting.

Alternatives

  • ape: The foundational R phylogenetics package, faster for basic plotting but lacks ggplot2 integration.
  • ggtreeExtra: An extension of ggtree designed specifically for presenting circular layouts with multiple rings of complex genomic data.
  • phyloseq: Excellent for microbiome-specific tree plotting, but less flexible for general phylogenetic annotations than ggtree.
  • tanggle: Specifically designed for visualizing phylogenetic networks rather than strict bifurcating trees.

Citations

  • Yu, G., Smith, D. K., Zhu, H., Guan, Y., & Lam, T. T. Y. (2017). ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods in Ecology and Evolution, 8(1), 28-36.
  • Yu, G. (2022). Integration, Manipulation and Visualization of Phylogenetic Trees (1st ed.). Chapman and Hall/CRC.

References


Run this on BioMate

This skill is the knowledge layer — when, why, and how to use ggtree. To run this analysis on your own data with managed compute, automated QC, and reproducible outputs, use BioMate — free to start.

▶ Open ggtree on BioMate →

Frequently asked questions

What does the Bioconductor Ggtree AI skill do?

'ggtree' extends the 'ggplot2' plotting system which implemented the grammar of graphics. 'ggtree' is designed for visualization and annotation of phylogenetic trees and other tree-like structures with their annotation data.

Why use Bioconductor Ggtree on TypingMind?

Because you install it once and use it with any model. Bioconductor Ggtree is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Bioconductor Ggtree in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bioMate-AI/biomate-bioconductor-kb/tree/main/skills/genomics/ggtree. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Bioconductor Ggtree?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Bioconductor Ggtree?

As many as you like. As long as a model supports skills, you can use Bioconductor Ggtree with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Bioconductor Ggtree AI skill free?

It is published on GitHub by bioMate-AI. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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