Bioconductor Kegggraph logo

Bioconductor Kegggraph

Organization
bioMate-AI
bioconductor-kegggraph

KEGGGraph is an interface between KEGG pathway and graph object as well as a collection of tools to analyze, dissect and visualize these graphs. It parses the regularly updated KGML (KEGG XML) files into graph models maintaining all essenti

Overview

PublisherbioMate-AI
Repositorybiomate-bioconductor-kb
Skill namebioconductor-kegggraph
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 Kegggraph 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/annotation/kegggraph
  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/annotation/kegggraph .claude/skills/bioconductor-kegggraph
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bioconductor Kegggraph 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 Kegggraph 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 Kegggraph 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.

KEGGgraph

Dependencies & Environment

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

  • Version: 1.72.0 · Bioconductor: 3.23 · R: ≥ 4.6
  • Imports: XML, graph, RCurl, Rgraphviz
  • Install: BiocManager::install("KEGGgraph")

When to Use

  • KGML Parsing: Parsing local or remote KGML (KEGG XML) files into standard R graphNEL objects using parseKGML2Graph.
  • Pathway Dissection: Subsetting complex pathways into smaller subgraphs based on node types (using subGraphByNodeType) or specific neighborhoods (using subKEGGgraph).
  • Graph Merging: Combining multiple related pathways (e.g., signaling pathways) into a single unified network using mergeGraphs.
  • Topology Analysis: Calculating graph characteristics like in-degrees, out-degrees, or betweenness centrality (via brandes.betweenness.centrality from RBGL) on biological pathways.

When NOT to Use

  • For dynamic visualization, interactive navigation, and manual editing of KEGG pathway diagrams, use KGML-ED instead.
  • For performing Signaling Pathway Impact Analysis directly without manual graph manipulation, use SPIA instead.
  • For modern ggplot2-based visualization of KEGG pathways, use ggkegg instead because KEGGgraph relies on Rgraphviz for rendering.

Data Requirements

  • KGML Files: Local KGML files or a valid internet connection to fetch them remotely via retrieveKGML.
  • Node Identifiers: Input data must align with KEGG standards (e.g., Entrez Gene IDs) to map correctly to the parsed graph nodes.

Key Parameters

  • genesOnly (FALSE): Logical passed to parseKGML2Graph; if TRUE, non-gene nodes (like compounds or maps) are removed from the graph.
  • expandGenes (TRUE): Logical passed to parseKGML2Graph or KEGGpathway2Graph; if TRUE, nodes representing multiple homologues are topologically expanded.
  • organism ("hsa"): Three-letter KEGG organism code used in retrieveKGML.
  • destfile: File path to save the remotely retrieved KGML file when using retrieveKGML.

Best Practices

  • Expand Gene Nodes: Set expandGenes=TRUE when parsing to ensure that nodes representing multiple gene products (homologues) are expanded into individual nodes.
  • Merge Related Pathways: Use mergeGraphs to union related pathways together, which helps resolve disconnected nodes that lack edges in a single specific disease pathway.
  • Translate Identifiers: Use translateGeneID2KEGGID to map Entrez Gene IDs from microarray data to KEGG IDs before performing graph operations.
  • Subset for Clarity: Use subKEGGgraph to divide and conquer large pathways, maintaining KEGG information while subsetting the graph.

Common Pitfalls

  • Disconnected Nodes: Disease pathways often contain genes with a degree of 0 because their interaction partners are in other pathways; fix this by using mergeGraphs to combine linked pathways.
  • Grouped Homologues: A single node in KEGG may represent multiple homologues (e.g., MAPK1 and MAPK3); fix this by setting expandGenes=TRUE during parsing.
  • Identifier Mismatch: Microarray data uses Entrez IDs but the graph uses KEGG IDs; fix this by using translateGeneID2KEGGID or translateKEGGID2GeneID.

Alternatives

  • SPIA: A package implementing the Signaling Pathway Impact Analysis algorithm that internally uses pathway topology but requires less manual graph manipulation.
  • KGML-ED: A standalone tool designed for the dynamic visualization, interactive navigation, and editing of KEGG pathway diagrams.

Citations

  • Zhang, J. D., & Wiemann, S. (2009). KEGGgraph: a graph approach to KEGG PATHWAY in R and Bioconductor. Bioinformatics, 25(11), 1470-1471.

References


Run this on BioMate

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

▶ Open kegggraph on BioMate →

Frequently asked questions

What does the Bioconductor Kegggraph AI skill do?

KEGGGraph is an interface between KEGG pathway and graph object as well as a collection of tools to analyze, dissect and visualize these graphs. It parses the regularly updated KGML (KEGG XML) files into graph models maintaining all essenti

Why use Bioconductor Kegggraph on TypingMind?

Because you install it once and use it with any model. Bioconductor Kegggraph 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 Kegggraph in TypingMind?

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

Which AI models can use Bioconductor Kegggraph?

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 Kegggraph?

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

Is the Bioconductor Kegggraph 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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