scientific-metabolomics-network

Construct metabolite interaction networks from KEGG/Reactome data.

3|1|Updated Feb 11, 2026

Search Tags:#metabolomics #network-analysis #reactome #pathway-enrichment #kegg #ggm #hub-metabolites

Ready to run

npx skills add https://github.com/nahisaho/satori --skill scientific-metabolomics-network

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scientific-metabolomics-network
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-metabolomics-network
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-metabolomics-network

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Constructs metabolite interaction networks from KEGG/Reactome data, enabling the discovery of relationships and network-level insights in metabolomics studies.

Core Features & Use Cases

KEGG/Reactome pathway graph construction for metabolite networks.
Metabolite correlation and partial correlation analysis (GGM / thresholded graphs) to identify connections.
Hub metabolite identification and integrated pathway-enrichment reporting (MetaboAnalyst).

Quick Start

Provide a metabolomics dataset and ask the tool to build a network, identify hub metabolites, and report enriched pathways.

Frequently Asked Questions about scientific-metabolomics-network

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build a metabolite interaction network from KEGG and Reactome data?▼

To build a metabolite network from KEGG and Reactome data, provide your metabolomics dataset to construct network graphs using glasso or pearson correlation methods, revealing metabolic relationships through reproducible Python code.

What is a hub metabolite and how do I identify it in a metabolomics dataset?▼

Hub metabolites are highly connected nodes within a metabolite network. You identify them by constructing a thresholded graph or GGM from your dataset and running hub detection analysis to pinpoint metabolites with high connectivity.

Can I perform pathway enrichment analysis directly on metabolite correlation networks?▼

Yes, you can perform integrated pathway enrichment reporting on your metabolite correlation networks. The tool combines network construction with MetaboAnalyst enrichment to report enriched pathways across diverse metabolomics datasets.

Does this tool use Gaussian Graphical Models for partial correlation network construction?▼

Yes, the tool uses Gaussian Graphical Models (GGM) for partial correlation network construction. It applies glasso or pearson methods to threshold graphs and construct metabolite interaction networks from your data.

What is the best way to integrate Reactome pathway graphs with metabolomics data?▼

The best way to integrate Reactome pathway graphs with metabolomics data is to construct pathway graphs directly from Reactome, then overlay your metabolite correlations to identify network-level insights and metabolic relationships.

Are there limitations when using thresholded graphs for metabolite correlation analysis?▼

Thresholded graphs for metabolite correlation analysis rely on accurate input data and chosen thresholds. Use glasso for partial correlations to mitigate indirect connections, but network accuracy depends on dataset quality and parameter tuning.