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tooluniverse-metabolomics-analysis
Maintainer FreedomIntelligence · Last updated April 1, 2026
Analyze metabolomics data including metabolite identification, quantification, pathway analysis, and metabolic flux. Processes LC-MS, GC-MS, NMR data from targeted and untargeted experiments. Performs normalization, statistical analysis, pathway enrichment, metabolite-enzyme integration, and biomarker discovery. Use when analyzing metabolomics datasets, identifying differential metabolites, studying metabolic pathwa….
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/tooluniverse-metabolomics-analysis
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- Comprehensive analysis of metabolomics data from metabolite identification through quantification, statistical analysis, pathway interpretation, and integration with other omics layers.
- User has metabolomics data (LC-MS, GC-MS, NMR).
- Questions about metabolite abundance or concentrations.
- Differential metabolite analysis requests.
- Metabolic pathway analysis.
Source Doc
Excerpt From SKILL.md
Core Capabilities
| Capability | Description |
|---|---|
| Data Import | LC-MS, GC-MS, NMR, targeted/untargeted platforms |
| Metabolite Identification | Match to HMDB, KEGG, PubChem, spectral libraries |
| Quality Control | Peak quality, blank subtraction, internal standard normalization |
| Normalization | Probabilistic quotient, total ion current, internal standards |
| Statistical Analysis | Univariate and multivariate (PCA, PLS-DA, OPLS-DA) |
| Differential Analysis | Identify significant metabolite changes |
| Pathway Enrichment | KEGG, Reactome, BioCyc metabolic pathway analysis |
| Metabolite-Enzyme Integration | Correlate with expression data |
| Flux Analysis | Metabolic flux balance analysis (FBA) |
| Biomarker Discovery | Multi-metabolite signatures |
Phase 1: Data Import & Metabolite Identification
Objective: Load data and identify metabolites from features.
Supported data types:
- Peak tables: Pre-processed metabolite abundance
- Raw spectra: LC-MS (.mzML, .mzXML), GC-MS
- NMR spectra: 1D/2D NMR data
Peak table format (typical):
Data loading:
Metabolite identification:
Confidence scoring:
Phase 2: Quality Control & Filtering
Objective: Remove low-quality features and background noise.
Quality control metrics:
Use cases
- User has metabolomics data (LC-MS, GC-MS, NMR).
- Questions about metabolite abundance or concentrations.
- Differential metabolite analysis requests.
- Metabolic pathway analysis.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
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