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tooluniverse-metabolomics-analysis

Analyze metabolomics data from identification through pathway enrichment and multi-omics integration.

1.7k|254|Updated Mar 3, 2025
Search Tags:#metabolomics#biomarker discovery#pathway enrichment#multi-omics#differential analysis#LC-MS#Normalization
One-click install
npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-metabolomics-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tooluniverse-metabolomics-analysis
Source: https://github.com/mims-harvard/ToolUniverse/tree/main/plugins/tooluniverse/skills/tooluniverse-metabolomics-analysis
Command: npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-metabolomics-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires tooluniverse, pandas, numpy, scipy, statsmodels, scikit-learn, matplotlib, and includes references (resource) components.

What problem does it solve?

Processing LC-MS, GC-MS, or NMR metabolomics data requires coordinated decisions about metabolite identification, normalization, QC filtering, and statistical testing, where wrong choices (e.g., treating missing values as zeros or ignoring batch effects) produce spurious differential metabolites.

Core Features & Use Cases

  • End-to-End Workflow: Eight phases covering data import, metabolite identification against HMDB/KEGG, QC filtering (CV < 30%, blank ratio > 3x), normalization (TIC, PQN, internal standards), PCA/PLS-DA, differential analysis with FDR correction, and pathway enrichment.
  • Multi-Omics Integration: Correlate metabolite levels with enzyme expression from RNA-seq or proteomics to score pathway dysregulation and infer metabolic phenotypes.
  • Use Case: Given a peak table from an untargeted LC-MS experiment comparing disease and control samples, identify differential metabolites, map them to dysregulated KEGG pathways like glycolysis and the TCA cycle, and build a biomarker panel.

Quick Start

Analyze this LC-MS metabolomics peak table for differential metabolites between disease and control groups and run pathway enrichment on the significant hits.

Frequently Asked Questions about tooluniverse-metabolomics-analysis

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

FAQPage Schema
How do I analyze LC-MS metabolomics data for differential metabolites?▼

Load the peak table, filter features by QC criteria (CV < 30% in QC samples, blank ratio > 3x, < 50% missing), normalize with PQN or TIC, then run Welch's t-test with Benjamini-Hochberg FDR correction. Significance thresholds are adj. p < 0.05 and |log2FC| > 1.0.

Which normalization method should I use for metabolomics data?▼

PQN is recommended as it is robust to large metabolite changes. TIC normalization is simpler but assumes similar total abundance across samples, while internal standard normalization is most accurate when spiked standards were added before sample processing.

How do I identify unknown metabolites from m/z values?▼

Match features to HMDB or KEGG by accurate mass within +/- 5 ppm using tools like MetabolomicsWorkbench_search_by_mz. Assign confidence levels: Level 1 with authentic standards, Level 2 with MS/MS, Level 3 with mass only, and Level 4 for unknowns.

Can metabolomics results be integrated with RNA-seq or proteomics data?▼

Yes, correlate metabolite levels with enzyme expression using Spearman correlation. Expected patterns are negative substrate-enzyme correlation (consumption) and positive product-enzyme correlation (production), enabling pathway-level dysregulation scoring.

What are the limitations of untargeted metabolomics analysis?▼

Many features remain unidentified (Level 4), quantification is relative rather than absolute without standards, structural isomers are hard to distinguish, and ion suppression from matrix effects can distort quantification. Coverage also depends on the analytical method used.

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