metabolomics-analysis

Processes untargeted and targeted MS data for metabolite identification and pathway analysis.

2|Updated Jun 4, 2026

Search Tags:#data analysis #bioinformatics #metabolomics #mass spectrometry #lc-ms #pathway mapping

Ready to run

npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill metabolomics-analysis

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: metabolomics-analysis
Source: https://github.com/Lord1Egypt/scientific-agent-toolkit/tree/main/scientific-skills/metabolomics-analysis
Command: npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill metabolomics-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyteomics, pandas, numpy, scipy, matplotlib, seaborn, scikit-learn, statsmodels, requests, lxml, matchms, mzmine.

What problem does it solve?

This skill addresses the complexity of processing raw mass spectrometry data, automating the transition from raw spectral files to biological insights like pathway enrichment and differential metabolite identification.

Core Features & Use Cases

Spectral Processing: Automates feature detection, peak picking, and alignment for LC-MS and GC-MS datasets.
Statistical Analysis: Provides built-in support for PCA, PLS-DA, and differential expression testing with FDR correction.
Use Case: Researchers can use this to process raw mzML files, normalize feature matrices, and map significant metabolites to KEGG pathways to identify biomarkers in clinical or experimental samples.

Quick Start

Use the metabolomics-analysis skill to process the raw mzML files in the current directory and generate a differential expression report between the control and treatment groups.

Frequently Asked Questions about metabolomics-analysis

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

FAQPage Schema

How do I process raw mzML files for LC-MS metabolomics data analysis?▼

To process raw mzML files for LC-MS metabolomics data analysis, you can automate feature detection, peak picking, and alignment to generate a normalized feature matrix for statistical modeling. This workflow extracts spectral data for downstream differential expression and pathway enrichment analysis.

What statistical methods are used for differential metabolite identification in mass spectrometry data?▼

Differential metabolite identification in mass spectrometry data commonly uses PCA, PLS-DA, and differential expression testing with FDR correction. These statistical methods identify significant biomarkers between experimental groups from normalized feature matrices.

How does MS2 spectral matching work for metabolite identification?▼

MS2 spectral matching for metabolite identification works by comparing experimental fragmentation spectra against public databases like HMDB and KEGG. This API-based querying matches MS2 spectra to known metabolite structures for automated annotation.

Can I map significant metabolites to KEGG pathways from untargeted metabolomics data?▼

Yes, you can map significant metabolites to KEGG pathways from untargeted metabolomics data by querying public databases after statistical analysis. This pathway mapping identifies biological insights and biomarker candidates from differential expression results.

Do I need scientific Python libraries like pandas and scipy for GC-MS metabolomics workflows?▼

Yes, you need scientific Python libraries like pandas, scipy, numpy, and scikit-learn for GC-MS metabolomics workflows. These dependencies are required for signal processing, statistical modeling, and data normalization of mass spectrometry outputs.

What is the best way to normalize feature matrices for untargeted metabolomics?▼

The best way to normalize feature matrices for untargeted metabolomics is using automated workflows that apply signal processing algorithms to raw spectral files. This normalization step ensures accurate statistical analysis and differential expression testing across samples.