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metabolomics-peak-detection

Detect metabolite peaks from LC-MS data using XCMS, MZmine 3 or MS-DIAL and extract features for downstream analysis.

155|26|Updated Mar 14, 2026
Search Tags:#data analysis#metabolomics#feature detection#peak detection#XCMS#MZmine#MS-DIAL
One-click install
npx skills add https://github.com/TianGzlab/OmicsClaw --skill metabolomics-peak-detection
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: metabolomics-peak-detection
Source: https://github.com/TianGzlab/OmicsClaw/tree/main/skills/metabolomics/metabolomics-peak-detection
Command: npx skills add https://github.com/TianGzlab/OmicsClaw --skill metabolomics-peak-detection

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, scipy, and includes scripts (resource) components.

What problem does it solve?

This Skill automates the challenging and time-consuming process of identifying and characterizing metabolite peaks in complex metabolomics data, which is crucial for downstream analysis and discovery.

Core Features & Use Cases

  • Automated Peak Picking: Identifies significant metabolite peaks from raw intensity data.
  • Feature Detection & Alignment: Detects chromatographic features and aligns them across samples.
  • Support for Multiple Tools: Integrates with established metabolomics software like XCMS, MZmine 3, and MS-DIAL.
  • Use Case: Analyze a batch of untargeted metabolomics LC-MS data to detect all potential metabolite features, enabling comparative analysis between different experimental conditions.

Quick Start

Use the metabolomics-peak-detection skill to detect peaks in the provided demo data.

Frequently Asked Questions about metabolomics-peak-detection

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

FAQPage Schema
How do I perform peak picking and feature alignment for untargeted metabolomics LC-MS data?▼

Peak picking and feature alignment for untargeted metabolomics LC-MS data are automated by processing raw intensity traces to identify and group chromatographic features across samples. This Skill handles signal detection using numpy, pandas, and scipy.

What is feature detection in metabolomics and when do I need it?▼

Feature detection in metabolomics is the process of identifying significant metabolite signals from complex raw intensity data. You need it to isolate relevant peaks before performing comparative analysis between different experimental conditions.

Can I use XCMS, MZmine 3, or MS-DIAL workflows for metabolite peak detection here?▼

Yes, metabolite peak detection integrates with established software workflows including XCMS, MZmine 3, and MS-DIAL. It supports both tabular intensity data and raw 1-D intensity traces for automated signal identification.

Do I need Python and scipy installed to run metabolomics feature detection?▼

Yes, you need Python with numpy, pandas, and scipy installed to run metabolomics feature detection. These libraries are required dependencies for signal processing, data manipulation, and executing the underlying scripts.

What's the best way to align chromatographic features across multiple metabolomics samples?▼

The best way to align chromatographic features across metabolomics samples is using automated grouping algorithms that process raw 1-D intensity traces. This ensures accurate comparative analysis by matching corresponding metabolite signals across datasets.

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