matchms

Name: matchms
Availability: InStock
Author: zeyuyang-0420

Process mass spectrometry data and calculate spectral similarities with Python libraries.

Updated May 8, 2026

Search Tags:#metabolomics #mass spectrometry #data processing #metabolite identification #spectral analysis

Ready to run

npx skills add https://github.com/Zeyuyang-0420/bio-ai-research-skills --skill matchms-zeyuyang-0420

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: matchms
Source: https://github.com/Zeyuyang-0420/bio-ai-research-skills/tree/main/categories/drug-discovery-molecular-modeling/matchms
Command: npx skills add https://github.com/Zeyuyang-0420/bio-ai-research-skills --skill matchms-zeyuyang-0420

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matchms, rdkit, numpy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates metabolomics analysis, streamlining mass spectrometry data processing and analysis, and saving researchers valuable time and effort.

Core Features & Use Cases

Mass Spectrometry Data Processing: Efficiently handle mass spectrometry data with features like importing/exporting, spectrum filtering, and processing.
Spectral Similarity Calculation: Compare spectra using various similarity metrics for metabolite identification and spectral matching.
Reproducible Workflows: Build and execute multi-step analysis workflows that can be replicated for consistent results.
Use Case: Imagine you have a set of mass spectrometry data and need to identify unknown compounds from spectral libraries. This Skill can compute similarity scores and match unknown compounds, saving hours of manual analysis.

Quick Start

Use the matchms skill to analyze a mass spectrometry data file 'ms_data.mgf'.

Frequently Asked Questions about matchms

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

FAQPage Schema

How do I automate mass spectrometry data processing for metabolite identification?▼

Automate mass spectrometry data processing by importing spectral files, filtering spectra, and calculating similarity scores against reference libraries. This approach matches unknown compounds and builds reproducible workflows, saving hours of manual analysis.

Can I use Python to calculate spectral similarity for mass spectrometry data?▼

Calculate spectral similarity for mass spectrometry data using Python libraries to compare spectra with various similarity metrics. This process computes similarity scores for metabolite identification and spectral library matching.

What is the best way to build reproducible metabolomics workflows in Python?▼

Build reproducible metabolomics workflows in Python by chaining mass spectrometry data import, spectrum filtering, and spectral similarity calculation steps. This ensures multi-step analysis can be replicated for consistent results.

Do I need rdkit to process chemical structures in metabolomics analysis?▼

Yes, rdkit is required to process chemical structures during metabolomics analysis. It works alongside matchms and numpy to handle mass spectrometry data, calculate spectral similarities, and perform chemical structure processing.

How does spectral library searching work for identifying unknown compounds?▼

Spectral library searching works by calculating similarity metrics between your experimental mass spectrometry spectra and reference library spectra. The computed similarity scores match unknown compounds, automating metabolite identification.

What mass spectrometry data formats can I import and export for metabolomics analysis?▼

Import and export mass spectrometry data formats like MGF files for metabolomics analysis. The process handles spectral data filtering and processing to prepare inputs for reproducible workflows and metabolite identification.