Does this skill help with data cleaning?

Absolutely. It includes over 40 filters to normalize intensities, remove noise peaks, and harmonize metadata like compound names and precursor m/z.

Can I use Matchms for compound identification?

Yes, Matchms is designed for compound ID by calculating similarity scores between query spectra and reference libraries using metrics like Modified Cosine.

Can I build automated analysis pipelines?

Yes, you can use the SpectrumProcessor class to define a sequence of filters and apply them consistently across thousands of spectra.

What file formats are supported by this Matchms skill?

The Matchms skill supports a wide range of formats including mzML, mzXML, MGF, MSP, JSON (GNPS-compatible), and USI references.

Matchms Mass Spectrometry Analysis

Name: Matchms Mass Spectrometry Analysis
Author: ovachiever

byovachiever

•

Data Science & ML

Processes, filters, and analyzes mass spectrometry data using the matchms Python library for metabolomics and chemical discovery.

About

The Matchms skill for Claude Code integrates the powerful matchms Python library into your development workflow, enabling advanced mass spectrometry data processing. It allows you to import spectral data from various formats like mzML and MGF, apply over 40 specialized filters for metadata harmonization and peak normalization, and calculate complex spectral similarities using algorithms such as Modified Cosine and Neutral Losses. This skill is ideal for researchers and bioinformaticians building reproducible metabolomics pipelines, matching unknown spectra against reference libraries, or performing large-scale chemical clustering and annotation within a coding environment.

Key Features

Multi-format support for importing and exporting mzML, MGF, MSP, and JSON spectral files
Comprehensive filtering suite for metadata harmonization and peak intensity normalization
Advanced similarity scoring including CosineGreedy, ModifiedCosine, and NeutralLosses
Reproducible SpectrumProcessor pipelines for automated, multi-step analysis workflows
Chemical annotation capabilities for deriving fingerprints, InChI, and SMILES strings
5 GitHub stars

Use Cases

Building large-scale spectral networks for metabolomics clustering and visualization
Matching unknown experimental spectra against reference libraries for compound identification
Automating the standardization and cleaning of large mass spectrometry datasets

About

Key Features

Multi-format support for importing and exporting mzML, MGF, MSP, and JSON spectral files
Comprehensive filtering suite for metadata harmonization and peak intensity normalization
Advanced similarity scoring including CosineGreedy, ModifiedCosine, and NeutralLosses
Reproducible SpectrumProcessor pipelines for automated, multi-step analysis workflows
Chemical annotation capabilities for deriving fingerprints, InChI, and SMILES strings
5 GitHub stars

Use Cases

Building large-scale spectral networks for metabolomics clustering and visualization
Matching unknown experimental spectra against reference libraries for compound identification
Automating the standardization and cleaning of large mass spectrometry datasets