What file formats does the Matchms skill support?

The Matchms skill supports common mass spectrometry formats including mzML, mzXML, MGF, MSP, GNPS-compatible JSON, and USI references.

Can this skill help with chemical structure identification?

Yes, it provides filters to derive InChI, SMILES, and molecular fingerprints, and can repair structural metadata mismatches during processing.

Which similarity metrics are available for spectrum comparison?

It includes CosineGreedy, CosineHungarian, ModifiedCosine (accounting for precursor mass shifts), NeutralLossesCosine, and FingerprintSimilarity.

Is it possible to automate multiple processing steps?

Yes, you can define a SpectrumProcessor pipeline that sequentially applies filters, normalization, and quality checks to entire spectral libraries.

Matchms MS Data Processing

Name: Matchms MS Data Processing
Author: lifangda

bylifangda

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Data Science & ML

Processes and analyzes mass spectrometry data using Python-based spectral similarity and metadata harmonization.

About

Matchms is a specialized Claude Code skill designed for researchers and bioinformaticians working with mass spectrometry data in metabolomics and proteomics. It provides a comprehensive toolkit for importing various spectral formats like mzML and MGF, standardizing metadata, and applying advanced peak filters. The skill excels at calculating spectral similarities using metrics such as CosineGreedy and ModifiedCosine, enabling users to match unknown queries against reference libraries. By facilitating the creation of reproducible processing pipelines and chemical annotations, Matchms streamlines the transition from raw spectral data to biological insight.

Key Features

Multi-format spectral data I/O supporting mzML, MGF, MSP, and JSON
Diverse spectral similarity metrics including Modified Cosine and Neutral Losses
Automated processing pipeline construction for reproducible workflows
Advanced metadata harmonization and chemical structure annotation
Comprehensive peak filtering, intensity normalization, and quality control
16 GitHub stars

Use Cases

Building automated pipelines for large-scale spectral clustering and networking
Identifying unknown compounds by matching query spectra against reference libraries
Cleaning and standardizing raw metabolomics datasets for downstream analysis

About

Key Features

Multi-format spectral data I/O supporting mzML, MGF, MSP, and JSON
Diverse spectral similarity metrics including Modified Cosine and Neutral Losses
Automated processing pipeline construction for reproducible workflows
Advanced metadata harmonization and chemical structure annotation
Comprehensive peak filtering, intensity normalization, and quality control
16 GitHub stars

Use Cases

Building automated pipelines for large-scale spectral clustering and networking
Identifying unknown compounds by matching query spectra against reference libraries
Cleaning and standardizing raw metabolomics datasets for downstream analysis