Is Matchms suitable for high-throughput analysis?

Absolutely. Matchms is designed for efficiency, featuring fast algorithms like CosineGreedy and a SpectrumProcessor class for building batch-processing pipelines.

How does Matchms handle metadata standardization?

It includes over 40 filters that automatically harmonize compound names, correct charges, standardize adducts, and derive chemical structures like InChI and SMILES.

Can Matchms help with compound identification?

Yes, it facilitates compound identification by calculating spectral similarities between query spectra and reference libraries using metrics like CosineGreedy and ModifiedCosine.

Do I need extra dependencies for chemical structural analysis?

For processing molecular structures (SMILES, InChI, and fingerprints), you should ensure the chemistry-enabled version is used by installing 'matchms[chemistry]'.

What file formats does Matchms support?

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

Matchms

Name: Matchms
Author: K-Dense-AI

byK-Dense-AI

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3,718

Data Science & ML

Processes and analyzes mass spectrometry data using advanced spectral similarity metrics and automated metadata harmonization.

About

Matchms is a specialized scientific skill for Claude that integrates the powerful Python library for mass spectrometry data processing. It enables researchers and developers to import spectra from various formats like mzML, MGF, and MSP, apply a suite of over 40 metadata filters, and calculate complex spectral similarities such as Modified Cosine scores. This skill is essential for metabolomics workflows, providing a standardized environment for cleaning data, matching unknown compounds against libraries, and building reproducible analytical pipelines directly within the AI interface.

Key Features

Advanced similarity metrics including Cosine and Modified Cosine
Comprehensive file support for mzML, MGF, MSP, and JSON formats
3,718 GitHub stars
Over 40 filters for metadata harmonization and peak processing
Chemical structure derivation for InChI, SMILES, and molecular fingerprints
Automated processing pipeline construction for reproducible research

Use Cases

Identifying unknown compounds by matching query spectra against reference libraries
Preprocessing and cleaning raw mass spectrometry data for metabolomics research
Building automated, high-throughput data processing workflows for large spectral datasets

About

Key Features

Advanced similarity metrics including Cosine and Modified Cosine
Comprehensive file support for mzML, MGF, MSP, and JSON formats
3,718 GitHub stars
Over 40 filters for metadata harmonization and peak processing
Chemical structure derivation for InChI, SMILES, and molecular fingerprints
Automated processing pipeline construction for reproducible research

Use Cases

Identifying unknown compounds by matching query spectra against reference libraries
Preprocessing and cleaning raw mass spectrometry data for metabolomics research
Building automated, high-throughput data processing workflows for large spectral datasets