Can I use this for compound identification?

Yes, Matchms allows you to match query spectra against reference libraries using various similarity metrics like CosineGreedy and ModifiedCosine.

What file formats does the Matchms skill support?

It supports standard mass spectrometry formats including mzML, mzXML, MGF, MSP, JSON (GNPS-compatible), and USI references.

Does it handle chemical structures?

Yes, it can derive InChI and SMILES from metadata, repair structural mismatches, and add molecular fingerprints for similarity analysis.

How do I install the necessary dependencies?

You can install the core library using 'uv pip install matchms' or include chemistry support with 'uv pip install matchms[chemistry]'.

Mass Spectrometry Analysis (Matchms)

Name: Mass Spectrometry Analysis (Matchms)
Author: K-Dense-AI

byK-Dense-AI

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2,188

Data Science & ML

Processes and analyzes mass spectrometry data using the Matchms library for spectral similarity and metadata harmonization.

About

Matchms provides a comprehensive Python-based toolkit for mass spectrometry data processing, enabling users to import spectra from multiple formats like mzML and MGF, apply standard filters, and calculate complex spectral similarities. This skill is essential for metabolomics researchers and data scientists who need to build reproducible analytical workflows, standardize metadata, and identify compounds through library matching or molecular fingerprints. It bridges the gap between raw spectral data and actionable chemical insights within the Claude environment.

Key Features

Apply 40+ metadata harmonization and peak filtering functions for data cleaning
Calculate spectral similarity using Cosine, Modified Cosine, and Neutral Loss metrics
Build reproducible multi-step processing pipelines with SpectrumProcessor
2,188 GitHub stars
Import and export diverse spectral formats including mzML, MGF, MSP, and JSON
Derive chemical structures (InChI, SMILES) and generate molecular fingerprints

Use Cases

Automating high-throughput mass spectrometry data processing and quality control
Matching experimental spectra against reference libraries for compound identification
Cleaning and standardizing large-scale metabolomics datasets for downstream analysis

About

Key Features

Apply 40+ metadata harmonization and peak filtering functions for data cleaning
Calculate spectral similarity using Cosine, Modified Cosine, and Neutral Loss metrics
Build reproducible multi-step processing pipelines with SpectrumProcessor
2,188 GitHub stars
Import and export diverse spectral formats including mzML, MGF, MSP, and JSON
Derive chemical structures (InChI, SMILES) and generate molecular fingerprints

Use Cases

Automating high-throughput mass spectrometry data processing and quality control
Matching experimental spectra against reference libraries for compound identification
Cleaning and standardizing large-scale metabolomics datasets for downstream analysis