How does Modified Cosine similarity differ from standard Cosine?

Modified Cosine similarity accounts for differences in precursor m/z values, making it significantly more effective for identifying related chemical analogs.

Can I calculate chemical structure similarities?

Yes, Matchms can derive InChI and SMILES metadata to calculate molecular structure similarity using fingerprints like Morgan (requires the chemistry extension).

Is this skill suitable for large-scale spectral library matching?

Absolutely. It is designed to build efficient processing pipelines and score objects capable of handling high-throughput comparison of query spectra against extensive reference libraries.

What file formats does the Matchms skill support?

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

Matchms MS Analysis

Name: Matchms MS Analysis
Author: x-cmd

byx-cmd

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Ciencia de Datos y ML

Processes mass spectrometry data to calculate spectral similarities, harmonize metadata, and identify chemical compounds.

Acerca de

Matchms is an open-source Python library integration for Claude Code designed for sophisticated mass spectrometry data processing and analysis. It allows users to import spectra from various formats like mzML, MGF, and MSP, apply over 40 specialized metadata and peak filters, and execute high-performance similarity calculations such as Modified Cosine and Neutral Losses. This skill is essential for researchers and developers building reproducible metabolomics workflows, performing large-scale spectral library searches, and automating chemical data standardization.

Características Principales

Robust spectrum processing pipelines for peak filtering, normalization, and quality control
Advanced similarity metrics including CosineGreedy, ModifiedCosine, and FingerprintSimilarity
Integration for chemical annotation using SMILES, InChI, and molecular fingerprints
Multi-format I/O support for mzML, MGF, MSP, and GNPS-compatible JSON
8 GitHub stars
Comprehensive metadata harmonization for compound names, adducts, and chemical structures

Casos de Uso

Automated metabolomics data processing and spectral library matching
Standardizing and cleaning large-scale mass spectrometry datasets for research
Identifying unknown chemical compounds through high-throughput similarity comparisons

Acerca de

Características Principales

Robust spectrum processing pipelines for peak filtering, normalization, and quality control
Advanced similarity metrics including CosineGreedy, ModifiedCosine, and FingerprintSimilarity
Integration for chemical annotation using SMILES, InChI, and molecular fingerprints
Multi-format I/O support for mzML, MGF, MSP, and GNPS-compatible JSON
8 GitHub stars
Comprehensive metadata harmonization for compound names, adducts, and chemical structures

Casos de Uso

Automated metabolomics data processing and spectral library matching
Standardizing and cleaning large-scale mass spectrometry datasets for research
Identifying unknown chemical compounds through high-throughput similarity comparisons