How does Matchms handle spectral similarity?

It provides various algorithms like CosineGreedy, Modified Cosine (which accounts for precursor mass differences), and NeutralLossesCosine to compare spectra.

Can I use this for chemical structure analysis?

Yes, Matchms includes specific filters to derive and repair InChI and SMILES strings, as well as generate molecular fingerprints like Morgan fingerprints.

What file formats does the Matchms skill support?

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

Is Matchms suitable for high-throughput data processing?

Absolutely. It features a SpectrumProcessor class designed to build and apply multi-step analysis pipelines to thousands of spectra efficiently.

Matchms Mass Spectrometry Analysis

Name: Matchms Mass Spectrometry Analysis
Author: Microck

byMicrock

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데이터 과학 및 ML

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

소개

The Matchms skill equips Claude with specialized capabilities for metabolomics and mass spectrometry data processing. It allows users to import spectra from common formats like mzML, MGF, and MSP, apply a wide range of metadata and peak filters, and perform advanced spectral similarity calculations using metrics like Modified Cosine. Ideal for researchers and bioinformaticians, this skill facilitates the building of reproducible workflows for spectrum cleaning, chemical annotation, and library matching, significantly speeding up the analysis of complex chemical data.

주요 기능

Chemical structure standardization using InChI, SMILES, and molecular fingerprints
Reproducible pipeline construction for large-scale metabolomics workflows
Import/export support for mzML, MGF, MSP, and GNPS-compatible JSON formats
81 GitHub stars
Advanced similarity metrics including Cosine, Modified Cosine, and Neutral Losses
40+ spectrum filters for metadata harmonization and peak intensity normalization

사용 사례

Cleaning and standardizing metadata in large-scale mass spectrometry datasets
Identifying unknown compounds by matching query spectra against reference libraries
Calculating spectral similarity networks for chemical clustering and discovery

소개

주요 기능

Chemical structure standardization using InChI, SMILES, and molecular fingerprints
Reproducible pipeline construction for large-scale metabolomics workflows
Import/export support for mzML, MGF, MSP, and GNPS-compatible JSON formats
81 GitHub stars
Advanced similarity metrics including Cosine, Modified Cosine, and Neutral Losses
40+ spectrum filters for metadata harmonization and peak intensity normalization

사용 사례

Cleaning and standardizing metadata in large-scale mass spectrometry datasets
Identifying unknown compounds by matching query spectra against reference libraries
Calculating spectral similarity networks for chemical clustering and discovery