What is the benefit of using the Modified Cosine similarity?

The Modified Cosine metric is specifically designed to account for differences in precursor m/z, allowing for better identification of related compounds with mass shifts.

How does the SpectrumProcessor help in metabolomics?

It allows you to define a standardized sequence of filters and normalization steps that can be applied consistently to thousands of spectra for reproducible results.

What mass spectrometry file formats are supported?

Matchms supports a wide range of formats including mzML, mzXML, MGF (Mascot Generic Format), MSP (spectral library format), and GNPS-compatible JSON.

Is Matchms suitable for large-scale data analysis?

Yes, Matchms is optimized for performance and provides vectorized scoring methods suitable for comparing large query sets against massive spectral libraries.

Can Claude use Matchms to identify chemical structures?

Yes, Matchms includes filters to derive InChI and InChIKey from SMILES strings and can calculate structural similarities using molecular fingerprints.

Matchms Mass Spectrometry Analysis

Name: Matchms Mass Spectrometry Analysis
Author: ricable

byricable

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데이터 과학 및 ML

Processes and analyzes mass spectrometry data using standardized metadata, peak filtering, and advanced spectral similarity metrics.

소개

Matchms is a powerful Python library integration for Claude designed specifically for scientific data processing in the field of mass spectrometry and metabolomics. It allows Claude to automate the ingestion of various spectral formats such as mzML and MGF, apply over 40 different filters for data cleaning, and calculate complex spectral similarities like modified cosine scores. This skill is essential for researchers and developers who need to build reproducible analytical workflows, identify chemical compounds, or perform large-scale spectral clustering within their AI-assisted development environment.

주요 기능

Extensive metadata harmonization and chemical structure derivation (SMILES, InChI)
Scalable processing pipelines using the SpectrumProcessor class
1 GitHub stars
Built-in peak filtering, normalization, and quality control validation
Comprehensive I/O support for mzML, MGF, MSP, and GNPS-compatible JSON formats
Advanced similarity metrics including CosineGreedy and ModifiedCosine algorithms

사용 사례

Standardizing and cleaning raw experimental metadata for scientific publication
Building reproducible metabolomics data processing and clustering pipelines
Automating the matching of unknown query spectra against large reference libraries

소개

주요 기능

Extensive metadata harmonization and chemical structure derivation (SMILES, InChI)
Scalable processing pipelines using the SpectrumProcessor class
1 GitHub stars
Built-in peak filtering, normalization, and quality control validation
Comprehensive I/O support for mzML, MGF, MSP, and GNPS-compatible JSON formats
Advanced similarity metrics including CosineGreedy and ModifiedCosine algorithms

사용 사례

Standardizing and cleaning raw experimental metadata for scientific publication
Building reproducible metabolomics data processing and clustering pipelines
Automating the matching of unknown query spectra against large reference libraries