What file formats does the Matchms skill support?

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

Does Matchms handle chemical structure data?

Yes, with the [chemistry] dependencies, it can derive InChI and SMILES codes, standardize chemical metadata, and calculate fingerprint-based similarities.

Can I use this skill to identify unknown chemical compounds?

Yes, Matchms allows you to calculate similarity scores between unknown query spectra and reference libraries using advanced algorithms like Modified Cosine.

Is Matchms optimized for large-scale data analysis?

Matchms is built for efficiency, offering fast similarity algorithms (like CosineGreedy) and batch processing capabilities suitable for high-throughput metabolomics.

How do I create a reproducible workflow with this skill?

You can use the SpectrumProcessor class to define a sequence of filters and processing steps that can be applied consistently across entire datasets.

Matchms Mass Spectrometry Analysis

Name: Matchms Mass Spectrometry Analysis
Author: sanand0

bysanand0

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데이터 과학 및 ML

Processes, filters, and analyzes mass spectrometry data for metabolomics research and chemical identification.

소개

Matchms is a powerful library integration designed for scientific research, specifically focusing on the processing and analysis of mass spectrometry data. It enables users to import spectra from various formats such as mzML and MGF, apply over 40 specialized filters for metadata harmonization and peak cleaning, and execute complex spectral similarity comparisons using metrics like Modified Cosine. This skill is essential for researchers building reproducible metabolomics workflows, identifying unknown compounds, and managing large-scale spectral libraries directly within their development environment.

주요 기능

3 GitHub stars
Reproducible analysis pipelines via the SpectrumProcessor class
Chemical annotation support using molecular fingerprints and InChI/SMILES
Over 40 built-in filters for metadata harmonization and peak processing
Multi-format support for mzML, MGF, MSP, and JSON spectral data
Advanced similarity metrics including CosineGreedy and ModifiedCosine

사용 사례

Building high-throughput spectral clustering and networking pipelines
Standardizing and cleaning raw mass spectrometry data for metabolomics
Identifying unknown chemical compounds by matching against reference libraries

소개

주요 기능

3 GitHub stars
Reproducible analysis pipelines via the SpectrumProcessor class
Chemical annotation support using molecular fingerprints and InChI/SMILES
Over 40 built-in filters for metadata harmonization and peak processing
Multi-format support for mzML, MGF, MSP, and JSON spectral data
Advanced similarity metrics including CosineGreedy and ModifiedCosine

사용 사례

Building high-throughput spectral clustering and networking pipelines
Standardizing and cleaning raw mass spectrometry data for metabolomics
Identifying unknown chemical compounds by matching against reference libraries