What file formats does the Matchms skill support?

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

Is it suitable for large-scale library matching?

Absolutely. Matchms is optimized for building efficient, reproducible pipelines that match query spectra against large reference databases using greedy or Hungarian algorithms.

Does Matchms handle metadata normalization?

Yes, it features over 40 filters designed to harmonize compound names, standardize adducts, correct charges, and validate precursor m/z data.

Can I use this for chemical structure analysis?

Yes, the skill includes specialized tools for deriving InChI and SMILES, as well as calculating molecular fingerprint similarities for chemical annotation.

Matchms Spectrometry Analysis

Name: Matchms Spectrometry Analysis
Author: pur3v4d3r

bypur3v4d3r

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데이터 과학 및 ML

Processes and analyzes mass spectrometry data using Python for metabolomics and chemical identification.

소개

Matchms provides a comprehensive open-source Python framework for mass spectrometry data processing, allowing users to import spectra from various formats like mzML, MGF, and MSP. It streamlines complex scientific workflows by offering over 40 metadata filters, peak normalization tools, and advanced similarity metrics such as Modified Cosine and NeutralLossesCosine. This skill is ideal for researchers and bioinformaticians needing reproducible analytical pipelines for compound identification, spectral clustering, and library matching within an AI-assisted environment.

주요 기능

Advanced spectral similarity metrics including Cosine and Modified Cosine
Direct spectral visualization and comparison capabilities
Standardized metadata harmonization and chemical annotation (InChI/SMILES)
1 GitHub stars
Reproducible processing pipelines with 40+ built-in filtering functions
Multi-format I/O support for mzML, MGF, MSP, JSON, and USI

사용 사례

Matching unknown query spectra against large reference libraries
Large-scale spectral similarity networking and clustering analysis
Building automated quality control and data cleaning pipelines for metabolomics

소개

주요 기능

Advanced spectral similarity metrics including Cosine and Modified Cosine
Direct spectral visualization and comparison capabilities
Standardized metadata harmonization and chemical annotation (InChI/SMILES)
1 GitHub stars
Reproducible processing pipelines with 40+ built-in filtering functions
Multi-format I/O support for mzML, MGF, MSP, JSON, and USI

사용 사례

Matching unknown query spectra against large reference libraries
Large-scale spectral similarity networking and clustering analysis
Building automated quality control and data cleaning pipelines for metabolomics