Process mass spectrometry spectra and calculate similarity scores with matchms.
npx skills add https://github.com/robinbarvaag/poynt --skill matchms-robinbarvaag
Please help me install this Agent Skill. Skill: matchms Source: https://github.com/robinbarvaag/poynt/tree/main/.github/skills/matchms Command: npx skills add https://github.com/robinbarvaag/poynt --skill matchms-robinbarvaag
This Skill streamlines the complex process of analyzing mass spectrometry data, enabling faster and more accurate identification and characterization of chemical compounds.
Use the matchms skill to load spectra from 'my_spectra.mgf' and calculate cosine similarity against 'library.mgf'.
High-intent search queries and answers about installing and using this skill.
You can import mass spectrometry spectra from formats like mzML, MGF, MSP, or JSON, apply filtering and standardization functions, and calculate similarity scores against a reference library for compound identification.
Supported mass spectrometry file formats include mzML, MGF, MSP, and JSON. The Skill imports and exports spectral data using these formats to facilitate processing and library matching in metabolomics research.
You calculate cosine similarity scores by loading experimental spectra and a reference library, then applying similarity analysis algorithms like Cosine or Modified Cosine to compare and match the mass spectrometry data.
Yes, processing metabolomics spectra requires Python libraries including matchms, rdkit, and numpy. These dependencies are essential for spectral manipulation, chemical structure handling, and numerical data analysis.
The best way to clean and filter raw mass spectrometry data is to apply built-in spectrum processing functions, which standardize spectral data to ensure high confidence matches during similarity analysis and library matching.
Yes, matchms works for proteomics research as well as metabolomics. It processes mass spectrometry data, applies similarity scoring, and facilitates compound identification across both proteomics and metabolomics workflows.
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