Process mass spectrometry spectra for similarity scoring and compound identification.
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill matchms-jasrajtulsi
Please help me install this Agent Skill. Skill: matchms Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/matchms Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill matchms-jasrajtulsi
This Skill helps metabolomics and mass spectrometry users standardize spectra, compare them reliably, and identify unknown compounds without manually stitching together preprocessing and scoring steps.
Use the matchms skill to load your spectra, run the recommended preprocessing pipeline, and return the top compound matches with scores.
High-intent search queries and answers about installing and using this skill.
To identify compounds via mass spectrometry library searching, you import reference and unknown spectra in MGF, MSP, or mzML format, harmonize metadata, normalize intensities, and apply similarity scoring like cosine or modified cosine to rank compound matches.
Yes, mzML and mzXML files are supported natively for metabolomics spectral similarity workflows, allowing you to load raw mass spectrometry data directly, apply peak filtering, and calculate scores without manual format conversion.
The best way to preprocess mass spectrometry data for library matching involves harmonizing metadata, normalizing intensities, removing precursor peaks, and adding neutral losses to ensure spectra are clean and comparable before calculating similarity scores.
Metadata harmonization directly impacts compound identification by standardizing spectra annotations across diverse formats, ensuring that similarity scoring algorithms compare consistent peak data and reducing false positives during library searching.
Available similarity metrics for ranking unknown spectra include cosine, modified cosine, neutral-loss, fingerprint, and metadata-based scoring, enabling comprehensive candidate ranking and confidence validation for compound identification.
After completing spectral similarity analysis, you can export processed mass spectrometry results to MGF, MSP, JSON, or pickle formats, ensuring interoperable workflows and reproducible analysis for downstream metabolomics research.
felixboehmCompare mass spectra from MGF, mzML, and MSP files using cosine similarity.
qinyan-aiMatch mass spectra to reference libraries using configurable similarity metrics and MGF/MSP/JSON formats.
OwnLabAIMatch mass spectra across libraries using multiple similarity metrics.
tassiovaleCompare mass spectra and identify compounds using spectral libraries.
