What it does

MatchMS provides a comprehensive Python toolkit for mass spectrometry data processing: importing/exporting mzML, MGF, MSP and JSON, harmonising metadata, filtering peaks, calculating spectral similarity (cosine, modified cosine, neutral-loss), and building reproducible processing pipelines for metabolomics. Concrete use cases include library searching, QC filtering, and large-scale similarity-based clustering.

When to use it

Use this skill when you need to preprocess raw LC/MS or GC/MS spectra, normalise and filter peaks, perform library matching or compute pairwise spectral similarities to identify compounds or annotate unknowns. It is suitable for batch pipelines, research reproducibility, and integrating with GNPS-style workflows.

What's included

Scripts: references/ contains detailed docs and example pipelines (has_references=true)
References: importing_exporting.md, filtering.md, similarity.md, workflows.md with usage examples
Instructions: how to load spectra, apply standard filters, normalise intensities, remove precursor peaks, and compute cosine/modified-cosine scores. Example Python snippets show typical pipelines and visualization steps.