matchms-spectral-matching
MS spectral matching and metabolite ID with matchms. Import spectra (mzML, MGF, MSP, JSON), filter/normalize peaks, score similarity (cosine, modified cosine, fingerprint), build reproducible pipelines, identify unknowns vs spectral libraries. Use pyopenms for full LC-MS/MS proteomics.
npx skills add BioTender-max/awesome-bio-agent-skills --skill matchms-spectral-matching --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Identifies unknown metabolites by matching MS/MS spectra against reference libraries, computes spectral similarity scores (cosine, modified cosine, fingerprint), processes and standardizes spectra from formats like mzML, MGF, MSP, JSON, and supports reproducible pipelines. It leverages pyopenms for LC-MS/MS proteomics workflows and provides multi-step modules for I/O, filtering, scoring, and processing pipelines.
How it works
- Module 1: Spectrum I/O — Import spectra from formats such as mzML, MGF, MSP, JSON, and export processed data (e.g., MGF, MSP, JSON, pickle).
- Module 2: Spectrum Filtering & Processing — Apply metadata harmonization and over 50 filters, normalize intensities, filter peaks by relative intensity and m/z, reduce peaks, remove peaks near precursor, require a minimum number of peaks, and add neutral losses. Optional chemistry-derived steps derive InChI/InChIKey from SMILES and add molecular fingerprints.
- Module 3: Similarity Scoring — Compute scores using multiple metrics (CosineGreedy, ModifiedCosine, CosineHungarian, NeutralLossesCosine, FingerprintSimilarity, PrecursorMzMatch) and access top matches per query.
- Module 4: Processing Pipelines — Build reusable SpectrumProcessor pipelines combining the above steps for reproducible workflows.
- The skill includes example code blocks showing how to load spectra, apply filters, compute scores, and extract best matches.
When to use it
- Identifying unknown metabolites by comparing MS/MS spectra to reference libraries
- Computing spectral similarity scores across formats (mzML, MGF, MSP, JSON)
- Building reproducible spectral processing pipelines for library matching and quality control
- Harmonizing metadata across spectral databases and performing large-scale library comparisons
- For full LC-MS/MS proteomics workflows, use pyopenms
What it can touch
- Tools: "pyopenms" is referenced for full LC-MS/MS proteomics workflows in the overview, and the code examples interact with the matchms API for I/O, filtering, scoring, and processing.
- No specific file-system or network actions are described beyond importing/exporting spectra in various formats and using the provided API calls (e.g., load_from_mgf, save_as_json, SpectrumProcessor).
Caveats
- License: Apache-2.0
- Prerequisites include Python 3.8+, NumPy, and optional chemistry dependencies for SMILES/InChI processing.
- The skill notes that for structure-based pre-filtering, FingerprintSimilarity requires fingerprints, and for certain workflows, pyopenms should be used; no outcomes are promised beyond the described capabilities.
# Matchms — Spectral Matching & Metabolite Identification ## Overview Matchms is a Python library for mass spectrometry data processing focused on spectral similarity calculation and compound identification. It provides multi-format I/O, 50+ spectrum filters for metadata harmonization and peak processing, 8 similarity scoring functions, and a pipeline framework for reproducible analytical workflows. ## When to Use - Identifying unknown metabolites by matching MS/MS spectra against reference libraries - Computing spectral similarity scores (cosine, modified cosine, fingerprint-based) - Processing and standardizing mass spectral data from multiple formats (mzML, MGF, MSP, JSON) - Building reproducible spectral processing pipelines for quality control - Harmonizing metadata across spectral databases (compound names, SMILES, InChI, adducts) - Large-scale spectral library comparisons and duplicate detection - For full LC-MS/MS proteomics workflows (feature detection, protein ID), use **pyopenms** instead - For chemical structure similarity without mass spectra, use **rdkit** fingerprint comparison ## Prerequisites ```bash uv pip install matchms numpy pandas # For chemical structure proc
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Module 1: Spectrum I/O
- Module 2: Spectrum Filtering & Processing
- Module 3: Similarity Scoring
- Module 4: Processing Pipelines
- Key Concepts
- Similarity Function Comparison
- Filter Categories
- Score Tuple Structure
- Common Workflows
uv pip install matchms numpy pandas For chemical structure processing (SMILES, InChI, fingerprints): uv pip install matchms[chemistry]
What does the matchms-spectral-matching skill do?
MS spectral matching and metabolite ID with matchms. Import spectra (mzML, MGF, MSP, JSON), filter/normalize peaks, score similarity (cosine, modified cosine, fingerprint), build reproducible pipelines, identify unknowns vs spectral libraries. Use pyopenms for full LC-MS/MS proteomics.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill matchms-spectral-matching --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.