pyopenms-mass-spectrometry
MS data processing with PyOpenMS for LC-MS/MS proteomics and metabolomics — mzML/mzXML I/O, signal processing (smoothing, peak picking, centroiding), feature detection/linking, peptide/protein ID with FDR, untargeted metabolomics. Use matchms for simple spectral matching.
npx skills add BioTender-max/awesome-bio-agent-skills --skill pyopenms-mass-spectrometry --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
MS data processing with PyOpenMS for LC-MS/MS proteomics and metabolomics, including file I/O (mzML, mzXML, etc.), signal processing (smoothing, peak picking, centroiding), feature detection and linking, peptide/protein identification with FDR, and untargeted metabolomics workflows. It also notes simple spectral matching with matchms.
How it works
Describes a Python-based workflow across modules:
- Module 1: File I/O & Data Access — read/write mzML/mzXML, idXML, featureXML; use IndexedMzMLFileLoader for large files; read FASTA, and write mzML.
- Module 2: Signal Processing & Peak Picking — apply Gaussian or Savitzky–Golay smoothing; centroid via PeakPickerHiRes; normalize data; filter peaks with ThresholdMower; optionally apply MorphologicalFilter for baseline reduction.
- Module 3: Feature Detection & Linking — detect chromatographic features with FeatureFinder; link features across samples using alignment and grouping algorithms; export results to a pandas DataFrame.
- Module 4: Peptide & Protein Identification — load identifications; inspect hits; apply FDR via FalseDiscoveryRate; perform basic protein inference and manipulate AA sequences.
- Module 5: Metabolomics Pipeline — centroid raw data; detect features; apply adduct decharger; RT alignment across samples; link features into a consensus map; export a metabolite table.
Key pattern for algorithms is to instantiate an algorithm, obtain parameters, set values, apply parameters, then execute.
When to use it
Use when processing raw LC-MS/MS data for proteomics or metabolomics, detecting features and linking across samples, identifying peptides/proteins with FDR, or performing untargeted metabolomics workflows from peak picking to annotation. Also suitable for converting between formats and simple spectral matching with matchms.
What it can touch
Core data structures and tools referenced include MSExperiment, MSSpectrum, FeatureMap, ConsensusMap, PeptideIdentification, AASequence, IdXMLFile, MzMLFile, FASTAFile, PeakPickerHiRes, GaussFilter, SavitzkyGolayFilter, FeatureFinder, MapAlignmentAlgorithmPoseClustering, FeatureGroupingAlgorithmQT, FalseDiscoveryRate, BasicProteinInferenceAlgorithm, ProteaseDigestion, MzIdentMLFile, and several format-specific File classes. The instructions show commands and file names exactly as written, such as "mzMLFile", "IndexEdMzMLFileLoader", "OnDiscMSExperiment", and Python imports like "import pyopenms as ms".
Caveats
License is BSD-3-Clause. The material lists prerequisites (Python 3.8+, numpy, pandas, matplotlib) and commands to install PyOpenMS. It presents a broad, multi-step workflow with explicit parameter names and defaults, but does not guarantee specific outcomes beyond what is described. No unsupported claims are made beyond what is stated in the steps and examples.
# PyOpenMS — Mass Spectrometry Analysis ## Overview PyOpenMS provides Python bindings to the OpenMS C++ library for computational mass spectrometry. It supports proteomics and metabolomics data processing including file I/O for 10+ MS formats, signal processing, feature detection, peptide/protein identification, and quantitative analysis across samples. ## When to Use - Processing raw LC-MS/MS data (mzML, mzXML) for proteomics or metabolomics - Detecting chromatographic features and linking them across multiple samples - Identifying peptides and proteins from MS/MS search engine results with FDR control - Running untargeted metabolomics workflows (peak picking → feature detection → alignment → annotation) - Converting between mass spectrometry file formats (mzML, mzXML, featureXML, idXML) - Smoothing, filtering, and centroiding raw spectral data - For simple spectral library matching and metabolite identification, use **matchms** instead - For protein sequence analysis (not mass spec), use **biopython** instead ## Prerequisites ```bash uv pip install pyopenms numpy pandas matplotlib ``` - Python 3.8+; NumPy for peak array operations - Input data: mzML files (standard MS format), FA
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Module 1: File I/O & Data Access
- Module 2: Signal Processing & Peak Picking
- Module 3: Feature Detection & Linking
- Module 4: Peptide & Protein Identification
- Module 5: Metabolomics Pipeline
- Key Concepts
- Algorithm Parameter Pattern
- Core Data Structures
- Supported File Formats
uv pip install pyopenms numpy pandas matplotlib
What does the pyopenms-mass-spectrometry skill do?
MS data processing with PyOpenMS for LC-MS/MS proteomics and metabolomics — mzML/mzXML I/O, signal processing (smoothing, peak picking, centroiding), feature detection/linking, peptide/protein ID with FDR, untargeted metabolomics. Use matchms for simple spectral matching.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill pyopenms-mass-spectrometry --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.