aipoch/pyopenms-skill
scientific-skills/Data Analysis/pyopenms-skill/SKILL.md
Comprehensive tool for computational mass spectrometry using PyOpenMS; use when you need to read/write MS formats (mzML/mzXML/MGF), run signal processing (smoothing/peak picking), detect isotope features, or perform peptide identification in proteomics/metabolomics workflows.
$ install --global
skillsauthnpx skillsauth add aipoch/medical-research-skills pyopenms-skillInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- pyopenms-skill
- description:
- Comprehensive tool for computational mass spectrometry using PyOpenMS; use when you need to read/write MS formats (mzML/mzXML/MGF), run signal processing (smoothing/peak picking), detect isotope features, or perform peptide identification in proteomics/metabolomics workflows.
- license:
- MIT
- skill-author:
- AIPOCH
When to Use
- Converting, validating, or batch-processing mass spectrometry files (e.g., mzML, mzXML, MGF) as part of a pipeline.
- Cleaning raw spectra before downstream analysis (smoothing, baseline correction, denoising, peak picking).
- Detecting and linking isotope patterns / features for proteomics or metabolomics feature tables.
- Running identification-oriented steps where peptide/protein identification integration is required.
- Building custom computational MS workflows in Python while leveraging OpenMS algorithms.
Key Features
- MS File I/O: Read/write common MS formats (mzML, mzXML, MGF).
- Signal Processing: Smoothing, baseline correction, filtering, and peak picking.
- Feature Detection: Isotope pattern detection and feature linking utilities.
- Identification Support: Hooks for peptide identification workflows via OpenMS-compatible components.
- Scripted Workflows: A ready-to-use “Load → Process → Analyze” workflow entry point.
Dependencies
Install the following Python packages:
pyopenms(version: compatible with your OpenMS/PyOpenMS distribution)pandas(version: latest recommended)numpy(version: latest recommended)
Installation:
uv pip install pyopenms pandas numpy
Example Usage
A complete runnable example using the provided workflow script (scripts/process_ms.py):
# run_example.py
from scripts.process_ms import run_workflow
def main():
# Load -> Process -> Analyze
# The script is expected to read the input mzML and apply optional filtering.
result = run_workflow("data.mzML", apply_filter=True)
# The returned object depends on the implementation of run_workflow.
# Common patterns include a processed experiment, a feature map, or a summary dict.
print("Workflow finished.")
print(result)
if __name__ == "__main__":
main()
Run:
python run_example.py
For manual/custom workflows, see:
- File operations:
references/file_io.md - Signal processing algorithms:
references/signal_processing.md
Implementation Details
- Binding Layer: This skill uses PyOpenMS, the Python bindings for the OpenMS C++ library, to expose core computational MS algorithms.
- Workflow Pattern: The default script follows a standard pipeline structure:
- Load an MS run from disk (e.g., mzML).
- Process spectra (optional filtering/smoothing/baseline correction).
- Analyze results (e.g., peak picking, feature detection, or downstream summaries).
- Configurable Processing: The
apply_filterflag inrun_workflow(...)is intended to toggle one or more preprocessing steps; exact filters and parameters should be documented inscripts/process_ms.pyand the referenced guides. - Algorithm Reference: Detailed descriptions of available filters and peak pickers, including parameterization, are maintained in
references/signal_processing.md.
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