---
name: pyopenms-skill-2fcf79270e85
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
author: AIPOCH
---
> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)

## 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:

```bash
uv pip install pyopenms pandas numpy
```

## Example Usage

A complete runnable example using the provided workflow script (`scripts/process_ms.py`):

```python
# 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:

```bash
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:
  1. **Load** an MS run from disk (e.g., mzML).
  2. **Process** spectra (optional filtering/smoothing/baseline correction).
  3. **Analyze** results (e.g., peak picking, feature detection, or downstream summaries).
- **Configurable Processing**: The `apply_filter` flag in `run_workflow(...)` is intended to toggle one or more preprocessing steps; exact filters and parameters should be documented in `scripts/process_ms.py` and the referenced guides.
- **Algorithm Reference**: Detailed descriptions of available filters and peak pickers, including parameterization, are maintained in `references/signal_processing.md`.

---
Source attribution: aipoch
Original project: https://github.com/aipoch/medical-research-skills/blob/686e09dfdb45d9fa1a08664f06bb93769b3c9d91/scientific-skills/Data%20Analysis/pyopenms-skill/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 AIpoch

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: aipoch
Original project: https://github.com/aipoch/medical-research-skills/blob/686e09dfdb45d9fa1a08664f06bb93769b3c9d91/scientific-skills/Data%20Analysis/pyopenms-skill/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Education & Research, Healthcare & Life Sciences
Roles: Clinical Research, Data Scientist, Researcher
Specialties: API, Backend
Tools: Numpy, Pandas, Pyopenms
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 9bc82b4bbfbfcb9d79fe1519f9e7847abed1287cb84306b7d0548dd5095bb7fc