domains / science / mass-spectrometry-analysis / pyopenms

pyopenms

Skill card

combined

The K-Dense-AI copy is a distinct, high-fidelity implementation that ships a suite of parameterized CLI scripts (e.g., `detect_features_metabo.py`, `align_link_quantify.py`) and provides specific installation constraints (Python 3.11+, pyOpenMS 3.6.0). The other three copies (Vibe-Skills, medical-research-skills, and OpenClaw-Medical-Skills) are largely identical or near-identical generic API guides that lack these scripts and instead point to `references/` files. Because the K-Dense copy provides a different, more operational procedure (running scripts vs. writing API code) and the others provide a different, more conceptual procedure (API exploration), crowning one would hide the alternative approach. The Vibe-Skills and OpenClaw copies are effectively duplicates of each other, while the medical-research-skills copy is a simplified variant of the same API-focused approach.

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Combined skill

# PyOpenMS

## Overview
PyOpenMS provides Python bindings to the OpenMS library for computational mass spectrometry. It supports proteomics and metabolomics workflows including file handling, signal processing, feature detection, and identification.

## Installation
Use `uv` to manage the environment. Ensure Python 3.11+ is available.
```bash
uv venv --python 3.13
uv pip install "pyopenms==3.6.0" pandas numpy matplotlib
```
Verify installation:
```python
import pyopenms as ms
print(ms.__version__)
```

## Workflow Options

### Option A: Script-Based (Recommended for Standard Pipelines)
If the skill directory contains a `scripts/` folder, prefer running these parameterized CLI tools over writing new code.

1. **Inspect & Convert**:
   - `python scripts/inspect_ms_data.py <input.mzML>`: Summarize data.
   - `python scripts/convert_format.py <input.mzML> --out <output.mzXML>`: Convert formats.
2. **Feature Detection & Quantification**:
   - `python scripts/detect_features_metabo.py <input.mzML> --out-csv features.csv`: Untargeted metabolomics.
   - `python scripts/align_link_quantify.py <s1.mzML> <s2.mzML> --out-prefix study`: Multi-sample quantification.
3. **Identification**:
   - `python scripts/process_identifications.py <search.idXML> --fasta db.fasta --fdr 0.01`: Post-process search results.

### Option B: API-Based (For Custom Workflows)
Use the Python API when no script fits the specific requirement.

1. **File I/O**:
   ```python
   import pyopenms as ms
   exp = ms.MSExperiment()
   ms.MzMLFile().load("data.mzML", exp)
   ```
2. **Signal Processing**:
   ```python
   gaussian = ms.GaussFilter()
   params = gaussian.getParameters()
   params.setValue("gaussian_width", 0.1)
   gaussian.setParameters(params)
   gaussian.filterExperiment(exp)
   ```
3. **Feature Detection**:
   ```python
   ff = ms.FeatureFinder()
   # Configure parameters and run
   ```
4. **Data Structures**:
   - `MSExperiment`: Collection of spectra.
   - `FeatureMap`: Collection of detected features.
   - `PeptideIdentification`: Search results.

## Conflicts
- **Installation**: K-Dense-AI specifies `pyopenms==3.6.0` and Python 3.13; Vibe-Skills uses `uv uv pip install pyopenms` (typo) without version pinning. Use the K-Dense-AI version for stability.
- **Execution**: K-Dense-AI prioritizes CLI scripts; Vibe-Skills prioritizes API code. Use scripts for standard tasks and API for custom logic.