# Pyopenms

Complete mass spectrometry analysis platform.

- Repository: https://github.com/LeonChaoX/qinyan-academic-skills (996 stars)
- Category: Engineering
- License: 3 clause BSD license
- Last updated: 2026-07-20
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add LeonChaoX/qinyan-academic-skills --skill pyopenms -g -a claude-code -y`
Codex: `npx skills add LeonChaoX/qinyan-academic-skills --skill pyopenms -g -a codex -y`
Cursor: `npx skills add LeonChaoX/qinyan-academic-skills --skill pyopenms -g -a cursor -y`
Gemini CLI: `npx skills add LeonChaoX/qinyan-academic-skills --skill pyopenms -g -a gemini-cli -y`

## Description

Complete mass spectrometry analysis platform. Use for proteomics workflows feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. Best for proteomics, comprehensive MS data processing. For simple spectral comparison and metabolite ID use matchms.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# PyOpenMS

## Overview

PyOpenMS provides Python bindings to the OpenMS library for computational mass spectrometry, enabling analysis of proteomics and metabolomics data. Use for handling mass spectrometry file formats, processing spectral data, detecting features, identifying peptides/proteins, and performing quantitative analysis.

## Installation

Install using uv:

```bash
uv uv pip install pyopenms
```

Verify installation:

```python
import pyopenms
print(pyopenms.__version__)
```

## Core Capabilities

PyOpenMS organizes functionality into these domains:

### 1. File I/O and Data Formats

Handle mass spectrometry file formats and convert between representations.

**Supported formats**: mzML, mzXML, TraML, mzTab, FASTA, pepXML, protXML, mzIdentML, featureXML, consensusXML, idXML

Basic file reading:

```python
import pyopenms as ms

# Read mzML file
exp = ms.MSExperiment()
ms.MzMLFile().load("data.mzML", exp)

# Access spectra
for spectrum in exp:
    mz, intensity = spectrum.get_peaks()
    print(f"Spectrum: {len(mz)} peaks")
```

**For detailed file handling**: See `references/file_io.md`

### 2. Signal Processing

Process raw spectral data with smoothing, filtering, centroiding, and normalization.

Basic spectrum processing:

```python
# Smooth spectrum with Gaussian filter
gaussian = ms.GaussFilter()
params = gaussian.getParameters()
params.setValue("gaussian_width", 0.1)
gaussian.setParameters(params)
gaussian.filterExperiment(exp)
```

**For algorithm details**: See `references/signal_processing.md`

### 3. Feature Detection

Detect and link features across spectra and samples for quantitative analysis.

```python
# Detect features
ff = ms.FeatureFinder()
ff.run("centroided", exp, features, params, ms.FeatureMap())
```

**For complete workflows**: See `references/feature_detection.md`

### 4. Peptide and Protein Identification

Integrate with search engines and process identification results.

The rest: https://github.com/LeonChaoX/qinyan-academic-skills/blob/HEAD/skills/06-化学信息与药物发现/pyopenms/SKILL.md