pyopenms
GitHubPyOpenMS是质谱分析平台，提供Python绑定。用于蛋白质组学工作流、特征检测、肽段鉴定、蛋白定量及LC-MS/MS复杂管道处理。支持广泛文件格式和算法，适用于全面质谱数据处理。
Trigger Scenarios
Install
npx skills add synthetic-sciences/openscience --skill pyopenms -g -y
SKILL.md
Frontmatter
{
"name": "pyopenms",
"license": "3 clause BSD license",
"category": "chemistry",
"metadata": {
"skill-author": "Synthetic Sciences"
},
"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."
}
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:
uv uv pip install pyopenms
Verify installation:
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:
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:
# 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.
# 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.
Supported engines: Comet, Mascot, MSGFPlus, XTandem, OMSSA, Myrimatch
Basic identification workflow:
# Load identification data
protein_ids = []
peptide_ids = []
ms.IdXMLFile().load("identifications.idXML", protein_ids, peptide_ids)
# Apply FDR filtering
fdr = ms.FalseDiscoveryRate()
fdr.apply(peptide_ids)
For detailed workflows: See references/identification.md
5. Metabolomics Analysis
Perform untargeted metabolomics preprocessing and analysis.
Typical workflow:
- Load and process raw data
- Detect features
- Align retention times across samples
- Link features to consensus map
- Annotate with compound databases
For complete metabolomics workflows: See references/metabolomics.md
Data Structures
PyOpenMS uses these primary objects:
- MSExperiment: Collection of spectra and chromatograms
- MSSpectrum: Single mass spectrum with m/z and intensity pairs
- MSChromatogram: Chromatographic trace
- Feature: Detected chromatographic peak with quality metrics
- FeatureMap: Collection of features
- PeptideIdentification: Search results for peptides
- ProteinIdentification: Search results for proteins
For detailed documentation: See references/data_structures.md
Common Workflows
Quick Start: Load and Explore Data
import pyopenms as ms
# Load mzML file
exp = ms.MSExperiment()
ms.MzMLFile().load("sample.mzML", exp)
# Get basic statistics
print(f"Number of spectra: {exp.getNrSpectra()}")
print(f"Number of chromatograms: {exp.getNrChromatograms()}")
# Examine first spectrum
spec = exp.getSpectrum(0)
print(f"MS level: {spec.getMSLevel()}")
print(f"Retention time: {spec.getRT()}")
mz, intensity = spec.get_peaks()
print(f"Peaks: {len(mz)}")
Parameter Management
Most algorithms use a parameter system:
# Get algorithm parameters
algo = ms.GaussFilter()
params = algo.getParameters()
# View available parameters
for param in params.keys():
print(f"{param}: {params.getValue(param)}")
# Modify parameters
params.setValue("gaussian_width", 0.2)
algo.setParameters(params)
Export to Pandas
Convert data to pandas DataFrames for analysis:
import pyopenms as ms
import pandas as pd
# Load feature map
fm = ms.FeatureMap()
ms.FeatureXMLFile().load("features.featureXML", fm)
# Convert to DataFrame
df = fm.get_df()
print(df.head())
Integration with Other Tools
PyOpenMS integrates with:
- Pandas: Export data to DataFrames
- NumPy: Work with peak arrays
- Scikit-learn: Machine learning on MS data
- Matplotlib/Seaborn: Visualization
- R: Via rpy2 bridge
Resources
- Official documentation: https://pyopenms.readthedocs.io
- OpenMS documentation: https://www.openms.org
- GitHub: https://github.com/OpenMS/OpenMS
References
references/file_io.md- Comprehensive file format handlingreferences/signal_processing.md- Signal processing algorithmsreferences/feature_detection.md- Feature detection and linkingreferences/identification.md- Peptide and protein identificationreferences/metabolomics.md- Metabolomics-specific workflowsreferences/data_structures.md- Core objects and data structures
Version History
- e9844a4 Current 2026-07-11 17:22
