pyopenms — Python-интерфейс для анализа масс-спектрометрии

--- name: pyopenms 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. license: 3 clause BSD license metadata: skill-author: K-Dense Inc. --- # 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. **Supported engines**: Comet, Mascot, MSGFPlus, XTandem, OMSSA, Myrimatch Basic identification workflow: ```python # 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: 1. Load and process raw data 2. Detect features 3. Align retention times across samples 4. Link features to consensus map 5. 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 ```python 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: ```python # 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: ```python 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 handling - `references/signal_processing.md` - Signal processing algorithms - `references/feature_detection.md` - Feature detection and linking - `references/identification.md` - Peptide and protein identification - `references/metabolomics.md` - Metabolomics-specific workflows - `references/data_structures.md` - Core objects and data structures