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Use for handling mass spectrometry file formats, processing spectral data, detecting features, identifying peptides\u002Fproteins, and performing quantitative analysis.\n\n## Installation\n\nInstall using uv:\n\n```bash\nuv uv pip install pyopenms\n```\n\nVerify installation:\n\n```python\nimport pyopenms\nprint(pyopenms.__version__)\n```\n\n## Core Capabilities\n\nPyOpenMS organizes functionality into these domains:\n\n### 1. File I\u002FO and Data Formats\n\nHandle mass spectrometry file formats and convert between representations.\n\n**Supported formats**: mzML, mzXML, TraML, mzTab, FASTA, pepXML, protXML, mzIdentML, featureXML, consensusXML, idXML\n\nBasic file reading:\n\n```python\nimport pyopenms as ms\n\n# Read mzML file\nexp = ms.MSExperiment()\nms.MzMLFile().load(\"data.mzML\", exp)\n\n# Access spectra\nfor spectrum in exp:\n    mz, intensity = spectrum.get_peaks()\n    print(f\"Spectrum: {len(mz)} peaks\")\n```\n\n**For detailed file handling**: See `references\u002Ffile_io.md`\n\n### 2. Signal Processing\n\nProcess raw spectral data with smoothing, filtering, centroiding, and normalization.\n\nBasic spectrum processing:\n\n```python\n# Smooth spectrum with Gaussian filter\ngaussian = ms.GaussFilter()\nparams = gaussian.getParameters()\nparams.setValue(\"gaussian_width\", 0.1)\ngaussian.setParameters(params)\ngaussian.filterExperiment(exp)\n```\n\n**For algorithm details**: See `references\u002Fsignal_processing.md`\n\n### 3. Feature Detection\n\nDetect and link features across spectra and samples for quantitative analysis.\n\n```python\n# Detect features\nff = ms.FeatureFinder()\nff.run(\"centroided\", exp, features, params, ms.FeatureMap())\n```\n\n**For complete workflows**: See `references\u002Ffeature_detection.md`\n\n### 4. Peptide and Protein Identification\n\nIntegrate with search engines and process identification results.\n\n**Supported engines**: Comet, Mascot, MSGFPlus, XTandem, OMSSA, Myrimatch\n\nBasic identification workflow:\n\n```python\n# Load identification data\nprotein_ids = []\npeptide_ids = []\nms.IdXMLFile().load(\"identifications.idXML\", protein_ids, peptide_ids)\n\n# Apply FDR filtering\nfdr = ms.FalseDiscoveryRate()\nfdr.apply(peptide_ids)\n```\n\n**For detailed workflows**: See `references\u002Fidentification.md`\n\n### 5. Metabolomics Analysis\n\nPerform untargeted metabolomics preprocessing and analysis.\n\nTypical workflow:\n1. Load and process raw data\n2. Detect features\n3. Align retention times across samples\n4. Link features to consensus map\n5. Annotate with compound databases\n\n**For complete metabolomics workflows**: See `references\u002Fmetabolomics.md`\n\n## Data Structures\n\nPyOpenMS uses these primary objects:\n\n- **MSExperiment**: Collection of spectra and chromatograms\n- **MSSpectrum**: Single mass spectrum with m\u002Fz and intensity pairs\n- **MSChromatogram**: Chromatographic trace\n- **Feature**: Detected chromatographic peak with quality metrics\n- **FeatureMap**: Collection of features\n- **PeptideIdentification**: Search results for peptides\n- **ProteinIdentification**: Search results for proteins\n\n**For detailed documentation**: See `references\u002Fdata_structures.md`\n\n## Common Workflows\n\n### Quick Start: Load and Explore Data\n\n```python\nimport pyopenms as ms\n\n# Load mzML file\nexp = ms.MSExperiment()\nms.MzMLFile().load(\"sample.mzML\", exp)\n\n# Get basic statistics\nprint(f\"Number of spectra: {exp.getNrSpectra()}\")\nprint(f\"Number of chromatograms: {exp.getNrChromatograms()}\")\n\n# Examine first spectrum\nspec = exp.getSpectrum(0)\nprint(f\"MS level: {spec.getMSLevel()}\")\nprint(f\"Retention time: {spec.getRT()}\")\nmz, intensity = spec.get_peaks()\nprint(f\"Peaks: {len(mz)}\")\n```\n\n### Parameter Management\n\nMost algorithms use a parameter system:\n\n```python\n# Get algorithm parameters\nalgo = ms.GaussFilter()\nparams = algo.getParameters()\n\n# View available parameters\nfor param in params.keys():\n    print(f\"{param}: {params.getValue(param)}\")\n\n# Modify parameters\nparams.setValue(\"gaussian_width\", 0.2)\nalgo.setParameters(params)\n```\n\n### Export to Pandas\n\nConvert data to pandas DataFrames for analysis:\n\n```python\nimport pyopenms as ms\nimport pandas as pd\n\n# Load feature map\nfm = ms.FeatureMap()\nms.FeatureXMLFile().load(\"features.featureXML\", fm)\n\n# Convert to DataFrame\ndf = fm.get_df()\nprint(df.head())\n```\n\n## Integration with Other Tools\n\nPyOpenMS integrates with:\n- **Pandas**: Export data to DataFrames\n- **NumPy**: Work with peak arrays\n- **Scikit-learn**: Machine learning on MS data\n- **Matplotlib\u002FSeaborn**: Visualization\n- **R**: Via rpy2 bridge\n\n## Resources\n\n- **Official documentation**: https:\u002F\u002Fpyopenms.readthedocs.io\n- **OpenMS documentation**: https:\u002F\u002Fwww.openms.org\n- **GitHub**: https:\u002F\u002Fgithub.com\u002FOpenMS\u002FOpenMS\n\n## References\n\n- `references\u002Ffile_io.md` - Comprehensive file format handling\n- `references\u002Fsignal_processing.md` - Signal processing algorithms\n- `references\u002Ffeature_detection.md` - Feature detection and linking\n- `references\u002Fidentification.md` - Peptide and protein identification\n- `references\u002Fmetabolomics.md` - Metabolomics-specific workflows\n- `references\u002Fdata_structures.md` - Core objects and data 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