PyOpenMS
Skill Verifiziert AktivComplete 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. Part of the AlterLab Academic Skills suite.
To serve as a complete platform for mass spectrometry analysis, empowering researchers in proteomics and metabolomics with robust tools for data processing and interpretation.
Funktionen
- Handles extensive mass spectrometry file formats (mzML, mzXML, idXML, etc.)
- Performs signal processing, smoothing, filtering, and normalization
- Detects and links features across samples for quantitative analysis
- Integrates with search engines for peptide and protein identification
- Supports untargeted metabolomics workflows and compound identification
Anwendungsfälle
- Processing large-scale proteomics datasets for feature detection and protein quantification
- Performing untargeted metabolomics analysis, including adduct detection and compound identification
- Analyzing complex LC-MS/MS pipelines for biological discovery
- Interfacing with various mass spectrometry data formats and standard analysis algorithms
Nicht-Ziele
- Performing simple spectral comparisons or metabolite identification (suggests using matchms for these tasks)
- Acting as a standalone application without the PyOpenMS library
Workflow
- Load mass spectrometry data (e.g., mzML files)
- Apply signal processing techniques (smoothing, filtering, normalization)
- Detect chromatographic features
- Link features across samples (feature detection and linking)
- Perform peptide and protein identification using search engines
- Analyze results using FDR control and protein inference
- Export results in various formats (e.g., mzTab, CSV)
Praktiken
- Mass Spectrometry Data Analysis
- Proteomics Workflows
- Metabolomics Workflows
- Bioinformatics Pipeline Development
Voraussetzungen
- Python environment
- pyopenms library installed
Execution
- info:Pinned dependenciesDependencies are managed via pip, but a lockfile is not explicitly mentioned in the context, so pinning is assumed but not confirmed.
Installation
npx skills add AlterLab-IEU/AlterLab-Academic-SkillsFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
VerifiziertVertrauenssignale
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