pyopenms
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.
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PyOpenMS - Mass Spectrometry Data Analysis Python Library
Skill Overview
PyOpenMS is the Python binding of OpenMS, providing a complete mass spectrometry data processing solution for proteomics and metabolomics research.
Applicable Scenarios
1. Proteomics Workflows
Suitable for complex LC-MS/MS data analysis pipelines, including peptide identification, protein recognition, and quantitative analysis. Supports major search engines like Comet, Mascot, MSGFPlus, and can handle large-scale proteomics datasets.
2. Metabolomics Data Preprocessing
Used for preprocessing in untargeted metabolomics, including feature detection, retention time correction, feature alignment, and compound annotation. The full data analysis pipeline supports end-to-end processing from raw data to biological interpretation.
3. Mass Spectrometry File Format Handling
Supports reading, converting, and exporting various mass spectrometry data formats, including standard formats like mzML, mzXML, featureXML, idXML, making integration with other MS analysis tools easy.
Core Features
1. Signal Processing and Feature Detection
Provides a complete MS signal processing toolchain, including spectrum smoothing, filtering, centroiding, and normalization. Built-in feature detection algorithms can automatically detect and extract chromatographic peaks, supporting cross-sample feature linking and quality assessment.
2. Peptide and Protein Identification
Integrates multiple search engines, supporting peptide-spectrum matching, FDR filtering, and protein inference. Can process identification results and perform subsequent quantitative analysis, offering a complete proteomics identification workflow.
3. Data Structures and Interoperability
Defines core data structures such as MSExperiment, MSSpectrum, FeatureMap, and supports seamless integration with Python ecosystem tools like Pandas, NumPy, and Scikit-learn, facilitating downstream data analysis and visualization.
Frequently Asked Questions
What types of data analysis is PyOpenMS suitable for?
PyOpenMS is designed for complex mass spectrometry data analysis and is best suited for complete proteomics workflows, including feature detection, peptide identification, protein quantification, and LC-MS/MS pipeline processing. It also supports metabolomics analysis, but if you only need simple spectrum comparison or metabolite identification, the lighter-weight matchms library is recommended.
Which file formats does PyOpenMS support?
PyOpenMS supports a wide range of mass spectrometry data formats, including mzML, mzXML, TraML, mzTab, FASTA, pepXML, protXML, mzIdentML, featureXML, consensusXML, and idXML. This allows compatibility with most mass spectrometers and data analysis software.
How to install PyOpenMS?
Install using uv:
uv pip install pyopenms. After installation, verify success with import pyopenms; print(pyopenms.__version__). PyOpenMS is released under the 3-clause BSD license and can be used freely for commercial and academic purposes.