pyopenms

You can analyze mass spectrometry data in Python by using bindings to OpenMS, which bridge Python workflows with C++ processing steps for file IO, feature detection, and peptide identification.

Yes, you can parse mzML files and extract feature maps by loading LC-MS/MS data into core OpenMS structures like MSExperiment and FeatureMap directly within Python scripts.

Yes, pyopenms supports both proteomics and metabolomics by enabling end-to-end pipelines for peptide identification and feature detection across standard mass spectrometry formats.

You can import standard mass spectrometry formats including mzML, mzXML, mzTab, FASTA, pepXML, protXML, and mzIdentML directly into Python for downstream analysis.

Yes, you need OpenMS Python bindings installed to build end-to-end pipelines because they provide the core data structures and processing algorithms required for identification and quantification steps.

The best way to integrate mass spectrometry processing with Python scientific tools is using OpenMS bindings, which allow you to run feature detection and identification within standard Python notebooks and pipelines.