msmu: a Python toolkit for modular and traceable LC-MS proteomics data analysis based on MuData
Abstract
Abstract Computational workflows for MS-based proteomics remain comparatively fragmented, with heterogeneous data formats and analysis pipelines that hinder their reproducibility, interoperability, and reuse of processed data. We present msmu, an open-source Python package that implements a flexible and reproducible end-to-end pipeline for post-search data preprocessing and statistical analysis. At its core, msmu leverages the highly structured MuData format, empowering comprehensive data provenance, transparency in data sharing and reuse, and interoperability with broader Python ecosystem. Together, msmu represents a unique and significant step toward realizing the FAIR (Findable, Accessible, Interoperable, and Reusable) principles in computational proteomics.
Fields of Study & Subjects
Advanced Proteomics Techniques and Applications
Scientific Computing and Data Management
Research Data Management Practices
Publication details
- Journal / source
- bioRxiv (Cold Spring Harbor Laboratory)
- Publication date
- 8 January 2026
- Document type
- Preprint