msmu: a Python toolkit for modular and traceable LC-MS proteomics data analysis based on MuData

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Authors: Hyung-Wook Choi, Byeongchan Lee, Un‐Beom Kang, Sunghyun Huh
Cited by: 0
References: 23

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