msmu: a Python toolkit for modular and traceable LC-MS proteomics data analysis based on MuData
Choi, H.-W.; Lee, B.; Kang, U.-B.; Huh, S.
Show 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.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Analysis and visualization of quantitative proteomics data using FragPipe-Analyst 97%
- MaxQuant and MSstats in Galaxy enable reproducible cloud-based analysis of quantitative proteomics experiments for everyone 97%
- Real-time spectral library matching for sample multiplexed quantitative proteomics. 96%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.