TidyMass: An Object-oriented Reproducible Analysis Framework for LC-MS Data

Abstract Reproducibility and transparency have been longstanding but significant problems for the metabolomics field. Here, we present the tidyMass project (https://www.tidymass.org/), a comprehensive computational framework that can achieve the shareable and reproducible workflow needs of data processing and analysis for LC-MS-based untargeted metabolomics. TidyMass was designed based on the following strategies to address the limitations of current tools: 1) Cross-platform utility. TidyMass can be installed on all platforms; 2) Uniformity, shareability, traceability, and reproducibility. A uniform data format has been developed, specifically designed to store and manage processed metabolomics data and processing parameters, making it possible to trace the prior analysis steps and parameters; 3) Flexibility and extensibility. The modular architecture makes tidyMass a highly flexible and extensible tool, so other users can improve it and integrate it with their own pipeline easily.