PipMet: Pipeline for processing GC-MS Metabolomics data and statistical graphics visualization Author information

Introduction: Metabolomics analysis, a key tool in life sciences, generates large amounts of multidimensional data that require robust computational pipelines. However, many existing data processing tools are costly, depending on online resources, or lack of user-friendly interfaces. This creates a significant barrier to entry, limiting the wider application of metabolomics. Objectives: To address these challenges, we developed PipMet, a cost-free, open-source, and user-friendly R package designed to provide a semi-automated, offline solution for untargeted GC-MS metabolomics data processing. Methods: The PipMet pipeline is built on a modular framework of open-source R packages and consists of four main steps: data reading, preprocessing, compound identification and/or annotation, and quantification. It supports standard '.mzML' and '.mzXML' formats. The pipeline's performance was validated through a comprehensive set of tests, including the analysis of standard mixtures, a standard curve for absolute quantification, and a complex sugarcane metabolomics dataset. Results and Discussion: Our validation demonstrates PipMet's outstanding performance in compound identification, with high scores and consistent results compared to other methods. The pipeline also showed excellent reproducibility, linearity, accuracy, and precision in both absolute and relative quantification analyses. Results from the complex dataset proved its efficacy in real-world applications. Conclusion: PipMet provides a flexible and accessible alternative to commercial software and online platforms. Its robust performance and user-friendly design make metabolomics data analysis more attainable for the scientific community, particularly for labs with limited budgets, thereby expanding the reach of this powerful analytical technique. Data availability: The PipMet pipeline is available on GitHub at https://github.com/PipMet/PipMet