MS-MINT: An Open-Source Data Analysis Software for Large-Scale Metabolomics Studies

Metabolomics has emerged as a mainstream approach for investigating the complex metabolic underpinnings of living systems, and over recent years, it has increasingly been applied to large cohort studies that tax the limits of existing computational tools. Most existing metabolomics software tools are effective at analyzing small data sets but exhibit a number of shortcomings that limit their utility when applied to large studies: they store entire data sets in memory, they use batch-dependent fitting algorithms, and they do not use concrete metrics for peak fitting, which not only results in inconsistent peak-picking results across samples but also complicates the documentation of data analyses. To address this, we developed the mass-spectrometry metabolomics integrator (MS-MINT), a Python application for processing, analyzing, and visualizing large liquid chromatography–mass spectrometry (LC-MS) data sets. To enable reproducible large-scale data processing, MS-MINT uses a region of interest (ROI)-based approach to extract data. We illustrate the function of this new tool by analyzing metabolites present in the media of a large data set (3334 files) of Staphylococcus aureus cultures. We show that MS-MINT accurately reproduces data generated from other software tools in a fraction of the time. In summary, MS-MINT offers a purpose-built software platform to support large-scale metabolomics data analyses. MS-MINT software is freely available at https://www.lewisresearchgroup.org/software .