MRM Processor: An Integrated Workflow for Automated MRM Data Processing via Relative Retention Time Correction

Targeted metabolomics using multiple reaction monitoring (MRM) provides sensitive and selective quantification, but large-scale data processing remains challenged by retention time (RT) drift, incorrect peak detection, and subjective integration. Here, we present the MRM Processor, an integrated, automated workflow that leverages relative retention time (RRT)-driven RT correction, derivative-based signal characterization, chromatographic peak integration, and calibration-based quantification. By dynamically updating analyte RTs using designated internal standards and evaluating chromatographic signals with derivative patterns, the workflow improves peak localization while reducing the level of manual intervention. Comprehensive validation using bile acid standards, matrix-spiked biological samples, and a pediatric sepsis fecal cohort demonstrated robust RT correction, accurate peak detection, reliable quantitative performance, and superior peak classification compared with existing data processing tools. Additional validation using structurally diverse metabolite standards further defined the current applicability boundary of the RRT correction strategy. The MRM Processor is freely available and compatible with standard MRM data acquired by liquid chromatography-tandem mass spectrometry (LC-MS/MS).