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Multiple reaction monitoring (MRM)-based targeted metabolomics can simultaneously analyze up to hundreds of metabolites with high-throughput, good reproducibility, and wide dynamic range. However, when hundreds or thousands of MRM transitions are measured with tens to hundreds of biological samples, the complexity of MRM dataset acquired is no longer amenable to manual evaluation, and presents a challenge for targeted metabolomics. Here, we developed an R package, namely MRMAnalyzer, to process large set of MRM-based targeted metabolomics data automatically without any manual intervention. To demonstrate our MRMAnalyzer program, we first developed a targeted metabolomic method that simultaneously analyzes 182 metabolites in one 15-min LC run, and demonstrated the data processing procedures using MRMAnalyzer. The data processing steps include ''pseudo'' accurate m/z transformation, peak detection and alignment, metabolite identification, quality control check and statistical analysis. Finally, a targeted metabolomic assay was designed and integrated with MRMAnalyzer to profile the metabolic changes in Escherichia coli subjected to the protein expression. The generated MRM dataset consisting of more than 8000 MRM transitions were readily processed using MRMAnalyzer within 20 min without any manual intervention. Fourty seven out of 140 detected metabolites, enriched in six metabolic pathways, were found significantly affected in E. coli metabolome. In summary, a targeted metabolomic platform is developed for high-throughput metabolite profiling and automated data processing, and the MRMAnalyzer program is a high efficient informatics tool for large scale targeted metabolomics.

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