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Recent advances in metabolomics technology have enabled large-scale comprehensive analyses of metabolites, but the throughput of data processing of non-targeted, quantitative differential analyses is very low. It is crucial to solve this problem to generate biological hypotheses from a large-scale dataset. To improve the analysis of metabolite data, we focused on the processing of quantitative differential analysis after multiple peak alignment. We have developed a program named FAQuant that automatically selects reliable peaks from each chromatogram, quantifies the mean of peak intensity to compare between sample groups, and selects the peaks with differences in accumulation of metabolites. This program was incorporated into a quantitative differential metabolome pipeline as a module to improve the throughput of gas chromatography-mass spectrometry dataset analysis. As a result, the module incorporation largely reduced the total processing time. Furthermore, differential analysis of metabolites in soybean (Glycine max) cultivars was demonstrated by use of the system. This system might facilitate biological hypothesis generation from large-scale comparative metabolome analysis.

Improvement of the quantitative differential metabolome pipeline for gas chromatography-mass spectrometry data by automated reliable peak selection