AntDAS-GCMS: A New Comprehensive Data Analysis Platform for GC-MS-Based Untargeted Metabolomics with the Advantage of Addressing the Time Shift Problem

文献类型： 外文期刊

第一作者： Ma, Gui-Mei

作者： Ma, Gui-Mei;Wang, Jia-Nan;Ma, Feng-Lian;Wang, Wen-Xin;Yu, Yong-Jie;Ma, Gui-Mei;Wang, Jia-Nan;Ma, Feng-Lian;Wang, Wen-Xin;Yu, Yong-Jie;Wang, Xing-Cai;She, Yuanbin;Li, Shu-Fang;Liu, Ping-Ping;Lv, Yi;Fu, Hai-Yan

作者机构：

期刊名称：ANALYTICAL CHEMISTRY （ 影响因子：7.4； 五年影响因子：7.0 ）

ISSN： 0003-2700

年卷期： 2024 年 96 卷 23 期

页码：

收录情况： SCI

摘要： Over the years, a number of state-of-the-art data analysis tools have been developed to provide a comprehensive analysis of data collected from gas chromatography-mass spectrometry (GC-MS). Unfortunately, the time shift problem remains unsolved in these tools. Here, we developed a novel comprehensive data analysis strategy for GC-MS-based untargeted metabolomics (AntDAS-GCMS) to perform total ion chromatogram peak detection, peak resolution, time shift correction, component registration, statistical analysis, and compound identification. Time shift correction was specifically optimized in this work. The information on mass spectra and elution profiles of compounds was used to search for inherent landmarks within analyzed samples to resolve the time shift problem across samples efficiently and accurately. The performance of our AntDAS-GCMS was comprehensively investigated by using four complex GC-MS data sets with various types of time shift problems. Meanwhile, AntDAS-GCMS was compared with advanced GC-MS data analysis tools and classic time shift correction methods. Results indicated that AntDAS-GCMS could achieve the best performance compared to the other methods.

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