代谢组学
化学
色谱法
质谱法
萃取(化学)
数据提取
数据集
数据挖掘
计算机科学
人工智能
生物化学
梅德林
作者
Xing-Cai Wang,Meng Zhai,Shufang Li,Hang Lv,Hui Ma,Chang Yang,Qingxia Zheng,Pingping Liu,Peng Lü,Yong‐Jie Yu,Haiyan Fu,Yuanbin She
标识
DOI:10.1021/acs.analchem.4c05768
摘要
The profile mode of ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) is commonly utilized in metabolomics for its ability to comprehensively retain compound information in mass spectra. However, current data-analysis methods have not been optimized for the entire profile-mode-based untargeted metabolomics. To address this issue, we developed a set of novel algorithms, including centroiding transformation, extracted ion chromatogram construction, and feature extraction. We integrated them into a new automatic data analysis platform, AntDAS-Profiler. The performance of these newly developed algorithms was demonstrated by distinguishing chrysanthemums from various production origins. Additionally, AntDAS-Profiler was comprehensively compared with several state-of-the-art tools such as MS-DIAL, XCMS, and MZmine. Results suggested that AntDAS-Profiler can provide researchers with a comprehensive solution for UHPLC-HRMS profile-mode-based metabolomics. AntDAS-Profiler can be accessed at http://www.pmdb.org.cn/antdasprofiler.
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