化学
代谢组学
色谱法
质谱法
保留时间
分辨率(逻辑)
分析化学(期刊)
模式识别(心理学)
生物系统
人工智能
计算机科学
生物
作者
Juanjuan Zhao,Yang Zhang,Xing-Cai Wang,Xuan Wang,Qian Zhang,Peng Lü,Pingping Liu,Yong‐Jie Yu,Lu Han,Huina Zhou,Qingxia Zheng,Haiyan Fu
标识
DOI:10.1016/j.aca.2021.339393
摘要
Substantial deviations in retention times among samples pose a great challenge for the accurate screening and identifying of metabolites by ultrahigh-performance liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS). In this study, a coarse-to-refined time-shift correction methodology was proposed to efficiently address this problem. Metabolites producing multiple fragment ions were automatically selected as landmarks to generate pseudo-mass spectra for a coarse time-shift correction. Refined peak alignment for extracted ion chromatograms was then performed by using a moving window-based multiple-peak alignment strategy. Based on this novel coarse-to-refined time-shift correction methodology, a new comprehensive UHPLC-HRMS data analysis platform was developed for UHPLC-HRMS-based metabolomics. Original datasets were employed as inputs to automatically extract and register features in the dataset and to distinguish fragment ions from metabolites for chemometric analysis. Its performance was further evaluated using complex datasets, and the results suggest that the new platform can satisfactorily resolve the time-shift problem and is comparable with commonly used UHPLC-HRMS data analysis tools such as XCMS Online, MS-DIAL, Mzmine2, and Progenesis QI. The new platform can be downloaded from: http://www.pmdb.org.cn/antdas2tsc.
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