Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection

doi:10.1016/j.aca.2018.05.001

	Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection
	Li, Zhucui 1,2,3; Lu, Yan1,2,4 ; Guo, Yufeng 3; Cao, Haijie 5; Wang, Qinhong 3; Shui, Wenqing2,4
	2018-10-31
发表期刊	ANALYTICA CHIMICA ACTA
ISSN	0003-2670
卷号	1029 页码:50-57
发表状态	已发表
DOI	10.1016/j.aca.2018.05.001
摘要	Data analysis represents a key challenge for untargeted metabolomics studies and it commonly requires extensive processing of more than thousands of metabolite peaks included in raw high-resolution MS data. Although a number of software packages have been developed to facilitate untargeted data processing, they have not been comprehensively scrutinized in the capability of feature detection, quantification and marker selection using a well-defined benchmark sample set. In this study, we acquired a benchmark dataset from standard mixtures consisting of 1100 compounds with specified concentration ratios including 130 compounds with significant variation of concentrations. Five software evaluated here (MS-Dial, MZmine 2, XCMS, MarkerView, and Compound Discoverer) showed similar performance in detection of true features derived from compounds in the mixtures. However, significant differences between untargeted metabolomics software were observed in relative quantification of true features in the benchmark dataset. MZmine 2 outperformed the other software in terms of quantification accuracy and it reported the most true discriminating markers together with the fewest false markers. Furthermore, we assessed selection of discriminating markers by different software using both the benchmark dataset and a real-case metabolomics dataset to propose combined usage of two software for increasing confidence of biomarker identification. Our findings from comprehensive evaluation of untargeted metabolomics software would help guide future improvements of these widely used bioinformatics tools and enable users to properly interpret their metabolomics results. (C) 2018 Elsevier B.V. All rights reserved.
关键词	Untargeted metabolomics Data processing software Feature detection Feature quantification Discriminating marker selection
收录类别	SCI ; SCIE ; EI
语种	英语
资助项目	National Natural Science Foundation of China[31401150]
WOS研究方向	Chemistry
WOS类目	Chemistry, Analytical
WOS记录号	WOS:000436586000008
出版者	ELSEVIER SCIENCE BV
WOS关键词	SPECTROMETRY-BASED METABOLOMICS ; MASS-SPECTROMETRY ; MISSING VALUES ; DATA SET ; DISCOVERY ; PERFORMANCE ; METABOLISM ; WORKFLOW ; PLATFORM ; URINE
原始文献类型	Article
引用统计
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/27444
专题	生命科学与技术学院_博士生 iHuman研究所_PI研究组_水雯箐组
通讯作者	Shui, Wenqing
作者单位	1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.ShanghaiTech Univ, iHuman Inst, Shanghai 201210, Peoples R China 3.Chinese Acad Sci, Tianjin Inst Ind Biotechnol, Tianjin 300308, Peoples R China 4.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 201210, Peoples R China 5.Nankai Univ, Coll Pharm, Tianjin 300071, Peoples R China
第一作者单位	iHuman研究所
通讯作者单位	iHuman研究所; 生命科学与技术学院
推荐引用方式 GB/T 7714	Li, Zhucui,Lu, Yan,Guo, Yufeng,et al. Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection[J]. ANALYTICA CHIMICA ACTA,2018,1029:50-57.
APA	Li, Zhucui,Lu, Yan,Guo, Yufeng,Cao, Haijie,Wang, Qinhong,&Shui, Wenqing.(2018).Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection.ANALYTICA CHIMICA ACTA,1029,50-57.
MLA	Li, Zhucui,et al."Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection".ANALYTICA CHIMICA ACTA 1029(2018):50-57.