Format

Send to

Choose Destination
Anal Chim Acta. 2018 Oct 31;1029:50-57. doi: 10.1016/j.aca.2018.05.001. Epub 2018 May 4.

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

Author information

1
University of Chinese Academy of Sciences, Beijing 100049, China; iHuman Institute, ShanghaiTech University, Shanghai 201210, China; Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.
2
University of Chinese Academy of Sciences, Beijing 100049, China; iHuman Institute, ShanghaiTech University, Shanghai 201210, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
3
Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.
4
College of Pharmacy, Nankai University, Tianjin 300071, China.
5
iHuman Institute, ShanghaiTech University, Shanghai 201210, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China. Electronic address: shuiwq@shanghaitech.edu.cn.

Abstract

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.

KEYWORDS:

Data processing software; Discriminating marker selection; Feature detection; Feature quantification; Untargeted metabolomics

PMID:
29907290
DOI:
10.1016/j.aca.2018.05.001
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center