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Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
Published on: March 14, 2013
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Automatic data analysis workflow for ultra-high performance liquid chromatography-high resolution mass
Yong-Jie Yu1, Qing-Xia Zheng2, Yue-Ming Zhang1
1College of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China; Ningxia Engineering and Technology Research Center for Modernization of Hui Medicine, Ningxia Medical University, Yinchuan, 750004, China.
Journal of Chromatography. A
|December 5, 2018
Summary
This study introduces AntDAS2, an automated workflow for ultra-performance liquid chromatography-high-resolution mass spectrometry metabolomics data analysis. AntDAS2 improves component identification and pattern recognition compared to existing methods.
Area of Science:
- Analytical Chemistry
- Biochemistry
- Computational Biology
Background:
- Metabolomics data analysis using ultra-performance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS) presents significant challenges.
- Existing computational tools often struggle with accurate component identification and sample-to-sample alignment.
Purpose of the Study:
- To develop an automated data analysis workflow, AntDAS2, for UPLC-HRMS metabolomics.
- To enhance the accuracy and efficiency of metabolite identification and data processing.
Main Methods:
- Development of a density-based ion clustering algorithm for extracted-ion chromatogram extraction.
- Implementation of a maximal value-based peak detection method with automatic baseline correction and noise estimation.
- Application of a modified dynamic programming approach for clustering high-resolution m/z peaks to correct time-shift issues across samples.
Main Results:
- AntDAS2 demonstrated superior performance in identifying underlying components and improving pattern recognition compared to XCMS Online, Mzmine2, and MS-DIAL.
- The workflow exhibited greater efficiency than XCMS Online and Mzmine2.
- A user-friendly MATLAB graphical user interface (GUI) was developed for AntDAS2.
Conclusions:
- AntDAS2 provides a robust and efficient solution for UPLC-HRMS metabolomics data analysis.
- The novel algorithms within AntDAS2 significantly advance the capabilities for metabolite discovery and data interpretation.
- The availability of a MATLAB GUI facilitates broader adoption and application in research settings.
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