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Tsai DM, Chang CY, Lin SM, Kuo TC, Wang SY, Chen GY, Kuo CH, Tseng YJ. MetaMOPE: a web service for mobile phase determination and fast chromatography peaks evaluation for metabolomics. Bioinform Adv 2023; 3:vbad061. [PMID: 37234699 PMCID: PMC10206287 DOI: 10.1093/bioadv/vbad061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 04/07/2023] [Accepted: 05/17/2023] [Indexed: 05/28/2023]
Abstract
Motivation Liquid chromatography coupled with mass spectrometry (LC-MS) is widely used in metabolomics studies, while HILIC LC-MS is particularly suited for polar metabolites. Determining an optimized mobile phase and developing a proper liquid chromatography method tend to be laborious, time-consuming and empirical. Results We developed a containerized web tool providing a workflow to quickly determine the optimized mobile phase by batch-evaluating chromatography peaks for metabolomics LC-MS studies. A mass chromatographic quality value, an asymmetric factor, and the local maximum intensity of the extracted ion chromatogram were calculated to determine the number of peaks and peak retention time. The optimal mobile phase can be quickly determined by selecting the mobile phase that produces the largest number of resolved peaks. Moreover, the workflow enables one to automatically process the repeats by evaluating chromatography peaks and determining the retention time of large standards. This workflow was validated with 20 chemical standards and successfully constructed a reference library of 571 metabolites for the HILIC LC-MS platform. Availability and implementation MetaMOPE is freely available at https://metamope.cmdm.tw. Source code and installation instructions are available on GitHub: https://github.com/CMDM-Lab/MetaMOPE. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Dong-Ming Tsai
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106216, Taiwan
- The Metabolomics Core Laboratory, Centers of Genomic and Precision Medicine, National Taiwan University, Taipei 100225, Taiwan
| | - Ching-Yao Chang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106216, Taiwan
| | - Shih-Ming Lin
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106216, Taiwan
| | - Tien-Chueh Kuo
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106216, Taiwan
- The Metabolomics Core Laboratory, Centers of Genomic and Precision Medicine, National Taiwan University, Taipei 100225, Taiwan
| | - San-Yuan Wang
- Master Program for Clinical Pharmacogenomics and Pharmacoproteomics, School of Pharmacy, Taipei Medical University, Taipei 110301, Taiwan
| | - Guan-Yuan Chen
- Forensic Medicine, College of Medicine, National Taiwan University, Taipei 100225, Taiwan
| | - Ching-Hua Kuo
- The Metabolomics Core Laboratory, Centers of Genomic and Precision Medicine, National Taiwan University, Taipei 100225, Taiwan
- School of Pharmacy, College of Medicine, National Taiwan University, Taipei 100225, Taiwan
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Luan H, Gu W, Li H, Wang Z, Lu L, Ke M, Lu J, Chen W, Lan Z, Xiao Y, Xu J, Zhang Y, Cai Z, Liu S, Zhang W. Serum metabolomic and lipidomic profiling identifies diagnostic biomarkers for seropositive and seronegative rheumatoid arthritis patients. J Transl Med 2021; 19:500. [PMID: 34876179 PMCID: PMC8650414 DOI: 10.1186/s12967-021-03169-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 11/23/2021] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum. METHODS We performed comprehensive metabolomic and lipidomic profiling in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals. RESULTS Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Both seropositive and seronegative patients were identified using this model. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity. CONCLUSIONS A panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients. Potential markers were also identified in stratifying RA cases based on disease activity.
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Affiliation(s)
- Hemi Luan
- School of Medicine, Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, 1088 Xueyuan Rd., Shenzhen, China
| | - Wanjian Gu
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu, China
| | - Hua Li
- Sustech Core Research Facilities, Southern University of Science and Technology, Shenzhen, China
| | - Zi Wang
- School of Medicine, Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, 1088 Xueyuan Rd., Shenzhen, China
| | - Lu Lu
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu, China
| | - Mengying Ke
- College of Pharmacy, Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine, Nanjing, 210046, China
| | - Jiawei Lu
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, 210009, China
| | - Wenjun Chen
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu, China
| | - Zhangzhang Lan
- School of Medicine, Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, 1088 Xueyuan Rd., Shenzhen, China
| | - Yanlin Xiao
- School of Medicine, Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, 1088 Xueyuan Rd., Shenzhen, China
| | - Jinyue Xu
- School of Medicine, Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, 1088 Xueyuan Rd., Shenzhen, China
| | - Yi Zhang
- School of Medicine, Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, 1088 Xueyuan Rd., Shenzhen, China
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis (SKLEBA), Department of Chemistry, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China.
| | - Shijia Liu
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu, China.
| | - Wenyong Zhang
- School of Medicine, Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, 1088 Xueyuan Rd., Shenzhen, China.
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Abstract
BACKGROUND Precision medicine, space exploration, drug discovery to characterization of dark chemical space of habitats and organisms, metabolomics takes a centre stage in providing answers to diverse biological, biomedical, and environmental questions. With technological advances in mass-spectrometry and spectroscopy platforms that aid in generation of information rich datasets that are complex big-data, data analytics tend to co-evolve to match the pace of analytical instrumentation. Software tools, resources, databases, and solutions help in harnessing the concealed information in the generated data for eventual translational success. AIM OF THE REVIEW In this review, ~ 85 metabolomics software resources, packages, tools, databases, and other utilities that appeared in 2020 are introduced to the research community. KEY SCIENTIFIC CONCEPTS OF REVIEW In Table 1 the computational dependencies and downloadable links of the tools are provided, and the resources are categorized based on their utility. The review aims to keep the community of metabolomics researchers updated with all the resources developed in 2020 at a collated avenue, in line with efforts form 2015 onwards to help them find these at one place for further referencing and use.
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