MMEASE: Online meta-analysis of metabolomic data by enhanced metabolite annotation, marker selection and enrichment analysis.

Affiliation

Yang Q(1), Li B(2), Chen S(3), Tang J(4), Li Y(3), Li Y(5), Zhang S(5), Shi C(5), Zhang Y(5), Mou M(5), Xue W(3), Zhu F(6).
Author information:
(1)College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
(2)College of Life Sciences, Chongqing Normal University, Chongqing, Chongqing 401331, China.
(3)School of Pharmaceutical Sciences, School of Big Data and Software Engineering, Chongqing University, Chongqing, Chongqing 401331, China.
(4)Department of Bioinformatics, Chongqing Medical University, Chongqing, Chongqing 400016, China.
(5)College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
(6)College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; School of Pharmaceutical Sciences, School of Big Data and Software Engineering, Chongqing University, Chongqing, Chongqing 401331, China. Electronic address: [Email]

Abstract

Large-scale and long-term metabolomic studies have attracted widespread attention in the biomedical studies yet remain challenging despite recent technique progresses. In particular, the ineffective way of experiment integration and limited capacity in metabolite annotation are known issues. Herein, we constructed an online tool MMEASE enabling the integration of multiple analytical experiments with an enhanced metabolite annotation and enrichment analysis (https://idrblab.org/mmease/). MMEASE was unique in capable of (1) integrating multiple analytical blocks; (2) providing enriched annotation for >330 thousands of metabolites; (3) conducting enrichment analysis using various categories/sub-categories. All in all, MMEASE aimed at supplying a comprehensive service for large-scale and long-term metabolomics, which might provide valuable guidance to current biomedical studies. SIGNIFICANCE: To facilitate the studies of large-scale and long-term metabolomic analysis, MMEASE was developed to (1) achieve the online integration of multiple datasets from different analytical experiments, (2) provide the most diverse strategies for marker discovery, enabling performance assessment and (3) significantly amplify metabolite annotation and subsequent enrichment analysis. MMEASE aimed at supplying a comprehensive service for long-term and large-scale metabolomics, which might provide valuable guidance to current biomedical studies.