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.
College of Life Sciences, Chongqing Normal University, Chongqing, Chongqing 401331, China.
J Proteomics. 2021 Feb 10;232:104023. doi: 10.1016/j.jprot.2020.104023. Epub 2020 Oct 29.
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.
大规模和长期代谢组学研究在生物医学研究中引起了广泛关注,但尽管最近技术取得了进展,仍然具有挑战性。特别是,实验集成的无效方式和代谢物注释的有限能力是已知的问题。在此,我们构建了一个在线工具 MMEASE,能够实现多个分析实验的集成,并增强代谢物注释和富集分析(https://idrblab.org/mmease/)。MMEASE 的独特之处在于能够:(1)集成多个分析模块;(2)为超过 33 万种代谢物提供丰富的注释;(3)使用各种类别/子类别进行富集分析。总之,MMEASE 旨在为大规模和长期代谢组学提供全面的服务,这可能为当前的生物医学研究提供有价值的指导。
为了促进大规模和长期代谢组学分析的研究,开发了 MMEASE 来:(1)实现来自不同分析实验的多个数据集的在线集成,(2)提供用于标志物发现的最多样化的策略,从而能够进行性能评估,(3)显著扩大代谢物注释和随后的富集分析。MMEASE 旨在为长期和大规模代谢组学提供全面的服务,这可能为当前的生物医学研究提供有价值的指导。