School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.
Zhejiang Institute of Tianjin University, Shaoxing, Shaoxing 312300, China.
Brief Bioinform. 2024 May 23;25(4). doi: 10.1093/bib/bbae264.
How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.
如何解决微生物的代谢暗物质一直是发现活性分子的一个具有挑战性的问题。多种组学工具已经被开发出来,以指导各种微生物代谢物的发现和特征描述,这使得逐渐有可能预测单个菌株的整体代谢物。多组学分析工具的结合有效地弥补了当前研究只关注单一组学或广泛类别的代谢物的缺点。在这篇综述中,我们系统地更新、分类和整理了过去五年中用于微生物代谢物预测的不同分析工具,呼吁在理解微生物代谢本质上进行多组学组合。首先,我们分别提供了基于基因组学、转录组学、蛋白质组学和代谢组学的不同更新预测数据库、网络服务器或软件的一般调查。然后,我们讨论了整合多组学数据来预测不同微生物菌株和群落代谢物的重要性,以及强调了与其他技术的结合,如系统生物学方法和数据驱动算法。最后,我们确定了开发多组学分析工具以更全面地预测对人类健康和疾病治疗有贡献的各种微生物代谢物的关键挑战和趋势。