Algorithms in Bioinformatics, Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany.
International Max Planck Research School "From Molecules to Organisms", Max Planck Institute for Biology Tübingen, Tübingen, Germany.
Brief Bioinform. 2023 Sep 22;24(6). doi: 10.1093/bib/bbad325.
A microbial community maintains its ecological dynamics via metabolite crosstalk. Hence, knowledge of the metabolome, alongside its populace, would help us understand the functionality of a community and also predict how it will change in atypical conditions. Methods that employ low-cost metagenomic sequencing data can predict the metabolic potential of a community, that is, its ability to produce or utilize specific metabolites. These, in turn, can potentially serve as markers of biochemical pathways that are associated with different communities. We developed MMIP (Microbiome Metabolome Integration Platform), a web-based analytical and predictive tool that can be used to compare the taxonomic content, diversity variation and the metabolic potential between two sets of microbial communities from targeted amplicon sequencing data. MMIP is capable of highlighting statistically significant taxonomic, enzymatic and metabolic attributes as well as learning-based features associated with one group in comparison with another. Furthermore, MMIP can predict linkages among species or groups of microbes in the community, specific enzyme profiles, compounds or metabolites associated with such a group of organisms. With MMIP, we aim to provide a user-friendly, online web server for performing key microbiome-associated analyses of targeted amplicon sequencing data, predicting metabolite signature, and using learning-based linkage analysis, without the need for initial metabolomic analysis, and thereby helping in hypothesis generation.
微生物群落通过代谢物串扰来维持其生态动力学。因此,了解代谢组及其群体将有助于我们理解群落的功能,还能预测其在非典型条件下将如何变化。采用低成本宏基因组测序数据的方法可以预测群落的代谢潜能,即其产生或利用特定代谢物的能力。这些代谢物反过来又可能成为与不同群落相关的生化途径的标志物。我们开发了 MMIP(微生物组代谢组集成平台),这是一个基于网络的分析和预测工具,可用于比较靶向扩增子测序数据中两组微生物群落的分类内容、多样性变化和代谢潜能。MMIP 能够突出显示与一组相比具有统计学意义的分类、酶和代谢特征,以及基于学习的特征。此外,MMIP 可以预测群落中物种或微生物群之间的联系、特定的酶谱、与该组生物相关的化合物或代谢物。借助 MMIP,我们旨在提供一个用户友好的在线网络服务器,用于对靶向扩增子测序数据执行关键的微生物组相关分析、预测代谢物特征,并使用基于学习的链接分析,而无需进行初始代谢组分析,从而有助于生成假说。