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微生物群落网络:探索微生物关联和代谢谱以获取机制性见解。

MicrobiomeNet: exploring microbial associations and metabolic profiles for mechanistic insights.

作者信息

Lu Yao, Hui Fiona, Zhou Guangyan, Xia Jianguo

机构信息

Institute of Parasitology, McGill University, Quebec, Canada.

Department of Microbiology and Immunology, McGill University, Quebec, Canada.

出版信息

Nucleic Acids Res. 2025 Jan 6;53(D1):D789-D796. doi: 10.1093/nar/gkae944.

Abstract

The growing volumes of microbiome studies over the past decade have revealed a wide repertoire of microbial associations under diverse conditions. Microbes produce small molecules to interact with each other as well as to modulate their environments. Their metabolic profiles hold the key to understanding these association patterns for translational applications. Based on this concept, we developed MicrobiomeNet, a comprehensive database that integrates microbial associations with their metabolic profiles for mechanistic insights. It currently contains a total of ∼5.8 million known microbial associations, coupled with >12 400 genome-scale metabolic models (GEMs) covering ∼6000 microbial species. Users can intuitively explore microbial associations and compare their corresponding metabolic profiles. Our case studies show that MicrobiomeNet can provide mechanistic insights that are consistent with the literature. MicrobiomeNet is freely available at https://www.microbiomenet.com/.

摘要

在过去十年中,微生物组研究的数量不断增加,揭示了在不同条件下广泛的微生物关联。微生物产生小分子以相互作用并调节其环境。它们的代谢谱是理解这些关联模式以用于转化应用的关键。基于这一概念,我们开发了MicrobiomeNet,这是一个综合数据库,整合了微生物关联及其代谢谱以获得机制性见解。它目前总共包含约580万个已知的微生物关联,以及涵盖约6000种微生物物种的超过12400个基因组规模代谢模型(GEMs)。用户可以直观地探索微生物关联并比较其相应的代谢谱。我们的案例研究表明,MicrobiomeNet可以提供与文献一致的机制性见解。可在https://www.microbiomenet.com/免费获取MicrobiomeNet。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48a7/11701532/b74f45d6c45e/gkae944figgra1.jpg

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