MIMOSA2：一种基于代谢网络的工具，用于推断微生物组-代谢组数据中机制支持的关系。

MIMOSA2: a metabolic network-based tool for inferring mechanism-supported relationships in microbiome-metabolome data.

机构信息

Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.

Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 6997801, Israel.

出版信息

Bioinformatics. 2022 Mar 4;38(6):1615-1623. doi: 10.1093/bioinformatics/btac003.

DOI:10.1093/bioinformatics/btac003

PMID:34999748

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8896604/

Abstract

MOTIVATION

Recent technological developments have facilitated an expansion of microbiome-metabolome studies, in which samples are assayed using both genomic and metabolomic technologies to characterize the abundances of microbial taxa and metabolites. A common goal of these studies is to identify microbial species or genes that contribute to differences in metabolite levels across samples. Previous work indicated that integrating these datasets with reference knowledge on microbial metabolic capacities may enable more precise and confident inference of microbe-metabolite links.

RESULTS

We present MIMOSA2, an R package and web application for model-based integrative analysis of microbiome-metabolome datasets. MIMOSA2 uses genomic and metabolic reference databases to construct a community metabolic model based on microbiome data and uses this model to predict differences in metabolite levels across samples. These predictions are compared with metabolomics data to identify putative microbiome-governed metabolites and taxonomic contributors to metabolite variation. MIMOSA2 supports various input data types and customization with user-defined metabolic pathways. We establish MIMOSA2's ability to identify ground truth microbial mechanisms in simulation datasets, compare its results with experimentally inferred mechanisms in honeybee microbiota, and demonstrate its application in two human studies of inflammatory bowel disease. Overall, MIMOSA2 combines reference databases, a validated statistical framework, and a user-friendly interface to facilitate modeling and evaluating relationships between members of the microbiota and their metabolic products.

AVAILABILITY AND IMPLEMENTATION

MIMOSA2 is implemented in R under the GNU General Public License v3.0 and is freely available as a web server at http://elbo-spice.cs.tau.ac.il/shiny/MIMOSA2shiny/ and as an R package from http://www.borensteinlab.com/software_MIMOSA2.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

最近的技术发展促进了微生物组-代谢组研究的扩展，在这些研究中，使用基因组和代谢组学技术对样本进行检测，以描述微生物分类群和代谢物的丰度。这些研究的一个共同目标是确定对样本间代谢物水平差异有贡献的微生物物种或基因。先前的工作表明，将这些数据集与微生物代谢能力的参考知识相结合，可以更精确和自信地推断微生物-代谢物之间的联系。

结果

我们提出了 MIMOSA2，这是一个用于微生物组-代谢组数据集的基于模型的综合分析的 R 包和网络应用程序。MIMOSA2 使用基因组和代谢参考数据库，根据微生物组数据构建基于群落的代谢模型，并使用该模型预测样本间代谢物水平的差异。将这些预测与代谢组学数据进行比较，以识别可能受微生物控制的代谢物和对代谢物变化有贡献的分类群。MIMOSA2 支持各种输入数据类型和用户定义的代谢途径的定制。我们确定了 MIMOSA2 在模拟数据集上识别真实微生物机制的能力，比较了它在蜜蜂微生物组中实验推断的机制的结果，并在两项炎症性肠病的人类研究中展示了它的应用。总的来说，MIMOSA2 结合了参考数据库、经过验证的统计框架和用户友好的界面，以促进建模和评估微生物群成员与其代谢产物之间的关系。

可用性和实现

MIMOSA2 是在 R 中实现的，遵循 GNU 通用公共许可证 v3.0，并可作为网络服务器在 http://elbo-spice.cs.tau.ac.il/shiny/MIMOSA2shiny/ 上免费获得，并作为 R 包从 http://www.borensteinlab.com/software_MIMOSA2.html 上获得。