q2-metnet：一个 QIIME2 软件包，通过对人类肠道微生物群进行高质量的代谢重建来分析 16S rRNA 数据。

q2-metnet: QIIME2 package to analyse 16S rRNA data via high-quality metabolic reconstructions of the human gut microbiota.

机构信息

Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra, San Sebastian 20018, Spain.

Area de Genómica y Salud, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana-Salud Pública, Valencia, 46020, Spain.

出版信息

Bioinformatics. 2024 Nov 1;40(11). doi: 10.1093/bioinformatics/btae455.

DOI:10.1093/bioinformatics/btae455

PMID:39018187

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11561042/

Abstract

MOTIVATION

16S rRNA gene sequencing is the most frequent approach for the characterization of the human gut microbiota. Despite different efforts in the literature, the inference of functional and metabolic interpretations from 16S rRNA gene sequencing data is still a challenging task. High-quality metabolic reconstructions of the human gut microbiota, such as AGORA and AGREDA, constitute a curated resource to improve functional inference from 16S rRNA data, but they are not typically integrated into standard bioinformatics tools.

RESULTS

Here, we present q2-metnet, a QIIME2 plugin that enables the contextualization of 16S rRNA gene sequencing data into AGORA and AGREDA. In particular, based on relative abundances of taxa, q2-metnet determines normalized activity scores for the reactions and subsystems involved in the selected metabolic reconstruction. Using these scores, q2-metnet allows the user to conduct differential activity analysis for reactions and subsystems, as well as exploratory analysis using PCA and hierarchical clustering. We apply q2-metnet to a dataset from our group that involves 16S rRNA data from stool samples from lean, allergic to cow's milk, obese and celiac children, and the Belgian Flemish Gut Flora Project cohort, which includes faecal 16S rRNA data from obese and normal-weight adult individuals. In the first case, q2-metnet outperforms existing algorithms in separating different clinical conditions based on predicted pathway abundances and subsystem scores. In the second case, q2-metnet complements competing approaches in predicting functional alterations in the gut microbiota of obese individuals. Overall, q2-metnet constitutes a powerful bioinformatics tool to provide metabolic context to 16S rRNA data from the human gut microbiota.

AVAILABILITY AND IMPLEMENTATION

Python code of q2-metnet is available in https://github.com/PlanesLab/q2-metnet and https://figshare.com/articles/dataset/q2-metnet_package/26180446.

摘要

动机

16S rRNA 基因测序是人类肠道微生物群落特征描述的最常用方法。尽管文献中有不同的努力，但从 16S rRNA 基因测序数据中推断功能和代谢解释仍然是一项具有挑战性的任务。高质量的人类肠道微生物群代谢重建，如 AGORA 和 AGREDA，构成了从 16S rRNA 数据中改进功能推断的精选资源，但它们通常不集成到标准生物信息学工具中。

结果

在这里，我们介绍了 q2-metnet，这是一个 QIIME2 插件，它能够将 16S rRNA 基因测序数据置于 AGORA 和 AGREDA 中。特别是，基于分类群的相对丰度，q2-metnet 为所选代谢重建中涉及的反应和子系统确定标准化活性分数。使用这些分数，q2-metnet 允许用户对反应和子系统进行差异活性分析，以及使用 PCA 和层次聚类进行探索性分析。我们将 q2-metnet 应用于我们小组的一个数据集，该数据集涉及来自瘦、对牛奶过敏、肥胖和乳糜泻儿童的粪便样本的 16S rRNA 数据，以及比利时佛兰芒肠道菌群项目队列，其中包括肥胖和正常体重成年人的粪便 16S rRNA 数据。在第一种情况下，q2-metnet 在基于预测途径丰度和子系统分数分离不同临床条件方面优于现有算法。在第二种情况下，q2-metnet 补充了预测肥胖个体肠道微生物群功能改变的竞争方法。总体而言，q2-metnet 是提供人类肠道微生物群 16S rRNA 数据代谢背景的强大生物信息学工具。