OGUs enable effective, phylogeny-aware analysis of even shallow metagenome community structures-猫眼课题宝

DOI：

10.1101/2021.04.04.438427

发表时间：

2021-04

期刊：

bioRxiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Qiyun Zhu;Shi Huang;Antonio Gonzalez;Imran McGrath;Daniel McDonald;N. Haiminen;George Armstrong;Y. Vázquez-Baeza;Julian Yu;Justin Kuczynski;G. D. Sepich-Poore;Austin D. Swafford;Promi Das;Justin P. Shaffer;F. Lejzerowicz;P. Belda-Ferre;A. Havulinna;G. Méric;T. Niiranen;L. Lahti;V. Salomaa;Ho-Cheol Kim;Mohit Jain;M. Inouye;J. Gilbert;R. Knight

我们介绍了操作基因组单元(OGU)，这是一种元基因组分析策略，直接利用单个参考基因组的序列比对命中作为评估微生物群落多样性及其与环境因素相关性的最小单位。这种方法独立于分类学分类，允许最大限度地分解群落组成，并使用系统发育树将特征组织成准确的层次结构。这些结果适用于当代群落生态学、差异丰度和监督学习的分析协议，同时支持系统发育方法，如UniFrac和系统分解，这些方法很少应用于鸟枪式元基因组学，尽管在16S rRNA基因扩增子研究中很普遍。正如在一个人工合成和两个真实世界的案例研究中所展示的那样，OGU方法从微生物组数据集产生具有生物学意义的模式。这样的模式在极低的元基因组测序深度下仍然可以检测到。与目前采用的基于分类单元的元基因组学工具和16S rRNA基因扩增序列变体的分析相比，该方法在提供与生物学相关的见解方面显示出优势，包括与人体微生物组项目数据集上的身体环境和宿主性别更强的相关性，以及通过芬兰人口中的肠道微生物群更准确地预测人类年龄。我们提供了实现该方法的生物信息学工具Woltka，并与QIIME 2程序包和QIITA网络平台完全集成，以促进OGU在未来的元基因组学研究中的采用。与16S rRNA基因扩增子测序相比，重要性散弹枪元基因组学是一种强大的、但在计算上具有挑战性的技术，用于解码微生物群落的组成和结构。然而，目前对元基因组数据的分析主要基于分类分类，与16S rRNA扩增子序列变异分析相比，这种分类在特征分辨率方面受到限制。为了解决这些挑战，我们引入了操作基因组单元(OGUS)，这是来自序列比对结果的单个参考基因组，但没有进一步指定它们的分类。OGU方法在两个维度上推进了当前基于Read的元基因组学：(I)提供群落组成的最大分辨率，同时(Ii)允许使用系统发育感知工具。我们对真实世界数据集的分析表明，在预测生物学特性方面，比目前采用的元基因组分析方法和最精细的16S rRNA分析方法有几个优势。因此，我们建议采用OGU作为元基因组研究的标准实践。

We introduce Operational Genomic Unit (OGU), a metagenome analysis strategy that directly exploits sequence alignment hits to individual reference genomes as the minimum unit for assessing the diversity of microbial communities and their relevance to environmental factors. This approach is independent from taxonomic classification, granting the possibility of maximal resolution of community composition, and organizes features into an accurate hierarchy using a phylogenomic tree. The outputs are suitable for contemporary analytical protocols for community ecology, differential abundance and supervised learning while supporting phylogenetic methods, such as UniFrac and phylofactorization, that are seldomly applied to shotgun metagenomics despite being prevalent in 16S rRNA gene amplicon studies. As demonstrated in one synthetic and two real-world case studies, the OGU method produces biologically meaningful patterns from microbiome datasets. Such patterns further remain detectable at very low metagenomic sequencing depths. Compared with taxonomic unit-based analyses implemented in currently adopted metagenomics tools, and the analysis of 16S rRNA gene amplicon sequence variants, this method shows superiority in informing biologically relevant insights, including stronger correlation with body environment and host sex on the Human Microbiome Project dataset, and more accurate prediction of human age by the gut microbiomes in the Finnish population. We provide Woltka, a bioinformatics tool to implement this method, with full integration with the QIIME 2 package and the Qiita web platform, to facilitate OGU adoption in future metagenomics studies. Importance Shotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene amplicon sequencing for decoding the composition and structure of microbial communities. However, current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution compared to 16S rRNA amplicon sequence variant analysis. To solve these challenges, we introduce Operational Genomic Units (OGUs), which are the individual reference genomes derived from sequence alignment results, without further assigning them taxonomy. The OGU method advances current read-based metagenomics in two dimensions: (i) providing maximal resolution of community composition while (ii) permitting use of phylogeny-aware tools. Our analysis of real-world datasets shows several advantages over currently adopted metagenomic analysis methods and the finest-grained 16S rRNA analysis methods in predicting biological traits. We thus propose the adoption of OGU as standard practice in metagenomic studies.

OGUs enable effective, phylogeny-aware analysis of even shallow metagenome community structures

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