Nickols William A, McIver Lauren J, Walsh Aaron, Zhang Yancong, Nearing Jacob T, Asnicar Francesco, Punčochář Michal, Segata Nicola, Nguyen Long H, Hartmann Erica M, Franzosa Eric A, Huttenhower Curtis, Thompson Kelsey N
Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
bioRxiv. 2024 Nov 9:2024.11.08.622677. doi: 10.1101/2024.11.08.622677.
Non-human-associated microbial communities play important biological roles, but they remain less understood than human-associated communities. Here, we assess the impact of key environmental sample properties on a variety of state-of-the-art metagenomic analysis methods. In simulated datasets, all methods performed similarly at high taxonomic ranks, but newer marker-based methods incorporating metagenomic assembled genomes outperformed others at lower taxonomic levels. In real environmental data, taxonomic profiles assigned to the same sample by different methods showed little agreement at lower taxonomic levels, but the methods agreed better on community diversity estimates and estimates of the relationships between environmental parameters and microbial profiles.
非人类相关的微生物群落发挥着重要的生物学作用,但与人类相关的群落相比,人们对它们的了解仍然较少。在这里,我们评估了关键环境样本属性对各种先进的宏基因组分析方法的影响。在模拟数据集中,所有方法在较高分类级别上表现相似,但结合宏基因组组装基因组的新型基于标记的方法在较低分类级别上优于其他方法。在实际环境数据中,不同方法分配给同一样本的分类学概况在较低分类级别上几乎没有一致性,但这些方法在群落多样性估计以及环境参数与微生物概况之间关系的估计上更为一致。