基因组
工作流程
生物
鉴定(生物学)
计算生物学
相对物种丰度
丰度(生态学)
基因组
古细菌
进化生物学
生态学
基因
计算机科学
遗传学
数据库
作者
Marco Gabrielli,Zihan Dai,Vincent Delafont,Peer H. A. Timmers,Paul W. J. J. van der Wielen,Manuela Antonelli,Ameet Pinto
标识
DOI:10.1021/acs.est.2c09010
摘要
The biogeography of eukaryotes in drinking water systems is poorly understood relative to that of prokaryotes or viruses, limiting the understanding of their role and management. A challenge with studying complex eukaryotic communities is that metagenomic analysis workflows are currently not as mature as those that focus on prokaryotes or viruses. In this study, we benchmarked different strategies to recover eukaryotic sequences and genomes from metagenomic data and applied the best-performing workflow to explore the factors affecting the relative abundance and diversity of eukaryotic communities in drinking water distribution systems (DWDSs). We developed an ensemble approach exploiting k-mer- and reference-based strategies to improve eukaryotic sequence identification and identified MetaBAT2 as the best-performing binning approach for their clustering. Applying this workflow to the DWDS metagenomes showed that eukaryotic sequences typically constituted small proportions (i.e., <1%) of the overall metagenomic data with higher relative abundances in surface water-fed or chlorinated systems with high residuals. The α and β diversities of eukaryotes were correlated with those of prokaryotic and viral communities, highlighting the common role of environmental/management factors. Finally, a co-occurrence analysis highlighted clusters of eukaryotes whose members' presence and abundance in DWDSs were affected by disinfection strategies, climate conditions, and source water types.
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