Environmental DNA metabarcoding is a powerful tool for studying biodiversity. However, bioinformatic approaches need to adjust to the diversity of taxonomic compartments targeted as well as to each barcode gene specificities. We built and tested a pipeline based on read correction with DADA2 allowing analysing metabarcoding data from prokaryotic (16S) and eukaryotic (18S, COI) life compartments. We implemented the option to cluster amplicon sequence variants (ASVs) into operational taxonomic units (OTUs) with swarm, a network-based clustering algorithm, and the option to curate ASVs/OTUs using LULU. Finally, taxonomic assignment was implemented via the Ribosomal Database Project Bayesian classifier (RDP) and BLAST. We validated this pipeline with ribosomal and mitochondrial markers using metazoan mock communities and 42 deep-sea sediment samples. The results show that ASVs and OTUs describe different levels of biotic diversity, the choice of which depends on the research questions. They underline the advantages and complementarity of clustering and LULU-curation for producing metazoan biodiversity inventories at a level approaching the one obtained using morphological criteria. While clustering removes intraspecific variation, LULU effectively removes spurious clusters, originating from errors or intragenomic variability. Swarm clustering affected alpha and beta diversity differently depending on genetic marker. Specifically, d-values > 1 appeared to be less appropriate with 18S for metazoans. Similarly, increasing LULU’s minimum ratio level proved essential to avoid losing species in sample-poor data sets. Comparing BLAST and RDP underlined that accurate assignments of deep-sea species can be obtained with RDP, but highlighted the need for a concerted effort to build comprehensive, ecosystem-specific databases.

中文翻译：

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结合去噪和聚类工具的生物信息学管道允许更全面的原核和真核元条形码

环境 DNA 元条形码是研究生物多样性的有力工具。然而，生物信息学方法需要适应目标分类区室的多样性以及每个条形码基因的特异性。我们使用 DADA2 构建并测试了基于读取校正的管道，允许分析来自原核 (16S) 和真核 (18S，COI) 生命区室的元条形码数据。我们实现了使用基于网络的聚类算法 swarm 将扩增子序列变体 (ASV) 聚类到操作分类单元 (OTU) 中的选项，以及使用 LULU 管理 ASV/OTU 的选项。最后，通过核糖体数据库项目贝叶斯分类器 (RDP) 和 BLAST 进行分类分配。我们使用后生动物模拟群落和 42 个深海沉积物样本，用核糖体和线粒体标记验证了这条管道。结果表明，ASV 和 OTU 描述了不同水平的生物多样性，其选择取决于研究问题。他们强调了聚类和 LULU 管理的优势和互补性，可以在接近使用形态标准获得的水平上产生后生动物生物多样性清单。在聚类消除种内变异的同时，LULU 有效地消除了源自错误或基因组内变异的虚假簇。群体聚类对 alpha 和 beta 多样性的影响取决于遗传标记。具体来说，他们强调了聚类和 LULU 管理的优势和互补性，可以在接近使用形态标准获得的水平上产生后生动物生物多样性清单。在聚类消除种内变异的同时，LULU 有效地消除了源自错误或基因组内变异的虚假簇。群体聚类对 alpha 和 beta 多样性的影响取决于遗传标记。具体来说，他们强调了聚类和 LULU 管理的优势和互补性，可以在接近使用形态标准获得的水平上产生后生动物生物多样性清单。在聚类消除种内变异的同时，LULU 有效地消除了源自错误或基因组内变异的虚假簇。群体聚类对 alpha 和 beta 多样性的影响取决于遗传标记。具体来说，对于后生动物，d-值 > 1 似乎不太适合 18S。同样，事实证明，提高 LULU 的最小比率水平对于避免在样本不足的数据集中丢失物种至关重要。BLAST 和 RDP 的比较强调了深海物种的准确分配可以通过 RDP 获得，但强调需要共同努力建立全面的、特定于生态系统的数据库。