New insights into biologic interpretation of bioinformatic pipelines for fish eDNA metabarcoding: A case study in Pearl River estuary

文献类型： 外文期刊

第一作者： Li, Zhuoying

作者： Li, Zhuoying;Zhao, Wencheng;Jiang, Yun;Wen, Yongjing;Liu, Li;Zou, Keshu;Li, Min

作者机构：

关键词： eDNA metabarcoding; Fish community; Bioinformatic pipelines; Distance matrix; Comparative evaluation

期刊名称：JOURNAL OF ENVIRONMENTAL MANAGEMENT （ 影响因子：8.0； 五年影响因子：7.9 ）

ISSN： 0301-4797

年卷期： 2024 年 368 卷

页码：

收录情况： SCI

摘要： Environmental DNA (eDNA) metabarcoding is an emerging tool for monitoring biological communities in aquatic ecosystems. The selection of bioinformatic pipelines significantly impacts the results of biodiversity assessments. However, there is currently no consensus on the appropriate bioinformatic pipelines for fish community analysis in eDNA metabarcoding. In this study, we compared three bioinformatic pipelines (Uparse, DADA2, and UNOISE3) using real and mock (constructed with 15/30 known fish) communities to investigate the differences in biological interpretation during the data analysis process in eDNA metabarcoding. Performance evaluation and diversity analyses revealed that the choice of bioinformatic pipeline could impact the biological results of metabarcoding experiments. Among the three pipelines, the operational taxonomic units (OTU)-based pipeline (Uparse) showed the best performance (sensitivity: 0.6250 +/- 0.0166; compositional similarity: 0.4000 +/- 0.0571), the highest richness (25-102) and minimal inter-group differences in alpha diversity. It suggested the OTU-based pipeline possessed superior capability in fish diversity monitoring compared to ASV/ZOTU-based pipeline. Additionally, the Bray-Curtis distance matrix achieved the highest discriminative effect in the PCoA (43.3%-53.89%) and inter-group analysis (P < 0.01), indicating it was better at distinguishing compositional differences or specific genera of fish community at different sampling sites than other distance matrices. These findings provide new insights into fish community monitoring through eDNA metabarcoding in estuarine environments.

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