Bioinformatic pipelines combining denoising and clustering tools allow for more comprehensive prokaryotic and eukaryotic metabarcoding

Type Article
Date 2021-08
Language English
Author(s) Brandt MiriamORCID1, Trouche Blandine2, Quintric Laure3, Günther BabettORCID6, Wincker Patrick4, 5, Poulain Julie4, 5, Arnaud‐haond SophieORCID1
Affiliation(s) 1 : MARBEC, Ifremer Univ. Montpellier IRD CNRS Sète, France
2 : Univ. Brest, CNRS, Ifremer Laboratoire de Microbiologie des Environnements Extrêmes Plouzané, France
3 : Ifremer Cellule Bioinformatique Brest ,France
4 : Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ. Evry, Université Paris‐Saclay 91057 Evry, France
5 : Research Federation for the study of Global Ocean Systems Ecology and Evolution FR2022 Tara, France
6 : MARBEC, Ifremer Univ. Montpellier IRD CNRS Sète, France
Source Molecular Ecology Resources (1755-098X) (Wiley), 2021-08 , Vol. 21 , N. 6 , P. 1904-1921
DOI 10.1111/1755-0998.13398
WOS© Times Cited 6
Keyword(s) DADA2, deep&#8208, sea biodiversity, LULU, mock communities, multimarker metabarcoding, swarm
Abstract

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 validate 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 datasets. 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.

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Brandt Miriam, Trouche Blandine, Quintric Laure, Günther Babett, Wincker Patrick, Poulain Julie, Arnaud‐haond Sophie (2021). Bioinformatic pipelines combining denoising and clustering tools allow for more comprehensive prokaryotic and eukaryotic metabarcoding. Molecular Ecology Resources, 21(6), 1904-1921. Publisher's official version : https://doi.org/10.1111/1755-0998.13398 , Open Access version : https://archimer.ifremer.fr/doc/00688/80057/