A flexible pipeline combining clustering and correction tools for prokaryotic and eukaryotic metabarcoding

Environmental metabarcoding is an increasingly popular tool for studying biodiversity in marine and terrestrial biomes. With sequencing costs decreasing, multiple-marker metabarcoding, spanning several branches of the tree of life, is becoming more accessible. 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 Illumina read correction with DADA2 allowing analyzing 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 v2, a network-based clustering algorithm, and to further curate the ASVs/OTUs based on sequence similarity and co-occurrence rates using a recently developed algorithm, LULU. Finally, flexible taxonomic assignment was implemented via Ribosomal Database Project (RDP) Bayesian classifier and BLAST. We validate this pipeline with ribosomal and mitochondrial markers using eukaryotic mock communities and 42 deep-sea sediment samples. The results show that ASVs, reflecting genetic diversity, may not be appropriate for alpha diversity estimation of organisms fitting the biological species concept. The results underline the advantages of clustering and LULU-curation for producing more reliable metazoan biodiversity inventories, and show that LULU is an effective tool for filtering metazoan molecular clusters, although the minimum identity threshold applied to co-occurring OTUs has to be increased for 18S. The comparison of BLAST and the RDP Classifier underlined the potential of the latter to deliver very good assignments, but highlighted the need for a concerted effort to build comprehensive, ecosystem-specific, databases adapted to the studied communities.