Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates

By Tobias Frøslev, R. Kjøller, H. H. Bruun, Rasmus Ejrnæs, Ane Kirstine Brunbjerg, Carlotta Pietroni, A. Hansen. Nature Communications, 2017

684 citations recorded by the source corpus.

DNA metabarcoding is promising for cost-effective biodiversity monitoring, but reliable diversity estimates are difficult to achieve and validate. Here we present and validate a method, called LULU, for removing erroneous molecular operational taxonomic units (OTUs) from community data derived by high-throughput sequencing of amplified marker genes. LULU identifies errors by combining sequence similarity and co-occurrence patterns. To validate the LULU method, we use a unique data set of high quality survey data of vascular plants paired with plant ITS2 metabarcoding data of DNA extracted from soil from 130 sites in Denmark spanning major environmental gradients. OTU tables are produced with several different OTU definition algorithms and subsequently curated with LULU, and validated against field survey data. LULU curation consistently improves α-diversity estimates and other biodiversity metrics, and does not require a sequence reference database; thus, it represents a promising method for reliable biodiversity estimation. A central problem in biodiversity estimation from genetic markers is the ability of algorithms to retain ‘true’ species while discarding artefacts. Here, the authors present a new post-clusturing curation algorithm using OTU co-occurrences to estimate plant biodiversity from soil samples.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0361a91cb3e0887435f166cac22fa3e7e84bf4fb

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In Molecular Biology

Citations in the corpus

Comparing environmental DNA metabarcoding and underwater visual census to monitor tropical reef fishes (2020, methodology): “A postclustering curation algorithm (LULU; Frøslev et al., 2017) was then applied to curate the data.”