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Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates
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Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates
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Journal Article
Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates
2017
Overview
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.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
