High-throughput sequencing of a targeted genetic marker is being widely used to analyze biodiversity across taxa and environments. Amid a multitude of exciting findings, scientists have also identified and addressed technical and biological limitations. Improved study designs and alternative sampling, lab and bioinformatic procedures have progressively enhanced data quality, but some problems persist. This article provides a framework to recognize and bypass the main types of errors that can affect metabarcoding data: false negatives, false positives, artifactual variants, disproportions and incomplete or incorrect taxonomic identifications. It is crucial to discern potential error impacts on different ecological parameters (e.g. taxon distribution, community structure, alpha and beta-diversity), as error management implies compromises and is thus directed by the research question. Synthesis of multiple plankton metabarcoding evaluations (mock sample sequencing or microscope comparisons) shows that high-quality data for qualitative and some semiquantitative goals can be achieved by implementing three checkpoints: first, rigorous protocol optimization; second, error minimization; and third, downstream analysis that considers potentially remaining biases. Conclusions inform us about the reliability of metabarcoding for plankton studies and, because plankton provides unique chances to compare genotypes and phenotypes, the robustness of this method in general.
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- Journal of Eukaryotic Microbiology
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- National Science Foundation
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