Exact sequence variants should replace operational taxonomic units in marker-gene data analysis

TLDR

High‑throughput marker‑gene sequencing can now be analyzed without constructing OTUs, as new methods resolve amplicon sequence variants (ASVs) to single‑nucleotide precision, offering finer resolution than traditional OTUs. The study aims to demonstrate that ASVs combine the computational efficiency of closed‑reference OTUs with the diversity accuracy of de novo OTUs, thereby justifying their adoption as the standard unit of analysis. The authors employ error‑controlled methods that resolve ASVs to single‑nucleotide differences, enabling linear‑time computation, straightforward dataset merging, forward prediction, and accurate diversity estimation even without deep reference coverage. The authors find that ASVs’ consistency and intrinsic biological meaning enhance reusability, reproducibility, and comprehensiveness, making them superior to OTUs and warranting their adoption as the standard analysis unit.

Abstract

Abstract Recent advances have made it possible to analyze high-throughput marker-gene sequencing data without resorting to the customary construction of molecular operational taxonomic units (OTUs): clusters of sequencing reads that differ by less than a fixed dissimilarity threshold. New methods control errors sufficiently such that amplicon sequence variants (ASVs) can be resolved exactly, down to the level of single-nucleotide differences over the sequenced gene region. The benefits of finer resolution are immediately apparent, and arguments for ASV methods have focused on their improved resolution. Less obvious, but we believe more important, are the broad benefits that derive from the status of ASVs as consistent labels with intrinsic biological meaning identified independently from a reference database. Here we discuss how these features grant ASVs the combined advantages of closed-reference OTUs—including computational costs that scale linearly with study size, simple merging between independently processed data sets, and forward prediction—and of de novo OTUs—including accurate measurement of diversity and applicability to communities lacking deep coverage in reference databases. We argue that the improvements in reusability, reproducibility and comprehensiveness are sufficiently great that ASVs should replace OTUs as the standard unit of marker-gene analysis and reporting.