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

By Benjamin J. Callahan, Paul J. McMurdie, Susan P. Holmes. The ISME Journal, 2017

2,783 citations recorded by the source corpus.

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.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f6d96940fd28f74d085821014778f9dbbd733654

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Citations in the corpus

QIIME 2 Enables Comprehensive End‐to‐End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data (2020, background): “The shorthand “artifact” in the per-sample Deblur statistics denotes artifactual sequences (i.e., those erroneously generated as byproducts of the PCR and DNA sequencing process), not a QIIME 2 artifact (i.e., a valid data product of QIIME 2).”
Microbiome definition re-visited: old concepts and new challenges (2020, neutral): “marker-gene analysis and reporting [161].”
Updating the 97% identity threshold for 16S ribosomal RNA OTUs (2017, background): “I agree with a recent perspective (Callahan et al., 2017) arguing that “improvements in reusability, reproducibility and comprehensiveness are sufficiently great that [ZOTUs] should replace [97”
MiFish metabarcoding: a high-throughput approach for simultaneous detection of multiple fish species from environmental DNA and other samples (2020, methodology): “ASV methods infer the biological sequences in the sample prior to the introduction of amplification and sequencing errors, and distinguish sequence variants differing by as little as one nucleotide (Callahan et al. 2017).”
Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment (2021, background): “However, due to recent advances in high-throughput sequencing technologies, OTUs are increasingly being replaced by amplicon sequence variants (ASVs), which are un-clustered error-corrected reads (Callahan et al., 2017).”
A practical guide to DNA metabarcoding for entomological ecologists (2020, methodology): “A better strategy is to incorporate single-nucleotide differences into OTU curation that probably retains all amplicon sequence variants (ASVs; Callahan et al., 2017).”
NetCoMi: network construction and comparison for microbiome data in R (2020, methodology): “MGS techniques allow the quantification of taxon abundances in the sample in form of operational taxonomic units (OTUs) [7, 8], amplicon sequencing variants (ASVs) [9], or metagenomic OTUs (mOTUs) [10].”
What drives study‐dependent differences in distance–decay relationships of microbial communities? (2021, background): “For example, increasing the similarity threshold at which operational taxonomic units are defined is thought to be equivalent to increasing the taxonomic resolution (Callahan et al., 2017).”
MicrobiomeAnalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data (2023, positive): “The marker gene data analysis has seen a shift from the traditional operational tax onom y units (O TUs), w hich ar e clusters of r eads based on similarity thresholds, towards high-resolution amplicon sequence variants (ASVs) identified based on their unique biological sequences ( 16 ).”