TACTIC: hybrid denoising and clustering for robust 16S amplicon diversity estimates

Abstract

Denoising algorithms for analyzing 16S rRNA amplicon data, such as UNOISE3 and DADA2, recover error-corrected sequence variants and enhance resolution by preserving single-nucleotide variation in contrast to previous clustering-based methods. However, sequence-level resolution does not necessarily capture species-level ecological diversity. Variability at both the intra-genomic and intra-species levels within 16S rRNA genes may split a single species into multiple inferred sequence variants (i.e., zOTUs), thereby inflating estimates of microbial diversity. Using 16S rRNA sequences from the All-Species Living Tree Project (LTP) and Greengenes2 databases, we demonstrated that relying solely on sequence variation inflates species-level ecological diversity. We used sequence length to introduce variation and observed an increase in richness when using sequence diversity, particularly in cases where within-species redundancy existed in the database. Moreover, we analyzed and illustrated the amplifying effect of within-species sequence variation on inferred alpha and beta diversity across 27 V4 Illumina sequencing runs from 11 mock communities of known composition. Within-species splits, caused or picked up by denoising algorithms, inflated inferred richness and phylogeny-agnostic distances (i.e., Jaccard, weighted Jaccard, and Bray-Curtis) to the expected compositions. Post-denoising clustering reduced inflation in diversity estimates, thereby reducing the bias introduced by dissimilarity measures and converging distance values. We propose that denoising and clustering should be viewed as two complementary measures for biological resolution. We introduce TACTIC, a 16S rRNA amplicon analysis tool that offers operational taxonomic unit (OTU) clustering (UPARSE) and zOTU inference (UNOISE3), as well as two hybrid post-denoising clustering strategies that overcome the limitations of each approach: Taxonomy-Informed Clustering (TIC) and Taxonomy-Agnostic Clustering (TAC).