Denoising the Denoisers: An independent evaluation of microbiome sequence error-correction methods

High-depth sequencing of universal marker genes such as the 16S rRNA gene are a common strategy to profile microbial communities. Traditionally, sequence reads are clustered into operational taxonomic units (OTUs) at a defined identity threshold to avoid sequencing errors generating spurious taxonomic units. However, there have been numerous bioinformatic methods recently released that attempt to correct sequencing errors to determine real biological sequences at single nucleotide resolution by generating amplicon sequence variants (ASVs). As the microbiome field moves from OTUs to higher resolution ASVs, there is a need for an in-depth and unbiased comparison of these novel “denoising” methods. In this study, we conduct a thorough comparison of three of the most widely-used denoising methods on mock, soil, and host-associated communities. We tested three different methods - DADA2, UNOISE3, and Deblur - on four mock communities and found that, although they produced similar microbial compositions based on relative abundance, the methods identified vastly different numbers of ASVs. Our analysis of a soil dataset also showed that the three methods were consistent in their per-sample compositions, resulting in only minor differences based on weighted UniFrac distances. However, DADA2 tended to find more ASVs than the other two methods when analyzing both the real soil data and two other host-associated datasets, suggesting that it could be better at finding rare organisms. The three tested methods were significantly different in their run times, with UNOISE3 running greater than 1200 and 15 times faster than DADA2 and Deblur, respectively. Our results indicate that the choice of denoising method will depend on a researcher’s individual importance for identifying rare ASVs, the availability of computational resources, and their willingness to support open-source or closed-source software.