Fig 1.
Effect of different USEARCH paired-end read merging parameters (“maxdiffs”).
Fig 2.
Hamming distance (no. of base differences) from each ASV/OTU sequence to the closest true sequence present in the mock community.
Fig 3.
Hamming distance from each ASV/OTU sequence to the closest other ASV/OTU sequence.
Dashed line marks the Hamming distance = 7 threshold, corresponding to the 97% identity threshold for OTUs in V4 16S rRNA gene amplicons. Blue ellipses highlight ASVs that are only 1 Hamming distance away from each other.
Table 1.
Sensitivity and specificity over three mock sequencing runs.
Values are reported as mean (standard deviation).
Table 2.
Inferred ratios of 16S rRNA gene variants.
Expected ratios (based on known copy numbers of the respective 16S rRNA gene variants) are shown in bold. USEARCH-UNOISE3 could not differentiate the two C. beijerinckii variants. Qiime2-Deblur could not differentiate any of the variants.
Table 3.
Proportion of counts assigned to either true or spurious OTUs/ASVs.
Fig 4.
Inferred mock community composition.
A) Comparison of QIIME-uclust vs. other pipelines. B) Comparison of DADA (no filter) vs. DADA2 (ee2). OTUs/ASVs whose abundance was under-estimated are indicated with arrows.
Fig 5.
Raw reads conversion to final counts.
Fig 6.
Spearman's rho correlation averaged across all samples of the HELIUS fecal sample dataset (N = 2170).
A) Actual values. B) Values scaled to range between 0 and 1. Hierarchical clustering was applied to both rows and columns in order to group pipelines based on the degree of correlation of their outputs.
Table 4.
Read tracking information and OTU/ASV outputs for the pipeline flows applied to the HELIUS data.
Fig 7.
Venn diagram showing the overlap between the ASVs produced by three denoising pipelines from the HELIUS fecal sample data (N = 2170).
Workflows shown are DADA2 (no filter), Qiime2-Deblur (e30.ee1), and USEARCH-UNOISE3. A) ASVs remaining after rarefaction to 10 000 counts. B) Filtered ASVs (mean relative abundance of at least 0.002% of rarefied counts).
Fig 8.
Alpha-diversity measures at different rarefaction levels.
Values shown are averages across all samples in the HELIUS fecal sample dataset. A) Sample richness (no. of OTUs/ASVs per individual sample). B) Shannon index. Only one workflow from each pipeline is shown: DADA2 (no filter), QIIME-uclust (e30.ee1), Qiime2-Deblur (e30.ee1) and MOTHUR (DGC.1).
Fig 9.
Alpha-diversity measures after downstream filtering of very low-abundance OTUs/ASVs.
X-axis shows the no. of counts that an OTU/ASV must reach (in the entire dataset) in order to be retained. All OTU/ASV tables rarefied to 10000 counts / sample prior to filtering. Values shown are averaged across all samples in the HELIUS fecal sample dataset. A) Sample richness. B) Shannon index. The blue vertical bar marks the filter threshold corresponding to 0.002% of rarefied counts.