Table 1.
Number of sequences failing quality screening criteria and total number of sequences remaining (bold, italics), for standard processing pipeline and for AmpliconNoise processing.
Figure 1.
Corresponding OTUs between data processing methods. A)
Each method generated some OTUs which consist entirely of sequence reads that were culled in the alternate method. These inconsistent OTUs were mostly singletons and were more abundant in the standard pipeline. B) Most OTUs had a clear corresponding OTU in the alternate method. Data shown are OTUs having a membership>50 reads in the AmpliconNoise dataset, and the proportion of the membership of each OTU that was shared with the best corresponding OTU in the standard pipeline dataset.
Figure 2.
Impacts of de-noising on the rank-abundance distribution of OTUs.
AmpliconNoise processing significantly altered the OTU rank-abundance distribution (two-sample Kolmogorov-Smirnov test; D = 0.20, p<0.0001), and increased evenness.
Figure 3.
Impacts of de-noising on OTU richness and diversity. A)
Relationship between OTU richness with and without de-noising, by sample. B) Relationship between OTU diversity (Shannon index) with and without de-noising, by sample. C) Ranking of samples by Shannon diversity index with and without de-noising.
Table 2.
ANOVA results tables for tests for differences in OTU diversity among two main treatment effects.
Figure 4.
Impacts of de-noising on phylogenetic diversity. A)
Relationship between phylogenetic diversity with and without de-noising, by sample. B) Ranking of samples by phylogenetic diversity with and without de-noising.