Abstract
BackgroundIn recent years, the microbiome field has undergone a shift from clustering-based methods of operational taxonomic unit (OTU) designation based on sequence similarity to denoising algorithms that identify exact amplicon sequence variants (ASVs), and methods to identify contaminating bacterial DNA sequences from low biomass samples have been developed. Although these methods improve accuracy when analyzing mock communities, their impact on real samples and downstream analysis of biological associations is less clear.ResultsHere, we re-processed our recently published milk microbiota data using Qiime1 to identify OTUs, and Qiime2 to identify ASVs, with or without contaminant removal using decontam. Qiime2 resolved the mock community more accurately, primarily because Qiime1 failed to detect Lactobacillus. Qiime2 also considerably reduced the average number of ASVs detected in human milk samples (364 ± 145 OTUs vs. 170 ± 73 ASVs, p < 0.001). Compared to the richness, the estimated diversity measures had a similar range using both methods albeit statistically different (inverse Simpson index: 14.3 ± 8.5 vs. 15.6 ± 8.7, p = 0.031) and there was strong consistency and agreement for the relative abundances of the most abundant bacterial taxa, including Staphylococcaceae and Streptococcaceae. One notable exception was Oxalobacteriaceae, which was overrepresented using Qiime1 regardless of contaminant removal. Downstream statistical analyses were not impacted by the choice of algorithm in terms of the direction, strength, and significance of associations of host factors with bacterial diversity and overall community composition.ConclusionOverall, the biological observations and conclusions were robust to the choice of the sequencing processing methods and contaminant removal.
Highlights
In recent years, the microbiome field has undergone a shift from clustering-based methods of operational taxonomic unit (OTU) designation based on sequence similarity to denoising algorithms that identify exact amplicon sequence variants (ASVs), and methods to identify contaminating bacterial DNA sequences from low biomass samples have been developed
In recent years there have been novel developments in several aspects of sequencing processing including a shift from clusteringbased methods of operational taxonomic unit (OTU) designation based on sequence similarity [1, 2] to denoising algorithms which identify exact amplicon sequence variants (ASVs) [3,4,5,6,7,8,9]; thereby increasing ecological precision
While the Qiime1 method detected an average (SD) of 223 (50) OTUs in the mock community samples, Qiime2 performance was closer to the expected composition, detecting 12 (3) ASVs (Table S1)
Summary
The microbiome field has undergone a shift from clustering-based methods of operational taxonomic unit (OTU) designation based on sequence similarity to denoising algorithms that identify exact amplicon sequence variants (ASVs), and methods to identify contaminating bacterial DNA sequences from low biomass samples have been developed These methods improve accuracy when analyzing mock communities, their impact on real samples and downstream analysis of biological associations is less clear. The detection of true biological and ecological variations appears to be robust to the choice of sequencing processing method (OTU vs ASV) in a few studies on soil, mouse feces, and human intestinal biopsies [12,13,14], but head-to-head comparisons are lacking for most human microbiota communities Another issue receiving increasing attention in sequencing-based microbiome analysis is contamination introduced during DNA extraction and library preparation. The datasets resulting from these four approaches (Fig. 1a) were used to assess the comparability of results in terms of microbiota features (taxonomy, alpha, and beta diversity) and test the hypothesis that biological associations are robust to the choice of upstream data processing
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
