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. 2010 Sep;7(9):668-9.
doi: 10.1038/nmeth0910-668b.

Rapidly denoising pyrosequencing amplicon reads by exploiting rank-abundance distributions

Jens Reeder, Rob Knight

Rapidly denoising pyrosequencing amplicon reads by exploiting rank-abundance distributions

Jens Reeder et al. Nat Methods. 2010 Sep.

Abstract

We developed a fast method for denoising pyrosequencing for community 16S rRNA analysis. We observe a 2–4 fold reduction in the number of observed OTUs (operational taxonomic units) comparing denoised with non-denoised data. ~50,000 sequences can be denoised on a laptop within an hour, two orders of magnitude faster than published techniques. We demonstrate the effects of denoising on alpha and beta diversity of large 16S rRNA datasets.

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Figures

Figure 1
Figure 1
Comparisons of non-denoised data (a–c) to denoised data (d–f) for alpha diversity for the Body Habitat study, and comparisons of beta diversity (g–h). Rarefaction plots of the “Body Habitat” study show a 3 to 4 fold decrease in the Chao1 estimate when comparing non-denoised (a) to denoised (b) data. Interestingly, denoising changes the relative order of OTU richness of individual body habitats: the gut exhibits the highest OTU richness without denoising, but falls back into the middle ranks after denoising. This holds true for both Chao1 estimates and phylogenetic diversity (PD). c) Scatter plots of alpha diversity metrics per sample show a high correlation overall, but a significant deviation from the average for gut and the oral cavity. (EAC = external auditory canal). g) Procrustes analysis of denoised and filtered unweighted UniFrac principal coordinates analysis (PCoA). Bars connect identical samples in the plot with the red side of the bar pointing towards the denoised data. There is no qualitative difference between denoised and filtered in the overall clustering, yet on a smaller scale we observe that the denoised samples are oriented more to the center than the filtered ones. This shows that denoising removes some of the artificial distance between samples introduced by false OTUs. h) Unweighted UniFrac distances for all pairs of samples for the denoised and filtered data set are highly correlated (r2=0.96). From the regression, it is clear that for similar samples noise has a greater effect than it has for dissimilar samples. The color bar gives the number of pairwise comparisons at a particular point.
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