Reference data and simulated communities for 16S rRNA GCN prediction

16S rRNA gene has been widely used in microbial diversity studies to
determine the community composition and structure. 16S rRNA gene copy
number (16S GCN) varies among microbial species and this variation
introduces biases to the relative cell abundance estimated using 16S rRNA
read counts. To correct the biases, methods (e.g., PICRUST2) have been
developed to predict 16S GCN. 16S GCN predictions come with inherent
uncertainty, which is often ignored in the downstream analyses. However, a
recent study suggests that the uncertainty can be so great that copy
number correction is not justified in practice. Despite the significant
implications in 16S rRNA-based microbial diversity studies, the
uncertainty associated with 16S GCN predictions has not been well
characterized. Here we develop a novel method to better model and capture
the inherent uncertainty. Using cross-validation, we show that our method
provides robust confidence estimates for the GCN predictions and
outperforms PICRUST2 in both precision and recall. We found that 16S GCN
correction should improve compositional and functional profiles estimated
using 16S rRNA reads. On the other hand, we found that GCN variation has
limited impacts on PCoA, PERMANOVA and random forest test, and 16S rRNA
GCN correction is unnecessary in beta-diversity analyses.