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. 

Reference genomes were downloaded from NCBI RefSeq database in April 2021. The annotated 16S rRNA genes were counted and one representative sequence was selected fro each genome. The reference phylogeny was inferred using RAxML version 8.2. The 16S rRNA GCN for SILVA non-redundant OTU99 was predicted using RasperGade16S. The simulated communities were generated using custom scripts.

The reference tree is provided in Newick format. The simulated communities are provided as binary RDS files that can be accessed through function readRDS() in R.