Critical Relevance of Stochastic Effects on Low-Bacterial-Biomass 16S rRNA Gene Analysis
John R. Erb‐Downward, Nicole R. Falkowski, Jennifer D’Souza, Lisa McCloskey, Roderick A. McDonald, Christopher A. Brown, Kerby Shedden, Robert P. Dickson, Christine M. Freeman, Kathleen A. Stringer, Betsy Foxman, Gary B. Huffnagle, Jeffrey L. Curtis, Sara D. Adar
Abstract
DNA contamination from external sources (reagents, environment, operator, etc.) has long been assumed to be the main cause of spurious signals that appear under low-bacterial-biomass conditions. Here, we demonstrate that contamination can be separated from another, random signal generated during low-biomass-sample sequencing. This stochastic noise is not reproduced between technical replicates; however, results for any one replicate taken alone could look like a microbial community different from the controls. Using this information, we investigated respiratory samples from healthy humans and determined the narrow range of bacterial biomass where samples transition from producing reproducible microbial sequences to ones dominated by noise. We present a rigorous approach to studies involving low-bacterial-biomass samples to detect this source of noise and provide a framework for deciding if a sample is likely to be dominated by noise. We anticipate that this work will facilitate increased reproducibility in the characterization of potentially important low-biomass communities.