Managing Contamination and Diverse Bacterial Loads in 16S rRNA Deep Sequencing of Clinical Samples: Implications of the Law of Small Numbers
Ruben Dyrhovden, Martin Rippin, Kjell Kåre Øvrebø, Randi Nygaard, Elling Ulvestad, Øyvind Kommedal
Abstract
There has been a gradual increase in 16S deep sequencing studies on infectious disease materials. Management of bacterial DNA contamination is a major challenge in such diagnostics, particularly in low biomass samples. Reporting a contaminant species as a relevant pathogen may cause unnecessary antibiotic treatment or even falsely classify a noninfectious condition as a bacterial infection. Yet, there are few studies on how to filter contamination in clinical microbiology. Here, we demonstrate that sequencing of extraction controls will not reveal the full spectrum of contaminants that could occur in the associated clinical samples. Only the most abundant contaminant species were consistently detected, and we present how this can be used to set sample specific thresholds for reliable identifications. We believe this work can facilitate the implementation of 16S deep sequencing in diagnostic laboratories. The new data we provide on the patterns of microbial DNA contamination is also important for microbiome research.