Abstract
ABSTRACTIn this article, we investigate patterns of microbial DNA contamination in targeted 16S rRNA amplicon sequencing (16S deep sequencing) and demonstrate how this can be used to filter background bacterial DNA in diagnostic microbiology. We also investigate the importance of sequencing depth. We first determined the patterns of contamination by performing repeat 16S deep sequencing of negative and positive extraction controls. This process identified a few bacterial species dominating across all replicates but also a high intersample variability among low abundance contaminant species in replicates split before PCR amplification. Replicates split after PCR amplification yielded almost identical sequencing results. On the basis of these observations, we suggest using the abundance of the most dominant contaminant species to define a threshold in each clinical sample from where identifications with lower abundances possibly represent contamination. We evaluated this approach by sequencing of a diluted, staggered mock community and of bile samples from 41 patients with acute cholangitis and noninfectious bile duct stenosis. All clinical samples were sequenced twice using different sequencing depths. We were able to demonstrate the following: (i) The high intersample variability between sequencing replicates is caused by events occurring before or during the PCR amplification step. (ii) Knowledge about the most dominant contaminant species can be used to establish sample-specific cutoffs for reliable identifications. (iii) Below the level of the most abundant contaminant, it rapidly becomes very demanding to reliably discriminate between background and true findings. (iv) Adequate sequencing depth can be claimed only when the analysis also picks up background contamination.
Highlights
In this article, we investigate patterns of microbial DNA contamination in targeted 16S rRNA amplicon sequencing (16S deep sequencing) and demonstrate how this can be used to filter background bacterial DNA in diagnostic microbiology
We used the results from this part of the study to formulate criteria for filtration of sequencing data from clinical samples, which we further evaluated on a staggered mock community and a collection of bile samples
Most current approaches for identifying and filtering contaminant bacteria rely on the assumption that sequencing of appropriate extraction controls will reveal the full spectrum of background contaminants that could possibly occur in the associated clinical samples [1,2,3,4]
Summary
We investigate patterns of microbial DNA contamination in targeted 16S rRNA amplicon sequencing (16S deep sequencing) and demonstrate how this can be used to filter background bacterial DNA in diagnostic microbiology. We first determined the patterns of contamination by performing repeat 16S deep sequencing of negative and positive extraction controls This process identified a few bacterial species dominating across all replicates and a high intersample variability among low abundance contaminant species in replicates split before PCR amplification. Accurate filtering of background contamination is more critical in weakly positive samples, where it constitutes a larger portion of the total bacterial DNA [1, 2] Sequencing depth is another essential factor, in particular for strongly positive, polymicrobial samples where the use of too few reads may result in failure to detect low abundance species. There has been little attention to the relationship between sequencing depth and sensitivity
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