Towards precision quantification of contamination in metagenomic sequencing experimentsM. S. Zinter, M. Y. Mayday, K. K. Ryckman, L. L. Jelliffe-Pawlowski, and J. L. DeRisi5
Microbiome, 2019Abstract: Metagenomic next-generation sequencing (mNGS) experiments involving small amounts of nucleic acid input are
highly susceptible to erroneous conclusions resulting from unintentional sequencing of occult contaminants,
especially those derived from molecular biology reagents. Recent work suggests that, for any given microbe
detected by mNGS, an inverse linear relationship between microbial sequencing reads and sample mass implicates
that microbe as a contaminant. By associating sequencing read output with the mass of a spike-in control, we
demonstrate that contaminant nucleic acid can be quantified in order to identify the mass contributions of each
constituent. In an experiment using a high-resolution (n = 96) dilution series of HeLa RNA spanning 3-logs of RNA
mass input, we identified a complex set of contaminants totaling 9.1 ± 2.0 attograms. Given the competition
between contamination and the true microbiome in ultra-low biomass samples such as respiratory fluid,
quantification of the contamination within a given batch of biological samples can be used to determine a
minimum mass input below which sequencing results may be distorted. Rather than completely censoring
contaminant taxa from downstream analyses, we propose here a statistical approach that allows separation of the
true microbial components from the actual contribution due to contamination. We demonstrate this approach
using a batch of n = 97 human serum samples and note that despite E. coli contamination throughout the dataset,
we are able to identify a patient sample with significantly more E. coli than expected from contamination alone.
Importantly, our method assumes no prior understanding of possible contaminants, does not rely on any prior collection
of environmental or reagent-only sequencing samples, and does not censor potentially clinically relevant taxa, thus
making it a generalized approach to any kind of metagenomic sequencing, for any purpose, clinical or otherwise.
