Journal article
Towards precision quantification of contamination in metagenomic sequencing experiments
Microbiome, Vol.7(1), pp.62-62
04/16/2019
DOI: 10.1186/s40168-019-0678-6
PMCID: PMC6469116
PMID: 30992055
Abstract
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.
Details
- Title: Subtitle
- Towards precision quantification of contamination in metagenomic sequencing experiments
- Creators
- M. S Zinter - San Francisco, CA USAM. Y Mayday - San Francisco, CA USAK. K Ryckman - Iowa City, IA USAL. L Jelliffe-Pawlowski - San Francisco, CA USA San Francisco, CA USAJ. L DeRisi - San Francisco, CA USA Chan Zuckerberg Biohub, San Francisco, CA USA 1700 4th St, 403C, Campus Box 2542, San Francisco, CA 94158-2330 USA
- Resource Type
- Journal article
- Publication Details
- Microbiome, Vol.7(1), pp.62-62
- DOI
- 10.1186/s40168-019-0678-6
- PMID
- 30992055
- PMCID
- PMC6469116
- NLM abbreviation
- Microbiome
- ISSN
- 2049-2618
- eISSN
- 2049-2618
- Publisher
- BioMed Central
- Grant note
- K12HD000850 / ; n/a / ; Preterm Birth Initiative (PTBi); Preterm Birth Initiative (PTBi) / ;
- Language
- English
- Date published
- 04/16/2019
- Academic Unit
- Stead Family Department of Pediatrics; Epidemiology
- Record Identifier
- 9984214834202771
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