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Research Article | Novel Systems Biology Techniques

Controlling for Contaminants in Low-Biomass 16S rRNA Gene Sequencing Experiments

Lisa Karstens, Mark Asquith, Sean Davin, Damien Fair, W. Thomas Gregory, Alan J. Wolfe, Jonathan Braun, Shannon McWeeney
Jack A. Gilbert, Editor
Lisa Karstens
aDivision of Bioinformatics and Computational Biology, Oregon Health and Science University, Portland, Oregon, USA
bDivision of Urogynecology, Oregon Health and Science University, Portland, Oregon, USA
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Mark Asquith
cDivision of Arthritis and Rheumatology, Oregon Health and Science University, Portland, Oregon, USA
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Sean Davin
cDivision of Arthritis and Rheumatology, Oregon Health and Science University, Portland, Oregon, USA
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Damien Fair
dDepartment of Behavioral Neuroscience, Oregon Health and Science University, Portland, Oregon, USA
eDepartment of Psychiatry, Oregon Health and Science University, Portland, Oregon, USA
fAdvanced Imaging Research Center, Oregon Health and Science University, Portland, Oregon, USA
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W. Thomas Gregory
bDivision of Urogynecology, Oregon Health and Science University, Portland, Oregon, USA
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Alan J. Wolfe
gDepartment of Microbiology and Immunology, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, USA
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Jonathan Braun
hCedars Sinai Medical Center, Los Angeles, California, USA
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Shannon McWeeney
aDivision of Bioinformatics and Computational Biology, Oregon Health and Science University, Portland, Oregon, USA
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Jack A. Gilbert
University of California San Diego
Roles: Editor
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DOI: 10.1128/mSystems.00290-19
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ABSTRACT

Microbial communities are commonly studied using culture-independent methods, such as 16S rRNA gene sequencing. However, one challenge in accurately characterizing microbial communities is exogenous bacterial DNA contamination, particularly in low-microbial-biomass niches. Computational approaches to identify contaminant sequences have been proposed, but their performance has not been independently evaluated. To identify the impact of decreasing microbial biomass on polymicrobial 16S rRNA gene sequencing experiments, we created a mock microbial community dilution series. We evaluated four computational approaches to identify and remove contaminants, as follows: (i) filtering sequences present in a negative control, (ii) filtering sequences based on relative abundance, (iii) identifying sequences that have an inverse correlation with DNA concentration implemented in Decontam, and (iv) predicting the sequence proportion arising from defined contaminant sources implemented in SourceTracker. As expected, the proportion of contaminant bacterial DNA increased with decreasing starting microbial biomass, with 80.1% of the most diluted sample arising from contaminant sequences. Inclusion of contaminant sequences led to overinflated diversity estimates and distorted microbiome composition. All methods for contaminant identification successfully identified some contaminant sequences, which varied depending on the method parameters used and contaminant prevalence. Notably, removing sequences present in a negative control erroneously removed >20% of expected sequences. SourceTracker successfully removed over 98% of contaminants when the experimental environments were well defined. However, SourceTracker misclassified expected sequences and performed poorly when the experimental environment was unknown, failing to remove >97% of contaminants. In contrast, the Decontam frequency method did not remove expected sequences and successfully removed 70 to 90% of the contaminants.

IMPORTANCE The relative scarcity of microbes in low-microbial-biomass environments makes accurate determination of community composition challenging. Identifying and controlling for contaminant bacterial DNA are critical steps in understanding microbial communities from these low-biomass environments. Our study introduces the use of a mock community dilution series as a positive control and evaluates four computational strategies that can identify contaminants in 16S rRNA gene sequencing experiments in order to remove them from downstream analyses. The appropriate computational approach for removing contaminant sequences from an experiment depends on prior knowledge about the microbial environment under investigation and can be evaluated with a dilution series of a mock microbial community.

  • Copyright © 2019 Karstens et al.

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

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Controlling for Contaminants in Low-Biomass 16S rRNA Gene Sequencing Experiments
Lisa Karstens, Mark Asquith, Sean Davin, Damien Fair, W. Thomas Gregory, Alan J. Wolfe, Jonathan Braun, Shannon McWeeney
mSystems Jun 2019, 4 (4) e00290-19; DOI: 10.1128/mSystems.00290-19

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Controlling for Contaminants in Low-Biomass 16S rRNA Gene Sequencing Experiments
Lisa Karstens, Mark Asquith, Sean Davin, Damien Fair, W. Thomas Gregory, Alan J. Wolfe, Jonathan Braun, Shannon McWeeney
mSystems Jun 2019, 4 (4) e00290-19; DOI: 10.1128/mSystems.00290-19
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KEYWORDS

16S rRNA gene sequencing
contamination
Decontam
low microbial biomass
microbiome
SourceTracker

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