Skip to main content
  • ASM Journals
    • Antimicrobial Agents and Chemotherapy
    • Applied and Environmental Microbiology
    • Clinical Microbiology Reviews
    • Clinical and Vaccine Immunology
    • EcoSal Plus
    • Infection and Immunity
    • Journal of Bacteriology
    • Journal of Clinical Microbiology
    • Journal of Microbiology & Biology Education
    • Journal of Virology
    • mBio
    • Microbiology and Molecular Biology Reviews
    • Microbiology Resource Announcements
    • Microbiology Spectrum
    • Molecular and Cellular Biology
    • mSphere
    • mSystems
  • Log in
  • My alerts
  • My Cart

Main menu

  • Home
  • Articles
    • Latest Articles
    • Special Issues
    • COVID-19 Special Collection
    • Special Series: Sponsored Minireviews and Video Abstracts
    • Archive
  • Topics
    • Applied and Environmental Science
    • Ecological and Evolutionary Science
    • Host-Microbe Biology
    • Molecular Biology and Physiology
    • Novel Systems Biology Techniques
    • Early-Career Systems Microbiology Perspectives
  • For Authors
    • Getting Started
    • Submit a Manuscript
    • Scope
    • Editorial Policy
    • Submission, Review, & Publication Processes
    • Organization and Format
    • Errata, Author Corrections, Retractions
    • Illustrations and Tables
    • Nomenclature
    • Abbreviations and Conventions
    • Publication Fees
    • Ethics
  • About the Journal
    • About mSystems
    • Editor in Chief
    • Board of Editors
    • For Reviewers
    • For the Media
    • For Librarians
    • For Advertisers
    • Alerts
    • RSS
    • FAQ
  • ASM Journals
    • Antimicrobial Agents and Chemotherapy
    • Applied and Environmental Microbiology
    • Clinical Microbiology Reviews
    • Clinical and Vaccine Immunology
    • EcoSal Plus
    • Infection and Immunity
    • Journal of Bacteriology
    • Journal of Clinical Microbiology
    • Journal of Microbiology & Biology Education
    • Journal of Virology
    • mBio
    • Microbiology and Molecular Biology Reviews
    • Microbiology Resource Announcements
    • Microbiology Spectrum
    • Molecular and Cellular Biology
    • mSphere
    • mSystems

User menu

  • Log in
  • My alerts
  • My Cart

Search

  • Advanced search
mSystems
publisher-logosite-logo

Advanced Search

  • Home
  • Articles
    • Latest Articles
    • Special Issues
    • COVID-19 Special Collection
    • Special Series: Sponsored Minireviews and Video Abstracts
    • Archive
  • Topics
    • Applied and Environmental Science
    • Ecological and Evolutionary Science
    • Host-Microbe Biology
    • Molecular Biology and Physiology
    • Novel Systems Biology Techniques
    • Early-Career Systems Microbiology Perspectives
  • For Authors
    • Getting Started
    • Submit a Manuscript
    • Scope
    • Editorial Policy
    • Submission, Review, & Publication Processes
    • Organization and Format
    • Errata, Author Corrections, Retractions
    • Illustrations and Tables
    • Nomenclature
    • Abbreviations and Conventions
    • Publication Fees
    • Ethics
  • About the Journal
    • About mSystems
    • Editor in Chief
    • Board of Editors
    • For Reviewers
    • For the Media
    • For Librarians
    • For Advertisers
    • Alerts
    • RSS
    • FAQ
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
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mark Asquith
cDivision of Arthritis and Rheumatology, Oregon Health and Science University, Portland, Oregon, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Sean Davin
cDivision of Arthritis and Rheumatology, Oregon Health and Science University, Portland, Oregon, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
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
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
W. Thomas Gregory
bDivision of Urogynecology, Oregon Health and Science University, Portland, Oregon, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Alan J. Wolfe
gDepartment of Microbiology and Immunology, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jonathan Braun
hCedars Sinai Medical Center, Los Angeles, California, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Shannon McWeeney
aDivision of Bioinformatics and Computational Biology, Oregon Health and Science University, Portland, Oregon, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jack A. Gilbert
University of California San Diego
Roles: Editor
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
DOI: 10.1128/mSystems.00290-19
  • Article
  • Figures & Data
  • Info & Metrics
  • PDF
Loading

Article Figures & Data

Figures

  • Tables
  • Supplemental Material
  • FIG 1
    • Open in new tab
    • Download powerpoint
    FIG 1

    Analysis of a mock microbial community dilution series reveals that contaminating bacteria increase with decreasing starting DNA. A mock microbial community consisting of 8 known bacteria was subject to 8 series of a 3-fold dilution (1:3 through 1:6,561), subject to bacterial DNA isolation and amplification, and sequenced with the Illumina MiSeq platform. (A) Number of reads per sample. (B) The proportion of reads from contaminant DNA increased with the amount of dilution. (C) Stacked bar plot representing the bacteria identified in each sample. The expected ASVs from the mock microbial community are displayed in color, while all other bacterial ASVs are in grayscale.

  • FIG 2
    • Open in new tab
    • Download powerpoint
    FIG 2

    Impact of contaminants on common alpha-diversity measures. Failure to remove contaminants from the data set leads to increased estimates of alpha diversity evaluated by the number of observed ASVs (Observed) inverse Simpson index (InvSimpson), and Shannon diversity index (Shannon). Expected results were calculated based on the expected mock community ASVs in each dilution sample.

  • FIG 3
    • Open in new tab
    • Download powerpoint
    FIG 3

    Classification of ASVs. Red, mock community ASVs correctly classified; light gray, mock community ASVs incorrectly classified; blue, contaminant ASVs correctly classified; dark gray, contaminant ASVs incorrectly classified. (A) Not correcting for contaminants leads to a large proportion of sequences being incorrectly considered mock ASVs across the dilution series. (B) Filtering by removing ASVs present in the negative control incorrectly classified many mock community ASVs as contaminants and misclassified many contaminant ASVs as mock community ASVs in the diluted samples. (C to E) Abundance filtering required an abundance of 1% to classify the majority of contaminant ASVs correctly, but it also removed mock community ASVs (E). (F to J) The Decontam frequency method did not misclassify any mock community ASV sequences and correctly classified the majority of contaminant ASVs. In highly diluted samples (D6 to D8), contaminant ASVs are misclassified as mock community ASVs. (K to N) SourceTracker performs well, correctly classifying the majority of mock community and contaminant ASVs, though some mock community ASVs are classified incorrectly as contaminant ASVs (K and L). However, in the scenario where the experimental environment is not well defined (M and N), many of the ASVs are incorrectly classified as mock community ASVs. S1, scenario 1; S2, scenario 2.

  • FIG 4
    • Open in new tab
    • Download powerpoint
    FIG 4

    Accuracy of methods to identify contaminants. The negative-control filter has poor accuracy, regardless of prevalence of contaminants. All other methods have high accuracy when the prevalence of contaminants is <5%. As the prevalence of contaminants increases, the accuracy of most methods drops, with the exception of SourceTracker with well-defined experimental conditions. Thr, threshold.

  • FIG 5
    • Open in new tab
    • Download powerpoint
    FIG 5

    Example of the effect of contaminant removal on common microbiome summary measures (relative abundance of ASVs, alpha diversity). (A) Relative abundance of Lactobacillus. (B) Relative abundance of Escherichia/Shigella. (C) Alpha diversity summarized by the inverse Simpson index. For brevity, results are shown for three dilution series samples representing low (less than 5%, D3), moderate (between 10% and 50%; D6), and high (greater than 50%, D8) levels of contaminant ASVs. Expected results shown by solid black line are based on the composition of the expected mock community ASVs in the specified dilution sample. See Fig. S1 and S2 for results across the entire dilution series.

Tables

  • Figures
  • Supplemental Material
  • TABLE 1

    Impact of decreasing starting material for 16S rRNA gene sequencing

    ParameterData by dilution
    D0D1D2D3D4D5D6D7D8
    No. of reads251,419172,915250,861247,581216,341136,081128,05341,07140,927
    No. of unique ASVs1820114172262312381147193
    No. of unique genera1516646180861075874
    % contaminantsa0.10.11.84.512.027.964.555.880.1
    • ↵a Percent contaminants calculated as the percentage of sequences in each sample that were not an exact match to the mock microbial community reference sequences.

  • TABLE 2

    Details of computational methods for identifying and removing contaminants

    MethodDetailsParameters evaluated
    Filter, negative controlASVs present in the negative control are removedNone
    Filter, abundanceASVs below a relative abundance threshold are removedAbundance threshold, 0.01%, 0.10%, or 1.00%
    Decontam, frequencyASVs with a correlation with DNA concentration are removedThreshold parameter, 0.1, 0.2, 0.3, 0.4, or 0.5
    SourceTracker, scenario 1; experimental source environment is well definedProportions of ASVs predicted to not be from a defined experimental source are removedSource environments; for case 1, mock community profile + contaminant profile + negative-control profile; for case 2, mock community profile + negative-control profile
    SourceTracker, scenario 2; experimental source environment is not definedProportion of ASVs predicted to be from a contaminant source are removedSource environments; for case 1, contaminant profile + negative-control profile; for case 2, negative-control profile

Supplemental Material

  • Figures
  • Tables
  • TABLE S1

    Estimated number of bacterial cells that yielded the DNA used in 16S rRNA gene PCR reaction for the mock microbial dilution series and DNA concentration measured by nanodrop. Download Table S1, CSV file, 0.00 MB.

    Copyright © 2019 Karstens et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • TABLE S2

    Taxonomic classification of contaminant ASVs with at least 1% abundance in at least 1 sample. Download Table S2, CSV file, 0.01 MB.

    Copyright © 2019 Karstens et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • TABLE S3

    Percentage of contaminant ASVs and mock community ASVs removed using each contaminant removal method. Download Table S3, XLSX file, 0.03 MB.

    Copyright © 2019 Karstens et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • FIG S1

    Recovered microbial community profiles after contaminant removal. Download FIG S1, PDF file, 0.02 MB.

    Copyright © 2019 Karstens et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

PreviousNext
Back to top
Download PDF
Citation Tools
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

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Print
Alerts
Sign In to Email Alerts with your Email Address
Email

Thank you for sharing this mSystems article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
Controlling for Contaminants in Low-Biomass 16S rRNA Gene Sequencing Experiments
(Your Name) has forwarded a page to you from mSystems
(Your Name) thought you would be interested in this article in mSystems.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
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
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Top
  • Article
    • ABSTRACT
    • INTRODUCTION
    • RESULTS
    • DISCUSSION
    • MATERIALS AND METHODS
    • ACKNOWLEDGMENTS
    • FOOTNOTES
    • REFERENCES
  • Figures & Data
  • Info & Metrics
  • PDF

KEYWORDS

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

Related Articles

Cited By...

About

  • About mSystems
  • Author Videos
  • Board of Editors
  • Policies
  • Overleaf Pilot
  • For Reviewers
  • For the Media
  • For Librarians
  • For Advertisers
  • Alerts
  • RSS
  • FAQ
  • Permissions
  • Journal Announcements

Authors

  • ASM Author Center
  • Submit a Manuscript
  • Author Warranty
  • Types of Articles
  • Getting Started
  • Ethics
  • Contact Us

Follow #mSystemsJ

@ASMicrobiology

       

 

ASM Journals

ASM journals are the most prominent publications in the field, delivering up-to-date and authoritative coverage of both basic and clinical microbiology.

About ASM | Contact Us | Press Room

 

ASM is a member of

Scientific Society Publisher Alliance

Copyright © 2021 American Society for Microbiology | Privacy Policy | Website feedback

Online ISSN: 2379-5077