Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data
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Abstract
BACKGROUND: The accuracy of microbial community surveys based on marker-gene and metagenomic sequencing (MGS) suffers from the presence of contaminants-DNA sequences not truly present in the sample. Contaminants come from various sources, including reagents. Appropriate laboratory practices can reduce contamination, but do not eliminate it. Here we introduce decontam ( https://github.com/benjjneb/decontam ), an open-source R package that implements a statistical classification procedure that identifies contaminants in MGS data based on two widely reproduced patterns: contaminants appear at higher frequencies in low-concentration samples and are often found in negative controls. RESULTS: Decontam classified amplicon sequence variants (ASVs) in a human oral dataset consistently with prior microscopic observations of the microbial taxa inhabiting that environment and previous reports of contaminant taxa. In metagenomics and marker-gene measurements of a dilution series, decontam substantially reduced technical variation arising from different sequencing protocols. The application of decontam to two recently published datasets corroborated and extended their conclusions that little evidence existed for an indigenous placenta microbiome and that some low-frequency taxa seemingly associated with preterm birth were contaminants. CONCLUSIONS: Decontam improves the quality of metagenomic and marker-gene sequencing by identifying and removing contaminant DNA sequences. Decontam integrates easily with existing MGS workflows and allows researchers to generate more accurate profiles of microbial communities at little to no additional cost.
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External contaminants tend to have frequencies inversely correlated with sample analyte concentration and can be detected statistically.
“recent literature demonstrated that external contaminants more consistently have frequencies inversely correlated with sample analyte concentration and can be detected using a robust statistical framework16,38.”
From RETRACTED ARTICLE: Microbiome analyses of blood and tissues suggest cancer diagnostic approach (2020) · cites this paper as [16]
External contaminants were identified and removed using sample analyte concentrations and a blacklist of microbes from reagents in similar sequencing kits.
“External contaminants were identified and removed using sample analyte concentrations for all TCGA samples (n=17,625), as recently described in the literature16,38, and by using a “blacklist” of microbes identified from reagents in sequencing kits similar to those used in TCGA14.”
From RETRACTED ARTICLE: Microbiome analyses of blood and tissues suggest cancer diagnostic approach (2020) · cites this paper as [16]
A robust statistical framework recently validated the principle that contaminant frequencies are inversely correlated with sample analyte concentration.
“A robust statistical framework recently validated this principle16, providing the opportunity to exploit sample DNA/RNA concentrations recorded by TCGA as a means to identify putative contaminants.”
From RETRACTED ARTICLE: Microbiome analyses of blood and tissues suggest cancer diagnostic approach (2020) · cites this paper as [16]
Sequences from contaminants generally have frequencies inversely correlated with sample analyte concentration.
“Additional putative external contamination was identified on the basis that sequences from contaminants generally have frequencies that are inversely correlated with sample analyte concentration16,38.”
From RETRACTED ARTICLE: Microbiome analyses of blood and tissues suggest cancer diagnostic approach (2020) · cites this paper as [16]
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Galeano Niño, J. L., Wu, H., LaCourse, K. D., Srinivasan, H., Fitzgibbon, M., Minot, S. S., Sather, C., Johnston, C. D., & Bullman, S. (2023) · Nature Protocols · doi:10.1038/s41596-023-00888-7 · Cited by 36 · see citing works
Sepich‐Poore, G. D., Guccione, C., Laplane, L., Pradeu, T., Curtius, K., & Knight, R. (2022) · BioEssays · doi:10.1002/bies.202100252 · Cited by 33 · see citing works
Deep Nasal Sinus Cavity Microbiota Dysbiosis in Parkinson’s Disease
Pal, G., Ramirez, V., Engen, P. A., Naqib, A., Forsyth, C. B., Green, S. J., Mahdavinia, M., Batra, P. S., Tajudeen, B. A., & Keshavarzian, A. (2021) · npj Parkinson s Disease · doi:10.1038/s41531-021-00254-y · Cited by 28 · see citing works
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- Authors
- Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A., & Callahan, B. J.
- Affiliations
- Stanford Medicine (US)
- University of California, San Francisco (US)
- Stanford University (US)
- VA Palo Alto Health Care System (US)
- North Carolina State University (US)
- Corresponding author
- Callahan, Benjamin J.
- Venue
- Microbiome
- Published
- 2018-12
- Volume
- 6
- Issue
- 1
- Pages
- 226-226
- ISSN
- 2049-2618 (ISSN-L)
- PMC
- PMC6298009
- OpenAlex
- W2952463830
- Cited by
- 3,493
- References
- 58
- Citations by year
- 2026: 528 · 2025: 557 · 2024: 635 · 2023: 536 · 2022: 472 · 2021: 433 · 2020: 226 · 2019: 84 · 2018: 18 · 2017: 1 · 2015: 1
- FWCI
- 67.86
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Microbiome (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute of Dental and Craniofacial Research (R01 DE023113)
- National Institute of Allergy and Infectious Diseases (R01 AI112401)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Physical Sciences › Environmental Science › Ecology › Environmental DNA in Biodiversity Studies
- Keywords
- pollutant removal, decontamination, metagenomics, marker gene sequencing, amplicon sequence variants, negative control, low-concentration samples, oral microbiota, preterm birth, microbial community analysis
- MeSH terms
- Bacteria, DNA, Bacterial, DNA, Ribosomal, Genetic Markers, Humans, Mouth, RNA, Ribosomal, 16S, Software, Models, Statistical, Sequence Analysis, DNA, Internet, Databases, Genetic, Metagenomics, DNA Contamination
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