Contamination in Low Microbial Biomass Microbiome Studies: Issues and Recommendations
Next-generation sequencing approaches in microbiome research have allowed surveys of microbial communities, their genomes, and their functions with higher sensitivity than ever before. However, this sensitivity is a double-edged sword because these tools also efficiently detect contaminant DNA and c...
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| Vydané v: | Trends in microbiology (Regular ed.) Ročník 27; číslo 2; s. 105 - 117 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
England
Elsevier Ltd
01.02.2019
Elsevier Science Ltd |
| Predmet: | |
| ISSN: | 0966-842X, 1878-4380, 1878-4380 |
| On-line prístup: | Získať plný text |
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| Abstract | Next-generation sequencing approaches in microbiome research have allowed surveys of microbial communities, their genomes, and their functions with higher sensitivity than ever before. However, this sensitivity is a double-edged sword because these tools also efficiently detect contaminant DNA and cross-contamination, which can confound the interpretation of microbiome data. Therefore, there is an urgent need to integrate key controls into microbiome research to improve the integrity of microbiome studies. Here, we review how contaminant DNA and cross-contamination arise within microbiome studies and discuss their negative impacts, especially during the analysis of low microbial biomass samples. We then identify several key measures that researchers can implement to reduce the impact of contaminant DNA and cross-contamination during microbiome research. We put forward a set of minimal experimental criteria, the ‘RIDE’ checklist, to improve the validity of future low microbial biomass research.
There is increasing interest in applying metagenomic techniques to find correlations between microorganisms and disease.
Metagenomic techniques are highly sensitive and can detect contaminant DNA (DNA from sources other than the samples under study) and cross-contamination (DNA exchange between samples).
Recent studies have shown that contaminant DNA and cross-contamination can confound metagenomic studies, especially for sample types that have low microbial biomass.
There is an urgent need for the field to adopt authentication criteria to prevent future metagenomic studies from falling prey to the pitfalls of contaminant DNA and cross-contamination. |
|---|---|
| AbstractList | Next-generation sequencing approaches in microbiome research have allowed surveys of microbial communities, their genomes, and their functions with higher sensitivity than ever before. However, this sensitivity is a double-edged sword because these tools also efficiently detect contaminant DNA and cross-contamination, which can confound the interpretation of microbiome data. Therefore, there is an urgent need to integrate key controls into microbiome research to improve the integrity of microbiome studies. Here, we review how contaminant DNA and cross-contamination arise within microbiome studies and discuss their negative impacts, especially during the analysis of low microbial biomass samples. We then identify several key measures that researchers can implement to reduce the impact of contaminant DNA and cross-contamination during microbiome research. We put forward a set of minimal experimental criteria, the 'RIDE' checklist, to improve the validity of future low microbial biomass research.Next-generation sequencing approaches in microbiome research have allowed surveys of microbial communities, their genomes, and their functions with higher sensitivity than ever before. However, this sensitivity is a double-edged sword because these tools also efficiently detect contaminant DNA and cross-contamination, which can confound the interpretation of microbiome data. Therefore, there is an urgent need to integrate key controls into microbiome research to improve the integrity of microbiome studies. Here, we review how contaminant DNA and cross-contamination arise within microbiome studies and discuss their negative impacts, especially during the analysis of low microbial biomass samples. We then identify several key measures that researchers can implement to reduce the impact of contaminant DNA and cross-contamination during microbiome research. We put forward a set of minimal experimental criteria, the 'RIDE' checklist, to improve the validity of future low microbial biomass research. Next-generation sequencing approaches in microbiome research have allowed surveys of microbial communities, their genomes, and their functions with higher sensitivity than ever before. However, this sensitivity is a double-edged sword because these tools also efficiently detect contaminant DNA and cross-contamination, which can confound the interpretation of microbiome data. Therefore, there is an urgent need to integrate key controls into microbiome research to improve the integrity of microbiome studies. Here, we review how contaminant DNA and cross-contamination arise within microbiome studies and discuss their negative impacts, especially during the analysis of low microbial biomass samples. We then identify several key measures that researchers can implement to reduce the impact of contaminant DNA and cross-contamination during microbiome research. We put forward a set of minimal experimental criteria, the 'RIDE' checklist, to improve the validity of future low microbial biomass research. Next-generation sequencing approaches in microbiome research have allowed surveys of microbial communities, their genomes, and their functions with higher sensitivity than ever before. However, this sensitivity is a double-edged sword because these tools also efficiently detect contaminant DNA and cross-contamination, which can confound the interpretation of microbiome data. Therefore, there is an urgent need to integrate key controls into microbiome research to improve the integrity of microbiome studies. Here, we review how contaminant DNA and cross-contamination arise within microbiome studies and discuss their negative impacts, especially during the analysis of low microbial biomass samples. We then identify several key measures that researchers can implement to reduce the impact of contaminant DNA and cross-contamination during microbiome research. We put forward a set of minimal experimental criteria, the ‘RIDE’ checklist, to improve the validity of future low microbial biomass research. There is increasing interest in applying metagenomic techniques to find correlations between microorganisms and disease. Metagenomic techniques are highly sensitive and can detect contaminant DNA (DNA from sources other than the samples under study) and cross-contamination (DNA exchange between samples). Recent studies have shown that contaminant DNA and cross-contamination can confound metagenomic studies, especially for sample types that have low microbial biomass. There is an urgent need for the field to adopt authentication criteria to prevent future metagenomic studies from falling prey to the pitfalls of contaminant DNA and cross-contamination. Next-generation sequencing approaches in microbiome research have allowed surveys of microbial communities, their genomes, and their functions with higher sensitivity than ever before. However, this sensitivity is a double-edged sword because these tools also efficiently detect contaminant DNA and cross-contamination, which can confound the interpretation of microbiome data. Therefore, there is an urgent need to integrate key controls into microbiome research to improve the integrity of microbiome studies. |
| Author | Weyrich, Laura S. Cooper, Alan Knight, Rob Marotz, Clarisse Eisenhofer, Raphael Minich, Jeremiah J. |
| Author_xml | – sequence: 1 givenname: Raphael orcidid: 0000-0002-3843-0749 surname: Eisenhofer fullname: Eisenhofer, Raphael email: raph.eisenhofer@gmail.com organization: Australian Centre for Ancient DNA, University of Adelaide, Adelaide, SA, Australia – sequence: 2 givenname: Jeremiah J. surname: Minich fullname: Minich, Jeremiah J. organization: Marine Biology Research Division, Scripps Institution of Oceanography, La Jolla, CA, USA – sequence: 3 givenname: Clarisse surname: Marotz fullname: Marotz, Clarisse organization: Department of Pediatrics, University of California San Diego, La Jolla, CA, USA – sequence: 4 givenname: Alan surname: Cooper fullname: Cooper, Alan organization: Australian Centre for Ancient DNA, University of Adelaide, Adelaide, SA, Australia – sequence: 5 givenname: Rob surname: Knight fullname: Knight, Rob organization: Department of Pediatrics, University of California San Diego, La Jolla, CA, USA – sequence: 6 givenname: Laura S. surname: Weyrich fullname: Weyrich, Laura S. organization: Australian Centre for Ancient DNA, University of Adelaide, Adelaide, SA, Australia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30497919$$D View this record in MEDLINE/PubMed |
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| Keywords | microbiota laboratory contamination DNA contamination microbiome methodology low biomass |
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| SubjectTerms | Biomass Contaminants Contamination cross contamination Deoxyribonucleic acid DNA DNA Contamination DNA, Bacterial - analysis DNA, Bacterial - genetics genome Genomes High-Throughput Nucleotide Sequencing Humans laboratory contamination low biomass Metagenomics methodology Microbial activity microbial biomass microbial communities Microbial contamination Microbiological Techniques - methods Microbiological Techniques - standards microbiome Microbiomes microbiota Microbiota - genetics Microorganisms Next-generation sequencing Reproducibility of Results Sensitivity Specimen Handling surveys |
| Title | Contamination in Low Microbial Biomass Microbiome Studies: Issues and Recommendations |
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