Contamination in Low Microbial Biomass Microbiome Studies: Issues and Recommendations.
By Raphael Eisenhofer, J. Minich, C. Marotz, Alan Cooper, Rob Knight, L. Weyrich. Trends in Microbiology, 2019
883 citations recorded by the source corpus.
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.
Subfields: Ecology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/8bfcbe840a496d4c45e4c35f29714e502b6a499f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecology
In Molecular Biology
Citations in the corpus
- Consistent and correctable bias in metagenomic sequencing experiments (2019, background): “Some of the challenges associated with building a statistical model for bias include modeling the presence of taxa thought to be absent from the community (but observed due to contamination or index switching (Eisenhofer et al. 2018)) and the absence of taxa known to be present (Yeh et al.”