Australian Centre for Ancient DNA, University of Adelaide, Adelaide, SA, Australia; Australian Research Council (ARC) Centre of Excellence for Australian Biodiversity and Heritage (CABAH), University of Adelaide, Adelaide, SA, Australia.
Marine Biology Research Division, Scripps Institution of Oceanography, La Jolla, CA, USA.
Trends Microbiol. 2019 Feb;27(2):105-117. doi: 10.1016/j.tim.2018.11.003. Epub 2018 Nov 26.
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.
下一代测序方法在微生物组研究中允许以比以往更高的灵敏度调查微生物群落、它们的基因组及其功能。然而，这种敏感性是一把双刃剑，因为这些工具也能有效地检测到污染物 DNA 和交叉污染，这可能会混淆微生物组数据的解释。因此，迫切需要将关键控制措施纳入微生物组研究中，以提高微生物组研究的完整性。在这里，我们回顾了污染物 DNA 和交叉污染在微生物组研究中是如何产生的，并讨论了它们的负面影响，特别是在分析低微生物生物量样本时。然后，我们确定了研究人员可以实施的一些关键措施，以减少微生物组研究中污染物 DNA 和交叉污染的影响。我们提出了一套最低实验标准，即“RIDE”清单，以提高未来低微生物生物量研究的有效性。
