DirtyGenes: testing for significant changes in gene or bacterial population compositions from a small number of samples.

Autor: Shaw LM; School of Biosciences, University of Nottingham, Loughborough, LE12 5RD, UK.; School of Science and Technology, Nottingham Trent University, Nottingham, NG11 8NS, UK., Blanchard A; School of Veterinary Medicine and Science, University of Nottingham, Loughborough, LE12 5RD, UK.; School of Animal, Rural and Environmental Sciences, Nottingham Trent University, Brackenhurst, NG25 0FQ, UK., Chen Q; Chinese Academy of Sciences, Institute of Urban Environment, Xiamen, 361021, China., An X; Chinese Academy of Sciences, Institute of Urban Environment, Xiamen, 361021, China., Davies P; School of Veterinary Medicine and Science, University of Nottingham, Loughborough, LE12 5RD, UK.; Institute of Infection and Global Health, University of Liverpool, Liverpool, L69 7BE, UK., Tötemeyer S; School of Veterinary Medicine and Science, University of Nottingham, Loughborough, LE12 5RD, UK., Zhu YG; Chinese Academy of Sciences, Institute of Urban Environment, Xiamen, 361021, China., Stekel DJ; School of Biosciences, University of Nottingham, Loughborough, LE12 5RD, UK. dov.stekel@nottingham.ac.uk.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2019 Feb 20; Vol. 9 (1), pp. 2373. Date of Electronic Publication: 2019 Feb 20.
DOI: 10.1038/s41598-019-38873-4
Abstrakt: High throughput genomics technologies are applied widely to microbiomes in humans, animals, soil and water, to detect changes in bacterial communities or the genes they carry, between different environments or treatments. We describe a method to test the statistical significance of differences in bacterial population or gene composition, applicable to metagenomic or quantitative polymerase chain reaction data. Our method goes beyond previous published work in being universally most powerful, thus better able to detect statistically significant differences, and through being more reliable for smaller sample sizes. It can also be used for experimental design, to estimate how many samples to use in future experiments, again with the advantage of being universally most powerful. We present three example analyses in the area of antimicrobial resistance. The first is to published data on bacterial communities and antimicrobial resistance genes (ARGs) in the environment; we show that there are significant changes in both ARG and community composition. The second is to new data on seasonality in bacterial communities and ARGs in hooves from four sheep. While the observed differences are not significant, we show that a minimum group size of eight sheep would provide sufficient power to observe significance of similar changes in further experiments. The third is to published data on bacterial communities surrounding rice crops. This is a much larger data set and is used to verify the new method. Our method has broad uses for statistical testing and experimental design in research on changing microbiomes, including studies on antimicrobial resistance.
Databáze: MEDLINE
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