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

Scientific Reports
Laurence M. ShawDov J. Stekel

Abstract

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 siz...Continue Reading

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Citations

Feb 6, 2020·Frontiers in Microbiology·Geraldine M Flaujac LafontaineIan F Connerton

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Methods Mentioned

BETA
soil treatments
SGA

Software Mentioned

R
PERMANOVA
Diamond
MetaPhlAn

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