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

Shaw, Laurence M.; Blanchard, Adam; Chen, Qinglin; An, Xinli; Davies, Peers; Totemeyer, Sabine; Zhu, Yong-Guan; Stekel, Dov J.


Laurence M. Shaw

Qinglin Chen

Xinli An

Peers Davies

Yong-Guan Zhu

Professor of Computational Biology


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 _rst 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.


Shaw, L. M., Blanchard, A., Chen, Q., An, X., Davies, P., Totemeyer, S., …Stekel, D. J. (2019). DirtyGenes: testing for significant changes in gene or bacterial population compositions from a small number of samples. Scientific Reports, 9, Article 2373.

Journal Article Type Article
Acceptance Date Dec 21, 2018
Online Publication Date Feb 20, 2019
Publication Date Feb 20, 2019
Deposit Date Jan 21, 2019
Publicly Available Date Feb 20, 2019
Journal Scientific Reports
Print ISSN 2045-2322
Electronic ISSN 2045-2322
Publisher Nature Publishing Group
Peer Reviewed Peer Reviewed
Volume 9
Article Number 2373
Public URL
Publisher URL
Additional Information Received: 19 February 2018; Accepted: 21 December 2018; First Online: 20 February 2019; : The authors declare no competing interests.


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