Statistical or biological significance?
© Saxon. 2015
Published: 5 November 2015
Oat plants grown at an agricultural research facility produce higher yields in Field 1 than in Field 2, under well fertilised conditions and with similar weather exposure; all oat plants in both fields are healthy and show no sign of disease. In this study, the authors hypothesised that the soil microbial community might be different in each field, and these differences might explain the difference in oat plant growth. They carried out a metagenomic analysis of the 16 s ribosomal ‘signature’ sequences from bacteria in 50 randomly located soil samples in each field to determine the composition of the bacterial community. The study identified >1000 species, most of which were present in both fields. The authors identified two plant growth-promoting species that were significantly reduced in soil from Field 2 (Student’s t-test P < 0.05), and concluded that these species might have contributed to reduced yield.
Another potential problem with this study is that although the 16 s ribosomal sequence is commonly used to identify bacterial species in metagenomic studies, many species have more than one copy of the 16 s sequence in their genome. Studies of bacterial abundance, such as this one, may, therefore, overestimate the number of bacterial species with a 16 s copy number greater than one. In a 2012 study, Kembel and coworkers  illustrated the importance of this problem by applying estimations of copy number to previously published metagenomic data sets, based on known copy numbers from diverse bacterial species. This adjustment for 16 s copy number changed some of the original outcomes reported in the published studies: in an oceanic data set, the ninth most abundant taxon became the second most abundant, and in a human microbiome study, the bacterial community found in the ear became more similar to that in the nostril rather than the sole of the foot — a more intuitive result.
The authors of that study created software designed to account for copy number, which can be used in conjunction with the open-source software already used for analysing metagenomic data sets, such as QIIME (Quantitative Insights Into Microbial Ecology) . Correcting for copy number can also be carried out using PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) .
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Kembel SW, Wu M, Eisen JA, Green JL. Incorporating 16S gene copy number information improves estimates of microbial diversity and abundance. PLoS Comput Biol. 2012;8:e1002743. doi:10.1371/journal.pcbi.1002743.PubMed CentralView ArticlePubMedGoogle Scholar
- Langille MGI, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013;31:814–21.View ArticlePubMedGoogle Scholar