Eisenhofer Raphael, Alberdi Antton, Woodcroft Ben J
Centre for Evolutionary Hologenomics, Globe Institute, University of Copenhagen, Copenhagen, Denmark.
Centre for Microbiome Research, School of Biomedical Sciences, Translational Research Institute, Brisbane, Queensland University of Technology (QUT), Woolloongabba, Australia.
ISME Commun. 2024 Sep 8;4(1):ycae111. doi: 10.1093/ismeco/ycae111. eCollection 2024 Jan.
Shotgun metagenomics is a powerful tool for studying the genomic traits of microbial community members, such as genome size, gene content, etc. While such traits can be used to better understand the ecology and evolution of microbial communities, the accuracy of their estimations can be critically influenced by both known and unknown factors. One factor that can bias trait estimations is the proportion of eukaryotic and viral DNA in a metagenome, as some bioinformatic tools assume that all DNA reads in a metagenome are bacterial or archaeal. Here, we add to a recent debate about the influence of eukaryotic DNA in the estimation of average genome size from a global soil sample dataset using a new bioinformatic tool. Contrary to what was assumed, our reanalysis of this dataset revealed that soil samples can contain a substantial proportion of non-microbial DNA, which severely inflated the original estimates of average genome size. Correcting for this bias significantly improves the statistical support for the negative relationship between average bacterial genome size and soil pH. These results highlight that metagenomes can contain large quantities of non-microbial DNA and that new methods that correct for this can improve microbial trait estimation.
鸟枪法宏基因组学是研究微生物群落成员基因组特征(如基因组大小、基因含量等)的强大工具。虽然这些特征可用于更好地理解微生物群落的生态和进化,但其估计的准确性可能受到已知和未知因素的严重影响。一个可能使特征估计产生偏差的因素是宏基因组中真核生物和病毒DNA的比例,因为一些生物信息学工具假定宏基因组中的所有DNA读数都是细菌或古细菌的。在此,我们使用一种新的生物信息学工具,加入了最近关于真核生物DNA对从全球土壤样本数据集中估计平均基因组大小影响的争论。与之前的假设相反,我们对该数据集的重新分析表明,土壤样本可能含有大量非微生物DNA,这严重夸大了平均基因组大小的原始估计值。校正这种偏差显著提高了对平均细菌基因组大小与土壤pH值之间负相关关系的统计支持。这些结果突出表明,宏基因组可能包含大量非微生物DNA,而校正此偏差的新方法可以改善微生物特征估计。