Abstract
Although Next-Generation Sequencing techniques have increased our access to the soil microbiome, each step of soil metagenomics presents inherent biases that prevent the accurate definition of the soil microbiome and its ecosystem function. In this study, we compared the effects of DNA extraction and sequencing depth on bacterial richness discovery from two soil samples. Four DNA extraction methods were used, and sequencing duplicates were generated for each DNA sample. The V3–V4 region of the 16S rRNA gene was sequenced to determine the taxonomical richness measured by each method at the amplicon sequence variant (ASV) level. Both the overall functional richness and antibiotic resistance gene (ARG) richness were evaluated by metagenomics sequencing. Despite variable DNA extraction methods, sequencing depth had a greater influence on bacterial richness discovery at both the taxonomical and functional levels. Sequencing duplicates from the same sample provided access to different portions of bacterial richness, and this was related to differences in the sequencing depth. Thus, the sequencing depth introduced biases in the comparison of DNA extraction methods. An optimisation of the soil metagenomics workflow is needed in order to sequence at a sufficient and equal depth. This would improve the accuracy of metagenomic comparisons and soil microbiome profiles.
Highlights
The soil ecosystem arguably harbours the highest diversity of microorganisms of any ecosystem [1]
The total abundance of the bacterial community extracted from two different soils, which was estimated by the number of copies of the 16S rRNA gene, varied between
The development of soil metagenomic techniques implied a significant breakthrough in the understanding of soil microbial ecology, biases associated with these approaches prevent the accurate definition of the soil microbiome [44] and its associated functions in the soil ecosystem [45]
Summary
The soil ecosystem arguably harbours the highest diversity of microorganisms of any ecosystem [1]. The use of Generation-Sequencing (NGS) techniques has increased our access to the microbial communities present in soil, especially to the large proportion of uncultured microorganisms [2,3]. Each methodological step from soil sampling to sequence annotation presents inherent biases that limit the depth and reliability of soil microbiome analyses [4,5,6,7]. Of all these biases, the ones associated with DNA extraction have been highlighted for their effects [8,9]. Organic matter and humic acids that are known to potentially inhibit enzymatic reactions [14,15]
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