Soil microbial communities contain the highest level of prokaryotic diversity of any environment, and metagenomic approaches involving the extraction of DNA from soil can improve our access to these communities. Most analyses of soil biodiversity and function assume that the DNA extracted represents the microbial community in the soil, but subsequent interpretations are limited by the DNA recovered from the soil. Unfortunately, extraction methods do not provide a uniform and unbiased subsample of metagenomic DNA, and as a consequence, accurate species distributions cannot be determined. Moreover, any bias will propagate errors in estimations of overall microbial diversity and may exclude some microbial classes from study and exploitation. To improve metagenomic approaches, investigate DNA extraction biases, and provide tools for assessing the relative abundances of different groups, we explored the biodiversity of the accessible community DNA by fractioning the metagenomic DNA as a function of (i) vertical soil sampling, (ii) density gradients (cell separation), (iii) cell lysis stringency, and (iv) DNA fragment size distribution. Each fraction had a unique genetic diversity, with different predominant and rare species (based on ribosomal intergenic spacer analysis [RISA] fingerprinting and phylochips). All fractions contributed to the number of bacterial groups uncovered in the metagenome, thus increasing the DNA pool for further applications. Indeed, we were able to access a more genetically diverse proportion of the metagenome (a gain of more than 80% compared to the best single extraction method), limit the predominance of a few genomes, and increase the species richness per sequencing effort. This work stresses the difference between extracted DNA pools and the currently inaccessible complete soil metagenome.

The soil ecosystem is critical for human health, affecting aspects of the environment from key agricultural and edaphic parameters to critical influence on climate change. Soil has more unknown biodiversity than any other ecosystem. We have applied diverse DNA extraction methods coupled with high throughput pyrosequencing to explore 4.88 × 10⁹ bp of metagenomic sequence data from the longest continually studied soil environment (Park Grass experiment at Rothamsted Research in the UK). Results emphasize important DNA extraction biases and unexpectedly low seasonal and vertical soil metagenomic functional class variations. Clustering-based subsystems and carbohydrate metabolism had the largest quantity of annotated reads assigned although <50% of reads were assigned at an E value cutoff of 10⁻⁵. In addition, with the more detailed subsystems, cAMP signaling in bacteria (3.24±0.27% of the annotated reads) and the Ton and Tol transport systems (1.69±0.11%) were relatively highly represented. The most highly represented genome from the database was that for a Bradyrhizobium species. The metagenomic variance created by integrating natural and methodological fluctuations represents a global picture of the Rothamsted soil metagenome that can be used for specific questions and future inter-environmental metagenomic comparisons. However, only 1% of annotated sequences correspond to already sequenced genomes at 96% similarity and E values of <10⁻⁵, thus, considerable genomic reconstructions efforts still have to be performed.