From soil to sequence: filling the critical gap in genome-resolved metagenomics is essential to the future of soil microbial ecology

Abstract

Soil microbiomes are heterogeneous, complex microbial communities. Metagenomic analysis is generating vast amounts of data, creating immense challenges in sequence assembly and analysis. Although advances in technology have resulted in the ability to easily collect large amounts of sequence data, soil samples containing thousands of unique taxa are often poorly characterized. These challenges reduce the usefulness of genome-resolved metagenomic (GRM) analysis seen in other fields of microbiology, such as the creation of high quality metagenomic assembled genomes and the adoption of genome scale modeling approaches. The absence of these resources restricts the scale of future research, limiting hypothesis generation and the predictive modeling of microbial communities. Creating publicly available databases of soil MAGs, similar to databases produced for other microbiomes, has the potential to transform scientific insights about soil microbiomes without requiring the computational resources and domain expertise for assembly and binning.

Keywords: FAIR Data Principles; Genome Resolved Metagenomics; Hybrid Assembly; Microbiome Assembled Genomes; Soil Microbiome.

Fig. 1

Percent of soil and human metagenomic reads mapped to the GEM and NCBI RefSeq databases as a function of nonpareil kmer diversity. Nonpareil kmer diversity is a measure of genetic diversity within a metagenome. Human microbiomes are less diverse than soil (agricultural, forest and desert) metagenomes. From the efforts of the Human Microbiome Project there is a large collection of bacterial genome sequences in NCBI’s RefSeq database and consequently a large proportion of reads from human metagenomes map to this database. Typically 50–90% of human metagenome reads map to the combined databases. In contrast very few soil metagenomic reads map to genomes in NCBI’s RefSeq, and less than 5% map to the GEM catalog