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Metagenomics has revolutionized our understanding of microbial communities, offering unprecedented insights into their genetic and functional diversity across Earth’s diverse ecosystems. Beyond their roles as environmental constituents, microbiomes act as symbionts, profoundly influencing the health and function of their host organisms. Given the inherent complexity of these communities and the diverse environments where they reside, the components of a metagenomics study must be carefully tailored to yield accurate results that are representative of the populations of interest. This Primer examines the methodological advancements and current practices that have shaped the field, from initial stages of sample collection and DNA extraction to the advanced bioinformatics tools employed for data analysis, with a particular focus on the profound impact of next-generation sequencing on the scale and accuracy of metagenomics studies. We critically assess the challenges and limitations inherent in metagenomics experimentation, available technologies and computational analysis methods. Beyond technical methodologies, we explore the application of metagenomics across various domains, including human health, agriculture and environmental monitoring. Looking ahead, we advocate for the development of more robust computational frameworks and enhanced interdisciplinary collaborations. This Primer serves as a comprehensive guide for advancing the precision and applicability of metagenomic studies, positioning them to address the complexities of microbial ecology and their broader implications for human health and environmental sustainability. 

ABSTRACT The field of microbial ecology, evolution, and biodiversity (EEB) is at the leading edge of understanding how microbes shape our biosphere and influence the well-being of humankind and Earth. To that end, EEB is developing new transdisciplinary tools to analyze these ecologically critical, complex microbial communities. The American Society for Microbiology’s Council on Microbial Sciences hosted a virtual retreat in 2023 to discuss the trajectory of EEB both within the Society and microbiology writ large. The retreat emphasized the interconnectedness of microbes and their outsized global influence on environmental and host health. The maximal potential impact of EEB will not be achieved without contributions from disparate fields that unite diverse technologies and data sets. In turn, this level of transdisciplinary efforts requires actively encouraging “broad” research, spanning inclusive global collaborations that incorporate both scientists and the public. Together, the American Society for Microbiology and EEB are poised to lead a paradigm shift that will result in a new era of collaboration, innovation, and societal relevance for microbiology. 

Despite substantial interest in the species diversity of the human microbiome and its role in disease, the scale of its genetic diversity, which is fundamental to deciphering human-microbe interactions, has not been quantified. Here, we conducted a cross-study meta-analysis of metagenomes from two human body niches, the mouth and gut, covering 3,655 samples from 13 studies. We found staggering genetic heterogeneity in the dataset, identifying a total of 45,666,334 non-redundant genes (23,961,508 oral and 22,254,436 gut) at the 95% identity level. Fifty percent of all genes were “singletons,” or unique to a single metagenomic sample. Singletons were enriched for different functions (compared with non-singletons) and arose from sub-population-specific microbial strains. Overall, these results provide potential bases for the unexplained heterogeneity observed in microbiome-derived human phenotypes. One the basis of these data, we built a resource, which can be accessed at https://microbial-genes.bio. 

Abstract MotivationAcross biology, we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities. ResultsHere, we present Aether (http://aether.kosticlab.org), an intuitive, easy-to-use, cost-effective and scalable framework that uses linear programming to optimally bid on and deploy combinations of underutilized cloud computing resources. Our approach simultaneously minimizes the cost of data analysis and provides an easy transition from users’ existing HPC pipelines. Availability and implementationData utilized are available at https://pubs.broadinstitute.org/diabimmune and with EBI SRA accession ERP005989. Source code is available at (https://github.com/kosticlab/aether). Examples, documentation and a tutorial are available at http://aether.kosticlab.org. Supplementary informationSupplementary data are available at Bioinformatics online.