The many microbial communities around us form interactive and dynamic ecosystems called microbiomes. Though concealed from the naked eye, microbiomes govern and influence macroscopic systems including human health, plant resilience, and biogeochemical cycling. Such feats have attracted interest from the scientific community, which has recently turned to machine learning and deep learning methods to interrogate the microbiome and elucidate the relationships between its composition and function. Here, we provide an overview of how the latest microbiome studies harness the inductive prowess of artificial intelligence methods. We start by highlighting that microbiome data – being compositional, sparse, and high-dimensional – necessitates special treatment. We then introduce traditional and novel methods and discuss their strengths and applications. Finally, we discuss the outlook of machine and deep learning pipelines, focusing on bottlenecks and considerations to address them.

The National Microbiome Data Collaborative (NMDC), a new initiative aimed at empowering microbiome research, is gearing up its pilot phase after receiving $10 million from the U.S. Department of Energy (DOE) Office of Science. Spearheaded by Lawrence Berkeley National Laboratory (Berkeley Lab), in partnership with Los Alamos (LANL), Oak Ridge (ORNL), and Pacific Northwest (PNNL) national laboratories, the NMDC will leverage DOE's existing data-science resources and high-performance computing systems to develop a framework that facilitates more efficient use of microbiome data for applications in energy, environment, health, and agriculture. Nearly every ecosystem and organism on Earth hosts a diverse community of microorganisms – its microbiome. Yet we know little about the functions of individual microbes, let alone how they interact with each other, their hosts, or their environments, and how their activity varies over time or in response to perturbations. The past decade has seen tremendous advances in genome and metagenome DNA-sequencing technologies, which has led to an unprecedented volume of microbiome data being generated.