Microbiome data management in action workshop: Atlanta, GA, USA, June 12–13, 2024 - Julia M. Kelliher; Mashael Aljumaah; Sarah R. Bordenstein; J. Rodney Brister; Patrick S. G. Chain; Jose Pablo Dundore-Arias; Joanne B. Emerson; Vanessa Moreira C. Fernandes; Roberto Flores; Antonio Gonzalez; Zoe A. Hansen; Eneida L. Hatcher; et al. (Environmental Microbiome)
Abstract Microbiome research is revolutionizing human and environmental health, but the value and reuse of microbiome data are significantly hampered by the limited development and adoption of data standards. While several ongoing efforts are aimed at improving microbiome data management, significant gaps still remain in terms of defining and promoting adoption of consensus standards for these datasets. The Strengthening the Organization and Reporting of Microbiome Studies (STORMS) guidelines for human microbiome research have been endorsed and successfully utilized by many research organizations, publishers, and funding agencies, and have been recognized as a consensus community standard. No equivalent effort has occurred for environmental, synthetic, and non-human host-associated microbiomes. To address this growing need within the microbiome research community, we convened the Microbiome Data Management in Action Workshop (June 12–13, 2024, in Atlanta, GA, USA), to bring together key decision makers in microbiome science including researchers, publishers, funders, and data repositories. The 50 attendees, representing the diverse and interdisciplinary nature of microbiome research, discussed recent progress and challenges, and brainstormed actionable recommendations and paths forward for coordinated environmental microbiome data management and the modifications necessary for the STORMS guidelines to be applied to environmental, non-human host, and synthetic microbiomes. The outcomes of this workshop will form the basis of a formalized data management roadmap to be implemented across the field. These best practices will drive scientific innovation now and in years to come as these data continue to be used not only in targeted reanalyses but in large-scale models and machine learning efforts.