Abstract
The interdisciplinary nature of microbiome research, coupled with the generation of complex multi-omics data, makes knowledge sharing challenging. The Strengthening the Organization and Reporting of Microbiome Studies (STORMS) guidelines provide a checklist for the reporting of study information, experimental design and analytical methods within a scientific manuscript on human microbiome research. Here, in this Consensus Statement, we present the standards for technical reporting in environmental and host-associated microbiome studies (STREAMS) guidelines. The guidelines expand on STORMS and include 67 items to support the reporting and review of environmental (for example, terrestrial, aquatic, atmospheric and engineered), synthetic and non-human host-associated microbiome studies in a standardized and machine-actionable manner. Based on input from 248 researchers spanning 28 countries, we provide detailed guidance, including comparisons with STORMS, and case studies that demonstrate the usage of the STREAMS guidelines. STREAMS, like STORMS, will be a living community resource updated by the Consortium with consensus-building input of the broader community.
| Original language | English |
|---|---|
| Pages (from-to) | 3059-3068 |
| Number of pages | 10 |
| Journal | Nature Microbiology |
| Volume | 10 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 2025 |
Funding
This material is based upon work supported by the National Science Foundation (NSF) under award number 2422717. We thank the American Society for Microbiology for support during the Microbiome Data Management in Action workshop in June 2024, and especially A. Corby and N. Nguyen for their participation. Any opinions, findings and conclusions or recommendations expressed in this presentation are those of the author(s) and do not necessarily reflect the views of the NSF, NOAA/OAR or the Department of Commerce. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the US Government. This material is based in part upon work supported by the National Ecological Observatory Network (NEON), a programme sponsored by the NSF and operated under cooperative agreement by Battelle. The work conducted by the National Microbiome Data Collaborative ( https://ror.org/05cwx3318 ) is supported by the Genomic Science Program in the US Department of Energy (DOE), Office of Science and Office of Biological and Environmental Research (BER) under contract numbers DE-AC02-05CH11231 (LBNL), 89233218CNA000001 (LANL) and DE-AC05-76RL01830 (PNNL). This work was supported by the US Geological Survey Coastal and Marine Hazards and Resources Program. This work was supported (in part) by the National Center for Biotechnology Information of the National Library of Medicine (NLM), National Institutes of Health (NIH). The contributions of the NIH author(s) are considered Works of the United States Government. The findings and conclusions presented in this paper are those of the author(s) and do not necessarily reflect the views of the NIH or the US Department of Health and Human Services. G.B. was supported by a Science Focus Area Grant from the DOE, BER, Biological System Science Division (BSSD) under grant number LANLF59T and the Great Lakes Bioenergy Research Center, DOE BER award number DE-SC0018409. J.P.D.A. was supported by the Foundation for Food and Agriculture Research New Innovator in Food and Agriculture Research award number FF-NIA20--0000000056. The work conducted by the US Department of Energy Joint Genome Institute ( https://ror.org/04xm1d337 ), a DOE Office of Science User Facility, is supported by the Office of Science of the DOE operated under contract number DE-AC02-05CH11231. P.B. was supported by MEYS LUC23152. M.S. was supported by NSF award number ABI2149505. Work conducted at Lawrence Livermore National Laboratory was supported under the auspices of the US DOE under award SCW1632 and contract DE-AC52-07NA27344. S. Record was supported by Hatch project award number [MEO-022425], from the US Department of Agriculture’s National Institute of Food and Agriculture. V.R. was supported by NSF Biology Integration Institutes Program award number 2022070. T.N. and V.N. were supported by DOE BER through the Trial Ecosystem Advancement for Microbiome Science (TEAMS) and m-CAFEs Microbial Community Analysis and Functional Evaluation in Soils, a Science Focus Area led by the Lawrence Berkeley National Laboratory under contract number DE-AC02-05CH11231. L.R.T. was supported by award NA21OAR4320190 to the Northern Gulf Institute from NOAA’s Office of Oceanic and Atmospheric Research, US Department of Commerce (LA-UR-25-22860). This material is based upon work supported by the National Science Foundation (NSF) under award number 2422717. We thank the American Society for Microbiology for support during the Microbiome Data Management in Action workshop in June 2024, and especially A. Corby and N. Nguyen for their participation. Any opinions, findings and conclusions or recommendations expressed in this presentation are those of the author(s) and do not necessarily reflect the views of the NSF, NOAA/OAR or the Department of Commerce. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the US Government. This material is based in part upon work supported by the National Ecological Observatory Network (NEON), a programme sponsored by the NSF and operated under cooperative agreement by Battelle. The work conducted by the National Microbiome Data Collaborative (https://ror.org/05cwx3318) is supported by the Genomic Science Program in the US Department of Energy (DOE), Office of Science and Office of Biological and Environmental Research (BER) under contract numbers DE-AC02-05CH11231 (LBNL), 89233218CNA000001 (LANL) and DE-AC05-76RL01830 (PNNL). This work was supported by the US Geological Survey Coastal and Marine Hazards and Resources Program.