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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Soil Management and Sugarbeet Research » Research » Publications at this Location » Publication #408543
Research Project: Agricultural Management for Long-Term Sustainability and Soil Health

Location: Soil Management and Sugarbeet Research

Title: Unveiling errors in soil microbial community sequencing: A case for reference soils and improved diagnostics for nanopore sequencing

Author
item Manter, Daniel
item Reardon, Catherine
item Ashworth, Amanda
item Ibekwe, Abasiofiok
item Lehman, Richard
item Maul, Jude
item Miller, Daniel
item Creed, Timothy
item Ewing, Patrick
item Park, Stanley
item Ducey, Thomas
item Tyler, Heather
item Veum, Kristen
item Weyers, Sharon
item Knaebel, David

Submitted to: Communications Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/17/2024
Publication Date: 7/28/2024
Citation: Manter, D.K., Reardon, C.L., Ashworth, A.J., Ibekwe, A.M., Lehman, R.M., Maul, J.E., Miller, D.N., Creed, T.B., Ewing, P.M., Park, S., Ducey, T.F., Tyler, H.L., Veum, K.S., Weyers, S.L., Knaebel, D.B. 2024. Unveiling errors in soil microbial community sequencing: A case for reference soils and improved diagnostics for nanopore sequencing. Communications Biology. 7. Article e913. https://doi.org/10.1038/s42003-024-06594-8.
DOI: https://doi.org/10.1038/s42003-024-06594-8

Interpretive Summary: A majority of the world’s biodiversity can be found in soils, but very little has been characterized. Scientists across USDA-ARS collaborated to test and improve current methods for measuring the diversity of soil microbiological communities. They found that current methods can result in errors when comparing populations from multiple soils. Their recommendations promise to improve the way that microbial communities are measured in future studies, particularly long-term and multi-location, with comparisons of multiple sequencing runs.

Technical Abstract: The sequencing platform and workflow strongly influence microbial community analyses through potential errors at each step. Effective diagnostics and experimental controls are needed to validate data and improve reproducibility. This cross-laboratory study evaluates sources of variability and error at three main steps of a standardized amplicon sequencing workflow (DNA extraction, polymerase chain reaction [PCR], and sequencing) using Oxford Nanopore MinION to analyze agricultural soils and a simple mock community. Variability in sequence results occurs at each step in the workflow with PCR errors and differences in library size greatly influencing diversity estimates. Common bioinformatic diagnostics and the mock community are ineffective at detecting PCR abnormalities. This work outlines several diagnostic checks and techniques to account for sequencing depth and ensure accuracy and reproducibility in soil community analyses. These diagnostics and the inclusion of a reference soil can help ensure data validity and facilitate the comparison of multiple sequencing runs within and between laboratories.