From DNA sequences to microbial ecology: Wrangling NEON soil microbe data with the neonMicrobe R package. Issue 11 (24th November 2021)

• Record Type: Journal Article
• Authors: Qin, Clara; Bartelme, Ryan; Chung, Y. Anny; Fairbanks, Dawson; Lin, Yang; Liptzin, Daniel; Muscarella, Chance; Naithani, Kusum; Peay, Kabir; Pellitier, Peter; St. Rose, Ayanna; Stanish, Lee; Werbin, Zoey; Zhu, Kai
• Is Part Of: Ecosphere. Volume 12:Issue 11(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-24
• Subjects: biogeography -- bioinformatics -- DADA2 -- macroecology -- marker gene sequences -- neonMicrobe -- R package -- soil microbial communities -- Special Feature: Harnessing the NEON Data Revolution; Ecology -- Periodicals; Ecology; Periodicals; 577.05
• DOI: 10.1002/ecs2.3842
• Languages: English
• ISSNs: 2150-8925

Abstract

Abstract: Soil microbial communities play critical roles in various ecosystem processes, but studies at a large spatial and temporal scale have been challenging due to the difficulty in finding the relevant samples in available data sets as well as the lack of standardization in sample collection and processing. The National Ecological Observatory Network (NEON) has been collecting soil microbial community data multiple times per year for 47 terrestrial sites in 20 eco‐climatic domains, producing one of the most extensive standardized sampling efforts for soil microbial biodiversity to date. Here, we introduce the neonMicrobe R package—a suite of downloading, preprocessing, data set assembly, and sensitivity analysis tools for NEON's newly published 16S and ITS amplicon sequencing data products which characterize soil bacterial and fungal communities, respectively. neonMicrobe is designed to make these data more accessible to ecologists without assuming prior experience with bioinformatic pipelines. We describe quality control steps used to remove quality‐flagged samples, report on sensitivity analyses used to determine appropriate quality filtering parameters for the DADA2 workflow, and demonstrate the immediate usability of the output data by conducting standard analyses of soil microbial diversity. The sequence abundance tables produced by neonMicrobe can be linked to NEON's other data products (e.g., soil physical and chemical properties, plant community composition) and soil subsamples archived in the NEON Biorepository. We provide recommendations for incorporating neonMicrobe into reproducible scientific workflows, discuss technical considerations for large‐scale amplicon sequence analysis, and outline future directions for NEON‐enabled microbial ecology. In particular, we believe that NEON marker gene sequence data will allow researchers to answer outstanding questions about the spatial and temporal dynamics of soil microbial communities while explicitly accounting for scale dependence. We expect that the data produced by NEON and the neonMicrobe R package will act as a valuable ecological baseline to inform and contextualize future experimental and modeling endeavors.