Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).Article
PubMed
Google Scholar
Manter, D. K., Moore, J. M., Lehman, R. & Hamm, A. K. Microbial community composition, diversity, and function. In Soil Health Series: Volume 2 Laboratory Methods for Soil Health Analysis 289–323 (Soil Science Society of America, Madison, 2021).Fierer, N., Wood, S. A. & de Mesquita, C. P. B. How microbes can, and cannot, be used to assess soil health. Soil Biol. Biochem. 153, 108111 (2021).Article
Google Scholar
Chatterjee, S., Mondal, K. C. & Chatterjee, S. (eds) Soil Health and Environmental Sustainability: Application of Geospatial Technology (Springer, 2022).Alteio, L. V. et al. A critical perspective on interpreting amplicon sequencing data in soil ecological research. Soil Biol. Biochem. 160, 108357 (2021).Article
Google Scholar
Walters, K. E. & Martiny, J. B. Alpha-, beta-, and gamma-diversity of bacteria varies across habitats. PLoS One 15, e0233872 (2020).Article
PubMed
PubMed Central
Google Scholar
Sinha, R. et al. Assessment of variation in microbial community amplicon sequencing by the microbiome quality control (MBQC) project consortium. Nat. Biotechnol. 35, 1077–1086 (2017).Article
PubMed
PubMed Central
Google Scholar
Bruner, E. A., Okubara, P. A., Abi-Ghanem, R., Brown, D. J. & Reardon, C. L. Use of pressure cycling technology for cell lysis and recovery of bacterial and fungal communities from soil. Biotechniques 58, 171–180 (2015).Article
PubMed
Google Scholar
Kennedy, N. A. et al. The impact of different DNA extraction kits and laboratories upon the assessment of human gut microbiota composition by 16S rRNA gene sequencing. PLoS One 9, e88982 (2014).Article
PubMed
PubMed Central
Google Scholar
Mori, H. et al. Assessment of metagenomic workflows using a newly constructed human gut microbiome mock community. DNA Res. 30, dsad010 (2023).Article
PubMed
PubMed Central
Google Scholar
Brooks, J. P. et al. The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies. BMC Microbiol. 15, 1–14 (2015).Article
Google Scholar
Tourlousse, D. M. et al. Characterization and demonstration of mock communities as control reagents for accurate human microbiome community measurements. Microbiol. Spectr. 10, e01915–e01921 (2022).Article
PubMed
PubMed Central
Google Scholar
Han, D. et al. Multicenter assessment of microbial community profiling using 16S rRNA gene sequencing and shotgun metagenomic sequencing. J. Adv. Res. 26, 111–121 (2020).Article
PubMed
PubMed Central
Google Scholar
Szóstak, N. et al. The standardisation of the approach to metagenomic human gut analysis: from sample collection to microbiome profiling. Sci. Rep. 12, 8470 (2022).Article
PubMed
PubMed Central
Google Scholar
Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1–e1 (2013).Article
PubMed
Google Scholar
Fouhy, F., Clooney, A. G., Stanton, C., Claesson, M. J. & Cotter, P. D. 16S rRNA gene sequencing of mock microbial populations-impact of DNA extraction method, primer choice and sequencing platform. BMC Microbiol. 16, 1–13 (2016).Article
Google Scholar
Ahn, J.-H., Kim, B.-Y., Song, J. & Weon, H.-Y. Effects of PCR cycle number and DNA polymerase type on the 16S rRNA gene pyrosequencing analysis of bacterial communities. J. Microbiol. 50, 1071–1074 (2012).Article
PubMed
Google Scholar
Lee, C. K. et al. Groundtruthing next-gen sequencing for microbial ecology-biases and errors in community structure estimates from PCR amplicon pyrosequencing. PLoS One 7, e44224 (2012).Article
PubMed
PubMed Central
Google Scholar
Keenum, I. et al. Amplicon sequencing minimal information (ASqMI): quality and reporting guidelines for actionable calls in biodefense applications. J. AOAC Int. 106, 1424–1430 (2023).Article
PubMed
PubMed Central
Google Scholar
Yilmaz, P. et al. Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications. Nat. Biotechnol. 29, 415–420 (2011).Article
PubMed
PubMed Central
Google Scholar
Costea, P. I. et al. Towards standards for human fecal sample processing in metagenomic studies. Nat. Biotechnol. 35, 1069–1076 (2017).Article
PubMed
Google Scholar
Bakker, M. G. A fungal mock community control for amplicon sequencing experiments. Mol. Ecol. Res. 18, 541–556 (2018).Article
Google Scholar
Singer, E. et al. Next generation sequencing data of a defined microbial mock community. Sci. Data 3, 1–8 (2016).Article
Google Scholar
Meslier, V. et al. Benchmarking second and third-generation sequencing platforms for microbial metagenomics. Sci. Data 9, 694 (2022).Article
PubMed
PubMed Central
Google Scholar
Sevim, V. et al. Shotgun metagenome data of a defined mock community using Oxford Nanopore, PacBio and Illumina technologies. Sci. Data 6, 285 (2019).Article
PubMed
PubMed Central
Google Scholar
Hardwick, S. A. et al. Synthetic microbe communities provide internal reference standards for metagenome sequencing and analysis. Nat. Commun. 9, 3096 (2018).Article
PubMed
PubMed Central
Google Scholar
Olivares, I. R. B., Souza, G., Nogueira, A., Toledo, G. & Marcki, D. C. Trends in developments of certified reference materials for chemical analysis-focus on food, water, soil, and sediment matrices. TrAC Trends Anal. Chem. 100, 53–64 (2018).Article
Google Scholar
Santos, A., van Aerle, R., Barrientos, L. & Martinez-Urtaza, J. Computational methods for 16S metabarcoding studies using nanopore sequencing data. Comput. Struct. Biotechnol. J. 18, 296–305 (2020).Article
PubMed
PubMed Central
Google Scholar
Zhang, T. et al. The newest Oxford nanopore R10. 4.1 full-length 16S rRNA sequencing enables the accurate resolution of species-level microbial community profiling. Appl. Environ. Microbiol. 89, e00605–e00623 (2023).Article
PubMed
PubMed Central
Google Scholar
Stevens, B. M., Creed, T. B., Reardon, C. L. & Manter, D. K. Comparison of Oxford nanopore technologies and Illumina MiSeq sequencing with mock communities and agricultural soil. Sci. Rep. 13, 9323 (2023).Article
PubMed
PubMed Central
Google Scholar
Kennedy, K., Hall, M. W., Lynch, M. D., Moreno-Hagelsieb, G. & Neufeld, J. D. Evaluating bias of Illumina-based bacterial 16S rRNA gene profiles. Appl. Environ. Microbiol. 80, 5717–5722 (2014).Article
PubMed
PubMed Central
Google Scholar
Ip, C. L. C. et al. MinION analysis and reference consortium: phase 1 data release and analysis. F1000Research 4, 1075 (2015).Article
PubMed
PubMed Central
Google Scholar
Delahaye, C. & Nicolas, J. Sequencing DNA with nanopores: troubles and biases. PloS one 16, e0257521 (2021).Article
PubMed
PubMed Central
Google Scholar
Mackey, E. et al. Certification of three NIST renewal soil standard reference materials for element content: SRM 2709a San Joaquin Soil, SRM 2710a Montana Soil I, and SRM 2711a Montana Soil II. NIST Spec. Publ. 260, 1–39 (2010).
Google Scholar
Bunge, J. & Fitzpatrick, M. Estimating the number of species: a review. J. Am. Stat. Assoc. 88, 364–373 (1993).Article
Google Scholar
Gotelli, N. J. & Colwell, R. K. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol. Lett. 4, 379–391 (2001).Article
Google Scholar
Chao, A. & Jost, L. Coverage‐based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology 93, 2533–2547 (2012).Article
PubMed
Google Scholar
Willis, A. D. Rarefaction, alpha diversity, and statistics. Front. Microbiol. 10, 492464 (2019).Article
Google Scholar
Cao, Y., Williams, W. P. & Bark, A. W. Effects of sample size (replicate number) on similarity measures in river benthic Aufwuchs community analysis. Water Environ. Res. 69, 107–114 (1997).Article
Google Scholar
Williams, J. D., Reardon, C. L., Wuest, S. B. & Long, D. S. Soil water infiltration after oilseed crop introduction into a Pacific Northwest winter wheat–fallow rotation. J. Soil Water Conserv. 75, 739–745 (2020).Article
Google Scholar
Halvorson, A. D., Del Grosso, S. J. & Stewart, C. E. Manure and inorganic nitrogen affect trace gas emissions under semi‐arid irrigated corn. J. Environ. Qual. 45, 906–914 (2016).Article
PubMed
Google Scholar
Lane, D. J. in Nucleic Acid Techniques in Bacterial Systematics (eds Stackebrandt, E. & Goodfellow, M.) (John Wiley & Sons Ltd., 1991).Muyzer, G., Teske, A., Wirsen, C. O. & Jannasch, H. W. Phylogenetic relationships ofThiomicrospira species and their identification in deep-sea hydrothermal vent samples by denaturing gradient gel electrophoresis of 16S rDNA fragments. Arch. Microbiol. 164, 165–172 (1995).Article
PubMed
Google Scholar
Wick, R. R. & Menzel, P. Filtlong. Available online: github.com/rrwick/Filtlong (accessed on 4 Mar 2022) (2018).Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).Article
Google Scholar
Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).Article
PubMed
PubMed Central
Google Scholar
O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745 (2016).Article
PubMed
Google Scholar
Stoddard, S. F., Smith, B. J., Hein, R., Roller, B. R. & Schmidt, T. M. rrn DB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. Nucleic Acids Res. 43, D593–D598 (2015).Article
PubMed
Google Scholar
Schoch, C. L. et al. NCBI taxonomy: a comprehensive update on curation, resources and tools. Database 2020, baaa062 (2020).Article
PubMed
PubMed Central
Google Scholar
Curry, K. D. et al. Emu: species-level microbial community profiling of full-length 16S rRNA Oxford nanopore sequencing data. Nat. Methods 19, 845–853 (2022).Article
PubMed
PubMed Central
Google Scholar
R: a language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2023).McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).Article
PubMed
PubMed Central
Google Scholar
Ben-Shachar, M. S., Lüdecke, D. & Makowski, D. effectsize: estimation of effect size indices and standardized parameters. J. Open Source Softw. 5, 2815 (2020).Article
Google Scholar
Schloss, P. D. Reintroducing mothur: 10 years later. Appl. Environ. Microbiol. 86, e02343–02319 (2020).Article
PubMed
PubMed Central
Google Scholar
Gkanogiannis, A. fastreeR: Phylogenetic, Distance And Other Calculations on VCF and Fasta Files https://github.com/gkanogiannis/fastreeR, https://github.com/gkanogiannis/BioInfoJava-Utils (2023).Oksanen, J. et al. Vegan: Community Ecology Package, R Package Version 2.6–4 https://CRAN.R-project.org/package=vegan, https://CRAN.R-project.org/package=vegan (2022).Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).Article
Google Scholar
Nearing, J. T. et al. Microbiome differential abundance methods produce different results across 38 datasets. Nat. Commun. 13, 342 (2022).Article
PubMed
PubMed Central
Google Scholar
Weiss, S. et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5, 1–18 (2017).Article
Google Scholar
Manter, D. et al. Cross-laboratory comparison of bacterial 16s rRNA communities in soil using nanopore sequencing [Dataset]. Zenodo https://zenodo.org/doi/10.5281/zenodo.11557861 (2024).