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Comparison of Oxford Nanopore Technologies and Illumina MiSeq Sequencing with Mock Communities and Agricultural Soil

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Journal Sci Rep
Specialty Science
Date 2023 Jun 8
PMID 37291169
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Abstract

Illumina MiSeq is the current standard for characterizing microbial communities in soil. The newer alternative, Oxford Nanopore Technologies MinION sequencer, is quickly gaining popularity because of the low initial cost and longer sequence reads. However, the accuracy of MinION, per base, is much lower than MiSeq (95% versus 99.9%). The effects of this difference in base-calling accuracy on taxonomic and diversity estimates remains unclear. We compared the effects of platform, primers, and bioinformatics on mock community and agricultural soil samples using short MiSeq, and short and full-length MinION 16S rRNA amplicon sequencing. For all three methods, we found that taxonomic assignments of the mock community at both the genus and species level matched expectations with minimal deviation (genus: 80.9-90.5%; species: 70.9-85.2% Bray-Curtis similarity); however, the short MiSeq with error correction (DADA2) resulted in the correct estimate of mock community species richness and much lower alpha diversity for soils. Several filtering strategies were tested to improve these estimates with varying results. The sequencing platform also had a significant influence on the relative abundances of taxa with MiSeq resulting in significantly higher abundances Actinobacteria, Chloroflexi, and Gemmatimonadetes and lower abundances of Acidobacteria, Bacteroides, Firmicutes, Proteobacteria, and Verrucomicrobia compared to the MinION platform. When comparing agricultural soils from two different sites (Fort Collins, CO and Pendleton, OR), methods varied in the taxa identified as significantly different between sites. At all taxonomic levels, the full-length MinION method had the highest similarity to the short MiSeq method with DADA2 correction with 73.2%, 69.3%, 74.1%, 79.3%, 79.4%, and 82.28% of the taxa at the phyla, class, order, family, genus, and species levels, respectively, showing similar patterns in differences between the sites. In summary, although both platforms appear suitable for 16S rRNA microbial community composition, biases for different taxa may make the comparison between studies problematic; and even with a single study (i.e., comparing sites or treatments), the sequencing platform can influence the differentially abundant taxa identified.

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References
1.
Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M . Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2012; 41(1):e1. PMC: 3592464. DOI: 10.1093/nar/gks808. View

2.
Straub D, Blackwell N, Langarica-Fuentes A, Peltzer A, Nahnsen S, Kleindienst S . Interpretations of Environmental Microbial Community Studies Are Biased by the Selected 16S rRNA (Gene) Amplicon Sequencing Pipeline. Front Microbiol. 2020; 11:550420. PMC: 7645116. DOI: 10.3389/fmicb.2020.550420. View

3.
Santos A, van Aerle R, Barrientos L, Martinez-Urtaza J . Computational methods for 16S metabarcoding studies using Nanopore sequencing data. Comput Struct Biotechnol J. 2020; 18:296-305. PMC: 7013242. DOI: 10.1016/j.csbj.2020.01.005. View

4.
Curry K, Wang Q, Nute M, Tyshaieva A, Reeves E, Soriano S . Emu: species-level microbial community profiling of full-length 16S rRNA Oxford Nanopore sequencing data. Nat Methods. 2022; 19(7):845-853. PMC: 9939874. DOI: 10.1038/s41592-022-01520-4. View

5.
Fujiyoshi S, Muto-Fujita A, Maruyama F . Evaluation of PCR conditions for characterizing bacterial communities with full-length 16S rRNA genes using a portable nanopore sequencer. Sci Rep. 2020; 10(1):12580. PMC: 7387495. DOI: 10.1038/s41598-020-69450-9. View