Comparison of Oxford Nanopore Technologies and Illumina MiSeq sequencing with mock communities and agricultural soil

Abstract

Illumina MiSeq is the current standard for characterizing microbial communities in soil. The 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 output of short MiSeq to short and full-length MinION 16S rRNA amplicons from a mock community and agricultural soil samples. 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 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.

Comprehensive Identification of Pathogenic Microbes and Antimicrobial Resistance Genes in Food Products Using Nanopore Sequencing-Based Metagenomics

“…This discrepancy could be attributed to the reduction in the number of reads following the filtering of low-quality reads. In addition, it was suggested that Illumina may have higher accuracy than nanopore sequencing (Kerkhof 2021, Stevens, Creed et al 2022), while we demonstrated an accuracy of over 90% for sequence lengths shorter than 300 bp and approximately 80% for sequence lengths longer than 800 bp in nanopore sequencing by aligning the TEM genes generated by nanopore to those generated by Illumina. Overall, we suggest that both platforms can be used for AMR profiling in food samples, but nanopore sequencing may be a better option.…”

Microbial Community Profiling Protocol with Full‐length 16S rRNA Sequences and Emu

“…Therefore, if the user requires classifying each read individually, further calculations are required. Yet when an accurate microbial community profile from 16S rRNA sequences is desired, recent studies are also favoring ONT sequencing paired with Emu (Petrone et al., 2023; Stevens et al., 2023).…”

Evaluating the Efficiency of 16S-ITS-23S operon Sequencing: A Comparison of Primer Pairs, Sequencing Platforms, and Taxonomic Classifiers

“…The introduction of third generation, or long-read, sequencing technologies has enhanced NGS capabilities by allowing the sequencing of longer DNA segments. This advancement has revolutionised 16S rRNA gene studies, allowing for full-length 16S gene sequencing (~ 1500 bp), thereby substantially improving phylogenetic resolution, often to the species level [23,[28][29][30][31][32][33]. The platforms provided by PacBio and Oxford Nanopore Technology (ONT) are at the forefront of long read sequencing.…”