Optimized bacterial community characterization through full-length 16S rRNA gene sequencing utilizing MinION nanopore technology

Abstract

Background: Accurate identification of bacterial communities is crucial for research applications, diagnostics, and clinical interventions. Although 16S ribosomal RNA (rRNA) gene sequencing is a widely employed technique for bacterial taxonomic classification, it often results in misclassified or unclassified bacterial taxa. This study sought to refine the full-length 16S rRNA gene sequencing protocol using the MinION sequencer, focusing on the V1-V9 regions. Our methodological enquiry examined several factors, including the number of PCR amplification cycles, choice of primers and Taq polymerase, and specific sequence databases and workflows employed. We used a microbial standard comprising eight bacterial strains (five gram-positive and three gram-negative) in known proportions as a validation control.

Results: Based on the MinION protocol, we employed the microbial standard as the DNA template for the 16S rRNA gene amplicon sequencing procedure. Our analysis showed that an elevated number of PCR amplification cycles introduced PCR bias, and the selection of Taq polymerase and primer sets significantly affected the subsequent analysis. Bacterial identification at genus level demonstrated Pearson correlation coefficients ranging from 0.73 to 0.79 when assessed using BugSeq, Kraken-Silva and EPI2ME-16S workflows. Notably, the EPI2ME-16S workflow exhibited the highest Pearson correlation with the microbial standard, minimised misclassification, and increased alignment accuracy. At the species taxonomic level, the BugSeq workflow was superior, with a Pearson correlation coefficient of 0.92.

Conclusions: These findings emphasise the importance of careful selection of PCR settings and a well-structured analytical framework for 16S rRNA full-length gene sequencing. The results showed a robust correlation between the predicted and observed bacterial abundances at both the genus and species taxonomic levels, making these findings applicable across diverse research contexts and with clinical utility for reliable pathogen identification.

Keywords: 16S rRNA gene-based sequencing; Bacterial DNA; Nanopore sequencing; V1–V9 region.

Fig. 1

Schematic diagram of the basic principle and process of nanopore sequencing workflow. DNA from a pre-set microbial community is used as a template for 16S rRNA gene amplification by polymerase chain reaction (PCR). We analysed the influence of the type of Taq polymerase, annealing temperature, type of primers, and number of cycles used during the reactions. Following a standard library preparation process, the 16S gene DNA fragment was subjected to sequencing using a nanopore sequencer (MinION), and the results were then screened and processed according to the workflows, databases and accuracy settings. (Diagram adapted from [22])

Fig. 2

A representative agarose gel (0.8%) showing amplified 16S gene DNA (~ 1,500 bp) after 15-20-25-30-35 PCR cycles. PCR amplification was performed using the LongAmp polymerase and primer set#1. (A, DNA concentrations are shown below). In the bar graph, four workflows, namely Kraken2-Silva, EPI2ME-16S, EPI2ME-WIMP, and BugSeq, were compared to determine the relative abundance of microbial genera in the mock community based on increasing PCR cycles (B). (MC = microbial community, others = misclassified sequences)

Fig. 3

The hierarchical clustering heat map shows the relative abundance of each genus in the microbial mock community using primer sets #1 and #2 at different annealing temperatures (48, 50, and 52°C) and Taq polymerases (LongAmp and iTaq). The results were analysed using BugSeq, and the bottom row of the table shows the Pearson correlation coefficient. The results were compared with the expected (theoretical) proportion of the mock community. The coloured gradient legend represents a linear scale of relative abundance. (others = misclassified sequences)

Fig. 4

Comparative analysis was performed to determine the most effective combination for accurate species identification of the two primer sets, Set#1 and Set#2, and the two Taq Polymerases, LongAmp and iTaq. The analysis was conducted using BugSeq at an annealing temperature of 48°C. The relative abundance of each species is presented as %