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Peer-reviewedCC BYBMC infectious diseases

Bacterial identification in clinical samples - comparing Nanopore and Illumina sequencing of the 16S rRNA and rpoB genes.

6 affiliations
  1. 1Department of Microbiology, Haukeland University Hospital, Bergen, Norway. [email protected].
  2. 2Department of Clinical Science, University of Bergen, Bergen, Norway. [email protected].
  3. 3Department of Microbiology, Haukeland University Hospital, Bergen, Norway.
  4. 4Pathogenomix, Santa Cruz, CA, USA.
  5. 5Division of Clinical Microbiology, Department of Laboratory Medicine and Pathology, Division of Public Health, Infectious Diseases, and Occupational Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
  6. 6Department of Medicine, Haukeland University Hospital, Bergen, Norway.

Abstract

Background

Recent improvements in the sequencing accuracy of the Oxford Nanopore Technologies (ONT) platform have increased its suitability for clinical microbiology applications, including broad-range amplification and sequencing of the 16S ribosomal RNA (rRNA) gene directly from clinical samples. This approach has a strong potential to improve diagnosis of bacterial infections. However, even full-length 16S rRNA sequencing may lack sufficient resolution to distinguish closely related species, a limitation exacerbated by the remaining ONT error rate. The rpoB gene represents a high-resolution bacterial phylogenetic marker that offers improved species-level discrimination. We evaluated a strategy combining ONT sequencing of the V1-V3 segment of the 16S rRNA gene and a segment of the rpoB gene. In addition, we assessed two novel pipelines designed specifically for the analysis of ONT amplicon data in clinical microbiology, where knowing the associated confidence level for a given identification is crucial, as is the ability to detect organisms that may not be represented in reference databases.Methods

A staggered mock community and 25 abdominal abscess samples were investigated using ONT sequencing of the 16S rRNA and rpoB genes, with Illumina sequencing of the same genes used as a comparator method to validate the analytical accuracy of the approach.Results

rpoB ONT sequencing achieved species-level identification in 91.2% of detections, as compared to 62.8% with 16S rRNA ONT sequencing. However, 16S rRNA ONT sequencing obtained a higher sensitivity of 93%, as compared to 83.7% for rpoB ONT sequencing. Combining 16S rRNA and rpoB ONT sequencing yielded a sensitivity of 98.8% with 89% species-level discrimination. For both gene targets, ONT sequencing paralleled Illumina sequencing.Conclusions

Combining ONT sequencing with high-sensitivity broad-range amplification of the 16S rRNA gene and amplification of the rpoB gene for enhanced taxonomic resolution enables accurate detection and species-level identification of bacteria directly from clinical samples. This approach represents a rapid, flexible and cost-efficient tool for culture-independent diagnostics in clinical microbiology.

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BibTeX
@article{bivand2026bacterial,
  title = {{Bacterial identification in clinical samples - comparing Nanopore and Illumina sequencing of the 16S rRNA and rpoB genes.}},
  author = {Joanna Małgorzata Bivand and Audun Sivertsen and Marit Gjerde Tellevik and Oystein Saebo and Robin Patel and Ruben Dyrhovden and Øyvind Kommedal},
  journal = {BMC infectious diseases},
  year = {2026},
  month = jul,
  doi = {10.1186/s12879-026-14101-z},
  url = {https://europepmc.org/article/MED/42928095},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Bacterial identification in clinical samples - comparing Nanopore and Illumina sequencing of the 16S rRNA and rpoB genes.
AU  - Joanna Małgorzata Bivand
AU  - Audun Sivertsen
AU  - Marit Gjerde Tellevik
AU  - Oystein Saebo
AU  - Robin Patel
AU  - Ruben Dyrhovden
AU  - Øyvind Kommedal
PY  - 2026
DA  - 2026/07/28
JO  - BMC infectious diseases
DO  - 10.1186/s12879-026-14101-z
UR  - https://europepmc.org/article/MED/42928095
AB  - Background  Recent improvements in the sequencing accuracy of the Oxford Nanopore Technologies (ONT) platform have increased its suitability for clinical microbiology applications, including broad-range amplification and sequencing of the 16S ribosomal RNA (rRNA) gene directly from clinical samples. This approach has a strong potential to improve diagnosis of bacterial infections. However, even full-length 16S rRNA sequencing may lack sufficient resolution to distinguish closely related species, a limitation exacerbated by the remaining ONT error rate. The rpoB gene represents a high-resolution bacterial phylogenetic marker that offers improved species-level discrimination. We evaluated a strategy combining ONT sequencing of the V1-V3 segment of the 16S rRNA gene and a segment of the rpoB gene. In addition, we assessed two novel pipelines designed specifically for the analysis of ONT amplicon data in clinical microbiology, where knowing the associated confidence level for a given identification is crucial, as is the ability to detect organisms that may not be represented in reference databases.Methods  A staggered mock community and 25 abdominal abscess samples were investigated using ONT sequencing of the 16S rRNA and rpoB genes, with Illumina sequencing of the same genes used as a comparator method to validate the analytical accuracy of the approach.Results  rpoB ONT sequencing achieved species-level identification in 91.2% of detections, as compared to 62.8% with 16S rRNA ONT sequencing. However, 16S rRNA ONT sequencing obtained a higher sensitivity of 93%, as compared to 83.7% for rpoB ONT sequencing. Combining 16S rRNA and rpoB ONT sequencing yielded a sensitivity of 98.8% with 89% species-level discrimination. For both gene targets, ONT sequencing paralleled Illumina sequencing.Conclusions  Combining ONT sequencing with high-sensitivity broad-range amplification of the 16S rRNA gene and amplification of the rpoB gene for enhanced taxonomic resolution enables accurate detection and species-level identification of bacteria directly from clinical samples. This approach represents a rapid, flexible and cost-efficient tool for culture-independent diagnostics in clinical microbiology.
SN  - 1471-2334
ER  -