What question did this study set out to answer?

This work aims to assess the accuracy and effectiveness of nanopore sequencing for pathogen genomics, focusing on genome assembly and genotyping.

February 28, 2026Open Access

Deciphering bacterial pathogen genomics: implementation and application of a bioinformatics framework for nanopore sequencing

Puntos clave

This work aims to assess the accuracy and effectiveness of nanopore sequencing for pathogen genomics, focusing on genome assembly and genotyping.
Evaluated ONT workflows for genome reconstruction and molecular characterization.
Investigated error patterns in a variety of zoonotic and healthcare-associated pathogens.
Assessed cgMLST and SNP-based approaches for high-resolution genotyping.
Performed in silico serotyping of Salmonella enterica using nanopore data.
Flye assembly followed by Medaka polishing yielded the highest genome accuracy.
Optimized workflows successfully reconstructed outbreak clusters despite ONT's higher error rate.
In silico serotyping showed high concordance with traditional assays.
Identified a resistance-carrying plasmid in Streptococcus uberis linked to bovine mastitis.

Resumen

Oxford Nanopore Technologies (ONT) sequencing offers real-time, long-read capabilities for pathogen genomics, yet concerns persist regarding its accuracy in critical applications like outbreak tracking. This dissertation evaluates ONT-based workflows for genome reconstruction, high-resolution genotyping, and molecular characterization, presenting a scalable, reproducible bioinformatics pipeline for bacterial whole-genome sequencing (WGS). Genome reconstruction and ONT specific error patterns were investigated across a diverse panel of zoonotic and healthcare-associated pathogens, including Bacillus anthracis, Brucella spp., and Klebsiella spp. Results show that assembly with Flye followed by Medaka polishing (using bacteria-specific models) provides the highest accuracy. However, persistent biases in homopolymer-rich, high-GC, and methylated regions highlight a continued need for algorithmic improvements. Regarding genotyping, both cgMLST and SNP-based approaches were assessed. Despite ONT's higher error rate, optimized long-read workflows reliably reconstructed outbreak clusters. Furthermore, in silico serotyping of Salmonella enterica with nanopore sequencing data showed high concordance with classical assays. The framework’s utility was demonstrated via a case study of Streptococcus uberis outbreaks in Thuringia. The analysis identified a resistance-carrying plasmid linked to bovine mastitis, a feature only resolvable through ONT sequencing. Despite these advantages, ONT’s evolving hardware, chemistry, and neural network-based basecallers introduce reproducibility challenges, particularly for rare taxa underrepresented in training datasets. This work underscores ONT’s strong potential as a standalone platform for rapid, field-deployable analysis of pathogen genomics, while emphasizing the need for continued benchmarking and careful consideration of sequencing biases in epidemiological applications.

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