Construction of a pathogenic microorganism detection method based on third-generation nanopore sequencing data
Abstract Background Pathogenic microorganisms can cause infections, allergies, diarrhoea, tumours, other diseases, and even death. Therefore, the detection of pathogenic microorganisms is important. Methods In this study, a bioinformatics pipeline was developed. First, a comprehensive and penetratin...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-02-01
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| Series: | BMC Infectious Diseases |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12879-025-10559-5 |
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| Summary: | Abstract Background Pathogenic microorganisms can cause infections, allergies, diarrhoea, tumours, other diseases, and even death. Therefore, the detection of pathogenic microorganisms is important. Methods In this study, a bioinformatics pipeline was developed. First, a comprehensive and penetrating reference database of nucleic acids from pathogenic microorganisms was constructed, and sequence alignment analysis was performed via nanopore sequencing of metagenomic data collected from 40 patients with lower respiratory tract infection symptoms. The similarity, abundance, and matching length of the sequences were integrated to obtain a comprehensive parameter, all_ratio, which can be used to identify pathogenic microorganisms effectively. Results Compared with two previous methods, i.e., pathogen identification via microbiological culture or a previously reported metagenomic pipeline, pathogens were detected in 23 out of the 40 samples using our method, with the same results as those of the previous two methods. In five samples, pathogens such as Streptococcus pneumoniae and Granulicatella adiacens that had not been detected via the previous two methods were detected using our method. Pathogens such as Streptococcus pneumoniae and Neisseria flavescens were detected in 12 samples via our method, in contrast to the results of the previous two methods. Therefore, it is presumed that the individuals from whom these samples were obtained may have been infected with two or more pathogenic microorganisms. Conclusions This study shows that our method can efficiently identify pathogenic microorganisms and even detect pathogenic microorganisms in samples that cannot be detected via other methods, thus supplementing existing methods. |
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| ISSN: | 1471-2334 |