Construction of a pathogenic microorganism detection method based on third-generation nanopore sequencing data.

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• Pathogenic microorganisms pose significant health risks, causing diverse diseases and mortality.
• Accurate and timely detection of these pathogens is crucial for effective treatment and public health.

Purpose of the Study:

• To develop and validate a novel bioinformatics pipeline for enhanced pathogenic microorganism detection.
• To improve upon existing methods for identifying microbial pathogens in clinical samples.

Main Methods:

• Construction of a comprehensive nucleic acid reference database for pathogenic microorganisms.
• Metagenomic data analysis using nanopore sequencing and a developed bioinformatics pipeline.
• Integration of sequence similarity, abundance, and matching length into an 'all_ratio' parameter for pathogen identification.

Main Results:

• The new method successfully identified pathogens in 23 out of 40 patient samples.
• Detected pathogens, including Streptococcus pneumoniae, in five samples missed by microbiological culture and previous metagenomic pipelines.
• Identified multiple pathogens in 12 samples, suggesting a higher sensitivity for polymicrobial infections.

Conclusions:

• The developed bioinformatics pipeline offers efficient and accurate pathogenic microorganism identification.
• This method enhances existing diagnostic capabilities by detecting pathogens missed by conventional techniques.
• The approach provides a valuable supplement to current pathogen detection strategies.