cgMSI: pathogen detection within species from nanopore metagenomic sequencing data

Area of Science:

• Genomics
• Bioinformatics
• Pathogen Detection

Background:

• Metagenomic sequencing offers an unbiased approach for detecting known and unknown pathogen strains.
• Nanopore sequencing provides rapid pathogen detection but faces challenges in strain-level identification due to high error rates.

Purpose of the Study:

• To develop a computational tool for accurate pathogen strain identification from nanopore metagenomic data.
• To overcome limitations in distinguishing pathogen strains within species using nanopore sequencing.

Main Methods:

• Developed the core gene alleles metagenome strain identification (cgMSI) tool.
• Employed a two-stage maximum a posteriori probability estimation method.
• Designed for low computational cost analysis.

Main Results:

• The cgMSI tool accurately identifies pathogen strains from nanopore metagenomic data.
• Enables estimation of relative strain abundance even at low coverage (1×).
• Demonstrates effectiveness in pathogen detection within species.

Conclusions:

• Introduced cgMSI for robust nanopore metagenomic pathogen detection at the strain level.
• cgMSI provides a solution for high-throughput, accurate pathogen identification.
• The tool is publicly available for research use.