cgMSI

cgMSI identifies pathogen strains from nanopore metagenomic sequencing data by estimating core gene alleles using a two-stage maximum a posteriori probability method to mitigate nanopore sequencing errors.

Key Features:

• Strain-Level Detection: Detects and differentiates strains within a species by analyzing core gene alleles.
• Low Computational Cost: Performs strain identification at 1× coverage with minimal computational resources.
• High Accuracy: Maintains accurate strain identification and relative abundance estimation despite high nanopore sequencing error rates.
• Rapid Turnaround Time: Enables rapid detection by leveraging nanopore sequencing's fast data generation.

Scientific Applications:

• Monitoring of pathogenic strains: Tracks the spread of specific pathogenic strains within populations using strain-level calls from metagenomes.
• Outbreak investigation: Identifies transmission pathways and strain-level relationships during outbreak analyses.
• Microbial diversity and evolution studies: Examines within-species diversity and evolutionary dynamics through core gene allele profiles.
• Vaccine development and efficacy studies: Supports tracking of strain-specific responses relevant to vaccine design and efficacy assessment.

Methodology:

Applies a two-stage maximum a posteriori probability estimation method focused on core gene alleles to identify strains from nanopore metagenomic sequencing data and mitigate sequencing errors.