EnteroBase

EnteroBase provides a curated repository and analysis framework for bacterial pathogen genomes to support genomic surveillance, population-structure analysis, cgMLST-based typing, and antimicrobial-resistance detection.

Key Features:

• Comprehensive Database: Manually curated genome sequence data and metadata for over 1.1 million bacterial isolates including Salmonella, Escherichia/Shigella, Mycobacterium tuberculosis, Streptococcus spp., Clostridioides, Vibrio, Helicobacter, Yersinia, Moraxella, and Clostridioides difficile.
• Antimicrobial Resistance Detection: Tools for detection of antimicrobial-resistance determinants directly from genomic data.
• Genomic Population Structure Visualization: A bubble-plot graphical tool visualizes bacterial genomic population structures based on pre-computed hierarchical clusters.
• Clostridioides difficile Specific Tools: Automatic retrieval and assembly of short reads from public databases, core-genome multilocus sequence typing (cgMLST), and organization of 18,254 quality-controlled C. difficile genomes into hierarchical clusters.
• Hierarchical Clustering for Epidemiology: cgMLST-based hierarchical clustering identifies and names populations of C. difficile across contexts from local transmission chains to epidemic strains.
• Statistical Associations with Epidemiological Data: Cluster assignments have been statistically associated with specific hospitals or wards and used to identify inter-hospital transmission in retrospective analyses.
• Integration with Existing Surveillance Methods: Hierarchical clustering correlates with k-mer-based classification and PCR ribotyping to enable comparisons with historical surveillance data.

Scientific Applications:

• Genomic surveillance and outbreak detection: Place newly collected isolates within a phylogenetic and epidemiological framework to identify transmission chains and outbreaks.
• Antimicrobial-resistance surveillance: Detect and track resistance determinants across populations and time.
• Population genetics and microbial diversity: Analyze population structure and diversity across multiple bacterial species.
• Epidemiological investigations: Associate genomic clusters with hospitals, wards, and inter-hospital transmission for retrospective and prospective studies.
• Comparative surveillance: Compare cgMLST-based hierarchical clusters with k-mer-based classification and PCR ribotyping for integration with historical datasets.

Methodology:

Automatic retrieval and assembly of short reads from public databases; core-genome multilocus sequence typing (cgMLST); pre-computed hierarchical clustering; k-mer-based classification comparisons; and statistical association analyses linking clusters to epidemiological metadata.