CABGen

CABGen provides integrated analysis of bacterial whole-genome shotgun and next-generation sequencing (NGS) data to perform genome assembly, annotation, MLST typing, antimicrobial resistance and virulence gene detection, plasmid identification, and point mutation analysis for surveillance and research.

Key Features:

• Data import and processing: Handles sequence data from diverse bacterial isolates including whole-genome shotgun and NGS reads.
• Analytical modules: Implements modular stages named Upload Sequences, Analyze Sequences, and Verify Results that support coverage estimation, species identification, and de novo genome assembly with quality assessment.
• Coverage estimation: Estimates sequencing coverage for input datasets to inform assembly and quality assessment.
• Species identification: Identifies bacterial species from sequencing data to support downstream analyses.
• De novo genome assembly: Performs de novo assembly of bacterial genomes with associated quality assessment metrics.
• Genome annotation: Produces detailed bacterial genome annotations to elucidate functional and metabolic pathways.
• MLST mapping: Performs multi-locus sequence typing (MLST) to support epidemiological tracking of strains.
• AMR and virulence gene detection: Detects antimicrobial resistance (AMR) genes, virulence factors, and plasmid-associated sequences.
• Point mutation analysis: Detects specific point mutations within AMR genes to inform resistance mechanism interpretation.
• Visualization: Provides visualization outputs to aid interpretation of genomic analyses.
• Clinical and surveillance reporting: Generates reports highlighting clinically relevant results for public health and clinical contexts.

Scientific Applications:

• Antimicrobial resistance surveillance: Enables detection and reporting of AMR genes and mutations to support surveillance efforts.
• Epidemiological tracking: Uses MLST and genomic data to track strain relatedness and dissemination.
• Functional genomics: Provides genome annotation and pathway information to study bacterial functional and metabolic landscapes.
• Clinical decision support: Produces clinically relevant genomic reports to inform public health and clinical investigations.

Methodology:

Computational steps explicitly include sequence upload and processing, coverage estimation, species identification, de novo genome assembly with quality assessment, genome annotation, MLST mapping, antimicrobial resistance and virulence gene detection (including plasmid sequences), and point mutation analysis.