LmCGST

LmCGST performs core-genome sequence typing of Listeria monocytogenes from next-generation sequencing data to enable molecular characterization and phylogenetically relevant sequence typing.

Key Features:

• High-Confidence Core (HCC) Genome Calculation: Identifies 1013 open-reading frames that constitute the HCC genome for ortholog identification and automated molecular characterization.
• Phylogenetically Relevant Nomenclature: Derives an evolutionarily relevant nomenclature based on phylogenetic analysis of HCC genomes.
• Database Comparison and Sequence Typing: Compares calculated HCC profiles to an expandable database containing profiles from 114 taxa to assign sequence types to isolates.
• Phylogenetic Analysis: Performs phylogenetic analysis to infer evolutionary relationships among Listeria monocytogenes isolates.
• Enhanced Discriminatory Power: Provides greater discriminatory power compared to pulsed-field gel electrophoresis, ribotyping, and in silico multi-locus sequence typing (MLST).
• Reproducibility and Standardization: Enables reproducible and standardized molecular characterization across datasets.
• Computational Efficiency and Error Resistance: Addresses limitations of single-nucleotide polymorphism detection and whole-chromosome sequence analysis by using a computationally efficient, error-resistant core-genome approach.

Scientific Applications:

• Epidemiological Surveillance: Supports epidemiological studies of Listeria monocytogenes that require precise typing and phylogenetic analysis.
• Outbreak Investigation: Aids tracking and classification of outbreak-related isolates via sequence type assignment and phylogenetic placement.
• Transmission Dynamics and Intervention Design: Facilitates analysis of transmission dynamics and development of targeted interventions based on phylogenetic relationships.
• Adaptive Genomic Profiling: Allows incorporation of additional loci and HCC profiles to expand and update the typing database as genomic data evolve.

Methodology:

Compute a High-Confidence Core (HCC) by identifying 1013 open-reading frames, calculate HCC profiles from sequencing data, compare profiles to an expandable database of 114 taxa, and perform phylogenetic analysis to derive nomenclature and infer relationships.