Integrated population clustering and genomic epidemiology with PopPIPE

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• Genetic distances in bacterial DNA aid in population clustering and transmission event detection.
• Reference-free methods offer sensitive detection of genetic differences, particularly in outbreak samples.
• Recombination challenges analysis across longer genetic distances, demanding bioinformatic expertise.

Purpose of the Study:

• To introduce a Population analysis PIPEline (PopPIPE) for analyzing bacterial genomes at both population-wide and outbreak scales.
• To provide a user-friendly, rapid, and reproducible pipeline for bacterial population structure and transmission analysis.
• To enhance the detection of transmission events and subpopulation clustering using reference-free methods.

Main Methods:

• Utilizes k-mer sketching for initial population splitting into strains.
• Employs split k-mer analysis and recombination removal for alignment and subcluster creation.
• Combines rapid reference-free genome analysis techniques for comprehensive bacterial genome analysis.

Main Results:

• PopPIPE successfully generates high-quality subclusters on a population-wide dataset of *Streptococcus pneumoniae*.
• Analysis of vancomycin-resistant *Enterococcus faecium* samples revealed more epidemiologically plausible transmission clusters compared to core genome and MLST approaches.
• The pipeline demonstrates speed, reproducibility, and the creation of interactive visualizations.

Conclusions:

• PopPIPE offers a user-friendly solution for bacterial genome analysis, from species-wide clustering to outbreak investigations.
• The pipeline effectively addresses challenges posed by variable gene content, order, and recombination in bacterial genomes.
• PopPIPE enhances the ability to detect transmission clusters and understand population dynamics in bacterial pathogens.