panRGP: a pangenome-based method to predict genomic islands and explore their diversity

Area of Science:

• Genomics
• Computational Biology
• Microbial Evolution

Background:

• Horizontal gene transfer (HGT) drives prokaryotic genome variability, often through genomic islands (GIs) and regions of genome plasticity (RGPs).
• Analyzing GIs at the species level is challenging due to the increasing volume of genomic data.
• Existing methods struggle to handle hundreds of genomes for comprehensive GI diversity studies.

Purpose of the Study:

• To develop a scalable method for identifying genomic islands (GIs) and regions of genome plasticity (RGPs) across large-scale prokaryotic pangenomes.
• To enable the exploration of GI diversity and the prediction of insertion hotspots within species.
• To provide a robust tool for comparative genomic studies.

Main Methods:

• Implementation of the panRGP method utilizing pangenome graphs constructed from all available genomes for a species.
• Benchmarking panRGP against existing genomic island detection tools using a reference dataset.
• Application of panRGP to metagenome assembled genomes, including the re-evaluation of the leuX tRNA hotspot in Escherichia coli.

Main Results:

• panRGP demonstrated superior predictive performance compared to other genomic island detection tools.
• The method successfully analyzed thousands of genomes, providing insights into RGP diversity.
• panRGP accurately redefined the boundaries of the leuX tRNA hotspot in Escherichia coli.

Conclusions:

• panRGP is a scalable and reliable tool for predicting genomic islands and insertion hotspots.
• The method facilitates large-scale comparative genomic studies in prokaryotes.
• panRGP enhances the analysis of genome plasticity and horizontal gene transfer events.