A Comprehensive Review on Graph-Based Pangenome Approaches for Bacterial Genomics: Advantages, Challenges, and Applications

The vast diversity, structural variation, and accessory genes that conventional linear reference genomes are unable to represent have been uncovered by the quick growth of bacterial genomic data. By storing numerous sequences, variations, and structural differences within a single graph structure, graph-based pan genome models like variation graphs, de Bruin graphs, and genome-graph frameworks provide a more thorough depiction of bacterial genomic diversity. An extensive overview of graph-based pan genome techniques is given in this paper, which also covers the key tools, computational strategies, and underlying algorithms. We assess how well they capture phylogenomic linkages, mobile genetic elements, gene content variability, and antimicrobial-resistance determinants compared to linear references. There is also a summary of applications in vaccine development, strain-level analysis, disease surveillance, and functional genomics. Graph-pan genomes present difficulties with computational complexity, scalability, annotation consistency, visualization, and integration with current genomic procedures, despite its revolutionary promise. Improved standardization, graph compression, interactive visualization, graph inference supported by machine learning, and real-time clinical applications are some of the future directions. In the age of huge sequencing data, graph-based pangenomics emerges as a key tool for precise, fair, and scalable bacterial genomics.