Pangenome Graphs: Concepts, Tools, and Emerging Trends in Genomic Analysis

The emergence of pangenome graphs represents a paradigm shift in genomics, moving beyond linear reference genomes to embrace the full spectrum of genetic diversity within and across species. These graph-based models provide a unified framework for representing alternative haplotypes, structural variants, and complex genomic rearrangements that are often missed by traditional approaches. This paper reviews the latest developments in pangenome graph construction, data structures, alignment algorithms, and variant inference. We explore recent human, plant, and microbial genomics applications, highlighting the advantages of graph representations in capturing population diversity and improving read mapping accuracy. We briefly discuss emerging directions such as machine learning-assisted graph analysis, although current applications remain limited and exploratory. Furthermore, we examine the emerging field of clinical genomics, where pangenome references have demonstrated measurable improvements in diagnostic yield—specifically increasing variant calling sensitivity for complex structural variants by up to 10–40% compared to linear models. We conclude by addressing ongoing challenges in graph scalability and standardization, aiming to guide future research and implementation in this rapidly evolving field.