The Genome Context Viewer is a web application for identifying, aligning, and visualizing genomic regions based on their micro and macrosyntenic structures. By using functional elements such as gene annotations as the unit of search and comparison, the Genome Context Viewer can compute and display relationships between regions across many assemblies from federated data sources in real-time, enabling users to rapidly explore multiple annotated genomes and identify divergence and structural events that can help provide insight into evolutionary mechanisms associated with functional consequences. In this work, we introduce version 2 of the Genome Context Viewer and highlight new features that enhance usability, performance, and ease of deployment.

A pangenome represents the entire sequence content and variation of a population. As collections of complete reference quality genomes become more common, so does the prevalence of pangenomes, necessitating the need for scalable computational methods for their analysis. Previously, we developed FindFRs for identifying Frequented Regions in pangenome graphs, where a Frequented Region is a subgraph that is frequently traversed by multiple sequences. In this work, we propose FindFRs3, which is an updated version of FindFRs capable of identifying Frequented Regions with improved runtime and memory efficiency, enabling the analysis of much larger pangenome graphs. In addition, FindFRs3 identifies Frequented Region Variants (the unique subpaths through each region). We demonstrate the utility of these variants by using them as input features for machine learning models that can predict genotype-to-phenotype associations in a large yeast pangenome. Biological insights gained from these variants show that this novel technique allows for a more nuanced and detailed analysis of larger pangenomes.

Complex diseases, with many associated genetic and environmental factors, are a challenging target for genomic risk assessment. Genome-wide association studies (GWAS) associate disease status with, and compute risk from, individual common variants, which can be problematic for diseases with many interacting or rare variants. In addition, GWAS typically employ a reference genome which is not built from the subjects of the study, whose genetic background may differ from the reference and whose genetic characterization may be limited. We present a complementary method based on disease association with collections of genotypes, called frequented regions, on a pangenomic graph built from subjects’ genomes. We introduce the pangenomic genotype graph, which is better suited than sequence graphs to human disease studies. Our method draws out collections of features, across multiple genomic segments, which are associated with disease status. We show that the frequented regions method consistently improves machine-learning classification of disease status over GWAS classification, allowing incorporation of rare or interacting variants. Notably, genomic segments that have few or no variants of genome-wide signif-icance ( p < 5 × 10 ⁻⁸ ) provide much-improved classification with frequented regions, encouraging their application across the entire genome. Frequented regions may also be utilized for purposes such as choice of treatment in addition to prediction of disease risk.