Approaches to studying virus pangenome variation graphs

TL;DR

This paper discusses the use of viral pangenome variation graphs (PVGs) as a powerful tool for capturing and analyzing genetic diversity in viruses, highlighting methods, challenges, and future directions.

Contribution

It introduces accessible tools and formats for constructing, analyzing, and visualizing PVGs in viral genomics, advancing beyond previous gene-centric approaches.

Findings

PVGs enable comprehensive viral diversity analysis

Tools for PVG construction and visualization are outlined

PVGs improve mutation detection and understanding of viral evolution

Abstract

Pangenome variation graphs (PVGs) allow for the representation of genetic diversity in a more nuanced way than traditional reference-based approaches. Here we focus on how PVGs are a powerful tool for studying genetic variation in viruses, offering insights into the complexities of viral quasispecies, mutation rates, and population dynamics. PVGs originated in human genomics and hold great promise for viral genomics. Previous work has been constrained by small sample sizes and gene-centric methods, PVGs enable a more comprehensive approach to studying viral diversity. Large viral genome collections should be used to make PVGs, which offer significant advantages: we outline accessible tools to achieve this. This spans PVG construction, PVG file formats, PVG manipulation and analysis, PVG visualisation, measuring PVG openness, and mapping reads to PVGs. Additionally, the development of PVG-specific formats for mutation representation and personalised PVGs that reflect specific research questions will further enhance PVG applications. Challenges remain, particularly in managing nested variants, optimising error detection, optimising k-mer/minimizer-based approaches for AT-rich genomes, incorporating long read sequencing data, and scalable visualisation approaches. Nevertheless, PVGs offer a new opportunities for viral population genomics, and a testing ground for tool development prior to application to larger eukaryotic genomes. These advances will enable more accurate and comprehensive detection of viral mutations, contributing to a deeper understanding of viral evolution and genotype-phenotype associations.