Using pangenome variation graphs to improve mutation detection in a large DNA virus

Accurately quantifying viral genetic diversity is essential for understanding pathogen evolution, transmission, and emergence. However, standard approaches that map sequencing reads to a single linear reference genome introduce substantial reference bias, particularly for samples that are divergent, recombinant, or belong to rare lineages. Pangenome variation graphs (PVGs) mitigate this issue by representing multiple genomes within a unified graph structure, enabling read mapping across all observed and potential haplotypes. Despite this, PVGs have rarely been applied to viruses. Here, we address this gap by constructing and evaluating the first PVG for lumpy skin disease virus (LSDV), an emerging poxvirus of global importance. We generated PVGs of different sizes and mapped Illumina datasets using Giraffe, benchmarking performance against linear reference mapping with Minimap2. A minimal three-sample PVG containing one representative from each major lineage recovered 97% of known LSDV nucleotide diversity while reducing PVG size by >95% relative to a 121-sample PVG. PVG-based mapping detected more SNPs than linear mapping, including variants at genes involved in host recognition and immune evasion, and identified lineage-specific mutations that improved subclade phylogenetic structure. Notably, 27% of SNPs discovered using PVGs could not be projected onto the linear reference, highlighting the extent of reference bias and the limitations of single-genome references. We propose a generalisable strategy for PVG construction that leverages viral population structure to maximise biological signal while minimising computational cost. Our findings demonstrate that PVGs substantially enhance SNP discovery in LSDV, with direct implications for genomic surveillance, outbreak tracing, and the detection of recombinant vaccine-related lineages in LSDV and other large DNA viruses.