PVGA: a precise viral genome assembler using an iterative alignment graph

Viral genome analysis is crucial for understanding virus evolution and mutation. Investigations into viral evolutionary dynamics and mutation patterns have garnered significant research attention since the outbreak of COVID-19. The basic structure of many virus genomes is highly conserved [1]. RNA viruses have high mutation rates, and single-nucleotide variations may induce substantial phenotypic alterations in terms of viral function and pathogenicity. Thus, special assembly methods are required for viral genome analysis.

PVGA starts with a reference genome and the sequencing reads. The first step in PVGA involves constructing an alignment graph based on a reference genome and the set of input sequencing reads. Then the optimal genomic path is determined through dynamic programming, maximizing the cumulative edge weights that reflect read support density across the alignment graph. The obtained path corresponds to a refined genome. Finally, we repeat the process by using the new reference genomes until no further improvement is possible. We evaluate PVGA's performance across both assembly and polishing tasks using simulated and real datasets, including both long reads and short reads. The experiments demonstrate that PVGA always outperforms popular existing programs in terms of the quality of assembly results, while the running time of our method is compatible to others. In particular, simulated Nanopore datasets show that our method can correctly report the true genomes with 0 mismatches and 0 indels.

PVGA is a novel viral genome assembler that seamlessly integrates assembly and polishing into a unified workflow. Its design prioritizes high accuracy, enabling the detection of subtle genomic variations that can impact viral function and pathogenicity. By addressing the unique challenges of viral genome assembly, PVGA provides a reliable and precise solution for advancing our understanding of viral evolution and behavior.