Abstract
The concept of viral quasispecies refers to a constantly mutating viral population occurring within hosts, which is essential for grasping the micro-evolutionary patterns of viruses. Despite its high error rate, long-read sequencing holds potential for advancing viral quasispecies research by resolving coverage limitations in next-generation sequencing. We introduce a refined workflow, QoALa, implemented in the longreadvqs R package. This workflow begins with nucleotide position-wise noise minimization of read alignments and sample size standardization, and extends to viral quasispecies comparison across related samples with integrated visualization capabilities. Benchmarking on simulated SARS-CoV-2 and HIV-1 datasets demonstrated that QoALa consistently outperformed existing error-correction methods in recovering quasispecies composition, particularly in preserving nucleotide diversity and hierarchical population structure. Real raw read samples from five studies of different viruses (HCV, HBV, HIV-1, SARS-CoV-2, and IAV), sequenced by major long-read platforms, were also used to evaluate these approaches. The comparative results provide novel insights into intra- and inter-host diversity dynamics in various scenarios and unveil rare haplotypes not reported in the original studies, underscoring the versatility and practicality of our methodology.