QoALa: A comprehensive workflow for viral quasispecies diversity comparison using long-read sequencing data

Area of Science:

• Virology
• Genomics
• Bioinformatics

Background:

• Viral quasispecies dynamics are crucial for understanding viral micro-evolution.
• Next-generation sequencing faces coverage limitations for complex viral populations.
• Long-read sequencing offers potential for improved viral quasispecies analysis.

Purpose of the Study:

• To develop and evaluate a refined workflow (QoALa) for viral quasispecies analysis using long-read sequencing data.
• To enhance the accuracy of viral population composition and diversity assessment.
• To enable comparison of viral quasispecies across different samples and hosts.

Main Methods:

• Developed QoALa workflow implemented in the longreadvqs R package.
• Incorporated nucleotide position-wise noise minimization and sample size standardization.
• Benchmarked QoALa on simulated SARS-CoV-2 and HIV-1 datasets and real-world data from HCV, HBV, HIV-1, SARS-CoV-2, and IAV.

Main Results:

• QoALa outperformed existing error-correction methods in recovering viral quasispecies composition.
• The workflow effectively preserved nucleotide diversity and hierarchical population structure.
• Identified rare haplotypes and provided insights into intra- and inter-host diversity dynamics across various viruses.

Conclusions:

• QoALa is a versatile and practical methodology for advancing viral quasispecies research using long-read sequencing.
• The approach enhances the resolution of viral population structure and diversity.
• QoALa facilitates novel discoveries in viral evolution and host-pathogen interactions.