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

Nakarin Pamornchainavakul1, Declan C Schroeder1, Kimberly VanderWaal1

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Summary

This study introduces QoALa, a new workflow for analyzing viral quasispecies using long-read sequencing. QoALa improves the accuracy of viral population composition analysis, revealing rare variants and population structures.

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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.