Recent advances in inferring viral diversity from high-throughput sequencing data

Area of Science:

• Virology
• Genomics
• Bioinformatics

Background:

• RNA viruses exist as diverse viral quasispecies within hosts.
• High-throughput sequencing (HTS) enables detailed assessment of viral genetic diversity.
• Analyzing HTS data for viral populations is complicated by short, error-prone reads.

Purpose of the Study:

• To review computational and statistical methods for analyzing HTS data of viral populations.
• To discuss challenges in HTS data analysis, including read alignment and reference bias.
• To explore experimental approaches for improving the biological signal-to-noise ratio in viral diversity studies.

Main Methods:

• Review of computational approaches for local diversity estimation.
• Review of computational approaches for global haplotype reconstruction.
• Discussion of experimental strategies to enhance sequencing data quality.

Main Results:

• Several computational and statistical methods exist to address HTS data limitations.
• Read alignment and reference biases significantly impact diversity estimates.
• Experimental methods can improve the biological signal-to-noise ratio.

Conclusions:

• Accurate analysis of viral quasispecies requires robust computational and statistical methods.
• Addressing challenges like read errors and reference bias is crucial for reliable diversity estimates.
• Future advancements will likely integrate computational and technological developments for analyzing heterogeneous virus populations.