Viral quasispecies assembly via maximal clique enumeration

Area of Science:

• Virology
• Computational Biology
• Genetics

Background:

• High genetic diversity within viral populations, known as viral quasispecies, significantly impacts disease progression and treatment efficacy.
• Understanding intra-host viral genetic structure is crucial for predicting disease outcomes and developing effective therapies.

Purpose of the Study:

• To introduce HaploClique, a novel computational method for reconstructing viral quasispecies from next-generation sequencing data.
• To accurately assemble viral haplotypes and detect genetic variations, including insertions and deletions, within mixed virus populations.

Main Methods:

• Developed a statistical model for paired-end reads that accounts for mutations, insertions, and deletions.
• Employed an iterative maximal clique enumeration approach to assemble read pairs into increasingly longer haplotypes.
• Assessed performance using simulated data with varying population characteristics and sequencing parameters.

Main Results:

• HaploClique accurately reconstructs error-free, full-length viral haplotypes even from low-coverage sequencing data.
• The method effectively detects large insertions and deletions at low frequencies within viral populations.
• Applied to Hepatitis C virus data, HaploClique identified a novel deletion validated by long-read sequencing.

Conclusions:

• HaploClique offers a robust and accurate approach for viral quasispecies reconstruction from next-generation sequencing data.
• The method enhances the ability to study viral genetic diversity, potentially improving diagnostics and treatment strategies.
• HaploClique's ability to detect structural variations provides new insights into viral evolution and pathogenesis.