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QSdpR: Viral quasispecies reconstruction via correlation clustering
Somsubhra Barik1, Shreepriya Das2, Haris Vikalo1
1ECE Department, The University of Texas at Austin, Austin, TX 78712, United States.
Genomics
|December 23, 2017
Summary
This study introduces QSdpR, a new method for reconstructing viral quasispecies from sequencing data. QSdpR accurately determines viral genetic diversity, aiding in understanding mutation patterns and developing antiviral drugs.
Area of Science:
- Virology
- Bioinformatics
- Genomics
Background:
- RNA viruses exhibit high mutation rates, forming diverse viral quasispecies populations.
- Viral heterogeneity is crucial for adaptation and infection progression.
- Accurate determination of viral genetic diversity is vital for understanding mutation patterns and developing effective antiviral therapies.
Purpose of the Study:
- To present QSdpR, a novel method and software for reconstructing viral quasispecies from short sequencing reads.
- To enable accurate inference of viral haplotypes and their proportions within a population.
Main Methods:
- QSdpR employs correlation clustering on a read-similarity graph to reconstruct quasispecies.
- Sub-species frequencies are estimated from clustering results.
- The number of sub-species is determined using the pseudo F index.
Main Results:
- QSdpR demonstrates strong performance in reconstructing viral quasispecies.
- The method was validated on both synthetic datasets and real-world data from HIV-1 and Zika virus.
- QSdpR favorably compares to existing methods across various performance metrics.
Conclusions:
- QSdpR is an effective tool for inferring viral genetic diversity from short sequencing reads.
- The method aids in understanding viral evolution and facilitates drug development for RNA viruses.
- QSdpR offers a valuable advancement in the analysis of viral quasispecies.
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