Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2022.10.21.513181v1?rss=1
Authors: Luo, R., Lin, Y.
Abstract:
With the high mutation rate in viruses, a mixture of closely related viral strains (called viral quasispecies) often co-infect an individual host. Reconstructing indi- vidual strains from viral quasispecies is a key step to characterizing the viral population, revealing strain-level genetic variability, and providing insights into biomedical and clin- ical studies. Reference-based approaches of reconstructing viral strains suffer from the lack of high-quality references due to high mutation rates and biased variant calling introduced by a selected reference. De novo methods require no references but face chal- lenges due to errors in reads, the high similarity of quasispecies, and uneven abundance of strains. In this paper, we propose VStrains, a de novo approach for reconstructing strains from viral quasispecies. VStrains incorporates contigs, paired-end reads, and coverage infor- mation to iteratively extract the strain-specific paths from assembly graphs. We bench- mark VStrains against multiple state-of-the-art de novo and reference-based approaches on both simulated and real datasets. Experimental results demonstrate that VStrains achieves the best overall performance on both simulated and real datasets under a com- prehensive set of metrics such as genome fraction, duplication ratio, NGA50, error rate, etc. VStrains is publicly available at https://github.com/MetaGenTools/VStrains.
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