VStrains: De Novo Reconstruction of Viral Strains via Iterative Path Extraction From Assembly Graphs

Abstract

With the high mutation rate in viruses, a mixture of closely related viral strains (called viral quasispecies) often coinfect an individual host. Reconstructing individual strains from viral quasispecies is a key step to characterizing the viral population, revealing strain-level genetic variability, and providing insights into biomedical and clinical 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 challenges 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 comprehensive set of metrics such as genome fraction, duplication ratio, NGA50, error rate, etc. VStrains is publicly available at https://github.com/MetaGenTools/VStrains.

HairSplitter: haplotype assembly from long, noisy reads

“…Many tools have been developed to overcome this problem in the context of short-read assemblies, such as OPERA-MS (Bertrand et al, 2019), Constrains (C Luo et al, 2015), STRONG (Quince et al, 2020), StrainXpress (Kang et al, 2022) and VStrains (R Luo and Lin, 2023). However, these methods are not designed for long-read sequencing and do not exploit the long-range information contained in long reads.…”