Full-length de novo viral quasispecies assembly through variation graph construction

Jasmijn A. Baaijens, Bastiaan Van der Roest, Johannes Köster, Leen Stougie, Alexander Schönhuth

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MOTIVATION: Viruses populate their hosts as a viral quasispecies: a collection of genetically related mutant strains. Viral quasispecies assembly is the reconstruction of strain-specific haplotypes from read data, and predicting their relative abundances within the mix of strains is an important step for various treatment-related reasons. Reference genome independent ('de novo') approaches have yielded benefits over reference-guided approaches, because reference-induced biases can become overwhelming when dealing with divergent strains. While being very accurate, extant de novo methods only yield rather short contigs. The remaining challenge is to reconstruct full-length haplotypes together with their abundances from such contigs. RESULTS: We present Virus-VG as a de novo approach to viral haplotype reconstruction from preassembled contigs. Our method constructs a variation graph from the short input contigs without making use of a reference genome. Then, to obtain paths through the variation graph that reflect the original haplotypes, we solve a minimization problem that yields a selection of maximal-length paths that is, optimal in terms of being compatible with the read coverages computed for the nodes of the variation graph. We output the resulting selection of maximal length paths as the haplotypes, together with their abundances. Benchmarking experiments on challenging simulated and real datasets show significant improvements in assembly contiguity compared to the input contigs, while preserving low error rates compared to the state-of-the-art viral quasispecies assemblers. AVAILABILITY AND IMPLEMENTATION: Virus-VG is freely available at https://bitbucket.org/jbaaijens/virus-vg. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Original languageEnglish
Pages (from-to)5086-5094
Number of pages9
JournalBioinformatics (Oxford, England)
Issue number24
Early online date30 May 2019
Publication statusPublished - 15 Dec 2019


This work was supported by the Netherlands Organisation for Scientific Research (NWO) through Vidi grant [679.072.309], Veni grant [639.021.648] and Gravitation Programme Networks [024.002.003].

FundersFunder number
Nederlandse Organisatie voor Wetenschappelijk Onderzoek679.072.309, 024.002.003, 639.021.648


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