De novo assembly of viral quasispecies using overlap graphs
Genome Research , Volume 27 - Issue 5 p. 835- 848
Aviral quasispecies, the ensemble of viral strains populating an infected person, can be highly diverse. For optimal assessment of virulence, pathogenesis, and therapy selection, determining the haplotypes of the individual strains can play a key role. As many viruses are subject to high mutation and recombination rates, high-quality reference genomes are often not available at the time of a new disease outbreak. We present SAVAGE, a computational tool for reconstructing individual haplotypes of intra-host virus strains without the need for a high-quality reference genome. SAVAGE makes use of either FM-index-based data structures or ad hoc consensus reference sequence for constructing overlap graphs frompatient sample data. In this overlap graph, nodes represent reads and/or contigs, while edges reflect that two reads/contigs, based on sound statistical considerations, represent identical haplotypic sequence. Following an iterative scheme, a new overlap assembly algorithm that is based on the enumeration of statistically well-calibrated groups of reads/contigs then efficiently reconstructs the individual haplotypes from this overlap graph. In benchmark experiments on simulated and on real deep-coverage data, SAVAGEdrastically outperforms generic de novo assemblers as well as the only specialized de novo viral quasispecies assembler available so far. When run on ad hoc consensus reference sequence, SAVAGE performs very favorably in comparison with state-of-theart reference genome-guided tools. We also apply SAVAGE on two deep-coverage samples of patients infected by the Zika and the hepatitis C virus, respectively, which sheds light on the genetic structures of the respective viral quasispecies.
|Organisation||Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands|
Baaijens, J.A, El Aabidine, A.Z, Rivals, E, & Schönhuth, A. (2017). De novo assembly of viral quasispecies using overlap graphs. Genome Research, 27(5), 835–848. doi:10.1101/gr.215038.116