Abstract
BackgroundRNA viruses mutate at extremely high rates, forming an intra-host viral population of closely related variants, which allows them to evade the host’s immune system and makes them particularly dangerous. Viral outbreaks pose a significant threat for public health, and, in order to deal with it, it is critical to infer transmission clusters, i.e., decide whether two viral samples belong to the same outbreak. Next-generation sequencing (NGS) can significantly help in tackling outbreak-related problems. While NGS data is first obtained as short reads, existing methods rely on assembled sequences. This requires reconstruction of the entire viral population, which is complicated, error-prone and time-consuming.ResultsThe experimental validation using sequencing data from HCV outbreaks shows that the proposed algorithm can successfully identify genetic relatedness between viral populations, infer transmission direction, transmission clusters and outbreak sources, as well as decide whether the source is present in the sequenced outbreak sample and identify it.ConclusionsIntroduced algorithm allows to cluster genetically related samples, infer transmission directions and predict sources of outbreaks. Validation on experimental data demonstrated that algorithm is able to reconstruct various transmission characteristics. Advantage of the method is the ability to bypass cumbersome read assembly, thus eliminating the chance to introduce new errors, and saving processing time by allowing to use raw NGS reads.
Highlights
Ribonucleic acid (RNA) viruses mutate at extremely high rates, forming an intra-host viral population of closely related variants
Viral outbreaks pose a significant threat for public health, and, in order to deal with it, it is critical to infer transmission clusters, i.e., decide whether two viral samples belong to the same outbreak
De Bruijn graph is the graph, that is constructed so that vertices represent every string over a finite alphabet of length l, and edges are added between vertices that have overlap of l − 1
Summary
RNA viruses mutate at extremely high rates, forming an intra-host viral population of closely related variants, which allows them to evade the host’s immune system and makes them dangerous. RNA viruses mutate at extremely high rates, forming an intra-host viral population of closely related variants (or quasi-species). Their high variability [1] allows them to evade the host’s immune system and makes them dangerous. We apply an alignment- and assemblyfree k-mer strategy to viral sequencing data This strategy was initially introduced for analyzing NGS data in metagenomic studies, where reads come from multiple related and unrelated genomes (see [9]), as well as for RNA-seq quantification [10]. Following [9], we build a De Bruijn graph for each sample, and calculate Earth Mover’s Distance (EMD) between two k-mer distributions
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