Viruses display high genetic diversity both within and among viral species, as well as within and among infected hosts. The composition of mixed samples can be assessed by metagenomics approaches, such as sequence read annotation by taxonomic classification using existing reference genomes and databases. For the majority of novel viral sequences encountered in biodiversity studies (WP 1.1), no reference genome or homolog is known, and discovery of de novo viral species by state-of-the-art genome assemblers ignores low-frequency variants and technical errors.
Low-frequency variants are of especially great interest for harbouring drug resistance mutations or affecting virulence. In this context, with the true interdisciplinary approach of VIROINF the following questions can be answered:
- How can we distinguish viral haplotypes in RNA-Seq data and characterise sequence-based evolution (ESR 3, ESR 8, ESR 4)?
- What is the role of quasispecies in virus pathogenesis and evolution (ESR 14, ESR 4)?
- How does intra-viral (ESR 1, ESR 2) and viral-host (ESR 6, ESR 15) selective pressure shape short-term evolution?
- Are RNA modifications also dictating some selective pressure (ESR 15)?
- Can we predict and design the fittest virus within a viral quasispecies (ESR 1)?
These questions will be mainly addressed through the integration of virus evolution experiments that generate high-resolution 2nd and 3rd generation sequences (ESR 4, ESR 9) and the development of novel bioinformatics tools to resolve quasispecies structures from the resulting data (ESR 8, ESR 3).
VIROINF will search for patterns which can emerge when host affiliation is projected onto viral taxonomy. Branch permutation techniques will be used to statistically determine at which level the viruses and hosts co-evolve. These patterns will reveal the evolution of virus-host associations for different classes of viruses. The associations will be validated by analysing both, known and predicted, host-associations independently (see WP 1.2). Moreover, virus-host associations will be used to validate the co-evolutionary signal.