MOTIVATION: The extraordinary genetic and antigenic variability of RNA viruses is arguably the greatest challenge to the development of broadly effective vaccines. No single viral variant can induce sufficiently broad immunity, and incorporating all known naturally circulating variants into one multivalent vaccine is not feasible. Furthermore, no objective strategies currently exist to select actual viral variants that should be included or excluded in polyvalent vaccines. RESULTS: To address this problem, we demonstrate a method based on graph theory that quantifies the relative importance of viral variants. We demonstrate our method through application to the envelope glycoprotein gene of a particularly diverse RNA virus of pigs: porcine reproductive and respiratory syndrome virus (PRRSV). Using distance matrices derived from sequence nucleotide difference, amino acid difference and evolutionary distance, we constructed viral networks and used common network statistics to assign each sequence an objective ranking of relative 'importance'. To validate our approach, we use an independent published algorithm to score our top-ranked wild-type variants for coverage of putative T-cell epitopes across the 9383 sequences in our dataset. Top-ranked viruses achieve significantly higher coverage than low-ranked viruses, and top-ranked viruses achieve nearly equal coverage as a synthetic mosaic protein constructed in silico from the same set of 9383 sequences. CONCLUSION: Our approach relies on the network structure of PRRSV but applies to any diverse RNA virus because it identifies subsets of viral variants that are most important to overall viral diversity. We suggest that this method, through the objective quantification of variant importance, provides criteria for choosing viral variants for further characterization, diagnostics, surveillance and ultimately polyvalent vaccine development.

MOTIVATION: In the event of an outbreak of a disease caused by an initially unknown pathogen, the ability to characterize anonymous sequences prior to isolation and culturing of the pathogen will be helpful. We show that it is possible to classify viral sequences by genome type (dsDNA, ssDNA, ssRNA positive strand, ssRNA negative strand, retroid) using amino acid distribution. RESULTS: In this paper we describe the results of analysis of amino acid preference in mammalian viruses. The study was carried out at the genome level as well as two shorter sequence levels: short (300 amino acids) and medium length (660 amino acids). The analysis indicates a correlation between the viral genome types dsDNA, ssDNA, ssRNA positive strand, ssRNA negative strand and retroid and amino acid preference. We investigated three different models of amino acid preference. The simplest amino acid preference model, 1-AAP, is a normalized description of the frequency of amino acids in genomes of a viral genome type. A slightly more complex model is the ordered pair amino acid preference model (2-AAP), which characterizes genomes of different viral genome types by the frequency of ordered pairs of amino acids. The most complex and accurate model is the ordered triple amino acid preference model (3-AAP), which is based on ordered triples of amino acids. The results demonstrate that mammalian viral genome types differ in their amino acid preference. AVAILABILITY: The tools used to format and analyze data and supplementary material are available at http://www.cse.sc.edu/~rose/aminoPreference/index.html CONTACT: [email protected].