Partitioning the Genetic Diversity of a Virus Family: Approach and Evaluation through a Case Study of Picornaviruses

Abstract

The recent advent of genome sequences as the only source available to classify many newly discovered viruses challenges the development of virus taxonomy by expert virologists who traditionally rely on extensive virus characterization. In this proof-ofprinciple study, we address this issue by presenting a computational approach (DEmARC) to classify viruses of a family into groups at hierarchical levels using a sole criterion-intervirus genetic divergence. To quantify genetic divergence, we used pairwise evolutionary distances (PEDs) estimated by maximum likelihood inference on a multiple alignment of family-wide conserved proteins. PEDs were calculated for all virus pairs, and the resulting distribution was modeled via a mixture of probability density functions. The model enables the quantitative inference of regions of distance discontinuity in the family-wide PED distribution, which define the levels of hierarchy. For each level, a limit on genetic divergence, below which two viruses join the same group, was objectively selected among a set of candidates by minimizing violations of intragroup PEDs to the limit. In a case study, we applied the procedure to hundreds of genome sequences of picornaviruses and extensively evaluated it by modulating four key parameters. It was found that the genetics-based classification largely tolerates variations in virus sampling and multiple alignment construction but is affected by the choice of protein and the measure of genetic divergence. In an accompanying paper (C. Lauber and A. E. Gorbalenya, J. Virol. 86:3905-3915, 2012), we analyze the substantial insight gained with the genetics-based classification approach by comparing it with the expert-based picornavirus taxonomy.

Toward Genetics-Based Virus Taxonomy: Comparative Analysis of a Genetics-Based Classification and the Taxonomy of Picornaviruses

“…We concluded that the alignment used in our study contains information compatible with taxonomy. Hence, we used this alignment as input for DEmARC in order to devise the GENETIC classification of picornaviruses (43). We identified three statistically most strongly supported positions of discontinuity (thresholds) in the picornavirus PED distribution that we assigned as defining species, genus, and supergenus levels of the classification.…”

“…We have developed DEmARC, a quantitative procedure for hierarchical classification of a virus family based on intervirus genetic divergence (43). It has been evaluated extensively for consistency and stability with respect to key parameters including the amount and/or diversity of the input data, the alignment construction method, and the measure of intervirus divergence.…”

Virus taxonomy in the age of metagenomics

“…Formalized clustering and network analysis methods that create similarity metrics that are based on the detection of homologous genes and their genetic divergence [55][56][57] could be valuable for taxonomic assignments and should be critically evaluated for their effectiveness in the development of a robust classification approach. Frameworks of this kind may have to be tailored to the virus group.…”

A genome-wide pairwise-identity-based proposal for the classification of viruses in the genus Mastrevirus (family Geminiviridae)

“…Despite these various issues, pairwise-identity-based virus classification criteria are extremely popular amongst virus taxonomists and are likely to grow in importance due to how easy they are to use and the fact that, once properly validated, they accurately reflect the biology of these organisms [2,32,33]. We have therefore devised a pairwise-identity-based approach for mastrevirus classification that almost completely removes all the alignment and gaphandling problems of the current ICTV-endorsed mastrevirus classification protocol.…”