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The viral family Coronaviridae comprises four genera, termed Alpha-, Beta-, Gamma -, and Deltacoronavirus. Recombination events have been described in many coronaviruses infecting humans and other animals. However, formal analysis of the recombination patterns, both in terms of the involved genome regions and the extent of genetic divergence between partners, are scarce. Common methods of recombination detection based on phylogenetic incongruences (e.g., a phylogenetic compatibility matrix) may fail in cases where too many events diminish the phylogenetic signal. Thus, an approach comparing genetic distances in distinct genome regions (pairwise distance deviation matrix) was set up. In alpha, beta, and delta-coronaviruses, a low incidence of recombination between closely related viruses was evident in all genome regions, but it was more extensive between the spike gene and other genome regions. In contrast, avian gammacoronaviruses recombined extensively and exist as a global cloud of genes with poorly corresponding genetic distances in different parts of the genome. Spike, but not other structural proteins, was most commonly exchanged between coronaviruses. Recombination patterns differed between coronavirus genera and corresponded to the modular structure of the spike: recombination traces were more pronounced between spike domains (N-terminal and C-terminal parts of S1 and S2) than within domains. The variability of possible recombination events and their uneven distribution over the genome suggest that compatibility of genes, rather than mechanistic or ecological limitations, shapes recombination patterns in coronaviruses.

Statistical phylogenetic methods are a powerful tool for inferring the evolutionary history of viruses through time and space. The selection of mathematical models and analysis parameters has a major impact on the outcome, and has been relatively well-described in the literature. The preparation of a sequence dataset is less formalized, but its impact can be even more profound. This article used simulated datasets of enterovirus sequences to evaluate the effect of sample bias on picornavirus phylogenetic studies. Possible approaches to the reduction of large datasets and their potential for introducing additional artefacts were demonstrated. The most consistent results were obtained using "smart sampling", which reduced sequence subsets from large studies more than those from smaller ones in order to preserve the rare sequences in a dataset. The effect of sequences with technical or annotation errors in the Bayesian framework was also analyzed. Sequences with about 0.5% sequencing errors or incorrect isolation dates altered by just 5 years could be detected by various approaches, but the efficiency of identification depended upon sequence position in a phylogenetic tree. Even a single erroneous sequence could profoundly destabilize the whole analysis by increasing the variance of the inferred evolutionary parameters.

Neurovirulent enterovirus 71 (EV-A71) caused a massive epidemic in China in 2008-2011. While subgenotype C4 was the major causative agent, a few isolates were almost identical to the prototype EV-A71 strain and belonged to genotype A. This variant was allegedly extinct since 1970, and its identification in this epidemic suggests reintroduction of the archive virus. Regression analysis of genetic distances (TempEst software) was of moderate utility due to the low resolution of classical phylogenetic methods. Bayesian phylogenetic analysis (BEAST software) suggested artificial introduction event based on highly aberrant phylogenetic tree branch rates that differed by over three standard deviations from the mean substitution rate for EV71. Manual nucleotide-level analysis was used to further explore the virus spread pattern after introduction into circulation. Upon reintroduction, the virus accumulated up to seven substitutions in VP1, most of them non-synonymous and located within the capsid's canyon or at its rims, compatible with readaptation of a lab strain to natural circulation., Competing Interests: The authors declare no conflicts of interest.