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The need to estimate divergence times in evolutionary histories in the presence of various sources of substitution rate variation has stimulated a rich development of relaxed molecular clock models. Viral evolutionary studies frequently adopt an uncorrelated clock model as a generic relaxed molecular clock process, but this may impose considerable estimation bias if discrete rate variation exists among clades or lineages. For HIV-1 group M, rate variation among subtypes has been shown to result in inconsistencies in time to the most recent common ancestor estimation. Although this calls into question the adequacy of available molecular dating methods, no solution to this problem has been offered so far. Here, we investigate the use of mixed effects molecular clock models, which combine both fixed and random effects in the evolutionary rate, to estimate divergence times. Using simulation, we demonstrate that this model outperforms existing molecular clock models in a Bayesian framework for estimating time-measured phylogenies in the presence of mixed sources of rate variation, while also maintaining good performance in simpler scenarios. By analysing a comprehensive HIV-1 group M complete genome data set we confirm considerable rate variation among subtypes that is not adequately modelled by uncorrelated relaxed clock models. The mixed effects clock model can accommodate this rate variation and results in a time to the most recent common ancestor of HIV-1 group M of 1920 (1915-25), which is only slightly earlier than the uncorrelated relaxed clock estimate for the same data set. The use of complete genome data appears to have a more profound impact than the molecular clock model because it reduces the credible intervals by 50 per cent relative to similar estimates based on short envelope gene sequences.

Viruses are the cause of a considerable burden to human, animal and plant health, while on the other hand playing an important role in regulating entire ecosystems. The power of new sequencing technologies combined with new tools for processing "Big Data" offers unprecedented opportunities to answer fundamental questions in virology. Virologists have an urgent need for virus-specific bioinformatics tools. These developments have led to the formation of the European Virus Bioinformatics Center, a network of experts in virology and bioinformatics who are joining forces to enable extensive exchange and collaboration between these research areas. The EVBC strives to provide talented researchers with a supportive environment free of gender bias, but the gender gap in science, especially in math-intensive fields such as computer science, persists. To bring more talented women into research and keep them there, we need to highlight role models to spark their interest, and we need to ensure that female scientists are not kept at lower levels but are given the opportunity to lead the field. Here we showcase the work of the EVBC and highlight the achievements of some outstanding women experts in virology and viral bioinformatics.

The hepatitis C virus (HCV) is a major human pathogen. Genetically related viruses in animals suggest a zoonotic origin of HCV. The closest relative of HCV is found in horses (termed equine hepacivirus [EqHV]). However, low EqHV genetic diversity implies relatively recent acquisition of EqHV by horses, making a derivation of HCV from EqHV unlikely. To unravel the EqHV evolutionary history within equid sister species, we analyzed 829 donkeys and 53 mules sampled in nine European, Asian, African, and American countries by molecular and serologic tools for EqHV infection. Antibodies were found in 278 animals (31.5%), and viral RNA was found in 3 animals (0.3%), all of which were simultaneously seropositive. A low RNA prevalence in spite of high seroprevalence suggests a predominance of acute infection, a possible difference from the mostly chronic hepacivirus infection pattern seen in horses and humans. Limitation of transmission due to short courses of infection may explain the existence of entirely seronegative groups of animals. Donkey and horse EqHV strains were paraphyletic and 97.5 to 98.2% identical in their translated polyprotein sequences, making virus/host cospeciation unlikely. Evolutionary reconstructions supported host switches of EqHV between horses and donkeys without the involvement of adaptive evolution. Global admixture of donkey and horse hepaciviruses was compatible with anthropogenic alterations of EqHV ecology. In summary, our findings do not support EqHV as the origin of the significantly more diversified HCV. Identification of a host system with predominantly acute hepacivirus infection may enable new insights into the chronic infection pattern associated with HCV., Importance: The evolutionary origins of the human hepatitis C virus (HCV) are unclear. The closest animal-associated relative of HCV occurs in horses (equine hepacivirus [EqHV]). The low EqHV genetic diversity implies a relatively recent acquisition of EqHV by horses, limiting the time span for potential horse-to-human infections in the past. Horses are genetically related to donkeys, and EqHV may have cospeciated with these host species. Here, we investigated a large panel of donkeys from various countries using serologic and molecular tools. We found EqHV to be globally widespread in donkeys and identify potential differences in EqHV infection patterns, with donkeys potentially showing enhanced EqHV clearance compared to horses. We provide strong evidence against EqHV cospeciation and for its capability to switch hosts among equines. Differential hepacivirus infection patterns in horses and donkeys may enable new insights into the chronic infection pattern associated with HCV., (Copyright © 2016 American Society for Microbiology.)