Background Transposable elements (TEs) are found in nearly all eukaryotic genomes and are implicated in a range of evolutionary processes. Despite considerable research attention on TEs, their annotation and characterisation remain challenging, particularly for non-specialists. Current methods of automated TE annotation are subject to several issues that can reduce their overall quality: (i) fragmented and overlapping TE annotations may lead to erroneous estimates of TE count and coverage; (ii) repeat models may represent small proportions of their total length, where 5’ and 3’ regions are poorly captured; (iii) resultant libraries may contain redundancy, with the same TE family represented more than once. Existing pipelines can also be challenging to install, run, and extract data from. To address these issues, we present Earl Grey: a fully automated transposable element annotation pipeline designed for the user-friendly curation and annotation of TEs in eukaryotic genome assemblies. Results Using a simulated genome, three model genome assemblies, and three non-model genome assemblies, Earl Grey outperforms current widely used TE annotation methodologies in ameliorating the issues mentioned above by producing longer TE consensus sequences in non-redundant TE libraries, which are then used to produce less fragmented TE annotations without the presence of overlaps. Earl Grey scores highly in benchmarking for TE annotation (MCC: 0.99) and classification (97% correctly classified) in comparison to existing software. Conclusions Earl Grey provides a comprehensive and fully automated TE annotation toolkit that provides researchers with paper-ready summary figures and outputs in standard formats compatible with other bioinformatics tools. Earl Grey has a modular format, with great scope for the inclusion of additional modules focussed on further quality control aspects and tailored analyses in future releases.

Background: Transposable elements (TEs) are found in nearly all eukaryotic genomes and are implicated in a range of evolutionary processes. Despite considerable research attention on TEs, their annotation and characterisation remain challenging, particularly for non-specialists. Current methods of automated TE annotation are subject to several issues that can reduce their overall quality: (i) fragmented and overlapping TE annotations may lead to erroneous estimates of TE count and coverage; (ii) repeat models may represent small proportions of their total length, where 5' and 3' regions are poorly captured; (iii) resultant libraries may contain redundancy, with the same TE family represented more than once. Existing pipelines can also be challenging to install, run, and extract data from. To address these issues, we present Earl Grey: a fully automated transposable element annotation pipeline designed for the user-friendly curation and annotation of TEs in eukaryotic genome assemblies. Results: Using a simulated genome, three model genome assemblies, and three non-model genome assemblies, Earl Grey outperforms current widely used TE annotation methodologies in ameliorating the issues mentioned above by producing longer TE consensus sequences in non-redundant TE libraries, which are then used to produce less fragmented TE annotations without the presence of overlaps. Earl Grey scores highly in benchmarking for TE annotation (MCC: 0.99) and classification (97% correctly classified) in comparison to existing software. Conclusions: Earl Grey provides a comprehensive and fully automated TE annotation toolkit that provides researchers with paper-ready summary figures and outputs in standard formats compatible with other bioinformatics tools. Earl Grey has a modular format, with great scope for the inclusion of additional modules focussed on further quality control aspects and tailored analyses in future releases.

Virus host shifts are a major source of outbreaks and emerging infectious diseases, and predicting the outcome of novel host and virus interactions remains a key challenge for virus research. The evolutionary relationships between host species can explain variation in transmission rates, virulence, and virus community composition between hosts, but it is unclear if correlations exist between related viruses in infection traits across novel hosts. Here, we measure correlations in viral load of four Cripavirus isolates across experimental infections of 45 Drosophilidae host species. We find positive correlations between every pair of viruses tested, suggesting that some host clades show broad susceptibility and could act as reservoirs and donors for certain types of viruses. Additionally, we find evidence of virus by host species interactions, highlighting the importance of both host and virus traits in determining the outcome of virus host shifts. Of the four viruses tested here, those that were more closely related tended to be more strongly correlated, providing tentative evidence that virus evolutionary relatedness may be a useful proxy for determining the likelihood of novel virus emergence, which warrants further research.

As a major source of outbreaks and emerging infectious diseases, virus host shifts cause significant health, social and economic damage. Predicting the outcome of infection with novel combinations of virus and host remains a key challenge in virus research. Host evolutionary relatedness can explain variation in transmission rates, virulence, and virus community composition between host species, but there is much to learn about the potential for virus evolutionary relatedness to explain variation in the ability of viruses to infect novel hosts. Here, we measure correlations in the outcomes of infection across 45 Drosophilidae host species with four Cripavirus isolates that vary in their evolutionary relatedness. We found positive correlations between every pair of viruses tested, with the strength of correlation tending to decrease with greater evolutionary distance between viruses. These results suggest that virus evolutionary relatedness can explain variation in the outcome of host shifts and may be a useful proxy for determining the likelihood of novel virus emergence.

Virus host shifts, where a virus transmits to and infects a novel host species, are a major source of emerging infectious disease. Genetic similarity between eukaryotic host species has been shown to be an important determinant of the outcome of virus host shifts, but it is unclear if this is the case for prokaryotes where anti-virus defences can be transmitted by horizontal gene transfer and evolve rapidly. Here, we measure the susceptibility of 64 strains of Staphylococcaceae bacteria (48 strains of Staphylococcus aureus and 16 non-S. aureus species spanning 2 genera) to the bacteriophage ISP, which is currently under investigation for use in phage therapy. Using three methods–plaque assays, optical density (OD) assays, and quantitative (q)PCR–we find that the host phylogeny explains a large proportion of the variation in susceptibility to ISP across the host panel. These patterns were consistent in models of only S. aureus strains and models with a single representative from each Staphylococcaceae species, suggesting that these phylogenetic effects are conserved both within and among host species. We find positive correlations between susceptibility assessed using OD and qPCR and variable correlations between plaque assays and either OD or qPCR, suggesting that plaque assays alone may be inadequate to assess host range. Furthermore, we demonstrate that the phylogenetic relationships between bacterial hosts can generally be used to predict the susceptibility of bacterial strains to phage infection when the susceptibility of closely related hosts is known, although this approach produced large prediction errors in multiple strains where phylogeny was uninformative. Together, our results demonstrate the ability of bacterial host evolutionary relatedness to explain differences in susceptibility to phage infection, with implications for the development of ISP both as a phage therapy treatment and as an experimental system for the study of virus host shifts.