Premise Azolla is a genus of floating ferns that has closely evolved with a vertically transmitted obligate cyanobacterium endosymbiont—Anabaena azollae—that fixes nitrogen. There are also other les...

Vacancy at School of Culture and Society - Department of Heritage Studies, Aarhus University

Premise Azolla is a genus of floating ferns that has closely evolved with a vertically transmitted obligate cyanobacterium endosymbiont—Anabaena azollae—that fixes nitrogen. There are also other les...

YouTube video by Computational Genomics Summer Institute CGSI

Procrustes analyses to infer co-phylogenetic matching between pairs of phylogenetic trees.

Advances in metagenomics have rapidly expanded viral discovery, revealing vast diversity across Earth's virosphere. Yet most virus-host interactions i.e., which viruses infect which hosts remain unrecorded. Identifying these interactions is essential for anticipating zoonotic spillover events and advancing biomedical applications such as bacteriophage therapy. However, the sheer diversity of viruses and hosts makes comprehensive experimental mapping infeasible, motivating the need for computational approaches. Most existing prediction methods rely on supervised learning strategies that use sequence derived features, such as codon usage bias or k-mer frequencies, and do not model the coevolutionary processes that shape virus-host interactions. This limits their ability to generalize and the evolutionary interpretability of their predictions. We introduce CoEvoLink, a framework for predicting virus-host interactions that integrates sequence-based evidence with phylogenetic signal by explicitly modeling the coevolutionary histories of viruses and hosts. CoEvoLink infers likely but unobserved interactions by minimizing the number of evolutionary events required to explain them, yielding the most parsimonious interaction under a coevolutionary model. This formulation generalizes classical maximum parsimony, typically defined on a single phylogeny, by jointly optimizing parsimony across both virus and host phylogenies. Sequence-based information is incorporated by assigning a cost to each potential interaction that reflects its likelihood based on genomic features. By drawing a connection between computing parsimony on interaction matrices and maximum parsimony on phylogenetic networks, we derive a polynomial-time algorithm that balances parsimony with sequence-derived prediction cost. We demonstrate the effectiveness of CoEvoLink on simulated data under diverse coevolutionary models. Applying CoEvoLink, we identified putative bat hosts of betacoronaviruses that have not yet been cataloged in the VIRION database. On a benchmark derived from metagenomic sequencing data, we demonstrate that CoEvoLink improves the performance of existing phage-host prediction tools using cophylogenetic information.

Advances in metagenomics have rapidly expanded viral discovery, revealing vast diversity across Earth's virosphere. Yet most virus-host interactions i.e., which viruses infect which hosts remain unrecorded. Identifying these interactions is essential for anticipating zoonotic spillover events and advancing biomedical applications such as bacteriophage therapy. However, the sheer diversity of viruses and hosts makes comprehensive experimental mapping infeasible, motivating the need for computational approaches. Most existing prediction methods rely on supervised learning strategies that use sequence derived features, such as codon usage bias or k-mer frequencies, and do not model the coevolutionary processes that shape virus-host interactions. This limits their ability to generalize and the evolutionary interpretability of their predictions. We introduce CoEvoLink, a framework for predicting virus-host interactions that integrates sequence-based evidence with phylogenetic signal by explicitly modeling the coevolutionary histories of viruses and hosts. CoEvoLink infers likely but unobserved interactions by minimizing the number of evolutionary events required to explain them, yielding the most parsimonious interaction under a coevolutionary model. This formulation generalizes classical maximum parsimony, typically defined on a single phylogeny, by jointly optimizing parsimony across both virus and host phylogenies. Sequence-based information is incorporated by assigning a cost to each potential interaction that reflects its likelihood based on genomic features. By drawing a connection between computing parsimony on interaction matrices and maximum parsimony on phylogenetic networks, we derive a polynomial-time algorithm that balances parsimony with sequence-derived prediction cost. We demonstrate the effectiveness of CoEvoLink on simulated data under diverse coevolutionary models. Applying CoEvoLink, we identified putative bat hosts of betacoronaviruses that have not yet been cataloged in the VIRION database. On a benchmark derived from metagenomic sequencing data, we demonstrate that CoEvoLink improves the performance of existing phage-host prediction tools using cophylogenetic information.

CONCLUSIONS: Our findings support the Supercontinent hypothesis and emphasize the relevance of the historical biogeography in Leishmania diversification. This comprehensive cophylogenetic analysis enhances our understanding of Leishmania origins and diversification, offering insights into host specificity, vector adaptation, and the long-term maintenance of its digenetic life cycle.

Bats, with their virus tolerance, social behaviors, and mobility, are reservoirs for emerging viruses, including coronaviruses (CoVs) known for genetic flexibility. Studying the cophylogenetic link between bats and CoVs provides vital insights into transmission dynamics and host adaptation. Prior re …