The severe acute respiratory syndrome coronavirus (SARS‐CoV) emerged in humans in 2002. Despite reports showing Chiroptera as the original animal reservoir of SARS‐CoV, many argue that Carnivora‐hosted viruses are the most likely origin. The emergence of the Middle East respiratory syndrome coronavirus (MERS‐CoV) in 2012 also involves Chiroptera‐hosted lineages. However, factors such as the lack of comprehensive phylogenies hamper our understanding of host shifts once MERS‐CoV emerged in humans and Artiodactyla. Since 2019, the origin of SARS‐CoV‐2, causative agent of coronavirus disease 2019 (COVID‐19), added to this episodic history of zoonotic transmission events. Here we introduce a phylogenetic analysis of 2006 unique and complete genomes of different lineages of Orthocoronavirinae . We used gene annotations to align orthologous sequences for total evidence analysis under the parsimony optimality criterion. Deltacoronavirus and Gammacoronavirus were set as outgroups to understand spillovers of Alphacoronavirus and Betacoronavirus among ten orders of animals. We corroborated that Chiroptera‐hosted viruses are the sister group of SARS‐CoV, SARS‐CoV‐2 and MERS‐related viruses. Other zoonotic events were qualified and quantified to provide a comprehensive picture of the risk of coronavirus emergence among humans. Finally, we used a 250 SARS‐CoV‐2 genomes dataset to elucidate the phylogenetic relationship between SARS‐CoV‐2 and Chiroptera‐hosted coronaviruses.

Responding to the ongoing and severe public health threat of viruses of the family Flaviviridae, including dengue, hepatitis C, West Nile, yellow fever, and Zika, demands a greater understanding of how these viruses emerge and spread. Updated phylogenies are central to this understanding. Most cladograms of Flaviviridae focus on specific lineages and ignore outgroups, hampering the efficacy of the analysis to test ingroup monophyly and relationships. This is due to the lack of annotated Flaviviridae genomes, which has gene content variation among genera. This variation makes analysis without partitioning difficult. Therefore, we developed an annotation pipeline for the genera of Flaviviridae (Flavirirus, Hepacivirus, Pegivirus, and Pestivirus, named “Fast Loci Annotation of Viruses” (FLAVi; http://flavi-web.com/), that combines ab initio and homology-based strategies. FLAVi recovered 100% of the genes in Flavivirus and Hepacivirus genomes. In Pegivirus and Pestivirus, annotation efficiency was 100% except for one partition each. There were no false positives. The combined phylogenetic analysis of multiple genes made possible by annotation has clear impacts over the tree topology compared to phylogenies that we inferred without outgroups or data partitioning. The final tree is largely congruent with previous hypotheses and adds evidence supporting the close phylogenetic relationship between dengue and Zika.

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The ongoing and severe public health threat of viruses of the family Flaviviridae, including dengue, hepatitis C, West Nile, yellow fever, and zika, demand a greater understanding of how these viruses evolve, emerge and spread in order to respond. Central to this understanding is an updated phylogeny of the entire family. Accurate gene annotations are pivotal to allow researchers to question how the fundamental genetic elements for these viruses evolve and function knowing they are comparing homologous genomic regions. To tackle these problems, our team has published a pipeline called FLAVi ("Fast Loci Annotation of Viruses"), an annotation pipeline for Flaviviridae that uses a combination of ab initio and homology‐based strategies. The pipeline recovered 100% of the genes in reference genomes and annotated over 97% of the expected genes in the remaining noncurated sequences. It was also further demonstrated that the combined analysis of genomes of all genera of Flaviviridae (Flavivirus, Hepacivirus, Pegivirus, and Pestivirus), as made possible by this annotation strategy, improves the phylogenetic analyses of these viruses for all optimality criteria that were tested (parsimony, maximum likelihood, and posterior probability). Here, we propose a user‐friendly web‐based application version of FLAVi, FLAVi‐Web (available at flavi‐web.com). FLAVi‐Web was created using Flask (Grinberg, 2018), a Python‐based application for the creation of web interfaces. Flask uses HTML and JavaScript to create the aesthetic properties of the web interface. The web application takes as input a FASTA file with the pathogen genomic information and outputs a compressed file with the main annotation results in a GFF3 file format. This novel web implementation enhances our ability to maintain and improve the FLAVi pipeline over time, ultimately promoting its usage among the virology community. Left ‐ Homepage of flavi‐web. com, Right ‐ Flowchart describing the application pipeline.