Phyloinformatics Lab

Working together to improve viral recombination detection

Denis Jacob Machado September 15, 2023
virusrecombinationaiphylogenetics

Update (December 2023): Although this proposal was well-evaluated, we are sorry to announce that is was not funded. We are prepared a new proposal for 2024.

High-performance computing for inferences of viral genome recombination

The Phyloinformatics lab is happy to announce that we have finalized the submission of a new proposal to Brazil’s National Council for Scientific and Technological Development (CNPq). Our project (process no. 443672/2023-7) is entitled “High-performance computing for inferences of viral genome recombination: Mechanisms, methods and evolutionary consequences.” The project was submitted in response to a public call for proposals (Chamada Pública MCTI/CNPq No. 14/2023 - Apoio a Projetos Internacionais de Pesquisa Científica, Tecnológica e de Inovação).

Our Proposal

New challenges continue to emerge in the research of viral infectious diseases, such as the COVID-19 pandemic, as well as ongoing issues like Zika, Dengue, Chikungunya, Yellow Fever, among others. One such challenge is the inference of viral recombination from genomic data. Viral recombination is significant because, as it accelerates genome evolution, it can increase genetic diversity, complicate diagnostics, and promote the expansion of host range. It can also enable pathogens to evade immune responses, current treatments, or vaccines. The LNCC and UNC Charlotte are among the research centers and universities leading the development of bioinformatics and phylogenomic pipelines to study viral evolution and propose responses to the risks that pathogens pose to public health. Combining the expertise of research groups from UNIMAG, KMU, UFG, UNMSM, and UPCH in phylogenomics and emerging diseases, we aim to enable a new class of viral recombination analyses using various arboviruses as models. Although several viral recombination detection tools have already been proposed, all are limited by the total number of sequences that can be analyzed simultaneously, and none can be executed in a high-performance computing environment. Here, we propose an interdisciplinary project to develop a computational solution for viral recombination inference with biotechnological potential. Our strategy will integrate phylogenomic techniques, machine learning, databases, scientific workflows, and high-performance computing. Our goal is to generate a genomic knowledge base to guide public health research on emerging and re-emerging viral zoonoses, aiming to develop epidemiological strategies, drugs, vaccines, and biomolecular kits for rapid diagnostics in Brazil.

Our team

Our core team (listed above in alphabetical order of first names) is composed of 9 researchers from 7 institutions in four countries:

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