Lingjie Meng

Lingjie Meng
令杰

Assistant Professor

Kyoto University

Ph.D. (Sc.)

Bio & Vision

Computational Virology

Hi! I am an Assistant Professor at Kyoto University, working at the cross of computational virology and big data integration.

A long-term goal of my research is to treat viruses rather than pathogens, but as fundamental nodes in the biosphere. My passion lies in investigating the links connected to these viral nodes. These links could be biological, ecological or even conceptual (like how human understanding of these networks challenges the definition of "life"). By tracing these invisible links, I believe we can connect everything into a one integrated system.

To map these intricate connections, my work is mainly data-driven. I leverage scalable metagenomics, pipeline development, and network-based inference to model how those viral nodes impact system stablity, which could bridge the gap between environmental virology, host-associated microbiomes, and broader biomedical contexts.

By organizing fragmented multi-omics data into an integrated, computable network of life, my current work focuses on building analytical frameworks independently and in collaboration with my supervisor, Hiroyuki Ogata. In the future, I hope to find more collaborators to explore these fundamental evolutionary networks together.

Highlight News

Featured Papers

  • Nat. Commun.
    Lingjie Meng, Tom O. Delmont, Morgan Gaïa, Eric Pelletier, Antonio Fernàndez-Guerra, Samuel Chaffron, Junyi Wu, Hiroto Kaneko, Hisashi Endo, Hiroyuki Ogata. (2023). Genomic adaptation of giant viruses in polar oceans. Nature Communications, 14: 6233.
    Highlight: In this study, we applied large-scale comparative genomics and environmental metagenomics to untangle the adaptive strategies of marine giant viruses in polar oceans. By resolving complex viral genomes from massive environmental datasets, we demonstrated how cold-adapted viruses modulate their extensive gene repertoires to synchronize with host dynamics and extreme environmental conditions. This work highlights our capacity to extract high-resolution evolutionary and ecological insights from complex, large-scale multi-omics data.
    Polar Virus Illustration

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