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  • Perspective
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Computational strategies for cross-species knowledge transfer

Nature Methods (2025)Cite this article

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Abstract

Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that use transcriptome data and/or molecular networks. Our Perspective addresses four key areas: (1) transferring disease and gene annotation knowledge across species, (2) identifying functionally equivalent molecular components, (3) inferring equivalent perturbed genes or gene sets and (4) identifying equivalent cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer, including introducing the concept of ‘agnology’ to describe functional equivalence of biological entities, regardless of their evolutionary origins. This concept is becoming pervasive in integrative data-driven models in which evolutionary origins of functions can remain unresolved.

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Fig. 1: Schematic diagram of each section of this Perspective.
Fig. 2: How to predict disease–gene or function–gene relationships across species.
Fig. 3: How to identify functionally equivalent molecular components across species.
Fig. 4: How to infer perturbed transcriptomes across species.
Fig. 5: How to map equivalent cell types and cell states across species.
Fig. 6: Definition of agnology.

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Acknowledgements

This work is supported by NIH R35 GM128765 and Simons Foundation 1017799 (to A.K.). (Zebra)fish–human research transfer in the Braasch laboratory has been supported by NIH R01 OD011116. We thank all members in the Krishnan and Braasch laboratories for helpful discussion and feedback on the manuscript.

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Authors and Affiliations

  1. Genetics and Genome Sciences Program, Michigan State University, East Lansing, MI, USA

    Hao Yuan & Ingo Braasch

  2. Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, MI, USA

    Hao Yuan & Ingo Braasch

  3. Department of Biostatistics and Informatics, University of Colorado Anschutz, Aurora, CO, USA

    Christopher A. Mancuso

  4. Department of Biomedical Informatics, University of Colorado Anschutz, Aurora, CO, USA

    Kayla Johnson & Arjun Krishnan

  5. Department of Integrative Biology, Michigan State University, East Lansing, MI, USA

    Ingo Braasch

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  1. Hao Yuan
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  2. Christopher A. Mancuso
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  3. Kayla Johnson
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  4. Ingo Braasch
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  5. Arjun Krishnan
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H.Y. drafted the manuscript. H.Y., C.A.M., K.J., I.B. and A.K. edited the manuscript.

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Correspondence to Ingo Braasch or Arjun Krishnan.

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Nature Methods thanks Ran Ran, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Madhura Mukhopadhyay, in collaboration with the Nature Methods team.

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Yuan, H., Mancuso, C.A., Johnson, K. et al. Computational strategies for cross-species knowledge transfer. Nat Methods (2025). https://doi.org/10.1038/s41592-025-02931-9

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