A compendium of human gene functions derived from evolutionary modelling

Marc Feuermann, Huaiyu Mi, Pascale Gaudet, Anushya Muruganujan, Suzanna E. Lewis, Dustin Ebert, Tremayne Mushayahama and Paul D. Thomas ()
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Marc Feuermann: Centre Medical Universitaire
Huaiyu Mi: University of Southern California Los Angeles
Pascale Gaudet: Centre Medical Universitaire
Anushya Muruganujan: University of Southern California Los Angeles
Suzanna E. Lewis: Lawrence Berkeley National Laboratory
Dustin Ebert: University of Southern California Los Angeles
Tremayne Mushayahama: University of Southern California Los Angeles
Paul D. Thomas: University of Southern California Los Angeles

Nature, 2025, vol. 640, issue 8057, 146-154

Abstract: Abstract A comprehensive, computable representation of the functional repertoire of all macromolecules encoded within the human genome is a foundational resource for biology and biomedical research. The Gene Ontology Consortium has been working towards this goal by generating a structured body of information about gene functions, which now includes experimental findings reported in more than 175,000 publications for human genes and genes in experimentally tractable model organisms1,2. Here, we describe the results of a large, international effort to integrate all of these findings to create a representation of human gene functions that is as complete and accurate as possible. Specifically, we apply an expert-curated, explicit evolutionary modelling approach to all human protein-coding genes. This approach integrates available experimental information across families of related genes into models that reconstruct the gain and loss of functional characteristics over evolutionary time. The models and the resulting set of 68,667 integrated gene functions cover approximately 82% of human protein-coding genes. The functional repertoire reveals a marked preponderance of molecular regulatory functions, and the models provide insights into the evolutionary origins of human gene functions. We show that our set of descriptions of functions can improve the widely used genomic technique of Gene Ontology enrichment analysis. The experimental evidence for each functional characteristic is recorded, thereby enabling the scientific community to help review and improve the resource, which we have made publicly available.

Date: 2025
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DOI: 10.1038/s41586-025-08592-0

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