A compendium of human gene functions derived from evolutionary modelling

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 organisms<sup>1,2</sup>. 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.

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Journal
Nature
Published
2025-02-26
DOI
https://doi.org/10.1038/s41586-025-08592-0
Citations
29
Primary Topic
Bioinformatics and Genomic Networks
Type
article
Field-Weighted Citation Impact
23.37

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article

A compendium of human gene functions derived from evolutionary modelling

Renzhi Su, Chantal Hulo, Elena Speretta, Stavros Diamantakis et al.
29 citations
Nature
Bioinformatics and Genomic Networks
23.37
article

A compendium of human gene functions derived from evolutionary modelling

Renzhi Su, Chantal Hulo, Elena Speretta, Stavros Diamantakis, Mary L. Kaldunski, Chris Mungall, Aaron M. Zorn, Emily Bowler-Barnett, Michele Magrane, Suvarna Nadendla, Steven J Marygold, Anushya Muruganujan, Harold Drabkin, Ruth C. Lovering, Deborah A. Siegele, Morgan Sheng, Eckart D. Gundelfinger, Armalya Pritazahra, Phani Garapati, Jan R.T. van Weering, Paul Denny, Leonore Reiser, SI-YAO HUANG, Eunjoon Kim, Shyamala Sundaram, Rainer Pielot, Pascale Gaudet, Hwajin Jung, Daniela Raciti, Judith A. Blake, Paul D. Thomas, Shirin C C Saverimuttu, Nadine Gruaz-Gumowski, Helen Attrill, Hema Bye‐A‐Jee, Mohamed Ali Kadhum, Mahima Vedi, Sierra Moxon, Cordelia Imig, Tremayne Mushayahama, James P. Balhoff, Antonia Lock, G. Thomas Hayman, Harold D. MacGillavry, Manuel Lera-Ramírez, Sandra Orchard, Matthijs Verhage, Vincent O’Connor, Nevila Hyka‐Nouspikel, Rex L. Chisholm, Karen Christie, David P. Hill, Giulia Antonazzo, Ivo Pedruzzi, Nomi L. Harris, Jaime de Juan‐Sanz, Paul W. Sternberg, Kimberly Van Auken, Meltem N Erdol, Kim Rutherford, Shuai Weng, Rossana Zaru, Stacia R. Engel, Marc Feuermann, Stanley J. F. Laulederkind, Jeffrey L De Pons, Petra Fey, Andrea H Auchincloss, Emmanuel Boutet, Noa Lipstein, Michelle Giglio, Catherine Rivoire, Lori E Corbani, Arnaud Gos, Yvonne Lussi, Virgilio Ponferrada, August Benjamin Smit, Christina James‐Zorn, Edith D. Wong, Rizwan Ishtiaq, Lionel Breuza, Kate Warner, Sridhar Ramachandran, Cristina Casals‐Casas, M. Eileen Dolan, Wendy Demos, Lucille Pourcel, Valerie Wood, Carlo Sala, Monte Westerfield, Marcelo P. Coba, Àlex Bayés, J. Michael Cherry, Anne Estreicher, Shur‐Jen Wang, Colin Logie, Dustin Ebert, Huaiyu Mi, Erika Bakker, Jolene Ramsey, Tanya Berardini, Kan Yan Chloe Li, Marie-Claude Blatter, Richard L. Huganir, Leyla Ruzicka, Saadullah H. Ahmed, L. Niels Cornelisse, Damien Lieberherr, Pedro Raposo, Maria Andres‐Alonso, Ruud F. Toonen, María Martin, Chiara Verpelli, Patrick Masson, Peter S. McPherson, Angeline Pesala, Peter D’Eustachio, Praoparn Asanitthong, Tilmann Achsel, Diana Luna Buitrago, Florence Jungo, Ghislaine Argoud‐Puy, Thomas Biederer, Marek S. Skrzypek, Suzi Aleksander, Suzanna Lewis, Reinhard Jahn, Miao Long, Kristian B. Axelsen, Marek Tutaj, Karl‐Heinz Smalla, Anne E. Kwitek, Malcolm E Fisher, Sylvain Poux, Nicholas H. Brown, Nidhi Tyagi, Kalpana Karra, Hana L. Goldschmidt, Cynthia L. Smith, Claudia Bagni, Matthew Gage, Nils Brose, Robert S Nash, Michael R. Kreutz, John Jia En Chua, Elisabeth Coudert, Melinda R. Dwinell, Seth Carbon, Raymond Lee, Dmitry Sitnikov, Alan Bridge, Stuart R. Miyasato, Alexandr Ignatchenko, Pascal S. Kaeser, Anne Morgat, Frank Koopmans, Philippe Le Mercier, Qiang Xu, Jennifer R. Smith, Maria Livia Famiglietti, Timothy P. Ryan, Aleksandra Michalak, Juancarlos Chan, Li Ni
article en
29 citations

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 organisms<sup>1,2</sup>. 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.

NatureVol. 640(8057)
Broad Institute (US), Northwestern University (US), Agency for Science, Technology and Research (SG), University of Rome Tor Vergata (IT), University of Southern California (US), California Institute of Technology (US), Universitat Autònoma de Barcelona (ES), University of Copenhagen (DK), University of Maryland, Baltimore (US), SIB Swiss Institute of Bioinformatics (CH), Cincinnati Children's Hospital Medical Center (US), Montreal Neurological Institute and Hospital (CA), European Bioinformatics Institute (GB), Harvard University (US), Johns Hopkins University (US), Radboud University Nijmegen (NL), Lawrence Berkeley National Laboratory (US), Korea Advanced Institute of Science and Technology (KR), Universität Hamburg (DE), National University of Singapore (SG), University of Oregon (US), Utrecht University (NL), Medical College of Wisconsin (US), Cornell University (US), University of Cambridge (GB), Discovery Institute (US), Hospital de Sant Pau (ES), Radboud University Medical Center (NL), Yale University (US), Sorbonne Université (FR), Phoenix Bioinformatics (US), Leibniz-Forschungsinstitut für Molekulare Pharmakologie (DE), Amsterdam Neuroscience (NL), University Medical Center Hamburg-Eppendorf (DE), Radboud Institute for Molecular Life Sciences (NL), Neuroscience Institute (IT), Jackson Laboratory (US), Center for Behavioral Brain Sciences (DE), Max Planck Institute for Biophysical Chemistry (DE), Institut du Cerveau (FR), University of Southampton (GB), Weill Cornell Medicine (US), Max Planck Institute for Multidisciplinary Sciences (DE), Institut de Recerca Sant Pau (ES), University College London (GB), McGill University (CA), New York University (US), Massachusetts Institute of Technology (US), Renaissance Computing Institute (US), Institute of Molecular and Cell Biology (SG), Vrije Universiteit Amsterdam (NL), Texas A&M University (US), Otto-von-Guericke-Universität Magdeburg (DE), Leibniz Institute for Neurobiology (DE), Stanford University (US), University of Lausanne (CH)
U.S. Department of Energy, Wellcome Trust, Australian Government, National Institute for Health and Care Research, University College London, National Institutes of Health, Office of Science, Medical Research Council, Staatssekretariat für Bildung, Forschung und Innovation, National Institute of Child Health and Human Development, Basic Energy Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Openalex Percentile: Top 0%
Bioinformatics and Genomic Networks
23.37
DOIOpen Access PDFOpenAlex
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