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Phenomics

From Wikipedia, the free encyclopedia

Phenomics is the study of phenotypic dynamics defining phenotype from the quantum to the ecosystem scales[1] and its emergence as a discipline is attributed to the Plant Sciences[2]. Phenomics uses phenotyping methods to systematically measure phenotypic traits that characterize an organism. Traits can vary their state over space and time, due to development, aging, metamorphosis or interactions with the environment. As such, phenomics investigates the dynamics generated by phenes constituting the phenome to form measurable phenotypes and their variation across all spatio-temporal scales in living systems, including plants[3], microbes[4], animals[5], and humans[6].

The definition of the phenome has evolved over the last few decades. Its first definition dates to 1949 as "... the sum total of extragenic, non-autoreproductive portions of the cell, whether cytoplasmic or nuclear. The phenome would be the material basis of the phenotype, just as the genome is the material basis of the genotype.[7]". Subsequently, the phenome was associated with an infinite set of traits that cannot be fully determined[8] and was attributed to the evolutionary biologist Michel Soule[9]. Later definitions, however, contradicted this initial notion of the phenome in which phenes are the fundamental building blocks of the phenotype[10], which implied a finite space. A recent definition unified both concepts by defining the phenome as a set phenes that denote an infinite set of transformations of unit traits that describe the dynamics forming a phenotype with respect to environmental interactions and any chosen set of trait measurements[1].

All phenome definitions enable researchers to better understand concepts like pleiotropy and phenotypic plasticity[11]. Yet, the major technical challenges involve improving, both qualitatively and quantitatively, the capacity to measure phenomes[12] and relate these measurements to biological and environmental processes.

Phenomics is also a transdisciplinary scientific discipline[13][14] that unites elements from the life, formal, and physical sciences. The phenotyping process involves the formulation of formal systems with their mathematical foundation to analyze and model phenotyping data that reflects the underlying biological multi-scale dynamics forming a phenotype. An essential aspect of this process is the quantitative and qualitative parametrization of phenes to capture phenotypic variation. However, as of September 2026, there is no undergraduate or graduate program to study phenomics specifically, yet its profession, the phenomicist, has been defined in basic and applied contexts[1].

Applications

[edit]

Plant sciences

[edit]

In plant sciences, phenomics research occurs in both field and controlled environments. Field phenomics encompasses the measurement of phenotypes that occur in both cultivated and natural conditions, whereas controlled environment phenomics research involves the use of greenhouses, growth chambers, and other systems where growth conditions can be manipulated. The International Plant Phenotyping Network maintains a publicly available database of facilities supporting field and controlled environment phenomics research globally[15] and can be exlored via an interactive online map[16].

Animal Science

[edit]

In animal science, phenomics research occurs mostly in field and farm environments. Specifically, the application of wearable sensors and computer vision allows to identify and breed behavioral, physiological and genetic traits in animals such as poultry, cattle and pigs.[17]

Human Science

[edit]

Human phenomics is an active research area in human health and the development of diagnostic tools and therapeutics and in evolutionary studies of the human origin[18].

Microbial Phenomics

[edit]

Microbial phenomics aims to link microbial phenotype to function in contexts of human, animal and plant health[19].

Professional Societies and Associations

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Plant Phenomics

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The International Plant Phenotyping Network (IPPN)[20] is an organization that seeks to enable exchange of knowledge, information, and expertise across many disciplines involved in plant phenomics by providing a network linking members, platform operators, users, research groups, developers, and policy makers. Regional networks include, the European Plant Phenotyping Network (EPPN)[21], the North American Plant Phenotyping Network (NAPPN)[22], German Plant Phenotyping Network (DPPN)[23], Japan Plant Phenotyping Network (JPPN)[24], Australian Plant Phenomics Network (APPN)[25], Nordic Plant Phenotyping Network (NPPN)[26] and the Latin American Plant Phenomics Network (LatPPN)[27]. Without current online presence, the China Plant Phenomics Network (CPPN), Pan Asia Pacific Phenomics Network (PAPPN) were founded.

Animal Phenomics

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The European Network on Livestock Phenomics[28] coordinates the European livestock phenomics community.

Human and Microbial Phenomics

[edit]

Currently no specific Human, Microbial or general phenomics societies are known

Phenomics Standards

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The Plant Phenomics community developed MIAPPE (Minimum Information About Plant Phenotyping Experiments) [29] as a standard to obtain consistent metadata across experiments, facilitating subsequent meta-analysis. Additionally, ontologies were created to encompass the intricacies of spatial and temporal scales.

The AgBioData Consortium, representing a group of agricultural genetic and genomic databases, has working groups and publications relevant to phenomics data management and federation[30]. BrAPI (Breeding API), a standardized RESTful web service API specification for communicating plant breeding data, serves as a software interface for interoperating genotyping and phenotyping data standards[31]. Projects like OpenALEA enable interoperability between various phenotyping and modeling software[32].

The cyberinfrastructure CyVerse[33], the successor of iPlant [34], empowers researchers to collaborate on their data and software, and drives the development of governance and practices for phenotyping data. These standards efficiently organize plant traits extracted using software libraries and platforms, ranging from end-user-friendly cyber-platforms in the cloud such as DIRT [35]and PlantIt[36] to programming frameworks for software developers like PlantCV[37].

Research coordination and communities

[edit]

The International Plant Phenotyping Network (IPPN)[20] is an organization that seeks to enable exchange of knowledge, information, and expertise across many disciplines involved in plant phenomics by providing a network linking members, platform operators, users, research groups, developers, and policy makers. Regional partners include, the European Plant Phenotyping Network (EPPN), the North American Plant Phenotyping Network (NAPPN),[22] and others.

The European research infrastructure for plant phenotyping, EMPHASIS[38], enables researchers to use facilities, services and resources for multi-scale plant phenotyping across Europe. EMPHASIS aims to promote future food security and agricultural business in a changing climate by enabling scientists to better understand plant performance and translate this knowledge into application.

Other resources

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Phenomics databases

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General Phenomics

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  1. PhenomicDB, a database combining phenotypic and genetic data from several species

Plant Phenomics

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  1. The Quantitative Plant, a comprehensive data base of plant phenotyping tools

Animal Phenomics

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  1. PhenomeAI, a platform aimed at advancing the of study the phenome with AI tools

Human Phenomics

[edit]
  1. Human Phenotype Ontology, a formal ontology of human phenotypes
  2. Centralized Interactive Phenomics Resource, a knowledgebase of computable electronic health records (EHR)-based phenotypes developed by the U.S. Department of Veterans Affairs (VA)
  3. PhenoBank ,a data sharing and collaborative research platform for human phenome data
  4. Phenomics Australia, a research infrastructure provider enabling research discovery and high-impact healthcare outcomes in precision medicine
  5. UK National Phenomics Resource , tools to help analyze Electronic Health Records
  6. The Human Phenotype Project, collects and shares human deep phenotype multiomic datasets

Microbial Phenomics

[edit]
  1. PROPHECY[4], a data base of high resolution phenomics of microbes

Phenomics Journals

[edit]

General Phenomics Journals

[edit]
  1. Phenomics

Plant Phenomics journals

[edit]
  1. Plant Phenomics
  2. The Plant Phenome Journal

Animal and Human Phenomics journals

[edit]

Currently no specific Animal or Human Phenomics Journals are known.

References

[edit]
  1. 1 2 3 Bucksch, Alexander; Chung, Yong S.; Clarke, Jennifer L.; Gerth, Stephan; von Gillhaussen, Philipp; Guo, Wei; Kholová, Jana; Pariyar, Shree; Pickering, Ethan; Sankaran, Sindhuja; Shafiee, Sahameh; Cervantes-Perez, Sergio Alan; Dhondt, Stijn; Han, Zhiguo; Hossain, Kabir (2026). "Plant Phenomics—the unrecognized rise of a scientific discipline". Trends in Plant Science. doi:10.1016/j.tplants.2026.08.001.
  2. ↑ Penberthy, Scott (2024). "The age of phenomics", Keynote Address Pacific Symposium on Biocomputing 2024, (0:00 min - 0:38min) https://www.youtube.com/watch?v=4zRbamGUbaA
  3. ↑ Pieruschka, Roland; Schurr, Uli (2019). "Plant Phenotyping: Past, Present, and Future". Plant Phenomics. 2019: 7507131. doi:10.34133/2019/7507131. PMC 7718630. PMID 33313536.{{cite journal}}: CS1 maint: article number as page number (link)
  4. 1 2 Fernandez-Ricaud, Luciano; Warringer, Jonas; Ericson, Elke; Pylvänäinen, Ilona; Kemp, Graham J. L.; Nerman, Olle; Blomberg, Anders (2005-01-01). "PROPHECY--a database for high-resolution phenomics". Nucleic Acids Research. 33 (Database issue): D369–373. doi:10.1093/nar/gki126. ISSN 1362-4962. PMC 540080. PMID 15608218.
  5. ↑ Steibel, Juan P. (2023), Zhang, Qin (ed.), "Phenomics in Animal Breeding", Encyclopedia of Smart Agriculture Technologies, Cham: Springer International Publishing, pp. 1–8, doi:10.1007/978-3-030-89123-7_149-1, ISBN 978-3-030-89123-7, retrieved 2026-09-27{{citation}}: CS1 maint: work parameter with ISBN (link)
  6. ↑ Ausiello, Dennis; Shaw, Stanley (2014). "Quantitative human phenotyping: the next frontier in medicine". Transactions of the American Clinical and Climatological Association. 125: 219–226, discussion 226–228. ISSN 0065-7778. PMC 4112685. PMID 25125736.
  7. ↑ Davis, B. D. (1949). "The Isolation of Biochemically Deficient Mutants of Bacteria by Means of Penicillin". Proceedings of the National Academy of Sciences of the United States of America. 35 (1): 1–10. doi:10.1073/pnas.35.1.1. ISSN 0027-8424. PMC 1062948. PMID 16588845.
  8. ↑ Houle, David (2010-01-26). "Numbering the hairs on our heads: The shared challenge and promise of phenomics". Proceedings of the National Academy of Sciences. 107 (suppl_1): 1793–1799. doi:10.1073/pnas.0906195106. ISSN 0027-8424. PMC 2868290. PMID 19858477.
  9. ↑ Soule, Michael (1967). "Phenetics of Natural Populations I. Phenetic Relationships of Insular Populations of the Side-Blotched Lizard". Evolution. 21 (3): 584. doi:10.2307/2406618.
  10. ↑ York, Larry M.; Nord, Eric A.; Lynch, Jonathan P. (2013). "Integration of root phenes for soil resource acquisition". Frontiers in Plant Science. 4. doi:10.3389/fpls.2013.00355. ISSN 1664-462X. PMC 3771073. PMID 24062755.
  11. ↑ Callahan, Hilary S.; Pigliucci, Massimo; Schlichting, Carl D. (1997). "Developmental phenotypic plasticity: Where ecology and evolution meet molecular biology". BioEssays. 19 (6): 519–525. doi:10.1002/bies.950190611. ISSN 0265-9247.
  12. ↑ Houle, David; Govindaraju, Diddahally R.; Omholt, Stig (2010). "Phenomics: the next challenge". Nature Reviews Genetics. 11 (12): 855–866. doi:10.1038/nrg2897. PMID 21085204. S2CID 14752610.
  13. ↑ Ninomiya, Seishi; Baret, Frédéric; Cheng, Zong-Ming (Max) (2019). "Plant Phenomics: Emerging Transdisciplinary Science". Plant Phenomics. 2019: 2765120. doi:10.34133/2019/2765120. PMC 7718629. PMID 33313524.{{cite journal}}: CS1 maint: article number as page number (link)
  14. ↑ Gerlai, R (2002-10-01). "Phenomics: fiction or the future?". Trends in Neurosciences. 25 (10): 506–509. doi:10.1016/S0166-2236(02)02250-6.
  15. ↑ "Global Phenotyping Infrastructure". www.plant-phenotyping.org. Retrieved 2026-09-27.
  16. ↑ "Interactive Phenotyping Infrastructure Map". www.plant-phenotyping.org. Retrieved 2026-09-27.
  17. ↑ Pérez-Enciso, Miguel; Steibel, Juan P. (2021-03-05). "Phenomes: the current frontier in animal breeding". Genetics, selection, evolution: GSE. 53 (1): 22. doi:10.1186/s12711-021-00618-1. ISSN 1297-9686. PMC 7934239. PMID 33673800.
  18. ↑ Lan, Lizhen; Feng, Kai; Wu, Yudan; Zhang, Wenbo; Wei, Ling; Che, Huiting; Xue, Le; Gao, Yidan; Tao, Ji; Qian, Shufang; Cao, Wenzhao; Zhang, Jun; Wang, Chengyan; Tian, Mei (2023). "Phenomic Imaging". Phenomics. 3 (6): 597–612. doi:10.1007/s43657-023-00128-8. ISSN 2730-583X. PMC 10781914. PMID 38223684.
  19. ↑ Hong, Jin-Kyung; Kim, Soo Bin; Lyou, Eun Sun; Lee, Tae Kwon (2021-03). "Microbial phenomics linking the phenotype to function: The potential of Raman spectroscopy". Journal of Microbiology. 59 (3): 249–258. doi:10.1007/s12275-021-0590-1. ISSN 1225-8873. {{cite journal}}: Check date values in: |date= (help)
  20. 1 2 "International Plant Phenotyping Network". www.plant-phenotyping.org. Retrieved 2026-09-27.
  21. ↑ "European Plant Phenotyping Infrastructure". emphasis.plant-phenotyping.eu. 2024-11-22. Retrieved 2026-09-27.
  22. 1 2 "North American Plant Phenotyping Network". North American Plant Phenotyping Network. Retrieved 2026-09-27.
  23. ↑ "Deutsches Planzen Phänotypisierungs Netzwerk". dppn.plant-phenotyping-network.de. Retrieved 2026-09-27.
  24. ↑ "Japan Plant Phenotyping Network". www.plant-phenotyping.jp. Retrieved 2026-09-27.
  25. ↑ "Australian Plant Phenomics Network". Australian Plant Phenomics Network. Retrieved 2026-09-27.
  26. ↑ "Nordic Baltic Plant Phenotyping Network". nordicphenotyping.org. University of Copenhagen. 2015-05-29. Retrieved 2026-09-27.
  27. ↑ Camargo, Anyela V.; Lobos, Gustavo A. (2016). "Latin America: A Development Pole for Phenomics". Frontiers in Plant Science. 7: 1729. doi:10.3389/fpls.2016.01729. ISSN 1664-462X. PMC 5138211. PMID 27999577.
  28. ↑ "EU-LI-PHE | COST Action CA22112 – European Network on Livestock Phenomics". Retrieved 2026-09-27.
  29. ↑ Papoutsoglou, Evangelia A.; Faria, Daniel; Arend, Daniel; Arnaud, Elizabeth; Athanasiadis, Ioannis N.; Chaves, Inês; Coppens, Frederik; Cornut, Guillaume; Costa, Bruno V.; Ćwiek-Kupczyńska, Hanna; Droesbeke, Bert; Finkers, Richard; Gruden, Kristina; Junker, Astrid; King, Graham J.; Krajewski, Paweł; Lange, Matthias; Laporte, Marie-Angélique; Michotey, Célia; Oppermann, Markus; Ostler, Richard; Poorter, Hendrik; Ramı́rez-Gonzalez, Ricardo; Ramšak, Živa; Reif, Jochen C.; Rocca-Serra, Philippe; Sansone, Susanna-Assunta; Scholz, Uwe; Tardieu, François; Uauy, Cristobal; Usadel, Björn; Visser, Richard G. F.; Weise, Stephan; Kersey, Paul J.; Miguel, Célia M.; Adam-Blondon, Anne-Françoise; Pommier, Cyril (2020). "Enabling reusability of plant phenomic datasets with MIAPPE 1.1". New Phytologist. 227 (1): 260–273. Bibcode:2020NewPh.227..260P. doi:10.1111/nph.16544. PMC 7317793. PMID 32171029.
  30. ↑ Callwood, Jodi; Celebioglu, Burcu; Gladman, Nicholas; Jung, Jinha; Lachowiec, Jennifer; Quezada Rodriguez, Elsa H; McNamara, John P; Clarke, Jennifer (2025-12-19). "The need for robust, FAIR phenomic databases supporting agricultural efficiency and resiliency". Science and Public Policy. 52 (6): 883–888. doi:10.1093/scipol/scaf039. ISSN 0302-3427.
  31. ↑ Selby, Peter; Abbeloos, Rafael; Backlund, Jan Erik; Basterrechea Salido, Martin; Bauchet, Guillaume; Benites-Alfaro, Omar E; Birkett, Clay; Calaminos, Viana C; Carceller, Pierre; Cornut, Guillaume; Vasques Costa, Bruno; Edwards, Jeremy D; Finkers, Richard; Yanxin Gao, Star; Ghaffar, Mehmood (2019-10-15). Wren, Jonathan (ed.). "BrAPI—an application programming interface for plant breeding applications". Bioinformatics. 35 (20): 4147–4155. doi:10.1093/bioinformatics/btz190. ISSN 1367-4803. PMC 6792114. PMID 30903186.
  32. ↑ Pradal, Christophe; Dufour-Kowalski, Samuel; Boudon, Frédéric; Fournier, Christian; Godin, Christophe (2008-11-11). "OpenAlea: a visual programming and component-based software platform for plant modelling". Functional Plant Biology. 35 (10): 751–760. doi:10.1071/FP08084. ISSN 1445-4408.
  33. ↑ Swetnam, Tyson L.; Antin, Parker B.; Bartelme, Ryan; Bucksch, Alexander; Camhy, David; Chism, Greg; Choi, Illyoung; Cooksey, Amanda M.; Cosi, Michele; Cowen, Cindy; Culshaw-Maurer, Michael; Davey, Robert; Davey, Sean; Devisetty, Upendra; Edgin, Tony (2024-02-07). "CyVerse: Cyberinfrastructure for open science". PLOS Computational Biology. 20 (2). Public Library of Science: e1011270. doi:10.1371/journal.pcbi.1011270. ISSN 1553-7358. PMC 10878509. PMID 38324613.{{cite journal}}: CS1 maint: article number as page number (link)
  34. ↑ Goff, Stephen A.; Vaughn, Matthew; McKay, Sheldon; Lyons, Eric; Stapleton, Ann E.; Gessler, Damian; Matasci, Naim; Wang, Liya; Hanlon, Matthew; Lenards, Andrew; Muir, Andy; Merchant, Nirav; Lowry, Sonya; Mock, Stephen; Helmke, Matthew (2011-07-25). "The iPlant Collaborative: Cyberinfrastructure for Plant Biology". Frontiers in Plant Science. 2. Frontiers. doi:10.3389/fpls.2011.00034. ISSN 1664-462X. PMC 3355756. PMID 22645531.
  35. ↑ Das, Abhiram; Schneider, Hannah; Burridge, James; Ascanio Martinez, Ana Karine; Wojciechowski, Tobias; Topp, Christopher N.; Lynch, Jonathan Paul; Weitz, Joshua; Bucksch, Alexander (2015). "Digital imaging of root traits (DIRT): a high-throughput computing and collaboration platform for field-based root phenomics". Plant Methods. 11 (1): 51ff. Bibcode:2015PlMet..11...51D. doi:10.1186/s13007-015-0093-3. PMC 4630929.
  36. ↑ PlantIt: free image-based plant phenotyping automation in the cloud
  37. ↑ PlantCV
  38. ↑ "Plant Phenotyping Infrastructure 🌱 EMPHASIS". emphasis.plant-phenotyping.eu. 2024-11-22. Retrieved 2026-09-27.
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