Phenomics

Phenomics is the systematic study of phenotypes,[1] and was coined by UC Berkeley and LBNL scientist Steven A. Garan.[2][3][4] As such, it is a transdisciplinary area of research that involves biology, data sciences, engineering and other fields. Phenomics is concerned with the measurement of phenomes where a phenome is the set of phenotypes (physical and biochemical traits) that can be produced by a given organism over the course of development and in response to genetic mutation and environmental influences. The relationship between phenotype and genotype enables researchers to understand and study pleiotropy.[5] Phenomics concepts are used in functional genomics, pharmaceutical research, metabolic engineering, agricultural research, and increasingly in phylogenetics.[6]

Technical challenges involve improving, both qualitatively and quantitatively, the capacity to measure phenomes.[5]

## Contents

* 1 Applications
* 1.1 Plant sciences
* 1.2 Pharmaceutical sciences
* 2 Standards, methods, tools, and instrumentation
* 3 Research coordination and communities
* 4 See also
* 5 References
* 6 Further reading

## 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 glass houses, growth chambers, and other systems where growth conditions can be manipulated. The TERRA-REF Gantry in Maricopa, Arizona is a platform developed to measure field phenotypes, and the Maize Genomes to Fields Initiative[7] is an example of a large-scale, distributed field phenomics project across many environments and years. Controlled environment systems include the Enviratron[8] at Iowa State University, the Plant Cultivation Hall under construction at IPK, and platforms at the Donald Danforth Plant Science Center, the University of Nebraska-Lincoln, and elsewhere.

### Pharmaceutical sciences[edit]

Phenomics profiling of cells is emerging as a powerful technology to study cellular responses to genetic or chemical perturbations.[9] High content-microscopy, in combination with fluorescent probes, can be used for capturing rich phenotypic information. Phenomics analysis of cells is used, among others, for the study of functional genomics, disease phenotyping, compound target identification, mechanism of action (MoA) prediction and toxicity studies.[10]

## Standards, methods, tools, and instrumentation[edit]

A Minimal Information About a Plant Phenotyping Experiment (MIAPPE) standard[11] is available and in use among many researchers collecting and organizing plant phenomics data. Emerging analysis methods exist, including a diverse set of software packages in computer vision available via PlantCV. Many research groups are focused on developing systems using the Breeding API, a Standardized RESTful Web Service API Specification for communicating Plant Breeding Data.

The Australian Plant Phenomics Facility (APPF), an initiative of the Australian government, has developed a number of new instruments for comprehensive and fast measurements of phenotypes in both the lab and the field.

## Research coordination and communities[edit]

The International Plant Phenotyping Network (IPPN) 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), and others.

The European research infrastructure for plant phenotyping, EMPHASIS,[12] 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.[12]

## See also[edit]

* PhenomicDB, a database combining phenotypic and genetic data from several species
* Phenotype microarray
* Human Phenotype Ontology, a formal ontology of human phenotypes

## References[edit]

1. ^ Bilder, R.M.; Sabb, F.W.; Cannon, TD; London, ED; Jentsch, JD; Parker, DS; Poldrack, RA; Evans, C; Freimer, NB (2009). "Phenomics: The systematic study of phenotypes on a genome-wide scale". Neuroscience. 164 (1): 30–42. doi:10.1016/j.neuroscience.2009.01.027. PMC 2760679. PMID 19344640.
2. ^ TODAY AT BERKELEY LAB (Aug 5, 2011). "Garan is a leading scientist in the field of aging research, best known for coining the term "phenomics" to describe the comprehensive study of phenotypes". TODAY AT BERKELEY LAB. Retrieved 2021-05-29.`{{cite web}}`: CS1 maint: url-status (link)
3. ^ Jin, Li (2021-02-01). "Welcome to the Phenomics Journal". Phenomics. 1 (1): 1–2. doi:10.1007/s43657-020-00009-4. ISSN 2730-5848.
4. ^ Guanghui, Yu; Xuanjun, Fang (2009). "Concept of phenomics and its development in plant science". Molecular Plant Breeding. ISSN 1672-416X - via The Food and Agriculture Organization (FAO) is a specialized agency of the United Nations.
5. ^ a b 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.
6. ^ O'Leary, M. A.; Bloch, J. I.; Flynn, J. J.; Gaudin, T. J.; Giallombardo, A.; Giannini, N. P.; Goldberg, S. L.; Kraatz, B. P.; Luo, Z.-X.; Meng, J.; Ni, X.; Novacek, M. J.; Perini, F. A.; Randall, Z.; Rougier, G. W.; Sargis, E. J.; Silcox, M. T.; Simmons, N. B.; Spaulding, M.; Velazco, P. M.; Weksler, M.; Wible, J. R.; Cirranello, A. L. (2013). "The placental mammal ancestor and the post-K-Pg radiation of placentals". Science. 332 (6120): 662–667. doi:10.1126/science.1229237. hdl:11336/7302. PMID 23393258. S2CID 206544776.
7. ^ AlKhalifah, Naser; Campbell, Darwin A.; Falcon, Celeste M.; Gardiner, Jack M.; Miller, Nathan D.; Romay, Maria Cinta; Walls, Ramona; Walton, Renee; Yeh, Cheng-Ting; Bohn, Martin; Bubert, Jessica; Buckler, Edward S.; Ciampitti, Ignacio; Flint-Garcia, Sherry; Gore, Michael A.; Graham, Christopher; Hirsch, Candice; Holland, James B.; Hooker, David; Kaeppler, Shawn; Knoll, Joseph; Lauter, Nick; Lee, Elizabeth C.; Lorenz, Aaron; Lynch, Jonathan P.; Moose, Stephen P.; Murray, Seth C.; Nelson, Rebecca; Rocheford, Torbert; Rodriguez, Oscar; Schnable, James C.; Scully, Brian; Smith, Margaret; Springer, Nathan; Thomison, Peter; Tuinstra, Mitchell; Wisser, Randall J.; Xu, Wenwei; Ertl, David; Schnable, Patrick S.; De Leon, Natalia; Spalding, Edgar P.; Edwards, Jode; Lawrence-Dill, Carolyn J. (9 July 2018). "Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets". BMC Research Notes. 11 (1): 452. doi:10.1186/s13104-018-3508-1. PMC 6038255. PMID 29986751.
8. ^ Bao, Yin; Zarecor, Scott; Shah, Dylan; Tuel, Taylor; Campbell, Darwin A.; Chapman, Antony V. E.; Imberti, David; Kiekhaefer, Daniel; Imberti, Henry; Lübberstedt, Thomas; Yin, Yanhai; Nettleton, Dan; Lawrence-Dill, Carolyn J.; Whitham, Steven A.; Tang, Lie; Howell, Stephen H. (23 October 2019). "Assessing plant performance in the Enviratron". Plant Methods. 15 (1): 117. doi:10.1186/s13007-019-0504-y. PMC 6806530. PMID 31660060.
9. ^ Bray, Mark-Anthony; Singh, Shantanu; Han, Han; Davis, Chadwick T; Borgeson, Blake; Hartland, Cathy; Kost-Alimova, Maria; Gustafsdottir, Sigrun M; Gibson, Christopher C; Carpenter, Anne E (September 2016). "Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes". Nature Protocols. 11 (9): 1757–1774. doi:10.1038/nprot.2016.105. ISSN 1754-2189. PMC 5223290. PMID 27560178.
10. ^ Caicedo, Juan C; Singh, Shantanu; Carpenter, Anne E (2016-06-01). "Applications in image-based profiling of perturbations". Current Opinion in Biotechnology. Systems biology • Nanobiotechnology. 39: 134–142. doi:10.1016/j.copbio.2016.04.003. ISSN 0958-1669. PMID 27089218.
11. ^ 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. doi:10.1111/nph.16544. PMC 7317793. PMID 32171029.
12. ^ a b EMPHASIS

## Further reading[edit]

* Schilling, C.H.; Edwards, J.S.; Palsson, B.O. (1999). "Toward metabolic phenomics: analysis of genomic data using flux balances". Biotechnology Progress. 15 (3): 288–295. doi:10.1021/bp9900357. PMID 10356245. S2CID 9677662.
* Gerlai, R. (2002). "Phenomics: fiction or the future?". Trends in Neurosciences. 25 (10): 506–509. doi:10.1016/S0166-2236(02)02250-6. PMID 12220878. S2CID 23587977.