Phenomics

Last updated

Phenomics studies phenotypes using phenotyping methods to characterize an organism with a set of traits [1] [2] which changes over time, due to development and aging or through metamorphosis such as when a caterpillar changes into a butterfly. Phenomics is a transdisciplinary area of research that involves biology, data sciences, engineering and other fields. Phenomics is concerned with the measurement of the phenotype where a phenome is a set of traits (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.

Contents

An organism's phenotype changes with time. The relationship between phenotype and genotype enables researchers to understand and study pleiotropy. [3] Phenomics concepts are used in functional genomics, pharmaceutical research, metabolic engineering, agricultural research, and increasingly in phylogenetics. [4]

Technical challenges involve improving, both qualitatively and quantitatively, the capacity to measure phenomes. [3]

Applications

Plant sciences

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 University of Arizona's Field Scanner [5] in Maricopa, Arizona is a platform developed to measure field phenotypes. Controlled environment systems include the Enviratron [6] at Iowa State University, the PhenoSphere [7] at the Leibniz-Institute of Plant Genetics and Crop Plant Research and platforms at the Donald Danforth Plant Science Center, the University of Nebraska–Lincoln, and elsewhere.

Standards, methods, tools, and instrumentation

A Minimal Information About a Plant Phenotyping Experiment (MIAPPE) standard [8] is available and in use among many researchers collecting and organizing plant phenomics data. A diverse set of computer vision methods exist to analyze 2D and 3D imaging data of plants. These methods are available to the community in various implementations, ranging from end-user ready cyber-platforms in the cloud such as DIRT [9] and PlantIt [10] to programming frameworks for software developers such as PlantCV. [11] 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

The International Plant Phenotyping Network (IPPN) [12] 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), [13] and others.

The European research infrastructure for plant phenotyping, EMPHASIS, [14] 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.

See also

References

  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. 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.
  3. 1 2 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.
  4. 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. Bibcode:2013Sci...339..662O. doi:10.1126/science.1229237. hdl: 11336/7302 . PMID   23393258. S2CID   206544776.
  5. The TerraRef Gantry System of the University of Arizona on the fields of the Maricopa Research Center
  6. 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. Bibcode:2019PlMet..15..117B. doi: 10.1186/s13007-019-0504-y . PMC   6806530 . PMID   31660060.
  7. "IPK PhenoSphere".
  8. 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.
  9. 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 .
  10. PlantIt: free image-based plant phenotyping automation in the cloud
  11. PlantCV
  12. IPPN - International Plant Phenotyping Network
  13. NAPPN - North American Plant Phenotyping Network
  14. EMPHASIS

Further reading