Omics

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Diagram illustrating genomics Genome-en.svg
Diagram illustrating genomics

The branches of science known informally as omics are various disciplines in biology whose names end in the suffix -omics , such as genomics, proteomics, metabolomics, metagenomics, phenomics and transcriptomics. Omics aims at the collective characterization and quantification of pools of biological molecules that translate into the structure, function, and dynamics of an organism or organisms. [1]

Contents

The related suffix -ome is used to address the objects of study of such fields, such as the genome, proteome or metabolome respectively. The suffix -ome as used in molecular biology refers to a totality of some sort; it is an example of a "neo-suffix" formed by abstraction from various Greek terms in -ωμα, a sequence that does not form an identifiable suffix in Greek.

Functional genomics aims at identifying the functions of as many genes as possible of a given organism. It combines different -omics techniques such as transcriptomics and proteomics with saturated mutant collections. [2]

Origin

"Omicum": Building of the Estonian Biocentre which houses the Estonian Genome Centre and Institute of Molecular and Cell Biology at the University of Tartu in Tartu, Estonia. Omicum.jpg
"Omicum": Building of the Estonian Biocentre which houses the Estonian Genome Centre and Institute of Molecular and Cell Biology at the University of Tartu in Tartu, Estonia.

The Oxford English Dictionary (OED) distinguishes three different fields of application for the -ome suffix:

  1. in medicine, forming nouns with the sense "swelling, tumour"
  2. in botany or zoology, forming nouns in the sense "a part of an animal or plant with a specified structure"
  3. in cellular and molecular biology, forming nouns with the sense "all constituents considered collectively"

The -ome suffix originated as a variant of -oma, and became productive in the last quarter of the 19th century. It originally appeared in terms like sclerome [3] or rhizome . [4] All of these terms derive from Greek words in -ωμα, [5] a sequence that is not a single suffix, but analyzable as -ω-μα, the -ω- belonging to the word stem (usually a verb) and the -μα being a genuine Greek suffix forming abstract nouns.

The OED suggests that its third definition originated as a back-formation from mitome , [6] Early attestations include biome (1916) [7] and genome (first coined as German Genom in 1920 [8] ). [9]

The association with chromosome in molecular biology is by false etymology. The word chromosome derives from the Greek stems χρωμ(ατ)- "colour" and σωμ(ατ)- "body". [9] While σωμα "body" genuinely contains the -μα suffix, the preceding -ω- is not a stem-forming suffix but part of the word's root. Because genome refers to the complete genetic makeup of an organism, a neo-suffix -ome suggested itself as referring to "wholeness" or "completion". [10]

Bioinformaticians and molecular biologists figured amongst the first scientists to apply the "-ome" suffix widely.[ citation needed ] Early advocates included bioinformaticians in Cambridge, UK, where there were many early bioinformatics labs such as the MRC centre, Sanger centre, and EBI (European Bioinformatics Institute); for example, the MRC centre carried out the first genome and proteome projects. [11]

Kinds of omics studies

Genomics

Epigenomics

The epigenome is the supporting structure of the genome, including protein and RNA binders, alternative DNA structures, and chemical modifications on DNA.

Microbiomics

Lipidomics

The lipidome is the entire complement of cellular lipids, including the modifications made to a particular set of lipids, produced by an organism or system.

Proteomics

The proteome is the entire complement of proteins, including the modifications made to a particular set of proteins, produced by an organism or system.

Glycomics

Glycomics is the comprehensive study of the glycome i.e. sugars and carbohydrates.

Foodomics

Foodomics was defined by Alejandro Cifuentes in 2009 as “a discipline that studies the food and nutrition domains through the application and integration of advanced omics technologies to improve consumer’s well-being, health, and knowledge.” [16] [17]

Transcriptomics

Transcriptome is the set of all RNA molecules, including mRNA, rRNA, tRNA, and other non-coding RNA, produced in one or a population of cells.

Metabolomics

The metabolome is the ensemble of small molecule found within a biological matrix.

Nutrition, pharmacology, and toxicology

Culture

Inspired by foundational questions in evolutionary biology, a Harvard team around Jean-Baptiste Michel and Erez Lieberman Aiden created the American neologism culturomics for the application of big data collection and analysis to cultural studies. [18]

Miscellaneous

A National Oceanic and Atmospheric Administration scientist using microbiomics to study marine ecosystems Scientist at AOML processes samples in the lab.jpg
A National Oceanic and Atmospheric Administration scientist using microbiomics to study marine ecosystems

Unrelated words in -omics

The word "comic" does not use the "omics" suffix; it derives from Greek "κωμ(ο)-" (merriment) + "-ικ(ο)-" (an adjectival suffix), rather than presenting a truncation of "σωμ(ατ)-".

Similarly, the word "economy" is assembled from Greek "οικ(ο)-" (household) + "νομ(ο)-" (law or custom), and "economic(s)" from "οικ(ο)-" + "νομ(ο)-" + "-ικ(ο)-". The suffix -omics is sometimes used to create names for schools of economics, such as Reaganomics.

Current usage

Many "omes" beyond the original "genome" have become useful and have been widely adopted by research scientists. "Proteomics" has become well-established as a term for studying proteins at a large scale. "Omes" can provide an easy shorthand to encapsulate a field; for example, an interactomics study is clearly recognisable as relating to large-scale analyses of gene-gene, protein-protein, or protein-ligand interactions. Researchers are rapidly taking up omes and omics, as shown by the explosion of the use of these terms in PubMed since the mid 1990s. [22]

See also

Notes

  1. https://www.mdpi.com/2673-592X/2/1/9
  2. Holtorf, Hauke; Guitton, Marie-Christine; Reski, Ralf (2002). "Plant functional genomics". Naturwissenschaften. 89 (6): 235–249. Bibcode:2002NW.....89..235H. doi:10.1007/s00114-002-0321-3. PMID   12146788. S2CID   7768096.
  3. "scleroma, n : Oxford English Dictionary" . Retrieved 2011-04-25.
  4. "rhizome, n : Oxford English Dictionary" . Retrieved 2011-04-25.
  5. "-oma, comb. form : Oxford English Dictionary" . Retrieved 2011-04-25.
  6. "Home : Oxford English Dictionary" . Retrieved 2011-04-25.
  7. "biome, n. : Oxford English Dictionary" . Retrieved 2011-04-25.
  8. Hans Winkler (1920). Verbreitung und Ursache der Parthenogenesis im Pflanzen – und Tierreiche. Verlag Fischer, Jena. p. 165. Ich schlage vor, für den haploiden Chromosomensatz, der im Verein mit dem zugehörigen Protoplasma die materielle Grundlage der systematischen Einheit darstellt den Ausdruck: das Genom zu verwenden ... " In English: " I propose the expression Genom for the haploid chromosome set, which, together with the pertinent protoplasm, specifies the material foundations of the species ...
  9. 1 2 Coleridge, H.; et alii. The Oxford English Dictionary
  10. Liddell, H.G.; Scott, R.; et alii. A Greek-English Lexicon [1996]. (Search at Perseus Project.)
  11. Grieve, IC; Dickens, NJ; Pravenec, M; Kren, V; Hubner, N; Cook, SA; Aitman, TJ; Petretto, E; Mangion, J (2008). "Genome-wide co-expression analysis in multiple tissues". PLOS ONE. 3 (12): e4033. Bibcode:2008PLoSO...3.4033G. doi: 10.1371/journal.pone.0004033 . ISSN   1932-6203. PMC   2603584 . PMID   19112506.
  12. O'Connell, Mary J.; McNally, Alan; McInerney, James O. (2017-03-28). "Why prokaryotes have pangenomes" (PDF). Nature Microbiology. 2 (4): 17040. doi:10.1038/nmicrobiol.2017.40. ISSN   2058-5276. PMID   28350002. S2CID   19612970.
  13. Tashiro, Satoshi; Lanctôt, Christian (2015-03-04). "The International Nucleome Consortium". Nucleus. 6 (2): 89–92. doi:10.1080/19491034.2015.1022703. PMC   4615172 . PMID   25738524.
  14. Cremer, Thomas; Cremer, Marion; Hübner, Barbara; Strickfaden, Hilmar; Smeets, Daniel; Popken, Jens; Sterr, Michael; Markaki, Yolanda; Rippe, Karsten (2015-10-07). "The 4D nucleome: Evidence for a dynamic nuclear landscape based on co-aligned active and inactive nuclear compartments". FEBS Letters. 589 (20PartA): 2931–2943. doi: 10.1016/j.febslet.2015.05.037 . ISSN   1873-3468. PMID   26028501. S2CID   10254118.
  15. Berg, Gabriele; Rybakova, Daria; Fischer, Doreen; Cernava, Tomislav; Vergès, Marie-Christine Champomier; Charles, Trevor; Chen, Xiaoyulong; Cocolin, Luca; Eversole, Kellye; Corral, Gema Herrero; Kazou, Maria; Kinkel, Linda; Lange, Lene; Lima, Nelson; Loy, Alexander; MacKlin, James A.; Maguin, Emmanuelle; Mauchline, Tim; McClure, Ryan; Mitter, Birgit; Ryan, Matthew; Sarand, Inga; Smidt, Hauke; Schelkle, Bettina; Roume, Hugo; Kiran, G. Seghal; Selvin, Joseph; Souza, Rafael Soares Correa de; Van Overbeek, Leo; et al. (2020). "Microbiome definition re-visited: Old concepts and new challenges". Microbiome. 8 (1): 103. doi:10.1186/s40168-020-00875-0. PMC   7329523 . PMID   32605663. CC-BY icon.svg Material was copied from this source, which is available under a Creative Commons Attribution 4.0 International License.
  16. Gunn, Sharon (27 November 2020). "Foodomics: The science of food". Front Line Genomics. Retrieved 2 June 2022.
  17. Cifuentes, Alejandro (October 2009). "Food analysis and Foodomics". Journal of Chromatography A. 1216 (43): 7109. doi:10.1016/j.chroma.2009.09.018. hdl:10261/154212. PMID   19765718 . Retrieved 2 June 2022.
  18. Michel, J-B; Shen, YK; Aiden, AP; Veres, A; Gray, MK; Google Books Team; Pickett, JP; Hoiberg, D; Clancy, D; Norvig, P; Orwant, J (2011). "Quantitative analysis of culture using millions of digitized books". Science. 331 (6014): 176–182. Bibcode:2011Sci...331..176M. doi:10.1126/science.1199644. ISSN   1095-9203. PMC   3279742 . PMID   21163965.
  19. Cumpson, Peter; Fletcher, Ian; Sano, Naoko; Barlow, Anders (2016). "Rapid multivariate analysis of 3D ToF-SIMSdata: graphical processor units (GPUs) and low-discrepancy subsampling for large-scale principal component analysis". Surface and Interface Analysis. 48 (12): 1328. doi: 10.1002/sia.6042 .
  20. Reiser, Michael (2009). "The ethomics era?". Nature Methods. 6 (6): 413–414. doi:10.1038/nmeth0609-413. PMID   19478800. S2CID   5151763.
  21. Chu, Su H.; Huang, Mengna; Kelly, Rachel S.; Benedetti, Elisa; Siddiqui, Jalal K.; Zeleznik, Oana A.; Pereira, Alexandre; Herrington, David; Wheelock, Craig E.; Krumsiek, Jan; McGeachie, Michael (2019-06-18). "Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective". Metabolites. 9 (6): E117. doi: 10.3390/metabo9060117 . ISSN   2218-1989. PMC   6630728 . PMID   31216675.
  22. "O M E S Page". bioinfo.mbb.yale.edu.

Further reading

Related Research Articles

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