Operational taxonomic unit

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An operational taxonomic unit (OTU) is an operational definition used to classify groups of closely related individuals. The term was originally introduced by Robert R. Sokal & Peter H. A. Sneath in the context of Numerical taxonomy, where an "Operational Taxonomic Unit" is simply the group of organisms currently being studied.[1] In this sense, an OTU is a pragmatic definition to group individuals by similarity, equivalent to but necessarily in line with classical Linnaean taxonomy or modern Evolutionary taxonomy.

Nowadays, however, the term "OTU" is generally used in a different context and refers to clusters of (uncultivated or unknown) microorganisms, grouped by DNA sequence similarity of a specific taxonomic marker gene.[2] In other words, OTUs are pragmatic proxies for microbial "species" at different taxonomic levels, in the absence of traditional systems of biological classification as are available for macroscopic organisms. For several years, OTUs have been the most commonly used units of microbial diversity, especially when analysing small subunit 16S or 18S rRNA marker gene sequence datasets.

Sequences can be clustered according to their similarity to one another, and operational taxonomic units are defined based on the similarity threshold (usually 97% similarity) set by the researcher. Typically, OTU's are based on similar 16S rRNA sequences. It remains debatable how well this commonly-used method recapitulates true microbial species phylogeny or ecology. Although OTUs can be calculated differently when using different algorithms or thresholds, recent research by Schmidt et al. demonstrated that microbial OTUs were generally ecologically consistent across habitats and several OTU clustering approaches.[3] The number of OTUs defined may be inflated due to errors in DNA sequencing.[4]


OTU classification approaches[edit]

See also[edit]

References[edit]

  1. ^ Sokal & Sneath: Principles of Numerical Taxonomy, San Francisco: W.H. Freeman, 1963
  2. ^ Blaxter, M.; Mann, J.; Chapman, T.; Thomas, F.; Whitton, C.; Floyd, R.; Abebe, E. (October 2005). "Defining operational taxonomic units using DNA barcode data.". Philos Trans R Soc Lond B Biol Sci. 360 (1462): 1935–43. doi:10.1098/rstb.2005.1725. PMC 1609233Freely accessible. PMID 16214751. 
  3. ^ Schmidt, Thomas S. B.; Rodrigues, João F. Matias; von Mering, Christian (24 April 2014). "Ecological Consistency of SSU rRNA-Based Operational Taxonomic Units at a Global Scale". PLoS Comput Biol. 10 (4): e1003594. doi:10.1371/journal.pcbi.1003594. ISSN 1553-7358. 
  4. ^ Kunin, V.; Engelbrektson, A.; Ochman, H.; Hugenholtz, P. (Jan 2010). "Wrinkles in the rare biosphere: pyrosequencing errors can lead to artificial inflation of diversity estimates.". Environ Microbiol. 12 (1): 118–23. doi:10.1111/j.1462-2920.2009.02051.x. PMID 19725865. 
  5. ^ Edgar, Robert C. (1 October 2010). "Search and clustering orders of magnitude faster than BLAST". Bioinformatics. 26 (19): 2460–2461. doi:10.1093/bioinformatics/btq461. ISSN 1367-4803. 
  6. ^ Fu, Limin; Niu, Beifang; Zhu, Zhengwei; Wu, Sitao; Li, Weizhong (1 December 2012). "CD-HIT: accelerated for clustering the next-generation sequencing data". Bioinformatics. 28 (23): 3150–3152. doi:10.1093/bioinformatics/bts565. ISSN 1367-4803. PMC 3516142Freely accessible. PMID 23060610. 
  7. ^ Fu, Limin; Niu, Beifang; Zhu, Zhengwei; Wu, Sitao; Li, Weizhong (1 December 2012). "CD-HIT: accelerated for clustering the next-generation sequencing data". Bioinformatics. 28 (23): 3150–3152. doi:10.1093/bioinformatics/bts565. ISSN 1367-4803. PMC 3516142Freely accessible. PMID 23060610.