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{{Short description|Distance metric used for comparing biological communities}}
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'''UniFrac''', a shortened version of '''unique fraction metric''', is a [[distance metric]] used for comparing [[Community (ecology)|biological communities]]. It differs from dissimilarity measures such as [[Bray-Curtis dissimilarity]] in that it incorporates information on the relative relatedness of community members by incorporating phylogenetic distances between observed organisms in the computation. 

Both weighted (quantitative) and unweighted (qualitative) variants of UniFrac<ref>{{cite journal |pmid=17220268 |pmc=1828774 |year=2007 |last1=Lozupone |first1=C. A. |title=Quantitative and qualitative beta diversity measures lead to different insights into factors that structure microbial communities |journal=Applied and Environmental Microbiology |volume=73 |issue=5 |pages=1576–85 |last2=Hamady |first2=M |last3=Kelley |first3=S. T. |last4=Knight |first4=R. |doi=10.1128/AEM.01996-06}}</ref> are widely used in [[microbial ecology]], where the former accounts for abundance of observed organisms, while the latter only considers their presence or absence. The method was devised by [[Catherine A. Lozupone|Catherine Lozupone]], when she was working with [[Rob Knight (biologist)|Rob Knight]]<ref>{{cite book |last=Knight |first=Rob |title=Follow Your Gut: The Enormous Impact of Tiny Microbes |url=https://books.google.com/books?id=VeBjAwAAQBAJ&pg=PT89 |date=2015 |publisher=Simon & Schuster/TED |isbn=978-1-4767-8475-5 |page=89}}</ref> of the [[University of Colorado at Boulder]] in 2005.<ref>{{cite journal 
| last1 = Lozupone | first1 = C. 
| last2 = Knight | first2 = R. 
| doi = 10.1128/AEM.71.12.8228-8235.2005 
| title = UniFrac: A New Phylogenetic Method for Comparing Microbial Communities 
| journal = Applied and Environmental Microbiology 
| volume = 71 
| issue = 12 
| pages = 8228–8235 
| year = 2005 
| pmid = 16332807 
| pmc =1317376 
}}</ref><ref>{{Cite journal
 | pmid = 19710709
| pmc = 2797552
| year = 2010
| last1 = Hamady
| first1 = M
| title = Fast Uni ''Frac'': Facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and Phylo ''Chip'' data
| journal = The ISME Journal
| volume = 4
| issue = 1
| pages = 17–27
| last2 = Lozupone
| first2 = C
| last3 = Knight
| first3 = R
| doi = 10.1038/ismej.2009.97
}}</ref>

==Research methods==
The distance is calculated between pairs of samples (each sample represents an organismal community). All taxa found in one or both samples are placed on a [[phylogenetic tree]]. A branch leading to taxa from both samples is marked as "shared" and branches leading to taxa which appears only in one sample are marked as "unshared". The distance between the two samples is then calculated as:


:::<math>\left ( \frac{sum~of~unshared~branch~lengths}{sum~of~all~tree~branch~lengths} \right ) {{=}} fraction~of~total~unshared~branch~lengths </math>


This definition satisfies the requirements of a [[distance metric]], being non-negative, zero only when entities are identical, transitive, and conforming to the [[triangle inequality]].

[[File:TriangleInequality.svg|thumb|Three examples of the triangle inequality for triangles with sides of lengths {{math|''x''}}, {{math|''y''}}, {{math|''z''}}. The top example shows a case where {{math|''z''}} is much less than the sum {{math|''x'' + ''y''}} of the other two sides,  and the bottom example shows a case where the side {{math|''z''}} is only slightly less than {{math|''x'' + ''y''}}.]]If there are several different samples, a distance matrix can be created by making a tree for each pair of samples and calculating their UniFrac measure. Subsequently, standard multivariate statistical methods such as [[data clustering]] and [[principal co-ordinates analysis]] can be used.

One can determine the statistical significance of the UniFrac distance between two  samples using [[Monte Carlo simulation]]s. By randomizing the sample classification of each taxon on the tree (leaving the branch structure unchanged) and creating a distribution of UniFrac distance values, one can obtain a distribution of UniFrac values. From this, a p-value can be given to the actual distance between the samples.

Additionally, there is a weighted version of the UniFrac metric which accounts for the relative abundance of each of the taxa within the communities. This is commonly used in [[metagenomic]] studies, where the number of metagenomic reads can be in the tens of thousands, and it is appropriate to 'bin' these reads into [[operational taxonomic unit]]s, or OTUs, which can then be dealt with as taxa within the UniFrac framework.

In 2012, a generalized UniFrac version,<ref>{{Cite journal | last1 = Chen | first1 = J. | last2 = Bittinger | first2 = K. | last3 = Charlson | first3 = E. S. | last4 = Hoffmann | first4 = C. | last5 = Lewis | first5 = J. | last6 = Wu | first6 = G. D. 
| last7 = Collman | first7 = R. G. | last8 = Bushman | first8 = F. D. | last9 = Li | first9 = H. | doi = 10.1093/bioinformatics/bts342 | title = Associating microbiome composition with environmental covariates using generalized UniFrac distances | journal = Bioinformatics | volume = 28 | issue = 16 | pages = 2106–2113 | year = 2012 | pmid = 22711789 | pmc =3413390 }}</ref> which unifies the weighted and unweighted UniFrac distance in a single framework, was proposed.  The authors argued that the weighted and unweighted UniFrac distances place too much emphasis on either abundant lineages or rare lineages, respectively, leading to “loss of power when the important composition change occurs in moderately abundant lineages”. The generalized UniFrac distance aims to address this limitation by down-weighting the emphasis on abundant or rare lineages.

== References ==
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{{Reflist}}

== External links ==
* [http://unifrac.colorado.edu UniFrac online] 
* [https://knightlab.ucsd.edu Knight Lab website]
* [http://readiab.org/book/latest/3/1 Description of UniFrac, with worked examples]

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[[Category:Metagenomics software]]
[[Category:Environmental microbiology]]
[[Category:Genomics]]
[[Category:Bioinformatics]]