The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
UniFrac: a New Phylogenetic Method for Comparing Microbial Communities
Open accessAbstract
We introduce here a new method for computing differences between microbial communities based on phylogenetic information. This method, UniFrac, measures the phylogenetic distance between sets of taxa in a phylogenetic tree as the fraction of the branch length of the tree that leads to descendants from either one environment or the other, but not both. UniFrac can be used to determine whether communities are significantly different, to compare many communities simultaneously using clustering and ordination techniques, and to measure the relative contributions of different factors, such as chemistry and geography, to similarities between samples. We demonstrate the utility of UniFrac by applying it to published 16S rRNA gene libraries from cultured isolates and environmental clones of bacteria in marine sediment, water, and ice. Our results reveal that (i) cultured isolates from ice, water, and sediment resemble each other and environmental clone sequences from sea ice, but not environmental clone sequences from sediment and water; (ii) the geographical location does not correlate strongly with bacterial community differences in ice and sediment from the Arctic and Antarctic; and (iii) bacterial communities differ between terrestrially impacted seawater (whether polar or temperate) and warm oligotrophic seawater, whereas those in individual seawater samples are not more similar to each other than to those in sediment or ice samples. These results illustrate that UniFrac provides a new way of characterizing microbial communities, using the wealth of environmental rRNA sequences, and allows quantitative insight into the factors that underlie the distribution of lineages among environments.
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“Our results show that the multiple sequence alignment (MSA) obtained from BLS (Fig. 1b and Methods) improves microbial phylogeny inference compared to other alignment methods and enables improved UniFrac26 effect size estimation for many variables known to be important to human digestive health.”
From Improving microbial phylogeny with citizen science within a mass-market video game (2025) · cites this paper as [26]
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“Principal Coordinates Unweighted UniFrac analysis 20 showed separation of ileum and colon samples along the first component, whereas the two rooms clearly separated along PC2 on Food A (Fig1A and B).”
From The composition of the gut microbiota shapes the colon mucus barrier (2015) · cites this paper as [20]
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Cites
Ecological Diversity and Its Measurement
Magurran, A. E. (1988) · Springer eBooks · doi:10.1007/978-94-015-7358-0 · Cited by 11,493
ARB: a software environment for sequence data
Ludwig, W. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh293 · Cited by 6,714
Measuring biological diversity
Magurran, A. E. (2004) · Choice Reviews Online · doi:10.5860/choice.42-1547 · Cited by 6,617
Obesity alters gut microbial ecology
Ley, R. E., Bäckhed, F., Turnbaugh, P., Lozupone, C. A., Knight, R. D., & Gordon, J. I. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504978102 · Cited by 6,176
Self-citationA Molecular View of Microbial Diversity and the Biosphere
Pace, N. R. (1997) · Science · doi:10.1126/science.276.5313.734 · Cited by 2,797
Determination of microbial diversity in environmental samples: pitfalls of PCR-based rRNA analysis
von Wintzingerode, F., Göbel, U. B., & Stackebrandt, E. S. (1997) · FEMS Microbiology Reviews · doi:10.1111/j.1574-6976.1997.tb00351.x · Cited by 2,085
The RDP (Ribosomal Database Project)
Maidak, B. L., Olsen, G. J., Larsen, N. E., Overbeek, R., McCaughey, M. J., & Woese, C. R. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.1.109 · Cited by 2,018
The Uncultured Microbial Majority
Rappé, M. S., & Giovannoni, S. J. (2003) · Annual Review of Microbiology · doi:10.1146/annurev.micro.57.030502.090759 · Cited by 2,017
Counting the Uncountable: Statistical Approaches to Estimating Microbial Diversity
Hughes, J. B., Hellmann, J. J., Ricketts, T. H., & Bohannan, B. J. M. (2001) · Applied and Environmental Microbiology · doi:10.1128/aem.67.10.4399-4406.2001 · Cited by 1,337
The RDP-II (Ribosomal Database Project)
Maidak, B. L. (2001) · Nucleic Acids Research · doi:10.1093/nar/29.1.173 · Cited by 1,180
Cited by
Sorted by citations
phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data
McMurdie, P. J., & Holmes, S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061217 · Cited by 21,942
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski, R. A., Oakley, B. B., Parks, D. H., Robinson, C. J., et al. (2009) · Applied and Environmental Microbiology · doi:10.1128/aem.01541-09 · Cited by 19,549
Naive Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy
Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007) · Applied and Environmental Microbiology · doi:10.1128/aem.00062-07 · Cited by 20,145
Metagenomic biomarker discovery and explanation
Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, W. S., & Huttenhower, C. (2011) · Genome Biology · doi:10.1186/gb-2011-12-6-r60 · Cited by 16,192
Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample
Caporaso, J. G., Lauber, C. L., Walters, W. A., Berg-Lyons, D., Lozupone, C., Turnbaugh, P. J., Fierer, N., & Knight, R. D. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1000080107 · Cited by 9,944
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Metadata
- Authors
- Lozupone, C., & Knight, R.
- Affiliations
- University of Colorado Boulder (US)
- Corresponding author
- Knight, Rob
- Venue
- Applied and Environmental Microbiology
- Publisher
- American Society for Microbiology
- Published
- 2005-12
- Volume
- 71
- Issue
- 12
- Pages
- 8228-8235
- ISSN
- 0099-2240 (ISSN-L)
- 1098-5336
- PMC
- PMC1317376
- OpenAlex
- W2170024099
- Cited by
- 8,227
- References
- 48
- Citations by year
- 2026: 311 · 2025: 420 · 2024: 500 · 2023: 564 · 2022: 681 · 2021: 832 · 2020: 766 · 2019: 654 · 2018: 676 · 2017: 607 · 2016: 508 · 2015: 401 · 2014: 363 · 2013: 281 · 2012: 210
- FWCI
- 31.33
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Applied and Environmental Microbiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Institute of General Medical Sciences (T32-GM08759, T32GM008759, T32GM142607)
- Topics
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Environmental Science › Ecology › Polar Research and Ecology
- Keywords
- UniFrac, phylogenetic distance, microbial community comparison, 16S rRNA gene library, bacterial communities, community clustering, ordination, sea ice bacteria, marine sediment bacteria, marine bacteria
- MeSH terms
- Bacteria, Genetic Techniques, Geography, Phylogeny, RNA, Ribosomal, 16S
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