UniFrac: an effective distance metric for microbial community comparison

By Catherine A. Lozupone, Manuel E. Lladser, Dan Knights, Jesse Stombaugh, Rob Knight. The ISME Journal, 2011

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View Paper: https://www.semanticscholar.org/paper/72a148e7915b2c419a059b2ab2568401016f83ca

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In Molecular Biology

Citations in the corpus

Tumor Microbiome Diversity and Composition Influence Pancreatic Cancer Outcomes (2019, methodology): “We used beta-diversity to generate a principal coordinate analysis (PCoA) using Unweighted-UniFrac distances (Lozupone et al., 2011) and using Bray-Curtis metric distances (McMurdie and Holmes, 2013).”
Effects of plant community history, soil legacy and plant diversity on soil microbial communities (2020, methodology): “For the differences in phylogenetic composition of the bacterial communities we used a multivariate ANOVA with UniFrac distances (Lozupone et al., 2011).”
Dysbiosis in the Gut Microbiota of Patients with Multiple Sclerosis, with a Striking Depletion of Species Belonging to Clostridia XIVa and IV Clusters (2015, methodology): “We then performed UniFrac Principal Coordinate Analysis (PCoA) and UniFrac distance analysis [17].”
Ecology and exploration of the rare biosphere (2015, background): “Indeed, increased weighting of the rare biosphere in βdiversity analyses (for example, by including the inci dence of conditionally rare taxa 25 or by using unweighted UniFrac 26 ) has provided novel insights into microbial communities 25 , 27 , 28 .”
Effects of library size variance, sparsity, and compositionality on the analysis of microbiome data (2015, background): “Thus, 119 samples with relatively few sequences can have inflated beta (, or between sample) diversity, 120 because authentically shared OTUs are erroneously scored as unique to samples with more 121 sequences (Lozupone et al. 2011).”
Microbiome Datasets Are Compositional: And This Is Not Optional (2017, background): “Three DD matrices dominate the literature; UniFrac (both the weighted and unweighted variants) (Lozupone et al., 2011), Bray-Curtis and Jensen-Shannon divergence, and while all have their uses, they do not account for the compositional nature of the data.”
Short‐chain fatty acids: microbial metabolites that alleviate stress‐induced brain–gut axis alterations (2018, methodology): “Alpha and beta diversities were generated in QIIME and calculated based on weighted Unifrac distance matrices (Lozupone et al., 2011).”
Microbiota organization is a distinct feature of proximal colorectal cancers (2014, methodology): “UniFrac distances (27) and principal coordinate analysis plots were computed”