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UniFrac: a New Phylogenetic Method for Comparing Microbial Communities

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TLDR
The 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.
Abstract
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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Citations
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Journal ArticleDOI

Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy

TL;DR: The RDP Classifier can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes, and the majority of the classification errors appear to be due to anomalies in the current taxonomies.
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phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data.

TL;DR: The phyloseq project for R is a new open-source software package dedicated to the object-oriented representation and analysis of microbiome census data in R, which supports importing data from a variety of common formats, as well as many analysis techniques.
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Metagenomic biomarker discovery and explanation

TL;DR: A new method for metagenomic biomarker discovery is described and validates by way of class comparison, tests of biological consistency and effect size estimation to address the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities.
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Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample

TL;DR: This work sequences a diverse array of 25 environmental samples and three known “mock communities” at a depth averaging 3.1 million reads per sample to demonstrate excellent consistency in taxonomic recovery and recapture diversity patterns that were previously reported on the basis of metaanalysis of many studies from the literature.
References
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Ecological Diversity and its Measurement

TL;DR: In this paper, the authors define definitions of diversity and apply them to the problem of measuring species diversity, choosing an index and interpreting diversity measures, and applying them to structural and structural diversity.
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Measuring Biological Diversity

TL;DR: In this paper, the authors focus on the pressure humanity is placing on the natural world, and on the continued ability of ecosystems to deliver the services on which we all depend, and develop strategies to ameliorate its impact.
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Obesity alters gut microbial ecology

TL;DR: Analysis of the microbiota of genetically obese ob/ob mice, lean ob/+ and wild-type siblings, and their ob/+ mothers, all fed the same polysaccharide-rich diet, indicates that obesity affects the diversity of the gut microbiota and suggests that intentional manipulation of community structure may be useful for regulating energy balance in obese individuals.
Journal ArticleDOI

Measuring Biological Diversity

TL;DR: In this article, a measure called the effective number of species is developed from a nonparametric probability inequality and is shown to have a simple interpretation in terms of comparing linear experiments.
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