Quantitative and Qualitative β Diversity Measures Lead to Different Insights into Factors That Structure Microbial Communities
01 Mar 2007-Applied and Environmental Microbiology (American Society for Microbiology)-Vol. 73, Iss: 5, pp 1576-1585
TL;DR: It is shown that applying qualitative and quantitative measures to the same data set can lead to dramatically different conclusions about the main factors that structure microbial diversity and can provide insight into the nature of community differences.
Abstract: The assessment of microbial diversity and distribution is a major concern in environmental microbiology. There are two general approaches for measuring community diversity: quantitative measures, which use the abundance of each taxon, and qualitative measures, which use only the presence/absence of data. Quantitative measures are ideally suited to revealing community differences that are due to changes in relative taxon abundance (e.g., when a particular set of taxa flourish because a limiting nutrient source becomes abundant). Qualitative measures are most informative when communities differ primarily by what can live in them (e.g., at high temperatures), in part because abundance information can obscure significant patterns of variation in which taxa are present. We illustrate these principles using two 16S rRNA-based surveys of microbial populations and two phylogenetic measures of community β diversity: unweighted UniFrac, a qualitative measure, and weighted UniFrac, a new quantitative measure, which we have added to the UniFrac website (http://bmf.colorado.edu/unifrac). These studies considered the relative influences of mineral chemistry, temperature, and geography on microbial community composition in acidic thermal springs in Yellowstone National Park and the influences of obesity and kinship on microbial community composition in the mouse gut. We show that applying qualitative and quantitative measures to the same data set can lead to dramatically different conclusions about the main factors that structure microbial diversity and can provide insight into the nature of community differences. We also demonstrate that both weighted and unweighted UniFrac measurements are robust to the methods used to build the underlying phylogeny.
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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.
Abstract: Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.
11,272 citations
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.
Abstract: The ongoing revolution in high-throughput sequencing continues to democratize the ability of small groups of investigators to map the microbial component of the biosphere. In particular, the coevolution of new sequencing platforms and new software tools allows data acquisition and analysis on an unprecedented scale. Here we report the next stage in this coevolutionary arms race, using the Illumina GAIIx platform to sequence a diverse array of 25 environmental samples and three known “mock communities” at a depth averaging 3.1 million reads per sample. We 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 (notably, the saline/nonsaline split in environmental samples and the split between host-associated and free-living communities). We also demonstrate that 2,000 Illumina single-end reads are sufficient to recapture the same relationships among samples that we observe with the full dataset. The results thus open up the possibility of conducting large-scale studies analyzing thousands of samples simultaneously to survey microbial communities at an unprecedented spatial and temporal resolution.
6,767 citations
Cites background or methods from "Quantitative and Qualitative β Dive..."
...When we incorporate abundance weighting into the results using the weighted UniFrac algorithm (31), we see that the clustering by sample type remains essentially the same, although somewhat more of the variance is explained....
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...These include samples fromhumanfeces (n=3), skin (n=3), and the dorsal tongue surface (n=3) (1); fecal samples fromhuman twins (31) (n=2); soil (n = 3) (5, 34); freshwater and freshwater sediment (n = 5), ocean (n = 3), and marine sediment (n = 3) samples; and “mock community” samples (n = 3) from genomic DNA isolated from 67bacterial strains and pooled at even abundances (26)....
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TL;DR: A strategy to understand the microbial components of the human genetic and metabolic landscape and how they contribute to normal physiology and predisposition to disease.
Abstract: A strategy to understand the microbial components of the human genetic and metabolic landscape and how they contribute to normal physiology and predisposition to disease.
4,730 citations
TL;DR: It is found that in direct contrast to the highly differentiated communities of their mothers, neonates harbored bacterial communities that were undifferentiated across multiple body habitats, regardless of delivery mode.
Abstract: Upon delivery, the neonate is exposed for the first time to a wide array of microbes from a variety of sources, including maternal bacteria. Although prior studies have suggested that delivery mode shapes the microbiota's establishment and, subsequently, its role in child health, most researchers have focused on specific bacterial taxa or on a single body habitat, the gut. Thus, the initiation stage of human microbiome development remains obscure. The goal of the present study was to obtain a community-wide perspective on the influence of delivery mode and body habitat on the neonate's first microbiota. We used multiplexed 16S rRNA gene pyrosequencing to characterize bacterial communities from mothers and their newborn babies, four born vaginally and six born via Cesarean section. Mothers' skin, oral mucosa, and vagina were sampled 1 h before delivery, and neonates' skin, oral mucosa, and nasopharyngeal aspirate were sampled <5 min, and meconium <24 h, after delivery. We found that in direct contrast to the highly differentiated communities of their mothers, neonates harbored bacterial communities that were undifferentiated across multiple body habitats, regardless of delivery mode. Our results also show that vaginally delivered infants acquired bacterial communities resembling their own mother's vaginal microbiota, dominated by Lactobacillus, Prevotella, or Sneathia spp., and C-section infants harbored bacterial communities similar to those found on the skin surface, dominated by Staphylococcus, Corynebacterium, and Propionibacterium spp. These findings establish an important baseline for studies tracking the human microbiome's successional development in different body habitats following different delivery modes, and their associated effects on infant health.
3,640 citations
TL;DR: The results indicate that the microbiota, although personalized, varies systematically across body habitats and time; such trends may ultimately reveal how microbiome changes cause or prevent disease.
Abstract: Elucidating the biogeography of bacterial communities on the human body is critical for establishing healthy baselines from which to detect differences associated with diseases. To obtain an integrated view of the spatial and temporal distribution of the human microbiota, we surveyed bacteria from up to 27 sites in seven to nine healthy adults on four occasions. We found that community composition was determined primarily by body habitat. Within habitats, interpersonal variability was high, whereas individuals exhibited minimal temporal variability. Several skin locations harbored more diverse communities than the gut and mouth, and skin locations differed in their community assembly patterns. These results indicate that our microbiota, although personalized, varies systematically across body habitats and time; such trends may ultimately reveal how microbiome changes cause or prevent disease.
2,839 citations
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"Quantitative and Qualitative β Dive..." refers background in this paper
...Each mother was heterozygous for a mutation in the gene for the hormone leptin, which affects appetite and causes obesity in homozygous mutants (10, 33 )....
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01 Jan 2004TL;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.
Abstract: Summary As prehistoric cave paintings illustrate, our species has had an enduring appreciation of the variety and abundance of life on Earth. Today, however, concern is focused 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. To understand the extent of this ‘biodiversity crisis’ and develop strategies to ameliorate its impact, it is essential to be able to accurately measure biological diversity (a term often contracted to biodiversity) and make informed predictions about how and why this diversity varies over space and time.
7,082 citations
TL;DR: The ARB program package comprises a variety of directly interacting software tools for sequence database maintenance and analysis which are controlled by a common graphical user interface.
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6,757 citations
"Quantitative and Qualitative β Dive..." refers methods in this paper
...It can thus be applied to phylogenetic trees that are generated from nonoverlapping sequence, such as trees generated based on top BLAST hits in viral metagenomic analyses (3) or from different regions of the rRNA molecule using ARB’s parsimony insertion tool (14)....
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...2f to k); (iii) an ARB parsimony insertion tree where we inserted the sequences into a tree containing 10,000 smallsubunit rRNA sequences (an augmented version of the ARB database described in reference 9), using the parsimony insertion tool of ARB (14), and then removed all but the sequences from this study (Fig....
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...It can thus be applied to phylogenetic trees that are generated from nonoverlapping sequence, such as trees generated based on top BLAST hits in viral metagenomic analyses (3) or from different regions of the rRNA molecule using ARB’s parsimony insertion tool (14)....
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...tion tool of ARB (14), and then removed all but the sequences...
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...For all but the ARB parsimony insertion tree (Fig....
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