A primary aim of microbial ecology is to determine patterns and drivers of community distribution, interaction, and assembly amidst complexity and uncertainty. Microbial community composition has been shown to change across gradients of environment, geographic distance, salinity, temperature, oxygen, nutrients, pH, day length, and biotic factors 1-6 . These patterns have been identified mostly by focusing on one sample type and region at a time, with insights extra polated across environments and geography to produce generalized principles. To assess how microbes are distributed across environments globally-or whether microbial community dynamics follow funda mental ecological 'laws' at a planetary scale-requires either a massive monolithic cross environment survey or a practical methodology for coordinating many independent surveys. New studies of microbial environments are rapidly accumulating; however, our ability to extract meaningful information from across datasets is outstripped by the rate of data generation. Previous meta analyses have suggested robust gen eral trends in community composition, including the importance of salinity 1 and animal association 2 . These findings, although derived from relatively small and uncontrolled sample sets, support the util ity of meta analysis to reveal basic patterns of microbial diversity and suggest that a scalable and accessible analytical framework is needed.The Earth Microbiome Project (EMP, http://www.earthmicrobiome. org) was founded in 2010 to sample the Earth's microbial communities at an unprecedented scale in order to advance our understanding of the organizing biogeographic principles that govern microbial commu nity structure 7,8 . We recognized that open and collaborative science, including scientific crowdsourcing and standardized methods 8 , would help to reduce technical variation among individual studies, which can overwhelm biological variation and make general trends difficult to detect 9 . Comprising around 100 studies, over half of which have yielded peer reviewed publications (Supplementary Table 1), the EMP has now dwarfed by 100 fold the sampling and sequencing depth of earlier meta analysis efforts 1,2 ; concurrently, powerful analysis tools have been developed, opening a new and larger window into the distri bution of microbial diversity on Earth. In establishing a scalable frame work to catalogue microbiota globally, we provide both a resource for the exploration of myriad questions and a starting point for the guided acquisition of new data to answer them. As an example of using this Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of r...

Background: Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems. Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses. Results: Effects on normalization: Most normalization methods enable successful clustering of samples according to biological origin when the groups differ substantially in their overall microbial composition. Rarefying more clearly clusters samples according to biological origin than other normalization techniques do for ordination metrics based on presence or absence. Alternate normalization measures are potentially vulnerable to artifacts due to library size. Effects on differential abundance testing: We build on a previous work to evaluate seven proposed statistical methods using rarefied as well as raw data. Our simulation studies suggest that the false discovery rates of many differential abundance-testing methods are not increased by rarefying itself, although of course rarefying results in a loss of sensitivity due to elimination of a portion of available data. For groups with large (~10×) differences in the average library size, rarefying lowers the false discovery rate. DESeq2, without addition of a constant, increased sensitivity on smaller datasets (<20 samples per group) but tends towards a higher false discovery rate with more samples, very uneven (~10×) library sizes, and/or compositional effects. For drawing inferences regarding taxon abundance in the ecosystem, analysis of composition of microbiomes (ANCOM) is not only very sensitive (for >20 samples per group) but also critically the only method tested that has a good control of false discovery rate. Conclusions: These findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study.

Summary The bacteria that colonize humans and our built environments have the potential to influence our health. Microbial communities associated with seven families and their homes over six weeks were assessed, including three families that moved home. Microbial communities differed significantly among homes, and the home microbiome was largely sourced from humans. The microbiota in each home were identifiable by family. Network analysis identified humans as the primary bacterial vector, and a Bayesian method significantly matched individuals to their dwellings. Draft genomes of potential human pathogens were observed on a kitchen counter could be matched to the hands of occupants. Following a house move, the microbial community in the new house rapidly converged on the microbial community of the occupants’ former house, suggesting rapid colonization by the family’s microbiota.

Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial interactions are little understood. This is due in part to the large number of bacteria, and the much larger number of interactions (easily in the millions), making experimental investigation very difficult at best and necessitating the nascent field of computational exploration through microbial correlation networks. We benchmark the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts. Also tested is the ability to distinguish signals from noise, and detect a range of ecological and time-series relationships. Finally, we provide specific recommendations for correlation technique usage. Although some methods perform better than others, there is still considerable need for improvement in current techniques.

Vertebrate corpse decomposition provides an important stage in nutrient cycling in most terrestrial habitats, yet microbially mediated processes are poorly understood. Here we combine deep microbial community characterization, community-level metabolic reconstruction, and soil biogeochemical assessment to understand the principles governing microbial community assembly during decomposition of mouse and human corpses on different soil substrates. We find a suite of bacterial and fungal groups that contribute to nitrogen cycling and a reproducible network of decomposers that emerge on predictable time scales. Our results show that this decomposer community is derived primarily from bulk soil, but key decomposers are ubiquitous in low abundance. Soil type was not a dominant factor driving community development, and the process of decomposition is sufficiently reproducible to offer new opportunities for forensic investigations.T he process of decay and decomposition in mammalian and other vertebrate taxa is a key step in biological nutrient cycling. Without the action of vertebrate and invertebrate scavengers, bacteria, archaea, fungi, and protists, chemical decomposition of animal waste would proceed extremely slowly and lead to reservoirs of biochemical waste (1). The coevolution of microbial decomposers with the availability of vertebrate corpses over the past 400 million years is expected to result in conservation of key biochemical metabolic pathways and cross-kingdom ecological interactions for efficient recycling of nutrient reserves. Although mammalian corpses likely represent a relatively small component of the detritus pool (2, 3) in most ecosystems, their role in nutrient cycling and community dynamics may be disproportionately large relative to input size, owing to the high nutrient content of corpses (3, 4) and their rapid rates of decomposition [e.g., up to three orders of magnitude faster than plant litter (2)]. These qualities make corpses a distinct and potentially critical driver of terrestrial function (5, 6).When a mammalian body is decomposing, microbial and biochemical activity results in a series of decomposition stages (5) that are associated with a reproducible microbial succession across mice (7), swine (8), and human corpses (9). Yet the microbial metabolism and successional ecology underpinning decomposition are still poorly understood. At present, we do not fully comprehend (i) whether microbial taxa that drive decomposition are ubiquitous across environment, season, and host phylogeny; (ii) whether microbes that drive decomposition derive primarily from the host or from the environment; and (iii) whether the metabolic succession of microbial decomposition is conserved across the physicochemical context of decay and host phylogeny.Several questions arise: Are microbial decomposer communities ubiquitous? What is the origin of the microbial decomposer community? How does mammalian decomposition affect the metabolic capacity of microbial communities? To answer these questions, we used mouse...

Author contributions In an academic-industry partnership, SomaLogic, Inc. and the academic collaborators worked together on study design, interpretation of the data and preparation of the manuscript. S.A.W., P.G. and N.W. were responsible for designing, writing and final editing of the manuscript and responses to reviewer comments. In addition to all authors being generally involved in the program, specific contributions were as follows: M.K. and M.J.S. were accountable for the data from the Whitehall II study and advised on the study design for the CV and diabetes models. C.L. and N.W. were accountable for the data from the Fenland study and advising on diabetes risk and behavioral models. C.B. and M.A.S. were accountable for the data from the Heritage Family study. C.J. was accountable for the data from the HUNT3 study. R.O. was accountable for the data from the Covance study.

BackgroundFecal microbiota transplantation (FMT) is an effective treatment for recurrent Clostridium difficile infection (CDI) that often fails standard antibiotic therapy. Despite its widespread recent use, however, little is known about the stability of the fecal microbiota following FMT.ResultsHere we report on short- and long-term changes and provide kinetic visualization of fecal microbiota composition in patients with multiply recurrent CDI that were refractory to antibiotic therapy and treated using FMT. Fecal samples were collected from four patients before and up to 151 days after FMT, with daily collections until 28 days and weekly collections until 84 days post-FMT. The composition of fecal bacteria was characterized using high throughput 16S rRNA gene sequence analysis, compared to microbiota across body sites in the Human Microbiome Project (HMP) database, and visualized in a movie-like, kinetic format. FMT resulted in rapid normalization of bacterial fecal sample composition from a markedly dysbiotic state to one representative of normal fecal microbiota. While the microbiome appeared most similar to the donor implant material 1 day post-FMT, the composition diverged variably at later time points. The donor microbiota composition also varied over time. However, both post-FMT and donor samples remained within the larger cloud of fecal microbiota characterized as healthy by the HMP.ConclusionsDynamic behavior is an intrinsic property of normal fecal microbiota and should be accounted for in comparing microbial communities among normal individuals and those with disease states. This also suggests that more frequent sample analyses are needed in order to properly assess success of FMT procedures.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-015-0070-0) contains supplementary material, which is available to authorized users.

A recent report warns that DNA extraction kits and other laboratory reagents are considerable sources of contamination in microbiome experiments. The issue of contamination is particularly problematic for samples of low biomass.See related research, http://www.biomedcentral.com/1741-7007/12/87