Pyrosequencing technology allows us to characterize microbial communities using 16S ribosomal RNA (rRNA) sequences orders of magnitude faster and more cheaply than has previously been possible. However, results from different studies using pyrosequencing and traditional sequencing are often difficult to compare, because amplicons covering different regions of the rRNA might yield different conclusions. We used sequences from over 200 globally dispersed environments to test whether studies that used similar primers clustered together mistakenly, without regard to environment. We then tested whether primer choice affects sequence-based community analyses using UniFrac, our recently-developed method for comparing microbial communities. We performed three tests of primer effects. We tested whether different simulated amplicons generated the same UniFrac clustering results as near-full-length sequences for three recent large-scale studies of microbial communities in the mouse and human gut, and the Guerrero Negro microbial mat. We then repeated this analysis for short sequences (100-, 150-, 200- and 250-base reads) resembling those produced by pyrosequencing. The results show that sequencing effort is best focused on gathering more short sequences rather than fewer longer ones, provided that the primers are chosen wisely, and that community comparison methods such as UniFrac are surprisingly robust to variation in the region sequenced.

The recent introduction of massively parallel pyrosequencers allows rapid, inexpensive analysis of microbial community composition using 16S ribosomal RNA (rRNA) sequences. However, a major challenge is to design a workflow so that taxonomic information can be accurately and rapidly assigned to each read, so that the composition of each community can be linked back to likely ecological roles played by members of each species, genus, family or phylum. Here, we use three large 16S rRNA datasets to test whether taxonomic information based on the full-length sequences can be recaptured by short reads that simulate the pyrosequencer outputs. We find that different taxonomic assignment methods vary radically in their ability to recapture the taxonomic information in full-length 16S rRNA sequences: most methods are sensitive to the region of the 16S rRNA gene that is targeted for sequencing, but many combinations of methods and rRNA regions produce consistent and accurate results. To process large datasets of partial 16S rRNA sequences obtained from surveys of various microbial communities, including those from human body habitats, we recommend the use of Greengenes or RDP classifier with fragments of at least 250 bases, starting from one of the primers R357, R534, R798, F343 or F517.

The vertebrate gut harbors a vast community of bacterial mutualists, the composition of which is modulated by the host immune system. Many gastrointestinal (GI) diseases are expected to be associated with disruptions of host-bacterial interactions, but relatively few comprehensive studies have been reported. We have used the rhesus macaque model to investigate forces shaping GI bacterial communities. We used DNA bar coding and pyrosequencing to characterize 141,000 sequences of 16S rRNA genes obtained from 100 uncultured GI bacterial samples, allowing quantitative analysis of community composition in health and disease. Microbial communities of macaques were distinct from those of mice and humans in both abundance and types of taxa present. The macaque communities differed among samples from intestinal mucosa, colonic contents, and stool, paralleling studies of humans. Communities also differed among animals, over time within individual animals, and between males and females. To investigate changes associated with disease, samples of colonic contents taken at necropsy were compared between healthy animals and animals with colitis and undergoing antibiotic therapy. Communities from diseased and healthy animals also differed significantly in composition. This work provides comprehensive data and improved methods for studying the role of commensal microbiota in macaque models of GI diseases and provides a model for the large-scale screening of the human gut microbiome.

Airborne microorganisms have been studied for centuries, but the majority of this research has relied on cultivation-dependent surveys that may not capture all of the microbial diversity in the atmosphere. As a result, our understanding of airborne microbial ecology is limited despite the relevance of airborne microbes to human health, various ecosystem functions, and environmental quality. Cultivation-independent surveys of small-subunit rRNA genes were conducted in order to identify the types of airborne bacteria and fungi found at a single site (Boulder, CO) and the temporal variability in the microbial assemblages over an 8-day period. We found that the air samples were dominated by ascomycete fungi of the Hypocreales order and a diverse array of bacteria, including members of the proteobacterial and Cytophaga-Flavobacterium-Bacteroides groups that are commonly found in comparable culture-independent surveys of airborne bacteria. Bacterium/fungus ratios varied by 2 orders of magnitude over the sampling period, and we observed large shifts in the phylogenetic diversity of bacteria present in the air samples collected on different dates, shifts that were not likely to be related to local meteorological conditions. We observed more phylogenetic similarity between bacteria collected from geographically distant sites than between bacteria collected from the same site on different days. These results suggest that outdoor air may harbor similar types of bacteria regardless of location and that the short-term temporal variability in airborne bacterial assemblages can be very large.

Fusion oncogene PTC presents with more extensive disease and aggressive pathology than BRAF(V) (600E) PTC in the pediatric population. The high prevalence of the NTRK1/NTRK3 fusion oncogene PTCs in the United States is unusual and needs further investigation.

The development of high-throughput sequencing methods allows for the characterization of microbial communities in a wide range of environments on an unprecedented scale. However, insight into microbial community composition is limited by our ability to detect patterns in this flood of sequences. Here we compare the performance of 51 analysis techniques using real and simulated bacterial 16S rRNA pyrosequencing datasets containing either clustered samples or samples arrayed across environmental gradients. We find that many diversity patterns are evident with severely undersampled communities, and that methods vary widely in their ability to detect gradients and clusters. Chi-squared distances and Pearson correlation distances perform especially well for detecting gradients, while Gower and Canberra distances perform especially well for detecting clusters. These results also provide a basis for understanding tradeoffs between number of samples and depth of coverage, tradeoffs which are important to consider when designing studies to characterize microbial communities.

We have implemented in Python the COmparative GENomic Toolkit, a fully integrated and thoroughly tested framework for novel probabilistic analyses of biological sequences, devising workflows, and generating publication quality graphics. PyCogent includes connectors to remote databases, built-in generalized probabilistic techniques for working with biological sequences, and controllers for third-party applications. The toolkit takes advantage of parallel architectures and runs on a range of hardware and operating systems, and is available under the general public license from http://sourceforge.net/projects/pycogent. RationaleThe genetic divergence of species is affected by both DNA metabolic processes and natural selection. Processes contributing to genetic variation that are undetectable with intraspecific data may be detectable by inter-specific analyses because of the accumulation of signal over evolutionary time scales. As a consequence of the greater statistical power, there is interest in applying comparative analyses to address an increasing number and diversity of problems, in particular analyses that integrate sequence and phenotype. Significant barriers that hinder the extension of comparative analyses to exploit genome indexed phenotypic data include the narrow focus of most analytical tools, and the diverse array of data sources, formats, and tools available. Theoretically coherent integrative analyses can be conducted by combining probabilistic models of different aspects of genotype. Probabilistic models of sequence change underlie many core bioinformatics tasks, including similarity search, sequence alignment, phylogenetic inference, and ancestral state reconstruction. Probabilistic models allow usage of likelihood inference, a powerful approach from statistics, to establish the significance of differences in support of competing hypotheses. Linking different analyses through a shared and explicit probabilistic model of sequence change is thus extremely valuable, and provides a basis for generalizing analyses to more complex models of evolution (for example, to incorporate dependence between sites). Numerous studies have established how biological factors representing metabolic or selective influences can be represented in substitution models as specific parameters that affect rates of interchange between sequence motifs or the spatial occurrence of such rates [1][2][3][4]. Given this solid grounding, it is desirable to have a toolkit that allows flexible parameterization of probabilistic models and interchange of appropriate modules.There are many existing software packages that can manipulate biological sequences and structures, but few allow specification of both truly novel statistical models and detailed workflow control for genome scale datasets. Traditional phylogenetic analysis applications [5,6] typically provide a number of explicitly defined statistical models that are difficult to modify. One exception in which the parameterization of entirely novel substitution models was poss...

SUMMARY Type 1 diabetes (T1D) is a chronic autoimmune disease that involves immune-mediated destruction of β cells. How β cells respond to immune attack is unknown. We identified a population of β cells during the progression of T1D in non-obese diabetic (NOD) mice that survives immune attack. This population develops from normal β cells confronted with islet infiltrates. Pathways involving cell movement, growth and proliferation, immune responses, and cell death and survival are activated in these cells. There is reduced expression of β cell identity genes and diabetes antigens and increased immune inhibitory markers and stemness genes. This new subpopulation is resistant to killing when diabetes is precipitated with cyclophosphamide. Human β cells show similar changes when cultured with immune cells. These changes may account for the chronicity of the disease and the long-term survival of β cells in some patients.