John Chase

Subsampled open-reference clustering creates consistent, comprehensive OTU definitions and scales to billions of sequences

We present a performance-optimized algorithm, subsampled open-reference OTU picking, for assigning marker gene (e.g., 16S rRNA) sequences generated on next-generation sequencing platforms to operational taxonomic units (OTUs) for microbial community analysis. This algorithm provides benefits over de novo OTU picking (clustering can be performed largely in parallel, reducing runtime) and closed-reference OTU picking (all reads are clustered, not only those that match a reference database sequence with high similarity). Because more of our algorithm can be run in parallel relative to “classic” open-reference OTU picking, it makes open-reference OTU picking tractable on massive amplicon sequence data sets (though on smaller data sets, “classic” open-reference OTU clustering is often faster). We illustrate that here by applying it to the first 15,000 samples sequenced for the Earth Microbiome Project (1.3 billion V4 16S rRNA amplicons). To the best of our knowledge, this is the largest OTU picking run ever performed, and we estimate that our new algorithm runs in less than 1/5 the time than would be required of “classic” open reference OTU picking. We show that subsampled open-reference OTU picking yields results that are highly correlated with those generated by “classic” open-reference OTU picking through comparisons on three well-studied datasets. An implementation of this algorithm is provided in the popular QIIME software package, which uses uclust for read clustering. All analyses were performed using QIIME’s uclust wrappers, though we provide details (aided by the open-source code in our GitHub repository) that will allow implementation of subsampled open-reference OTU picking independently of QIIME (e.g., in a compiled programming language, where runtimes should be further reduced). Our analyses should generalize to other implementations of these OTU picking algorithms. Finally, we present a comparison of parameter settings in QIIME’s OTU picking workflows and make recommendations on settings for these free parameters to optimize runtime without reducing the quality of the results. These optimized parameters can vastly decrease the runtime of uclust-based OTU picking in QIIME.

Bacterial diversity in two Neonatal Intensive Care Units (NICUs).

Infants in Neonatal Intensive Care Units (NICUs) are particularly susceptible to opportunistic infection. Infected infants have high mortality rates, and survivors often suffer life-long neurological disorders. The causes of many NICU infections go undiagnosed, and there is debate as to the importance of inanimate hospital environments (IHEs) in the spread of infections. We used culture-independent next-generation sequencing to survey bacterial diversity in two San Diego NICUs and to track the sources of microbes in these environments. Thirty IHE samples were collected from two Level-Three NICU facilities. We extracted DNA from these samples and amplified the bacterial small subunit (16S) ribosomal RNA gene sequence using ‘universal’ barcoded primers. The purified PCR products were pooled into a single reaction for pyrosequencing, and the data were analyzed using QIIME. On average, we detected 93+/−39 (mean +/− standard deviation) bacterial genera per sample in NICU IHEs. Many of the bacterial genera included known opportunistic pathogens, and many were skin-associated (e.g., Propionibacterium). In one NICU, we also detected fecal coliform bacteria (Enterobacteriales) in a high proportion of the surface samples. Comparison of these NICU-derived sequences to previously published high-throughput 16S rRNA amplicon studies of other indoor environments (offices, restrooms and healthcare facilities), as well as human- and soil-associated environments, found the majority of the NICU samples to be similar to typical building surface and air samples, with the notable exception of the IHEs which were dominated by Enterobacteriaceae. Our findings provide evidence that NICU IHEs harbor a high diversity of human-associated bacteria and demonstrate the potential utility of molecular methods for identifying and tracking bacterial diversity in NICUs.

Geography and Location Are the Primary Drivers of Office Microbiome Composition

Our study highlights several points that should impact the design of future studies of the microbiology of BEs. First, projects tracking changes in BE bacterial communities should focus sampling efforts on surveying different locations in offices and in different cities but not necessarily different materials or different offices in the same city. Next, disturbance due to repeated sampling, though detectable, is small compared to that due to other variables, opening up a range of longitudinal study designs in the BE. Next, studies requiring more samples than can be sequenced on a single sequencing run (which is increasingly common) must control for run effects by including some of the same samples in all of the sequencing runs as technical replicates. Finally, detailed tracking of indoor and material environment covariates is likely not essential for BE microbiome studies, as the normal range of indoor environmental conditions is likely not large enough to impact bacterial communities. ABSTRACT In the United States, humans spend the majority of their time indoors, where they are exposed to the microbiome of the built environment (BE) they inhabit. Despite the ubiquity of microbes in BEs and their potential impacts on health and building materials, basic questions about the microbiology of these environments remain unanswered. We present a study on the impacts of geography, material type, human interaction, location in a room, seasonal variation, and indoor and microenvironmental parameters on bacterial communities in offices. Our data elucidate several important features of microbial communities in BEs. First, under normal office environmental conditions, bacterial communities do not differ on the basis of surface material (e.g., ceiling tile or carpet) but do differ on the basis of the location in a room (e.g., ceiling or floor), two features that are often conflated but that we are able to separate here. We suspect that previous work showing differences in bacterial composition with surface material was likely detecting differences based on different usage patterns. Next, we find that offices have city-specific bacterial communities, such that we can accurately predict which city an office microbiome sample is derived from, but office-specific bacterial communities are less apparent. This differs from previous work, which has suggested office-specific compositions of bacterial communities. We again suspect that the difference from prior work arises from different usage patterns. As has been previously shown, we observe that human skin contributes heavily to the composition of BE surfaces. IMPORTANCE Our study highlights several points that should impact the design of future studies of the microbiology of BEs. First, projects tracking changes in BE bacterial communities should focus sampling efforts on surveying different locations in offices and in different cities but not necessarily different materials or different offices in the same city. Next, disturbance due to repeated sampling, though detectable, is small compared to that due to other variables, opening up a range of longitudinal study designs in the BE. Next, studies requiring more samples than can be sequenced on a single sequencing run (which is increasingly common) must control for run effects by including some of the same samples in all of the sequencing runs as technical replicates. Finally, detailed tracking of indoor and material environment covariates is likely not essential for BE microbiome studies, as the normal range of indoor environmental conditions is likely not large enough to impact bacterial communities.

The role of curcumin in modulating colonic microbiota during colitis and colon cancer prevention

Background:Intestinal microbiota influences the progression of colitis-associated colorectal cancer. With diet being a key determinant of the gut microbial ecology, dietary interventions are an attractive avenue for the prevention of colitis-associated colorectal cancer. Curcumin is the most active constituent of the ground rhizome of the Curcuma longa plant, which has been demonstrated to have anti-inflammatory, antioxidative, and antiproliferative properties. Methods:Il10−/− mice on 129/SvEv background were used as a model of colitis-associated colorectal cancer. Starting at 10 weeks of age, wild-type or Il10−/− mice received 6 weekly intraperitoneal injections of azoxymethane (AOM) or phosphate-buffered saline (PBS) and were started on either a control or a curcumin-supplemented diet. Stools were collected every 4 weeks for microbial community analysis. Mice were killed at 30 weeks of age. Results:Curcumin-supplemented diet increased survival, decreased colon weight/length ratio, and, at 0.5%, entirely eliminated tumor burden. Although colonic histology indicated improvement with curcumin, no effects of mucosal immune responses have been observed in PBS/Il10−/− mice and limited effects were seen in AOM/Il10−/− mice. In wild-type and in Il10−/− mice, curcumin increased bacterial richness, prevented age-related decrease in alpha diversity, increased the relative abundance of Lactobacillales, and decreased Coriobacterales order. Taxonomic profile of AOM/Il10−/− mice receiving curcumin was more similar to those of wild-type mice than those fed control diet. Conclusions:In AOM/Il10−/− model, curcumin reduced or eliminated colonic tumor burden with limited effects on mucosal immune responses. The beneficial effect of curcumin on tumorigenesis was associated with the maintenance of a more diverse colonic microbial ecology.

Reduced Epithelial Na+/H+ Exchange Drives Gut Microbial Dysbiosis and Promotes Inflammatory Response in T Cell-Mediated Murine Colitis

Inflammatory bowel diseases (IBD) are associated with functional inhibition of epithelial Na+/H+ exchange. In mice, a selective disruption of NHE3 (Slc9a3), a major apical Na+/H+ exchanger, also promotes IBD-like symptoms and gut microbial dysbiosis. We hypothesized that disruption of Na+/H+ exchange is necessary for the development of dysbiosis, which promotes an exacerbated mucosal inflammatory response. Therefore, we performed a temporal analysis of gut microbiota composition, and mucosal immune response to adoptive T cell transfer was evaluated in Rag2-/- and NHE3-/-/Rag2-/- (DKO) mice with and without broad-spectrum antibiotics. Microbiome (16S profiling), colonic histology, T cell and neutrophil infiltration, mucosal inflammatory tone, and epithelial permeability were analyzed. In adoptive T cell transfer colitis model, Slc9a3 status was the most significant determinant of gut microbial community. In DKO mice, NHE3-deficiency and dysbiosis were associated with dramatically accelerated and exacerbated disease, with rapid body weight loss, increased mucosal T cell and neutrophil influx, increased mucosal cytokine expression, increased permeability, and expansion of CD25-FoxP3+ Tregs; this enhanced susceptibility was alleviated by oral broad-spectrum antibiotics. Based on these results and our previous work, we postulate that epithelial electrolyte homeostasis is an important modulator in the progression of colitis, acting through remodeling of the gut microbial community.

ghost-tree: creating hybrid-gene phylogenetic trees for diversity analyses

BackgroundFungi play critical roles in many ecosystems, cause serious diseases in plants and animals, and pose significant threats to human health and structural integrity problems in built environments. While most fungal diversity remains unknown, the development of PCR primers for the internal transcribed spacer (ITS) combined with next-generation sequencing has substantially improved our ability to profile fungal microbial diversity. Although the high sequence variability in the ITS region facilitates more accurate species identification, it also makes multiple sequence alignment and phylogenetic analysis unreliable across evolutionarily distant fungi because the sequences are hard to align accurately. To address this issue, we created ghost-tree, a bioinformatics tool that integrates sequence data from two genetic markers into a single phylogenetic tree that can be used for diversity analyses. Our approach starts with a “foundation” phylogeny based on one genetic marker whose sequences can be aligned across organisms spanning divergent taxonomic groups (e.g., fungal families). Then, “extension” phylogenies are built for more closely related organisms (e.g., fungal species or strains) using a second more rapidly evolving genetic marker. These smaller phylogenies are then grafted onto the foundation tree by mapping taxonomic names such that each corresponding foundation-tree tip would branch into its new “extension tree” child.ResultsWe applied ghost-tree to graft fungal extension phylogenies derived from ITS sequences onto a foundation phylogeny derived from fungal 18S sequences. Our analysis of simulated and real fungal ITS data sets found that phylogenetic distances between fungal communities computed using ghost-tree phylogenies explained significantly more variance than non-phylogenetic distances. The phylogenetic metrics also improved our ability to distinguish small differences (effect sizes) between microbial communities, though results were similar to non-phylogenetic methods for larger effect sizes.ConclusionsThe Silva/UNITE-based ghost tree presented here can be easily integrated into existing fungal analysis pipelines to enhance the resolution of fungal community differences and improve understanding of these communities in built environments. The ghost-tree software package can also be used to develop phylogenetic trees for other marker gene sets that afford different taxonomic resolution, or for bridging genome trees with amplicon trees.Availabilityghost-tree is pip-installable. All source code, documentation, and test code are available under the BSD license at https://github.com/JTFouquier/ghost-tree.

cual­id: globally unique, correctable, and human­friendly sample identifiers for comparative ­omics studies

The adoption of identifiers that are globally unique, correctable, and easily handwritten or manually entered into a computer will be a major step forward for sample tracking in comparative omics studies. As the fields transition to more-centralized sample management, for example, across labs within an institution, across projects funded under a common program, or in systems designed to facilitate meta- and/or integrated analysis, sample identifiers generated with cual-id will not need to change; thus, costly and error-prone updating of data and metadata identifiers will be avoided. Further, using cual-id will ensure that transcription errors in sample identifiers do not require the discarding of otherwise-useful samples that may have been expensive to obtain. Finally, cual-id is simple to install and use and is free for all use. No centralized infrastructure is required to ensure global uniqueness, so it is feasible for any lab to get started using these identifiers within their existing infrastructure. ABSTRACT The number of samples in high-throughput comparative “omics” studies is increasing rapidly due to declining experimental costs. To keep sample data and metadata manageable and to ensure the integrity of scientific results as the scale of these projects continues to increase, it is essential that we transition to better-designed sample identifiers. Ideally, sample identifiers should be globally unique across projects, project teams, and institutions; short (to facilitate manual transcription); correctable with respect to common types of transcription errors; opaque, meaning that they do not contain information about the samples; and compatible with existing standards. We present cual-id, a lightweight command line tool that creates, or mints, sample identifiers that meet these criteria without reliance on centralized infrastructure. cual-id allows users to assign universally unique identifiers, or UUIDs, that are globally unique to their samples. UUIDs are too long to be conveniently written on sampling materials, such as swabs or microcentrifuge tubes, however, so cual-id additionally generates human-friendly 4- to 12-character identifiers that map to their UUIDs and are unique within a project. By convention, we use “cual-id” to refer to the software, “CualID” to refer to the short, human-friendly identifiers, and “UUID” to refer to the globally unique identifiers. CualIDs are used by humans when they manually write or enter identifiers, while the longer UUIDs are used by computers to unambiguously reference a sample. Finally, cual-id optionally generates printable label sticker sheets containing Code 128 bar codes and CualIDs for labeling of sample collection and processing materials. IMPORTANCE The adoption of identifiers that are globally unique, correctable, and easily handwritten or manually entered into a computer will be a major step forward for sample tracking in comparative omics studies. As the fields transition to more-centralized sample management, for example, across labs within an institution, across projects funded under a common program, or in systems designed to facilitate meta- and/or integrated analysis, sample identifiers generated with cual-id will not need to change; thus, costly and error-prone updating of data and metadata identifiers will be avoided. Further, using cual-id will ensure that transcription errors in sample identifiers do not require the discarding of otherwise-useful samples that may have been expensive to obtain. Finally, cual-id is simple to install and use and is free for all use. No centralized infrastructure is required to ensure global uniqueness, so it is feasible for any lab to get started using these identifiers within their existing infrastructure.

Keemei: cloud-based validation of tabular bioinformatics file formats in Google Sheets

BackgroundBioinformatics software often requires human-generated tabular text files as input and has specific requirements for how those data are formatted. Users frequently manage these data in spreadsheet programs, which is convenient for researchers who are compiling the requisite information because the spreadsheet programs can easily be used on different platforms including laptops and tablets, and because they provide a familiar interface. It is increasingly common for many different researchers to be involved in compiling these data, including study coordinators, clinicians, lab technicians and bioinformaticians. As a result, many research groups are shifting toward using cloud-based spreadsheet programs, such as Google Sheets, which support the concurrent editing of a single spreadsheet by different users working on different platforms. Most of the researchers who enter data are not familiar with the formatting requirements of the bioinformatics programs that will be used, so validating and correcting file formats is often a bottleneck prior to beginning bioinformatics analysis.Main textWe present Keemei, a Google Sheets Add-on, for validating tabular files used in bioinformatics analyses. Keemei is available free of charge from Google’s Chrome Web Store. Keemei can be installed and run on any web browser supported by Google Sheets. Keemei currently supports the validation of two widely used tabular bioinformatics formats, the Quantitative Insights into Microbial Ecology (QIIME) sample metadata mapping file format and the Spatially Referenced Genetic Data (SRGD) format, but is designed to easily support the addition of others.ConclusionsKeemei will save researchers time and frustration by providing a convenient interface for tabular bioinformatics file format validation. By allowing everyone involved with data entry for a project to easily validate their data, it will reduce the validation and formatting bottlenecks that are commonly encountered when human-generated data files are first used with a bioinformatics system. Simplifying the validation of essential tabular data files, such as sample metadata, will reduce common errors and thereby improve the quality and reliability of research outcomes.

An Introduction to Applied Bioinformatics: a free, open, and interactive text.

Statement of need: Due to the increasing rate of biological data generation, bioinformatics is rapidly growing as a field and is now an essential part of scientific advances in human health and environmental sciences. Online and publicly accessible resources for learning bioinformatics exist (e.g., Rosalind, (Searls, 2012, 2014)), and there are excellent textbooks and courses in the area, some focused heavily on theory (Durbin, Eddy, Krogh, & Mitchison, 1998; Felsenstein, 2003), and others geared toward learning specific skills such as Python programming or the Unix shell (Dunn & Haddock, 2010; Wilson, 2016). An Introduction to Applied Bioinformatics (IAB) is a free, online bioinformatics text that bridges the gap between theory and application by teaching fundamentals of bioinformatics in the context of their implementation, using an interactive framework based on highly relevant tools including Python 3, Jupyter Notebooks, and GitHub. IAB is geared toward students who are completely new to bioinformatics, though having completed an introductory course (or book) in both Computer Science and Biology are useful prerequisites. IAB readers begin on the project website. While it is possible to view the content statically from this page, we recommend that readers work interactively by installing IAB. Readers progress through chapters that introduce fundamental topics, such as sequence homology searching and multiple sequence alignment, and presents their Python 3 implementation. Because the content is presented in Jupyter Notebooks, students can edit and execute the code, for example to explore how changing k-word size or an alignment gap penalty might impact the results of a database search. The Python code that readers interact with is intended for educational purposes, where the implementation is made as simple as possible, sometimes at the cost of computational efficiency. Chapters therefore also include examples of performing the same analyses with scikit-bio, a production-quality bioinformatics Python 3 library. This enables a rapid transition from learning theory, or how an algorithm works, to applying techniques in a real-world setting. IAB additionally contains Wikipedia-style “Edit” links in each section of the text. When one of these links is followed, the reader is taken to the GitHub online editor where they can submit a pull request to modify content or code. Readers are therefore introduced to GitHub through a user-friendly web interface, and can begin building their GitHub activity history (commonly reviewed by bioinformatics hiring managers). Finally, every time a change is proposed via GitHub, all of the executable content of IAB is automatically tested. This continuous integration testing ensures that IAB example code remains functional as changes are introduced, solving an issue that plagues printed applied computational texts (for example because they describe an outdated software interface). IAB evolved from lecture materials developed by Dr. Caporaso for an introductory bioinformatics course targeted toward computer science and biology undergraduates (typically juniors or seniors) at Northern Arizona University. Since the early stages of its development, it has been used to teach at least ten courses and short (e.g., one day) bioinformatics workshops. As it became clear that the content and format was useful for teaching bioinformatics, Dr. Caporaso applied for and received grants from the Arizona Technology and Research Initiative and the Alfred P Sloan Foundation to further develop the resource.The content was originally written in Jupyter Notebooks, but as the project grew, it became difficult to maintain the notebooks and in particular to review submissions from others. The Jupyter Notebooks were transitioned to markdown files which are now the source for static HTML and Jupyter Notebook renderings of the content. The current version of IAB contains six chapters covering fundamental concepts and their applications. It is a dynamic resource that will be expanded, revised and updated over time. Its lifecycle is thus more similar to an active software project than a textbook: a practical approach to education in a rapidly changing field.