BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures.

The literature of human and other host-associated microbiome studies is expanding rapidly, but systematic comparisons among published results of host-associated microbiome signatures of differential abundance remain difficult. We present BugSigDB, a community-editable database of manually curated microbial signatures from published differential abundance studies accompanied by information on study geography, health outcomes, host body site and experimental, epidemiological and statistical methods using controlled vocabulary. The initial release of the database contains >2,500 manually curated signatures from >600 published studies on three host species, enabling high-throughput analysis of signature similarity, taxon enrichment, co-occurrence and coexclusion and consensus signatures. These data allow assessment of microbiome differential abundance within and across experimental conditions, environments or body sites. Database-wide analysis reveals experimental conditions with the highest level of consistency in signatures reported by independent studies and identifies commonalities among disease-associated signatures, including frequent introgression of oral pathobionts into the gut.

An integrated remediation approach using combinations of biochar, Rhizobium leguminosarum, and Vigna radiata for immobilizing and dissipating cadmium contaminants from the soil-mustard plant system.

Cadmium (Cd) contamination of soils is an environmental concern, as cadmium harms food crops and can therefore impact human health. The use of combinations of biochar (seeded with Rhizobium leguminosarum) and Vigna radiata (as an intercrop) has the potential to reduce the mobilization of Cd from soil via mustard plants (Brassica juncea). Mustard plants are grown as a food and oil production crop that is consumed worldwide. However, this plant has the property of hyperaccumulation; thus, it bioaccumulates Cd in its tissues, which in turn, if eaten, can become part of the human food chain. Hence, reducing Cd bioaccumulation in mustard plants is crucial to making these plants a reliable and safe source of food for consumption. To improve soil sorption capacity and immobilization efficiency, biochar (in the form of wheat husk) was mixed with R. leguminosarum and intercropped (using V. radiata) with mustard plants for further investigation. Sampling was performed at an early growth stage (i.e., at 30 days) and at maturity (i.e., at 60 days) to determine the impact of Cd on a plant's morphophysiological attributes. Data were analyzed in two ways: first by analysis of variance (ANOVA) and then by the post hoc Tukey's honestly significant difference (HSD) test. The statistical analysis concluded that combinations effectively improved plant traits by 65%-90% in the early growth stage and by 70%-90% in the maturity stage. The T6 treatment combination [i.e., biochar + R. leguminosarum + V. radiata (BC + RL + VR)] provided the most effective results in terms of growth, biomass, pod yield, and pigmentation content. In addition, this combination reduced the translocation of Cd in mustard plants by 70%-95%. The combination of BC + RL + VR effectively reduced Cd contamination of mustard tissue and provided a suitable growing environment for the plants. A post-harvesting soil analysis using X-ray diffraction (XRD) found that Cd was undetectable in soil. This provides clear confirmation that these approaches can lead to Cd soil remediation. Moreover, this study provided insight into the responses of different morphophysiological attributes of mustard plants to Cd stress and could aid in developing Cd stress tolerance in mustard plants.

A Structured Review of Commercially Available Cardiac Rehabilitation mHealth Applications Using the Mobile Application Rating Scale.

PURPOSE: This study systematically evaluated the quality and functionalities of patient-facing, commercially available mobile health (mHealth) apps for cardiac rehabilitation (CR). METHODS: We performed our search in two of the most widely used commercial mobile app stores: Apple iTunes Appstore and Google Play Store (Android apps). Six search terms were used to query relevant CR apps: "cardiac rehabilitation," "heart disease and remote therapy," "heart failure exercise," "heart therapy and cardiac recovery," "cardiac recovery," and "heart therapy." App quality was evaluated using the Mobile Application Rating Scale (MARS). App functionality was evaluated using the IQVIA functionality scale, and app content was evaluated against the American Heart Association guidelines for CR. Apps meeting our inclusion criteria were downloaded and evaluated by two to three reviewers, and interclass correlations between reviewers were calculated. RESULTS: We reviewed 3121 apps and nine apps met our inclusion criteria. On average, the apps scored a 3.0 on the MARS (5-point Likert scale) for overall quality. The two top-ranking mHealth apps for CR for all three quality, functionality, and consistency with evidence-based guidelines were My Cardiac Coach and Love My Heart for Women, both of which scored ≥4.0 for behavior change. CONCLUSION: Overall, the quality and functionality of free apps for mobile CR was high, with two apps performing the best across all three quality categories. High-quality CR apps are available that can expand access to CR for patients with cardiovascular disease.

Reporting guidelines for human microbiome research: the STORMS checklist.

The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.

Multiomic Integration of Public Oncology Databases in Bioconductor.

PURPOSE: Investigations of the molecular basis for the development, progression, and treatment of cancer increasingly use complementary genomic assays to gather multiomic data, but management and analysis of such data remain complex. The cBioPortal for cancer genomics currently provides multiomic data from > 260 public studies, including The Cancer Genome Atlas (TCGA) data sets, but integration of different data types remains challenging and error prone for computational methods and tools using these resources. Recent advances in data infrastructure within the Bioconductor project enable a novel and powerful approach to creating fully integrated representations of these multiomic, pan-cancer databases. METHODS: We provide a set of R/Bioconductor packages for working with TCGA legacy data and cBioPortal data, with special considerations for loading time; efficient representations in and out of memory; analysis platform; and an integrative framework, such as MultiAssayExperiment. Large methylation data sets are provided through out-of-memory data representation to provide responsive loading times and analysis capabilities on machines with limited memory. RESULTS: We developed the curatedTCGAData and cBioPortalData R/Bioconductor packages to provide integrated multiomic data sets from the TCGA legacy database and the cBioPortal web application programming interface using the MultiAssayExperiment data structure. This suite of tools provides coordination of diverse experimental assays with clinicopathological data with minimal data management burden, as demonstrated through several greatly simplified multiomic and pan-cancer analyses. CONCLUSION: These integrated representations enable analysts and tool developers to apply general statistical and plotting methods to extensive multiomic data through user-friendly commands and documented examples.

HMP16SData: Efficient Access to the Human Microbiome Project Through Bioconductor.

Phase 1 of the Human Microbiome Project (HMP) investigated 18 body subsites of 242 healthy American adults to produce the first comprehensive reference for the composition and variation of the "healthy" human microbiome. Publicly available data sets from amplicon sequencing of two 16S ribosomal RNA variable regions, with extensive controlled-access participant data, provide a reference for ongoing microbiome studies. However, utilization of these data sets can be hindered by the complex bioinformatic steps required to access, import, decrypt, and merge the various components in formats suitable for ecological and statistical analysis. The HMP16SData package provides count data for both 16S ribosomal RNA variable regions, integrated with phylogeny, taxonomy, public participant data, and controlled participant data for authorized researchers, using standard integrative Bioconductor data objects. By removing bioinformatic hurdles of data access and management, HMP16SData enables epidemiologists with only basic R skills to quickly analyze HMP data.

Software for the Integration of Multiomics Experiments in Bioconductor.

Multiomics experiments are increasingly commonplace in biomedical research and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multiomics experiments. The MultiAssayExperiment software package, implemented in R and leveraging Bioconductor software and design principles, provides for the coordinated representation of, storage of, and operation on multiple diverse genomics data. We provide the unrestricted multiple 'omics data for each cancer tissue in The Cancer Genome Atlas as ready-to-analyze MultiAssayExperiment objects and demonstrate in these and other datasets how the software simplifies data representation, statistical analysis, and visualization. The MultiAssayExperiment Bioconductor package reduces major obstacles to efficient, scalable, and reproducible statistical analysis of multiomics data and enhances data science applications of multiple omics datasets. Cancer Res; 77(21); e39-42. ©2017 AACR.