MBGapp: A Shiny application for teaching model-based geostatistics to population health scientists.
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ABSTRACT:
SUBMITTER: Johnson O
PROVIDER: S-EPMC8719748 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Inference of toxicological and mechanistic properties of untested chemicals through structural or biological similarity is a commonly employed approach for initial chemical characterization and hypothesis generation. We previously developed a web-based application, Tox21Enricher-Grails, on the Grails framework that identifies enriched biological/toxicological properties of chemical sets for the purpose of inferring properties of untested chemicals within the set. It was able to detect significantly overrepresented biological (e.g., receptor binding), toxicological (e.g., carcinogenicity), and chemical (e.g., toxicologically relevant chemical substructures) annotations within sets of chemicals screened in the Tox21 platform. Here, we present an R Shiny application version of Tox21Enricher-Grails, Tox21Enricher-Shiny, with more robust features and updated annotations. Tox21Enricher-Shiny allows users to interact with the web application component (available at http://hurlab.med.und.edu/Tox21Enricher/) through a user-friendly graphical user interface or to directly access the application's functions through an application programming interface. This version now supports InChI strings as input in addition to CASRN and SMILES identifiers. Input chemicals that contain certain reactive functional groups (nitrile, aldehyde, epoxide, and isocyanate groups) may react with proteins in cell-based Tox21 assays: this could cause Tox21Enricher-Shiny to produce spurious enrichment analysis results. Therefore, this version of the application can now automatically detect and ignore such problematic chemicals in a user's input. The application also offers new data visualizations, and the architecture has been greatly simplified to allow for simple deployment, version control, and porting. The application may be deployed onto a Posit Connect or Shiny server, and it uses Postgres for database management. As other Tox21-related tools are being migrated to the R Shiny platform, the development of Tox21Enricher-Shiny is a logical transition to use R's strong data analysis and visualization capacities and to provide aesthetic and developmental consistency with other Tox21 applications developed by the Division of Translational Toxicology (DTT) at the National Institute of Environmental Health Sciences (NIEHS).
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Project description:UnlabelledSynRio is a Shiny and R based web analysis portal for viewing Synechocystis PCC 6803 genome, a cyanobacterial genome with data analysis capabilities. The web based user interface is created using R programming language powered by Shiny package. This web interface helps in creating interactive genome visualization based on user provided data selection along with selective data download options.AvailabilitySinRio is available to download freely from Github - https://github.com/NFMC/SynRio or from http://www.nfmc.res.in/synrio/. In addition an online version of the platform is also hosted at nfmc.res.in/synrio, using shiny server (open source edition) installation.
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Project description:Household electricity access data in Africa are scarce, particularly at the subnational level. We followed a model-based Geostatistics approach to produce maps of electricity access between 2000 and 2013 at a 5 km resolution. We collated data from 69 nationally representative household surveys conducted in Africa and incorporated nighttime lights imagery as well as land use and land cover data to produce maps of electricity access between 2000 and 2013. The information produced here can be an aid for understanding of how electricity access has changed in the region during this 14 year period. The resolution and the continental scale makes it possible to combine these data with other sources in applications in the socio-economic field, both at a local or regional level.
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Project description:Owning to advancements in sensor-based, non-destructive phenotyping platforms, researchers are increasingly collecting data with higher temporal resolution. These phenotypes collected over several time points are cataloged as longitudinal traits and used for genome-wide association studies (GWAS). Longitudinal GWAS typically yield a large number of output files, posing a significant challenge to data interpretation and visualization. Efficient, dynamic, and integrative data visualization tools are essential for the interpretation of longitudinal GWAS results for biologists; however, these tools are not widely available to the community. We have developed a flexible and user-friendly Shiny-based online application, ShinyAIM, to dynamically view and interpret temporal GWAS results. The main features of the application include (a) interactive Manhattan plots for single time points, (b) a grid plot to view Manhattan plots for all time points simultaneously, (c) dynamic scatter plots for p-value-filtered selected markers to investigate co-localized genomic regions across time points, (d) and interactive phenotypic data visualization to capture variation and trends in phenotypes. The application is written entirely in the R language and can be used with limited programming experience. ShinyAIM is deployed online as a Shiny web server application at https://chikudaisei.shinyapps.io/shinyaim/, enabling easy access for users without installation. The application can also be launched on a local machine in RStudio.
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Project description:BACKGROUND:Maps of influenza activity are important tools to monitor influenza epidemics and inform policymakers. In France, the availability of a high-quality data set from the Oscour® surveillance network, covering 92% of hospital emergency department (ED) visits, offers new opportunities for disease mapping. Traditional geostatistical mapping methods such as Kriging ignore underlying population sizes, are not suited to non-Gaussian data and do not account for uncertainty in parameter estimates. OBJECTIVE:Our objective was to create reliable weekly interpolated maps of influenza activity in the ED setting, to inform Santé publique France (the French national public health agency) and local healthcare authorities. METHODS:We used Oscour® data of ED visits covering the 2016-2017 influenza season. We developed a Bayesian model-based geostatistical approach, a class of generalized linear mixed models, with a multivariate normal random field as a spatially autocorrelated random effect. Using R-INLA, we developed an algorithm to create maps of the proportion of influenza-coded cases among all coded visits. We compared our results with maps obtained by Kriging. RESULTS:Over the study period, 45 565 (0.82%) visits were coded as influenza cases. Maps resulting from the model are presented for each week, displaying the posterior mean of the influenza proportion and its associated uncertainty. Our model performed better than Kriging. CONCLUSIONS:Our model allows producing smoothed maps where the random noise has been properly removed to reveal the spatial risk surface. The algorithm was incorporated into the national surveillance system to produce maps in real time and could be applied to other diseases.
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Project description:ObjectiveDifferential expression (DE) is a fundamental step in the analysis of RNA-Seq count data. We had previously developed an R/Bioconductor package (called TCC) for this purpose. While this package has the unique feature of an in-built robust normalization method, its use has so far been limited to R users only. There is thus, a need for an alternative to DE analysis by TCC for non-R users.ResultsHere, we present a graphical user interface for TCC (called TCC-GUI). Non-R users only need a web browser as the minimum requirement for its use ( https://infinityloop.shinyapps.io/TCC-GUI/ ). TCC-GUI is implemented in R and encapsulated in Shiny application. It contains all the major functionalities of TCC, including DE pipelines with robust normalization and simulation data generation under various conditions. It also contains (i) tools for exploratory analysis, including a useful score termed average silhouette that measures the degree of separation of compared groups, (ii) visualization tools such as volcano plot and heatmap with hierarchical clustering, and (iii) a reporting tool using R Markdown. By virtue of the Shiny-based GUI framework, users can obtain results simply by mouse navigation. The source code for TCC-GUI is available at https://github.com/swsoyee/TCC-GUI under MIT license.
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Project description:Public health laboratories obtain whole-genome sequences of pathogens to confirm outbreaks and identify transmission routes. Here, we present tinselR, an open-source and user-friendly application for visualization and annotation of relatedness among pathogens with phylogenetic trees. By combining annotation and phylogenetics, we simplify one critical step in the pathogen analysis workflow.
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Project description:SummaryGenotype Query Tools (GQT) were developed to discover disease-causing variations from billions of genotypes and millions of genomes, processes data at substantially higher speed over other existing methods. While GQT has been available to a wide audience as command-line software, the difficulty of constructing queries among non-IT or non-bioinformatics researchers has limited its applicability. To overcome this limitation, we developed webGQT, an easy-to-use tool with a graphical user interface. With pre-built queries across three modules, webGQT allows for pedigree analysis, case-control studies, and population frequency studies. As a package, webGQT allows researchers with less or no applied bioinformatics/IT experience to mine potential disease-causing variants from billions.ResultswebGQT offers a flexible and easy-to-use interface for model-based candidate variant filtering for Mendelian diseases from thousands to millions of genomes at a reduced computation time. Additionally, webGQT provides adjustable parameters to reduce false positives and rescue missing genotypes across all modules. Using a case study, we demonstrate the applicability of webGQT to query non-human genomes. In addition, we demonstrate the scalability of webGQT on large data sets by implementing complex population-specific queries on the 1000 Genomes Project Phase 3 data set, which includes 8.4 billion variants from 2504 individuals across 26 different populations. Furthermore, webGQT supports filtering single-nucleotide variants, short insertions/deletions, copy number or any other variant genotypes supported by the VCF specification. Our results show that webGQT can be used as an online web service, or deployed on personal computers or local servers within research groups.AvailabilitywebGQT is made available to the users in three forms: 1) as a webserver available at https://vm1138.kaj.pouta.csc.fi/webgqt/, 2) as an R package to install on personal computers, and 3) as part of the same R package to configure on the user's own servers. The application is available for installation at https://github.com/arumds/webgqt.
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