Interactive Web App Research Articles

TBIO-15. MODELING DEVELOPMENTAL GENE EXPRESSION DYNAMICS AT CELLULAR RESOLUTION TO INTERPRET PEDIATRIC BRAIN TUMOR TRANSCRIPTIONAL PROGRAMS

A central challenge in understanding the biology of pediatric brain tumors is defining the cellular and molecular context where oncogenesis occurs. We hypothesize that spatiotemporally restricted cell types are uniquely susceptible to specific genetic alterations, which alter normal neurodevelopmental programs and ultimately lead to oncogenesis. The resulting tumors retain some transcriptomic features of their lineage of origin. To delineate these origins, we assembled a densely sampled developmental time course of the mouse forebrain and pons, doubling our recently published single-cell atlas. This dataset comprises >100,000 cells at 9 timepoints from E10-P6. However, while single cell transcriptomics reveal rich gene dynamics during cell differentiation, interpretation of individual genes can be challenging due to data sparsity. Leveraging this time-series, we present strategies to model and visualize the expression of a given gene across differentiation of distinct lineages. We demonstrate an interactive web app to interrogate the expression of genes or gene sets during brain development, extract temporally correlated genes, and search active transcription factor regulatory modules. Finally, we profile the expression of core transcriptional programs of several pediatric brain tumor entities during development. Our analyses reveal genes with restricted expression patterns that elucidate tumor etiology. More broadly, these resources harness single cell data to enable exploration of neurodevelopmental gene programs with great relevance to pediatric brain tumor oncogenesis.

Developing a digital toolkit to enhance the communication of health claims: The Health Claims Unpacked project

AbstractConsumer understanding and acceptance of health claims are influenced by a variety of factors including personal knowledge and familiarity with the information, characteristics of the product (such as the ingredients) and the way the claim is presented (e.g. wording and visual aids such as symbols). The official wording of authorised EU health claims is set by the European Commission (EC), though there is some flexibility, in that food companies can change the wording to aid consumer understanding of the claim as long as the original meaning is retained and the modified version does not mislead. An EIT Food‐funded project with the consumer‐facing title Health Claims Unpacked, consists of an international consortium aiming to develop a digital toolkit to investigate consumers’ responses to health claims and aid their understanding thereof. Developed firstly in English (and now also in other EU languages such as German, French and Polish), the toolkit is informed by research in linguistics, design, nutrition science, technology and behavioural economics as well as focus groups with consumers and interviews with the food industry. The format of the toolkit is an interactive web app comprising activities which are used to gather data regarding consumers’ responses to different kinds of linguistic, semiotic (signs and symbols) and marketing cues related to health claims and how these cues impact their understanding of the relationship between nutrients/food components and health, trust in the message and willingness to pay for products. The data will be used to provide information on the preferences of different demographic groups on the wording of health claims and to provide recommendations for stakeholders aiming to enhance the communication of health claims on food and drink labels.

Feasibility of informing syndrome-level empiric antibiotic recommendations using publicly available antibiotic resistance datasets

Background: Antibiotics are often prescribed empirically to treat infection syndromes before causative bacteria and their susceptibility to antibiotics are identified. Guidelines on empiric antibiotic prescribing are key to effective treatment of infection syndromes, and need to be informed by likely bacterial aetiology and antibiotic resistance patterns. We aimed to create a clinically-relevant composite index of antibiotic resistance for common infection syndromes to inform recommendations at the national level. Methods: To create our index, we used open-access antimicrobial resistance (AMR) surveillance datasets, including the ECDC Surveillance Atlas, CDDEP ResistanceMap, WHO GLASS and the newly-available Pfizer ATLAS dataset. We integrated these with data on aetiology of common infection syndromes, existing empiric prescribing guidelines, and pricing and availability of antibiotics. Results: The ATLAS dataset covered many more bacterial species (287) and antibiotics (52) than other datasets (ranges = 8-11 and 16-32 respectively), but had a similar number of samples per country per year. Using these data, we were able to make empiric prescribing recommendations for bloodstream infection, pneumonia and cellulitis/skin abscess in up to 44 countries. There was insufficient data to make national-level recommendations for the other six syndromes investigated. Results are presented in an interactive web app, where users can visualise underlying resistance proportions to first-line empiric antibiotics for infection syndromes and countries of interest. Conclusions: We found that whilst the creation of a composite resistance index for empiric antibiotic therapy was technically feasible, the ATLAS dataset in its current form can only inform on a limited number of infection syndromes. Other open-access AMR surveillance datasets are largely limited to bloodstream infection specimens and cannot directly inform treatment of other syndromes. With improving availability of international AMR data and better understanding of infection aetiology, this approach may prove useful for informing empiric prescribing decisions in settings with limited local AMR surveillance data.

Feasibility of informing syndrome-level empiric antibiotic recommendations using publicly available antibiotic resistance datasets

Background: Antibiotics are often prescribed empirically to treat infection syndromes before causative bacteria and their susceptibility to antibiotics are identified. Guidelines on empiric antibiotic prescribing are key to effective treatment of infection syndromes, and need to be informed by likely bacterial aetiology and antibiotic resistance patterns. We aimed to create a clinically-relevant composite index of antibiotic resistance for common infection syndromes to inform recommendations at the national level. Methods: To create our index, we used open-access antimicrobial resistance (AMR) surveillance datasets, including the ECDC Surveillance Atlas, CDDEP ResistanceMap, WHO GLASS and the newly-available Pfizer ATLAS dataset. We integrated these with data on aetiology of common infection syndromes, existing empiric prescribing guidelines, and pricing and availability of antibiotics. Results: The ATLAS dataset covered many more bacterial species (287) and antibiotics (52) than other datasets (ranges = 8-11 and 16-32 respectively), but had a similar number of samples per country per year. Using these data, we were able to make empiric prescribing recommendations for bloodstream infection, pneumonia and cellulitis/skin abscess in up to 44 countries. There was insufficient data to make national-level recommendations for the other six syndromes investigated. Results are presented in an interactive web app, where users can visualise underlying resistance proportions to first-line empiric antibiotics for infection syndromes and countries of interest. Conclusions: We found that whilst the creation of a composite resistance index for empiric antibiotic therapy was technically feasible, the ATLAS dataset in its current form can only inform on a limited number of infection syndromes. Other open-access AMR surveillance datasets are largely limited to bloodstream infection specimens and cannot directly inform treatment of other syndromes. With improving availability of international AMR data and better understanding of infection aetiology, this approach may prove useful for informing empiric prescribing decisions in settings with limited local AMR surveillance data

Risk-of-bias VISualization (robvis): An R package and Shiny web app for visualizing risk-of-bias assessments.

Despite a major increase in the range and number of software offerings now available to help researchers produce evidence syntheses, there is currently no generic tool for producing figures to display and explore the risk-of-bias assessments that routinely take place as part of systematic review. However, tools such as the R programming environment and Shiny (an R package for building interactive web apps) have made it straightforward to produce new tools to help in producing evidence syntheses. We present a new tool, robvis (Risk-Of-Bias VISualization), available as an R package and web app, which facilitates rapid production of publication-quality risk-of-bias assessment figures. We present a timeline of the tool's development and its key functionality.

Feasibility of informing syndrome-level empiric antibiotic recommendations using publicly available antibiotic resistance datasets.

Background: Antibiotics are most often prescribed empirically, meaning that they are used to treat infection syndromes prior to identification of the causative bacteria and their susceptibility to antibiotics. The effectiveness of antibiotic therapies is now compromised by the emergence and spread of antibiotic-resistant bacteria. Guidelines on empiric antibiotic therapy are a key component of effective clinical care for infection syndromes, as treatment needs to be informed by knowledge of likely aetiology and bacterial resistance patterns. Methods: We used open-access antimicrobial resistance (AMR) surveillance datasets, including the newly available ATLAS dataset from Pfizer, to derive a composite index of antibiotic resistance for common infection syndromes. Results: We developed a framework that integrated data on antibiotic prescribing guidelines, aetiology of infections, access to and cost of antibiotics, with antibiotic susceptibilities from global AMR surveillance datasets to create an empirical prescribing index. The results are presented in an interactive web app to allow users to visualise underlying resistance rates to first-line empiric antibiotics for their infection syndromes and countries of interest. Conclusions: We found that whilst an index for empiric antibiotic therapy based on resistance data can technically be created, the ATLAS dataset in its current form can only inform on a limited number of infection syndromes. Other open-access AMR surveillance datasets (ECDC Surveillance Atlas, CDDEP ResistanceMap and WHO GLASS datasets) are largely limited to bacteraemia-derived specimens and cannot directly inform treatment of other infection syndromes. With improving data availability on international rates of AMR and better understanding of infection aetiology, our approach may prove useful for informing empiric prescribing decisions in settings with limited local AMR surveillance data. Syndrome-level resistance could be a more clinically relevant measure of resistance to inform on the appropriateness of empiric antibiotic therapies at the country-level.

Sharing Collections of Historical Maps Online

Abstract. Today’s expectations for historical maps are widely different from what they were ten or even five years ago. Today, maps are expected to be freely available online and viewed in easy-to-use, interactive web apps. Collections of maps, such as maps in atlases or in a map series, are no different. While there have been great strides in developing methods for scanning and sharing smaller collections of maps online, less effort has been directed toward sharing larger and more varied collections of historical maps. Even less energy has been focused on the development of common workflows and off-the-shelf resources that could be used by many who want to digitize and share their own collections.These deficiencies are complicated by the fact that different types of map collections require different solutions. There is wide variety in the types of map collections, including those with many maps of the same extent and map scale but varying themes (as in a thematic atlas), those with maps of varying scales and extents but a single theme (as in a topographic map series), and those with combinations of both. The workflows to convert the maps in these collections to digital format that can then be shred online must assure expediency and accuracy in the processing of the maps, despite their variations. For example, the workflow in figure 1 could be used for collections of maps with varying scales, extents, and themes.Another requirement for sharing historical map collections online is the ability to add new images or replace faulty images in the collection. The workflow in figure 1 allows for this by first updating the metadata, then adding new images to the mosaic dataset, and finally updating the image service. The view of the collection in the web app is updated automatically so no edits need be made to the app code.The web apps used to display the map collections must also vary in order to provide an optimal experience for viewers to interact with the collection. The app in figure 2 was developed to allow viewers to explore a very large set (over 186,000) of historical topographic maps of the United States. This collection includes maps of varying scales, dates, and sizes. The app allows viewers to find maps, compare maps using transparency sliders, download maps images that can be used in software applications such as ArcGIS, and share the current map view with others via social media or a hyperlink.Variations of this app could be used for other types of collections. We have explored modifications to support viewing more than 500 maps compiled by the U.S. Department of Defence during the Vietnam conflict (figure 3). The Vietnam map collection requires a slightly different app solution because there is no significant variation in the dates of the maps, although there are two map scales (1:50,000 and 1:250,000). Additionally, the marginalia on the maps contains information critical to reading the maps, such as the legend and a glossary of Vietnamese names.Our work to date provides solutions to sharing large collections of maps in a series or from atlases, even if the maps have different scales, dates, sizes, and themes. These collections can be shared via web apps that provide viewers with useful interactivity and functionality. We have also developed the means to update collections on a regular basis – for example, the USGS topographic map collection is now being updated quarterly. To support developers of these collections, we provide documentation on the workflow, share example datasets to allow them to test the methodology, and allow access to the web app which can be configured to conform to users’ requirements. In this presentation, we detail the workflows and resources we have developed, and we demonstrate solutions for map collections of different types.

An interactive web app for retrieval, visualization, and analysis of hydrologic and meteorological time series data

As wireless sensor networks become more prevalent tools for analyzing watershed dynamics, the integration and management of the myriad of available data streams presents a unique challenge. Web-based platforms provide tools to retrieve and visualize hydrologic and meteorologic data from various sources while addressing syntactic and semantic differences in data formats. However, current platforms are limited by a lack of data analysis tools. The Stream Hydrology And Rainfall Knowledge System (SHARKS) app was developed to expand upon existing platforms by providing a suite of exploratory data analysis features including the ability to calculate the total precipitation depth recorded for any period, interpolate the average annual recurrence interval for precipitation events, perform hydrograph separations, and calculate the volume of runoff for any period. A case study provides a conceptual description of the types of rapid analysis that can be performed using the SHARKS app.

Revelation of the metabolic pathway of hederacoside C using an innovative data analysis strategy for dynamic multiclass biotransformation experiments

Although some herbal remedies have been used for decades, little is known about the active compounds and the mechanism of action. Many natural products, such as glycosides, can be considered as prodrugs, which become active after biotransformation.To optimize the workflow of in vitro biotransformation followed by automated data analysis, hederacoside C was used as a model compound for saponins. Hederacoside C was subjected to gastrointestinal enzymes and fecal microflora. Samples were analyzed with UHPLC-PDA-HRMS before, during and after in vitro biotransformation, which allowed the monitoring of the relative abundances of the compound and its metabolites.The data-analysis workflow was optimized to render as much information as possible from the longitudinal LCMS data. XCMS was used to convert the raw data into features via peak-picking, followed by grouping, and EDGE was used for the extraction of significant differential profiles. To evaluate if the workflow was suitable for dynamic multiclass metabolomics data, an interactive Shiny web app was developed in R to rate the quality of the resulting features. These ratings were used to train a random forest model for predicting experts response. A performance analysis revealed that the random forest model was capable of correctly predicting the reviewers response in most cases (AUC 0.926 with 10 fold cross validation).The automated data analysis workflow was used for unbiased screening for metabolites and revealed the biotransformation of hederacoside C. As expected, a decrease in relative abundance of hederacoside C was observed over time. Additionally, the relative abundance of metabolites increased, illustrating the biotransformation of hederacoside C, especially in the colon phase, where microbial fermentation takes place. Stepwise progressive elimination of sugar moieties was the major metabolic pathway.

Interactive Web Apps for Visualizations in Mathematics and Engineering

AbstractDepicting numbers in a graphical way has always been crucial to understand and explain the physical phenomena behind. Interactive graphical applications bring the understanding to a higher level, where the users get actively engaged. Moreover, interactive visualization of data, equations etc.… help to motivate students and strengthen the sharing of knowledge. At the Technical University of Munich, the Chair of Structural Mechanics and the Research Group Algebra developed interactive applications in the fields of mechanics and higher mathematics. Students can access the applications online at any time, without installation or special requirements, and, thus, can actively explore the content with a very low entry barrier. For example, they can experiment with a spring‐mass‐damper system and let it enter the resonance by changing the frequency of the excitation; or they can search for local maxima and minima of a function with side conditions with the Lagrange multipliers in an interactive and graphical way. The interactive process enhances the understanding, even if the user does not interact with a strategy but just uses his intuition. These applications can be also used to explain complex concepts to the general public. The source code is publicly available on https://github.com/ChairOfStructuralMechanicsTUM/Mechanics_Apps .

GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, miRNA and mRNA data in GDC.

The large-scale multidimensional omics data in the Genomic Data Commons (GDC) provides opportunities to investigate the crosstalk among different RNA species and their regulatory mechanisms in cancers. Easy-to-use bioinformatics pipelines are needed to facilitate such studies. We have developed a user-friendly R/Bioconductor package, named GDCRNATools, for downloading, organizing and analyzing RNA data in GDC with an emphasis on deciphering the lncRNA-mRNA related competing endogenous RNAs regulatory network in cancers. Many widely used bioinformatics tools and databases are utilized in our package. Users can easily pack preferred downstream analysis pipelines or integrate their own pipelines into the workflow. Interactive shiny web apps built in GDCRNATools greatly improve visualization of results from the analysis. GDCRNATools is an R/Bioconductor package that is freely available at Bioconductor (http://bioconductor.org/packages/devel/bioc/html/GDCRNATools.html). Detailed instructions, manual and example code are also available in Github (https://github.com/Jialab-UCR/GDCRNATools).

C-State: an interactive web app for simultaneous multi-gene visualization and comparative epigenetic pattern search

BackgroundComparative epigenomic analysis across multiple genes presents a bottleneck for bench biologists working with NGS data. Despite the development of standardized peak analysis algorithms, the identification of novel epigenetic patterns and their visualization across gene subsets remains a challenge.ResultsWe developed a fast and interactive web app, C-State (Chromatin-State), to query and plot chromatin landscapes across multiple loci and cell types. C-State has an interactive, JavaScript-based graphical user interface and runs locally in modern web browsers that are pre-installed on all computers, thus eliminating the need for cumbersome data transfer, pre-processing and prior programming knowledge.ConclusionsC-State is unique in its ability to extract and analyze multi-gene epigenetic information. It allows for powerful GUI-based pattern searching and visualization. We include a case study to demonstrate its potential for identifying user-defined epigenetic trends in context of gene expression profiles.

Interactive web site and APP for early magnetic resonance education

Introduction Teaching and understanding basic Magnetic Resonance (MR) is a challenge. This is clear from the educational literature that often repeats misinterpretations of quantum mechanics reminiscent of its earliest formulations (see www.drcmr.dk/MR that also links to the developed software). Modern quantum formulations of MR are much closer to classical descriptions than to typical quantum inspired myths frequent in literature. This opens for intuitive educational computer simulation using modern web technologies offering excellent interactive possibilities for experimentation. Purpose An educational web page and a corresponding free Android app, CompassMR, were developed for teaching of basic MR. They simulate Compass Magnetic Resonance that is easy to understand by all, and serves as an excellent starting point for introducing precession, nutation, FIDs and spectra, that are also simulated. Materials and methods The web page was developed in JavaScript/HTML5 which is increasingly supported by modern browsers. A corresponding Android app was made using the PhoneGap web service that also offers iPhone and Windows phone support (but due to cost, currently no app is offered for these). Results The web page works well, e.g. in recent Chrome and Firefox browsers that supports HTML5. Soon after release, the corresponding app was running on more than 60 devices worldwide, and got top ratings. Conclusion Modern web technologies are suited for designing much needed educational simulation tools for MR. Disclosure Nothing to disclose.

E-IBA: Development of new technology to reduce adolescent alcohol use

Event Abstract Back to Event e-IBA: Development of new technology to reduce adolescent alcohol use Robert Patton1* 1 University of Surrey, School of Psychology, UK Background: The SIPS Jr. Research programme (1) is funded by the NIHR to screen for alcohol use and deliver effective brief interventions to 14-17yr olds presenting to Emergency departments. Ongoing Patient & Public Involvement (PPI) work has identified a need for paperless screening / baseline data collection and an enthusiasm for interventions based on mobile technologies. Two recent systematic reviews by the research team (Patton et al, 2014; Donovan et al, 2014) support the use of such technology to identify and intervene with adolescent drinkers (2, 3). Description: Together with a specialist software development company we have developed a bespoke data collection / participant management tool for the iPad which is currently being used as part of an ongoing RCT (4), with data being collected from 10 Emergency Departments in England (London, Hull and the North East). This method of data collection has already demonstrated considerable cost savings and is popular with participants and research staff (over 6000 participants recruited to date in the current trial and earlier prevalence study). We have developed an interactive WebApp to reduce alcohol consumption and related harm among adolescents. This works in conjunction with the data collection tool to provide personalised feedback to users, to record ongoing consumption and to provide access to information and advice presented in an engaging and age appropriate format. Conclusion: Both the Research Tool and e-intervention have been developed and refined by the research team working collaboratively with the Web Developer, PPI groups and local youth organisations. The use of this technology is effective and cost effective.

Synergy Maps: exploring compound combinations using network-based visualization.

BackgroundThe phenomenon of super-additivity of biological response to compounds applied jointly, termed synergy, has the potential to provide many therapeutic benefits. Therefore, high throughput screening of compound combinations has recently received a great deal of attention. Large compound libraries and the feasibility of all-pairs screening can easily generate large, information-rich datasets. Previously, these datasets have been visualized using either a heat-map or a network approach—however these visualizations only partially represent the information encoded in the dataset.ResultsA new visualization technique for pairwise combination screening data, termed “Synergy Maps”, is presented. In a Synergy Map, information about the synergistic interactions of compounds is integrated with information about their properties (chemical structure, physicochemical properties, bioactivity profiles) to produce a single visualization. As a result the relationships between compound and combination properties may be investigated simultaneously, and thus may afford insight into the synergy observed in the screen. An interactive web app implementation, available at http://richlewis42.github.io/synergy-maps, has been developed for public use, which may find use in navigating and filtering larger scale combination datasets. This tool is applied to a recent all-pairs dataset of anti-malarials, tested against Plasmodium falciparum, and a preliminary analysis is given as an example, illustrating the disproportionate synergism of histone deacetylase inhibitors previously described in literature, as well as suggesting new hypotheses for future investigation.ConclusionsSynergy Maps improve the state of the art in compound combination visualization, by simultaneously representing individual compound properties and their interactions. The web-based tool allows straightforward exploration of combination data, and easier identification of correlations between compound properties and interactions.

Probability Distributome: A Web Computational Infrastructure for Exploring the Properties, Interrelations, and Applications of Probability Distributions.

Probability distributions are useful for modeling, simulation, analysis, and inference on varieties of natural processes and physical phenomena. There are uncountably many probability distributions. However, a few dozen families of distributions are commonly defined and are frequently used in practice for problem solving, experimental applications, and theoretical studies. In this paper, we present a new computational and graphical infrastructure, the Distributome, which facilitates the discovery, exploration and application of diverse spectra of probability distributions. The extensible Distributome infrastructure provides interfaces for (human and machine) traversal, search, and navigation of all common probability distributions. It also enables distribution modeling, applications, investigation of inter-distribution relations, as well as their analytical representations and computational utilization. The entire Distributome framework is designed and implemented as an open-source, community-built, and Internet-accessible infrastructure. It is portable, extensible and compatible with HTML5 and Web2.0 standards (http://Distributome.org). We demonstrate two types of applications of the probability Distributome resources: computational research and science education. The Distributome tools may be employed to address five complementary computational modeling applications (simulation, data-analysis and inference, model-fitting, examination of the analytical, mathematical and computational properties of specific probability distributions, and exploration of the inter-distributional relations). Many high school and college science, technology, engineering and mathematics (STEM) courses may be enriched by the use of modern pedagogical approaches and technology-enhanced methods. The Distributome resources provide enhancements for blended STEM education by improving student motivation, augmenting the classical curriculum with interactive webapps, and overhauling the learning assessment protocols.

LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures.

For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology. The LCB application is available at http://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of the http://lincscloud.org and http://lincs.hms.harvard.edu websites.