Resources
REALGAR
Reducing Associations by Linking Genes And omics Results facilitates the translation of raw data into biological knowledge by integrating gene-, exposure-, and disease-specific results, thereby providing insights helpful for prioritizing and designing functional validation studies for genetic associations. Results of over 75 datasets are currently available, with output that includes forest plots of gene fold-changes of differential expression and genome tracks with ChIP-Seq and GWAS results.
PREVALENCEMAPS
This R Shiny web application that is based on Behavioral Risk Factor Surveillance System (BRFSS) data relates various health outcomes to demographic risk factors at the level of Metropolitan and Micropolitan Statistical Areas. In addition, it compares association results obtained with individual income, median aggregate income, and Area Deprivation Index (ADI)—a validated score of U.S. socioeconomic deprivation. Analyses take into account survey weights from the BRFSS source data and these weighted values form the basis of results visualized in maps and various plots.
PARGASITE
Pollution-Associated Risk Geospatial Analysis SITE, an online web-application and R package, can be used to estimate levels of pollutants in the U.S. at user-defined geographic locations and time ranges. While Environmental Protection Agency (EPA) data is readily available, estimating pollution exposure at a given latitude-longitude location remains computationally intensive. By providing interpolated pollution measures, PARGASITE facilitates the study of associations between exposures and health outcomes.
SAPPHIRINE
Sensor-based Analysis of Pollution in the Philadelphia Region with Information on Neighborhoods and the Environment is a web app with integrated environmental data corresponding to the greater Philadelphia area. Data sources include low-cost pollution sensors, criteria pollutant measures from the EPA, traffic from the PA department of transportation, and area deprivation index computed from American Community Survey variables. high degrees of geographic and temporal specificity. This app supports research efforts of the Center of Excellence in Environmental Toxicology (CEET).