In recent years, developments in deep-sequencing-based genomic assays, single-cell technologies, and machine learning methods have greatly improved our understanding of epigenetics and gene regulation, which in turn has advanced our understanding of cell type differentiation and human disease. Recent studies have begun to apply these techniques to understand how non-coding regulatory elements, such as enhancers and insulators, contribute to infectious, autoinflammatory, and autoimmune diseases. Studies of the immune system, especially hematopoiesis and genome-wide associations of autoimmune diseases and blood-cell traits, have produced promising results, discovering unique enhancer landscapes of immune cell types and identifying genetic variants with clinical relevance for prognosis and treatment of immune diseases. However, many existing analyses are gene-centric and do not take advantage of regulatory information in non-coding regions, leaving these data underutilized. An atlas of regulatory elements and their impact on molecular and clinical phenotypes will greatly expand our understanding of the biology of immune-related diseases and has the potential to expand precision medicine for predicting clinical outcomes and treatment pathways. As members of the ENCODE project, we have developed the Registry of candidate cis-regulatory elements (cCREs), a collection of roughly two million candidate enhancers, promoters, and insulators in the human genome with activity profiles in more than 1500 human cell types, presented by a powerful web-based knowledgebase SCREEN (screen.encodeproject.org). The Registry and SCREEN are powerful resources for the study of non-coding elements, but they do not include any disease-specific data and are not designed to take advantage of special features of immune data or immune-related disorders. Here, we propose to draw on the wealth of genomic and epigenetic data from immune samples rapidly accumulating in the literature and public repositories to build a data integration and visualization platform, iSCREEN, for supporting a wide range of immune-mediated disease research. This project has three aims: Aim 1. Develop a web-based data integration platform iSCREEN for immune cCREs and their cell-type-specific epigenetic signals with the goal of predicting the impact of human genetic variants on immune traits; Aim 2. Build analysis and visualization tools to map single-cell data onto the space of well-annotated immune cell types and integrate their annotations; and Aim 3. Perform community outreach, expand our user base, and develop new computational tools and visualizations to meet user needs.