iSCREEN: An Integrative Data and Annotation Platform of Gene Regulation for Immune-mediated Disease Research

iSCREEN：免疫介导疾病研究基因调控的综合数据和注释平台

• 批准号: 10576561
• 负责人: Zhiping Weng
• 金额: $ 49.16万
• 依托单位: UNIV OF MASSACHUSETTS MED SCH WORCESTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-12 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10576561
• 关键词: 2019-nCoV; 3-Dimensional; ATAC-seq; Architecture; Atlases; Autoimmune Diseases; Autoimmune Responses; Biological Assay; Biology; Blood Cells; Cells; Cellular Assay; Chromatin; Clinical; Collection; Communities; Community Outreach; DNA Methylation; Data; Data Analyses; Databases; Development; Disease; Elements; Enhancers; Epigenetic Process; Gene Expression Regulation; Genes; Genetic; Genome; Genomics; Goals; Hematopoiesis; Heritability; Human; Human Genetics; Human Genome; Immune; Immune System Diseases; Immune response; Immune system; Immunize; Immunobiology; Immunologics; Immunologist; Individual; Infection; Letters; Literature; Machine Learning; Malignant Neoplasms; Maps; Mediating; Metadata; Modeling; Online Systems; Pathway interactions; Patient Care; Persons; Prognosis; Recommendation; Registries; Regulatory Element; Research; Research Personnel; Research Project Grants; Resolution; Resources; Sampling; Signal Transduction; Symptoms; Techniques; Technology; Time; Tissues; Transcriptional Regulation; Tumor-infiltrating immune cells; Untranslated RNA; Variant; Virus; Visual; Visualization; Visualization software; autoinflammatory; base; cell type; clinical phenotype; clinically relevant; community engagement; computer framework; computerized tools; computing resources; data integration; data repository; data visualization; deep sequencing; design; epigenetic regulation; epigenome; experience; gene function; genetic makeup; genetic variant; genome wide association study; genomic tools; histone modification; human disease; immunoregulation; improved; improved outcome; informatics tool; insight; knowledgebase; machine learning method; member; molecular phenotype; novel; precision medicine; predict clinical outcome; predictive modeling; promoter; public repository; response; single cell technology; single-cell RNA sequencing; syntax; tool; trait; transcription factor; transcriptome; transcriptome sequencing

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.

近年来，基于深度测序的基因组测定、单细胞技术和 机器学习方法大大提高了我们对表观遗传学和基因调控的理解， 反过来又促进了我们对细胞类型分化和人类疾病的理解。最近的研究 开始应用这些技术来了解非编码调控元件，如增强子和 绝缘体，有助于感染性，自身炎症和自身免疫性疾病。免疫研究 系统，特别是造血和自身免疫性疾病与血细胞 性状，已经产生了有希望的结果，发现了免疫细胞类型的独特增强子景观， 鉴定与免疫疾病的预后和治疗具有临床相关性的遗传变体。然而，在这方面， 许多现有的分析是以基因为中心的，并且没有利用非编码区中的调控信息。 这些数据没有得到充分利用。调控元件图谱及其对分子和 临床表型将大大扩展我们对免疫相关疾病生物学的理解， 扩大精准医学的潜力，以预测临床结果和治疗途径。 作为ENCODE项目的成员，我们已经开发了候选顺式调控元件的注册表 cCREs是人类基因组中大约200万个候选增强子、启动子和绝缘子的集合。 基因组与活动概况在1500多个人类细胞类型，提出了一个强大的网络为基础的 知识库屏幕（screen.encodeproject.org）。Registry和SCREEN是强大的资源， 非编码元件的研究，但它们不包括任何疾病特异性数据，也不旨在 利用免疫数据或免疫相关疾病的特殊特征。在此，我们建议借鉴 来自免疫样品的基因组和表观遗传数据的财富在文献中迅速积累， 公共存储库，用于构建数据集成和可视化平台iSCREEN，以支持广泛的 免疫介导的疾病研究。该项目有三个目标：目标1。开发基于网络的数据 免疫cCREs及其细胞类型特异性表观遗传信号的整合平台iSCREEN，目标是 预测人类遗传变异对免疫性状的影响;目标2.构建分析和可视化工具 将单细胞数据映射到注释良好的免疫细胞类型的空间上并整合它们的注释;以及 目标3.执行社区外展，扩大我们的用户群，并开发新的计算工具， 可视化以满足用户需求。