ImmGen: Gene Expression and Regulation in Immune Cells

免疫基因组计划(ImmGen)是由15个免疫学和 在标准化条件下对基因进行彻底剖析的计算生物学实验室 在小鼠免疫系统中的表达及其调控。我们还调查了它们是如何应对 细胞因子和免疫学挑战，免疫基因表达如何与染色质结构相关，以及 使用尖端的计算算法来推断监管联系。ImmGen数据是公开的 在专用的Web和智能手机支持上提供，使用交互式图形显示来实现结果 对用户来说很直观。现在免疫学研究界经常使用这些药物。建议数 继续将利用ImmGen小组的集体专业知识来进一步发展这一重要的公众 资源。(1)扩展和深化纲要，应用单细胞RNAseq来非常广泛地定义 免疫系统中发挥作用的细胞类型的铸型，它们的分化轨迹，以及它们对 这项工作将得到对细胞类型的更深层次的批量剖析的补充 已确认身份。我们将扩大对细胞因子签名的分析，继续绘制非编码RNA(CircRNAs)的图表， 并说服来自社区的协调样本贡献的“开放源码”计划。(2) 以14种细胞类型为核心的免疫基因组调控网络的机制剖析 它们代表了所有主要的免疫谱系。我们将生成高分辨率的转录地图 超深ATACseq的足迹因子；hic染色体构象捕捉绘制3D结构图； 应用ChIPseq定义增强子和超级增强子、染色质修饰结构域和结构 锚；使用机器学习方法将这些互补数据集成到全面的 从精细的TF足迹到与拓扑相关的域的监管计划；验证这些 通过与KOMP项目合作分析核心集转录本和染色质进行推断 具有转录因子突变的小鼠的配置决定了功能相关的方面 免疫细胞活性。(三)：公开展示。ImmGen数据已成为免疫学中广泛使用的资源 研究，这将得到维护和管理。实现可扩展性、成本效益，并实现简便性 计算方面，我们将开发和部署符合NIH标准的基于云的数据存储和访问解决方案 《公共指南》。我们将添加到现有的数据库浏览器中，以允许用户查询 扩展数据(差异表达、调控网络、染色质状态和结构、细胞因子 签名)，增加了与其他数据源的连接。我们将继续开发广受欢迎的移动应用程序， 并探索这种媒介的特殊用途。ImmGen对中国的免疫学研究产生了积极影响 当前的十年，提供了详细和严格的免疫群体基因组解析，由 对他们的免疫学有很强的了解。这就是我们将继续和扩大的。

The Immunological Genome Project (ImmGen) is a collaborative group of 15 Immunology and Computational Biology laboratories who perform, under standardized conditions, a thorough dissection of gene expression and its regulation in the mouse immune system. We also investigated how these respond to cytokines and immunologic challenges, how immune gene expression relates to chromatin configuration, and used cutting-edge computational algorithms to deduce regulatory connections. ImmGen data are publicly available on dedicated web and smartphone supports, using interactive graphic displays that make the results intuitive to users. These are now frequently used by the Immunology research community. The proposed continuation will harness the collective expertise of the ImmGen group to further develop this important public resource. (1) Expand and deepen the compendium, applying single-cell RNAseq to define very broadly the cast of cell-types at play in the immune system, their trajectories of differentiation, and their adaptation to specific organismal locations This effort will be complemented by deeper bulk profiling of cell-types thus identified. We will expand the analysis of cytokine signatures, continue to chart non-coding RNA (circRNAs), and persue the “OpenSource” program of coordinated sample contributions from the community. (2) Mechanistic Dissection of Immunogenomic Regulatory Networks focusing on a Core Set of 14 cell-types that represent all the major immunological lineages. We will generate high-resolution maps of transcription factor footprints by ultra-deep ATACseq; map 3D architectures by HiC chromosome conformation capture; apply ChIPseq to define enhancers and super-enhancers, chromatin modification domains and structural anchors; use machine learning approaches to integrate these complementary data into a comprehensive regulatory plan which spans from fine TF footprints to topologically associated domaiins; validate these inferences by collaborating with the KOMP project to analyze Core Set transcriptomes and chromatin configuration in mice with mutations in transcription factors that determine functionally relevant facets of immunocyte activity. (3): Public Display. ImmGen data have become a widely used resource in Immunology research, which will be maintained and curated. For scalability, cost-effectiveness, and to enable facile computation, we will develop and deploy cloud-based data storage and access solutions, compliant with NIH Commons guidelines. We will add to the existing databrowsers to allow users to query different facets of the expanding data (differential expression, regulatory network, chromatin states and architecture, cytokine signatures), increasing connectivity to other data sources. We will continue developing the popular mobile app, and explore the particular uses of that medium. ImmGen has positively impacted immunological research in the current decade, providing detailed and rigorous genomic resolution of immune populations, informed by a strong understanding of their immunology. This is what we will continue and amplify.