A Comprehensive Genomic Community Resource of Transcriptional Regulation

转录调控的综合基因组群落资源

• 批准号: 10625529
• 负责人: Anshul Kundaje
• 金额: $ 80.94万
• 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: 2022-06-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10625529
• 关键词: ATAC-seq; Algorithms; Atlases; Automobile Driving; Base Pairing; Benchmarking; Binding; CRISPR/Cas technology; Catalogs; Cells; ChIP-seq; Chromatin; Code; Collaborations; Collection; Communities; Community Outreach; Computer Models; DNA; DNA Sequence; Data; Data Analyses; Data Set; Development; Disease; Education and Outreach; Educational workshop; Elements; Epigenetic Process; Exons; Functional disorder; Future; Genes; Genomics; Histones; Human; Human BioMolecular Atlas Program; Human Genome; Human Genome Project; Human body; Individual; International; Interruption; Maps; Mediating; Methods; Modeling; Nematoda; Online Systems; Organism; Pattern; Physiology; Process; Quality Control; Registries; Regulatory Element; Research; Research Personnel; Resolution; Resources; Role; Scheme; Signal Transduction; Specific qualifier value; Techniques; Technology; Testing; Time; Tissues; Training; Trans-Omics for Precision Medicine; Transcriptional Regulation; Untranslated RNA; Variant; Visualization; Work; base; cell type; community building; community setting; data analysis pipeline; data repository; deep learning; deep learning model; deep sequencing; design; epigenome; epigenomics; experimental study; follow-up; genome wide association study; in silico; in vivo Model; machine learning model; model development; novel; online resource; outreach; predictive modeling; public repository; repository; sequence learning; syntax; tool; trait; transcription factor

Project Summary/Abstract The Human Genome Project (HGP) completed the first draft human genome sequence two decades ago. The HGP revealed that human complexity arises from only approximately 20,000 coding genes, roughly the same number as much simpler organisms such as nematodes. Intricate patterns of transcriptional regulation mediated by non-coding regulatory elements specify the myriad cell types and states required for human complexity. Genome-wide association studies have subsequently identified thousands of disease-associated variants, many of which interrupt the function of these non-coding elements to disrupt transcriptional regulation. Thus, in order to better understand human physiology and pathophysiology, comprehensive atlases of regulatory elements are essential. Many previous efforts, including the International Human Epigenome Consortium (IHEC), the FANTOM Consortium, the Roadmap Epigenomics Project, and the ENCODE Project, have aimed to build comprehensive collections of regulatory elements, as well as computational models to better predict regulatory activity and understand the sequence features underlying regulatory function. ENCODE (2003-2022) is a large- scale consortium effort which aims to annotate every functional non-coding element of the human genome; during our work on the project, we built a Registry of approximately 1 million human candidate cis-regulatory elements (cCREs). We further developed deep-learning approaches which model the transcription factor motif syntax that underlies element function at base-pair resolution and built two web-based resources, SCREEN and Factorbook, to make our results accessible to the scientific community. Here, we propose to extend this framework to build the Community Resource for Transcriptional Regulation (CRTR), a comprehensive atlas of non-coding regulatory elements and machine-learning models which will encompass community and consortium deep-sequencing data, both bulk and single cell, across a broad array of cell types and states. Our project has five aims. First, we aim to curate community and consortium data for inclusion in CRTR and perform uniform processing and quality control. Second, we aim to train deep-learning sequence models on bulk epigenetic datasets to identify transcription factor motif syntax driving regulatory element activity in distinct tissues and cell types. Third, we aim to train sequence models on single cell datasets to identify transcription factor motif syntax driving transcriptional regulation in high-resolution cell states and during cell state transitions. Fourth, we aim to use the aforementioned results to build comprehensive benchmark datasets and machine-learning model collections, which will aid future analysts in designing new models to predict regulatory readouts. Fifth, we aim to build a state-of-the-art web-based user interface to enable users to perform integrative analyses and in silico experimentation with CRTR, and hold workshops and other outreach to maximize the impact of the resource and its accessibility to the broader scientific community.

项目摘要/摘要 人类基因组计划(HGP)在20年前完成了第一个人类基因组序列草案。这个 HGP揭示，人类的复杂性仅由大约2万个编码基因产生，大致相同 像线虫这样简单得多的生物的数量。错综复杂的转录调控模式 通过非编码，调节元件指定了人类复杂性所需的无数细胞类型和状态。 全基因组关联研究随后确定了数千种与疾病相关的变异，其中许多 它们干扰这些非编码元件的功能，从而扰乱转录调控。因此，按照顺序 为了更好地了解人体生理学和病理生理学，调控元素的综合图集包括 必不可少的。许多以前的努力，包括国际人类表观基因组联合会(IHEC)， 幻影联盟、路线图表观基因组学项目和ENCODE项目的目标是建立 全面的监管要素集合，以及更好地预测监管的计算模型 活动，并了解潜在的调控功能的序列特征。Encode(2003-2022)是一个大型- Scale联盟的努力，旨在注释人类基因组的每个功能非编码元件； 在我们对该项目的工作中，我们建立了一个约有100万名人类候选人顺式监管的注册表 元素(CCRE)。我们进一步开发了对转录因子基序建模的深度学习方法 以碱基对分辨率作为元素函数基础的语法，并构建了两个基于Web的资源：Screen和 Factorbook，让科学界了解我们的成果。在这里，我们建议延长这一期限 建立转录调控社区资源的框架，这是一本全面的地图集 非编码监管元素和机器学习模式，将涵盖社区和联盟 对大量细胞类型和状态的大量和单个细胞进行深度测序。我们的项目已经 五个目标。首先，我们的目标是整理社区和财团数据，以纳入CRTR并执行统一 加工和质量控制。第二，我们的目标是训练大量表观遗传的深度学习序列模型 识别在不同组织和细胞中驱动调节元件活性的转录因子基序语法的数据集 类型。第三，我们的目标是在单细胞数据集上训练序列模型来识别转录因子基序语法 在高分辨率细胞状态和细胞状态转换期间驱动转录调控。第四，我们的目标是 使用上述结果构建全面的基准数据集和机器学习模型 这将帮助未来的分析师设计新的模型来预测监管读数。第五，我们的目标是 构建最先进的基于Web的用户界面，使用户能够在Silico中执行综合分析 试验社区研究与培训项目，举办讲习班和其他外联活动，以最大限度地发挥资源和资源的影响 其对更广泛的科学界的可及性。