Mechanisms of Transcriptional Control Revealed by Nascent Transcript Sequencing

新生转录本测序揭示的转录控制机制

• 批准号: 10171878
• 负责人: Lee Stirling Churchman
• 金额: $ 51.35万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10171878
• 关键词: Binding Sites; Bioinformatics; Cell Differentiation process; Cells; ChIP-seq; Chromatin; Code; Communities; Computational algorithm; Consumption; DNA-Directed RNA Polymerase; DNase I hypersensitive sites sequencing; Data; Data Set; Detection; Diagnosis; Elements; Embryonic Development; Enhancers; Erythropoiesis; Eukaryotic Cell; Event; Gene Expression Regulation; Genes; Genetic Transcription; Goals; Hand; Human; Investigation; KDM1A gene; Kinetics; Knowledge; Label; Maps; Measures; Mediating; Messenger RNA; Metabolic; Methods; Mission; Molecular; Neural Network Simulation; Nucleic Acid Regulatory Sequences; Nucleotides; Output; Phenotype; Play; Polymerase; Positioning Attribute; Process; Protocols documentation; Public Health; RNA; RNA Polymerase II; RNA purification; Regulator Genes; Reporting; Research; Resolution; Ribonucleases; Ribosomal RNA; Role; Sampling; Science; Series; Small Nuclear RNA; Small Nucleolar RNA; Speed; System; Testing; Time; Transcript; Transcriptional Regulation; United States National Institutes of Health; Untranslated RNA; cell type; computerized tools; deep learning; deep neural network; density; flexibility; functional genomics; genome-wide; genomic data; hematopoietic differentiation; human disease; improved; innovation; network models; novel; promoter; transcription factor; transcriptome; virtual

Large consortium efforts have collected hundreds of genome-wide datasets that have delineated myriad regulatory regions, transcription factor binding sites and large numbers of coding and non-coding transcripts. Even with this massive amount of data, it remains a significant challenge to determine how the mapped elements function together in regulatory networks. This is due in large part to our inability to accurately and quantitatively detect all forms of nascent transcription, the instantaneous output of transcriptional regulation. Moreover, our understanding of global gene regulation is restricted by a lack of computational tools that seamlessly integrate genome-wide datasets. The overall goal of this proposal is to maximize the impact of nascent transcriptome studies and enable facile integration with other functional genomic data. My group developed native elongating transcript sequencing (NET-seq), that enables the strand-specific nucleotide-resolution mapping of RNA polymerase density, highlighting all transcriptional activity regardless of transcript half-lives and revealing precise positions of Pol II pausing where regulatory control is applied. Here, we will develop a new version of NET-seq – NET-seq 2.0 – that enables the routine, scalable and flexible application to diverse human cell types (or any eukaryotic system). Moreover, we will increase the potential of NET-seq analysis by developing two innovative bioinformatics strategies to seamlessly integrate NET-seq data with other genome-wide datasets that will have applications beyond NET-seq studies. To demonstrate the broad utility of our integrated approach, we will study regulatory networks and cell differentiation for which instantaneous nascent transcriptional analysis will be highly impactful. In Aim 1, our goal is to make NET-seq easier, cheaper, and more flexible. Our improvements will reduce background and increase usable reads, dramatically reduce cell input requirements (100-1000-fold), enable dense, region-specific RNA transcription analyses, and enable quantitative comparisons between samples and conditions. In Aim 2, we will determine transcription kinetics through integrating NET-seq with metabolic RNA labeling (TT-seq) data which report local synthesis rates. This integrative approach yields a rich transcriptional phenotype that we will use to develop gene regulatory network models. In Aim 3, we will create new computational algorithms that circumvent the need to determine each molecular event separately, and instead infer the status of unmapped events using information-rich datasets, such as NET-seq. We will use integrative deep neural networks (`deep-learning') that use available genome-wide datasets to predict unavailable datasets from data already on hand. We will apply this approach to study erythropoiesis using a well- defined primary human hematopoietic differentiation system by a time series NET-seq and DNase-seq analysis. These data will inform deep neural network models to predict ChIP-seq data for myriad transcription factors and chromatin marks to investigate key regulatory events without additional expense.

大型财团的努力已经收集了数百个全基因组数据集，这些数据集描绘了无数 调控区、转录因子结合位点以及大量编码和非编码转录物。 即使有如此大量的数据，确定映射的元素如何 在监管网络中共同发挥作用。这在很大程度上是由于我们无法准确和定量地 检测所有形式的新生转录，转录调节的瞬时输出。而且我们 对全局基因调控的理解受到缺乏无缝整合的计算工具的限制， 全基因组数据集。这项提案的总体目标是使新生转录组的影响最大化 研究，并能够与其他功能基因组数据轻松整合。我的团队发展出了 转录测序（NET-seq），这使得RNA的链特异性核苷酸分辨率映射成为可能。 聚合酶密度，突出显示所有转录活性，无论转录物半衰期如何，并揭示精确的 Pol II暂停的位置适用于监管控制。在这里，我们将开发新版本的NET-seq - NET-seq 2.0 -使常规，可扩展和灵活的应用程序，以不同的人类细胞类型（或任何 真核生物系统）。此外，我们将通过开发两种创新的方法来增加NET-seq分析的潜力。 生物信息学策略，将NET-seq数据与其他全基因组数据集无缝集成， NET-seq研究之外的应用。为了证明我们的综合方法的广泛实用性，我们将研究 调控网络和细胞分化，瞬时新生转录分析将是高度 有影响力在Aim 1中，我们的目标是使NET-seq更容易，更便宜，更灵活。我们的改进将 减少背景并增加可用读数，显著减少单元输入要求（100-1000倍）， 能够进行密集的区域特异性RNA转录分析，并能够在 样品和条件。在目标2中，我们将通过整合NET-seq与 代谢RNA标记（TT-seq）数据，其报告局部合成速率。这种综合方法产生了丰富的 转录表型，我们将使用它来开发基因调控网络模型。在目标3中，我们将创建 新的计算算法，避免了单独确定每个分子事件的需要， 相反，使用信息丰富的数据集（如NET-seq）推断未映射事件的状态。我们将使用 集成深度神经网络（“深度学习”），使用可用的全基因组数据集来预测 现有数据中的不可用数据集。我们将应用这种方法来研究红细胞生成，使用良好的- 通过时间序列NET-seq和DNase-seq分析确定了原代人造血分化系统。 这些数据将为深度神经网络模型提供信息，以预测无数转录因子的ChIP-seq数据， 染色质标记来研究关键的调控事件，而无需额外的费用。