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

基本信息

批准号：
9762140
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
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-04-01 至 2022-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9762140
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

项目摘要

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本。即使有如此大量的数据，确定映射元素的方式仍然是一个重大挑战这在很大程度上是由于我们无法准确和定量地在监管网络中共同发挥作用。检测所有形式的新生转录，即转录调控的瞬时输出。对全球基因调控的理解因缺乏无缝集成的计算工具而受到限制该提案的总体目标是最大限度地发挥新生转录组的影响。研究并能够与其他功能基因组数据轻松集成。转录本测序 (NET-seq)，可实现 RNA 链特异性核苷酸分辨率映射聚合酶密度，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 暂停应用监管控制的位置在这里，我们将开发新版本的 NET-seq。 – NET-seq 2.0 – 能够对不同的人类细胞类型（或任何细胞）进行常规、可扩展和灵活的应用此外，我们将通过开发两种创新来增加 NET-seq 分析的潜力。将 NET-seq 数据与其他全基因组数据集无缝集成的生物信息学策略为了证明我们的集成方法的广泛实用性，我们将研究 NET-seq 研究之外的应用。瞬时新生转录分析将高度重视调节网络和细胞分化在目标 1 中，我们的目标是使 NET-seq 更容易、更便宜、更灵活。减少背景并增加可用读数，显着降低细胞输入要求（100-1000 倍），实现密集的、区域特异性的 RNA 转录分析，并实现之间的定量比较在目标 2 中，我们将通过集成 NET-seq 来确定转录动力学。报告局部合成率的代谢 RNA 标记 (TT-seq) 数据这种综合方法产生了丰富的数据。在目标 3 中，我们将创建转录表型，用于开发基因调控网络模型。新的计算算法可以避免单独确定每个分子事件的需要，以及相反，我们将使用信息丰富的数据集（例如 NET-seq）来推断未映射事件的状态。使用可用的全基因组数据集进行预测的综合深度神经网络（“深度学习”）我们将应用这种方法来使用现有的数据来研究红细胞生成。通过时间序列 NET-seq 和 DNase-seq 分析定义了初级人类造血分化系统。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 数据染色质标记可用于研究关键调控事件，无需额外费用。