Mechanisms of Transcriptional Control Revealed by Nascent Transcript Sequencing

大型联盟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 调控区域、转录因子结合位点以及大量的编码和非编码转录本。 即使有如此海量的数据，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，瞬时输出转录调控。而且，我们的 对全球基因调控的理解受到缺乏无缝集成的计算工具的限制 全基因组数据集。这项提议的总体目标是最大限度地发挥新生转录组的影响。 研究并实现与其他功能基因组数据的便捷集成。我的团队发展出了天然的伸长 转录测序(net-seq)，使RNA的链特定核苷酸分辨图谱成为可能 聚合酶密度，突出所有转录活性，而不考虑转录半衰期，并揭示准确 适用监管控制的Pol II暂停位置。在这里，我们将开发一个新版本的Net-seq -Net-seq 2.0-使常规、可扩展和灵活的应用程序能够应用于不同的人类细胞类型(或任何 真核系统)。此外，我们将通过开发两个创新的方法来提高Net-Seq分析的潜力 将Net-Seq数据与其他基因组范围的数据集无缝集成的生物信息学战略 网络序贯研究之外的应用。为了展示我们的综合方法的广泛效用，我们将研究 调节网络和细胞分化的瞬时新生转录分析将是非常重要的 很有影响力。在目标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，大幅减少单元格输入要求(100-1000倍)， 实现密集的、特定于区域的RNA转录分析，并实现 样本和条件。在目标2中，我们将通过将net-seq与 报告局部合成速率的代谢RNA标记(TT-SEQ)数据。这种综合的方法产生了丰富的 我们将用来开发基因调控网络模型的转录表型。在目标3中，我们将创造 新的计算算法避免了单独确定每个分子事件的需要，以及 相反，使用信息丰富的数据集(如net-seq)来推断未映射事件的状态。我们将使用 综合深度神经网络(深度学习)，使用可用的全基因组数据集来预测 手头已有数据中的数据集不可用。我们将使用这种方法来研究红细胞生成，使用的井- 通过时间序列Net-Seq和DNase-Seq分析确定了人类原始造血分化系统。 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和 染色质标记用于调查关键的监管事件，无需额外费用。

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.