ABI Innovation: Data Driven Model of Polymerase Activity

ABI Innovation：聚合酶活性的数据驱动模型

• 批准号: 1759949
• 负责人: Robin Dowell
• 金额: $ 71.49万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-15 至 2022-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1759949&HistoricalAwards=false
• 关键词: ABI; Innovation; Data; Driven; Model

The majority of cells in an organism contain the same DNA, and yet they are able to develop along different paths to achieve different functions and phenotypes. The cell type identity is defined, at least in part, by the specific regions of the genome that are transcribed from DNA to RNA. Transcription is the basic cellular process that creates the RNA intermediate that either codes for proteins, mRNAs, or regulates other processes, producing various non-coding RNAs (ncRNAs) like enhancer RNAs (eRNAs). Both types of transcript cell differentiation paths or cellular responses to environmental influences. RNA polymerases are the enzymes that produce RNA transcripts, such as mRNAs and ncRNAs, and they exist in cells either as molecules that are maintain their association with the DNA template or are released being transported away from the DNA and acted on by other cellular machinery. One complication to analyzing the transcription process is that genes can overlap, so the presence of a polymerase and a nascent RNA transcript might indicate activity of either gene using current methodologies. Since RNA transcripts have a high propensity to fold into three dimensional shapes they that can affect the downstream processes, both sequence and shape of the nascent RNA are important characteristics of nascent transcripts still associated with DNA as well as the RNA transcripts that are released from the DNA. Only recently have high throughput nascent transcription assays become available; these allow researchers to assess the activity of cellular polymerases directly. This project seeks to develop an integrated analysis framework for nascent transcription data. Critically, changes in the shapes or levels of transcripts provide information as to how mutations, or other perturbations, have affected an RNA polymerase's functional properties. Understanding the detailed molecular basis of transcription is critical to many research areas including biochemistry, molecular biology and computational biology. As such, careful attention will be paid to the development of educational and computational resources to support of the larger community. The overall goal of this project is to develop of an integrated framework for the interpretation and analysis of nascent transcription. This project leverages a mathematical model of RNA polymerase II that, when fit to nascent transcription data, quantitatively characterizes not only the levels but also the shapes of all transcripts genome-wide. Specifically, this project has 4 overarching goals, as follows: Leverage patterns of differential nascent transcription to identify overlapping transcripts; detection of overlapping transcription at regulatory regions like enhancers may provide insight into how such ncRNAs function. Capture termination of transcription within a principled mathematical model of polymerase behavior; the algorithm developed here will inform on the conflicting hypotheses about how transcription termination occurs. Identify the impact of technical choices, such as protocol selection and sequencing depth, on the detection and characterization of transcripts, thereby recommending best practice in nascent transcription studies. Enable broader adoption of nascent transcription analysis through construction of an educational module that supports the use of these computational tools; the module will encompass the software, websites, and user documentation. In summary, the research proposed here will provide an unprecedented perspective on differences in transcription characteristics at individual genes and how perturbations affect them. By quantifying alterations in transcription at individual genes and their magnitude, the end result of this project will be an integrated framework for the interpretation of nascent transcription. Datasets and codebase links, with descriptions, may be found at http://dowell.colorado.edu/resources.html .This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

生物体中的大多数细胞含有相同的DNA，但它们能够沿着不同的路径发育，以实现不同的功能和表型。细胞类型身份至少部分地由基因组中从DNA转录为RNA的特定区域来定义。转录是基本的细胞过程，它产生编码蛋白质、mrna或调节其他过程的RNA中间体，产生各种非编码RNA (ncrna)，如增强RNA （erna）。这两种类型的转录细胞分化途径或细胞对环境影响的反应。RNA聚合酶是产生RNA转录物的酶，如mrna和ncrna，它们存在于细胞中，要么作为分子保持与DNA模板的联系，要么被释放出来，从DNA中运输出来，并受到其他细胞机制的作用。分析转录过程的一个复杂之处在于基因可以重叠，因此使用当前的方法，聚合酶和新生RNA转录物的存在可能表明其中任何一个基因的活性。由于RNA转录物具有高度的折叠成三维形状的倾向，它们可以影响下游过程，因此新生RNA的序列和形状是与DNA相关的新生转录物以及从DNA释放的RNA转录物的重要特征。直到最近才有高通量的新生转录测定方法可用；这使得研究人员可以直接评估细胞聚合酶的活性。该项目旨在为新生转录数据开发一个综合分析框架。关键的是，转录物形状或水平的变化提供了突变或其他扰动如何影响RNA聚合酶功能特性的信息。了解转录的详细分子基础对包括生物化学、分子生物学和计算生物学在内的许多研究领域至关重要。因此，将认真关注教育和计算资源的发展，以支持更大的社区。该项目的总体目标是为新生转录的解释和分析开发一个综合框架。该项目利用RNA聚合酶II的数学模型，当适合新生的转录数据时，不仅定量表征了所有转录物的水平，而且还表征了全基因组的形状。具体来说，该项目有4个总体目标，如下：利用差异新生转录模式来识别重叠转录；在像增强子这样的调控区域检测重叠转录可能会让我们深入了解这些ncrna的功能。在聚合酶行为的原则数学模型中捕获转录终止；这里开发的算法将告知关于转录终止如何发生的相互矛盾的假设。确定技术选择（如方案选择和测序深度）对转录本检测和表征的影响，从而为新生转录研究推荐最佳实践。通过构建支持使用这些计算工具的教育模块，使新生转录分析得到更广泛的采用；该模块将包括软件、网站和用户文档。总之，这里提出的研究将为个体基因的转录特征差异以及扰动如何影响它们提供前所未有的视角。通过量化单个基因的转录变化及其幅度，这个项目的最终结果将是一个解释新生转录的综合框架。数据集和代码库链接及其描述可在http://dowell.colorado.edu/resources.html上找到。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Combining signal and sequence to detect RNA polymerase initiation in ATAC-seq data

结合信号和序列来检测 ATAC-seq 数据中的 RNA 聚合酶起始

• DOI: 10.1371/journal.pone.0232332
• 发表时间: 2020
• 期刊: PLOS ONE
• 影响因子: 3.7
• 作者: Tripodi, Ignacio J.;Chowdhury, Murad;Gruca, Margaret;Dowell, Robin D.
• 通讯作者: Dowell, Robin D.

TFIID Enables RNA Polymerase II Promoter-Proximal Pausing

• DOI: 10.1016/j.molcel.2020.03.008
• 发表时间: 2020-05-21
• 期刊: MOLECULAR CELL
• 影响因子: 16
• 作者: Fant, Charli B.;Levandowski, Cecilia B.;Taatjes, Dylan J.
• 通讯作者: Taatjes, Dylan J.