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都包含相同的DNA，但它们能够沿着不同的路径发展以实现不同的功能和表型。细胞类型的身份至少部分由从DNA转录为RNA的基因组的特定区域定义。 转录是创建RNA中间体的基本细胞过程，该过程代码为蛋白质，mRNA或调节其他过程，从而产生各种非编码RNA（NCRNA）（NCRNA），例如增强子RNA（ERNAS）。两种类型的转录细胞分化路径或对环境影响的细胞反应。 RNA聚合酶是产生RNA转录本（例如mRNA和NCRNA）的酶，它们存在于细胞中，要么是将其与DNA模板保持缔合或释放的分子，要么被释放到DNA中并由其他细胞机械运行。分析转录过程的一种并发症是基因可以重叠，因此聚合酶的存在和新生的RNA转录本可以使用当前方法来指示任何一种基因的活性。由于RNA转录本具有很高的倾向，可以折叠成三维形状，它们可能影响下游过程，因此，新生RNA的序列和形状都是与DNA以及从DNA释放的RNA转录物相关的新生转录本的重要特征。直到最近才有高通量的新生转录测定。这些使研究人员能够直接评估细胞聚合酶的活性。该项目旨在为新生的转录数据开发一个集成的分析框架。 至关重要的是，成绩单的形状或水平的变化提供了有关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于许多研究领域至关重要，包括生物化学，分子生物学和计算生物学。因此，将仔细关注教育和计算资源以支持大型社区的发展。该项目的总体目标是开发一个综合框架来解释和分析新生的转录。该项目利用了RNA聚合酶II的数学模型，该模型符合新生的转录数据，它不仅代表了所有转录本范围内基因组的水平，而且还表征了所有转录本的形状。 具体而言，该项目具有4个总体目标，如下所示：利用差异新生转录的模式以识别重叠的转录本；检测在增强子等监管区域中重叠转录的检测可能会洞悉这种NCRNA的功能。在聚合酶行为的原则数学模型中捕获转录终止；此处开发的算法将告知有关转录终止方式的相互矛盾的假设。确定技术选择的影响，例如协议选择和测序深度，对转录本的检测和表征，从而建议在新生的转录研究中进行最佳实践。通过构建支持这些计算工具的使用的教育模块，可以更广泛地采用新生的转录分析；该模块将涵盖软件，网站和用户文档。 总而言之，此处提出的研究将为单个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观点。 通过量化单个基因的转录变化及其大小，该项目的最终结果将是解释新生转录的综合框架。可以在http://dowell.colorado.edu/resources.html上找到带有描述的数据集和代码库链接。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估来通过评估来获得支持的。

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.

Lessons from eRNAs: understanding transcriptional regulation through the lens of nascent RNAs

• DOI: 10.1080/21541264.2019.1704128
• 发表时间: 2019-12-21
• 期刊: TRANSCRIPTION-AUSTIN
• 影响因子: 3.6
• 作者: Cardiello, Joseph F.;Sanchez, Gilson J.;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.