ABI Innovation: Data Driven Model of Polymerase Activity

ABI Innovation：聚合酶活性的数据驱动模型

基本信息

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

项目摘要

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。细胞类型特性至少部分是由从 DNA 转录为 RNA 的基因组特定区域定义的。转录是产生 RNA 中间体的基本细胞过程，该中间体要么编码蛋白质、mRNA，要么调节其他过程，产生各种非编码 RNA (ncRNA)，如增强子 RNA (eRNA)。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（例如 mRNA 和 ncRNA）的酶，它们以分子形式存在于细胞中，与 DNA 模板保持关联，或者从 DNA 中释放并被其他细胞机器作用。分析转录过程的一个复杂问题是基因可能重叠，因此聚合酶和新生 RNA 转录物的存在可能表明使用当前方法的任一基因的活性。由于 RNA 转录本非常倾向于折叠成三维形状，它们可以影响下游过程，因此新生 RNA 的序列和形状都是仍与 DNA 相关的新生转录本以及从 DNA 释放的 RNA 转录本的重要特征。脱氧核糖核酸。直到最近，高通量新生转录测定才变得可用。这些使得研究人员能够直接评估细胞聚合酶的活性。该项目旨在为新生转录数据开发一个综合分析框架。至关重要的是，转录本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生物化学、分子生物学和计算生物学等许多研究领域至关重要。因此，我们将认真关注教育和计算资源的开发，以支持更大的社区。该项目的总体目标是开发一个用于解释和分析新生转录的综合框架。该项目利用 RNA 聚合酶 II 的数学模型，当适合新生转录数据时，不仅可以定量表征全基因组范围内所有转录本的水平，还可以表征其形状。具体来说，该项目有 4 个总体目标，如下：利用差异新生转录的模式来识别重叠的转录本；检测增强子等调控区域的重叠转录可能有助于了解此类 ncRNA 的功能。在聚合酶行为的原理数学模型中捕获转录终止；这里开发的算法将告知有关转录终止如何发生的相互矛盾的假设。确定技术选择（例如方案选择和测序深度）对转录本检测和表征的影响，从而推荐新生转录研究的最佳实践。通过构建支持使用这些计算工具的教育模块，使新兴转录分析得到更广泛的采用；该模块将包含软件、网站和用户文档。总之，这里提出的研究将为单个基因转录特征的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，该项目的最终结果将是解释新生转录的综合框架。数据集和代码库链接及其说明可在 http://dowell.colorado.edu/resources.html 找到。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持标准。