ABI INNOVATION: Characterizing protein-DNA interactions from high-resolution assays

特定类型细胞的身份和健康取决于执行其功能的蛋白质，而功能如何变化取决于细胞的环境。细胞中的许多反应都是由于蛋白质产生的变化;当一组特定的蛋白质，调节蛋白质与DNA相互作用时，细胞就会启动蛋白质的产生，或者关闭蛋白质的产生。调节蛋白可以单独或成组地作用以打开和关闭基因，并且这些结合事件中的组合的时间和数量可能非常复杂。 有一些实验显示了特定蛋白质附着在DNA上的位置，为我们提供了可能发生调控的线索;随着实验室方法的改进，我们能够非常特异地找到越来越多蛋白质的DNA结合位置（图谱）。这项研究将开发特殊的计算和统计方法来分析所有这些数据，以便我们可以看到蛋白质的组合一起调节基因。从这些结果中，我们将更多地了解基因调控如何在功能细胞中发挥作用。本研究产生的所有产品将免费提供给其他研究人员和公众。本科生和研究生将接受使用这些生物信息学研究技术的培训，并将努力从代表性不足的群体中招收学生。有趣的练习将根据研究方法和结果，为本科生物信息学学生和研讨会学生创建，以及适合高中生在生物学课程中学习遗传调控的课程。目前的生物信息学分析技术不能完全捕获由高分辨率基因组测定中产生的读段分布的形状提供的结构信息。例如，仔细分析ChIP-exo数据集中的交联模式可以潜在地告知哪些蛋白质在高阶蛋白质-DNA复合物中相互作用。该项目旨在开发一套形状感知的机器学习工具，用于分析高分辨率蛋白质-DNA结合数据，该工具将：1）从单个数据集中解卷积不同的基因组相互作用模式; 2）检测和纠正新测定中出现的实验伪像和偏差; 3）表征跨多种数据类型的高阶蛋白质-DNA复合物的组织;和4）检测在多个实验条件下基因组事件位置和相互作用模式的变化。因此，该项目将使不同的蛋白质-DNA复合物的整合模型，直接影响我们对各种生物体中基因调控的理解。

The identity and health of a particular type of cell depends on the proteins carrying out its functions, and how the functions change depends on the cell's environment. Many of the responses in a cell are due to changes in what proteins are produced; cells turn on protein production, or turn it off, when a particular group of proteins, regulatory proteins, interact with the DNA. Regulatory proteins may act alone or in groups to turn genes on and off, and the timing and number of combinations in these binding events can be very complicated. There are experiments that show where a specific protein attaches to the DNA, giving us clues that regulation might be occurring; with improving laboratory methods we are able to find the DNA binding locations (profiles) of more and more proteins very specifically. This research will develop special computational and statistical methods for analyzing all of this data together, so that we can see what combinations of proteins act together to regulate genes. From these results we will understand much more about how gene regulation is working in functioning cells. All products produced by this research will be made freely available and accessible to other researchers and the public. Undergraduate and graduate students will be trained to use these bioinformatics research techniques, and strong efforts will be made to recruit students from under-represented groups. Interesting exercises will be created, based on the research methods and results, for undergraduate bioinformatics students and for students in workshops, as well as lessons suitable for high school students studying genetic regulation in their biology courses. Current bioinformatics analysis techniques do not fully capture the structural information provided by the shape of read distributions produced in high-resolution genomic assays. For example, careful analysis of cross-linking patterns in collections of ChIP-exo datasets can potentially inform which proteins are interacting with one another in higher-order protein-DNA complexes. This project aims to develop a suite of shape-aware machine-learning tools for the analysis of high-resolution protein-DNA binding data that will: 1) deconvolve distinct genomic interaction modes from a single dataset; 2) detect and correct experimental artifacts and biases that arise in the new assays; 3) characterize the organization of higher-order protein-DNA complexes across multiple data types; and 4) detect changes in genomic event locations and interaction modes across multiple experimental conditions. This project will therefore enable integrative models of diverse protein-DNA complexes, directly impacting our understanding of gene regulation in a wide variety of organisms.