Data Analysis and Modeling

项目总结/摘要-数据分析和建模 在这个数据分析和建模（DAM）模块中，我们将开发计算方法， 关于核划分及其功能影响的新知识。首先，我们将制定一个 计算方法，该方法结合了全基因组DamID提供的不同类型的信息， TSA-Seq转化为一个统一的模型，该模型预测基因组区域的位置和动态， 第一次有了不同的核隔间。结果将是具体的预测，将在 使用显微镜的生物学确认开发模块。其次，我们将使用计算策略， 预测基因组位点靶向特定核区室的机制。 具体来说，我们将预测哪些DNA元件和/或表观遗传特征对于这种靶向很重要- 产生可测试的预测，将在生物学验证开发中进行系统验证 模块，使用在附加工具开发或数据生成模块中开发的新技术。 这些结果将为第一个驱动基因组区室化的机制提供关键见解。 时间第三，我们将整合TRIP，d-Seq和其他数据，以便计算预测功能性 定位到特定核隔间的后果-这些预测将再次在 生物学确认开发模块。在这个DAM模块中的三个目标是紧密耦合到三个 生物学验证开发模块的目标。我们设想了收集数据用于输入的迭代周期 我们的计算模型，根据这些模型进行预测，然后进行实验测试， 这些预测。所提出的方法将导致强大的原则和新的知识的核 组织利用在整个项目中开发的新的和改进的绘图技术。

PROJECT SUMMARY / ABSTRACT – DATA ANALYSIS AND MODELING In this Data Analysis and Modeling (DAM) module, we will develop computational methods to derive new knowledge about nuclear compartmentalization and its functional impact. First, we will develop a computational method that combines the distinct types of information provided by genome-wide DamID and TSA-Seq into a unified model that predicts the position and dynamics of genomic regions with respect to various nuclear compartments for the first time. The results will be concrete predictions that will be tested in the Biological Validation Development module using microscopy. Second, we will use computational strategies to predict the mechanisms through which genomic loci are targeted to specific nuclear compartments. Specifically, we will predict which DNA elements and/or epigenetic features are important for this targeting – yielding testable predictions that will be systematically validated in the Biological Validation Development module, using new technologies developed in the Additional Tool Development or Data Generation module. The results will provide key insights into the mechanisms that drive genome compartmentalization for the first time. Third, we will integrate TRIP, Repli-Seq and other data in order to computationally predict the functional consequences of localization to specific nuclear compartments – again these predictions will be tested in the Biological Validation Development module. The three aims in this DAM module are tightly coupled to the three aims of the Biological Validation Development module. We envision iterative cycles of collecting data for input to our computational models, making predictions based on these models, and then experimentally testing these predictions. The proposed methods will lead to robust principles and new knowledge of nuclear organization utilizing the new and improved mapping technologies developed in the entire project.