Data Analysis and Modeling

项目摘要/摘要--数据分析和建模 在此数据分析和建模(DAM)模块中，我们将开发计算方法来推导 关于核区划及其功能影响的新知识。首先，我们将开发一种 结合全基因组DamID和DAMID提供的不同类型信息的计算方法 TSA-Seq到一个统一的模型中，该模型预测基因组区域相对于 第一次出现了不同的核弹舱。结果将是具体的预测，将在 使用显微镜的生物验证开发模块。第二，我们将使用计算策略来 预测基因组基因定位于特定核室的机制。 具体地说，我们将预测哪些DNA元件和/或表观遗传特征对这种靶向- 产生可测试的预测，将在生物验证开发中进行系统验证 模块，使用其他工具开发或数据生成模块中开发的新技术。 这一结果将首次为推动基因组区划的机制提供关键的见解 时间到了。第三，我们将综合TRIP、REPLI-SEQ和其他数据，以计算预测泛函 本地化到特定核舱室的后果--这些预测将再次在 生物验证开发模块。这个大坝模块中的三个目标与三个目标紧密相连 生物验证开发模块的目标。我们设想了为输入收集数据的迭代周期 到我们的计算模型，根据这些模型进行预测，然后进行实验测试 这些预测。提出的方法将带来可靠的原理和对核能的新认识。 利用在整个项目中开发的新的和改进的地图技术的组织。

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.