Big Data Methods for Decoding Gene Regulation

项目摘要 全面了解基因的活动是如何在时间和空间上受到控制的，对于 研究人类发展和疾病。转录因子是一类重要的调控因子。 通过与靶基因的调控DNA序列结合来控制基因转录活性的蛋白质 称为顺式调节元件(Cres)。所有细胞中Cres或“调节体”的全基因组活动图 类型和生物条件将为研究生物学的基本运行规律提供基础， 解释基因变异如何导致疾病，并指导疾病治疗策略的制定。 遗憾的是，现有的试验性Regulome图谱技术无法分析大量样本 EFfi明显升高。到目前为止，它们只被应用于绘制所有生物学背景中的一小部分调节体。 因此，今天仍然缺乏一张全面的人类监管版图。 这项研究旨在开发一种解决方案，以绘制大量生物样本中的调控组 通过利用公开可用的功能基因组数据，实现不同的细胞类型和条件。我们将利用富人 由DNA元素百科全书(ENCODE)项目生成的基因表达和调节组数据 开发一种新的预测方法，利用生物样品的转录组来预测其调节组(目标1)。 然后，我们将把训练好的预测模型应用于29万个公开可用的人类基因表达样本 在基因表达总览(GEO)数据库中创建涵盖数十万人的调节组图谱 比现有的调节组数据更多的生物学背景(目标2)。我们还将开发一种方法来帮助研究人员 探索海量数据集，通过将数据投影到其最低层来获得对基因调控的生物学见解- 空间结构反映了它们的发展轨迹(目标3)。 我们的研究将创造新的分析方法，用于预测超高维结果 高维预测器，在培训和应用数据更通用时进行跨平台预测- 由不同的技术平台以系统的平台差异，检索低维的 从海量数据集生成树结构。将这些新方法应用于大量公开的 可用的基因表达数据将使我们能够解决调节组映射中的一个重大挑战，而这一挑战不可能 利用现有的实验技术解决了这个问题。通过实现快速且经济实惠的fi映射和分析 人类基因调控格局，拟议的研究可以对未来人类研究产生重大影响 发展和疾病。

Project Summary A comprehensive understanding of how genes' activities are controlled temporally and spatially is crucial for studying human development and diseases. Transcription factors (TFs) are an important class of regulatory proteins that can control genes' transcriptional activities by binding to target genes' regulatory DNA sequences called cis-regulatory elements (CREs). A map of genome-wide activities of CREs, or “regulome”, in all cell types and biological conditions will provide a foundation for investigating the basic operating rules of biology, interpreting how genetic variants cause diseases, and guiding the development of disease treatment strategies. Unfortunately, existing experimental regulome mapping technologies cannot analyze a large number of samples efficiently. Thus far, they have only been applied to map regulomes in a small fraction of all biological contexts. As a result, today a comprehensive map of human regulatory landscape is still lacking. This study aims to develop a solution to mapping regulomes in a massive number of biological samples from diverse cell types and conditions by leveraging publicly available functional genomic data. We will use the rich gene expression and regulome data generated by the Encyclopedia of DNA Elements (ENCODE) project to develop a new prediction approach that predicts a biological sample's regulome using its transcriptome (Aim 1). We will then apply the trained prediction models to 290,000+ publicly available human gene expression samples in the Gene Expression Omnibus (GEO) database to create a regulome map that covers hundreds of thousands more biological contexts than existing regulome data (Aim 2). We will also develop a method to help researchers explore the massive datasets to gain biological insights into gene regulation by projecting the data to their low- dimensional structure reflecting their developmental trajectory (Aim 3). Our research will create new analytical methods for predicting ultra-high-dimensional outcomes using ultra- high-dimensional predictors, making cross-platform predictions when the training and application data are gener- ated by different technological platforms with systematic platform differences, and retrieving the low-dimensional spanning tree structure from a massive dataset. Applying these new methods to the vast amounts of publicly available gene expression data will allow us to address a major challenge in regulome mapping that cannot be solved using existing experimental technologies. By enabling fast and cost-efficient mapping and analysis of human gene regulatory landscape, the proposed research can have a major impact on future studies of human development and diseases.