Big Data Methods for Decoding Gene Regulation

项目概要 全面了解基因活动如何在时间和空间上受到控制对于 研究人类发展和疾病。转录因子（TF）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（CRE）。所有细胞中 CRE 或“调节组”的全基因组活性图谱 类型和生物条件将为研究生物学的基本运行规则提供基础， 解释遗传变异如何导致疾病，并指导疾病治疗策略的制定。 不幸的是，现有的实验性规则组图谱技术无法分析大量样本 有效地。到目前为止，它们仅被应用于绘制所有生物环境中一小部分的调节组图谱。 因此，今天仍然缺乏人类监管格局的全面地图。 本研究旨在开发一种解决方案，在大量生物样本中绘制调控组图谱。 通过利用公开可用的功能基因组数据来实现不同的细胞类型和条件。我们将利用富人 DNA 元素百科全书 (ENCODE) 项目生成的基因表达和调控组数据 开发一种新的预测方法，利用生物样本的转录组来预测其调控组（目标 1）。 然后，我们将训练好的预测模型应用于 290,000 个公开的人类基因表达样本 在基因表达综合 (GEO) 数据库中创建涵盖数十万个的调节组图谱 比现有的调节组数据有更多的生物学背景（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低位点来获得对基因调控的生物学见解。 反映其发展轨迹的维度结构（目标 3）。 我们的研究将创建新的分析方法，使用超预测超高维结果 高维预测器，在训练和应用数据生成时进行跨平台预测 由具有系统平台差异的不同技术平台进行计算，并检索低维 来自海量数据集的生成树结构。将这些新方法应用到大量的公共领域 可用的基因表达数据将使我们能够解决调节组图谱中无法解决的重大挑战 使用现有的实验技术解决。通过实现快速且经济高效的映射和分析 人类基因调控景观，拟议的研究可能对人类未来的研究产生重大影响 发育和疾病。

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.