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， 研究人类发展和疾病。转录因子（TF）是一类重要的调节因子， 通过与靶基因的调节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（克雷斯）。在所有细胞中，克雷斯或“调节组”的全基因组活性图 类型和生物学条件将为研究生物学的基本运行规律提供基础， 解释遗传变异如何导致疾病，并指导疾病治疗策略的发展。 遗憾的是，现有的实验性规则组作图技术不能分析大量样品 有效地。到目前为止，它们只被应用于所有生物背景中的一小部分中的规则组。 因此，今天仍然缺乏一个全面的人类监管格局图。 这项研究的目的是开发一种解决方案，在大量的生物样品中绘制规则组， 通过利用公众可获得的功能基因组数据，使细胞类型和条件多样化。我们会利用富人 基因表达和调节组数据由DNA元件百科全书（ENCODE）项目生成， 开发一种新的预测方法，使用其转录组预测生物样品的调节组（Aim 1）。 然后，我们将训练的预测模型应用于290，000多个公开可用的人类基因表达样本 在基因表达综合数据库（GEO）中创建一个覆盖数十万个 更多的生物背景比现有的调节组数据（目标2）。我们还将开发一种方法来帮助研究人员 探索大量的数据集，通过将数据投影到它们的低水平， 三维结构反映其发展轨迹（目标3）。 我们的研究将创造新的分析方法，用于预测超高维的结果，使用超 高维预测器，在训练和应用数据生成时进行跨平台预测， 系统平台差异的不同技术平台， 从海量数据集生成树结构。将这些新方法应用于大量的公共 现有的基因表达数据将使我们能够解决调节组作图中的一个主要挑战， 利用现有的实验技术。通过快速、经济高效地绘制和分析 人类基因调控景观，拟议的研究可能会对未来的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.