Big Data Methods for Decoding Gene Regulation

解码基因调控的大数据方法

• 批准号: 9762143
• 负责人: Hongkai Ji
• 金额: $ 42.08万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-10 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9762143
• 关键词: Address; Big Data; Big Data Methods; Binding; Biological; Biological Factors; Biology; Cells; Complex; Coupling; DNA; DNA Sequence; Data; Data Set; Databases; Development; Dimensions; Disease; Elements; Encyclopedia of DNA Elements; Foundations; Future; Gene Expression; Gene Expression Regulation; Genes; Genetic Transcription; Human; Human Development; Maps; Measures; Methods; Modeling; Outcome; Play; Regenerative Medicine; Regulator Genes; Regulatory Element; Research; Research Personnel; Role; Sample Size; Sampling; Structure; Technology; Text; Time; Training; Trees; analytical method; cell type; computerized tools; cost; design; functional genomics; genetic regulatory protein; genetic variant; genome-wide; genomic data; high dimensionality; human disease; improved; innovation; insight; predicting response; predictive modeling; programs; response; transcription factor; transcriptome; treatment strategy; Address; Big Data; Big Data Methods; Binding; Biological; Biological Factors; Biology; Cells; Complex; Coupling; DNA; DNA Sequence; Data; Data Set; Databases; Development; Dimensions; Disease; Elements; Encyclopedia of DNA Elements; Foundations; Future; Gene Expression; Gene Expression Regulation; Genes; Genetic Transcription; Human; Human Development; Maps; Measures; Methods; Modeling; Outcome; Play; Regenerative Medicine; Regulator Genes; Regulatory Element; Research; Research Personnel; Role; Sample Size; Sampling; Structure; Technology; Text; Time; Training; Trees; analytical method; cell type; computerized tools; cost; design; functional genomics; genetic regulatory protein; genetic variant; genome-wide; genomic data; high dimensionality; human disease; improved; innovation; insight; predicting response; predictive modeling; programs; response; transcription factor; transcriptome; treatment strategy

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.

项目摘要 对基因活动如何临时和空间控制的全面了解对于 研究人类发展和疾病。转录因子（TF）是一类重要的调节 可以通过与靶基因调节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（CRE）。在所有细胞中，CRE或“调节型”的全基因组活动图 类型和生物条件将为研究生物学基本操作规则提供基础， 解释遗传变异如何引起疾病，并指导疾病治疗策略的发展。 不幸的是，现有的实验调节映射技术无法分析大量样品 有效。这远，它们仅应用于在所有生物环境中的一小部分中绘制调节器。 结果，如今仍缺乏人类监管格局的全面地图。 这项研究旨在开发一种解决方案，以绘制来自大量生物样品的调节 通过利用公开可用的功能基因组数据来多样化的细胞类型和条件。我们将使用富人 DNA元素百科全书（编码）项目生成的基因表达和调节数据 开发一种新的预测方法，该方法使用其转录组来预测生物样品的调节组（AIM 1）。 然后，我们将将经过训练的预测模型应用于290,000多个公开的人类基因表达样本 在基因表达综合（GEO）数据库中，创建一个覆盖数十万的调节图 比现有调节数据的生物环境更多（AIM 2）。我们还将开发一种方法来帮助研究人员 探索大量数据集，以通过将数据投射到其低 - 尺寸结构反映其发育轨迹（AIM 3）。 我们的研究将创建新的分析方法，用于使用超高维结局使用超高维结局 高维预测变量，当培训和应用数据是生成时，进行跨平台预测 由具有系统平台差异的不同技术平台的ATED，并检索低维 从大量数据集中跨越树结构。将这些新方法应用于大量公开 可用的基因表达数据将使我们能够解决调节映射中的重大挑战 使用现有的实验技术解决。通过启用快速，成本效益的映射和分析 人类基因调节局势，拟议的研究可能对人类的未来研究产生重大影响 发展和疾病。