Integrating multi-omics datasets to infer phenotype-specific driver genes, regulatory interactions and drug response

整合多组学数据集来推断表型特异性驱动基因、调控相互作用和药物反应

• 批准号: 10188564
• 负责人: Serdar Bozdag
• 金额: $ 34.23万
• 依托单位: UNIVERSITY OF NORTH TEXAS
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10188564
• 关键词: All of Us Research Program; Architecture; Biological; Biology; Clinical; Computing Methodologies; DNA; DNA Methylation; Data; Data Set; Disease; Environmental Exposure; Epigenetic Process; Gene Expression; Generations; Genes; Genetic; Goals; Health Care Costs; Individual; International; Methods; MicroRNAs; Patients; Pharmaceutical Preparations; Phenotype; Records; Regulator Genes; Regulatory Element; Research; Research Personnel; Techniques; Tissues; Underrepresented Populations; Work; biobank; computerized tools; computing resources; cost; cost effective; effective therapy; graduate student; high throughput technology; human disease; innovation; insight; multiple omics; novel; open source; precision medicine; programs; response; secondary analysis; tool; undergraduate student

PROJECT SUMMARY My lab’s research goal is to develop open source integrative computational tools that perform secondary analysis of publicly available multi-omics biological, clinical and environmental exposure datasets to infer context-specific regulatory interactions and modules, and to predict disease associated genes and patient-specific drug response. With the recent advances in high-throughput technologies in biology, the cost of data generation has reduced tremendously, which enabled the generation of vast amounts of multi-omics datasets such as gene expression, microRNA expression, copy number alteration, and DNA methylation. Numerous international and national consortiums have been established to generate these multi-omics datasets to study regulatory elements in DNA, disease and healthy tissues, epigenetic signatures, and drug responses. Furthermore, ongoing large initiatives such as UK Biobank, Million Records Project, and the All of Us research program will bring vast amounts of multi-omics datasets from millions of individuals. Consequently, there is a tremendous need for scalable methods that can integrate different layers of multi-omics datasets across millions of individuals from different backgrounds. These methods would produce valuable insights into human diseases and pave the way towards precision medicine. My research program is devoted to utilizing these multi-omics datasets cost effectively by developing open-source innovative and integrative computational resources. My lab has been successful in developing open source integrative computational methods to integrate such datasets to infer gene regulatory interactions and modules and to predict disease drivers. In the next five years, we aim to extend our recent and ongoing work to infer context-specific regulatory interactions and modules, and to predict disease associated genes and patient- specific drug response. We will integrate various types of heterogenous multi-omics datasets to build integrative and scalable computational tools. The computational tools we develop through this research will enable us to elucidate the genetic and epigenetic architecture of regulatory interactions and drug response and discover novel disease associated genes. Our tools will be applicable for any disease type and will enable researchers to leverage publicly available multi- omics datasets to their full extent and pave the road towards precision medicine. Through this research program, I will create research opportunities for graduate and undergraduate students particularly those from under-represented groups.

项目概要 我实验室的研究目标是开发开源综合计算工具来执行二次分析 公开的多组学生物、临床和环境暴露数据集，以推断特定背景的监管 相互作用和模块，并预测疾病相关基因和患者特异性药物反应。随着最近 随着生物学高通量技术的进步，数据生成的成本大大降低，这 能够生成大量的多组学数据集，例如基因表达、microRNA 表达、复制 数量改变和DNA甲基化。许多国际和国家财团已成立 生成这些多组学数据集来研究 DNA、疾病和健康组织、表观遗传学中的调控元件 签名和药物反应。此外，正在进行的大型举措，例如英国生物银行、百万记录项目和 “我们所有人”研究计划将带来来自数百万人的大量多组学数据集。 因此，非常需要能够集成多组学数据集不同层的可扩展方法 涵盖数百万来自不同背景的人。这些方法将对人类产生有价值的见解 疾病并为精准医疗铺平道路。我的研究计划致力于利用这些多组学 通过开发开源创新和集成计算资源，数据集具有成本效益。我的实验室已经 成功开发开源综合计算方法来整合此类数据集以推断基因调控 相互作用和模块并预测疾病驱动因素。在未来五年中，我们的目标是扩展我们最近和正在进行的 致力于推断特定环境的调控相互作用和模块，并预测疾病相关基因和患者 特异性药物反应。我们将整合各种类型的异构多组学数据集，构建综合性的、 可扩展的计算工具。我们通过这项研究开发的计算工具将使我们能够阐明 调控相互作用和药物反应的遗传和表观遗传结构，并发现相关的新疾病 基因。我们的工具将适用于任何疾病类型，并使研究人员能够利用公开的多种方法 充分发挥组学数据集，为精准医学铺平道路。通过这个研究计划，我将 为研究生和本科生，特别是那些来自代表性不足群体的学生创造研究机会。