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

项目总结 我的实验室的研究目标是开发开放源码的集成计算工具，这些工具可以对 公开可用的多组学生物、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定的药物反应。随着最近的 随着生物高通量技术的进步，数据生成的成本大大降低，这 能够生成海量的多组学数据集，如基因表达、microRNA表达、复制 数字改变和DNA甲基化。许多国际和国家财团已经成立，以 生成这些多组学数据集，以研究DNA、疾病和健康组织、表观遗传学 签名和药物反应。此外，正在进行的大型倡议，如英国生物库，百万唱片项目，以及 我们所有人的研究计划将带来来自数百万个人的海量多组学数据集。 因此，非常需要能够集成不同层的多组学数据集的可扩展方法 来自不同背景的数百万人。这些方法将对人类产生有价值的见解 并为精准医学铺平了道路。我的研究计划致力于利用这些多组学 通过开发开源、创新和综合的计算资源，使数据集具有成本效益。我的实验室已经 成功地开发了开源综合计算方法，以整合这些数据集来推断基因调控 相互作用和模块，并预测疾病驱动因素。在未来五年，我们的目标是延长我们最近和正在进行的 致力于推断特定于环境的调控相互作用和模块，并预测疾病相关基因和患者- 特定的药物反应。我们将整合各种类型的异质多组学数据集，以构建完整的 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 调节相互作用和药物反应的遗传和表观遗传结构，并发现与之相关的新疾病 基因。我们的工具将适用于任何疾病类型，并将使研究人员能够利用公开可用的多个 最大限度地收集组学数据，并为精准医学铺平道路。通过这个研究项目，我将 为研究生和本科生创造研究机会，特别是那些来自代表性不足群体的学生。

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.