Integrative computational models for functional epigenomics and transcriptional regulation

功能表观基因组学和转录调控的综合计算模型

• 批准号: 10005372
• 负责人: Chongzhi Zang
• 金额: $ 39.98万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10005372
• 关键词: ATAC-seq; Affect; Algorithms; Architecture; Biochemical; Bioinformatics; Biological Process; Biomedical Research; Cell physiology; Cells; ChIP-seq; Chromatin; Communities; Computer Analysis; Computer Models; Computing Methodologies; DNase I hypersensitive sites sequencing; Data; Data Analyses; Disease; Eukaryotic Cell; Gene Expression; Gene Expression Regulation; Genetic Transcription; Genetic Variation; Genomic DNA; Genomic approach; Genomics; Goals; Human; Joints; Mammalian Cell; Methods; Modeling; Molecular Profiling; Multiomic Data; Outcome; Pattern; Phenotype; Physics; Play; Proteins; Regulator Genes; Research; Role; Software Tools; Statistical Models; System; Transcriptional Regulation; Vision; base; bioinformatics tool; cell type; computer science; epigenomics; genomic data; high dimensionality; human disease; insight; mammalian genome; mathematical sciences; novel; online resource; open source; predictive modeling; programs; statistics

PROJECT SUMMARY/ABSTRACT Transcriptional regulation of gene expression plays a critical role in numerous cellular processes. Epigenomics refers to the study of global patterns and dynamic changes of protein molecules and biochemical factors that interact with genomic DNA to affect the chromatin architecture and to regulate gene expression. Epigenomics bridges the mechanistic gaps between genetic variations and cellular phenotypes. Identification of functional epigenomics and transcriptional regulatory relations is essential for understanding fundamental gene regulatory mechanisms. High-throughput genomic approaches have been increasingly applied in the field and a large amount of multi-level genomics data have been generated to characterize molecular profiles of different cell types in various systems. One major challenge in such genomics studies is unbiased model-based computational analysis and integration of these high-dimensional multi-omics data from different platforms to retrieve functional insights. The research program of my lab focuses on developing quantitative models and computational methods for functional multi-omics data analysis. We have developed several computational models and bioinformatics methods for ChIP-seq data analysis and predictive models for functional transcriptional regulation by integrating publicly available multi-omics data. Our long-term vision is that by using novel computational methodologies with adapted cross-disciplinary approaches from statistics, physics, mathematics and computer science, we will be able to understand fundamental mechanisms of gene regulation in human cells and their role in many diseases. Specifically, in the next five years, my lab will mainly focus on the following objectives: (1) Developing accurate predictive models for functional transcriptional regulatory relations and networks with smart integration of multi-omics data. (2) Developing statistical models for unbiased quantification and analysis of chromatin accessibility sequencing (ATAC-seq and DNase-seq) data. (3) Developing computational methods for joint analysis for integrating cross-scale bulk and single-cell multi-omics data to study functional regulatory dynamics in a single-cell level. In the meantime, we collaborate with a few experimental labs and apply our developed computational methods for studying functional epigenomics and transcriptional regulation in a variety of mammalian cell systems. We commit to make all methods and algorithms that we develop into open-source bioinformatics software tools, APIs, and web-based resources that are accessible and useful to the biomedical research community.

项目摘要/摘要 基因表达的转录调节在众多细胞过程中起着至关重要的作用。表观基因组学 是指研究全球模式和蛋白质分子和生化因素的动态变化，这些因素 与基因组DNA相互作用以影响染色质结构并调节基因表达。表观基因组学 桥接遗传变异与细胞表型之间的机械差距。识别功能 表观基因组学和转录调节关系对于理解基本基因至关重要 监管机制。高通量基因组方法已越来越多地应用于该领域， 已经生成了大量的多级基因组数据，以表征不同的分子曲线 各种系统中的细胞类型。此类基因组学研究中的一个主要挑战是基于模型的公正 从不同平台到这些高维的多态数据的计算分析和集成 检索功能见解。 我实验室的研究计划着重于开发定量模型和计算方法 功能多摩变数据分析。我们已经开发了几种计算模型和生物信息学 芯片序列数据分析的方法和功能转录调控的预测模型通过 集成了公开可用的多媒体数据。我们的长期视野是使用新颖的计算 统计，物理，数学和计算机的适应性跨学科方法的方法 科学，我们将能够理解人类细胞中基因调节的基本机制 在许多疾病中的作用。具体来说，在接下来的五年中，我的实验室将主要关注以下目标： （1）开发与功能转录调节关系和网络的准确预测模型 智能集成多摩变数据。 （2）开发用于无偏定和分析的统计模型 染色质可访问性测序（ATAC-SEQ和DNASE-SEQ）数据。 （3）开发计算 联合分析的方法，用于整合跨尺度和单细胞多摩变数据以研究功能 单细胞水平的调节动力学。同时，我们与一些实验实验室合作， 应用我们开发的计算方法来研究功能基因组学和转录调节 在各种哺乳动物细胞系统中。我们承诺制造我们发展成的所有方法和算法 开源生物信息授权软件工具，API和基于Web的资源可访问且有用 生物医学研究界。