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

基本信息

批准号：
10809380
负责人：
Chongzhi Zang
金额：
$ 1.46万
依托单位：
UNIVERSITY OF VIRGINIA
依托单位国家：
美国
项目类别：
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/10809380
关键词：
ATAC-seq Affect Algorithms Architecture Biochemical Bioinformatics Biological Process Biomedical Research Cell physiology Cells ChIP-seq Chromatin Collaborations 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 bioinformatics tool cell type computer science epigenomics genomic data high dimensionality human disease insight laboratory experiment mammalian genome mathematical sciences novel online resource open source predictive modeling programs statistics

项目摘要

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.

项目概要/摘要基因表达的转录调控在许多细胞过程中发挥着关键作用。表观基因组学是指研究蛋白质分子和生化因子的整体模式和动态变化，与基因组 DNA 相互作用，影响染色质结构并调节基因表达。表观基因组学弥合了遗传变异和细胞表型之间的机制差距。功能性鉴定表观基因组学和转录调控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学数据来表征不同的分子谱各种系统中的细胞类型。此类基因组学研究的一项主要挑战是基于模型的公正性对来自不同平台的这些高维多组学数据进行计算分析和整合检索功能见解。我实验室的研究项目侧重于开发定量模型和计算方法功能性多组学数据分析。我们开发了几种计算模型和生物信息学 ChIP-seq 数据分析方法和功能转录调控的预测模型整合公开的多组学数据。我们的长期愿景是通过使用新颖的计算技术采用统计学、物理学、数学和计算机的跨学科方法的方法论通过科学，我们将能够了解人类细胞及其基因调控的基本机制在许多疾病中发挥作用。具体来说，未来五年，我的实验室将主要围绕以下目标进行： (1) 开发功能性转录调控关系和网络的准确预测模型多组学数据的智能整合。 (2) 开发统计模型以进行无偏量化和分析染色质可及性测序（ATAC-seq 和 DNase-seq）数据。 (3) 发展计算整合跨尺度批量和单细胞多组学数据以研究功能的联合分析方法单细胞水平的调控动态。与此同时，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表观基因组学和转录调控在多种哺乳动物细胞系统中。我们致力于将我们开发的所有方法和算法变成开源生物信息学软件工具、API 和基于 Web 的资源，可供用户访问和使用生物医学研究界。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

BART Cancer: a web resource for transcriptional regulators in cancer genomes.

BART Cancer：癌症基因组转录调节因子的网络资源。

DOI：
发表时间：
2021-03
期刊：
影响因子：
5.1
作者：
Thomas, Zachary V;Wang, Zhenjia;Zang, Chongzhi
通讯作者：
Zang, Chongzhi

An epilepsy-associated mutation in the nuclear import receptor KPNA7 reduces nuclear localization signal binding.

核输入受体 KPNA7 中与癫痫相关的突变减少了核定位信号结合。

DOI：
发表时间：
2020
期刊：
影响因子：
4.6
作者：
Oostdyk, Luke T;Wang, Zhenjia;Zang, Chongzhi;Li, Hui;McConnell, Michael J;Paschal, Bryce M
通讯作者：
Paschal, Bryce M

RECOGNICER: A coarse-graining approach for identifying broad domains from ChIP-seq data.

RECOGNICER：一种从 ChIP-seq 数据中识别广泛域的粗粒度方法。

DOI：
发表时间：
2020-12-24
期刊：
影响因子：
0
作者：
Zang, Chongzhi;Wang, Yiren;Peng, Weiqun
通讯作者：
Peng, Weiqun

Author Correction: Single-nucleus chromatin accessibility profiling highlights regulatory mechanisms of coronary artery disease risk.

作者更正：单核染色质可及性分析强调了冠状动脉疾病风险的调节机制。

DOI：
发表时间：
2022-08
期刊：
影响因子：
30.8
作者：
Turner, Adam W;Hu, Shengen Shawn;Mosquera, Jose Verdezoto;Ma, Wei Feng;Hodonsky, Chani J;Wong, Doris;Auguste, Gaëlle;Song, Yipei;Sol;Farber, Emily;Kundu, Soumya;Kundaje, Anshul;Lopez, Nicolas G;Ma, Lijiang;Ghosh, Saikat Kumar B
通讯作者：
Ghosh, Saikat Kumar B

Aggregated understanding of characteristics of wheat straw node and internode with their interfacial bonding mechanisms

小麦秸秆节间特征及其界面结合机制的综合认识