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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：
10228663
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
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/10228663
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

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.

项目总结/文摘