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.
Transcriptional regulation of gene expression plays a critical role in numerous cellular processes, but its fundamental mechanism in eukaryotic cells is not fully understood. The main goal of the proposed research program is to develop integrative statistical models and computational methods for analyzing cross-platform multi-scale omics data to study functional transcriptional regulation in mammalian genomes. The anticipated outcome of the proposed research will provide the biomedical research community with insightful computational models and useful bioinformatics tools, which will make potentially impact on understanding fundamental mechanisms of gene regulation in many biological processes and human diseases.
