# Integrative computational models for functional epigenomics and transcriptional regulation

> **NIH NIH R35** · UNIVERSITY OF VIRGINIA · 2022 · $399,831

## Abstract

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.

## Key facts

- **NIH application ID:** 10460972
- **Project number:** 5R35GM133712-04
- **Recipient organization:** UNIVERSITY OF VIRGINIA
- **Principal Investigator:** Chongzhi Zang
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $399,831
- **Award type:** 5
- **Project period:** 2019-09-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10460972

## Citation

> US National Institutes of Health, RePORTER application 10460972, Integrative computational models for functional epigenomics and transcriptional regulation (5R35GM133712-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10460972. Licensed CC0.

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