# Integrative computational models for functional epigenomics and transcriptional regulation

> **NIH NIH R35** · UNIVERSITY OF VIRGINIA · 2024 · $444,125

## Abstract

PROJECT SUMMARY/ABSTRACT
Transcriptional regulation of gene expression is an essential process in determining cell identity in multicellular
organisms. Through this process, one single genome can create numerous different cell types. Dysregulation
of transcription can result in aberrant gene expression and cause diseases. Transcription factors (TFs) play a
critical role in altering and controlling the transcription program in every cell. Identification of functional
transcriptional regulators is an important task for transcriptional regulation research, but the performance of
current computational tools still has room for improvement. The global distribution of cis-regulatory elements
(CREs) where TFs bind to DNA in the genome and its potential association with TF functions, and mechanisms
of TF recruitment to the regulatory genome are still not fully understood. With the availability of large amounts
of multi-modal genomics data in the public domain, innovative computational methods with rigorous statistical
models are needed to leverage such big data for studying these fundamental problems in functional genomics.
The research program of my lab focuses on developing statistical models and computational methods for
functional data analysis to study transcriptional regulation, genomics and epigenomics. In the next five years,
we will focus our research efforts on the following directions: (1) Developing improved statistical models and
computational methods for functional TR prediction; (2) Investigating genomic clustering tendencies of CREs
and their association with TF functions; and (3) Identifying novel mechanisms of TF recruitment to the genome
by integrative analysis of CTCF binding with lncRNAs, R-loops, and DNA secondary structures using
computational approaches. We are committed to developing all computational methods and tools as open-
source, rigorous, robust, and user-friendly for the biomedical research community.

## Key facts

- **NIH application ID:** 10841922
- **Project number:** 2R35GM133712-06
- **Recipient organization:** UNIVERSITY OF VIRGINIA
- **Principal Investigator:** Chongzhi Zang
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $444,125
- **Award type:** 2
- **Project period:** 2019-09-01 → 2029-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10841922

## Citation

> US National Institutes of Health, RePORTER application 10841922, Integrative computational models for functional epigenomics and transcriptional regulation (2R35GM133712-06). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10841922. Licensed CC0.

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