# A systems biology approach for dissecting enhancer hierarchy

> **NIH NIH R01** · DANA-FARBER CANCER INST · 2020 · $191,064

## Abstract

In a multi-cellular organism, the genome in different cells is nearly identical, but each cell type has
its own gene expression pattern and biological function. The variation of chromatin states plays an
important role in regulating cell-type specific gene expression activities. Genome-wide chromatin state
analyses have identified a large number of putative enhancer elements that are associated with distinct
chromatin marks; however, it remains a challenge to distinguish functional elements from spurious ones,
especially where multiple enhancer elements are adjacent to each other forming dense clusters. Recent
studies suggest that these enhancer clusters are hotspots for functional elements that play important roles
in maintenance of cell-identity and regulating cell-type specific gene expression patterns; however, the
underlying mechanism remains unclear. Our recent experimental work strongly suggests that enhancer
clusters are composed of a functional hierarchy of interdependent constituent elements. In this project, we
will develop computational methods to predict and experimentally validate the enhancer hierarchy within
super-enhancers, integrating information from Hi-C, ChIPseq, and RNAseq datasets. Our proposed
research will provide powerful computational tools that will facilitate functional characterization of
noncoding DNA.

## Key facts

- **NIH application ID:** 9954128
- **Project number:** 5R01HG009663-04
- **Recipient organization:** DANA-FARBER CANCER INST
- **Principal Investigator:** Guo-Cheng Yuan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $191,064
- **Award type:** 5
- **Project period:** 2017-09-01 → 2020-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9954128

## Citation

> US National Institutes of Health, RePORTER application 9954128, A systems biology approach for dissecting enhancer hierarchy (5R01HG009663-04). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/9954128. Licensed CC0.

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