# Joint analysis of 3D chromatin organization and 1D epigenome

> **NIH NIH R35** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $432,839

## Abstract

Abstract
After the completion of the Human Genome Project, thousands of experiments from ENCODE and Roadmap
Epigenomics projects have successfully profiled regulatory elements and epigenetic landscape along the genome.
More recently, over 2,000 chromatin organization datasets have been generated from 4D Nucleome (4DN) Project,
and they provide complementary information about how these genomic and epigenomic elements are spatially
organized in a nucleus. Joint analysis of 3D chromatin organization with previously profiled 1D epigenome in
different cell types will be a key step to understand the mechanisms underlying transcriptional regulation over
long genomic distances. However, there are two challenges. First, there is a resolution mismatch between
chromatin organization data (e.g. Hi-C contacts) which are usually measured at 10k base pair resolution, and
epigenome-based chromatin state features (e.g. ChIP-seq peaks) whose signals are usually at tens to hundreds of
base pairs. Second, existing computational approaches for analyzing epigenome, such as annotating genome and
understanding regulatory elements, all treat the DNA sequence as one-dimensional data, leaving the important
3D structural information unutilized. We aim to develop the most cutting-edge deep learning approaches for
understanding the relationship between chromatin state features and chromatin organization, performing 3D and 4D
genome annotation, and identifying spatially collaborative transcription factors, respectively. After the completion of
the proposed work, we expect to have: (1) an accurate and interpretable computational model to predict chromatin
contact maps at nucleosome resolution for a wide range of cell lines, (2) 3D and 4D genome annotations over
dynamic chromatin organization, regulatory elements and epigenomic features, and (3) a computational method for
identifying spatially collaborative transcription factors which can help us understand the orchestration of noncoding
genetic variants. These results will provide fundamental understanding of disease-relevant genetic variation in the
light of the spatial organization of these genomic and epigenomic elements and their functional implications.

## Key facts

- **NIH application ID:** 10046394
- **Project number:** 1R35HG011279-01
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Jie Liu
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $432,839
- **Award type:** 1
- **Project period:** 2020-09-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10046394

## Citation

> US National Institutes of Health, RePORTER application 10046394, Joint analysis of 3D chromatin organization and 1D epigenome (1R35HG011279-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10046394. Licensed CC0.

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