# Reconstruction of 3D Genome Architecture from Chromatin Conformation Capture Data

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2020 · $317,000

## Abstract

Abstract
We are poised to enter a new era of conformational biology. Genome conformation is critical for
numerous cellular processes, including gene regulation, with certain alterations (translocations, fu-
sions) being oncogenic. While recent assays, notably Hi-C, have already transformed understanding
of chromatin architecture, even newer technologies have the potential to dramatically improve
accuracy and resolution of three-dimensional (3D) genome reconstructions. However, to fully
realize this potential, new statistical methods and algorithms will be required to operate on the
resultant data and structures, and to integrate concomitant biomedical data. This project aims at
developing such methods. A concrete example is provided by current findings identifying an
instance of insulated neighborhood disruption as a novel oncogenic mechanism. Instead of an
individual instance, we will develop methods to detect, and prioritize, genome-wide candidates,
building on our previous work on 3D hotspot elicitation. In particular, we will devise original
reconstruction-free approaches to avert uncertainties in inferring architecture.
Despite these uncertainties, reconstructions confer several advantages. We will deploy newly
devised assays, in conjunction with recent algorithmic advances, to improve reconstruction accuracy
and resolution. Multiplexed FISH provides richer imaging of chromatin conformation, enabling
refinement of transfer functions linking Hi-C contacts to distances, a precursor to reconstruction.
Protein-centric HiChIP provides gains in informative reads, as does multi-read rescue. Combining
these advances will produce enhanced approaches to 3D genome reconstruction.
The very notion of ‘a’ 3D genome reconstruction has been questioned since the underlying Hi- C
assays are based on large cell populations. Multiplexed in situ Hi-C has enabled generation of
thousands of single-cell datasets which we will couple with a new multi-track reconstruction
algorithm to dissect inter-cellular structural heterogeneity. We will also use this data to develop
classifiers, based on structural differences, for between cell-type discrimination.
Much downstream interpretation of Hi-C data has derived from spectral analysis of the contact
matrix, especially delineation of chromatin compartments. Spectral summarization has limitations
including compartment identification at high resolution, sensitivity to normalization, and extent of
explained variation. We will evaluate spectral analysis of contact matrices with emphasis on the
impact of approximations on 3D reconstructions, assessed via (i) inferred distance matrices, (ii)
derived reconstructions, and (iii) subsequent hotspot detection.

## Key facts

- **NIH application ID:** 10000929
- **Project number:** 5R01GM109457-08
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** MARK R SEGAL
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $317,000
- **Award type:** 5
- **Project period:** 2013-09-01 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10000929

## Citation

> US National Institutes of Health, RePORTER application 10000929, Reconstruction of 3D Genome Architecture from Chromatin Conformation Capture Data (5R01GM109457-08). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10000929. Licensed CC0.

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