# Decoding the Noncoding Regulatory Genome with Super-resolution via Single-cell Multiomics Integration

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA-IRVINE · 2023 · $394,000

## Abstract

PROJECT SUMMARY
 In eukaryotes, transcriptional regulation is essential to maintaining cell identity, responding to
intra- and extra-cellular signals, and coordinating gene activities, whereas its dysregulation can
cause a broad range of disorders. Previous methods mainly used averaged genomic signals from
thousands of cells to investigate gene regulation, failing to reveal the regulatory heterogeneity
across diverse cell states. Recent advances in multimodal single-cell technologies provide new
opportunities to decipher the cell-type-specific regulation code at the finest resolution possible.
However, its computational modeling is still in its infancy due to the high dimensionality,
missingness, vulnerabilities to confounding factors, and complex feature interactions. In this
project, we aim to develop a suite of computational models to construct gene-centric, personal
regulome via single-cell multiome integration and link multi-scale dysregulations to disease.
 Distinct from previous efforts reporting a set of one-dimensional (1D) functional cis-regulatory
elements (CREs) from only one genome and applying it to all samples, we aim to construct
personal, compact, gene-centric, and cell-type-specific transcriptional regulome from sc-
multiome data. Specifically, we will first propose a scalable multimodal deep generative model to
integrate single-cell data with single-, multi-, and hybrid modalities. Distinct to existing methods,
we will include an invariant representation learning scheme to derive latent cell representations
uncorrelated with confounding factors (e.g., age, gender, read depth, and batch effects) for bias-
free transcriptome and epigenome reconstruction (Aim 1). Then, we will go beyond the 1D
genome annotation by deciphering multi-scale gene regulation code (Aim 2), including i)
functional CREs at a base-pair resolution; ii) CRE target genes for functional interpretation; iii)
transcription factor regulatory networks. Lastly, we will develop interpretable deep learning
models to link multi-scale dysregulations to disease with mechanistic explanation (Aim 3).
 This proposal is built on a close and long-term collaboration between Dr. Jing Zhang, an
expert in computational biology and machine learning at the University of California, Irvine, with
Dr. Feng Yue, an expert in regulatory genomics and 3D genome organization at the Northwestern
University. Upon completion, our proposed methods will substantially deepen our understanding
of transcriptional regulation to a single-cell level resolution and quantitatively relate multi-scale
risk factors to genetic disorders. In addition, our aims will yield open-source software for the
scientific community as essential tools for single-cell multi-omics data processing and integration.

## Key facts

- **NIH application ID:** 10694940
- **Project number:** 5R01HG012572-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA-IRVINE
- **Principal Investigator:** JING ZHANG
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $394,000
- **Award type:** 5
- **Project period:** 2022-09-01 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10694940

## Citation

> US National Institutes of Health, RePORTER application 10694940, Decoding the Noncoding Regulatory Genome with Super-resolution via Single-cell Multiomics Integration (5R01HG012572-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10694940. Licensed CC0.

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