# Deep learning for decoding genetic regulation and cellular maps in craniofacial development

> **NIH NIH R01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2021 · $585,184

## Abstract

Project Summary
A deep understanding of gene regulation and function during craniofacial development is not only important for
our biological knowledge, but also critical to identify causal variants and genes underlying many dental, oral, and
craniofacial (DOC) diseases. Numerous -omics datasets at the genomic, epigenomic, (single-cell) transcriptomic
levels have been generated for craniofacial development and DOC diseases. These datasets are highly
heterogeneous (e.g. platforms, species, tissues, developmental stages) and cross-species (e.g. human and
mouse), requiring novel analytical approaches for decoding genetic regulation, molecular function, and cellular
maps in craniofacial development. Critically, because of practical unavailability of human embryonic craniofacial
tissue, there is a big gap between the abundant -omics and functional studies in murine craniofacial development
and large-scale human genetic studies of DOC diseases. In this proposal, we combine machine learning,
genomics, single-cell RNA sequencing (scRNA-seq), complex disease genetics, developmental biology to
design novel methods aiming to decode complex genetic regulation and cellular maps during craniofacial
development. We propose three specific aims. Aim 1. To develop a deep learning method, DeepFace, for
characterizing and prioritizing genetic variants and regulation during craniofacial development. DeepFace is
designed to decipher functional impact of noncoding variants and will be the first deep learning method to
integrate cross-species functional features in craniofacial development. We will validate DeepFace by using data
from genome-wide association studies (15 datasets) and case-parent trio-based whole genome sequencing (3
datasets) of orofacial clefts (OFCs). This validation will identify potential causal variants, both common and de
novo mutations, in OFCs. Aim 2. To develop deep learning methods for time-series scRNA-seq data analysis in
craniofacial development. We will develop novel algorithms including TTNNet for integrating time-series scRNA-
seq data and DrivAER for tracing developmental trajectories and identifying driving transcription factors in
craniofacial development. We will validate the methods using scRNA-seq datasets from the FaceBase
consortium and to-be-generated data for mouse palate formation. Aim 3. To experimentally validate and
characterize the top ranked novel mutations (Aim 1) and regulators (Aim 2). Building on our previous studies,
strong preliminary data and highly experienced team, this proposal is timely to develop machine learning
methods to effectively address the current gap between the genomics studies in murine craniofacial development
and human genetic studies of orofacial clefts. The successful completion will provide 1) the NIDCR research
community a suite of novel methods and analytical tools for genomic/epigenomic/scRNA-seq data, and 2) the
mechanistic assessment on the mutations/genes and transcriptional regulators th...

## Key facts

- **NIH application ID:** 10235696
- **Project number:** 1R01DE030122-01A1
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** Junichi Iwata
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $585,184
- **Award type:** 1
- **Project period:** 2021-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10235696

## Citation

> US National Institutes of Health, RePORTER application 10235696, Deep learning for decoding genetic regulation and cellular maps in craniofacial development (1R01DE030122-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10235696. Licensed CC0.

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