# Computational Method for Identification of Master Transcription Factors in Craniofacial Tissues

> **NIH NIH R03** · UNIVERSITY OF IOWA · 2020 · $154,500

## Abstract

PROJECT SUMMARY
Craniofacial anomalies (CFA) are a heterogeneous group of congenital anomalies that
collectively represent the most common form of human structural birth defects. Proper
craniofacial development requires transcription factors to establish cell identities for all
participating cells in a precisely orchestrated spatial and temporal manner. Although half of the
~1,500 transcription factors are expressed in any given cell/tissue-type, only a handful of
master transcription factors (MTFs) are required to establish or change a cell’s identity. Our
long term goal is to identify MTFs and elucidate gene regulatory networks that control
development of craniofacial regions. The objective of this application is to identify MTFs
candidates by our computational model, MTFinder, with FaceBase’s transcriptome and
epigenome data of human cranial Neural Crest Cells (cNCCs) and sub-regions of the
developing mouse face at three critical stages. Current computational methods for MTFs
prediction suffer from high false positive rates because they only use transcriptome data.
Fortunately, recent studies have found that MTFs also have distinct epigenetic features. We
developed a MTFs prediction method termed “MTFinder” that incorporates both transcriptome
and epigenome data into a Bayesian statistics model. Our preliminary studies show that
MTFinder successfully rank all known MTFs of mouse embryonic stem cell(mESC) and liver
hepatocytes within top 20 out of all 1,500 transcription factors with their transcriptome (RNA-
seq) and epigenome (H3K27ac ChIP-seq) data. In this project, we will first evaluate and
determine the optimal configuration of MTFinder in various cell/tissue-types (Aim 1). And then
apply the optimal MTFinder configuration to FaceBase’s transcriptome and epigenome data to
identify potential MTFs for craniofacial cell/tissue-types including cNCCs and sub-regions of
the developing mouse face at three critical stages of development (Aim 2). At the completion
of this project, we will generate a list of known and novel MTFs candidates for craniofacial
cell/tissue-types that are critical for craniofacial development. These results will not only
significantly advance our understanding of transcriptional regulation during craniofacial
development but also provide potential MTFs for cell linage reprogramming, which holds great
promise for regenerative medicine, disease modeling, drug screening and other applications.

## Key facts

- **NIH application ID:** 9988396
- **Project number:** 5R03DE028354-02
- **Recipient organization:** UNIVERSITY OF IOWA
- **Principal Investigator:** Huojun Cao
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $154,500
- **Award type:** 5
- **Project period:** 2019-08-02 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9988396

## Citation

> US National Institutes of Health, RePORTER application 9988396, Computational Method for Identification of Master Transcription Factors in Craniofacial Tissues (5R03DE028354-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9988396. Licensed CC0.

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