# A Technique for Measuring Transcription Factor Activity

> **NIH NIH R01** · UNIVERSITY OF COLORADO · 2021 · $20,203

## Abstract

Summary
Almost every division of NIH has invested heavily in understanding transcription factors (TFs). TFs are the
managers of the cell, controlling everything from cell type to cellular response to stress. With their great power,
it is no wonder, many human disorders (cancer, familial platelet disorder, Waardenburg syndrome, etc.) result
from mutations in transcription factors. Moreover, over 75% of disease causing variants within the human
genome reside in regulatory regions, which are dense with TF binding sites. Currently we can measure the
location of TF binding, but binding does not equate with regulatory activity. Furthermore, binding analysis is
conducted one TF at a time. What is desperately needed is a technology that is able to measure the activity of
all TFs in a cell simultaneously. We have developed a novel approach, called eRNA profiling, that leverages
enhancer RNAs to infer the activity of all TFs in a cell simultaneously using the location of all eRNAs. The
primary goal of this diversity supplement extension request is to evaluate in vivo the limits of our computational
modeling towards predicting the activity of TFs. In this way, we seek to optimize our technology, making eRNA
profiling more accurate, fast and broadly applicable.

## Key facts

- **NIH application ID:** 10351235
- **Project number:** 3R01GM125871-03S1
- **Recipient organization:** UNIVERSITY OF COLORADO
- **Principal Investigator:** Robin DeAnne Dowell-Deen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $20,203
- **Award type:** 3
- **Project period:** 2018-09-05 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10351235

## Citation

> US National Institutes of Health, RePORTER application 10351235, A Technique for Measuring Transcription Factor Activity (3R01GM125871-03S1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10351235. Licensed CC0.

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