# Mechanisms of Transcriptional Control Revealed by Nascent Transcript Sequencing

> **NIH NIH R01** · HARVARD MEDICAL SCHOOL · 2021 · $513,500

## Abstract

Large consortium efforts have collected hundreds of genome-wide datasets that have delineated myriad
regulatory regions, transcription factor binding sites and large numbers of coding and non-coding transcripts.
Even with this massive amount of data, it remains a significant challenge to determine how the mapped elements
function together in regulatory networks. This is due in large part to our inability to accurately and quantitatively
detect all forms of nascent transcription, the instantaneous output of transcriptional regulation. Moreover, our
understanding of global gene regulation is restricted by a lack of computational tools that seamlessly integrate
genome-wide datasets. The overall goal of this proposal is to maximize the impact of nascent transcriptome
studies and enable facile integration with other functional genomic data. My group developed native elongating
transcript sequencing (NET-seq), that enables the strand-specific nucleotide-resolution mapping of RNA
polymerase density, highlighting all transcriptional activity regardless of transcript half-lives and revealing precise
positions of Pol II pausing where regulatory control is applied. Here, we will develop a new version of NET-seq
– NET-seq 2.0 – that enables the routine, scalable and flexible application to diverse human cell types (or any
eukaryotic system). Moreover, we will increase the potential of NET-seq analysis by developing two innovative
bioinformatics strategies to seamlessly integrate NET-seq data with other genome-wide datasets that will have
applications beyond NET-seq studies. To demonstrate the broad utility of our integrated approach, we will study
regulatory networks and cell differentiation for which instantaneous nascent transcriptional analysis will be highly
impactful. In Aim 1, our goal is to make NET-seq easier, cheaper, and more flexible. Our improvements will
reduce background and increase usable reads, dramatically reduce cell input requirements (100-1000-fold),
enable dense, region-specific RNA transcription analyses, and enable quantitative comparisons between
samples and conditions. In Aim 2, we will determine transcription kinetics through integrating NET-seq with
metabolic RNA labeling (TT-seq) data which report local synthesis rates. This integrative approach yields a rich
transcriptional phenotype that we will use to develop gene regulatory network models. In Aim 3, we will create
new computational algorithms that circumvent the need to determine each molecular event separately, and
instead infer the status of unmapped events using information-rich datasets, such as NET-seq. We will use
integrative deep neural networks (`deep-learning') that use available genome-wide datasets to predict
unavailable datasets from data already on hand. We will apply this approach to study erythropoiesis using a well-
defined primary human hematopoietic differentiation system by a time series NET-seq and DNase-seq analysis.
These data will inform deep neural net...

## Key facts

- **NIH application ID:** 10171878
- **Project number:** 5R01HG007173-09
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** Lee Stirling Churchman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $513,500
- **Award type:** 5
- **Project period:** 2013-04-01 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10171878

## Citation

> US National Institutes of Health, RePORTER application 10171878, Mechanisms of Transcriptional Control Revealed by Nascent Transcript Sequencing (5R01HG007173-09). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10171878. Licensed CC0.

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