# A cloud-based learning module to analyze ATAC-seq and single cell ATAC-seq data

> **NIH NIH P20** · UNIVERSITY OF NEBRASKA MEDICAL CENTER · 2024 · $106,732

## Abstract

Abstract
The recent surge in genomic datasets can be attributed to advancements in genomic methodologies and the
availability of user-friendly kits, enabling genome-wide research across a diverse array of scientific disciplines.
These datasets harbor invaluable information, facilitating groundbreaking conclusions. Among these
methodologies, ChIP-seq to measure the chromatin occupancy of proteins has been a staple for many
researchers. CUT&RUN and CUT&Tag are two new methods measuring protein occupancy and require their
own bioinformatic considerations. These technologies have transformed our understanding of TF-mediated
gene regulation and the responsiveness of chromatin modifications. Despite the recognized value of these
data, the analysis often becomes a bottleneck due to the need for bioinformatic expertise and specific
computational resources. We will address these challenges and aid researchers in integrating chromatin
occupancy data with chromatin accessibility (ATAC-seq) and gene expression (RNA-seq) taught in other
NIH/NIGMS Sandbox modules. Therefore, we propose the development of cloud-based training focused on
Chromatin Occupancy by Next-Generation Sequencing, including ChIP-seq, CUT&RUN, and CUT&Tag. By
integrating these with other Sandbox modules (ATAC-seq and RNA-seq), users will learn how to perform basic
processing steps as well as critical downstream analyses. Our approach will use interactive lessons in Jupyter
notebooks that follow an example analysis of p63, H3K27ac in relation to the BAF complex. This module
targets a broad audience, including students, postdocs, INBRE scholars, and researchers with minimal or no
programming background. The project leaders, who are experienced in teaching and using these methods, are
uniquely qualified to develop this training module. Through this initiative, we aim to democratize access to
bioinformatics training, facilitating the analysis of chromatin occupancy genome-wide.

## Key facts

- **NIH application ID:** 11037534
- **Project number:** 3P20GM103427-23S1
- **Recipient organization:** UNIVERSITY OF NEBRASKA MEDICAL CENTER
- **Principal Investigator:** PAUL L SORGEN
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $106,732
- **Award type:** 3
- **Project period:** 2001-09-30 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11037534

## Citation

> US National Institutes of Health, RePORTER application 11037534, A cloud-based learning module to analyze ATAC-seq and single cell ATAC-seq data (3P20GM103427-23S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/11037534. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*