A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

Summary

This protocol provides a streamlined computational pipeline for quantifying nascent enhancer transcripts. By integrating chromatin accessibility, chromatin feature, and transcriptional data, it enables accurate detection and strand-specific analysis of enhancer activity in complex intragenic regions, while remaining accessible to researchers without extensive bioinformatics training.

Abstract

Core cis-regulatory elements known as enhancers play a central role in enabling precise transcriptional regulation of target genes that control diverse cellular functions and developmental processes. These enhancers are often transcribed in both directions, producing long non-coding transcripts referred to as enhancer RNAs (eRNAs). The expression of eRNAs is closely linked to active chromatin features, such as H3K27ac and co-activator recruitment, and functionally contributes to the transcriptional activation of target genes. Nevertheless, the detection and quantification of eRNAs remain challenging, especially when they overlap with host-gene transcription. To address this, we present a standardized, user-friendly computational workflow for analyzing enhancer transcription from nascent RNA sequencing data. The protocol guides users through data preprocessing, read mapping, and quality control, followed by strand-specific quantification of enhancer-associated transcription, with dedicated procedures for intragenic enhancers where signal assignment is complex. Visualization modules enable clear inspection of enhancer activity across genomic contexts, and built-in options support analyses of both intergenic and intragenic enhancers. Designed for researchers with limited bioinformatics expertise, this workflow provides a practical framework for consistent, reproducible, and scalable studies of enhancer transcription, facilitating broader application of enhancer biology across diverse systems.

Introduction

Enhancers are cis-regulatory DNA elements that control target gene transcription by organizing chromatin looping and recruiting the transcriptional machinery^1,2,3. Their tissue-specific activity enables precise regulation during development and lineage commitment^4,5,6,7,8. Active enhancers show characteristic chromatin features such as H3K4me1 (histone H3 Lysine 4 mono-methylation) and H3K27ac (histone H3 Lysine 27....

Protocol

NOTE: All raw datasets used in workflow are listed in Table 1. Details of bioinformatics tools are provided in the Table of Materials. The number of threads used in this pipeline can be adjusted by modifying the THREADS variable defined at the top of each script. Users may increase the number to accelerate analysis depending on the user's CPU resources.
After each step, a log file is generated. For quick failure checks, use commands like cat StepXX_log.txt; grep -qF "ERROR" StepXX_log.txt && echo "ERROR found. Fix before next step." || echo "OK: no ERROR markers". If ....

Results

Schematic workflow for enhancer transcript quantification pipeline
Publicly available ChIP-seq (H3K27ac, H3K4me1), ATAC-seq, and GRO-seq datasets (Table 1) were processed with a standardized pipeline designed primarily for validation. Adapter trimming and quality filtering were performed with Trim Galore and Cutadapt, followed by alignment to the mm10 reference genome using Bowtie2 (detailed in protocol Step 6). For ChIP-seq and ATAC-seq, peaks were i.......

Discussion

Following the discovery of enhancer-derived transcripts^13,14,15, accurately quantifying eRNAs has remained a major challenge, particularly in intragenic contexts where eRNAs often overlap with host gene transcripts. This overlap complicates strand assignment and signal attribution, making it difficult to distinguish genuine enhancer transcription from background gene expression^13,

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
bedtools	Quinlan Lab, University of Utah	v2.31.1	Utilities for editing BED files
bowtie2	Langmead Lab, Johns Hopkins University	v2.5.4	Multi-threaded aligner for mapping reads to a reference genome
cowplot	Wilke Lab, University of Texas	v1.2.0	Tools for combining and aligning ggplot2-based figures
cutadapt	Science For Life Laboratory, Stockholm University	v5.1	Adaptor and poly-A/G tail trimmer
deeptools	Bioinformatics Facility, Max Planck Institute	v3.5.6	Read counting tool for quantifying reads in defined genomic regions
fastqc	Babraham Bioinformatics, Babraham Institute	v0.12.1	Quality control for sequencing reads
featureCounts (subread)	Shi Lab, Monash University	v2.1.1	Raw read counting tools for specified genomic regions
homer	Benner Lab, University of California San Diego (UCSD)	v5.1	Toolkit for ChIP-seq, ATAC-seq, and nascent RNA analysis; includes tag directory creation and signal profiling
macs3	Chan Zuckerberg Initiative	v3.0.3	Peak calling for ChIP-seq and ATAC-seq datasets
pigz	.	v2.8	Multi-threaded compression tool for generating gzip-compressed files
sambamba	Petersburg State University	v1.0.1	Multi-threaded SAM/BAM file processing toolkit
samtools	Wellcome Trust Sanger Institute	v1.22.1	Tools for processing and manipulating SAM/BAM files
sra-tools	National Center for Biotechnology Information (NCBI)	v3.2.0	For downloading SRR files from the NCBI SRA database
tidyverse	Posit PBC	v2.0.0	Collection of R packages for data manipulation and visualization
trim-galore	Altos Labs, Cambridge Institute of Science	v0.6.10	Adapter and low-quality base trimming using multi-threading
Ubuntu 20.04			Developing and testing the pipeline
ucsc-bedgraphtobigwig	Kent Lab, University of California Santa Cruze	v482	Tools for generating bigWig signal tracks