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

Our group investigate enhancer epigenetics and epitranscriptomics, focusing on developing accurate method to quantify enhancer RNAs as indicators of enhancer activation. Because intragenic enhancers were with the host transcripts, distinguishing enhancer DER signals and quantifying them precise is challenging. To begin open the terminal and type the command to prepare the necessary files and references for enhancer identification.

Type the command in the terminal to enter the working directory. Then run the command that copies the enhancer identification scripts into the current directory. Then run the corresponding command to generate BED files for promoter regions, gene bodies and protein coding genes using gen code annotation.

To prepare the ATAC-seq and histone ChIP-seq files for enhancer identification. Enter the appropriate pre-processing command in the terminal. Next, run the command to define and classify enhancers using chromatin peak data.

Type the command to assign temporary strand information to the intergenic enhancer BED file. Next, enter the command to determine the correct strand direction for intergenic enhancers that overlap with genes located on both strands. Afterward, calculate strand specific RPKM for genes overlapping enhancers on the same strand.

Then enter the command to finalize strand assignment for intergenic enhancers. Assign the final strand information to all intergenic enhancers and their corresponding summit regions. Type the command in the terminal to move to the pipeline root directory.

Then use the appropriate command to copy the script for preparing downstream analysis Using the appropriate command, prepare all necessary files for enhancer aggregation, GRO-seq signal processing and enhancer RNA quantification. Next, type the script to enter the working directory and then copy the necessary scripts for downstream analysis. To create aggregation plots, showing chromatin signal patterns around each type of enhancer summit.

Type the corresponding command in the terminal. Once the prompt returns enter the command to quantify enhancer RNA expression levels from GRO-seq using feature counts. Finally run the script to visualize and compare enhancer RNA expression levels across enhancer groups using R.In the GRO-seq dataset.

Polynucleotide tails were present before trimming and successfully removed after applying the trimming parameters impaired end ATAC-seq datasets NextEra adapter sequences were detected in both red pairs before trimming and were removed after pre-processing ChIP-seq data sets for H3K27 acetylation showed minimal adapter contamination requiring only default trimming. Filtering of ATAC-seq data yielded 71, 165 non-motor open chromatin regions, which included 47, 317 enhancer regions, of which 23, 147 were classified as active enhancers and 24, 170 as non-active enhancers based on overlap with histone marks, among all enhancer regions 45.2%were intergenic and 54.8%were intragenic. This distribution was similar across non-active and active enhancer categories.

An intergenic enhancer upstream of the NENO gene showed strong enrichment for ATAC-seq, H3K4 mono methylation, H3K27 acetylation and GRO-seq signals. An intragenic enhancer near the CHD2 gene displayed similar chromatin and transcriptional features as the NENOG enhancer with clear signals in all four data sets. Aggregation plots showed that active enhancers had coordinated enrichment of ATAC-seq, H3K4 monomethyl, and H3K27 acetylation signals.

While non-active enhancers lacked H3K27 acetylation enrichment, but retained strong ATAC-seq and H3K4 monomethyl signals. Active enhancers showed significantly higher GRO-seq derived transcription than non-active enhancers in both intergenic and intragenic regions. We assemble those trend aware eRNA computation pipeline validated by activity dependent transcription in intergenic or intragenic context.

We address DER of a standardized accessible eRNA quantification, especially for intragenic enhancers confounded by host genome. We'll revise self-driven identification, separate operating transcripts from both trends and benchmark analysis pipeline across genome assemblies, experimental protocols, and cell types.