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Related Experiment Video

Updated: Dec 23, 2025

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Prenet: Predictive network from ATAC-SEQ data.

Nazmus Salehin^1,2, Patrick P L Tam^1,2, Pierre Osteil^1,2

¹Embryology Unit, Children's Medical Research Institute, The University of Sydney, Westmead, NSW 2145, Australia.

Journal of Bioinformatics and Computational Biology

|April 28, 2020

Summary

This summary is machine-generated.

This study introduces PreNet, a novel pipeline using transposase-accessible chromatin sequencing (ATAC-seq) data to identify genuine transcription factor footprints and predict gene regulatory networks. PreNet enhances understanding of molecular pathways from sequencing data.

Keywords:

ATAC-seq footprints gene regulatory network

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Mapping Genome-wide Accessible Chromatin in Primary Human T Lymphocytes by ATAC-Seq

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Published on: November 13, 2017

Area of Science:

Genomics and Molecular Biology
Epigenetics
Bioinformatics

Background:

Transposase-accessible chromatin sequencing (ATAC-seq) is a key method for studying chromatin accessibility across cell types.
Transcription factor binding sites, identified as footprints in ATAC-seq data, are crucial for gene regulation.
Existing footprint identification methods suffer from high false positive rates, limiting their utility.

Purpose of the Study:

To develop a novel computational approach for accurately identifying genuine transcription factor footprints from ATAC-seq data.
To integrate footprint analysis with differentially accessible chromatin (DACs) regions to build predictive gene regulatory networks (PreNets).
To demonstrate the utility of PreNet in uncovering molecular regulatory pathways.

Main Methods:

Development of a new bioinformatics pipeline integrating DACs and bona fide footprints derived from ATAC-seq data.
Utilized state-of-the-art algorithms for DACs identification (e.g., DESeq2).
Implemented a novel strategy to identify genuine footprints, overcoming limitations of previous methods.

Main Results:

Successfully generated a Predictive gene regulatory Network (PreNet) solely from ATAC-seq data.
The PreNet pipeline effectively identifies genuine footprints, reducing false positives compared to existing methods.
Demonstrated that PreNet can reveal significant molecular regulatory pathways within specific cell types.

Conclusions:

The developed PreNet pipeline offers a robust method for analyzing ATAC-seq data to predict gene regulatory networks.
PreNet facilitates a deeper understanding of gene expression regulation by accurately identifying transcription factor binding and interactions.
This approach provides a powerful tool for exploring complex molecular mechanisms in various biological contexts.