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Updated: Feb 22, 2026

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

PETScan: score-based genome-wide association analysis of RNA-Seq and ATAC-Seq data.

Yajing Hao¹, Tal Kafri^2,3,4, Fei Zou^1,5

¹Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

Bioinformatics (Oxford, England)

|February 20, 2026

Summary

This summary is machine-generated.

PETScan efficiently analyzes gene expression and chromatin accessibility data, improving systems biology insights. This tool enhances genome-wide association studies by considering distant gene-regulatory element interactions.

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Chromatin Interaction Analysis with Paired-End Tag Sequencing ChIA-PET for Mapping Chromatin Interactions and Understanding Transcription Regulation

Chromatin Interaction Analysis with Paired-End Tag Sequencing ChIA-PET for Mapping Chromatin Interactions and Understanding Transcription Regulation

Published on: April 30, 2012

Area of Science:

Systems biology
Genomics
Bioinformatics

Background:

High-dimensional sequencing data like RNA-Seq (gene expression) and ATAC-Seq (chromatin accessibility) are crucial for systems biology.
Current methods often overlook long-range cis-regulatory interactions and fail to adequately model count-based sequencing data.
Understanding gene regulation requires analyzing associations between gene expression and chromatin accessibility.

Purpose of the Study:

To develop a computationally efficient method for genome-wide association analysis of RNA-Seq and ATAC-Seq data.
To address limitations of existing tools by incorporating distant regulatory element interactions and accommodating count-based data.
To provide a robust tool for uncovering gene regulatory mechanisms.

Main Methods:

Introduced PETScan, a PEak-Transcript Score-based association analysis method.
Utilized negative binomial models to accurately represent RNA-Seq count data.
Employed score tests and matrix calculations for computational efficiency, combined with empirical permutation and genomic control for valid p-value calculations.

Main Results:

PETScan demonstrated computational efficiency, achieving speeds three orders of magnitude faster than Wald tests.
The method identified significant gene-peak pairs comparable to existing approaches.
PETScan effectively analyzes genome-wide associations between gene expression and chromatin accessibility.

Conclusions:

PETScan offers a computationally efficient and statistically robust approach for analyzing high-dimensional sequencing data.
The tool enhances the study of gene regulatory mechanisms by considering both local and distant interactions.
PETScan provides valuable insights for systems biology research and is available as an R package.