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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Jan 10, 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

Computational Analyses and Challenges of Single-cell ATAC-seq.

Chenfei Wang^1,2, Jiaojiao Zhou³, Hong Zhang³

¹Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, Tongji Hospital, School of Life Science and Technology, Tongji University, Shanghai 200092, China.

Genomics, Proteomics & Bioinformatics

|November 21, 2025

Summary

This review covers computational workflows for single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) analysis. It addresses data sparsity challenges and explores emerging AI and spatial technologies for gene regulation studies.

Keywords:

Computational analysis Epigenetic landscape Gene regulation Multiomics integration scATAC-seq

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Last Updated: Jan 10, 2026

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

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Published on: June 30, 2022

Area of Science:

Genomics
Epigenetics
Computational Biology

Background:

Single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) is vital for understanding cell-specific epigenetic landscapes and gene regulation.
scATAC-seq data inherently suffers from sparsity and missing data due to low genomic coverage per cell, posing significant computational challenges.

Purpose of the Study:

To provide a comprehensive overview of current computational workflows for scATAC-seq data analysis.
To guide researchers in selecting appropriate tools and developing customized pipelines for gene regulation and cellular diversity exploration.
To highlight emerging trends including deep learning, AI foundation models, and spatial ATAC-seq technologies.

Main Methods:

Review of published computational methods for scATAC-seq analysis, from preprocessing to downstream applications.
Survey of scATAC-seq databases and curated resources.
Discussion of advanced computational approaches, including deep learning, AI, and spatial ATAC-seq.

Main Results:

Identification of key computational steps in scATAC-seq analysis: quality control, alignment, peak calling, dimensionality reduction, clustering, gene regulation scoring, and cell type annotation.
Cataloging of essential scATAC-seq databases for accessible data resources.
Overview of cutting-edge deep learning, AI, and spatial ATAC-seq computational strategies.

Conclusions:

A clear understanding of current scATAC-seq methodologies is crucial for effective data analysis.
The review equips researchers with the knowledge to navigate the complex computational landscape of scATAC-seq.
Future directions involve leveraging AI and spatial technologies for more sophisticated epigenetic analyses.