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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 microarray-based...

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Updated: Jun 19, 2026

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

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

Published on: November 13, 2017

Incorporating network diffusion and peak location information for better single-cell ATAC-seq data analysis.

Jiating Yu^1,2,3, Jiacheng Leng^2,3,4, Zhichao Hou^2,3

¹School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing 210044, China.

Briefings in Bioinformatics

|March 17, 2024

Summary

This summary is machine-generated.

SCARP, a new computational method, enhances single-cell ATAC-seq analysis by refining network diffusion. It effectively addresses data challenges, improving cell clustering and revealing transcriptional regulation insights.

Keywords:

accessible regions cellular heterogeneity genomic distance network diffusion scATAC-seq

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Last Updated: Jun 19, 2026

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

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

Published on: November 13, 2017

ATAC-seq Assay with Low Mitochondrial DNA Contamination from Primary Human CD4+ T Lymphocytes

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ATAC-Seq Optimization for Cancer Epigenetics Research

ATAC-Seq Optimization for Cancer Epigenetics Research

Published on: June 30, 2022

Area of Science:

Genomics
Computational Biology
Epigenetics

Background:

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) offers insights into epigenetic heterogeneity and transcriptional regulation.
Analyzing scATAC-seq data is challenging due to high sparsity, binarization, and dimensionality.
There is a need for advanced computational methods to extract more information from scATAC-seq datasets.

Purpose of the Study:

To develop a novel computational method for comprehensive scATAC-seq data analysis.
To address the inherent challenges of scATAC-seq data, including missing signals and high dimensionality.
To improve the elucidation of cell heterogeneity and transcriptional regulation using scATAC-seq data.

Main Methods:

Proposed a network diffusion-based computational method named Single-Cell ATAC-seq Analysis via Network Refinement with Peaks Location Information (SCARP).
Formulated Network Refinement diffusion under a graph theory framework to aggregate information from different network orders.
Incorporated genomic distance information between adjacent peaks to depict co-accessibility.

Main Results:

SCARP effectively compensates for missing signals in scATAC-seq data.
Achieved superior cell clustering, enabling better identification of cell heterogeneity and subpopulations.
Successfully depicted peak co-accessibility relationships, offering new insights into transcriptional regulation.
Identified disease-related genes in KEGG pathways and predicted cis-regulatory interactions.

Conclusions:

SCARP is a promising computational tool for comprehensive scATAC-seq data analysis.
The method effectively handles data sparsity and dimensionality, improving biological insights.
SCARP facilitates deeper understanding of epigenetic heterogeneity and gene regulation.