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Related Experiment Video

Updated: Sep 13, 2025

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

Cell-Type Annotation for scATAC-Seq Data by Integrating Chromatin Accessibility and Genome Sequence.

Guo Wei¹, Long Wang¹, Yan Liu²

¹State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210000, China.

|July 29, 2025

Summary

scAttG improves cell-type annotation for single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) data. This deep learning framework integrates genomic sequence features for more accurate and robust cell identification.

Keywords:

convolutional neural networks cross-omics genome graph attention networks

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Last Updated: Sep 13, 2025

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 Optimization for Cancer Epigenetics Research

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Published on: June 30, 2022

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

Area of Science:

Genomics
Epigenetics
Computational Biology

Background:

Single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) provides single-cell resolution of chromatin accessibility.
Existing cell annotation methods for scATAC-seq data have limitations, including cross-omics alignment issues and intra-omics batch effects.

Purpose of the Study:

To develop a novel deep learning framework for robust and accurate cell-type annotation using scATAC-seq data.
To address the limitations of current cross-omics and intra-omics annotation methods.

Main Methods:

Proposed scAttG, a deep learning framework integrating Graph Attention Networks (GATs) and Convolutional Neural Networks (CNNs).
Incorporated genomic sequence information corresponding to scATAC-seq peaks into the model.
Evaluated performance across multiple scATAC-seq datasets.

Main Results:

scAttG demonstrated enhanced robustness and accuracy in cell-type annotation.
The framework effectively captured both chromatin accessibility signals and genomic sequence features.
Experimental results showed scAttG performed favorably compared to existing methods.

Conclusions:

scAttG offers a significant advancement in single-cell chromatin accessibility-based cell-type annotation.
Integrating genomic sequence information improves the accuracy and reliability of scATAC-seq data analysis.
The proposed method provides a competitive alternative for epigenetic heterogeneity and cellular differentiation studies.