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Chromatin Immunoprecipitation- ChIP

Chromatin Immunoprecipitation- ChIP

Chromatin immunoprecipitation, or ChIP, is an antibody-based technique used to identify sites on DNA that bind to transcription factors of interest or histone proteins. It also helps determine the type of histone modifications such as acetylation, phosphorylation, or methylation.
Types of ChIP
ChIP can be divided into two types - X-ChIP and N-ChIP. X-ChIP involves in vivo cross-linking of histones and regulatory proteins to DNA, fragmenting the DNA by sonication, and isolating the protein-DNA...

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Related Experiment Video

Updated: Jul 1, 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

scAuto as a comprehensive framework for single-cell chromatin accessibility data analysis.

Meiqin Gong¹, Yun Yu², Zixuan Wang³

¹Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.

Computers in Biology and Medicine

|March 5, 2024

Summary

This summary is machine-generated.

We developed scAuto, a deep learning framework for analyzing single-cell chromatin accessibility data. This tool enhances intercellular heterogeneity insights and offers a user-friendly web server for precision medicine applications.

Keywords:

Chromatin accessibility Data analysis tools Deep learning Single-cell genomics Web server

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Last Updated: Jul 1, 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

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

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Published on: November 13, 2017

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Published on: April 21, 2023

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Interpreting single-cell chromatin accessibility data is vital for understanding cellular heterogeneity.
Existing computational methods lack a comprehensive framework and user-friendly tools for analysis.

Purpose of the Study:

To develop a deep learning-based framework, scAuto, for analyzing single-cell chromatin accessibility data.
To provide an accessible online platform for researchers with varying programming expertise.

Main Methods:

Developed scAuto, a deep learning framework utilizing pre-training and fine-tuning on DNA sequences.
Applied a self-supervised pre-training approach on unlabeled human genome data, followed by supervised fine-tuning for scATAC-seq data.
Created an interactive web server integrating tutorial-style interfaces.

Main Results:

scAuto demonstrated superior performance in chromatin accessibility prediction, single-cell clustering, and data denoising on the Buenrostro2018 dataset.
The developed web server offers a user-friendly platform for single-cell chromatin accessibility analysis.
The framework effectively learns DNA sequence grammar for biological interpretation.

Conclusions:

scAuto provides a powerful and accessible tool for single-cell chromatin accessibility data analysis.
This framework is expected to advance the understanding of intercellular heterogeneity and support precision medicine development.
The integrated web server democratizes access to advanced computational tools in genomics.