Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

SDP-ChroNet: A shared dilated pooling network for chromatin interaction prediction.

Gene·2026

Same author

EPIFBMC: A New Model for Enhancer-Promoter Interaction Prediction.

International journal of molecular sciences·2025

Same author

Prokaryotic expression and potential application of the truncated PCV-2 capsid protein.

Virologica Sinica·2010

Same author

Serum and urinary cell-free MiR-146a and MiR-155 in patients with systemic lupus erythematosus.

The Journal of rheumatology·2010

Same author

Peptide dendrimers as efficient and biocompatible gene delivery vectors: Synthesis and in vitro characterization.

Journal of controlled release : official journal of the Controlled Release Society·2010

Same author

The amplification and evolution of orthologous 22-kDa α-prolamin tandemly arrayed genes in coix, sorghum and maize genomes.

Plant molecular biology·2010

Same journal

Mutation T71R enhanced the structural stability and functional activity of wild type superoxide dismutase cloned from soil metagenome.

Gene·2026

Same journal

Reduced ATXN1 expression as an adverse prognostic indicator in Acute myeloid leukemia.

Gene·2026

Same journal

Constructing regulatory networks of Rubisco post-translational modifications: a novel avenue for engineering environment adaptive plants.

Gene·2026

Same journal

Traumatic brain injury enhances fracture healing by upregulating VNN1 to activate the Wnt/β-catenin signaling pathway.

Gene·2026

Same journal

Single-cell transcriptomics reveals CCL2-mediated macrophage-endothelial cell interactions drive apoptosis in varicose veins.

Gene·2026

Same journal

Development of a gene signature related to phospholipid metabolism for prognosis and therapeutic Prediction in Osteosarcoma: Focus on VAC14.

Gene·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 15, 2025

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

TECM-ChI: A TECM network-based method for chromatin interaction prediction.

Yu Chen¹, Chengfeng Bao¹, Gang Wang¹

¹College of Computer and Control Engineering, Northeast Forestry University, Hexing Road 26, 150040 Heilongjiang Province, China.

|July 13, 2025

Summary

This summary is machine-generated.

A new model, TECM-ChI, accurately predicts chromatin interactions using DNA sequences and genomic features. This method improves upon existing techniques, offering enhanced accuracy for understanding genome function and disease development.

Keywords:

Chromatin interactions Deep learning Gene expression Genomic characteristics Three-dimensional genome organization

More Related Videos

Genome-wide Mapping of Protein-DNA Interactions with ChEC-seq in Saccharomyces cerevisiae

Genome-wide Mapping of Protein-DNA Interactions with ChEC-seq in Saccharomyces cerevisiae

Published on: June 3, 2017

The ChIP-exo Method: Identifying Protein-DNA Interactions with Near Base Pair Precision

The ChIP-exo Method: Identifying Protein-DNA Interactions with Near Base Pair Precision

Published on: December 23, 2016

Related Experiment Videos

Last Updated: Sep 15, 2025

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

Genome-wide Mapping of Protein-DNA Interactions with ChEC-seq in Saccharomyces cerevisiae

Genome-wide Mapping of Protein-DNA Interactions with ChEC-seq in Saccharomyces cerevisiae

Published on: June 3, 2017

The ChIP-exo Method: Identifying Protein-DNA Interactions with Near Base Pair Precision

The ChIP-exo Method: Identifying Protein-DNA Interactions with Near Base Pair Precision

Published on: December 23, 2016

Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Chromatin interactions are crucial for genome regulation and disease development.
Traditional experimental methods for studying chromatin interactions are resource-intensive.
Existing computational methods struggle with data imbalance and prediction accuracy.

Purpose of the Study:

To develop a novel computational model for predicting chromatin interactions.
To address limitations of existing methods, including data imbalance and low accuracy.
To leverage DNA sequences and genomic features for improved prediction.

Main Methods:

Developed the TECM-ChI model incorporating DNA sequence and genomic feature analysis.
Implemented the Forward Combine Reverse (FCR) method for sample balancing.
Utilized a Three-Encoding module for comprehensive sequence feature extraction.
Employed the CMANet network, combining convolutional and attention mechanisms for feature recognition.

Main Results:

TECM-ChI demonstrated superior performance compared to existing models across K562, IMR90, and GM12878 cell lines.
Achieved accuracy improvements of 4.68%, 1.31%, and 2.41% on the respective datasets.
Validated the model's effectiveness and generalization ability through rigorous experiments.

Conclusions:

TECM-ChI offers a significant advancement in predicting chromatin interactions.
The model's accuracy and generalization capabilities hold promise for biomedical research.
The developed computational approach provides a valuable tool for studying genome regulation and disease mechanisms.