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Chromatin Immunoprecipitation- ChIP

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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.
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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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Updated: Nov 16, 2025

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

Published on: October 5, 2018

A sequence-based deep learning approach to predict CTCF-mediated chromatin loop.

Hao Lv¹, Fu-Ying Dao¹, Hasan Zulfiqar¹

¹Informational Biology at University of Electronic Science and Technology of China.

Briefings in Bioinformatics

|February 26, 2021

Summary

This summary is machine-generated.

Deep-loop, a new computational model, accurately predicts chromosome 3D architecture and chromatin loops. This method integrates genomic sequence features for efficient cell type-specific loop identification.

Keywords:

CTCF chromosome conformation deep learning loop sequence feature

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Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

The three-dimensional (3D) organization of chromosomes is vital for gene regulation and DNA replication.
CTCF-mediated chromatin loops are key structural elements, but their cell type specificity and experimental detection challenges limit large-scale studies.

Purpose of the Study:

To develop an efficient computational method for predicting chromatin loops.
To address the limitations of experimental techniques in studying cell type-specific chromatin architecture.

Main Methods:

Developed Deep-loop, a convolutional neural network model.
Integrated features including k-tuple nucleotide frequency, nucleotide pair spectrum encoding, position conservation, position scoring, and natural vectors.
Utilized cross-validation for performance assessment.

Main Results:

Deep-loop demonstrates excellent performance in identifying chromatin loops.
The model accurately predicts chromatin loops across different cell types.
The computational approach overcomes limitations of time-consuming experimental methods.

Conclusions:

Deep-loop offers a powerful and efficient tool for predicting chromatin loops.
This method facilitates large-scale analysis of cell type-specific 3D genome organization.
The freely available source code promotes wider research application.