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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...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Related Experiment Video

Updated: Jun 12, 2026

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

ChIP-PaM: an algorithm to identify protein-DNA interaction using ChIP-Seq data.

Song Wu¹, Jianmin Wang, Wei Zhao

¹Department of Biostatistics, St, Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA. song.wu@stjude.org

Theoretical Biology & Medical Modelling

|June 8, 2010

Summary

This summary is machine-generated.

ChIP-PaM accurately identifies transcription factor binding sites from ChIP-Seq data by integrating tag counts, patterns, and motif searches. This method improves discovery accuracy and reduces false positives without needing technical controls.

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Chromatin Interaction Analysis with Paired-End Tag Sequencing (ChIA-PET) for Mapping Chromatin Interactions and Understanding Transcription Regulation

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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
Molecular Biology
Bioinformatics

Background:

Chromatin immunoprecipitation sequencing (ChIP-Seq) is crucial for identifying genomic regulator-DNA interactions, particularly transcription factor binding sites.
High costs and false discovery rates limit the application of current ChIP-Seq analysis methods.

Purpose of the Study:

To develop a novel algorithm, ChIP-PaM, for accurate identification of transcription factor target regions in ChIP-Seq datasets.
To overcome the limitations of existing methods by improving accuracy and reducing false discoveries.

Main Methods:

ChIP-PaM utilizes a protein-DNA binding pattern by integrating three evidence lines: tag count modeling, tag count distribution pattern matching, and genome-wide motif searching.
A novel data-based two-step empirical false discovery rate (eFDR) procedure is employed to combine these evidence lines.
The algorithm does not require technical controls and relies solely on ChIP-Seq data.

Main Results:

ChIP-PaM effectively discriminates true transcription factor (TF) binding regions from falsely enriched regions using only ChIP-Seq data.
The algorithm demonstrated its capability through analysis of real genomic data.

Conclusions:

ChIP-PaM offers more accurate binding site discovery compared to existing methods.
The algorithm maintains comparable statistical power while enhancing precision in identifying transcription factor binding sites.