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Related Concept Videos

Chromatin Immunoprecipitation- ChIP02:36

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...
DNA Microarrays02:34

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 15, 2026

An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues
10:41

An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues

Published on: April 5, 2018

Comparing genome-wide chromatin profiles using ChIP-chip or ChIP-seq.

Frank Johannes1, René Wardenaar, Maria Colomé-Tatché

  • 1Groningen Bioinformatics Centre, University of Groningen, Kerklaan 30, Biologisch Centrum, 9751 NN Haren, The Netherlands. f.johannes@rug.nl

Bioinformatics (Oxford, England)
|March 9, 2010
PubMed
Summary
This summary is machine-generated.

New statistical methods enable simultaneous analysis of multiple ChIP-seq and ChIP-chip samples to compare epigenetic patterns. This approach aids in identifying genome-wide and locus-specific chromatin differences across tissues or individuals.

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A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies
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A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies

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Generation of High Quality Chromatin Immunoprecipitation DNA Template for High-throughput Sequencing (ChIP-seq)
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Generation of High Quality Chromatin Immunoprecipitation DNA Template for High-throughput Sequencing (ChIP-seq)

Published on: April 19, 2013

Related Experiment Videos

Last Updated: Jun 15, 2026

An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues
10:41

An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues

Published on: April 5, 2018

A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies
08:04

A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies

Published on: August 13, 2020

Generation of High Quality Chromatin Immunoprecipitation DNA Template for High-throughput Sequencing (ChIP-seq)
09:52

Generation of High Quality Chromatin Immunoprecipitation DNA Template for High-throughput Sequencing (ChIP-seq)

Published on: April 19, 2013

Area of Science:

  • Epigenetics and Genomics
  • Computational Biology
  • Statistical Genetics

Background:

  • Chromatin immunoprecipitation (ChIP) coupled with microarrays (ChIP-chip) and sequencing (ChIP-seq) offers high-resolution, genome-wide epigenomic profiling.
  • Comparative analysis of multiple ChIP samples is crucial for understanding developmental and population-level epigenetic variations.
  • Existing analytical methods are primarily designed for single-sample analysis, limiting their utility for multi-sample comparisons.

Purpose of the Study:

  • To develop a statistical framework for the simultaneous analysis of two or more ChIP samples.
  • To enable robust identification of both genome-wide and locus-specific epigenetic differences.
  • To overcome the limitations of current single-sample-focused analytical approaches.

Main Methods:

  • Introduction of a parametric classification approach for multi-sample ChIP data analysis.
  • Utilizing multivariate mixture models to infer chromatin differences based on biological assumptions.
  • Employing an incremental version of the Expectation-Maximization algorithm (IEM) for parameter estimation.
  • Demonstrating scalability and application to diverse ChIP-chip and ChIP-seq datasets.

Main Results:

  • The proposed method efficiently analyzes multiple ChIP samples simultaneously.
  • It accurately identifies locus-specific and genome-wide chromatin variations.
  • Performance evaluation shows favorable comparison against existing ChIP-chip and ChIP-seq software.

Conclusions:

  • The developed parametric classification approach provides a valuable tool for comparative epigenomics.
  • It serves as an effective first-pass algorithm for identifying candidate regulatory regions.
  • Recommended for initial screening of epigenomic data, potentially followed by targeted second-pass analysis.