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Related Concept Videos

Chromatin Immunoprecipitation- ChIP02:36

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.
Types of ChIP
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The extent of chromatin compaction can be studied by staining chromatin using specific DNA binding dyes. Under the microscope, the dense-compacted regions take up more dye, appearing darker, while the less-compact areas take up less dye and appear lighter. Based on the compaction level, chromatins are classified into two primary forms – euchromatin and heterochromatin.
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An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues
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ChAsE: chromatin analysis and exploration tool.

Hamid Younesy1, Cydney B Nielsen2, Matthew C Lorincz3

  • 1Graphics Usability and Visualization Lab, Simon Fraser University, Burnaby, Canada Canada's Michael Smith Genome Sciences Centre, Vancouver, Canada Biomedical Research Centre, University of British Columbia, Vancouver, Canada.

Bioinformatics (Oxford, England)
|July 6, 2016
PubMed
Summary
This summary is machine-generated.

We introduce ChAsE, a user-friendly application for visualizing and clustering epigenomic data, like ChIP-seq. This tool aids biologists and bioinformaticians in exploring complex datasets for enhanced research insights.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Epigenomic data analysis, particularly from ChIP-seq experiments, requires specialized tools for effective visualization and exploration.
  • Existing methods may lack the interactivity and user-centric design needed for complex biological datasets.

Purpose of the Study:

  • To develop ChAsE, a cross-platform desktop application for interactive visualization, exploration, and clustering of epigenomic data.
  • To enhance usability and interactivity in epigenomic data analysis through close collaboration with biologists and bioinformaticians.

Main Methods:

  • ChAsE employs k-means clustering for data analysis.
  • It allows specifying signal presence/absence in epigenetic data and performing set operations between clusters.
  • Interactive heat maps and profile plots are utilized for results exploration.

Main Results:

  • ChAsE provides an interactive interface for visualizing and exploring epigenomic data.
  • The application supports clustering and set operations for in-depth data analysis.
  • Results can be exported as high-quality figures for publications.

Conclusions:

  • ChAsE offers a powerful and user-friendly solution for epigenomic data analysis.
  • The application facilitates interactive exploration and clustering, aiding in the interpretation of ChIP-seq and similar data.
  • ChAsE is available as open-source software with comprehensive tutorials.