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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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ChIP-on-Chip Analysis methods for Affymetrix Tiling Arrays.

Sean J Yoder¹, Steven A Enkemann

¹Microarray Core Facility, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.

Methods in Molecular Biology (Clifton, N.J.)

|April 22, 2009

Summary

This summary is machine-generated.

This review guides researchers through Chromatin Immunoprecipitation-chip (ChIP-Chip) microarray data analysis. It details common applications and workflows for identifying ChIP-binding sites, aiding tool selection.

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Area of Science:

Genomics and Molecular Biology
Bioinformatics and Computational Biology

Background:

Chromatin Immunoprecipitation-chip (ChIP-Chip) microarray data analysis is complex, involving multiple sequential steps.
Numerous software applications exist for each stage of the ChIP-Chip analysis pipeline, from raw data processing to peak identification.
Selecting appropriate tools requires careful consideration of experimental specifics and researcher computational expertise.

Purpose of the Study:

To provide a comprehensive review of commonly available applications for Affymetrix ChIP-Chip data analysis.
To outline the general workflow for ChIP-Chip data analysis.
To empower researchers in selecting the most suitable applications and guide them through the analysis process.

Main Methods:

Review of existing ChIP-Chip data analysis software and platforms.
Description of a typical ChIP-Chip analysis workflow.
Evaluation criteria for application suitability based on experimental design and user technical skills.

Main Results:

Identification of key applications for Affymetrix ChIP-Chip data processing and analysis.
A structured overview of the ChIP-Chip data analysis pipeline.
Guidance on matching specific analytical needs with available computational tools.

Conclusions:

Effective ChIP-Chip data analysis relies on informed selection of appropriate bioinformatics tools.
Understanding the analysis workflow and available applications facilitates successful identification of ChIP-binding sites.
This review serves as a valuable resource for researchers navigating ChIP-Chip data analysis.