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Related Concept Videos

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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Starr: Simple Tiling ARRay analysis of Affymetrix ChIP-chip data.

Benedikt Zacher1, Pei Fen Kuan, Achim Tresch

  • 1Gene Center, Department of Chemistry and Biochemistry, Ludwig-Maximilians-University of Munich, Feodor-Lynen-Str, 25, D-81377 Munich, Germany.

BMC Bioinformatics
|April 20, 2010
PubMed
Summary
This summary is machine-generated.

Starr is a new R package for analyzing ChIP-chip data, enabling researchers to compare binding profiles across experiments and platforms. This tool aids in understanding gene expression by identifying DNA-protein interactions and chromatin modifications.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) is crucial for studying DNA-protein interactions and histone modifications.
  • Standardized bioinformatic processing for high-throughput ChIP-chip data is lacking.
  • Identifying and localizing genomic binding regions is key for understanding gene expression.

Purpose of the Study:

  • To develop a comprehensive R package for the analysis of ChIP-chip data.
  • To facilitate comparative analysis of ChIP-chip data across different experiments and microarray platforms.
  • To provide tools for reliable identification and localization of genomic protein-binding regions.

Main Methods:

  • Developed Starr, a free, open-source R/Bioconductor package.
  • Implemented functions for data import, quality assessment, and visualization.
  • Included high-level analysis tools: signal alignment, correlation, peak-finding, and profile comparison.

Main Results:

  • Starr supports comparative analysis of ChIP-chip data across diverse experiments and platforms.
  • The package offers automated probe annotation updates via efficient sequence remapping.
  • Starr integrates ChIP signals with complementary genomic data for advanced analysis.

Conclusions:

  • Starr streamlines the entire ChIP-chip workflow, from data processing to binding pattern detection.
  • The package enhances high-level data analysis by integrating and statistically analyzing binding profiles.
  • Starr enables systematic assessment of gene group binding behavior along genomic features.