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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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Gene Expression Browser: large-scale and cross-experiment microarray data integration, management, search &

Ming Zhang1, Yudong Zhang, Li Liu

  • 1GeneExp, 310 South Third Street, San Jose, CA 95112, USA.

BMC Bioinformatics
|August 24, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces the Gene Expression Browser, a tool for integrating and analyzing microarray data. It standardizes diverse experiments, enabling users to search and visualize gene expression patterns and relationships effectively.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Large volumes of microarray gene expression data are publicly available.
  • Integrating and analyzing this data is crucial for understanding gene function, networks, and pathways.
  • Existing tools struggle to integrate, search, and visualize large-scale, diverse gene expression datasets.

Purpose of the Study:

  • To develop a powerful tool for integrating, managing, and processing microarray data.
  • To enable effective web-based search and visualization of gene expression information.
  • To standardize microarray data from diverse experiments for homogeneous analysis.

Main Methods:

  • Developed a system called Gene Expression Browser.
  • Implemented an innovative method to standardize 'treatment over control' for all experiments.
  • Utilized a 2-layer dynamic web display for online visualization of pre-processed data.
  • Computed relationships between genes and treatments over control based on expression ratios.

Main Results:

  • Gene Expression Browser provides Gene View, Treatment over Control View, and Slide View for data exploration.
  • Users can identify genes affected by specific treatments and genes that change under selected conditions.
  • Identified co-responsive genes and co-regulation treatments over control based on expression profiles.

Conclusions:

  • Gene Expression Browser is a comprehensive software suite including data extraction, management, annotation, processing, and visualization tools.
  • It is available as a free public web service, integrating 301 ATH1 gene microarray experiments.
  • The software is adaptable for large-scale expression data from other platforms and species.