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CARMAweb: comprehensive R- and bioconductor-based web service for microarray data analysis.

Johannes Rainer1, Fatima Sanchez-Cabo, Gernot Stocker

  • 1Institute for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria.

Nucleic Acids Research
|July 18, 2006
PubMed
Summary
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CARMAweb is a free, web-based tool for comprehensive microarray data analysis. It offers reproducible gene expression analysis, including differential expression and clustering, for various microarray platforms.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray analysis is crucial for understanding gene expression patterns.
  • Existing tools may lack comprehensive features or user-friendliness.
  • Reproducibility and transparency are key challenges in bioinformatics analyses.

Purpose of the Study:

  • To introduce CARMAweb, a novel web application for integrated microarray data analysis.
  • To provide a user-friendly platform for preprocessing, differential gene expression analysis, clustering, and functional annotation.
  • To ensure transparency and reproducibility through automated report generation.

Main Methods:

  • CARMAweb utilizes R and Bioconductor packages for statistical analysis.
  • It incorporates the R function Sweave for automated report generation.

Related Experiment Videos

  • The web application is built using Java and J2EE technologies.

    • Main Results:

    • CARMAweb supports data from Affymetrix, spotted two-color, and ABI microarrays.
    • It performs essential analyses: preprocessing, differential expression, clustering, dimension reduction, classification, and Gene Ontology analysis.
    • Automated reports detail all analysis steps, enhancing reproducibility.

    Conclusions:

    • CARMAweb offers a comprehensive, reproducible, and transparent solution for microarray data analysis.
    • The platform is freely accessible, promoting wider adoption in biological research.
    • It facilitates deeper insights into gene expression data across various platforms.