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Asterias: integrated analysis of expression and aCGH data using an open-source, web-based, parallelized software

Ramón Díaz-Uriarte1, Andreu Alibés, Edward R Morrissey

  • 1Statistical Computing Team, Structural and Computational Biology Programme, Spanish National Cancer Center (CNIO), Melchor Fernández Almagro 3, Madrid, 28029, Spain. rdiaz02@gmail.com

Nucleic Acids Research
|May 10, 2007
PubMed
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Asterias is a free, web-based software suite for analyzing gene expression and array comparative genomic hybridization (aCGH) data. It offers advanced statistical methods, parallel computing, and integrated biological annotations for comprehensive genomic analysis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression and aCGH data analysis are crucial for understanding biological processes and disease.
  • Existing tools may lack comprehensive features or efficient computational capabilities.
  • Integrating diverse biological annotations enhances data interpretation.

Purpose of the Study:

  • To introduce Asterias, an open-source, web-based suite for gene expression and aCGH data analysis.
  • To provide a platform that integrates validated statistical methods with parallel computing.
  • To facilitate access to functional information and annotations for enhanced biological insights.

Main Methods:

  • Asterias is a web-based suite implementing validated statistical methods.

Related Experiment Videos

  • It utilizes parallel computing for efficient processing on multicore CPUs and clusters.
  • The suite offers applications for array normalization, imputation, preprocessing, differential gene expression, class and survival prediction, and aCGH analysis.

    • Main Results:

    • Asterias provides a comprehensive workflow for genomic data analysis.
    • It enables efficient analysis through parallelization and access to extensive biological annotations.
    • The open-source nature allows for software extension and reuse.

    Conclusions:

    • Asterias is a unique, web-based suite offering integrated analysis of gene expression and aCGH data.
    • Its features, including parallelization and functional annotation access, enhance its utility for researchers.
    • The open-source availability promotes collaboration and further development in bioinformatics.