Journal Article

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

Ramón Díaz-Uriarte, Andreu Alibés, Edward R. Morrissey, Andrés Cañada, Oscar M. Rueda and Mariana L. Neves

in Nucleic Acids Research

Volume 35, issue suppl_2, pages W75-W80
Published in print July 2007 | ISSN: 0305-1048
Published online July 2007 | e-ISSN: 1362-4962 | DOI: http://dx.doi.org/10.1093/nar/gkm229
Asterias: integrated analysis of expression and aCGH data using an open-source, web-based, parallelized software suite

More Like This

Show all results sharing these subjects:

  • Chemistry
  • Biochemistry
  • Bioinformatics and Computational Biology
  • Genetics and Genomics
  • Molecular and Cell Biology

GO

Show Summary Details

Preview

Asterias (http://www.asterias.info) is an open-source, web-based, suite for the analysis of gene expression and aCGH data. Asterias implements validated statistical methods, and most of the applications use parallel computing, which permits taking advantage of multicore CPUs and computing clusters. Access to, and further analysis of, additional biological information and annotations (PubMed references, Gene Ontology terms, KEGG and Reactome pathways) are available either for individual genes (from clickable links in tables and figures) or sets of genes. These applications cover from array normalization to imputation and preprocessing, differential gene expression analysis, class and survival prediction and aCGH analysis. The source code is available, allowing for extention and reuse of the software. The links and analysis of additional functional information, parallelization of computation and open-source availability of the code make Asterias a unique suite that can exploit features specific to web-based environments.

Journal Article.  2843 words.  Illustrated.

Subjects: Chemistry ; Biochemistry ; Bioinformatics and Computational Biology ; Genetics and Genomics ; Molecular and Cell Biology

Full text: subscription required

How to subscribe Recommend to my Librarian

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.