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Database (2009) Vol. 2009:bap007; doi:10.1093/database/bap007 published on July 28, 2009
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© 2009 The Author(s).
This is Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Radiation Genes: a database devoted to microarrays screenings revealing transcriptome alterations induced by ionizing radiation in mammalian cells

Francesco Chiani1, Camilla Iannone1, Rodolfo Negri1, Daniele Paoletti2, Mattia D’Antonio2, Paolo D’onorio De Meo2 and Tiziana Castrignanò2,*

1Laboratory of Functional Genomics and Proteomics of Model Systems, Department of Cell Biology and Development, University of Rome, La Sapienza and 2Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Università e Ricerca, Rome, Italy

*Corresponding author: Tel: +390644486704; Fax: +39064957083; Email: tiziana.castrignano{at}caspur.it


   Abstract

The analysis of the great extent of data generated by using DNA microarrays technologies has shown that the transcriptional response to radiation can be considerably different depending on the quality, the dose range and dose rate of radiation, as well as the timing selected for the analysis. At present, it is very difficult to integrate data obtained under several experimental conditions in different biological systems to reach overall conclusions or build regulatory models which may be tested and validated. In fact, most available data is buried in different websites, public or private, in general or local repositories or in files included in published papers; it is often in various formats, which makes a wide comparison even more difficult. The Radiation Genes Database (http://www.caspur.it/RadiationGenes) collects microarrays data from various local and public repositories or from published papers and supplementary materials. The database classifies it in terms of significant variables, such as radiation quality, dose, dose rate and sampling timing, as to provide user-friendly tools to facilitate data integration and comparison.

Received January 14, 2009; Revised May 7, 2009; Accepted June 13, 2009


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