Chiani Francesco, Iannone Camilla, Negri Rodolfo, Paoletti Daniele, D'Antonio Mattia, De Meo Paolo D'onorio, Castrignanò Tiziana
Laboratory of Functional Genomics and Proteomics of Model Systems, Department of Cell Biology and Development, University of Rome, La Sapienza and Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Università e Ricerca, Rome, Italy.
Database (Oxford). 2009;2009:bap007. doi: 10.1093/database/bap007. Epub 2009 Jul 28.
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.
对使用DNA微阵列技术产生的大量数据进行分析表明,根据辐射的质量、剂量范围和剂量率以及分析所选的时间,对辐射的转录反应可能会有很大差异。目前,很难整合在不同生物系统的几种实验条件下获得的数据,以得出总体结论或建立可测试和验证的调控模型。事实上,大多数现有数据都埋藏在不同的网站中,包括公共或私人网站、一般或本地存储库或已发表论文中包含的文件中;这些数据通常格式各异,这使得进行广泛比较更加困难。辐射基因数据库(http://www.caspur.it/RadiationGenes)收集来自各种本地和公共存储库或已发表论文及补充材料中的微阵列数据。该数据库根据辐射质量、剂量、剂量率和采样时间等重要变量对数据进行分类,以提供用户友好的工具来促进数据整合和比较。