Cross-SAGE: SAGE Data Mining Tool Based on Set Theory
Cheng‐Hong Yang, Tsung-Mu Shih, 谷德倫, 張學偉, Li‐Yeh Chuang · 2008
technique for analyzing gene expression of a series of tags obtained from cDNA. This technology lets biologists analyze and observe the relative gene expressions of many samples in silico. To date, some visible analyzing platforms for SAGE were not provided in a biologically significance way for cross-analysis and-comparison, thus limiting its application. Therefore, we retrieved various SAGE databases of Homo sapiens from NCBI SAGEmap and proposed a powerful tool for cross-analyzing gene expression among different SAGE libraries of tissue sources. In this paper, we combine the mathematical set theory with a unique multi-group method to analyze SAGE data, and provide the function for gaining the corresponding information between tags and genes. Some upor down-regulated tissue-specific markers which are or are not common to others could be identified computationally. This method is a viable and convenient way to analyze gene expression in complex comparison, and can obtain analysis results by biological significance. Index Terms—cDNA, gene expression, restriction enzyme, SAGE, Set theory. I.