Wang San Ming
Center for Functional Genomics, ENH Research Institute, Robert H. Lurie Comprehensive Cancer Center, Northwestern University, 1001 University Place, Evanston, IL 60201, USA.
Trends Genet. 2007 Jan;23(1):42-50. doi: 10.1016/j.tig.2006.11.001. Epub 2006 Nov 15.
Serial analysis of gene expression (SAGE) is a method for identifying and quantifying transcripts from eukaryotic genomes. Since its invention, SAGE has been widely applied to analyzing gene expression in many biological and medical studies. Vast amounts of SAGE data have been collected and more than a thousand SAGE-related studies have been published since the mid-1990s. The principle of SAGE has been developed to address specific issues such as determination of normal gene structure and identification of abnormal genome structural changes. This review focuses on the general features of SAGE data, including the specificity of SAGE tags with respect to their original transcripts, the quantitative nature of SAGE data for differentially expressed genes, the reproducibility, the comparability of SAGE with microarray and the future potential of SAGE. Understanding these basic features should aid the proper interpretation of SAGE data to address biological and medical questions.
基因表达序列分析（SAGE）是一种用于识别和定量真核生物基因组转录本的方法。自发明以来，SAGE已广泛应用于许多生物学和医学研究中的基因表达分析。自20世纪90年代中期以来，已收集了大量的SAGE数据，并且发表了一千多项与SAGE相关的研究。SAGE的原理已得到发展，以解决特定问题，如正常基因结构的确定和异常基因组结构变化的识别。本综述重点关注SAGE数据的一般特征，包括SAGE标签相对于其原始转录本的特异性、差异表达基因的SAGE数据的定量性质、可重复性、SAGE与微阵列的可比性以及SAGE的未来潜力。了解这些基本特征应有助于正确解释SAGE数据，以解决生物学和医学问题。
