Anisimov Sergey V
Department of Hematology Research, V.A. Almazov Federal Center for Heart, Blood, Endocrinology, Saint-Petersburg, Russia.
Curr Pharm Biotechnol. 2008 Oct;9(5):338-50. doi: 10.2174/138920108785915148.
A number of molecular methods of gene expression analysis can approach genomic level. Among those, Serial Analysis of Gene Expression (SAGE) stands out. Unlike many other techniques, SAGE allows both qualitative and quantitative analysis of previously unknown transcripts. Over the course of the last 13 years, SAGE has became a recognized tool of large-scale gene expression profiling, being used extensively in human, animal, yeast and plant studies of various nature. A number of important adaptations was introduced both to the protocol of SAGE library construction and to the analytical algorithm employed. Moreover, some variations of the original protocol (MAGE, SADE, microSAGE, miniSAGE, longSAGE, superSAGE, deepSAGE, etc.) were derived to improve the utility of SAGE in certain conditions. Current review aims comparing the benefits and drawbacks of the techniques for high-throughput gene expression analysis (including SAGE) in a realistic, balanced manner. Issues related to modifications to the original protocol and further development of the SAGE are discussed.
许多基因表达分析的分子方法可以达到基因组水平。其中,基因表达序列分析(SAGE)脱颖而出。与许多其他技术不同,SAGE允许对以前未知的转录本进行定性和定量分析。在过去的13年里,SAGE已成为大规模基因表达谱分析的公认工具,广泛应用于人类、动物、酵母和植物的各种研究中。在SAGE文库构建方案和所采用的分析算法方面都引入了一些重要的改进。此外,还衍生出了原始方案的一些变体(MAGE、SADE、microSAGE、miniSAGE、longSAGE、superSAGE、deepSAGE等),以提高SAGE在某些条件下的实用性。当前的综述旨在以现实、平衡的方式比较高通量基因表达分析技术(包括SAGE)的优缺点。讨论了与原始方案修改和SAGE进一步发展相关的问题。