Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, and Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
PLoS One. 2010 Dec 17;5(12):e14369. doi: 10.1371/journal.pone.0014369.
SAGE (serial analysis of gene expression) is a powerful method of analyzing gene expression for the entire transcriptome. There are currently many well-developed SAGE tools. However, the cross-comparison of different tissues is seldom addressed, thus limiting the identification of common- and tissue-specific tumor markers.
METHODOLOGY/PRINCIPAL FINDINGS: To improve the SAGE mining methods, we propose a novel function for cross-tissue comparison of SAGE data by combining the mathematical set theory and logic with a unique "multi-pool method" that analyzes multiple pools of pair-wise case controls individually. When all the settings are in "inclusion", the common SAGE tag sequences are mined. When one tissue type is in "inclusion" and the other types of tissues are not in "inclusion", the selected tissue-specific SAGE tag sequences are generated. They are displayed in tags-per-million (TPM) and fold values, as well as visually displayed in four kinds of scales in a color gradient pattern. In the fold visualization display, the top scores of the SAGE tag sequences are provided, along with cluster plots. A user-defined matrix file is designed for cross-tissue comparison by selecting libraries from publically available databases or user-defined libraries.
CONCLUSIONS/SIGNIFICANCE: The hSAGEing tool provides a combination of friendly cross-tissue analysis and an interface for comparing SAGE libraries for the first time. Some up- or down-regulated genes with tissue-specific or common tumor markers and suppressors are identified computationally. The tool is useful and convenient for in silico cancer transcriptomic studies and is freely available at http://bio.kuas.edu.tw/hSAGEing.
SAGE（基因表达序列分析）是一种分析整个转录组基因表达的强大方法。目前有许多开发良好的 SAGE 工具。然而，不同组织之间的交叉比较很少被涉及，从而限制了常见和组织特异性肿瘤标志物的鉴定。
方法/主要发现：为了改进 SAGE 挖掘方法，我们提出了一种新的功能，用于通过将数学集合理论和逻辑与独特的“多池方法”相结合来进行 SAGE 数据的跨组织比较，该方法单独分析多个成对病例对照的池。当所有设置都在“包含”中时，挖掘出常见的 SAGE 标签序列。当一种组织类型在“包含”中，而其他类型的组织不在“包含”中时，生成选定的组织特异性 SAGE 标签序列。它们以标签百万分之一（TPM）和倍数值显示，并以颜色梯度模式的四种比例视觉显示。在倍数可视化显示中，提供了 SAGE 标签序列的最高分数，以及聚类图。通过从公共数据库或用户定义的库中选择库，设计了一个用户定义的矩阵文件用于跨组织比较。
结论/意义：hSAGEing 工具首次提供了友好的跨组织分析组合和用于比较 SAGE 库的界面。通过计算，确定了一些具有组织特异性或常见肿瘤标志物和抑制剂的上调或下调基因。该工具对于计算机癌症转录组学研究非常有用和方便，可在 http://bio.kuas.edu.tw/hSAGEing 上免费获得。
