Zhang Yan, Eberhard David A, Frantz Gretchen D, Dowd Patrick, Wu Thomas D, Zhou Yan, Watanabe Colin, Luoh Shiuh-Ming, Polakis Paul, Hillan Kenneth J, Wood William I, Zhang Zemin
Department of Bioinformatics, Genentech Inc., South San Francisco, CA 94080, USA.
Bioinformatics. 2004 Oct 12;20(15):2390-8. doi: 10.1093/bioinformatics/bth256. Epub 2004 Apr 8.
Expression profiling in diverse tissues is fundamental to understanding gene function as well as therapeutic target identification. The vast collection of expressed sequence tags (ESTs) and the associated tissue source information provides an attractive opportunity for studying gene expression.
To facilitate EST-based expression analysis, we developed GEPIS (gene expression profiling in silico), a tool that integrates EST and tissue source information to compute gene expression patterns in a large panel of normal and tumor samples. We found EST-based expression patterns to be consistent with published papers as well as our own experimental results. We also built a GEPIS Regional Atlas that depicts expression characteristics of all genes in a selected genomic region. This program can be adapted for large-scale screening for genes with desirable expression patterns, as illustrated by our large-scale mining for tissue- and tumor-specific genes.
The email server version of the GEPIS application is freely available at http://share.gene.com/share/gepis. An interactive version of GEPIS will soon be freely available at http://www.cgl.ucsf.edu/Research/genentech/gepis/. The source code, modules, data and gene lists can be downloaded at http://share.gene.com/share/gepis.
不同组织中的表达谱分析对于理解基因功能以及确定治疗靶点至关重要。大量的表达序列标签（EST）及相关的组织来源信息为研究基因表达提供了一个有吸引力的机会。
为便于基于EST的表达分析，我们开发了GEPIS（计算机基因表达谱分析），这是一种整合EST和组织来源信息以计算大量正常和肿瘤样本中基因表达模式的工具。我们发现基于EST的表达模式与已发表论文以及我们自己的实验结果一致。我们还构建了一个GEPIS区域图谱，描绘了选定基因组区域中所有基因的表达特征。该程序可用于大规模筛选具有理想表达模式的基因，我们对组织和肿瘤特异性基因的大规模挖掘就说明了这一点。
GEPIS应用程序的电子邮件服务器版本可在http://share.gene.com/share/gepis免费获取。GEPIS的交互式版本很快将在http://www.cgl.ucsf.edu/Research/genentech/gepis/免费提供。源代码、模块、数据和基因列表可在http://share.gene.com/share/gepis下载。
