GENT: gene expression database of normal and tumor tissues.
GENT: gene expression database of normal and tumor tissues.
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作者：
Shin G;Kang TW;Yang S;Baek SJ;Jeong YS;Kim SY
一些癌基因，如ERBB2和EGFR只在一小部分患者中过度表达。癌症异常值分布分析是识别基因表达数据中异常值的计算方法之一。在搜索仅在部分患者中过度表达的基因时，拥有大样本量的数据库将是一个巨大的优势。GENT(正常组织和肿瘤组织的基因表达数据库)是一个可通过网络访问的数据库，它提供了各种人类癌症和正常组织的基因表达模式。超过40000个样本，由Affymetrix U133A或U133plus2平台在世界各地的许多不同实验室进行分析，从公共资源收集并合并到两个大型数据集，帮助识别仅在部分患者中过度表达的癌症异常值。还提供了近1000个人类癌细胞系的基因表达模式。在每个组织中，用户可以检索到根据更详细的临床信息分类的基因表达模式。GENT的大样本量(U133plus2为>；24300，U133A为>；16400)在识别癌症异常值方面具有优势。肿瘤细胞系基因表达数据库可用于体外实验的靶点验证。我们希望GENT在从靶点发现到靶点验证的许多阶段都能成为癌症研究人员的有用资源。可在http://medicalgenome.kribb.re.kr/GENT/或http://genome.kobic.re.kr/GENT/.上购买Gent。
Some oncogenes such as ERBB2 and EGFR are over-expressed in only a subset of patients. Cancer outlier profile analysis is one of computational approaches to identify outliers in gene expression data. A database with a large sample size would be a great advantage when searching for genes over-expressed in only a subset of patients. GENT (Gene Expression database of Normal and Tumor tissues) is a web-accessible database that provides gene expression patterns across diverse human cancer and normal tissues. More than 40000 samples, profiled by Affymetrix U133A or U133plus2 platforms in many different laboratories across the world, were collected from public resources and combined into two large data sets, helping the identification of cancer outliers that are over-expressed in only a subset of patients. Gene expression patterns in nearly 1000 human cancer cell lines are also provided. In each tissue, users can retrieve gene expression patterns classified by more detailed clinical information. The large samples size (>24300 for U133plus2 and >16400 for U133A) of GENT provides an advantage in identifying cancer outliers. A cancer cell line gene expression database is useful for target validation by in vitro experiment. We hope GENT will be a useful resource for cancer researchers in many stages from target discovery to target validation. GENT is available at http://medicalgenome.kribb.re.kr/GENT/ or http://genome.kobic.re.kr/GENT/.