Congruence of tissue expression profiles from Gene Expression Atlas, SAGEmap and TissueInfo databases.
Congruence of tissue expression profiles from Gene Expression Atlas, SAGEmap and TissueInfo databases.
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DOI：
10.1186/1471-2164-4-31
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发表时间：
2003-07-29
期刊：
影响因子：
4.4
通讯作者：
Wolfe KH
中科院分区：
文献类型：
--
作者：
Huminiecki L;Lloyd AT;Wolfe KH
从公共数据库中存储的大量基因表达信息中提取生物学知识是后基因组时代的主要挑战。通过数据整合和表达谱的跨平台比较可以获得其他见解。然而，由于实验技术、数据后处理、数据库格式以及基因和样本注释不一致，数据库荟萃分析变得复杂。我们分析了三个公共数据库的表达谱：基因表达图谱，SAGEmap和TissueInfo。这些数据库分别是寡核苷酸微阵列、基因表达系列分析和表达序列标签人类基因表达数据库。我们设计了一种方法，优先表达测量，以确定在任何给定组织中显着过度表达或表达不足的基因。我们研究了数据库内和数据库间的一致性偏好表达措施。寡核苷酸微阵列数据的重复实验之间存在良好的相关性，但通过基因表达系列分析和表达序列标签计数测量的表达谱的一致性较差。我们研究了六种组织类别的数据库间相关性，这些数据存在于三个数据库中。与脑、前列腺和血管内皮细胞呈显著正相关，但与卵巢、肾脏和胰腺无相关性。我们表明，从基因表达图谱，SAGEmap和TissueInfo的数据可以整合使用UniGene基因索引，表达谱相关性相对较好时，大量的标签或组织细胞组成是简单的。最后，就大脑而言，我们证明，当PEM值显示良好的相关性时，基于综合数据的组织特异性表达预测非常准确。
Extracting biological knowledge from large amounts of gene expression information deposited in public databases is a major challenge of the postgenomic era. Additional insights may be derived by data integration and cross-platform comparisons of expression profiles. However, database meta-analysis is complicated by differences in experimental technologies, data post-processing, database formats, and inconsistent gene and sample annotation. We have analysed expression profiles from three public databases: Gene Expression Atlas, SAGEmap and TissueInfo. These are repositories of oligonucleotide microarray, Serial Analysis of Gene Expression and Expressed Sequence Tag human gene expression data respectively. We devised a method, Preferential Expression Measure, to identify genes that are significantly over- or under-expressed in any given tissue. We examined intra- and inter-database consistency of Preferential Expression Measures. There was good correlation between replicate experiments of oligonucleotide microarray data, but there was less coherence in expression profiles as measured by Serial Analysis of Gene Expression and Expressed Sequence Tag counts. We investigated inter-database correlations for six tissue categories, for which data were present in the three databases. Significant positive correlations were found for brain, prostate and vascular endothelium but not for ovary, kidney, and pancreas. We show that data from Gene Expression Atlas, SAGEmap and TissueInfo can be integrated using the UniGene gene index, and that expression profiles correlate relatively well when large numbers of tags are available or when tissue cellular composition is simple. Finally, in the case of brain, we demonstrate that when PEM values show good correlation, predictions of tissue-specific expression based on integrated data are very accurate.
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DOI：
10.1073/pnas.95.19.11423
发表时间：
1998-09-15
影响因子：
11.1
作者：
Ju, YT;Chang, ACY;Lin-Chao, S
通讯作者：
Lin-Chao, S
影响因子：
4.4
作者：
Craxton, M
通讯作者：
Craxton, M
影响因子：
64.8
作者：
Hu, K;Carroll, J;Davletov, B
通讯作者：
Davletov, B
影响因子：
11.4
作者：
BADER, BL;MAGIN, TM;FRANKE, WW
通讯作者：
FRANKE, WW
影响因子：
64.8
作者：
Fernández-Chacón, R;Königstorfer, A;Südhof, TC
通讯作者：
Südhof, TC