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共调控数据库（CORD）：一种用于识别协同表达基因的工具。

The CO-Regulation Database (CORD): a tool to identify coordinately expressed genes.

作者信息

Fahrenbach John P, Andrade Jorge, McNally Elizabeth M

机构信息

Department of Medicine, The University of Chicago, Chicago, Illinois, United States of America.

Center for Research Informatics, The University of Chicago, Chicago, Illinois, United States of America.

出版信息

PLoS One. 2014 Mar 5;9(3):e90408. doi: 10.1371/journal.pone.0090408. eCollection 2014.

DOI:10.1371/journal.pone.0090408
PMID:24599084
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3944024/
Abstract

BACKGROUND

Meta-analysis of gene expression array databases has the potential to reveal information about gene function. The identification of gene-gene interactions may be inferred from gene expression information but such meta-analysis is often limited to a single microarray platform. To address this limitation, we developed a gene-centered approach to analyze differential expression across thousands of gene expression experiments and created the CO-Regulation Database (CORD) to determine which genes are correlated with a queried gene.

RESULTS

Using the GEO and ArrayExpress database, we analyzed over 120,000 group by group experiments from gene microarrays to determine the correlating genes for over 30,000 different genes or hypothesized genes. CORD output data is presented for sample queries with focus on genes with well-known interaction networks including p16 (CDKN2A), vimentin (VIM), MyoD (MYOD1). CDKN2A, VIM, and MYOD1 all displayed gene correlations consistent with known interacting genes.

CONCLUSIONS

We developed a facile, web-enabled program to determine gene-gene correlations across different gene expression microarray platforms. Using well-characterized genes, we illustrate how CORD's identification of co-expressed genes contributes to a better understanding a gene's potential function. The website is found at http://cord-db.org.

摘要

背景

对基因表达阵列数据库进行荟萃分析有潜力揭示有关基因功能的信息。基因-基因相互作用的识别可从基因表达信息中推断出来，但这种荟萃分析通常限于单个微阵列平台。为解决这一局限性，我们开发了一种以基因为中心的方法来分析数千个基因表达实验中的差异表达，并创建了共调控数据库（CORD）以确定哪些基因与一个查询基因相关。

结果

利用基因表达综合数据库（GEO）和ArrayExpress数据库，我们分析了来自基因微阵列的超过120,000个分组实验，以确定超过30,000个不同基因或假设基因的相关基因。给出了CORD输出数据的样本查询结果，重点关注具有知名相互作用网络的基因，包括p16（CDKN2A）、波形蛋白（VIM）、肌细胞生成素（MyoD，即MYOD1）。CDKN2A、VIM和MYOD1均显示出与已知相互作用基因一致的基因相关性。

结论

我们开发了一个便捷的、基于网络的程序来确定不同基因表达微阵列平台之间的基因-基因相关性。利用特征明确的基因，我们说明了CORD对共表达基因的识别如何有助于更好地理解一个基因的潜在功能。该网站网址为http://cord-db.org。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e597/3944024/8e6533936bab/pone.0090408.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e597/3944024/dbc753345de6/pone.0090408.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e597/3944024/4815ab2b8ea0/pone.0090408.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e597/3944024/33c10f2ea85c/pone.0090408.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e597/3944024/86c9046ef364/pone.0090408.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e597/3944024/8e6533936bab/pone.0090408.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e597/3944024/dbc753345de6/pone.0090408.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e597/3944024/4815ab2b8ea0/pone.0090408.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e597/3944024/33c10f2ea85c/pone.0090408.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e597/3944024/86c9046ef364/pone.0090408.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e597/3944024/8e6533936bab/pone.0090408.g005.jpg

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