A gene regulatory process is the result of the concerted action of transcription factors, co-factors, regulatory non-coding RNAs (ncRNAs) and chromatin interactions. Therefore, the combination of protein-DNA, protein-protein, ncRNA-DNA, ncRNA-protein and DNA-DNA data in a single graph database offers new possibilities regarding generation of biological hypotheses. GREG (The Gene Regulation Graph Database) is an integrative database and web resource that allows the user to visualize and explore the network of all above-mentioned interactions for a query transcription factor, long non-coding RNA, genomic range or DNA annotation, as well as extracting node and interaction information, identifying connected nodes and performing advanced graphical queries directly on the regulatory network, in a simple and efficient way. In this article, we introduce GREG together with some application examples (including exploratory research of Nanog's regulatory landscape and the etiology of chronic obstructive pulmonary disease), which we use as a demonstration of the advantages of using graph databases in biomedical research. Database URL: https://mora-lab.github.io/projects/greg.html, www.moralab.science/GREG/.

中文翻译：

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使用综合图数据库研究GREG的转录调控。

基因调节过程是转录因子，辅助因子，调节性非编码RNA（ncRNA）和染色质相互作用共同作用的结果。因此，在单个图形数据库中将蛋白质-DNA，蛋白质-蛋白质，ncRNA-DNA，ncRNA-蛋白质和DNA-DNA数据组合在一起，就可以提供有关生物学假设生成的新可能性。GREG（基因调控图数据库）是一个集成的数据库和网络资源，它使用户可以可视化和探索上述所有相互作用的网络，包括查询转录因子，长非编码RNA，基因组范围或DNA注释，例如以及提取节点和交互信息，识别连接的节点并以简单有效的方式直接在监管网络上执行高级图形查询。在这篇文章中，我们将介绍GREG，并提供一些应用示例（包括对Nanog的监管格局和慢性阻塞性肺疾病的病因进行探索性研究），以证明在生物医学研究中使用图形数据库的优势。数据库网址：https：//mora-lab.github.io/projects/greg.html，www.moralab.science/GREG/。