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使用综合图谱数据库研究转录调控。

GREG-studying transcriptional regulation using integrative graph databases.

机构信息

School of Basic Medical Sciences, Guangzhou Medical University, Panyu Campus of Guangzhou Medical University, Xinzao, 511436 Guangzhou, P.R. China.

Joint School of Life Sciences, Guangzhou Medical University and Guangzhou Institutes of Biomedicine and Health (Chinese Academy of Sciences), Panyu Campus of Guangzhou Medical University, Xinzao, 511436 Guangzhou, P.R. China.

出版信息

Database (Oxford). 2020 Jan 1;2020. doi: 10.1093/database/baz162.

DOI:10.1093/database/baz162
PMID:32055858
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7018612/
Abstract

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/.

摘要

基因调控过程是转录因子、共因子、调控非编码 RNA(ncRNA)和染色质相互作用协同作用的结果。因此,将蛋白质-DNA、蛋白质-蛋白质、ncRNA-DNA、ncRNA-蛋白质和 DNA-DNA 数据组合在单个图形数据库中,为生成生物学假设提供了新的可能性。GREG(基因调控图形数据库)是一个集成的数据库和网络资源,允许用户可视化和探索查询转录因子、长非编码 RNA、基因组范围或 DNA 注释的所有上述相互作用的网络,以及提取节点和交互信息,识别连接节点,并以简单有效的方式直接在调控网络上执行高级图形查询。在本文中,我们介绍了 GREG 及其一些应用示例(包括对 Nanog 调控景观和慢性阻塞性肺疾病病因的探索性研究),我们将其用作在生物医学研究中使用图形数据库的优势的演示。数据库网址:https://mora-lab.github.io/projects/greg.html,www.moralab.science/GREG/。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c995/7018612/6e5a0773ca59/baz162f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c995/7018612/387e7eaa2018/baz162f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c995/7018612/6e5a0773ca59/baz162f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c995/7018612/387e7eaa2018/baz162f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c995/7018612/6e5a0773ca59/baz162f2.jpg

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本文引用的文献

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Involvement of Lamin B1 Reduction in Accelerated Cellular Senescence during Chronic Obstructive Pulmonary Disease Pathogenesis.层粘连蛋白 B1 减少参与慢性阻塞性肺疾病发病过程中的细胞加速衰老。
J Immunol. 2019 Mar 1;202(5):1428-1440. doi: 10.4049/jimmunol.1801293. Epub 2019 Jan 28.
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Cistrome Data Browser: expanded datasets and new tools for gene regulatory analysis.染色质数据浏览器:扩展数据集和新的基因调控分析工具。
Nucleic Acids Res. 2019 Jan 8;47(D1):D729-D735. doi: 10.1093/nar/gky1094.
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BioGraph: a web application and a graph database for querying and analyzing bioinformatics resources.
Database (Oxford). 2022 Jun 27;2022. doi: 10.1093/database/baac044.
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An overview of graph databases and their applications in the biomedical domain.图数据库及其在生物医学领域中的应用概述。
Database (Oxford). 2021 May 18;2021. doi: 10.1093/database/baab026.
BioGraph:一个用于查询和分析生物信息学资源的网络应用程序和图形数据库。
BMC Syst Biol. 2018 Nov 20;12(Suppl 5):98. doi: 10.1186/s12918-018-0616-4.
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Nucleic Acids Res. 2019 Jan 8;47(D1):D766-D773. doi: 10.1093/nar/gky955.
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LnChrom: a resource of experimentally validated lncRNA-chromatin interactions in human and mouse.LnChrom:人类和小鼠中经过实验验证的 lncRNA-染色质相互作用的资源。
Database (Oxford). 2018 Jan 1;2018. doi: 10.1093/database/bay039.
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Reactome graph database: Efficient access to complex pathway data.Reactome 图形数据库:高效访问复杂的通路数据。
PLoS Comput Biol. 2018 Jan 29;14(1):e1005968. doi: 10.1371/journal.pcbi.1005968. eCollection 2018 Jan.
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MELODI: Mining Enriched Literature Objects to Derive Intermediates.MELODI:挖掘丰富的文献对象以推导中间体。
Int J Epidemiol. 2018 Jan 12;47(2):369-79. doi: 10.1093/ije/dyx251.
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