The BioGRID Database: An Open International Resource for Biological Interactions

BioGRID 数据库：生物相互作用的开放国际资源

• 批准号: BB/F010486/1
• 负责人: Peter Swain
• 金额: $ 118.58万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2008
• 资助国家: 英国
• 起止时间: 2008 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FF010486%2F1
• 关键词: BioGRID; Database; Open; International; Resource

Complex organisms, such as humans, are composed of trillions of cells of many hundreds of different types, organized into tissues and organs. Each cell is itself composed of tens of thousands of proteins encoded by the genome; in turn, the cell's behavior is dictated by a vast network of gene and protein interactions that control virtually every cellular function, whether it be division, gene expression, cell shape and movement, development, or energy production and metabolism. The difficulty in understanding just how cells function, either normally, or when processes run awry in disease, is that cellular networks are so intricately interconnected that it is difficult to dissect the contribution of each individual gene. While analyzing the components individually, as in biochemistry, we learn much about each individual process, but not necessarily how they work together. Conversely, if we attempt to remove key components from the puzzle by genetic mutations, we may lose the function of the entire process, and thus not be able to discern the precise function of the gene in question. These difficulties are further confounded by the often multiple functions of different genes, and by the built in redundancies that can occur in cellular networks. The approach of systems biology is to combine many different types of measurements, both biochemical and genetic, in order to understand the cumulative effect of each individual protein and gene. A crucial first step in attempting to understand how cells and organisms function is to keep track of the many hundreds of thousands of interactions in the cell. The scientific literature records the results obtained by thousands of researchers around the globe, who study many different species. One of the profound insights to come from genome sequence analysis, as well as from the study of specific gene functions in species that range from yeast to humans, is that all organisms are in fact very closely related. Thus, many of the mechanisms that operate in a yeast cell are fundamentally the same as those in a human cell. For this reason, the study of 'model' organisms, such as yeast, worms, flies and plants, has proven extremely informative in understanding, for example, human disease. Indeed, fundamental discoveries in one organism invariably lead to a better understanding of all species. A primary means of understanding is to elucidate the genetic and protein interactions of the cell. A variety of techniques have been devised to detect various interactions, and many of these methods can now be carried out in a rapid high throughput fashion. Although all interactions discovered are recorded in the scientific literature, a problem has emerged in that there is no central repository for such interactions, which are thus difficult to analyze in a systematic manner. To solve this issue, we have assembled a team of computer programmers and biologists who curate the scientific literature for thousands of protein and genetic interactions, which we systematically classify by various criteria and then house permanently in an open access on-line database called the BioGRID (www.thebiogrid.org). An associated graphical interface called Osprey enables biologists to easily assemble networks from these interactions, and thereby deduce their function. We propose to establish a primary portal for the BioGRID database at the University of Edinburgh and to expand the capacity of the BioGRID/Osprey system through the curation of gene and protein interactions from yeasts, worms, flies, plants and human cells. We will also develop new software tools to enhance the performance of BioGRID/Osprey, including building links to other sophisticated analysis platforms at the Centre for Systems Biology in Edinburgh. The large interaction datasets housed in BioGRID will prove invaluable to the international academic life sciences community and to the biotechnology and pharmaceutical industries.

复杂的生物体，如人类，是由数以万亿计的细胞组成的数百种不同类型，组织成组织和器官。每个细胞本身由基因组编码的数万种蛋白质组成;反过来，细胞的行为由基因和蛋白质相互作用的巨大网络决定，这些网络控制着几乎所有的细胞功能，无论是分裂，基因表达，细胞形状和运动，发育还是能量生产和代谢。无论是正常情况下，还是在疾病中，细胞的功能发生了变化，理解细胞功能的困难在于，细胞网络是如此错综复杂地相互连接，以至于很难剖析每个基因的作用。在生物化学中，当我们单独分析这些成分时，我们对每个过程都有很多了解，但不一定了解它们如何协同工作。相反，如果我们试图通过基因突变从这个难题中移除关键成分，我们可能会失去整个过程的功能，从而无法识别所讨论的基因的精确功能。这些困难进一步被不同基因的多种功能以及细胞网络中可能发生的内置冗余所混淆。系统生物学的方法是联合收割机结合许多不同类型的测量，包括生物化学和遗传学，以了解每个蛋白质和基因的累积效应。试图了解细胞和生物体如何运作的关键第一步是跟踪细胞中数十万种相互作用。科学文献记载了地球仪上千名研究人员对许多不同物种的研究结果。从基因组序列分析以及从酵母到人类的物种中特定基因功能的研究中得出的深刻见解之一是，所有生物实际上都是非常密切相关的。因此，在酵母细胞中运作的许多机制基本上与人类细胞中的机制相同。出于这个原因，对“模型”生物的研究，如酵母，蠕虫，苍蝇和植物，已经证明在理解人类疾病等方面提供了非常丰富的信息。事实上，在一个有机体中的基本发现总是会导致对所有物种的更好理解。理解的主要手段是阐明细胞的遗传和蛋白质相互作用。已经设计了多种技术来检测各种相互作用，并且这些方法中的许多现在可以以快速高通量的方式进行。虽然所有发现的相互作用都记录在科学文献中，但出现了一个问题，即没有这种相互作用的中央储存库，因此难以系统地分析。为了解决这个问题，我们组建了一个由计算机程序员和生物学家组成的团队，他们为数千种蛋白质和遗传相互作用整理科学文献，我们根据各种标准对这些文献进行系统分类，然后永久保存在一个名为BioGRID（www.thebiogrid.org）的开放式在线数据库中。一个名为Osprey的相关图形界面使生物学家能够轻松地从这些相互作用中组装网络，从而推断出它们的功能。我们建议在爱丁堡大学建立一个BioGRID数据库的主要门户网站，并通过酵母，蠕虫，苍蝇，植物和人类细胞的基因和蛋白质相互作用的策展来扩大BioGRID/鱼鹰系统的能力。我们还将开发新的软件工具，以提高BioGRID/Osprey的性能，包括与爱丁堡系统生物学中心的其他复杂分析平台建立联系。BioGRID中的大型交互数据集将证明对国际学术生命科学界以及生物技术和制药行业是非常宝贵的。

项目成果

The BioGRID interaction database: 2017 update.

• DOI: 10.1093/nar/gkw1102
• 发表时间: 2017-01-04
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Chatr-Aryamontri A;Oughtred R;Boucher L;Rust J;Chang C;Kolas NK;O'Donnell L;Oster S;Theesfeld C;Sellam A;Stark C;Breitkreutz BJ;Dolinski K;Tyers M
• 通讯作者: Tyers M

How to link ontologies and protein-protein interactions to literature: text-mining approaches and the BioCreative experience.

• DOI: 10.1093/database/bas017
• 发表时间: 2012
• 期刊: Database : the journal of biological databases and curation
• 作者: Krallinger M;Leitner F;Vazquez M;Salgado D;Marcelle C;Tyers M;Valencia A;Chatr-aryamontri A
• 通讯作者: Chatr-aryamontri A

ELM--the database of eukaryotic linear motifs.

• DOI: 10.1093/nar/gkr1064
• 发表时间: 2012-01
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Dinkel H;Michael S;Weatheritt RJ;Davey NE;Van Roey K;Altenberg B;Toedt G;Uyar B;Seiler M;Budd A;Jödicke L;Dammert MA;Schroeter C;Hammer M;Schmidt T;Jehl P;McGuigan C;Dymecka M;Chica C;Luck K;Via A;Chatr-Aryamontri A;Haslam N;Grebnev G;Edwards RJ;Steinmetz MO;Meiselbach H;Diella F;Gibson TJ
• 通讯作者: Gibson TJ

The BioGRID interaction database: 2013 update.

• DOI: 10.1093/nar/gks1158
• 发表时间: 2013-01
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Chatr-Aryamontri A;Breitkreutz BJ;Heinicke S;Boucher L;Winter A;Stark C;Nixon J;Ramage L;Kolas N;O'Donnell L;Reguly T;Breitkreutz A;Sellam A;Chen D;Chang C;Rust J;Livstone M;Oughtred R;Dolinski K;Tyers M
• 通讯作者: Tyers M

Benchmarking of the 2010 BioCreative Challenge III text-mining competition by the BioGRID and MINT interaction databases.

• DOI: 10.1186/1471-2105-12-s8-s8
• 发表时间: 2011-10-03
• 期刊: BMC bioinformatics
• 影响因子: 3
• 作者: Chatr-Aryamontri A;Winter A;Perfetto L;Briganti L;Licata L;Iannuccelli M;Castagnoli L;Cesareni G;Tyers M
• 通讯作者: Tyers M