BioGRID: An open resource for biological interactions and network analysis

复杂的物理和遗传相互作用网络决定了所有生物系统和 是人类发展、健康和疾病的基础。几十年的生物实验已经确认了无数 在主要的生物医学文献中描述的支撑特定生物过程的分子过程。 更新的技术创新与完整的基因组序列信息相结合，导致了 开发多种高通量(HTP)方法以产生物理和遗传互作 史无前例的数据。因为人类互动网络通常直接类似于 在更易驯服的生物模型网络中，至关重要的是数十万生物 在主要模式生物中发现的相互作用，以及人类，都被保存在一口井中- 带注释的方式，提供了严格分析和计算的方法。捕获、集成和 在询问文献和HTP数据集的丰富数据后，我们开发了BioGRID数据库 作为物理和遗传相互作用的开放储存库(www.thebiogrid.org)。BioGRID包含超过 来自75,760个出版物的总共2,000,000个互动。2020年，BioGRID的平均页面浏览量为151,735次，19,407次 每月有独立访问者和7537次文件下载。我们最近发布的用于CRISPR的开放存储库 屏幕(兽人)，平均8,725个页面浏览量，1,646个独立访问者，每月268次下载。在……里面 此外，内容广泛的BioGRID数据概要由许多合作伙伴数据库广泛传播，Meta- 数据库和软件工具。在这里，我们建议显著增强数据内容，数据库 架构，以及BioGRID的用户界面。我们将扩大可用数据的数量和类型 通过BioGRID，特别关注将知识从模型生物体网络转化为人类 使用同源基因作图和一种新的框架来绘制跨物种的表型和疾病。我们会 显著扩展基于CRISPR的遗传交互、化学和药物交互以及翻译后 修改，我们将与我们的核心物理和遗传交互作用相结合，并围绕 围绕特定的生物学主题开展重点策划工作。使用文本挖掘算法和人工智能方法将 扩大范围以提高蛋白质组的精确率和覆盖率。用户访问中的大型数据集 BioGRID将通过数据丰富的界面、用户定义的搜索和显示参数以及 可视化的方法。所有软件将继续是开源的，并将朝着兼容性和 将与其他数据库和软件开发工作相辅相成。BioGRID将提供 用于模拟生物体数据库和其他相关方的交互数据和软件工具 限制。BioGRID资源将使生物医学研究社区能够访问经过验证的 用于假设生成和网络的跨模型生物和人类的生物相互作用数据集 分析，从而推动国家卫生研究院的总体使命。

Complex physical and genetic interaction networks determine the properties of all biological systems and underlie human development, health and disease. Decades of biological experiments have identified myriad molecular processes that underpin specific biological processes, described in the primary biomedical literature. More recent technological innovations combined with complete genome sequence information have led to the development of a wide variety high-throughput (HTP) methods to generate physical and genetic interaction data on an unprecedented scale. Because human interaction networks are often directly analogous to networks in more tractable model organisms, it is essential that the hundreds of thousands of biological interactions discovered across the major model organisms, as well as humans, are archived in a well- annotated manner that provides a means for rigorous analysis and computation. To capture, integrate, and interrogate this wealth of data from both the literature and HTP datasets, we developed the BioGRID database as an open repository for physical and genetic interactions (www.thebiogrid.org). BioGRID contains over 2,000,000 total interactions from 75,760 publications. In 2020, BioGRID averaged 151,735 page views, 19,407 unique visitors and 7,537 file downloads per month. Our recently released Open Repository for CRISPR Screens (ORCS), averages 8,725 page views, 1,646 unique visitors, and 268 downloads per month. In addition, the extensive BioGRID data compendium is widely disseminated by many partner databases, meta- databases, and software tools. Here, we propose to markedly enhance the data content, the database architecture, and the user interface of BioGRID. We will expand the amount and types of data available through BioGRID, with a particular focus on translating knowledge from model organism networks to humans using ortholog mapping and a novel framework for mapping phenotypes and diseases across species. We will significantly expand CRISPR-based genetic interactions, chemical and drug interactions, and post-translational modifications, which we will integrate with our core physical and genetic interactions and organize around focused curation efforts around particular biological themes. Use of text-mining algorithms and AI methods will be extended to enhance curation rates and coverage of the proteome. User access to the large datasets in BioGRID will be facilitated by data-rich interfaces, user-defined search and display parameters, and multiple methods of visualization. All software will continue to be open source and engineered toward compatibility and will be complementary with other database and software development efforts. The BioGRID will provide interaction data and software tools to model organism databases and other interested parties without restriction. The BioGRID resource will enable the biomedical research community to access validated biological interaction datasets across model organisms and humans for hypothesis generation and network analysis, and thereby further the general mission of the NIH.