BioGRID: An open resource for biological interactions and network analysis

BioGRID：生物相互作用和网络分析的开放资源

• 批准号: 10819019
• 负责人: KARA DOLINSKI
• 金额: $ 93.99万
• 依托单位: HOSPITAL FOR SICK CHLDRN (TORONTO)
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10819019
• 关键词: Algorithms; Alleles; Architecture; Archives; Area; Behavior; Binding; Biological; Biological Process; Biomedical Research; COVID-19; CRISPR screen; Cells; Chemical Agents; Chemicals; Clustered Regularly Interspaced Short Palindromic Repeats; Collaborations; Collection; Communities; Complex; Computer software; Data; Data Set; Databases; Development; Discipline; Disease; Drug Interactions; Elasticity; Engineering; Funding; Future; Generations; Genes; Genetic; Genome; Health; Human; Human Development; International; Intuition; Knowledge; Literature; Malignant Neoplasms; Maps; Methods; Mission; Molecular; Organism; Orthologous Gene; Pathway Analysis; Phenotype; Post-Translational Protein Processing; Process; Property; Protein Structure Initiative; Proteins; Proteome; Publications; Records; Research; Research Personnel; Resources; Services; Software Tools; Source; System; Technology; Tissues; Translating; Translational Research; Ubiquitin; United States National Institutes of Health; Visualization; biological systems; data pipeline; data tools; experimental study; functional genomics; fundamental research; gene function; genome-wide; human disease; improved; interest; knowledge graph; large datasets; model organism; model organisms databases; multicatalytic endopeptidase complex; new therapeutic target; novel; open source; pathogen; protein complex; protein function; repository; software development; statistics; technological innovation; text searching; tool; web site

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.

复杂的物理和遗传相互作用网络决定了所有生物系统的特性， 是人类发展、健康和疾病的基础。几十年的生物学实验已经发现了无数 在主要生物医学文献中描述的支持特定生物过程的分子过程。 最近的技术创新与完整的基因组序列信息相结合， 开发多种高通量（HTP）方法，以产生物理和遗传相互作用 前所未有的数据。因为人类互动网络通常直接类似于 在更易处理的模式生物中，成千上万的生物网络是至关重要的。 在主要的模式生物中发现的相互作用，以及人类，都被存档在一个很好的- 注释的方式，提供了一种严格的分析和计算的手段。捕捉、整合和 从文献和HTP数据集中查询这些丰富的数据，我们开发了BioGRID数据库 作为物理和遗传相互作用的开放式知识库（www.thebiogrid.org）。BioGRID包含超过 来自75，760篇出版物的2，000，000次互动。2020年，BioGRID平均页面浏览量为151，735次， 每个月有7，537次文件下载。我们最近发布的CRISPR开放库 屏幕（ORCS），平均每月8，725次页面浏览，1，646次独立访问，268次下载。在 此外，许多合作伙伴数据库、Meta数据库、 数据库和软件工具。在这里，我们建议显著增强数据内容， 架构和BioGRID的用户界面。我们将扩大可用数据的数量和类型 通过BioGRID，特别关注将模型生物网络的知识转化为人类 使用直系同源物作图和一种新的框架来绘制跨物种的表型和疾病。我们将 显著扩展了基于CRISPR的遗传相互作用、化学和药物相互作用以及翻译后 修改，我们将与我们的核心物理和遗传相互作用整合，并围绕 围绕特定的生物学主题进行策展。使用文本挖掘算法和人工智能方法将 扩展以提高蛋白质组的管理率和覆盖率。用户对大型数据集的访问 BioGRID将通过数据丰富的界面，用户定义的搜索和显示参数，以及多个 可视化的方法。所有的软件将继续是开源的，并朝着兼容性和 将与其他数据库和软件开发工作相辅相成。BioGRID将提供 相互作用数据和软件工具，以模拟生物数据库和其他相关方， 限制. BioGRID资源将使生物医学研究界能够获得经过验证的 跨模型生物体和人类的生物相互作用数据集，用于假设生成和网络 分析，从而进一步促进国家卫生研究院的一般使命。

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

The BioGRID Interaction Database: 2011 update.

• DOI: 10.1093/nar/gkq1116
• 发表时间: 2011-01
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Stark C;Breitkreutz BJ;Chatr-Aryamontri A;Boucher L;Oughtred R;Livstone MS;Nixon J;Van Auken K;Wang X;Shi X;Reguly T;Rust JM;Winter A;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

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

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