Illuminating the Druggable Genome Knowledge Management Center (IDG KMC)

阐明可药物基因组知识管理中心 (IDG KMC)

• 批准号: 9558162
• 负责人: TUDOR I OPREA
• 金额: $ 36.8万
• 依托单位: UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2018-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9558162
• 关键词: Algorithms; Chemicals; Clinical; Communities; Coupled; Data; Databases; Denmark; Disease; Disease Pathway; Elements; Florida; G-Protein-Coupled Receptors; Genes; Genome; Genomics; Goals; Human; Imagery; Information Resources Management; Ion Channel; Knowledge; Legal patent; Link; Literature; Mining; Modeling; New Mexico; Nuclear Receptors; Phosphotransferases; Process; Proteins; Research; Resources; Scientist; Source; Stratification; System; TRD@ gene cluster; Tissues; Universities; base; dark matter; design; drug discovery; experience; improved; link protein; macromolecule; prospective; software development; tool

DESCRIPTION: The overall goal of the Illuminating the Druggable Genome Knowledge Management Center (IDG KMC) is to evaluate and organize (via the Data Organizing Core, DOC), present and visualize (via the User Interface Portal, UIP) and rank (in cooperation with the IDG Consortium) all prospective disease-linked proteins, as potential druggable targets for four protein superfamilies: G-protein-coupled receptors (GPCRs), nuclear receptors (NRs), ion channels (IC) and kinases. By combining data extracted from multiple sources, coupled with algorithmic processing, prediction and human curation, the emerging knowledge will be associated with the appropriate proteins. The KMC will link disease, pathway, protein, gene, chemical, bioactivity, drug discovery and clinical information elements from databases, literature, patents and other documents in the DOC "Target Central" Resource Database. TCRD will serve as primary source for the IDG Query Platform, the UlP-developed system that will enable scientists to access, visualize and analyze IDG-specific data. Coordinating DOC and UIP activities, the Administrative Core, AC, will assist with human curation by organizing class-specific External Target Panels to categorize proteins into 4 classes (Tclin - clinical; Tchem - manipulated by chemicals; Tmacro - manipulated by macromolecules; and Tdark - the genomic "dark matter"). Tissue and cellular localization for both disease and protein will serve as central filtes for ranking. The specific aims of the KMC are based on the demonstrated experience of the Oprea-Sklar team at the University of New Mexico (data capture, processing, mining and modeling), and the Simeonov-led team at NCATS (software development, visualization and modeling), supported by teams based in Denmark, Florida and UK. Using automated tools, we performed disease-protein associations for each protein superfamily, obtained preliminary stratification (e.g., Tclin 22%, Tdark 30%), and designed Specific Aims that enable us to further annotate this genome subset. It is expected that within 12 months, the TCRD-based IDG Querly Platform will be operational, which may dramatically improve the target prioritization process for the research community at large and the IDG Consortium, in exploring "dark matter" for GPCRs, NRs, ICs and kinases.

产品说明： 照亮可药用基因组知识管理中心（IDG KMC）的总体目标是评估和组织（通过数据组织核心，DOC），呈现和可视化（通过用户界面门户，UIP）和排名（与IDG联盟合作）所有有前景的疾病相关蛋白，作为四个蛋白超家族的潜在药物靶标：G蛋白偶联受体（GPCR）、核受体（NR）、离子通道（IC）和激酶。通过结合从多个来源提取的数据，再加上算法处理，预测和人工管理，新兴的知识将与适当的蛋白质相关联。KMC将从DOC“目标中心”资源数据库中的数据库、文献、专利和其他文件中链接疾病、途径、蛋白质、基因、化学、生物活性、药物发现和临床信息元素。TCRD将作为IDG查询平台的主要来源，该平台是UlP开发的系统，将使科学家能够访问，可视化和分析IDG特定的数据。 协调DOC和UIP活动，行政核心AC将通过组织特定类别的外部靶向小组将蛋白质分为4类（Tclin -临床; Tchem-临床; Tchem-临床和Tchem-临床）来协助人类管理。 - Tmacro -由大分子操纵; Tdark -基因组“黑暗” matter”）。疾病和蛋白质的组织和细胞定位将作为排名的中心过滤器。 KMC的具体目标是基于新墨西哥州大学的Oprea-Sklar团队（数据采集、处理、挖掘和建模）和NCATS的Simeonov领导的团队（软件开发、可视化和建模）的经验，并得到了丹麦、佛罗里达和英国团队的支持。使用自动化工具，我们对每个蛋白质超家族进行了疾病-蛋白质关联，获得了初步分层（例如，Tclin 22%，Tdark 30%），并设计了特异性目的，使我们能够进一步注释该基因组子集。预计在12个月内，基于TCRD的IDG Querly平台将投入运行，这可能会大大改善整个研究界和IDG联盟在探索GPCR、NR、IC和激酶的“暗物质”方面的目标优先级排序过程。

项目成果

The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods.

• DOI: 10.1093/nar/gkad1004
• 发表时间: 2024-01-05
• 期刊: Nucleic acids research
• 影响因子: 14.9

Comprehensive comparison of large-scale tissue expression datasets.

• DOI: 10.7717/peerj.1054
• 发表时间: 2015
• 期刊: PeerJ
• 影响因子: 2.7
• 作者: Santos A;Tsafou K;Stolte C;Pletscher-Frankild S;O'Donoghue SI;Jensen LJ
• 通讯作者: Jensen LJ

The ChEMBL database in 2017.

• DOI: 10.1093/nar/gkw1074
• 发表时间: 2017-01-04
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Gaulton A;Hersey A;Nowotka M;Bento AP;Chambers J;Mendez D;Mutowo P;Atkinson F;Bellis LJ;Cibrián-Uhalte E;Davies M;Dedman N;Karlsson A;Magariños MP;Overington JP;Papadatos G;Smit I;Leach AR
• 通讯作者: Leach AR

Illuminating the druggable genome through patent bioactivity data.

• DOI: 10.7717/peerj.15153
• 发表时间: 2023
• 期刊: PeerJ
• 影响因子: 2.7
• 作者: Magariños MP;Gaulton A;Félix E;Kiziloren T;Arcila R;Oprea TI;Leach AR
• 通讯作者: Leach AR

Formalizing drug indications on the road to therapeutic intent.

• DOI: 10.1093/jamia/ocx064
• 发表时间: 2017-11-01
• 期刊: Journal of the American Medical Informatics Association : JAMIA
• 作者: Nelson SJ;Oprea TI;Ursu O;Bologa CG;Zaveri A;Holmes J;Yang JJ;Mathias SL;Mani S;Tuttle MS;Dumontier M
• 通讯作者: Dumontier M