Data Organizing Core

Illuminating the Druggable Genome Knowledge Management Center（IDG KMC）的数据组织核心DOC的主要目标是评估、组织和排名四个蛋白质超家族的所有预期疾病相关蛋白质：G蛋白偶联受体（GPCR）、核受体（NR）、离子通道（IC）和激酶。作为主要的知识库，DOC将通过合并从多个来源提取的数据来开发“目标中心”资源数据库（TCRD），这些数据将疾病、途径、蛋白质、化学、基因、生物活性、药物发现和来自数据库、文献、专利、药物标签和其他文件的临床信息元素联系起来。TCRD将作为IDG查询平台的中心资源，该平台由KMC的用户界面门户（UIP）核心开发。DOC将开发用于算法处理和预测的工具，这将改善人类策展支持的疾病-蛋白质关联。四个外部目标小组将策划新兴的协会，排名适当的蛋白质。DOC将蛋白质分为4类（Tclin -临床; Tchem -由化学物质操纵; Tmacro -由大分子操纵; Tdark -基因组“暗物质”），由蛋白质（TTL）和疾病的组织和细胞定位数据支持。UNM的Oprea将领导DOC，分别由团队负责人Brunak和詹森（丹麦蛋白质研究中心）、Overington（欧洲生物信息学研究所）和Schurer（迈阿密）支持。具体目标：1。开发自动提取和处理存入TCRD的数据的工具; 2.开发用于路径、疾病和相关本体的半自动数据提取的工具，这将支持TTL分层; 3.开发用于文献和专利数据、批准的药物标签和临床试验的专家策展工具; 4.开发分析、建模和可视化工具，用于基于疾病的目标优先级排序。初步分层（例如，使用自动化工具对每个蛋白质超家族进行疾病-蛋白质关联的Tclin 22%，Tdark 30%。在12个月内，基于TCRD的IDG Querly平台将投入运营，改善整个研究社区和IDG联盟的目标优先级，探索GPCR，NR，IC和激酶的“暗物质”。

The main goal of the Data Organizing Core, DOC, of the Illuminating the Druggable Genome Knowledge Management Center (IDG KMC) is to evaluate, organize and rank all prospective disease-linked proteins for four protein superfamilies: G-protein-coupled receptors (GPCRs), nuclear receptors (NRs), ion channels (IC) and kinases. As main knowledge repository, the DOC will develop the "Target Central" Resource Database (TCRD) by combining data extracted from multiple sources linking disease, pathway, protein, chemical, gene, bioactivity, drug discovery and clinical information elements from databases, literature, patents, drug labels and other documents. TCRD will serve as central source for the IDG Query Platform, which is developed by KMC's User Interface Portal (UIP) core. DOC will develop tools for algorithmic processing and prediction, which will improve disease-protein associations supported by human curation. Four External Target Panels will curate emerging associations, ranking appropriate proteins. DOC will stratify proteins into 4 classes (Tclin - clinical; Tchem - manipulated by chemicals; Tmacro - manipulated by macromolecules; and Tdark - the genomic "dark matter"), supported by tissue and cellular localization data for proteins (TTL) and diseases. Oprea at UNM will lead the DOC, supported by team leaders Brunak and Jensen (at Center for Protein Research, Denmark), Overington (European Bioinformatics Institute) and Schurer (University of Miami), respectively. Specific Aims: 1. Develop tools for the automated extraction and processing of data, deposited into TCRD; 2. Develop tools for the semi-automated data extraction for pathways, diseases and associated ontologies, which will support TTL stratification; 3. Develop tools for expert curation of literature and patent data, approved drug labels and clinical trials; 4. Develop analytics, modeling and visualization tools for disease-based target prioritization. Preliminary stratification (e.g., Tclin 22%, Tdark 30%) of disease-protein associations was performed for each protein superfamily, using automated tools. Within 12 months, the TCRD-based IDG Querly Platform will be operational, improving target prioritization for the research community at large and the IDG Consortium, in exploring "dark matter" for GPCRs, NRs, ICs and kinases.