Systematic data curation and integration to link models of human disease

系统数据管理和整合以链接人类疾病模型

• 批准号: 8332357
• 负责人: KARA DOLINSKI
• 金额: $ 63.85万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-14 至 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8332357
• 关键词: Address; Algorithms; Animal Model; Architecture; Area; Bayesian Method; Binding; Biological; Biological Models; Biological Process; Biomedical Research; Cell Line; Cells; Chemicals; Collaborations; Communities; Computer software; Custom; Data; Data Analyses; Data Collection; Data Set; Data Sources; Databases; Development; Disease; Drug Interactions; Elements; Encapsulated; Failure; Gene Deletion; Gene Proteins; Generations; Genes; Genetic; Goals; Health; Human; Human Identifications; Imagery; Individual; Infectious Agent; Knowledge; Lead; Link; Literature; Metabolism; Methods; Modeling; Molecular; Monoclonal Antibody R24; Mutation; Neighborhoods; Nematoda; Network-based; Neurobiology; Ontology; Organ; Pathway interactions; Pharmaceutical Preparations; Phenotype; Process; Proteins; Protocols documentation; Publications; RNA Interference; Reporting; Research; Research Infrastructure; Resources; Set protein; Software Tools; Source; Structure; Techniques; Testing; Therapeutic Intervention; Time; Tissues; Toxic Environmental Substances; Yeasts; Zebrafish; base; chemical genetics; data exchange; data integration; data modeling; design; drug discovery; fly; gene interaction; human disease; innovation; insight; mouse model; natural language; novel; quality assurance; repository; research study; small hairpin RNA; small molecule; software development; text searching; tool

DESCRIPTION (provided by applicant): Decades of experiments have produced vast amounts of data and identified a multitude of molecular processes that underlie specific biological functions directly relevant to human health. However, the potential of these data to inform about human health and disease have not yet been fully realized because publications report results in natural language that is not easily identifiable or computable. To capture and interrogate this wealth of data from the literature, we developed the BioGRID, an open repository for molecular interactions. BioGRID is a widely used resource, with on average over 6,500 unique visitors per month who explore the > 360,000 interactions in the database with custom search and visualization tools. In addition, BioGRID data sets are the source of interaction information for a host of partner databases. An analogous challenge exists with the description of models of human disease. While much information is available from years of research in powerful models of human disease, including yeast, nematode, fly, zebrafish and mouse models, the relationship of these models to each other and to human disease has not been systematically organized. In this and other proposals connected through the Linking Animal Models to Human Disease Initiative (LAMHDl), we will undertake a systematic, coordinated effort to expand the BioGRID database through curation of pivotal new data compendia, application of sophisticated new methods for data integration, organization of data into predicted networks, and critically, linkage of networks between model systems and human disease processes. Our curation effort will comprehensively annotate RNAi phenotype data and chemical genetic data, which are crucial for accurate models of human disease and therapeutic intervention in disease, respectively. We will apply data analysis techniques to integrate these and other data across species to link human diseases with all relevant models to predict new features of human disease. We will also develop software tools to allow facile access of the research community to all of these results. Thus, we will enable the biomedical community to access fully comprehensive, integrated datasets across multiple models for hypothesis generation and analysis of human diseases.

描述（由申请人提供）：数十年的实验已经产生了大量的数据，并确定了许多分子过程，这些过程是与人类健康直接相关的特定生物学功能的基础。然而，这些数据提供人类健康和疾病信息的潜力尚未完全实现，因为出版物报告的结果是不容易识别或计算的自然语言。为了从文献中捕获和查询这些丰富的数据，我们开发了BioGRID，这是一个开放的分子相互作用库。BioGRID是一个广泛使用的资源，平均每月有超过6，500名独立访问者使用自定义搜索和可视化工具探索数据库中的360，000多个交互。此外，BioGRID数据集是许多合作伙伴数据库相互作用信息的来源。人类疾病模型的描述也存在类似的挑战。虽然从多年来对人类疾病的强大模型（包括酵母、线虫、苍蝇、斑马鱼和小鼠模型）的研究中获得了许多信息，但这些模型之间的关系以及与人类疾病的关系尚未得到系统的组织。在这个和其他通过连接动物模型人类疾病倡议（LAMHDl）连接的提案中，我们将进行系统的，协调的努力，通过关键的新数据纲要的管理，数据集成的复杂的新方法的应用，数据组织到预测的网络中，以及关键的是，模型系统和人类疾病过程之间的网络连接来扩展BioGRID数据库。我们的策展工作将全面注释RNAi表型数据和化学遗传数据，这分别对人类疾病的准确模型和疾病的治疗干预至关重要。我们将应用数据分析技术来整合这些和其他跨物种的数据，将人类疾病与所有相关模型联系起来，以预测人类疾病的新特征。我们还将开发软件工具，使研究界能够轻松访问所有这些结果。因此，我们将使生物医学界能够访问跨多个模型的全面综合数据集，用于人类疾病的假设生成和分析。