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

基本信息

批准号：
8705064
负责人：
KARA DOLINSKI
金额：
$ 63.21万
依托单位：
PRINCETON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-09-14 至 2016-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8705064
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

项目摘要

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）：数十年的实验产生了大量数据，并确定了与人类健康直接相关的特定生物功能基础的多种分子过程。然而，这些数据了解人类健康和疾病的潜力尚未完全实现，因为出版物报告的自然语言结果不易识别或计算。为了捕获和查询文献中的大量数据，我们开发了 BioGRID，这是一个分子相互作用的开放存储库。 BioGRID 是一种广泛使用的资源，平均每月有超过 6,500 名独立访问者，他们使用自定义搜索和可视化工具探索数据库中超过 360,000 次交互。此外，BioGRID 数据集是许多合作伙伴数据库交互信息的来源。人类疾病模型的描述也存在类似的挑战。虽然通过多年对强大的人类疾病模型（包括酵母、线虫、苍蝇、斑马鱼和小鼠模型）的研究可以获得很多信息，但这些模型之间的关系以及这些模型与人类疾病的关系尚未系统地组织起来。在通过“将动物模型与人类疾病联系起来”倡议（LAMHDl）相关的这项提案和其他提案中，我们将采取系统、协调的努力，通过整理关键的新数据概要、应用复杂的新数据集成方法、组织来扩展 BioGRID 数据库。将数据转化为预测网络，最重要的是，将模型系统与人类疾病过程之间的网络联系起来。我们的管理工作将全面注释 RNAi 表型数据和化学遗传数据，这分别对于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跨多个模型访问完全全面、集成的数据集，以生成和分析人类疾病。