Resource Project

资源项目

• 批准号: 9209992
• 负责人: Paul D. Thomas
• 金额: $ 142.04万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9209992
• 关键词: Animal Model; Area; Bioinformatics; Biological; Biological Models; Biological Process; Biology; Biomedical Research; Case Study; Communication; Communities; Computer software; Computers; Data; Data Set; Development; Documentation; Ensure; Environment; Focus Groups; Gene Family; Genealogical Tree; Genes; Genetic; Genomics; Health; Heart; Human; Human Biology; Human Genome; Informatics; Information Resources; Interest Group; Intuition; Kidney; Knowledge; Link; Lipids; Literature; Measurement; Metabolism; Mission; Modeling; Molecular; Mus; Ontology; Organism; Pathway interactions; Persons; Phenotype; Phylogenetic Analysis; Physiological; Practice Guidelines; Process; Productivity; Protein Family; Proteins; PubMed; Readability; Research; Research Infrastructure; Research Personnel; Resources; Role; Running; Scientist; Side; Structure; System; Technology; Time; Training; Update; Videoconferences; WNT Signaling Pathway; Zebrafish; annotation system; base; biological research; biological systems; comparative; computer based Semantic Analysis; computer infrastructure; computerized; data resource; flexibility; gene function; human disease; improved; innovation; insight; interest; interoperability; knowledge base; lipid metabolism; member; migration; model building; molecular scale; new technology; quality assurance; social media; tool; web portal; web services; webinar

PROJECT SUMMARY Because of the staggering complexity of biological systems, biomedical research is becoming increasingly dependent on knowledge stored in a computable form. The Gene Ontology (GO) is by far the largest knowledgebase of how genes function, and has become a critical component of the computational infrastructure enabling the genomic revolution. It has become nearly indispensible in the interpretation of large- scale molecular measurements in biological research. Crucially, for human health research, GO is also one of a suite of complementary ontologies constructed in such as way to maximally promote interoperability and comparability of data sets. It represents the gene functions and biological processes that are perturbed in human disease, e.g. via the links from Human Phenotype Ontology (HPO) class abnormality of lipid metabolism, defined in relation to the GO class lipid metabolic process (GO_0006629), researchers or clinicians can find the set of genes that are known to be involved in this process. GO is a knowledge resource that can be statistically mined, either standalone or in combination with data from other knowledge resources, which enables experts to discover connections and form new hypotheses from the biological networks GO represents. All knowledge in GO is represented using semantic web technologies and so is amenable to computational integration and consistency checking. The proposed GO knowledge environment will enable a wider community of scientists to contribute to, and to utilize, a common, computable representation of biology. To ensure the knowledge environment meets the requirements of biomedical researchers, we will: a) deliver a comprehensive, detailed, computable knowledgebase of gene function, encoded in the Gene Ontology and annotations (computer-readable statements about the how specific genes function), focusing on human biology; b) provide a “hub” for a broad community of scientists to collaboratively extend, correct and improve the knowledgebase; c) ensure the GO knowledge resource is of the highest quality with regards to depth, breadth and accuracy; d) facilitate the transfer of insights obtained from studies of non-human organisms, such as the mouse and zebrafish, to human biology; and e) enable the scientific community to use the knowledgebase in analyses of large-scale genetic and -omics data. Our aims reflect the essential requirements for realizing the overarching objectives for a biomedical data resource: efficiently capturing and integrating biological knowledge and adhering to the highest possible standard for accuracy and detail; constructing and providing a robust, flexible, powerful, and extensible technological infrastructure available not only for internal use but just as easily by the wider community; and lastly, leveraging state-of-the-art social media, web services and other technologies to disseminate the GO resource to the entire biomedical research community.

项目概要 由于生物系统极其复杂，生物医学研究变得越来越重要 依赖于以可计算形式存储的知识 基因本体（GO）是迄今为止最大的。 基因如何发挥作用的知识库，已成为计算的重要组成部分 它已成为基因组革命的基础设施的解释几乎不可或缺的。 至关重要的是，对于人类健康研究，GO 也是生物研究中规模分子测量之一。 一套以最大限度地促进互操作性和 它代表了受干扰的基因功能和生物过程。 人类疾病，例如通过人类表型本体 (HPO) 类脂质异常的链接 代谢，根据 GO 类脂质代谢过程 (GO_0006629) 进行定义，研究人员或 战士们可以找到已知参与这一过程的一组基因。 GO 是一种可以专门挖掘的知识资源，可以单独使用，也可以与来自以下数据的数据结合使用： 其他知识资源，使专家能够发现联系并形成新的假设 GO 所代表的生物网络。GO 中的所有知识均使用语义网络技术来表示。 所以适合计算集成和一致性检查。 环境将使更广泛的科学家群体能够为并利用一个共同的、可计算的 生物学的表征。 为了确保知识环境满足生物医学研究人员的要求，我们将： 全面、详细、可计算的基因功能知识库，编码在基因本体和 注释（关于特定基因如何发挥作用的计算机可读语句），重点关注人类 b) 为广大科学家群体提供一个协作扩展、纠正和改进的“中心” c) 确保 GO 知识资源在深度方面具有最高质量， d) 促进从非人类生物体研究中获得的见解的传播，例如 正如小鼠和斑马鱼对人类生物学的影响；以及 e) 使科学界能够利用 大规模遗传和组学数据分析的知识库我们的目标反映了基本要求。 实现生物医学数据资源的总体目标：有效捕获和集成 生物学知识并遵守尽可能高的准确性和细节标准； 提供强大、灵活、强大且可扩展的技术基础设施，不仅可供内部使用 更广泛的社区也可以轻松使用；最后，利用最先进的社交媒体、网络服务 和其他技术将 GO 资源传播给整个生物医学研究界。