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.

项目摘要 由于生物系统的惊人复杂性，生物医学研究变得越来越多 取决于以可计算形式存储的知识。基因本体论（GO）是迄今为止最大的 知识基因的功能，并已成为计算的关键组成部分 基础设施实现了基因组革命。在大型解释中，它几乎是不可或缺的 生物学研究中的比例分子测量。对于人类健康研究而言，GO也是之一 以最大促进互操作性和 数据集的可比性。它代表了受到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论（HPO）类别异常的链接 代谢，根据GO类脂质代谢过程（GO_0006629），研究人员或研究人员或 临床医生可以找到已知参与此过程的基因集。 GO是一种知识资源，可以在统计上开采，无论是独立的还是与来自 其他知识资源，使专家能够发现联系并形成新的假设 生物网络代表。 GO中的所有知识都使用语义Web技术代表 因此，适合计算集成和一致性检查。拟议的Go Wearkge 环境将使更广泛的科学家社区能够为一个常见的，可计算的贡献和利用 生物学的代表。 为了确保知识环境满足生物医学研究人员的要求，我们将：a）提供 基因本体论和 注释（有关特定基因功能的计算机可读语句），重点是人类 生物学; b）为广泛的科学家社区提供一个“枢纽”，以协作，纠正和改进 知识库； c）确保GO知识资源在深度方面是最高质量的 广泛和准确性； d）促进从非人类生物研究获得的见解的转移， 作为小鼠和斑马鱼，对人类生物学； e）使科学界能够使用 在大规模遗传和 - 组数据的分析中，知识库。我们的目标反映了基本要求 用于实现生物医学数据资源的总体目标：有效捕获和集成 生物学知识并遵守准确性和细节的最高标准；构造和 提供强大，灵活，强大和可扩展的技术基础架构，不仅可用于内部 使用但更容易被更广泛的社区使用；最后，利用最先进的社交媒体，网络服务 以及将GO资源传播到整个生物医学研究界的其他技术。