Gene Ontology Consortium

项目摘要 由于生物系统惊人的复杂性，生物医学研究正变得越来越多， 依赖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 基因如何运作的知识库，并已成为计算的关键组成部分， 基因组革命的基础设施。它几乎已经成为不可或缺的解释大- 生物学研究中的分子尺度测量。至关重要的是，对于人类健康研究来说，GO也是 一套互补的本体论，以最大限度地促进互操作性的方式构建， 数据集的可比性。它代表了基因功能和生物学过程， 人类疾病，例如通过来自人类表型本体（HPO）类脂质异常的链接 代谢，定义与GO类脂质代谢过程（GO_0006629）相关，研究人员或 临床医生可以找到已知参与这一过程的一组基因。 GO是一种知识资源，可以单独或与来自 其他知识资源，使专家能够发现联系，并形成新的假设 GO代表的生物网络。GO中的所有知识都使用语义Web技术表示， 因此易于计算积分和一致性检查。建议的GO知识 环境将使更广泛的科学家社区能够为一个共同的、可计算的 代表生物学。 为了确保知识环境符合生物医学研究人员的要求，我们将：a）提供一个 基因功能的全面、详细、可计算的知识库，编码在基因本体中， 注释（关于特定基因功能的计算机可读声明），专注于人类 生物学; B）为广大科学家提供一个“中心”，以协作扩展、纠正和改进 知识库; 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转移， 如老鼠和斑马鱼，对人类生物学的影响;以及e）使科学界能够使用 大规模遗传和组学数据分析中的知识库。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：有效地捕获和整合 生物学知识和坚持尽可能高的标准的准确性和细节;建设和 提供一个强大、灵活、强大和可扩展的技术基础设施，不仅可用于内部 更广泛的社区可以轻松使用;最后，利用最先进的社交媒体、网络服务 和其他技术来传播GO资源到整个生物医学研究社区。

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.