Reactome and the Gene Ontology: Digital pathway convergence for core data resources

基因本体(GO)知识库是世界上最大的关于 基因的功能，是生物医学研究中计算分析的基础。反应- Tome集成了关于描述人类的相互关联的生物路径的信息 分子水平上的生物学。拟议的工作将扩大GO和GO之间的合作 Reactome项目，以更好地整合目前孤立在两个资源和几个资源中的内容 相关参考资源，为 生物医学研究社区。 GO提供了广泛使用的工具，用于将单个基因产物与细胞成分联系起来- 它是其中的一部分，它所调节的分子功能，以及它所参与的生物过程 参与其中。反应组知识库集成中的生物反应注释 许多这样的原子围棋断言涉及基因产物，因为它们在生物前... Cess，对使用Go注释的研究人员具有巨大潜在价值的信息，但很难 之所以使用，是因为两者的配方不同。最近发展起来的围棋因果活动 模型(GO-CAM)形式化方法，它扩展了原子GO注释来捕获和集成En-CAM。 轮胎的生物过程，可以形成围棋和反应体的路径整合的基础- 方式模型，增加它们的实用性。 我们建议开发一个集成的路径/本体资源，通过开发工具来有效地对路径/本体资源进行管理。 科学提取GO-CAM模型内容，进一步将GO-CAM/Reactome模型与 关键参考资源，如RHEA(专家策划的生化反应数据库) 以及蛋白质本体(PRO)，它是蛋白质相关实体的本体表示。 具体地说，我们将1)为每个反应组途径生成GO-CAM模型 正常的人体生物过程；2)协调Reactome和GO之间的策划和质量保证； 3)支持资源之间的数据交换并交叉验证映射；4)扩展此功能 努力支持高效的、部分自动化的模式生物途径资源的创建。 最终结果将是一套开源、集成的工具和数据，这些工具和数据准确地表示 人类和模拟生物生物过程，支持各种数据挖掘、分析和 建模活动。这些工具和数据将被整合到GO、Reactome、PRO和 参与模式生物资源，支持重点社区长期对接 消息人士称，这一项目完成后。

The Gene Ontology (GO) knowledgebase is the world’s largest source of information on the functions of genes, and is a foundation for computational analysis in biomedical research. Reac- tome integrates information about the interconnected biological pathways that describe human biology at a molecular level. The proposed work will extend a collaboration between the GO and Reactome projects to better integrate content currently siloed in the two resources and several related reference resources to provide a more fully interoperable open access resource for the biomedical research community. GO provides widely-used tools for associating an individual gene product with a cellular compo- nent of which it is a part, a molecular function it mediates, and a biological process in which it participates. Annotations of biological reactions in the Reactome Knowledgebase integrate many such atomic GO assertions concerning gene products as they interact in biological pro- cesses, information of great potential value to researchers who use GO annotations, but hard to use because the two are formulated differently. The recently developed GO Causal Activity Model (GO-CAM) formalism, which extends atomic GO annotation to capture and integrate en- tire biological processes, can form the basis of integration between GO and Reactome's path- way models, increasing their utility. We propose to develop an integrated pathway/ontology resource by developing tools to effi- ciently extract GO-CAM model content and further integrate GO-CAM / Reactome models with key reference resources such as Rhea (a database of expert-curated biochemical reactions) and the Protein Ontology (PRO), an ontological representation of protein-related entities. Specifically, we will 1) generate GO-CAM models for every Reactome pathway that represents a normal human biological process; 2) coordinate curation and QA between Reactome and GO; 3) support data exchange among resources and cross-validate the mappings; and 4) extend this work to support efficient, partly automated creation of pathway resources for model organisms. The end result will be an open-source, integrated set of tools and data that accurately represent human and model organism biological processes, supporting diverse data mining, analysis, and modeling activities. These tools and data will be incorporated into GO, Reactome, PRO, and participating model organism resources, supporting long-term alignment of key community re- sources after the completion of this project.