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

Reactome 和基因本体：核心数据资源的数字路径融合

基本信息

批准号：
10657749
负责人：
PETER G DEUSTACHIO
金额：
$ 68.13万
依托单位：
NEW YORK UNIVERSITY SCHOOL OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-24 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10657749
关键词：
Automated Annotation Automation Biochemical Reaction Bioinformatics Biological Biological Models Biological Process Biomedical Research Caenorhabditis elegans Chemicals Chemistry Collaborations Communicable Diseases Communities Complex Computer Analysis Coupled Data Databases Development Ensure Foundations Funding Generations Genes Goals Human Human Biology Individual Laboratory mice Life Maps Mediating Modeling Molecular National Human Genome Research Institute Nematoda Ontology Pathway interactions Planet Earth Procedures Process Protein Family Proteins Reaction Research Personnel Resources Rhea Source System Translating Validation Work biomedical resource data exchange data mining data resource data standards digital diverse data gene function gene product interoperability knowledgebase model organism open source protein complex prototype tool

项目摘要

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.

基因本体（GO）知识库是世界上最大的基因本体信息来源基因的功能，是生物医学研究中计算分析的基础。 tome 整合了描述人类的相互关联的生物途径的信息拟议的工作将扩大 GO 和分子水平上的生物学之间的合作。 Reactome 项目旨在更好地整合目前两个资源和多个资源中孤立的内容相关参考资源，为生物医学研究团体。 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关联起来。它是其中的一部分，它介导的分子功能，以及它所参与的生物过程 Reactome 知识库中的生物反应注释参与整合。许多此类关于基因产物的原子 GO 断言，因为它们在生物亲和性中相互作用。访问，对于使用 GO 注释的研究人员来说具有巨大潜在价值的信息，但很难使用，因为两者的表述不同，最近开发的 GO 因果活动。模型（GO-CAM）形式主义，它扩展了原子 GO 注释以捕获和集成 en- 轮胎生物过程，可以形成GO和Reactome路径之间整合的基础- 方式模型，增加其实用性。我们建议通过开发工具来开发集成的路径/本体资源快速提取GO-CAM模型内容，并进一步将GO-CAM / Reactome模型与关键参考资源，例如 Rhea（专家策划的生化反应数据库）以及蛋白质本体论（PRO），蛋白质相关实体的本体论表示。具体来说，我们将 1) 为代表的每个 Reactome 途径生成 GO-CAM 模型正常的人类生物过程；2) 协调 Reactome 和 GO 之间的管理和 QA； 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；致力于支持高效、部分自动化地创建模式生物资源途径。最终结果将是一套开源、集成的工具和数据，可以准确地表示人类和模型生物体的生物过程，支持各种数据挖掘、分析和这些工具和数据将被纳入 GO、Reactome、PRO 和参与模式生物资源，支持关键社区重建的长期协调本项目完成后的资源。