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建立一个全球基因组观测站:利用 GEOME(基因组观测站元数据库)加快和改进生物多样性研究遗传数据和元数据的存储和检索。

Building a global genomics observatory: Using GEOME (the Genomic Observatories Metadatabase) to expedite and improve deposition and retrieval of genetic data and metadata for biodiversity research.

机构信息

School of Biological Sciences, The University of Queensland, St Lucia, Qld, Australia.

Department of Biology and Chemistry, California State University, Seaside, CA, USA.

出版信息

Mol Ecol Resour. 2020 Nov;20(6):1458-1469. doi: 10.1111/1755-0998.13269. Epub 2020 Oct 27.

Abstract

Genetic data represent a relatively new frontier for our understanding of global biodiversity. Ideally, such data should include both organismal DNA-based genotypes and the ecological context where the organisms were sampled. Yet most tools and standards for data deposition focus exclusively either on genetic or ecological attributes. The Genomic Observatories Metadatabase (GEOME: geome-db.org) provides an intuitive solution for maintaining links between genetic data sets stored by the International Nucleotide Sequence Database Collaboration (INSDC) and their associated ecological metadata. GEOME facilitates the deposition of raw genetic data to INSDCs sequence read archive (SRA) while maintaining persistent links to standards-compliant ecological metadata held in the GEOME database. This approach facilitates findable, accessible, interoperable and reusable data archival practices. Moreover, GEOME enables data management solutions for large collaborative groups and expedites batch retrieval of genetic data from the SRA. The article that follows describes how GEOME can enable genuinely open data workflows for researchers in the field of molecular ecology.

摘要

遗传数据代表了我们对全球生物多样性理解的一个相对较新的领域。理想情况下,这些数据应包括基于生物体 DNA 的基因型和生物体采样的生态背景。然而,大多数数据存储工具和标准仅专注于遗传或生态属性。基因组观测站元数据库 (GEOME:geome-db.org) 为维护国际核苷酸序列数据库合作组织 (INSDC) 存储的遗传数据集与其相关生态元数据之间的链接提供了一个直观的解决方案。GEOME 便于将原始遗传数据存入 INSDC 序列读取档案 (SRA),同时保持与 GEOME 数据库中符合标准的生态元数据的持久链接。这种方法促进了可查找、可访问、可互操作和可重复使用的数据归档实践。此外,GEOME 为大型协作组提供数据管理解决方案,并加快了从 SRA 批量检索遗传数据的速度。接下来的文章描述了 GEOME 如何为分子生态学领域的研究人员实现真正开放的数据工作流程。

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