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系统指南:有效利用 COVID-19 数据库进行基因组学、流行病学和临床研究。

Systematic Guidelines for Effective Utilization of COVID-19 Databases in Genomic, Epidemiologic, and Clinical Research.

机构信息

Department of Medical Informatics, College of Medicine, Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea.

Graduate School of Medical Science and Engineering, Korea Advanced Institute and Technology (KAIST), Daejeon 34141, Republic of Korea.

出版信息

Viruses. 2023 Mar 6;15(3):692. doi: 10.3390/v15030692.

Abstract

The pandemic has led to the production and accumulation of various types of data related to coronavirus disease 2019 (COVID-19). To understand the features and characteristics of COVID-19 data, we summarized representative databases and determined the data types, purpose, and utilization details of each database. In addition, we categorized COVID-19 associated databases into epidemiological data, genome and protein data, and drug and target data. We found that the data present in each of these databases have nine separate purposes (clade/variant/lineage, genome browser, protein structure, epidemiological data, visualization, data analysis tool, treatment, literature, and immunity) according to the types of data. Utilizing the databases we investigated, we created four queries as integrative analysis methods that aimed to answer important scientific questions related to COVID-19. Our queries can make effective use of multiple databases to produce valuable results that can reveal novel findings through comprehensive analysis. This allows clinical researchers, epidemiologists, and clinicians to have easy access to COVID-19 data without requiring expert knowledge in computing or data science. We expect that users will be able to reference our examples to construct their own integrative analysis methods, which will act as a basis for further scientific inquiry and data searching.

摘要

大流行导致了与 2019 年冠状病毒病(COVID-19)相关的各种类型数据的产生和积累。为了了解 COVID-19 数据的特征和特点,我们总结了有代表性的数据库,并确定了每个数据库的数据类型、目的和利用细节。此外,我们将与 COVID-19 相关的数据库分为流行病学数据、基因组和蛋白质数据以及药物和靶点数据。我们发现,根据数据类型,这些数据库中的数据具有九个独立的用途(进化枝/变体/谱系、基因组浏览器、蛋白质结构、流行病学数据、可视化、数据分析工具、治疗、文献和免疫)。利用我们研究的数据库,我们创建了四个查询作为综合分析方法,旨在回答与 COVID-19 相关的重要科学问题。我们的查询可以有效地利用多个数据库来产生有价值的结果,通过综合分析揭示新的发现。这使得临床研究人员、流行病学家和临床医生无需具备计算或数据科学方面的专业知识即可轻松访问 COVID-19 数据。我们希望用户能够参考我们的示例来构建自己的综合分析方法,这将为进一步的科学探究和数据搜索提供基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66a3/10059256/99db64f1ba70/viruses-15-00692-g001.jpg

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