银河系统中敏感临床研究数据的 FAIR 数据检索。

IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES GigaScience Pub Date : 2024-01-02 DOI:10.1093/gigascience/giad099
Jasper Ouwerkerk, Helena Rasche, John D Spalding, Saskia Hiltemann, Andrew P Stubbs
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引用次数: 0

摘要

背景:在临床研究中,数据必须是可访问和可重复的,但生成的数据越来越大,分析也越来越复杂。在此,我们提出了一个可查找、可访问、可互操作和可重用(FAIR)的数据访问平台,以创建可重现的研究结果。通过 PyEGA3 等应用程序接口服务,已经实现了对欧洲基因组-表型组档案(EGA)这一主要基因组资源库的标准化访问。我们的目标是通过检索 EGA 的基因组数据,在 Galaxy 中提供 FAIR 数据分析服务,并为 FAIR 数据分析提供一个通用的 "omics "平台:为了证明这一点,我们实施了一个端到端的 Galaxy 工作流程,以复制来自 EGA 的 RD-Connect 合成数据集 Beyond the 1 Million Genomes (synB1MG) 的研究结果。我们在 Galaxy 中开发了 PyEGA3 连接器,以便从 EGA 轻松下载多个数据集。我们将用于精准基因组学诊断环境的 gene.iobio 工具添加到了 Galaxy 中,并证明它为三元组分析结果提供了更动态、更可解释的视图。我们开发了一套 Galaxy 三元组分析工作流程,利用 GEMINI 和 gene.iobio 工具确定 synB1MG 三元组中的致病变体。完整的工作流程可在 WorkflowHub 上找到,在 Galaxy 培训网络中还创建了相关教程,帮助不熟悉 Galaxy 的研究人员运行工作流程:我们展示了通过 PyEGA3 在 Galaxy 中重用 EGA 数据的可行性,并通过在合成数据中重新发现尖峰变异验证了工作流程。最后,我们改进了 Galaxy 中的现有工具,并创建了一个三元组分析工作流,以展示 Galaxy 中 FAIR 基因组学分析的价值。
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FAIR data retrieval for sensitive clinical research data in Galaxy.

Background: In clinical research, data have to be accessible and reproducible, but the generated data are becoming larger and analysis complex. Here we propose a platform for Findable, Accessible, Interoperable, and Reusable (FAIR) data access and creating reproducible findings. Standardized access to a major genomic repository, the European Genome-Phenome Archive (EGA), has been achieved with API services like PyEGA3. We aim to provide a FAIR data analysis service in Galaxy by retrieving genomic data from the EGA and provide a generalized "omics" platform for FAIR data analysis.

Results: To demonstrate this, we implemented an end-to-end Galaxy workflow to replicate the findings from an RD-Connect synthetic dataset Beyond the 1 Million Genomes (synB1MG) available from the EGA. We developed the PyEGA3 connector within Galaxy to easily download multiple datasets from the EGA. We added the gene.iobio tool, a diagnostic environment for precision genomics, to Galaxy and demonstrate that it provides a more dynamic and interpretable view for trio analysis results. We developed a Galaxy trio analysis workflow to determine the pathogenic variants from the synB1MG trios using the GEMINI and gene.iobio tool. The complete workflow is available at WorkflowHub, and an associated tutorial was created in the Galaxy Training Network, which helps researchers unfamiliar with Galaxy to run the workflow.

Conclusions: We showed the feasibility of reusing data from the EGA in Galaxy via PyEGA3 and validated the workflow by rediscovering spiked-in variants in synthetic data. Finally, we improved existing tools in Galaxy and created a workflow for trio analysis to demonstrate the value of FAIR genomics analysis in Galaxy.

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来源期刊
GigaScience
GigaScience MULTIDISCIPLINARY SCIENCES-
CiteScore
15.50
自引率
1.10%
发文量
119
审稿时长
1 weeks
期刊介绍: GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.
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