Ouwerkerk Jasper, Rasche Helena, Spalding John D, Hiltemann Saskia, Stubbs Andrew P
Clinical Bioinformatics Group, Department of Pathology, Erasmus Medical Center, 3015 CN, Rotterdam, the Netherlands.
CSC-IT Center for Science, 02101 Espoo, Finland.
Gigascience. 2024 Jan 2;13. doi: 10.1093/gigascience/giad099.
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.
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.
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.
在临床研究中，数据必须易于获取且可重复，但生成的数据量越来越大，分析也越来越复杂。在此，我们提出了一个用于可查找、可访问、可互操作和可重用（FAIR）数据访问并创建可重复结果的平台。通过诸如PyEGA3之类的API服务，已实现对主要基因组存储库欧洲基因组-表型档案库（EGA）的标准化访问。我们旨在通过从EGA检索基因组数据，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“组学”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“超越百万基因组”（synB1MG）的结果。我们在Galaxy中开发了PyEGA3连接器，以便轻松从EGA下载多个数据集。我们将基因.iobio工具（一种用于精准基因组学的诊断环境）添加到Galaxy中，并证明它为三人组分析结果提供了更动态且可解释的视图。我们开发了一个Galaxy三人组分析工作流程，使用GEMINI和基因.iobio工具从synB1MG三人组中确定致病变异。完整的工作流程可在WorkflowHub上获取，并且在Galaxy培训网络中创建了相关教程，这有助于不熟悉Galaxy的研究人员运行该工作流程。
我们展示了通过PyEGA3在Galaxy中重用EGA数据的可行性，并通过重新发现合成数据中掺入的变异来验证工作流程。最后，我们改进了Galaxy中的现有工具，并创建了一个三人组分析工作流程，以证明Galaxy中FAIR基因组学分析的价值。
