Zheng Christina L, Ratnakar Varun, Gil Yolanda, McWeeney Shannon K
Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA.
Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA.
Genome Med. 2015 Jul 25;7(1):73. doi: 10.1186/s13073-015-0202-y.
Recent highly publicized cases of premature patient assignment into clinical trials, resulting from non-reproducible omics analyses, have prompted many to call for a more thorough examination of translational omics and highlighted the critical need for transparency and reproducibility to ensure patient safety. The use of workflow platforms such as Galaxy and Taverna have greatly enhanced the use, transparency and reproducibility of omics analysis pipelines in the research domain and would be an invaluable tool in a clinical setting. However, the use of these workflow platforms requires deep domain expertise that, particularly within the multi-disciplinary fields of translational and clinical omics, may not always be present in a clinical setting. This lack of domain expertise may put patient safety at risk and make these workflow platforms difficult to operationalize in a clinical setting. In contrast, semantic workflows are a different class of workflow platform where resultant workflow runs are transparent, reproducible, and semantically validated. Through semantic enforcement of all datasets, analyses and user-defined rules/constraints, users are guided through each workflow run, enhancing analytical validity and patient safety.
To evaluate the effectiveness of semantic workflows within translational and clinical omics, we have implemented a clinical omics pipeline for annotating DNA sequence variants identified through next generation sequencing using the Workflow Instance Generation and Specialization (WINGS) semantic workflow platform.
We found that the implementation and execution of our clinical omics pipeline in a semantic workflow helped us to meet the requirements for enhanced transparency, reproducibility and analytical validity recommended for clinical omics. We further found that many features of the WINGS platform were particularly primed to help support the critical needs of clinical omics analyses.
This is the first implementation and execution of a clinical omics pipeline using semantic workflows. Evaluation of this implementation provides guidance for their use in both translational and clinical settings.
近期，因不可重复的组学分析导致患者被过早分配到临床试验中的案例被高度曝光，这促使许多人呼吁对转化组学进行更全面的审视，并凸显了确保患者安全对透明度和可重复性的迫切需求。诸如Galaxy和Taverna等工作流平台的使用极大地提高了组学分析流程在研究领域的应用、透明度和可重复性，在临床环境中也将是一个非常有价值的工具。然而，使用这些工作流平台需要深厚的领域专业知识，尤其是在转化组学和临床组学的多学科领域，临床环境中可能并不总是具备这些知识。这种领域专业知识的缺乏可能会危及患者安全，并使这些工作流平台在临床环境中难以实施。相比之下，语义工作流是另一类工作流平台，其生成的工作流运行是透明、可重复且经过语义验证的。通过对所有数据集、分析以及用户定义的规则/约束进行语义强化，用户在每次工作流运行过程中都能得到引导，从而提高分析的有效性和患者安全性。
为了评估语义工作流在转化组学和临床组学中的有效性，我们使用工作流实例生成与专业化（WINGS）语义工作流平台，实施了一个临床组学流程，用于注释通过下一代测序识别出的DNA序列变异。
我们发现，在语义工作流中实施和执行我们的临床组学流程有助于我们满足临床组学所推荐的提高透明度、可重复性和分析有效性的要求。我们还进一步发现，WINGS平台的许多功能特别适合帮助支持临床组学分析的关键需求。
这是首次使用语义工作流实施和执行临床组学流程。对该实施过程的评估为其在转化和临床环境中的应用提供了指导。
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