Use of semantic workflows to enhance transparency and reproducibility in clinical omics.
BACKGROUND
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.


METHODS
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.


RESULTS
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.


CONCLUSIONS
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.
Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA. zheng@ohsu.edu.|Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA. zheng@ohsu.edu.
Christina L
CL
Zheng
Information Sciences Institute, University of Southern California, Los Angeles, CA, USA. varunr@isi.edu.
Varun
V
Ratnakar
Information Sciences Institute, University of Southern California, Los Angeles, CA, USA. gil@isi.edu.
Yolanda
Y
Gil
Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA. mcweeney@ohsu.edu.|Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA. mcweeney@ohsu.edu.|Division of Biostatistics, Department of Public Health and Preventative Medicine, Oregon Health & Science University, Portland, OR, USA. mcweeney@ohsu.edu.
Shannon K
SK
McWeeney
D019295:Computational Biology / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000235:genetics; D015203:Reproducibility of Results; D017422:Sequence Analysis, DNA; D057188:Workflow