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

Overview of attention for article published in Genome Medicine, July 2015
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)

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8 X users
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2 Facebook pages

Citations

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15 Dimensions

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46 Mendeley
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3 CiteULike
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Title
Use of semantic workflows to enhance transparency and reproducibility in clinical omics
Published in
Genome Medicine, July 2015
DOI 10.1186/s13073-015-0202-y
Pubmed ID
Authors

Christina L. Zheng, Varun Ratnakar, Yolanda Gil, Shannon K. McWeeney

Abstract

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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Luxembourg 1 2%
Canada 1 2%
Brazil 1 2%
Unknown 42 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 20%
Student > Ph. D. Student 8 17%
Student > Master 5 11%
Student > Doctoral Student 4 9%
Student > Bachelor 4 9%
Other 11 24%
Unknown 5 11%
Readers by discipline Count As %
Computer Science 11 24%
Biochemistry, Genetics and Molecular Biology 9 20%
Agricultural and Biological Sciences 8 17%
Engineering 3 7%
Social Sciences 2 4%
Other 7 15%
Unknown 6 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 December 2015.
All research outputs
#6,617,870
of 24,629,540 outputs
Outputs from Genome Medicine
#1,080
of 1,517 outputs
Outputs of similar age
#71,086
of 268,262 outputs
Outputs of similar age from Genome Medicine
#30
of 38 outputs
Altmetric has tracked 24,629,540 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 1,517 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.2. This one is in the 28th percentile – i.e., 28% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 268,262 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.