Evidence map›Paper›PMID 26289940›Full record

ArticleGenome medicine2015

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

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

Abstract read
In one paragraph

Article in Genome medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Big Data in Gastroenterology Research.International journal of molecular sciences · 2023
    Review
  5. Review
  6. Review
  7. Semantic workflows for benchmark challenges: Enhancing comparability, reusability and reproducibility.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019
    Article
  8. Article
  9. Review
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Christina L ZhengDivision of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA. zheng@ohsu.edu.
Varun RatnakarInformation Sciences Institute, University of Southern California, Los Angeles, CA, USA. varunr@isi.edu.
Yolanda GilInformation Sciences Institute, University of Southern California, Los Angeles, CA, USA. gil@isi.edu.
Shannon K McWeeneyDivision of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA. mcweeney@ohsu.edu.

Funding

OREGON CLINICAL AND TRANSLATIONAL SCIENCE INSTITUTEUL1RR024140 · NCRR · OREGON HEALTH & SCIENCE UNIVERSITY · PI ORWOLL, ERIC S. · 2006 to 2011
$61.1M
NCI NIH HHS 5P30CA06533NCRR NIH HHS 5UL1RR024140NCRR NIH HHS UL1 RR024140
6 · The paper itself

Abstract

backgroundRecent 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.

methodsTo 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.

resultsWe 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.

conclusionsThis 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.

Indexed as

WorkflowComputational BiologyHumansNeoplasmsReproducibility of ResultsSequence Analysis, DNA

Identifiers

PMID26289940
PMCPMC4545705

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.