Translational research platforms integrating clinical and omics data: a review of publicly available solutions
Open accessAbstract
The rise of personalized medicine and the availability of high-throughput molecular analyses in the context of clinical care have increased the need for adequate tools for translational researchers to manage and explore these data. We reviewed the biomedical literature for translational platforms allowing the management and exploration of clinical and omics data, and identified seven platforms: BRISK, caTRIP, cBio Cancer Portal, G-DOC, iCOD, iDASH and tranSMART. We analyzed these platforms along seven major axes. (1) The community axis regrouped information regarding initiators and funders of the project, as well as availability status and references. (2) We regrouped under the information content axis the nature of the clinical and omics data handled by each system. (3) The privacy management environment axis encompassed functionalities allowing control over data privacy. (4) In the analysis support axis, we detailed the analytical and statistical tools provided by the platforms. We also explored (5) interoperability support and (6) system requirements. The final axis (7) platform support listed the availability of documentation and installation procedures. A large heterogeneity was observed in regard to the capability to manage phenotype information in addition to omics data, their security and interoperability features. The analytical and visualization features strongly depend on the considered platform. Similarly, the availability of the systems is variable. This review aims at providing the reader with the background to choose the platform best suited to their needs. To conclude, we discuss the desiderata for optimal translational research platforms, in terms of privacy, interoperability and technical features.
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Patient clinical and omics data, as part of health-related big data, is now more widely accessible.
“Health-based big data including linked information for patients, such as their clinical data (for example gender, age, pathological and physiological history) and omics data (such as genetics, proteomics and metabolomics) has now become more widely available (Canuel et al., 2015;Singhal et al., 2016).”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Canuel et al., 2015
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- Authors
- Canuel, V., Rance, B., Avillach, P., Degoulet, P., & Burgun, A.
- Affiliations
- Inserm (FR)
- Corresponding author
- Rance, B.
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-03-07
- Volume
- 16
- Issue
- 2
- Pages
- 280-290
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- PMC
- PMC4364065
- OpenAlex
- W2157164548
- Cited by
- 97
- References
- 42
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 6 · 2023: 6 · 2022: 6 · 2021: 4 · 2020: 7 · 2019: 7 · 2018: 7 · 2017: 19 · 2016: 20 · 2015: 7 · 2014: 3
- FWCI
- 1.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Briefings in Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Keywords
- clinical data management, omics data, data integration, data privacy, interoperability, cancer genomics, tranSMART, Icod, BRISK, personalized medicine, data visualization tools
- MeSH terms
- Humans, Software, Computational Biology, Genomics, Databases, Genetic, Translational Research, Biomedical, Precision Medicine
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