Exchange of Clinical and Omics Data According to FAIR Principles: A Review of Open Source Solutions.

BACKGROUND: Due to the ongoing increase and importance of the sustainable reusability of data, the findable, accessible, interoperable, reusable or FAIR principles were developed which are also relevant in translational research. OBJECTIVES: The study aims at identification of platforms by literature search that are suitable for implementation in translational research, in particular with regard to their FAIRness. METHODS: The collected information is summarized and compared. RESULTS: Platforms have been identified which are suitable for linking with other translational platforms with regard to documentation, long-term archiving, and processing as well as for FAIR handling of bioinformatic data. CONCLUSION: There are already platforms in the translational environment that take FAIR principles into account and thus improve translational research. Due to the specialization of the research platforms and the fact that FAIR are only principles and not standards, the platforms have to be examined in individual cases to see whether and how they can be integrated with other platforms.

Developing New Analysis Functions for a Translational Research Platform: Extending the cBioPortal for Cancer Genomics.

BACKGROUND: The cBioPortal is a prevalent open-source translational research platform, allowing private instances and extensions. OBJECTIVE: Our aim was to build up an own instance of cBioPortal, identify missing functionality by interviewing researchers, and implementing these extensions. METHODS: We examined the code base of the cBioPortal and conducted a requirements analysis with researchers. Then an own extension was implemented and a usability evaluation was performed. RESULTS: We developed a new tab in the results view of cBioPortal adding the option to analyze the correlation of gene expression and mutation patterns. CONCLUSION: While extending the cBioPortal is possible, there are still some challenges to overcome. A plug-in concept and a more detailed documentation would greatly facilitate the development of own extensions.

Pan-European Data Harmonization for Biobanks in ADOPT BBMRI-ERIC.

BACKGROUND: High-quality clinical data and biological specimens are key for medical research and personalized medicine. The Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortium (BBMRI-ERIC) aims to facilitate access to such biological resources. The accompanying ADOPT BBMRI-ERIC project kick-started BBMRI-ERIC by collecting colorectal cancer data from European biobanks. OBJECTIVES: To transform these data into a common representation, a uniform approach for data integration and harmonization had to be developed. This article describes the design and the implementation of a toolset for this task. METHODS: Based on the semantics of a metadata repository, we developed a lexical bag-of-words matcher, capable of semiautomatically mapping local biobank terms to the central ADOPT BBMRI-ERIC terminology. Its algorithm supports fuzzy matching, utilization of synonyms, and sentiment tagging. To process the anonymized instance data based on these mappings, we also developed a data transformation application. RESULTS: The implementation was used to process the data from 10 European biobanks. The lexical matcher automatically and correctly mapped 78.48% of the 1,492 local biobank terms, and human experts were able to complete the remaining mappings. We used the expert-curated mappings to successfully process 147,608 data records from 3,415 patients. CONCLUSION: A generic harmonization approach was created and successfully used for cross-institutional data harmonization across 10 European biobanks. The software tools were made available as open source.

Samply.MDR - A Metadata Repository and Its Application in Various Research Networks.

Collaboration in medical research is becoming common, especially for collecting relevant cases across institutional boundaries. If the data, which is usually very heterogeneously formalized and structured, can be integrated, such a collaboration can facilitate research. An absolute prerequisite for this is an extensive description about the formalization and exact meaning of every data element contained in a dataset. This information is commonly known as metadata. Various research networking projects tackle this challenge with the development of concepts and IT tools. The Samply Metadata Repository (Samply.MDR) is a solution for managing and publishing such metadata in a standardized and reusable way. In this article we present the structure and features of the Samply.MDR as well as its flexible usability by giving an overview about its application in various projects.

Developing Interactive Plug-ins for tranSMART Using the SmartR Framework: The Case of Survival Analysis.

BACKGROUND: TranSMART has become an important and widely used platform for the translational research. Included features for analysing data have significant deficiencies concerning user interactivity with and export of the generated results. SmartR, a plugin for tranSMART, promises to close this gap with its interactive workflows. OBJECTIVES: Test with a proof of concept whether an own SmartR workflow is possible. Improvement of the integrated survival analysis with a self-build workflow, taking specific user requirements into account. METHODS: Analysis of existing SmartR workflows to understand the underlying SmartR architecture. Implementation of a SmartR workflow, which provides an improved survival analysis. RESULTS: Extension of SmartR and thus tranSMART is possible and could be successfully achieved with a prototype for survival analysis. The framework still lacks some functionality, like binning of continuous variables, and documentation. CONCLUSION: SmartR workflows are a good way for realising interactive analysis, but the SmartR framework still needs further improvements to become a full alternative to the already existing Rmodules.

Two Years of tranSMART in a University Hospital for Translational Research and Education.

BACKGROUND: For translational research, software platforms such as tranSMART with an integrated view of both clinical and omics data have gained more and more attention in the last years. OBJECTIVES: We wanted to examine the success and failures of tranSMART in the fields of translational research and education by looking at the examples of six use cases at our hospital. We wanted to point out suitable scenarios and user groups as well as still existing challenges and limitations. METHODS: We sum up the experience we made with our use cases with a focus on lessons learned. RESULTS: tranSMART was successfully established by a bottom-up approach at our university hospital and has been running for more than two years now. It has been used in four translational research projects as well as in education in the context of lectures and bachelor/master theses. CONCLUSION: tranSMART can be a very useful tool for translational research and education. But it should be used with both care and statistical knowledge to avoid wrong conclusions. Some technical constraints, especially for data modeling, still limit many applications. Version control and data provenance are remaining challenges.