Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Journal Article

Data and knowledge management in translational research: implementation of the eTRIKS platform for the IMI OncoTrack consortium


Lehrach,  Hans
Emeritus Group of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;
Alacris Theranostics GmbH, Berlin, Germany;
Dahlem Centre for Genome Research and Medical Systems Biology, Berlin, Germany;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

(Publisher version), 2MB

Supplementary Material (public)
There is no public supplementary material available

Gu, W., Yildirimman, R., Van der Stuyft, E., Verbeeck, D., Herzinger, S., Satagopam, V., et al. (2019). Data and knowledge management in translational research: implementation of the eTRIKS platform for the IMI OncoTrack consortium. BMC Bioinformatics, 20: 20:164. doi:10.1186/s12859-019-2748-y.

Cite as: https://hdl.handle.net/21.11116/0000-0006-3B08-0

For large international research consortia, such as those funded by the European Union's Horizon 2020 programme or the Innovative Medicines Initiative, good data coordination practices and tools are essential for the successful collection, organization and analysis of the resulting data. Research consortia are attempting ever more ambitious science to better understand disease, by leveraging technologies such as whole genome sequencing, proteomics, patient-derived biological models and computer-based systems biology simulations.

The IMI eTRIKS consortium is charged with the task of developing an integrated knowledge management platform capable of supporting the complexity of the data generated by such research programmes. In this paper, using the example of the OncoTrack consortium, we describe a typical use case in translational medicine. The tranSMART knowledge management platform was implemented to support data from observational clinical cohorts, drug response data from cell culture models and drug response data from mouse xenograft tumour models. The high dimensional (omics) data from the molecular analyses of the corresponding biological materials were linked to these collections, so that users could browse and analyse these to derive candidate biomarkers.

In all these steps, data mapping, linking and preparation are handled automatically by the tranSMART integration platform. Therefore, researchers without specialist data handling skills can focus directly on the scientific questions, without spending undue effort on processing the data and data integration, which are otherwise a burden and the most time-consuming part of translational research data analysis.