TY - JOUR
T1 - Data and knowledge management in translational research
T2 - Implementation of the eTRIKS platform for the IMI OncoTrack consortium
AU - Gu, Wei
AU - Yildirimman, Reha
AU - Van Der Stuyft, Emmanuel
AU - Verbeeck, Denny
AU - Herzinger, Sascha
AU - Satagopam, Venkata
AU - Barbosa-Silva, Adriano
AU - Schneider, Reinhard
AU - Lange, Bodo
AU - Lehrach, Hans
AU - Guo, Yike
AU - Henderson, David
AU - Rowe, Anthony
N1 - Publisher Copyright:
© 2019 The Author(s).
PY - 2019/4/1
Y1 - 2019/4/1
N2 - Background: 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. Results: 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. Conclusions: 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.
AB - Background: 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. Results: 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. Conclusions: 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.
KW - Data management
KW - Oncology
KW - Precision medicine
KW - Translational medicine
UR - https://www.webofscience.com/wos/woscc/full-record/WOS:000463047100001
UR - https://openalex.org/W2941381127
UR - https://www.scopus.com/pages/publications/85063784330
U2 - 10.1186/s12859-019-2748-y
DO - 10.1186/s12859-019-2748-y
M3 - Journal Article
C2 - 30935364
SN - 1471-2105
VL - 20
JO - BMC Bioinformatics
JF - BMC Bioinformatics
IS - 1
M1 - 164
ER -