It is a collaborative project focused on increasing the efficiency of translational research by:

Reducing the cost of translational research data & knowledge management


Helping projects find and implement cost efficient solutions for:

Data capture and tracking such as:

  • eCRF/ECD systems, image LIMS, NGS/Omic LIMS, etc.
  • Sample tracking systems
  • Central storage of raw data

Data integration such as:

  • Data cleaning, curation and loading into tranSMART
  • tranSMART integration platform
  • data models for translational research studies

Knowledge management and tracking such as:

  • Contextual knowledge management – e.g. pathway, gene function knowledge
  • Management of study protocols, analysis processes, general study documentation
  • Management of results and observation of study

Enabling non-statisticians to perform exploratory analyses

One of the most valuable features of the tranSMART platform is that it allows non-statisticians to perform exploratory analyses. eTRIKS looks to extend that capability through:

  • Further developing tranSMART analytics
  • Training on the use of tranSMART analytics
  • Plugging in existing analytics packages

Facilitating cross study analyses

Cross study analysis holds the potential to facilitate validation of biomarkers and computational models. It also holds forth promise of enabling cross disease mechanism focused research. eTRIKS aims to:

  • Implement federated search capability
  • Develop cross study analytics
  • Provide data hosting which will also help to ensure project data legacy

"eTRIKS is more than just tranSMART. We aim to support projects with different types of open source software, standards, hosted content, business analysis, curation processes and training."

Ian Dix, eTRIKS Coordinator, Astra Zeneca

"eTRIKS will provide an open and collaborative platform  to support the knowledge generation and management needs of Translational Research in European IMI projects and beyond. The platform facilitates data driven translational medicine initiatives with its rich support on big data analytics and collaborative research for clinical investigators, analysts and bioinformatics  developers.  It aims to foster new approaches for disease prevention, diagnosis, and treatment, ultimately redefining the way biomedical research is translated to better health."

Prof. Yike Guo

Direct of Data Science Institute,  Imperial College London

Managing Entity  of eTRIKS Project

"We are entering a era where the data space is increasing and there is more and more need to collaborate on data. eTRIKS will make that possible."

            Scott Wagers, eTRIKS Collaboration Moderator, CEO BioSci Consulting

Shared Vision

Technical

Improving the operational efficiency 0f translational research by enabling access to software.

Scientific

Bringing together data from related studies, in a controlled manner. 

 

Community

A ‘commons’ comprised of translational researchers and technology providers in which software, technology and data, are shared.  

    

 

Delivering knowledge management solutions for translational research projects through collaborative consultation.

Addressing the IMI's knowledge management needs.

The original IMI strategic agenda listed knowledge management as one of the required pillars of translational research.  From the agenda itself:

Set-up a KM Platform team that conceives the overall architecture and delivers an integrated biomedical data platform and interactive scientific exploration tools.

 

The KM Platform is an integrative tool that assures synergies with management and exploitation of re- search results by bringing data together in an open and consistent format that is suitable for overall data analysis. The creation of such a platform can lead to new biopharmaceutical insight through extensive data sharing.

 

The KM Platform team will publish project calls that address the development of components of the KM Platform that are currently lacking or that need specific biomedical extensions. The evolving KM Platform will, over time, trigger and support new types of joint project that exploit the availability of new IMI data.

 

The scientific and functional requirements for the KM Platform can be summarised as follows:

 

  • Data federation: seamless search and navigation across heterogeneous data sources, both private and public;
  • Data integration: the capacity to pool data from heterogeneous sources in a scientifically, semantically and mathematically consistent manner for further computation;
  • Shared services: the development, sharing and integration of relevant and powerful data exploitation tools such as modelling and simulation.

The requirements can be met using a distributed/federated, multi-layer, service oriented, and ontology- driven architecture.

 

 

In 2011 the eTRIKS IMI call process was formally initiated as part of IMI round 4 calls. The thesis of the call was to:

  • Start with a proven platform, tranSMART, thus minimising risk of a complex KM project.
  • Call deliverables to reflect real Efficacy and Safety Project demand.
  • Keep the applicant consortium small ensuring delivery accountability & focus as well as concentrating resources.
  • Limit funding in a first phase, anticipating scale up if successful.
  • Be explicit regarding consortia capabilities & skills.

Data security and re-use across IMI:

  • Coordination of limited number of backed-up project archives (central and local)
  • Federated querying across archives
  • Technology and process support for controlled data access.

Standardisation in software use across IMI:

  • Brokering conversation between projects on requirements
  • Between projects and open source technology projects
  • Software development

Standardisation in software MetaData and interoperability across IMI:

  • Re-use standards
  • Development data standards
  • Coordination with standards organisations (e.g CDISC)

Adoption and education in IMI of standards and tech:

  • Training and help desk support for projects (users and tech)
  • Curation support – support in getting content in
  • Bug fixing

Public reference data management (for IMI projects):

  • Ensuring relevant public data is available for IMI projects to consume

Community and communication within IMI

  • Cross project information sharing between projects re data management approaches
  • IMI Community workshops

The Vasca De Gama Guide to Translational Research

Exlorers

A sad truth about all the translational research data that is being collected.

combine harvester

An invigorated tranSMART community

Community5

Case Study

Data integration in support of stratified medicine.

Situation

Project (U-BIOPRED) collecting a rich set of study participant characterisation data: clinical, patient reported outcomes, imaging, and omics (proteomics, lipidomics, trancriptomics, breathomics, physiomics, genomics) with a plan to integrate the data into a sub-phenotyping 'handprint of severe asthma'. 

Critical needs

  • A platform for integrating data
  • Curation support
  • Means of organizing the analysis process
  • Data repository
  • Ability to relate collected data to other studies

Solution

  • Project based instance of eTRIKS 1.1
  • Workarounds - repurposing tranSMART modules to house different data types
  • ETL scripts and curation
  • Knowledge portal with scientific quesitons linking back to eTRIKS platform
  • Loading of project relevant public data into an eTRIKS public server
  • User training sessions

Significant Achievements

 

  • eTRIKS 1.1 - parallel development with tranSMART 1.1

  • Standards Advisory Board Convened

  • Public Server established with loaded data - launch on January 31st 2014

  • 5 projects supported