FAIR in action - a flexible framework to guide FAIRification

The COVID-19 pandemic has highlighted the need for FAIR (Findable, Accessible, Interoperable, and Reusable) data more than any other scientific challenge to date. We developed a flexible, multi-level, domain-agnostic FAIRification framework, providing practical guidance to improve the FAIRness for both existing and future clinical and molecular datasets. We validated the framework in collaboration with several major public-private partnership projects, demonstrating and delivering improvements across all aspects of FAIR and across a variety of datasets and their contexts. We therefore managed to establish the reproducibility and far-reaching applicability of our approach to FAIRification tasks.

Pilot Study on the Intercalibration of a Categorisation System for FAIRer Digital Objects Related to Sensitive Data in the Life Sciences

Sharing sensitive data is a specific challenge for research infrastructures in the field of life sciences. For that reason a toolbox has been developed, providing resources for researchers who wish to share and use sensitive data, to support the workflows for handling these kinds of digital objects. Common and community approved annotations are required to be compliant with FAIR principles (Findability, Accessibility, Interoperability, Reusability). The toolbox makes use of a tagging (categorisation) system, allowing consistent labelling and categorisation of digital objects, in terms relevant to data sharing tasks and activities. A pilot study was performed within the Horizon 2020 project EOSC-Life, in which 2 experts from 6 life sciences research infrastructures were recruited to independently assign tags to the same set of 10 to 25 resources related to sensitive data management and data sharing (in total 110). Summary statistics of agreement and observer variation per research infrastructure are provided. The pilot study has shown that experts were able to attribute tags but in most cases with a considerable observer variation between experts. In the context of CWFR (Canonical Workflow Frameworks for Research), this indicates the necessity for careful definition, evaluation and validation of parameters and processes related to workflow descriptions. The results from this pilot study were used to tackle this issue by revising the categorisation system and providing an updated version.

Systematically linking tranSMART, Galaxy and EGA for reusing human translational research data

The availability of high-throughput molecular profiling techniques has provided more accurate and informative data for regular clinical studies. Nevertheless, complex computational workflows are required to interpret these data. Over the past years, the data volume has been growing explosively, requiring robust human data management to organise and integrate the data efficiently. For this reason, we set up an ELIXIR implementation study, together with the Translational research IT (TraIT) programme, to design a data ecosystem that is able to link raw and interpreted data. In this project, the data from the TraIT Cell Line Use Case (TraIT-CLUC) are used as a test case for this system. Within this ecosystem, we use the European Genome-phenome Archive (EGA) to store raw molecular profiling data; tranSMART to collect interpreted molecular profiling data and clinical data for corresponding samples; and Galaxy to store, run and manage the computational workflows. We can integrate these data by linking their repositories systematically. To showcase our design, we have structured the TraIT-CLUC data, which contain a variety of molecular profiling data types, for storage in both tranSMART and EGA. The metadata provided allows referencing between tranSMART and EGA, fulfilling the cycle of data submission and discovery; we have also designed a data flow from EGA to Galaxy, enabling reanalysis of the raw data in Galaxy. In this way, users can select patient cohorts in tranSMART, trace them back to the raw data and perform (re)analysis in Galaxy. Our conclusion is that the majority of metadata does not necessarily need to be stored (redundantly) in both databases, but that instead FAIR persistent identifiers should be available for well-defined data ontology levels: study, data access committee, physical sample, data sample and raw data file. This approach will pave the way for the stable linkage and reuse of data.

Integration of EGA secure data access into Galaxy

High-throughput molecular profiling techniques are routinely generating vast amounts of data for translational medicine studies. Secure access controlled systems are needed to manage, store, transfer and distribute these data due to its personally identifiable nature. The European Genome-phenome Archive (EGA) was created to facilitate access and management to long-term archival of bio-molecular data. Each data provider is responsible for ensuring a Data Access Committee is in place to grant access to data stored in the EGA. Moreover, the transfer of data during upload and download is encrypted. ELIXIR, a European research infrastructure for life-science data, initiated a project (2016 Human Data Implementation Study) to understand and document the ELIXIR requirements for secure management of controlled-access data. As part of this project, a full ecosystem was designed to connect archived raw experimental molecular profiling data with interpreted data and the computational workflows, using the CTMM Translational Research IT (CTMM-TraIT) infrastructure http://www.ctmm-trait.nl as an example. Here we present the first outcomes of this project, a framework to enable the download of EGA data to a Galaxy server in a secure way. Galaxy provides an intuitive user interface for molecular biologists and bioinformaticians to run and design data analysis workflows. More specifically, we developed a tool -- ega_download_streamer - that can download data securely from EGA into a Galaxy server, which can subsequently be further processed. This tool will allow a user within the browser to run an entire analysis containing sensitive data from EGA, and to make this analysis available for other researchers in a reproducible manner, as shown with a proof of concept study.  The tool ega_download_streamer is available in the Galaxy tool shed: https://toolshed.g2.bx.psu.edu/view/yhoogstrate/ega_download_streamer.

Abstract 3166: Querying, viewing and analyzing colorectal cancer translational research studies in tranSMART

All translational research projects basically share the same design, which is correlating variation in disease phenotype to variation in underlying biology. Typical questions to be addressed are: ‘Which (out of thousands) biomarkers predict good/bad outcome?’ and ‘Which (out of thousands) biomarkers predict whether a patient will benefit from a particular therapy?’. In line with this concept, many research teams have generated large amounts of experimental molecular data from patient samples, yet generally this information is inaccessible for examination due to local storage of both (meta)data and the data processing workflows used. Alternatively, data stored in central databases may only be available for exploration and interpretation by data specialists, provided that the processing workflow has been published and is available. Thus, if data is not recorded and easily retrievable, validation of obtained results (e.g. promising biomarkers) may be virtually impossible. Additionally, it will be difficult to query existing data sets to answer new questions, which may lead to experiments being unnecessarily repeated and biological materials being wasted. In the Netherlands, the Translational Research IT (TraIT) project initiated by the Center for Translational Molecular Medicine (CTMM, www.ctmm-trait.nl) aims to provide IT solutions to support translational research from start to end, including sustainable management and analysis of data. These data types involve the clinical, biomedical imaging, biobanking, (molecular) experimental data domains and their associated workflows. In addition to domain-specific solutions to manage these data types, the processed or ‘final’ data of these different domains will become available in the data-integration platform tranSMART for querying, visualization and analysis. To ensure sustainable data stewardship and provide easy access to existing data for the CTMM colorectal cancer project ‘Decrease in Colorectal Cancer Death (DeCoDe)’, data has been made available in tranSMART and more datasets are being added. The data encompasses both clinical and (molecular) experimental data from non-high-throughput molecular profiling (NHTMP) assays and array-based profiling techniques.

It is now possible with tranSMART to explore the respective DeCoDe datasets and perform various analyses (survival, Fisher-exact tests, ANOVA, aCGH tests etc.), without needing deep bioinformatics expertise. Through metadata tags it is possible to trace back raw or pre-processed data in other tools (e.g. clinical data in OpenClinica, array-data in GEO, or histological images of tissue microarrays). Data can be examined in more detail by domain experts using tools they are familiar with or other tools provided by CTMM-TraIT. Thus, existing rich data are made findable, accessible, interoperable and reusable (FAIR) and source data can be traced back for customized processing.

Citation Format: Mariska Bierkens, Wim van der Linden, Ward Weistra, Kees van Bochove, Jeroen A.M. Beliën, Remond J.A. Fijneman, Rita Azevedo, Jan-Willem Boiten, Gerrit A. Meijer. Querying, viewing and analyzing colorectal cancer translational research studies in tranSMART. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3166.