Showing papers by "Sofie Bekaert published in 2019"

Biobank Quality Management in the BBMRI.be Network.

Abstract: From as early as 2005, different guidelines and quality standards covering biobank activities and sample handling methods have been developed to improve and guarantee the reproducibility of biomarker research. Ten years on, the BBMRI.be Quality working group wanted to gauge the current situation of these aspects in the biobanks of the BBMRI.be network. To this end, two online surveys were launched (fall 2017 and fall 2018) to the biobank quality managers in the BBMRI.be network to determine the status and setup of their current quality management system (QMS) and how their QMS and related practices have evolved over a 14 month time period. All biobanks addressed by the two surveys provided a complete response (12 and 13, respectively). A QMS was implemented in 85% of biobanks, with 4 standards emerging as primary basis. Supplementary guidelines were used, with a strong preference for the ISBER best practices for biobanks. The Standard Preanalytical Code-an indicator of the preanalytical lifecycle of a biospecimen impacting the downstream analysis results-was already implemented in 50% of the biobanks while the other half intends future implementation. To assess and maintain the quality of their QMS, 62% of biobanks used self-assessment tools and 71% participated in proficiency testing schemes. The majority of biobanks had implemented procedures for general and biobank specific activities. However, policies regarding the business and sustainability aspect of biobank were only implemented in a limited number of biobanks. A clear desire for a peer-review audit was expressed by 69% of biobanks, with over half of them intending to implement the recently published biobank standard ISO20387. Overall, the biobanks of the BBMRI.be network have actively implemented a solid quality approach in their practices. The implementation of ISO 20387 may bring further professionalization of activities. Based on the needs expressed in this survey, the Quality working group will be setting up an audit program for the BBMRI.be biobanks, to enhance, harmonize and streamline their activities. On the whole, the biobanks in the BBMRI.be network are able to substantially contribute to translational research, as a primary facilitator guaranteeing high quality standards and reproducibility.

High‐quality biobanks : pivotal assets for reproducibility of OMICS‐data in biomedical translational research

Abstract: Human biospecimen samples (HBS) and associated data stored in biobanks (also called "biotrusts," "biorepositories," or "biodistributors") are very critical resources for translational research. As HBS quality is decisive to the reproducibility of research results, biobanks are also key assets for new developments in precision medicine. Biobanks are more than infrastructures providing HBS and associated data. Biobanks have pioneered in identifying and standardizing sources of preanalytical variations in HBS, thus paving the way for the current biospecimen science. To achieve this milestone, biobankers have successively assumed the role of "detective," and then "architect," to identify new detrimental impact of preanalytical variables on the tissue integrity. While standardized methods in omics are required to be practiced throughout research communities, the accepted best practices and standards on biospecimen handling are generally not known nor applied by researchers. Therefore, it is mandatory to raise the awareness within omics communities regarding not only the basic concepts of collecting, storing, and utilizing HBS today, but also to suggest insights on biobanking in the cancer omics context.

How to assess FAIRness to improve crediting and rewarding processes for data sharing? A step forward towards an extensive assessment grid.

Abstract: The SHARC (SHAring Reward & Credit) interest group (IG) is an interdisciplinary group set up in the framework of RDA (Research Data Alliance) to improve crediting and rewarding mechanisms in the sharing process throughout the data life cycle. Notably, one of the objectives is to promote data sharing activities in research assessment schemes at national and European levels. To this aim, the RDA-SHARC IG is developing assessment grids using criteria to establish if data are compliant to the FAIR principles (findable /accessible / interoperable / reusable). The grid is aiming to be extensive, generic and trans-disciplinary. It is meant to be used by evaluators to assess the quality of the sharing practice of the researcher/scientist over a given period, taking into account the means & support available over that period. The grid displays a mind-mapped tree-graph structure based on previous works on FAIR data management (Reymonet et al., 2018; Wilkinson et al., 2016; Wilkinson et al., 2018; and E.U.Guidelines about FAIRness Data Management Plans). The criteria used are based on the work from FORCE 11*, and the Open Science Career Assessment Matrix designed by the EC Working group on Rewards under Open science. The criteria are organised in 5 clusters: ‘Motivations for sharing’; ‘Findable’, ‘Accessible’, ‘Interoperable’ and ‘Reusable’. For each criterion, 4 graduations are proposed (‘Never / Not Assessable’; ‘If mandatory’; ‘Sometimes’; ‘Always’). Only one value must be selected per criterion. Evaluation should be done by cluster; the final overall assessment will be based on the sum of the number of each ticked value / total number of criteria in each cluster; the ‘motivations for sharing’ should be appreciated qualitatively in the final interpretation. The final goals are to develop a graduated assessment of the researcher FAIRness literacy and help identifying needs to build FAIRness guidelines to improve the sharing capacity of researchers.

Rationalized Development of a Campus-Wide Cell Line Dataset for Implementation in the Biobank LIMS System at Bioresource Center Ghent.

Abstract: The Bioresource center Ghent is the central hospital-integrated biobank of Ghent University Hospital. Our mission is to facilitate translational biomedical research by collecting, storing and providing high quality biospecimens to researchers. Several of our biobank partners store large amounts of cell lines. As cell lines are highly important both in basic research and preclinical screening phases, good annotation, authentication, and quality of these cell lines is pivotal in translational biomedical science. A Biobank Information Management System (BIMS) was implemented as sample and data management system for human bodily material. The samples are annotated by the use of defined datasets, based on the BRISQ (Biospecimen Reporting for Improved Study Quality) and Minimum Information About Biobank data Sharing (MIABIS) guidelines completed with SPREC (Standard PREanalytical Coding) information. However, the defined dataset for human bodily material is not ideal to capture the specific cell line data. Therefore, we set out to develop a rationalized cell line dataset. Through comparison of different datasets of online cell banks (human, animal, and stem cell), we established an extended cell line dataset of 156 data fields that was further analyzed until a smaller dataset-the survey dataset of 54 data fields-was obtained. The survey dataset was spread throughout our campus to all cell line users to rationalize the fields of the dataset and their potential use. Analysis of the survey data revealed only small differences in preferences in data fields between human, animal, and stem cell lines. Hence, one essential dataset for human, animal and stem cell lines was compiled consisting of 33 data fields. The essential dataset was prepared for implementation in our BIMS system. Good Clinical Data Management Practices formed the basis of our decisions in the implementation phase. Known standards, reference lists and ontologies (such as ICD-10-CM, animal taxonomy, cell line ontology…) were considered. The semantics of the data fields were clearly defined, enhancing the data quality of the stored cell lines. Therefore, we created an essential cell line dataset with defined data fields, useable for multiple cell line users.