Since 2019, the 101 Genomes Foundation has been taking part in the Genomes4Brussels project co-funded by the Brussels region (Innoviris).
This project brings together the Foundation and three organizations specializing in bioinformatics, genetics and algorithms: theInteruniversity Institute of Bioinformatics Brussels (IB)², theULB Center of Human Genetic (CHG) and theULB Machine Learning Group (MLG).
This consortium has set up an ecosystem to optimize the development of bioinformatics tools for genome analysis to assist doctors and researchers in the field of rare diseases.
Emma Verkinderen, bioinformatician at (IB)², answers Ludivine’s questions and explains her role in this groundbreaking project.
Ludivine – Emma, can you tell us a little about your academic background?
Emma – I obtained a BSc in Bioengineering Sciences and an MSc in Bioinformatics at the KU Leuven. After graduating, I did a five-month internship with a start-up specializing in human genetics and rare diseases.
L. – How is your past experience an asset for the Genome4Brussels project?
E.- During my internship, I was trained in genetics, variant analysis and machine learning in the context of rare diseases, which are of course very relevant areas of expertise for the Genome4Brussels project. I also gained technical experience, learning the main concepts and best practices of web application development. This internship has greatly nurtured my interest in bioinformatics software development, so I’m very happy to continue working on a project where biomedicine and informatics come together as a technical bioinformatician for the Genome4Brussels project.
L.- Can you tell us more about the tools you’re currently working on?
E.- Up to now, I’ve mainly worked on ORVAL, a bioinformatics tool developed at the Institut Interuniversitaire de Bioinformatique de Bruxelles (IB)². ORVAL is an online oligogenic variant analysis platform where users can submit the variants they have found in a patient’s genome. ORVAL then creates all possible digenic combinations (a pair of variants in two different genes) and annotates them with data from public bioinformatics resources. The annotations are then used to run the machine learning predictor (VarCoPP) which produces the probability that each digenic combination is the cause of the disease. Finally, the results are presented in a comprehensive way, and the user can perform additional analyses on the digenic variant combinations of interest.
As part of the Genome4Brussels project, we want to transfer this tool from the (IB)² infrastructure to the FairGX cloud, to ensure its continuity. I’m currently preparing the ORVAL application for this transfer.
L.- What do you think of the Genome4Brussels project?
E.- I think it’s very interesting to see how the Genome4Brussels project is creating an ecosystem that centralizes genomic data (and its collection) and bioinformatics analysis tools. This will stimulate research into rare diseases and, potentially, lead to more effective diagnosis.
The fact that all the tools and data are hosted in the cloud is an essential additional advantage, as the cloud can accommodate a large volume of genomic data while meeting the FAIR principles’ requirement for transparency in data processing and scientific analysis.
So I’m delighted to be part of this innovative project!
