Since 2019, the Fondation 101 génomes has the privilege to work with three organisations specialised in bioinformatics, genetics and algorithms (IB², CHG and MLG) on the Genomes4Brussels project co-financed by the Brussels region (Innoviris).
Genome4Brussels aims to create an ecosystem to optimise the development of bioinformatics tools for genome analysis and to facilitate the transfer of the innovation and knowledge acquired during the project to citizens.
Sofia Papadimitriou, researcher (IB)², answers Ludivine's questions and explains her involvement in this unique project.
Ludivine - Sofia, can you summarise your academic career?
Sofia - I graduated with a BSc in Biology in 2013 from Aristotle University of Thessaloniki, Greece, and later obtained a MSc in Bioinformatics in 2016 from Wageningen University, the Netherlands. I then continued with a PhD in bioinformatics and Machine Learning applied to oligogenic diseases at the Université Libre de Bruxelles and the Vrije Universiteit Brussel. I am involved in the Genome4Brussels project as a postdoctoral researcher since January 2021, while in October 2022 I obtained a F.R.S.-FNRS grant to carry out research on the detection of genetic modifiers for retinal diseases at the Université Libre de Bruxelles, under the supervision of Professor Tom Lenaerts and in collaboration with the laboratory of Professor Elfride de Baere at Ghent University.
L. - How is your past experience an asset for the Genome4Brussels project?
S.- My Bachelor's and Master's degree in science gave me a broad knowledge of biology and bioinformatics, and through them I gained experience in NGS analysis (New Generation Sequencing), Machine Learning, genetics, phylogenetics, data management and network inference. My specialisation in the genetics and prediction of oligogenic diseases during my PhD is very relevant for the Genome4Brussels project. As part of the oligogenic group at the Interuniversity Institute of Bioinformatics in Brussels (IB)2 , I focused my research on how bioinformatics and machine learning methods can facilitate the detection of combinations of pathogenic genetic variants involved in genetic diseases, as the presence of an individual pathogenic variant is often not sufficient to explain a patient's symptoms. During my PhD, I developed VarCoPP, a tool that predicts pathogenic combinations of genetic variants in gene pairs in an individual. This tool is new in its field and we are pleased to see that it is already being used by the scientific community. My expertise in this field can significantly help, in the framework of the Genome4Brussels project, to understand how this tool can be further improved and used more efficiently in the detection of genetic modifiers in Marfan syndrome patients with very different phenotypes.
L.- Can you tell us more about the tools you are working on at the moment?
S.- Within the oligogenic group, we continue to focus on how the methodologies of Machine Learning can be applied to oligogenic diseases. At the moment, my colleagues and I are actively working on improving the performance of the VarCoPP tool, by re-collecting better training data, re-evaluating its model structure and collecting new relevant features. The improvement of VarCoPP will be directly useful for the Genome4Brussels project, in order to detect genetic modifiers more accurately. In addition, I will soon start working on network theory and on ways to create heterogeneous graphs that will link patients according to their VarCoPP predictions and symptoms, with the aim of better understanding the link between the diverse genetic architectures of a particular disease and the development of variable symptoms for that disease.
We strive to provide (as much as possible) a hypothesis-free approach, where we are more interested in helping to discover new knowledge than in identifying genes already known to be involved in a particular disease. Indeed, we have found that for many genetic diseases, current knowledge seems limited and cannot provide a sufficient explanation for their phenotypic variability.
L.- What do you think of the Genome4Brussels project?
S.- I am very excited to be part of the Genome4Brussels project as it is an important initiative for understanding the genetic architecture of Marfan syndrome and for more efficient diagnosis. Genetic modifiers have been suspected for some time in Marfan syndrome, but they are not easily detectable with current methodological approaches. Detecting these modifiers is very important to better understand why some patients with the same primary pathogenic mutation show different symptoms and may pave the way for personalised therapies and more effective counselling for patients and parents. I am therefore very motivated to be part of this project and conduct innovative research to help Marfan syndrome patients and their parents.
Furthermore, what I really like about this project is that it aims to promote the fair and transparent use of bioinformatics and Machine Learning in the field of medical genomics, which I believe is extremely important given the popularity of Machine Learning in this field. As a researcher myself specialising in Machine Learning, I am very excited to contribute to this initiative.
