Premier biocluster français, Genopole est un incubateur de projets d’excellence dédié aux biotechnologies. Situé à Evry-Courcouronnes, il offre un environnement unique aux chercheurs et aux entrepreneurs qui souhaitent innover et faire avancer la recherche.
Découvrir >Que vous soyez chercheur, post-doctorant ou une jeune startup, Genopole vous accompagne à toutes les étapes de votre projet pour vous offrir les meilleures conditions possibles de développement business.
Découvrir >Chaque jour à Genopole chercheurs, entrepreneurs et étudiants se croisent, cohabitent et collaborent, pour une véritable émulation au service de l’innovation.
Découvrir >Donner de l’envergure à la recherche et au travail de notre communauté fait aussi partie de nos missions à Genopole. Retrouvez les dernières avancées scientifiques, les succès des acteurs de la biotechnologie et les événements qui animent notre biocluster.
Découvrir >Venez assister à la conférence d’ouverture de la Genopole Summer School.
Au programme de cette conférence « Bioinformatics and Biostatistical Tools in Genomics for Health », 5 sessions proposées :
Multi-omics decipher medulloblastoma biology; Modelling space in tumors
Emmanuel BARILLOT,Big Data in Biology: what the pandemic has taught us
Ewan BIRNEY,Sequencing the general population to help find genes and variants involved in disease
Emmanuelle GENIN,Drug Discovery in the era of large-scale genetics and genomics data
Philippe SANSEAU,Info : En raison des restrictions de voyage « Covid » qui sont toujours en vigueur, certains orateurs donneront leur conférence à distance (ZOOM).
Abstracts :
Title: Multi-omics decipher medulloblastoma biology; Modelling space in tumors
Multi-omics characterization of tumors enable to uncover hidden biological characteristics of cancer subtypes and offer new opportunities for innovative treatments. In the first part of my talk, I will show how proteomics and phosphor-proteomics complements genomic, epigenomic and transcriptomic perspectives, and opens fresh perspective in medulloblastoma research and treatment.
Last decade has shown that tumor onset and progression rely crucially on their microenvironment and heterogeneous composition. In a second and unrelated part, I will present an approach to model computationnally in three dimensions the growth and treatment of tumor ecosystem models.
Title : Big Data in Biology: what the pandemic has taught us
Molecular biology is now a leading example of a data intensive science, with both pragmatic and theoretical challenges being raised by data volumes and dimensionality of the data. These changes are present in both “large scale” consortia science and small scale science, and across now a broad range of applications – from human health, through to agriculture and ecosystems. All of molecular life science is feeling this effect.
This shift in modality is creating a wealth of new opportunities and has some accompanying challenges. In particular there is a continued need for a robust information infrastructure for molecular biology. This ranges from the physical aspects of dealing with data volume through to the more statistically challenging aspects of interpreting it. A particular problem is finding causal relationships in the high level of correlative data. Genetic data are particular useful in resolving these issues.
The pandemic has brought together operational public health delivery (eg, testing and DNA sequencing of the infectious agent) alongside research and models. The rate of learning has increased between these two domains and delivered better and better products for both policy makers and research. I will illustrate this with examples including the expansion of the Alpha and Delta SARS-CoV-2 genomes and integrating genomic and contact tracing work.
Title : Drug Discovery in the era of large-scale genetics and genomics data
Drug discovery remains a very long and costly process. We have shown previously that using human genetics evidence may increase the chance of success in the clinic for drug targets. In addition, functional genomics technologies that can measure or perturb the genome at scale can also be used to identify and validate new drug targets at scale.
In this talk, I will describe how the combination of genetics and functional genomics data with computational biology analyses and more advanced analytics are applied to drug discovery.
Title: Sequencing the general population to help find genes and variants involved in disease
Next-generation sequencing methods now offer the possibility to sequence large samples of patients and controls to search for genetic variants involved in human diseases. To carry out these studies, however, it is often necessary to share data while respecting their confidentiality. Over the last few years, we have been working on setting up reference panels of individuals from the French population. Exomes and genomes of individuals with ancestry in the different French regions were sequenced to create catalogues of variants and their frequencies. These data are useful in the study of monogenic diseases to filter out neutral variants from patients’ genomes. They are also useful in the study of complex disease as they can serve as controls in case-control association studies. In this presentation, we will describe these panels and the tools we are developing to share them and perform rare variant association tests.
Title : Unsupervised learning for personalised medicine – from pharmacogenomics to disease subty
Two drugs, even with the same target, rarely have the same potency across all patients – so how do we objectively select the right patients to treat with each drug? How do you specifically diagnose patients with a disease that is only defined by symptoms and has no root cause? These are a couple of questions and challenges for personalised medicine. In this talk, we show how unsupervised machine learning (ML) approaches can be used to compare the pharmacological response relationships between 327 pairs of cancer therapies. This approach integrates measures of dose-response to identify subpopulations that react differently to inhibitors of the same or different targets to understand mechanisms of resistance and pathway cross-talk. In another example, we use the same unsupervised ML to untangle the heterogeneity of pulmonary arterial hypertension (PAH) using whole-blood RNA transcriptomes from a cohort of 359 patients with idiopathic or heritable PAH .
We identified five patient subgroups with three of the largest subgroups accounting for 92% of the cohort, each with a unique transcriptomic and clinical feature fingerprint which was validated in a further 197 patients.
Through these two examples, you will see how the same computational approach can be applied to two seemingly different biomedical problems.
