[From gene to cell: Functional validation of RYR1 variants].

Autor:	Reynaud Dulaurier R; Université Grenoble Alpes, Inserm U1216, Grenoble Institut Neurosciences, Grenoble, France., Brocard J; Université Grenoble Alpes, Inserm U1216, Grenoble Institut Neurosciences, Grenoble, France., Rendu J; Université Grenoble Alpes, Inserm U1216, Grenoble Institut Neurosciences, Grenoble, France - CHU Grenoble Alpes, Grenoble, France., Debbah N; Université Grenoble-Alpes, Département de Pharmacochimie Moléculaire (DPM), CNRS UMR 5063, Grenoble, France - Laboratoire des Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, UMR 5525, Grenoble, France., Fauré J; Université Grenoble Alpes, Inserm U1216, Grenoble Institut Neurosciences, Grenoble, France - CHU Grenoble Alpes, Grenoble, France., Marty I; Université Grenoble Alpes, Inserm U1216, Grenoble Institut Neurosciences, Grenoble, France.
Jazyk:	francouzština
Zdroj:	Medecine sciences : M/S [Med Sci (Paris)] 2024 Nov; Vol. 40 Hors série n° 1, pp. 30-33. Date of Electronic Publication: 2024 Nov 18.
DOI:	10.1051/medsci/2024135
Abstrakt:	Genetic screening of rare diseases allows identification of the responsible gene(s) in about 50% of patients. The remaining cases are in a diagnostic deadlock as current knowledge fails to identify the correct gene or determine if the detected variant on the gene is pathogenic. These are named "variants of unknown significance" (VUS). In the case of neuromuscular diseases, the RYR1 gene is often implicated, with the majority of variants classified as VUS, requiring reliable classification to help patient diagnosis. Our project aims to create an efficient classification pipeline, integrating artificial intelligence, structural biology data, and functional analyses to enhance genetic diagnosis of RYR1-related diseases. (© 2024 médecine/sciences – Inserm.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu