Identification of Protein Features Encoded by Alternative Exons Using Exon Ontology
Downloaded from genome.cshlp.org on October 2, 2021 - Published by Cold Spring Harbor Laboratory Press Resource Identification of protein features encoded by alternative exons using Exon Ontology Léon-Charles Tranchevent,1 Fabien Aubé,1 Louis Dulaurier,1 Clara Benoit-Pilven,1 Amandine Rey,1 Arnaud Poret,1 Emilie Chautard,2 Hussein Mortada,1 François-Olivier Desmet,1 Fatima Zahra Chakrama,1 Maira Alejandra Moreno-Garcia,1 Evelyne Goillot,3 Stéphane Janczarski,1 Franck Mortreux,1 Cyril F. Bourgeois,1,4 and Didier Auboeuf1,4 1Université Lyon 1, ENS de Lyon, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, F-69007, Lyon, France; 2Laboratoire de Biométrie et Biologie Évolutive, Université Lyon 1, UMR CNRS 5558, INRIA Erable, Villeurbanne, F-69622, France; 3Institut NeuroMyoGène, CNRS UMR 5310, INSERM U1217, Université Lyon 1, Lyon, F-69007 France Transcriptomic genome-wide analyses demonstrate massive variation of alternative splicing in many physiological and pathological situations. One major challenge is now to establish the biological contribution of alternative splicing var- iation in physiological- or pathological-associated cellular phenotypes. Toward this end, we developed a computational approach, named Exon Ontology, based on terms corresponding to well-characterized protein features organized in an ontology tree. Exon Ontology is conceptually similar to Gene Ontology-based approaches but focuses on exon-encod- ed protein features instead of gene level functional annotations. Exon Ontology describes the protein features encoded by a selected list of exons and looks for potential Exon Ontology term enrichment. By applying this strategy to exons that are differentially spliced between epithelial and mesenchymal cells and after extensive experimental validation, we demonstrate that Exon Ontology provides support to discover specific protein features regulated by alternative splic- ing.
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