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 variation 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-encoded 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 splicing. We also show that Exon Ontology helps to unravel biological processes that depend on suites of coregulated alternative exons, as we uncovered a role of epithelial cell-enriched splicing factors in the AKT signaling pathway and of mesenchymal cell-enriched splicing factors in driving splicing events impacting on autophagy. Freely available on the web, Exon Ontology is the first computational resource that allows getting a quick insight into the protein features encoded by alternative exons and investigating whether coregulated exons contain the same biological information. [Supplemental material is available for this article.]Alternative splicing is a major step in the gene expression process leading to the production of different transcripts with different exon content (or alternative splicing variants) from one single gene. This mechanism is the rule, as 95% of human genes produce at least two splicing variants (Nilsen and Graveley 2010;de Klerk and 't Hoen 2015;Lee and Rio 2015). Alternative splicing decisions rely on splicing factors binding on pre-mRNA molecules more or less close to splicing sites and regulating their recognition by the spliceosome (Lee and Rio 2015). Other mechanisms, including usage of alternative promoters and alternative polyadenylation sites, also increase the diversity of transcripts and drive both quantitative and qualitative effects (Tian and Manley 2013;de Klerk and 't Hoen 2015). Indeed, alternative promoters and alternative polyadenylation sites can impact mRNA 5 ′ -and 3 ′ -untranslated regions, which can have consequences on transcript stability or translation (Tian and Manley 2013;de Klerk and 't Hoen 2015). In addition, alternative splicing can lead to the biogenesis of nonproductive mRNAs degraded by the nonsense-mediated mRNA decay pathway (Hamid and Makeyev 2014). These mechanisms can also change the gene coding sequence. Alternative promoters and alternative polyadenylation sites can change protein N-and C-terminal domains, respec...

Target identification, one of the steps of drug discovery, aims at identifying biomolecules whose function should be therapeutically altered in order to cure the considered pathology. This work proposes an algorithm for in silico target identification using Boolean network attractors. It assumes that attractors of dynamical systems, such as Boolean networks, correspond to phenotypes produced by the modeled biological system. Under this assumption, and given a Boolean network modeling a pathophysiology, the algorithm identifies target combinations able to remove attractors associated with pathological phenotypes. It is tested on a Boolean model of the mammalian cell cycle bearing a constitutive inactivation of the retinoblastoma protein, as seen in cancers, and its applications are illustrated on a Boolean model of Fanconi anemia. The results show that the algorithm returns target combinations able to remove attractors associated with pathological phenotypes and then succeeds in performing the proposed in silico target identification. However, as with any in silico evidence, there is a bridge to cross between theory and practice, thus requiring it to be used in combination with wet lab experiments. Nevertheless, it is expected that the algorithm is of interest for target identification, notably by exploiting the inexpensiveness and predictive power of computational approaches to optimize the efficiency of costly wet lab experiments.