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Drug-set enrichment analysis: a novel tool to investigate drug mode of action

Motivation: Automated screening approaches are able to rapidly identify a set of small molecules inducing a desired phenotype from large small-molecule libraries. However, the resulting set of candidate molecules is usually very diverse pharmacologically, thus little insight on the shared mechanism...

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Detalles Bibliográficos
Autores principales: Napolitano, Francesco, Sirci, Francesco, Carrella, Diego, di Bernardo, Diego
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4795590/
https://www.ncbi.nlm.nih.gov/pubmed/26415724
http://dx.doi.org/10.1093/bioinformatics/btv536
Descripción
Sumario:Motivation: Automated screening approaches are able to rapidly identify a set of small molecules inducing a desired phenotype from large small-molecule libraries. However, the resulting set of candidate molecules is usually very diverse pharmacologically, thus little insight on the shared mechanism of action (MoA) underlying their efficacy can be gained. Results: We introduce a computational method (Drug-Set Enrichment Analysis—DSEA) based on drug-induced gene expression profiles, which is able to identify the molecular pathways that are targeted by most of the drugs in the set. By diluting drug-specific effects unrelated to the phenotype of interest, DSEA is able to highlight phenotype-specific pathways, thus helping to formulate hypotheses on the MoA shared by the drugs in the set. We validated the method by analysing five different drug-sets related to well-known pharmacological classes. We then applied DSEA to identify the MoA shared by drugs known to be partially effective in rescuing mutant cystic fibrosis transmembrane conductance regulator (CFTR) gene function in Cystic Fibrosis. Availability and implementation: The method is implemented as an online web tool publicly available at http://dsea.tigem.it. Contact: dibernardo@tigem.it Supplementary information: Supplementary data are available at Bioinformatics online.