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Drug-set enrichment analysis: a novel tool to investigate drug mode of action
- Source :
- Bioinformatics
- Publication Year :
- 2015
- Publisher :
- Oxford University Press (OUP), 2015.
-
Abstract
- 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.
- Subjects :
- Statistics and Probability
Drug
Cystic Fibrosis
media_common.quotation_subject
Cystic Fibrosis Transmembrane Conductance Regulator
Computational biology
Biology
Biochemistry
Small Molecule Libraries
Humans
Mode of action
Set (psychology)
Molecular Biology
media_common
Genetics
Regulation of gene expression
Systems Biology
Original Papers
Phenotype
Cystic fibrosis transmembrane conductance regulator
3. Good health
Computer Science Applications
Computational Mathematics
Gene Expression Regulation
Computational Theory and Mathematics
Mutation
biology.protein
Transcriptome
Function (biology)
Subjects
Details
- ISSN :
- 13674811 and 13674803
- Volume :
- 32
- Database :
- OpenAIRE
- Journal :
- Bioinformatics
- Accession number :
- edsair.doi.dedup.....3fda99093e98942a8a9a2d26453440da
- Full Text :
- https://doi.org/10.1093/bioinformatics/btv536