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- Title
KEA: kinase enrichment analysis.
- Authors
Alexander Lachmann; Avi Maayan
- Abstract
Motivation: Multivariate experiments applied to mammalian cells often produce lists of proteins/genes altered under treatment versus control conditions. Such lists can be projected onto prior knowledge of kinaseâsubstrate interactions to infer the list of kinases associated with a specific protein list. By computing how the proportion of kinases, associated with a specific list of proteins/genes, deviates from an expected distribution, we can rank kinases and kinase families based on the likelihood that these kinases are functionally associated with regulating the cell under specific experimental conditions. Such analysis can assist in producing hypotheses that can explain how the kinome is involved in the maintenance of different cellular states and can be manipulated to modulate cells towards a desired phenotype. Summary: Kinase enrichment analysis (KEA) is a web-based tool with an underlying database providing users with the ability to link lists of mammalian proteins/genes with the kinases that phosphorylate them. The system draws from several available kinaseâsubstrate databases to compute kinase enrichment probability based on the distribution of kinaseâsubstrate proportions in the background kinaseâsubstrate database compared with kinases found to be associated with an input list of genes/proteins. Availability: The KEA system is freely available at http://amp.pharm.mssm.edu/lib/kea.jsp Contact: avi.maayan@mssm.edu
- Publication
Bioinformatics, 2009, Vol 25, Issue 5, p684
- ISSN
1367-4803
- Publication type
Academic Journal
- DOI
10.1093/bioinformatics/btp026