KEA: Kinase enrichment analysis

Department of Pharmacology and Systems Therapeutics, Systems Biology Center in New York, Icahn Medical Institute, Mount Sinai School of Medicine, 1425 Madison Avenue, New York, NY 10029, USA.
Bioinformatics (Impact Factor: 4.98). 02/2009; 25(5):684-6. DOI: 10.1093/bioinformatics/btp026
Source: PubMed


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 kinase-substrate 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.
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 kinase-substrate databases to compute kinase enrichment probability based on the distribution of kinase-substrate proportions in the background kinase-substrate database compared with kinases found to be associated with an input list of genes/proteins.
The KEA system is freely available at

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Available from: Avi Ma'ayan, Sep 30, 2015
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    • "These downstream analyses usually rely on protein-level information, such as protein access numbers or gene names. Here, we used IPA for mapping proteins onto existing networks and pathways and classifying the proteins based on gene ontology (GO) annotations as well as KEA, a kinase enrichment analysis tool [23], to gain biological insight into the phosphoproteome data from our HaCaT keratinocyte experiments. The IPA analysis results of the manually processed datasets as well as the PhosFox-processed datasets are shown in Table 1. "
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    ABSTRACT: Background It is possible to identify thousands of phosphopeptides and –proteins in a single experiment with mass spectrometry-based phosphoproteomics. However, a current bottleneck is the downstream data analysis which is often laborious and requires a number of manual steps. Results Toward automating the analysis steps, we have developed and implemented a software, PhosFox, which enables peptide-level processing of phosphoproteomic data generated by multiple protein identification search algorithms, including Mascot, Sequest, and Paragon, as well as cross-comparison of their identification results. The software supports both qualitative and quantitative phosphoproteomics studies, as well as multiple between-group comparisons. Importantly, PhosFox detects uniquely phosphorylated peptides and proteins in one sample compared to another. It also distinguishes differences in phosphorylation sites between phosphorylated proteins in different samples. Using two case study examples, a qualitative phosphoproteome dataset from human keratinocytes and a quantitative phosphoproteome dataset from rat kidney inner medulla, we demonstrate here how PhosFox facilitates an efficient and in-depth phosphoproteome data analysis. PhosFox was implemented in the Perl programming language and it can be run on most common operating systems. Due to its flexible interface and open source distribution, the users can easily incorporate the program into their MS data analysis workflows and extend the program with new features. PhosFox source code, implementation and user instructions are freely available from Conclusions PhosFox facilitates efficient and more in-depth comparisons between phosphoproteins in case–control settings. The open source implementation is easily extendable to accommodate additional features for widespread application use cases.
    Proteome Science 06/2014; 12(1):36. DOI:10.1186/1477-5956-12-36 · 1.73 Impact Factor
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    • "A literature-based protein-protein interaction (PPI) network was constructed by combining interactions from the following databases: BioGRID [27], HPRD [28], MINT [29], IntAct [30], KEA [31], KEGG [32], SNAVI [33] and MIPS [34]. Only interactions from publications that reported ten or fewer interactions were retained. "
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    ABSTRACT: \emph{De novo} loss-of-function (dnLoF) mutations are found twofold more often in autism spectrum disorder (ASD) probands than their unaffected siblings. Multiple independent dnLoF mutations in the same gene implicate the gene in risk and hence provide a systematic, albeit arduous, path forward for ASD genetics. It is likely that using additional non-genetic data will enhance the ability to identify ASD genes. To accelerate the search for ASD genes, we developed a novel algorithm, DAWN, to model two kinds of data: rare variations from exome sequencing and gene co-expression in the mid-fetal prefrontal and motor-somatosensory neocortex, a critical nexus for risk. The algorithm casts the ensemble data as a hidden Markov random field in which the graph structure is determined by gene co-expression and it combines these interrelationships with node-specific observations, namely gene identity, expression, genetic data and the estimated effect on risk. Using currently available genetic data and a specific developmental time period for gene co-expression, DAWN identified 127 genes that plausibly affect risk, and a set of likely ASD subnetworks. Validation experiments making use of published targeted resequencing results demonstrate its efficacy in reliably predicting ASD genes. DAWN also successfully predicts known ASD genes, not included in the genetic data used to create the model. Validation studies demonstrate that DAWN is effective in predicting ASD genes and subnetworks by leveraging genetic and gene expression data. The findings reported here implicate neurite extension and neuronal arborization as risks for ASD. Using DAWN on emerging ASD sequence data and gene expression data from other brain regions and tissues would likely identify novel ASD genes. DAWN can also be used for other complex disorders to identify genes and subnetworks in those disorders.
    Molecular Autism 03/2014; 5(1):22. DOI:10.1186/2040-2392-5-22 · 5.41 Impact Factor
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    • ", 2012 ; Culhane et al . , 2010 ; Lachmann and Ma ' ayan , 2009 ; Liberzon et al . , 2011 ) , includ - ing some from our previous publications , new gene - set libraries were added , including a gene - set library created from the Encyclopedia of DNA Elements ( ENCODE ) project ( Rosenbloom et al . "
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    ABSTRACT: Motivation: Networks are vital to computational systems biology research, but visualizing them is a challenge. For networks larger than ∼100 nodes and ∼200 links, ball-and-stick diagrams fail to convey much information. To address this, we developed Network2Canvas (N2C), a web application that provides an alternative way to view networks. N2C visualizes networks by placing nodes on a square toroidal canvas. The network nodes are clustered on the canvas using simulated annealing to maximize local connections where a node's brightness is made proportional to its local fitness. The interactive canvas is implemented in HyperText Markup Language (HTML)5 with the JavaScript library Data-Driven Documents (D3). We applied N2C to visualize 30 canvases made from human and mouse gene-set libraries and 6 canvases made from the Food and Drug Administration (FDA)-approved drug-set libraries. Given lists of genes or drugs, enriched terms are highlighted on the canvases, and their degree of clustering is computed. Because N2C produces visual patterns of enriched terms on canvases, a trained eye can detect signatures instantly. In summary, N2C provides a new flexible method to visualize large networks and can be used to perform and visualize gene-set and drug-set enrichment analyses.Availability: N2C is freely available at and is open source.Contact: Supplementary information: Supplementary data are available at Bioinformatics online. © 2013 The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: [email protected] /* */
    Bioinformatics 06/2013; 29(15). DOI:10.1093/bioinformatics/btt319 · 4.98 Impact Factor
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