Phosphoproteomics data annotation using hypothesis driven kinase perturbation analysis


Kinase perturbation analysis (KinasePA) is a web tool that allows you to identify key kinases that are perturbed in two treatments compared to a control condition.
(1) The input data should be a csv file separated by comma. The rows of the data file are phosphorylation sites and the columns are treatment1 vs control and treatment2 vs control. The values of the data file should be log2 fold changes.

Please download this file as an example for required data format: [exampleData] (hint: right click and save)

Note that the same data is loaded into KinasePA when it is initiated for demonstrating its function.

(2) Click here access KinasePA []
The R package version of KinasePA is available from CRAN. You can also install this package in R by typing:


Using the following code as an example on how to use the functions:


# load the phosphoproteomics dataset

# load the kinase-substrate annotations

# Example: test combined effect of Torin1 and Rapamycin vs insulin both on "down-regulation"
# (180 degree to original direction)
par(mfrow=c(1, 2))
kPA <- kinasePA(Tc=HEK, direction=pi, annotation=PhosphoSite.mouse)
# rank substrates on the direciton of interest

# Generate kinase perturbation plot
perturbPlot2d(Tc=HEK, annotation=PhosphoSite.mouse, cex=3)


Yang, P., Patrick, E., Humphrey, S., Ghazanfar, S., James, D., Jothi, R. & Yang, J. (2016). KinasePA: Phosphoproteomics data annotation using hypothesis driven kinase perturbation analysis. Proteomics, 16(13), 1868-1871