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bioRxiv · 10.1101/2020.08.26.268581

KRSA: Network-based Prediction of Differential Kinase Activity from Kinome Array Data

DePasquale, E. A.·Alganem, K.·Bentea, E.·Nawreen, N.·McGuire, J.·Naji, F.·Hilhorst, R.·Meller, J.·McCullumsmith, R.
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Abstract

MotivationPhosphorylation by serine-threonine and tyrosine kinases is critical for determining protein function. Array-based approaches for measuring multiple kinases allow for the testing of differential phosphorylation between conditions for distinct sub-kinomes. While bioinformatics tools exist for processing and analyzing such kinome array data, current open-source tools lack the automated approach of upstream kinase prediction and network modeling. The presented tool, alongside other tools and methods designed for gene expression and protein-protein interaction network analyses, help the user better understand the complex regulation of gene and protein activities that forms biological systems and cellular signaling networks. ResultsWe present the Kinome Random Sampling Analyzer (KRSA), a web-application for kinome array analysis. While the underlying algorithm has been experimentally validated in previous publications, we tested the full KRSA application on dorsolateral prefrontal cortex (DLPFC) in male (n=3) and female (n=3) subjects to identify differential phosphorylation and upstream kinase activity. Kinase activity differences between males and females were compared to a previously published kinome dataset (11 female and 7 male subjects) which showed similar patterns to the global phosphorylation signal. Additionally, kinase hits were compared to gene expression databases for in silico validation at the transcript level and showed differential gene expression of kinases. Availability and implementationKRSA as a web-based application can be found at http://bpg-n.utoledo.edu:3838/CDRL/KRSA/. The code and data are available at https://github.com/kalganem/KRSA. Supplementary informationSupplementary data are available online.

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DePasquale, E. A., Alganem, K., Bentea, E., Nawreen, N., McGuire, J., Naji, F., Hilhorst, R., Meller, J., McCullumsmith, R.. 2020-08-27. KRSA: Network-based Prediction of Differential Kinase Activity from Kinome Array Data. https://doi.org/10.1101/2020.08.26.268581

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