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Sci. Signal., 17 April 2012
Vol. 5, Issue 220, p. pl2
[DOI: 10.1126/scisignal.2002429]

PROTOCOLS

A Systematic Approach for Analysis of Peptide Array Kinome Data

Yue Li1*, Ryan J. Arsenault2,3, Brett Trost1, Jillian Slind1, Philip J. Griebel3,4, Scott Napper2,3, and Anthony Kusalik1{dagger}

1 Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5C9, Canada.
2 Department of Biochemistry, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5E5, Canada.
3 Vaccine and Infectious Disease Organization, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5E3, Canada.
4 School of Public Health, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5E5, Canada.

* Current address: Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.

Abstract: The central roles of kinases in cellular processes and diseases make them highly attractive as indicators of biological responses and as therapeutic targets. Peptide arrays are emerging as an important means of characterizing kinome activity. Currently, the computational tools used to perform high-throughput kinome analyses are not specifically tailored to the nature of the data, which hinders extraction of biological information and overall progress in the field. We have developed a method for kinome analysis, which is implemented as a software pipeline in the R environment. Components and parameters were chosen to address the technical and biological characteristics of kinome microarrays. We performed comparative analysis of kinome data sets that corresponded to stimulation of immune cells with ligands of well-defined signaling pathways: bovine monocytes treated with interferon-{gamma} (IFN-{gamma}), CpG-containing nucleotides, or lipopolysaccharide (LPS). The data sets for each of the treatments were analyzed with our methodology as well as with three other commonly used approaches. The methods were evaluated on the basis of statistical confidence of calculated values with respect to technical and biological variability, and the statistical confidence (P values) by which the known signaling pathways could be independently identified by the pathway analysis of InnateDB (a Web-based resource for innate immunity interactions and pathways). By considering the particular attributes of kinome data, we found that our approach identified more of the peptides involved in the pathways than did the other compared methods and that it did so at a much higher degree of statistical confidence.

{dagger} To whom correspondence should be addressed. E-mail: kusalik{at}cs.usask.ca

Citation: Y. Li, R. J. Arsenault, B. Trost, J. Slind, P. J. Griebel, S. Napper, A. Kusalik, A Systematic Approach for Analysis of Peptide Array Kinome Data. Sci. Signal. 5, pl2 (2012).

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R. J. Arsenault, Y. Li, P. Maattanen, E. Scruten, K. Doig, A. Potter, P. Griebel, A. Kusalik, and S. Napper (2013)
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