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Proteomics. 2016 Jul;16(13):1868-71. doi: 10.1002/pmic.201600068. Epub 2016 May 27.

KinasePA: Phosphoproteomics data annotation using hypothesis driven kinase perturbation analysis.

Author information

1
School of Mathematics and Statistics, University of Sydney, Sydney, NSW, Australia.
2
Charles Perkins Centre, School of Molecular Biosciences, University of Sydney, Sydney, NSW, Australia.
3
Systems Biology Section, Epigenetics & Stem Cell Biology Laboratory, National Institute of Environmental, Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA.
4
Brigham and Women's Hospital, Harvard Medical School, Broad Institute, Boston, MA, USA.
5
Department of Proteomics and Signal Transduction, Max Planck Institute for Biochemistry, Martinsried, Germany.

Abstract

Mass spectrometry (MS)-based quantitative phosphoproteomics has become a key approach for proteome-wide profiling of phosphorylation in tissues and cells. Traditional experimental design often compares a single treatment with a control, whereas increasingly more experiments are designed to compare multiple treatments with respect to a control. To this end, the development of bioinformatic tools that can integrate multiple treatments and visualise kinases and substrates under combinatorial perturbations is vital for dissecting concordant and/or independent effects of each treatment. Here, we propose a hypothesis driven kinase perturbation analysis (KinasePA) to annotate and visualise kinases and their substrates that are perturbed by various combinatorial effects of treatments in phosphoproteomics experiments. We demonstrate the utility of KinasePA through its application to two large-scale phosphoproteomics datasets and show its effectiveness in dissecting kinases and substrates within signalling pathways driven by unique combinations of cellular stimuli and inhibitors. We implemented and incorporated KinasePA as part of the "directPA" R package available from the comprehensive R archive network (CRAN). Furthermore, KinasePA also has an interactive web interface that can be readily applied to annotate user provided phosphoproteomics data (http://kinasepa.pengyiyang.org).

KEYWORDS:

Bioinformatics; Hypothesis testing; Kinase; Perturbation; Phosphoproteomics; Signalling

PMID:
27145998
PMCID:
PMC5027648
DOI:
10.1002/pmic.201600068
[Indexed for MEDLINE]
Free PMC Article

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