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Nucleic Acids Res. 2015 Jul 1;43(W1):W276-82. doi: 10.1093/nar/gkv459. Epub 2015 May 6.

SELPHI: correlation-based identification of kinase-associated networks from global phospho-proteomics data sets.

Author information

1
Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, M5G 1X8, Canada petsalakis@lunenfeld.ca.
2
Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, M5G 1X8, Canada Program in Developmental and Stem Cell Biology, Hospital for Sick Children, Toronto, Ontario, M5G 1X5, Canada.
3
Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, M5G 1X8, Canada.
4
Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, M5G 1X8, Canada Donnelly Centre and Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, Ontario, M5G 1X8, Canada Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, 02215, USA Canadian Institute for Advanced Research, Toronto, Ontario, M5G 1Z8, Canada fritz.roth@utoronto.ca.

Abstract

While phospho-proteomics studies have shed light on the dynamics of cellular signaling, they mainly describe global effects and rarely explore mechanistic details, such as kinase/substrate relationships. Tools and databases, such as NetworKIN and PhosphoSitePlus, provide valuable regulatory details on signaling networks but rely on prior knowledge. They therefore provide limited information on less studied kinases and fewer unexpected relationships given that better studied signaling events can mask condition- or cell-specific 'network wiring'. SELPHI is a web-based tool providing in-depth analysis of phospho-proteomics data that is intuitive and accessible to non-bioinformatics experts. It uses correlation analysis of phospho-sites to extract kinase/phosphatase and phospho-peptide associations, and highlights the potential flow of signaling in the system under study. We illustrate SELPHI via analysis of phospho-proteomics data acquired in the presence of erlotinib-a tyrosine kinase inhibitor (TKI)-in cancer cells expressing TKI-resistant and -sensitive variants of the Epidermal Growth Factor Receptor. In this data set, SELPHI revealed information overlooked by the reporting study, including the known role of MET and EPHA2 kinases in conferring resistance to erlotinib in TKI sensitive strains. SELPHI can significantly enhance the analysis of phospho-proteomics data contributing to improved understanding of sample-specific signaling networks. SELPHI is freely available via http://llama.mshri.on.ca/SELPHI.

PMID:
25948583
PMCID:
PMC4489257
DOI:
10.1093/nar/gkv459
[Indexed for MEDLINE]
Free PMC Article

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