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PLoS Comput Biol. 2015 Aug 7;11(8):e1004403. doi: 10.1371/journal.pcbi.1004403. eCollection 2015 Aug.

Knowledge-Based Analysis for Detecting Key Signaling Events from Time-Series Phosphoproteomics Data.

Author information

1
Epigenetics & Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, Durham, North Carolina, United States of America; Biostatistics Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, Durham, North Carolina, United States of America.
2
Epigenetics & Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, Durham, North Carolina, United States of America.
3
Centre for Mathematical Biology, School of Mathematics and Statistics, University of Sydney, Sydney, Australia.

Abstract

Cell signaling underlies transcription/epigenetic control of a vast majority of cell-fate decisions. A key goal in cell signaling studies is to identify the set of kinases that underlie key signaling events. In a typical phosphoproteomics study, phosphorylation sites (substrates) of active kinases are quantified proteome-wide. By analyzing the activities of phosphorylation sites over a time-course, the temporal dynamics of signaling cascades can be elucidated. Since many substrates of a given kinase have similar temporal kinetics, clustering phosphorylation sites into distinctive clusters can facilitate identification of their respective kinases. Here we present a knowledge-based CLUster Evaluation (CLUE) approach for identifying the most informative partitioning of a given temporal phosphoproteomics data. Our approach utilizes prior knowledge, annotated kinase-substrate relationships mined from literature and curated databases, to first generate biologically meaningful partitioning of the phosphorylation sites and then determine key kinases associated with each cluster. We demonstrate the utility of the proposed approach on two time-series phosphoproteomics datasets and identify key kinases associated with human embryonic stem cell differentiation and insulin signaling pathway. The proposed approach will be a valuable resource in the identification and characterizing of signaling networks from phosphoproteomics data.

PMID:
26252020
PMCID:
PMC4529189
DOI:
10.1371/journal.pcbi.1004403
[Indexed for MEDLINE]
Free PMC Article

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