PhosR enables processing and functional analysis of phosphoproteomic data

Journal article

Kim, Hani Jieun, Kim, Taiyun, Hoffman, Nolan J., Xiao, Di, James, David E., Humphrey, Sean J. and Yang, Pengyi. (2021). PhosR enables processing and functional analysis of phosphoproteomic data. Cell Reports. 34, p. 108771. https://doi.org/10.1101/2020.08.31.276329
AuthorsKim, Hani Jieun, Kim, Taiyun, Hoffman, Nolan J., Xiao, Di, James, David E., Humphrey, Sean J. and Yang, Pengyi
Abstract

Mass spectrometry (MS)-based phosphoproteomics has revolutionised our ability to profile phosphorylation-based signalling in cells and tissues on a global scale. To infer the action of kinases and signalling pathways in phosphoproteomic experiments, we present PhosR, a set of tools and methodologies implemented in a suite of R packages facilitating comprehensive analysis of phosphoproteomic data. By applying PhosR to both published and new phosphoproteomic datasets, we demonstrate capabilities in data imputation and normalisation using a novel set of ‘stably phosphorylated sites’, and in functional analysis for inferring active kinases and signalling pathways. In particular, we introduce a ‘signalome’ construction method for identifying a collection of signalling modules to summarise and visualise the interaction of kinases and their collective actions on signal transduction. Together, our data and findings demonstrate the utility of PhosR in processing and generating novel biological knowledge from MS-based phosphoproteomic data.

Year2021
JournalCell Reports
Journal citation34, p. 108771
PublisherCell Press
ISSN2211-1247
Digital Object Identifier (DOI)https://doi.org/10.1101/2020.08.31.276329
Open accessPublished as ‘gold’ (paid) open access
Research or scholarlyResearch
Page range1-9
FunderAustralian Research Council (ARC)
National Health and Medical Research Council (NHMRC)
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online23 Feb 2021
Publication process dates
Accepted28 Jan 2021
Deposited30 Jun 2021
ARC Funded ResearchThis output has been funded, wholly or partially, under the Australian Research Council Act 2001
Grant IDARC/DE170100759
NHMRC/1173469
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