Protocol for the processing and downstream analysis of phosphoproteomic data with PhosR

Analysis of phosphoproteomic data requires advanced computational methodologies. To this end, we developed PhosR, a set of tools and methodologies implemented in R to allow the comprehensive analysis of phosphoproteomic data. PhosR enables processing steps such as imputation, normalization, and func...

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Bibliographic Details
Published in:STAR protocols 2021-06, Vol.2 (2), p.100585-100585, Article 100585
Main Authors: Kim, Hani Jieun, Kim, Taiyun, Xiao, Di, Yang, Pengyi
Format: Article
Language:English
Subjects:
Bioinformatics
Proteomics
Protocol
Systems biology
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Summary:Analysis of phosphoproteomic data requires advanced computational methodologies. To this end, we developed PhosR, a set of tools and methodologies implemented in R to allow the comprehensive analysis of phosphoproteomic data. PhosR enables processing steps such as imputation, normalization, and functional analysis such as kinase activity inference and signalome construction. Together, PhosR facilitates interpretation and discovery from large-scale phosphoproteomic data sets. For complete details on the use and execution of this protocol, please refer to Kim et al. (2021). [Display omitted] •This protocol describes how to run and interpret the results of PhosR•PhosR performs filtering, imputation, and normalization of phosphoproteomic data•PhosR enables kinase-substrate predictions and signalome construction•The step-by-step protocol provides a comprehensive introduction to phospho-data analysis Analysis of phosphoproteomic data requires advanced computational methodologies. To this end, we developed PhosR, a set of tools and methodologies implemented in R to allow the comprehensive analysis of phosphoproteomic data. PhosR enables processing steps such as imputation, normalization, and functional analysis such as kinase activity inference and signalome construction. Together, PhosR facilitates interpretation and discovery from large-scale phosphoproteomic data sets.
ISSN:2666-1667
2666-1667
DOI:10.1016/j.xpro.2021.100585