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PhosPiR : an automated phosphoproteomic pipeline in R

Hong, Ye ; Flinkman, Dani LU ; Suomi, Tomi ; Pietilä, Sami ; James, Peter LU orcid ; Coffey, Eleanor and Elo, Laura L. (2022) In Briefings in Bioinformatics 23(1).
Abstract

Large-scale phosphoproteome profiling using mass spectrometry (MS) provides functional insight that is crucial for disease biology and drug discovery. However, extracting biological understanding from these data is an arduous task requiring multiple analysis platforms that are not adapted for automated high-dimensional data analysis. Here, we introduce an integrated pipeline that combines several R packages to extract high-level biological understanding from large-scale phosphoproteomic data by seamless integration with existing databases and knowledge resources. In a single run, PhosPiR provides data clean-up, fast data overview, multiple statistical testing, differential expression analysis, phosphosite annotation and translation... (More)

Large-scale phosphoproteome profiling using mass spectrometry (MS) provides functional insight that is crucial for disease biology and drug discovery. However, extracting biological understanding from these data is an arduous task requiring multiple analysis platforms that are not adapted for automated high-dimensional data analysis. Here, we introduce an integrated pipeline that combines several R packages to extract high-level biological understanding from large-scale phosphoproteomic data by seamless integration with existing databases and knowledge resources. In a single run, PhosPiR provides data clean-up, fast data overview, multiple statistical testing, differential expression analysis, phosphosite annotation and translation across species, multilevel enrichment analyses, proteome-wide kinase activity and substrate mapping and network hub analysis. Data output includes graphical formats such as heatmap, box-, volcano- and circos-plots. This resource is designed to assist proteome-wide data mining of pathophysiological mechanism without a need for programming knowledge.

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author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Bioinformatics, Data visualization, Phosphoproteomics, Pipeline, Proteomics, Statistics
in
Briefings in Bioinformatics
volume
23
issue
1
article number
bbab510
publisher
Oxford University Press
external identifiers
  • scopus:85126760300
  • pmid:34882763
ISSN
1467-5463
DOI
10.1093/bib/bbab510
language
English
LU publication?
yes
id
d8ef8ce9-89e8-4dac-8c8d-18b3187b322b
date added to LUP
2022-04-20 15:41:04
date last changed
2025-04-08 12:59:00
@article{d8ef8ce9-89e8-4dac-8c8d-18b3187b322b,
  abstract     = {{<p>Large-scale phosphoproteome profiling using mass spectrometry (MS) provides functional insight that is crucial for disease biology and drug discovery. However, extracting biological understanding from these data is an arduous task requiring multiple analysis platforms that are not adapted for automated high-dimensional data analysis. Here, we introduce an integrated pipeline that combines several R packages to extract high-level biological understanding from large-scale phosphoproteomic data by seamless integration with existing databases and knowledge resources. In a single run, PhosPiR provides data clean-up, fast data overview, multiple statistical testing, differential expression analysis, phosphosite annotation and translation across species, multilevel enrichment analyses, proteome-wide kinase activity and substrate mapping and network hub analysis. Data output includes graphical formats such as heatmap, box-, volcano- and circos-plots. This resource is designed to assist proteome-wide data mining of pathophysiological mechanism without a need for programming knowledge.</p>}},
  author       = {{Hong, Ye and Flinkman, Dani and Suomi, Tomi and Pietilä, Sami and James, Peter and Coffey, Eleanor and Elo, Laura L.}},
  issn         = {{1467-5463}},
  keywords     = {{Bioinformatics; Data visualization; Phosphoproteomics; Pipeline; Proteomics; Statistics}},
  language     = {{eng}},
  month        = {{01}},
  number       = {{1}},
  publisher    = {{Oxford University Press}},
  series       = {{Briefings in Bioinformatics}},
  title        = {{PhosPiR : an automated phosphoproteomic pipeline in R}},
  url          = {{http://dx.doi.org/10.1093/bib/bbab510}},
  doi          = {{10.1093/bib/bbab510}},
  volume       = {{23}},
  year         = {{2022}},
}