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PSEA-Quant: a protein set enrichment analysis on label-free and label-based protein quantification data

Academic Article
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Overview

authors

  • Lavallee-Adam, M.
  • Rauniyar, N.
  • McClatchy, D. B.
  • Yates III, John

publication date

  • December 2014

journal

  • Journal of Proteome Research  Journal

abstract

  • The majority of large-scale proteomics quantification methods yield long lists of quantified proteins that are often difficult to interpret and poorly reproduced. Computational approaches are required to analyze such intricate quantitative proteomics data sets. We propose a statistical approach to computationally identify protein sets (e.g., Gene Ontology (GO) terms) that are significantly enriched with abundant proteins with reproducible quantification measurements across a set of replicates. To this end, we developed PSEA-Quant, a protein set enrichment analysis algorithm for label-free and label-based protein quantification data sets. It offers an alternative approach to classic GO analyses, models protein annotation biases, and allows the analysis of samples originating from a single condition, unlike analogous approaches such as GSEA and PSEA. We demonstrate that PSEA-Quant produces results complementary to GO analyses. We also show that PSEA-Quant provides valuable information about the biological processes involved in cystic fibrosis using label-free protein quantification of a cell line expressing a CFTR mutant. Finally, PSEA-Quant highlights the differences in the mechanisms taking place in the human, rat, and mouse brain frontal cortices based on tandem mass tag quantification. Our approach, which is available online, will thus improve the analysis of proteomics quantification data sets by providing meaningful biological insights.

subject areas

  • Algorithms
  • Animals
  • Brain
  • Cell Line
  • Chromatography, Liquid
  • Computational Biology
  • Cystic Fibrosis Transmembrane Conductance Regulator
  • Databases, Protein
  • Humans
  • Mice
  • Mutation
  • Proteins
  • Proteome
  • Proteomics
  • Rats
  • Tandem Mass Spectrometry
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Research

keywords

  • bioinformatics
  • computational biology
  • cystic fibrosis
  • gene ontology
  • gene set enrichment analysis
  • isobaric tandem mass tagging
  • mass spectrometry
  • protein quantification
  • spectral counting
  • statistics
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Identity

PubMed Central ID

  • PMC4258137

International Standard Serial Number (ISSN)

  • 1535-3893

Digital Object Identifier (DOI)

  • 10.1021/pr500473n

PubMed ID

  • 25177766
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Additional Document Info

start page

  • 5496

end page

  • 5509

volume

  • 13

issue

  • 12

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