EBprotV2: A Perseus Plugin for Differential Protein Abundance Analysis of Labeling-Based Quantitative Proteomics Data

Hiromi W.L. Koh, Yunbin Zhang, Christine Vogel, Hyungwon Choi

Research output: Contribution to journalArticle

Abstract

We present EBprotV2, a Perseus plugin for peptide-ratio-based differential protein abundance analysis in labeling-based proteomics experiments. The original version of EBprot models the distribution of log-transformed peptide-level ratios as a Gaussian mixture of differentially abundant proteins and nondifferentially abundant proteins and computes the probability score of differential abundance for each protein based on the reproducible magnitude of peptide ratios. However, the fully parametric model can be inflexible, and its R implementation is time-consuming for data sets containing a large number of peptides (e.g., >100000). The new tool built in the C++ language is not only faster in computation time but also equipped with a flexible semiparametric model that handles skewed ratio distributions better. We have also developed a Perseus plugin for EBprotV2 for easy access to the tool. In addition, the tool now offers a new submodule (MakeGrpData) to transform label-free peptide intensity data into peptide ratio data for group comparisons and performs differential abundance analysis using mixture modeling. This approach is especially useful when the label-free data have many missing peptide intensity data points.

Original languageEnglish (US)
Pages (from-to)748-752
Number of pages5
JournalJournal of Proteome Research
Volume18
Issue number2
DOIs
StatePublished - Feb 1 2019

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Proteomics
Labeling
Peptides
Proteins
Labels
Language
Experiments

Keywords

  • differential abundance
  • label-free data
  • labeling-based proteomics
  • peptide-level analysis
  • Perseus plugin
  • R package
  • semiparametric modeling
  • statistics

ASJC Scopus subject areas

  • Biochemistry
  • Chemistry(all)

Cite this

EBprotV2 : A Perseus Plugin for Differential Protein Abundance Analysis of Labeling-Based Quantitative Proteomics Data. / Koh, Hiromi W.L.; Zhang, Yunbin; Vogel, Christine; Choi, Hyungwon.

In: Journal of Proteome Research, Vol. 18, No. 2, 01.02.2019, p. 748-752.

Research output: Contribution to journalArticle

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