gpGrouper: A Peptide Grouping Algorithm for Gene-Centric Inference and Quantitation of Bottom-Up Proteomics Data

Mol Cell Proteomics. 2018 Nov;17(11):2270-2283. doi: 10.1074/mcp.TIR118.000850. Epub 2018 Aug 9.


In quantitative mass spectrometry, the method by which peptides are grouped into proteins can have dramatic effects on downstream analyses. Here we describe gpGrouper, an inference and quantitation algorithm that offers an alternative method for assignment of protein groups by gene locus and improves pseudo-absolute iBAQ quantitation by weighted distribution of shared peptide areas. We experimentally show that distributing shared peptide quantities based on unique peptide peak ratios improves quantitation accuracy compared with conventional winner-take-all scenarios. Furthermore, gpGrouper seamlessly handles two-species samples such as patient-derived xenografts (PDXs) without ignoring the host species or species-shared peptides. This is a critical capability for proper evaluation of proteomics data from PDX samples, where stromal infiltration varies across individual tumors. Finally, gpGrouper calculates peptide peak area (MS1) based expression estimates from multiplexed isobaric data, producing iBAQ results that are directly comparable across label-free, isotopic, and isobaric proteomics approaches.

Keywords: Bioinformatics software; Cancer Biology; Label-free quantification; Mass Spectrometry; Mouse models; Quantification; iTRAQ; patient derived xenograft; protein inference; shared peptides.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms*
  • Animals
  • Genes
  • HeLa Cells
  • Humans
  • Mice
  • Mice, SCID
  • NIH 3T3 Cells
  • Peptides / metabolism*
  • Proteome / metabolism
  • Proteomics / methods*
  • Reproducibility of Results
  • Xenograft Model Antitumor Assays


  • Peptides
  • Proteome