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

gpGrouper is a gene-centric peptide inference and quantitation algorithm that prevents gene origin mixing and isoform omission in parsimonious protein-centric approaches. A simple classification schema indicates distinguishable gene products, with shared peptide quantities distributed by ratios of corresponding unique peptides. This approach accurately determines tumor content and deconvolution of proteomes from mixed species patient derived xenografts without elimination of species-shared peptides. iBAQ quantities are calculated from label-free, isotopic, or isobaric data, allowing comparisons within and across samples and methodologies. Graphical Abstract Highlights Gene-centric inference algorithm with classification for distinguishable groups. Shared peptides are split proportionally to corresponding unique peptide ratios. iBAQ values are calculated for label-free, isotopic or isobaric labeling methods. Universally handles single or mixed species PDX data with accurate deconvolution. 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.