Journal of Proteome Research· 2026Q1
Manchester Proteome Profiler: A User-Friendly Platform for Quantitative Proteomic Analysis
- 0citations
- Q1SCImago
- 2026year
Short summary
The Manchester Proteome Profiler (MPP) is a new open-source R Shiny application that simplifies quantitative proteomic data analysis, integrating filtering, normalization, imputation, and differential expression analysis for various experimental designs.
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Key points
- MPP is an open-source R Shiny application for quantitative proteomic data analysis.
- It integrates multiple analysis steps: filtering, normalization, imputation, differential expression, and clustering.
- MPP supports various data inputs (MaxQuant, FragPipe, Proteome Discoverer) and experimental designs (single/dual datasets, affinity purification, proximity labeling).
- Benchmarking demonstrated its ability to identify reproducible protein clusters and biologically meaningful patterns.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract Manchester Proteome Profiler (MPP) is an open-source R Shiny application that streamlines the downstream analysis of quantitative proteomic data. Compatible with grouped protein intensities tables from MaxQuant, FragPipe, Proteome Discoverer, and other custom layouts, MPP provides an integrated platform for filtering, normalization, imputation, differential expression analysis, and cluster analysis across user-chosen experimental conditions. MPP supports both single- and dual-data set comparisons, incorporates SAINTexpress for affinity purification and proximity labeling experiments, and enables downstream analysis of the significant protein list clusters to functional enrichment and interaction networks via Gene Ontology, BioGRID, and STRING. Benchmarking with a KRAS proximity biotinylation data set demonstrated the ability of MPP to identify reproducible clusters of differentially expressed proteins and reveal biologically meaningful patterns, including enrichment of solute carrier transporters and adhesion molecules. With interactive visualizations, customizable reports, and support for complex experimental designs, MPP offers a novel, versatile, and user-friendly environment for proteomic data exploration and hypothesis generation.
The authors' abstract, as published at the source. Journal of Proteome Research, 2026 · DOI ↗
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Field: Spectroscopy
SpectroscopyChemistry