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Updated: Jan 18, 2026

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Published on: August 19, 2025

An updated Bioconductor workflow for correlation profiling subcellular proteomics.

Charlotte Hutchings¹, Thomas Krueger², Oliver M Crook³

¹Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QR, UK.

|September 11, 2025

Summary

This summary is machine-generated.

This study presents a new R workflow for analyzing subcellular proteomics data from mass spectrometry. It enables accurate protein localization classification and prediction of differential localization events across conditions.

Keywords:

LOPIT QFeatures Subcellular spatial proteomics bandle correlation profiling mass spectrometry pRoloc protein localisation

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Last Updated: Jan 18, 2026

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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

Area of Science:

Proteomics
Cell Biology
Bioinformatics

Background:

Protein subcellular localization is crucial for protein function.
Mass spectrometry-based correlation profiling aids in classifying protein localization.
Comparing static localizations reveals differential protein localization events.

Purpose of the Study:

To provide a comprehensive workflow for processing and analyzing subcellular proteomics data.
To enable high-confidence protein localization classification and differential localization predictions.
To facilitate the adaptation of the workflow for various mass spectrometry data types.

Main Methods:

Utilizes open-source R software packages from Bioconductor.
Employs the QFeatures infrastructure for protein correlation profile generation.
Integrates machine learning for protein subcellular localization classification.

Main Results:

A robust workflow for processing and analyzing subcellular proteomics data.
High-quality protein correlation profiles generated.
Accurate protein localization classifications and differential localization predictions.

Conclusions:

A comprehensive start-to-end workflow for correlation profiling subcellular proteomics experiments.
The workflow is implemented in R version 4.5.0 with Bioconductor version 3.21.