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Related Concept Videos

Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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Updated: May 25, 2026

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
07:01

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

Published on: August 19, 2025

ProteoIntegrator: A Scalable and Integrated Platform for Robust Multi-Source Proteomics Data Harmonization,

Yan Ren1,2, Zhaomei Shi3, Dan Chen4

  • 1Experiment Center for Science and Technology, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.

Analytical Chemistry
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

ProteoIntegrator is a new no-code platform that harmonizes and analyzes proteomics data. It effectively handles missing values and batch effects, improving the detection of differentially expressed proteins.

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Large-scale proteomics data integration is challenging due to missing values and batch effects.
  • Existing methods often struggle with multi-source and multi-platform data harmonization.
  • Accurate analysis is crucial for identifying biological insights and disease biomarkers.

Purpose of the Study:

  • To develop and validate ProteoIntegrator, a scalable, web-based platform for robust proteomics data harmonization and analysis.
  • To benchmark imputation and batch correction methods for optimal integration of diverse proteomics datasets.
  • To provide a no-code tool for automated quality control, differential expression analysis, and pathway analysis.

Main Methods:

  • Benchmarking of 12 imputation methods, identifying Bayesian Principal Component Analysis (BPCA) as optimal.
  • Integration of BPCA with parallelized ComBat for efficient batch correction (>1000 samples/min).
  • Development of a no-code workflow for quality control, normalization, imputation, differential expression analysis, and network analysis.

Main Results:

  • ProteoIntegrator successfully harmonized multi-platform, multi-gradient, and multi-laboratory proteomics data with >98% quantification accuracy.
  • The platform corrected 7-year quality control drifts across three instruments.
  • ProteoIntegrator outperformed HarmonizR in detecting differentially expressed proteins (DEPs) and recapitulated known metabolic shifts in kidney cancer data.

Conclusions:

  • ProteoIntegrator offers a powerful, user-friendly solution for complex proteomics data integration and analysis.
  • The platform enhances the discovery of biological insights and biomarkers by addressing key data challenges.
  • Public accessibility empowers reproducible proteomics research and accelerates scientific discovery.