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Proteomics

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...

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A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

Published on: April 18, 2025

Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference.

Hui Peng^1,2, He Wang^1,2, Weijia Kong^1,2

¹Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.

Nature Communications

|May 9, 2024

Summary

This summary is machine-generated.

Optimizing proteomics workflows involves identifying the best combination of analysis steps. This study found conserved properties in high-performing workflows and developed predictable models, improving differential proteome coverage.

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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Identifying differentially expressed proteins is crucial in proteomics.
Numerous analytical choices exist, complicating workflow optimization.
Lack of standardized optimal workflows hinders reproducible results.

Purpose of the Study:

To identify optimal proteomics workflows and their conserved properties.
To develop predictive models for workflow performance.
To enhance differential proteome coverage using ensemble inference.

Main Methods:

Conducted 34,576 combinatoric experiments on 24 spike-in datasets.
Applied frequent pattern mining and machine learning techniques.
Developed an ensemble inference method for integrating workflow results.

Main Results:

Discovered conserved properties in high-performing proteomics workflows.
Achieved predictable workflow performance with high accuracy (F1 > 0.84).
Ensemble inference improved proteome coverage (pAUC up to 4.61%) and accuracy (G-mean up to 11.14%).

Conclusions:

Optimal proteomics workflows possess predictable and conserved characteristics.
Ensemble inference effectively integrates results from diverse quantification methods.
Further research is needed to standardize ensemble inference frameworks for proteomics.