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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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Related Experiment Video

Updated: Dec 29, 2025

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Human body-fluid proteome: quantitative profiling and computational prediction.

Lan Huang¹, Dan Shao^1,2, Yan Wang¹

¹College of Computer Science and Technology in the Jilin University.

Briefings in Bioinformatics

|February 6, 2020

Summary

This summary is machine-generated.

High-throughput technologies advance body-fluid proteome research, identifying disease biomarkers. Computational methods, including machine learning, are crucial for analyzing protein modifications and discovering clinical biomarkers.

Keywords:

biomarker discovery body-fluid proteome clinical application protein prediction

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Last Updated: Dec 29, 2025

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Published on: February 27, 2015

Area of Science:

Proteomics
Biotechnology
Computational Biology

Background:

High-throughput bio-technologies have significantly advanced body-fluid proteome research.
Over 15,000 proteins have been identified in human body fluids, aiding biomarker and drug discovery.
Challenges persist in handling diverse protein modifications within body fluids.

Purpose of the Study:

To review experimental advancements and discoveries in body-fluid proteome research.
To survey computational approaches for protein prediction and biomarker identification.
To discuss future perspectives for clinical disease biomarker discovery.

Main Methods:

Summary of experimental progress using conventional and high-throughput technologies.
Focus on the research status of 16 types of body-fluid proteins.
Survey of computational methods including support vector machines, ranking algorithms, and protein-protein interaction networks.

Main Results:

Significant progress in identifying novel disease biomarkers and therapeutic drugs from body fluids.
Early successes in computational identification of biomarker proteins using statistical and machine-learning approaches.
Discussion of algorithmic approaches and their applications in proteomics.

Conclusions:

Integration of experimental and computational methods is key to overcoming proteomics challenges.
Machine learning and network analysis show promise for biomarker discovery.
Future research should focus on translating these findings into clinical applications for disease diagnosis.