Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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 proteomics...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Ovarian Reproductive and Endocrine Function Investigated in Microgravity Environment.

Methods in molecular biology (Clifton, N.J.)·2026

Same author

Integrative structural analysis of the human LRP2-LRPAP1 complex reveals multiple regulatory sites.

Communications biology·2026

Same author

Molecular recognition of thyroglobulin by sortilin.

Nature communications·2026

Same author

Correction: Microgravity impairs endocrine signaling and reproductive health of women. A narrative review.

Frontiers in physiology·2025

Same author

The assembly of cancer-specific ribosomes by the lncRNA LISRR suppresses melanoma anti-tumor immunity.

The Journal of experimental medicine·2025

Same author

TBC1D22B Regulates ER-to-Golgi Trafficking via RAB1B Inactivation and Promotes Oncogenic Programs in Breast Cancer.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025

Same journal

Region-aware bridge modeling enables interpretable mesoscale representation of spatial transcriptomic tissue sections.

Bioinformatics advances·2026

Same journal

Microbiome differential abundance methodologies to detect relevant taxa associated with chemotherapy toxicity rate in colorectal cancer.

Bioinformatics advances·2026

Same journal

maldipickr dereplicates microbial MALDI-TOF spectra to facilitate multiplexed isolation.

Bioinformatics advances·2026

Same journal

RAM-MSA: an anytime memory-bounded method for exact multiple sequence alignment using path finding.

Bioinformatics advances·2026

Same journal

Interpretable machine learning for low-sample multi-omics: a case study of ferret vaccine response.

Bioinformatics advances·2026

Same journal

DeepTaxa: a hybrid CNN-BERT framework for 16S rRNA taxonomic classification.

Bioinformatics advances·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 23, 2026

Quantitative Analysis of Chromatin Proteomes in Disease

Quantitative Analysis of Chromatin Proteomes in Disease

Published on: December 28, 2012

QproMS: a web application for label-free proteomic data analysis.

Fabio Bedin¹, Giorgia Cucina¹, Giampaolo Martinello¹

¹Department of Experimental Oncology at IEO, European Institute of Oncology IRCCS, Milan 20139, Italy.

Bioinformatics Advances

|June 22, 2026

Summary

This summary is machine-generated.

Quantitative Proteomics Made Simple (QProMS) offers a user-friendly pipeline for analyzing label-free proteomics data. This tool simplifies complex analyses, making advanced proteomics accessible to all researchers.

More Related Videos

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

Dissecting Multi-protein Signaling Complexes by Bimolecular Complementation Affinity Purification (BiCAP)

Dissecting Multi-protein Signaling Complexes by Bimolecular Complementation Affinity Purification (BiCAP)

Published on: June 15, 2018

Related Experiment Videos

Last Updated: Jun 23, 2026

Quantitative Analysis of Chromatin Proteomes in Disease

Quantitative Analysis of Chromatin Proteomes in Disease

Published on: December 28, 2012

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

Dissecting Multi-protein Signaling Complexes by Bimolecular Complementation Affinity Purification (BiCAP)

Dissecting Multi-protein Signaling Complexes by Bimolecular Complementation Affinity Purification (BiCAP)

Published on: June 15, 2018

Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Proteomics research has advanced with new data acquisition methods (DDA, DIA) and analysis tools.
Current proteomics software often requires expert knowledge, limiting accessibility.
Existing tools have unique data analysis packages for reduction, imputation, and visualization.

Purpose of the Study:

Introduce Quantitative Proteomics Made Simple (QProMS), a user-friendly, search engine-agnostic pipeline.
To provide a simplified platform for reproducible proteomic data analysis for both novice and experienced users.
To enable state-of-the-art data analysis for various label-free proteomic workflows.

Main Methods:

Developed QProMS with a graphical interface for guided data analysis and statistical testing.
Integrated established R functions for statistical tests compatible with label-free quantification.
Implemented a novel mixed imputation method for handling missing values without machine learning.
Enabled interaction analyses via Gene Ontology or protein-protein interaction databases.
Incorporated interactive figures for detailed protein investigation and standalone report generation.

Main Results:

QProMS offers a user-friendly, search engine-agnostic pipeline for proteomics data analysis and visualization.
The pipeline supports diverse label-free quantification experiments, including global and targeted methods.
Features include guided analysis, mixed imputation for missing values, and interactive visualizations.
QProMS facilitates reproducible analysis and interaction studies.

Conclusions:

QProMS democratizes advanced proteomics data analysis, catering to users of all skill levels.
The pipeline enhances reproducibility and accessibility in label-free quantitative proteomics.
QProMS integrates key features from existing software with novel imputation methods.