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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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Updated: Mar 17, 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

msVolcano: A flexible web application for visualizing quantitative proteomics data.

Sukhdeep Singh¹, Marco Y Hein², A Francis Stewart³

¹Genomics, Biotechnology Center, Technische Universität Dresden, Dresden, Germany. sukhdeepsingh.bio@gmail.com.

|July 22, 2016

Summary

This summary is machine-generated.

We present msVolcano, a web tool for visualizing label-free mass spectrometry data from interactomics studies. This application helps identify significant protein interactors and calculate stoichiometries, generating publication-quality results.

Keywords:

Bioinformatics Data visualization Interaction proteomics Label free quantification Web Application

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Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Label-free mass spectrometry is crucial for interactomics.
Analyzing large datasets from pipelines like MaxQuant requires specialized visualization tools.
Identifying significant protein interactors and their stoichiometries is key in understanding protein complexes.

Purpose of the Study:

To introduce msVolcano, a user-friendly web application for visualizing label-free mass spectrometry data.
To provide a tool optimized for MaxQuant output, facilitating interactomics data analysis.
To enable flexible identification and characterization of interacting proteins.

Main Methods:

Development of a web application using R and Shiny.
Implementation of volcano plot generation for interactomics data.
Inclusion of customizable cutoff values for identifying significant interactors.
Option for calculating stoichiometries of interacting proteins.

Main Results:

msVolcano generates volcano plots with lists of interacting proteins.
Users can adjust cutoff values to refine the identification of significant interactors.
Publication-quality graphical and tabular outputs are available for download.
The tool supports flexible customization for diverse research needs.

Conclusions:

msVolcano offers an efficient and flexible platform for visualizing and analyzing label-free mass spectrometry interactomics data.
The application simplifies the identification of meaningful protein interactions and their quantitative characteristics.
It serves as a valuable resource for researchers in proteomics and systems biology.