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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: Jun 30, 2025

Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry

Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry

Published on: October 24, 2018

CloudProteoAnalyzer: scalable processing of big data from proteomics using cloud computing.

Jiancheng Li¹, Yi Xiong², Shichao Feng¹

¹Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States.

Bioinformatics Advances

|March 18, 2024

Summary

This summary is machine-generated.

Researchers can now process large proteomics datasets faster using a new cloud-based Software as a Service (SaaS) application. This scalable solution addresses big data challenges in system biology and mass spectrometry analysis.

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Last Updated: Jun 30, 2025

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Area of Science:

Proteomics
System Biology
Bioinformatics

Background:

Shotgun proteomics is crucial for global protein expression profiling in various biological systems.
Traditional local data processing methods struggle with the rapidly increasing volume of proteomics data.
The big data challenge hinders timely analysis in large-scale system biology studies.

Purpose of the Study:

To develop a scalable, cloud-based solution for end-to-end proteomics data analysis.
To provide a user-friendly Software as a Service (SaaS) application for researchers.
To overcome the limitations of local computing for large proteomics datasets.

Main Methods:

Developed a cloud-based parallel computing application accessible via a web interface.
Implemented a Software as a Service (SaaS) model for proteomics data analysis.
Utilized distributed computing across multiple supercomputer nodes for scalability.

Main Results:

Demonstrated a viable cloud computing solution for large-scale proteomics data processing.
Enabled users to upload data, configure parameters, and monitor jobs through a web interface.
Achieved scalability for handling rapidly growing mass spectrometry datasets.

Conclusions:

Cloud-based SaaS offers a scalable and efficient approach to proteomics data analysis.
The developed application effectively addresses the big data challenges in system biology.
This solution empowers researchers to conduct timely and comprehensive proteomic studies.