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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 proteomics...

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Related Experiment Video

Updated: Jun 13, 2026

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

Enhancing Proteomics Quality Control: Insights from the Visualization Tool QCeltis.

Manasa Vegesna^1,2, Niveda Sundararaman^1,2, Ajay Bharadwaj^1,2

¹Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States.

Journal of Proteome Research

|February 24, 2025

Summary

QCeltis is a new Python package for automated quality control in large-scale proteomics. It helps identify technical biases and verify consistency in mass spectrometry data, improving research reliability.

Keywords:

DIA mass spectrometry proteomics quality control visualization tool

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Last Updated: Jun 13, 2026

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

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Published on: November 15, 2017

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

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Published on: June 15, 2018

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

Area of Science:

Proteomics
Analytical Chemistry
Bioinformatics

Background:

Large-scale mass-spectrometry-based proteomics experiments are complex and prone to analytical variability.
Rigorous quality control (QC) is essential throughout the workflow, from sample preparation to bioinformatics.
Existing QC approaches often focus on outlier detection and instrument performance monitoring.

Purpose of the Study:

To introduce QCeltis, a Python package for automated QC analysis in proteomics.
To facilitate the identification of technical biases and verify consistency in large-scale proteomics data.
To aid in differentiating between QC issues and batch effects in data-independent acquisition (DIA) proteomics.

Main Methods:

Development of the QCeltis Python package for automated QC analysis.
Application of QCeltis to various proteomics workflows, including sample preparation and mass spectrometry.
Utilizing command-line interface for Windows and Linux environments.
Case studies involving depleted plasma, whole blood vs. plasma, and dried blood spot samples.

Main Results:

QCeltis effectively automates QC analysis across the proteomics workflow.
The package aids in identifying sample preparation and acquisition issues.
QCeltis assists in distinguishing between QC issues and batch effects in DIA proteomics data.
Demonstrated utility across diverse sample types.

Conclusions:

QCeltis enhances data reliability in large-scale proteomics projects.
The package enables more nuanced downstream data analysis and interpretation.
QCeltis is a valuable tool for ensuring consistency and identifying biases in proteomics research.