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Proteomics

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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.
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Related Experiment Video

Updated: Apr 16, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

RepExplore: addressing technical replicate variance in proteomics and metabolomics data analysis.

Enrico Glaab¹, Reinhard Schneider¹

¹Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg.

Bioinformatics (Oxford, England)

|February 27, 2015

Summary

RepExplore is a new web service that uses technical replicate variance in omics data to improve differential expression and abundance statistics. It offers automated analysis and visualizations for more reliable omics data interpretation.

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Last Updated: Apr 16, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

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Published on: November 10, 2023

Quantitative Analysis of Chromatin Proteomes in Disease

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Published on: December 28, 2012

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Area of Science:

Genomics
Proteomics
Metabolomics
Systems Biology

Background:

High-throughput omics datasets frequently include technical replicates to manage measurement noise.
Summarizing replicates with averages can reduce noise but discards valuable variance information.
This lost variance information is crucial for robust statistical analyses in omics studies.

Purpose of the Study:

To introduce RepExplore, a web service designed to leverage technical replicate variance for enhanced omics data analysis.
To provide more reliable and informative differential expression and abundance statistics.
To make advanced statistical methods accessible to researchers without extensive programming experience.

Main Methods:

RepExplore utilizes previously established statistical methods for analyzing technical replicate variance.
The service automates data processing, including interactive ranking tables.
It offers visualizations such as whisker plots, heat maps, and principal component analysis.

Main Results:

RepExplore exploits technical replicate variance to yield more dependable omics statistics.
The web service simplifies the application of complex statistical approaches.
It facilitates comprehensive interpretation of omics data and derived statistics.

Conclusions:

RepExplore enhances the reliability of omics data analysis by incorporating technical replicate variance.
The tool democratizes access to advanced statistical methods for omics research.
It provides a user-friendly platform for interpreting complex biological datasets.