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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: Jul 8, 2025

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

A data analysis framework for combining multiple batches increases the power of isobaric proteomics experiments.

Jonathon J O'Brien¹, Anil Raj², Aleksandr Gaun²

¹Calico Life Sciences LLC, South San Francisco, CA, USA. obrien@golgistat.com.

|December 19, 2023

Summary

This study introduces msTrawler, a novel framework for analyzing multiplexed mass spectrometry proteomics data. It enhances the detection of subtle biological changes and improves proteome coverage by accounting for data variations across batches.

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Last Updated: Jul 8, 2025

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

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Published on: November 15, 2017

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

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Published on: April 1, 2017

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

Published on: April 18, 2025

Area of Science:

Proteomics
Mass Spectrometry
Bioinformatics

Background:

Analyzing multiplexed mass spectrometry proteomics data is challenging due to variations in observation numbers and quality across batches.
Combining data from multiple isobaric batches often leads to estimation errors, complicating statistical analyses.
Existing methods may struggle with missing data or discard valuable low-signal measurements.

Purpose of the Study:

To present a computational framework that reduces estimation error in multiplexed mass spectrometry proteomics data analysis.
To improve the statistical power for detecting biological associations, particularly for subtle changes.
To enhance quantitative proteome coverage by effectively utilizing all available data, including low-signal observations.

Main Methods:

Development of a novel framework utilizing statistical models that explicitly account for known sources of variation across batches.
Implementation of an open-source software tool, msTrawler, to apply the proposed framework.
Benchmarking the software on a multi-batch experiment and previously published datasets.

Main Results:

The msTrawler framework significantly increases the power to detect statistical associations, especially for changes less than twofold.
Quantitative proteome coverage is enhanced by incorporating more low-signal observations.
The method effectively handles complex missing data patterns without imputation or data discarding.

Conclusions:

The proposed framework and msTrawler software offer a robust solution for analyzing complex multiplexed mass spectrometry proteomics data.
This approach improves the reliability and depth of proteomic analyses, facilitating more sensitive biological discovery.
The software provides a valuable tool for researchers dealing with multi-batch proteomics datasets and missing data challenges.