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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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Updated: Jun 22, 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

A statistical framework for protein quantitation in bottom-up MS-based proteomics.

Yuliya Karpievitch¹, Jeff Stanley, Thomas Taverner

¹Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, USA.

Bioinformatics (Oxford, England)

|June 19, 2009

Summary

This summary is machine-generated.

This study introduces a new statistical model for quantitative proteomics, improving protein-level estimation by accounting for missing data. The model enhances discovery rates compared to standard methods.

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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

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Last Updated: Jun 22, 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

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
Quantitative Mass Spectrometry
Statistical Modeling

Background:

Quantitative mass spectrometry-based proteomics necessitates accurate protein-level estimates and confidence measures.
Key challenges include low-quality peptide identifications, missing data, and aggregating peptide information to the protein level.

Purpose of the Study:

To develop a statistical model for unbiased, model-based protein-level estimation and inference in quantitative proteomics.
To address challenges of informative missingness in peak intensities and integrate peptide-level data.

Main Methods:

A novel statistical model is presented that accounts for informative missingness in peak intensities.
The model supports both label-based and label-free quantitative proteomics experiments.
Automated, model-based algorithms for protein/peptide filtering and missing value imputation are provided.

Main Results:

The developed model enables unbiased, model-based protein-level estimation and inference.
Demonstrated applicability to both label-based and label-free quantitative mass spectrometry data.
Simulation studies show substantially increased discovery rates compared to standard methods.

Conclusions:

The statistical model effectively handles informative missingness for improved protein quantification.
Automated algorithms facilitate data processing and enhance the reliability of proteomics results.
The approach offers a significant advancement for quantitative proteomics analysis.