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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: May 9, 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

Large scale proteomic studies create novel privacy considerations.

Andrew C Hill¹, Claire Guo¹, Elizabeth M Litkowski²

¹National Jewish Health, Denver, CO, USA.

Scientific Reports

|June 7, 2023

Summary

This summary is machine-generated.

Linking proteomic data to genomes is now possible with high accuracy using genotype information. This breakthrough in omics research enhances data integrity and supports diverse population studies.

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Area of Science:

Genomics
Proteomics
Biostatistics

Background:

Genomic research prioritizes privacy, unlike proteomic research.
Linking proteomic data to specific individuals was previously challenging.

Purpose of the Study:

To develop and validate a method for linking proteomic data to individual genomes.
To assess the accuracy of this method across diverse populations.

Main Methods:

Identified single nucleotide polymorphism (SNP) quantitative trait loci (pQTL).
Calculated protein level genotype probabilities.
Applied a Bayesian approach to link SomaScan proteomes to genomes in large cohorts.

Main Results:

Achieved 90-95% accuracy in linking proteomes to genomes, with 95-99% identifying the top 1% of links.
Linking accuracy improved to >99% with larger proteomic profiling and diverse training data.
Successfully inferred biological features like sex and ancestry from proteomic data alone.

Conclusions:

Large-scale proteomic datasets can be accurately linked to genomes using pQTLs.
Diverse population data is crucial for robust omics linking.
This method can identify and correct mislabeled samples, improving data quality.