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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: Jan 12, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Variant Classification Using Proteomics-Informed Large Language Models Increases Power of Rare Variant Association

Christopher E Gillies¹, Joelle Mbatchou¹, Lukas Habegger¹

¹Regeneron Genetics Center, Tarrytown, New York, USA.

Genetic Epidemiology

|November 3, 2025

Summary

This summary is machine-generated.

Large language models (LLMs) for predicting damaging genetic variants are improved using proteomics data. This refined approach enhances the discovery of gene-trait associations in human genetic studies.

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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

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Published on: August 20, 2019

Area of Science:

Genetics
Bioinformatics
Proteomics

Background:

Rare variant association analysis is crucial for understanding human biology.
Predicting the impact of genetic variants is essential for this analysis.
Deep learning and large language models (LLMs) show promise in variant impact prediction.

Purpose of the Study:

To evaluate and refine LLM predictors of damaging genetic variants using proteomics data.
To assess the performance of a proteomics-guided LLM in identifying gene-trait associations.

Main Methods:

Utilized proteomics data from 46,665 individuals across 2898 proteins.
Developed and refined LLM-based variant predictors.
Evaluated model performance on 241 positive control gene-trait pairs and 10 UK Biobank traits.

Main Results:

The proteomics-guided LLM outperformed conventional and machine learning methods in identifying damaging missense variants.
The model recapitulated 36.5% of known gene-trait associations, exceeding alternatives.
177 novel gene-trait associations were identified using the model on UK Biobank data.

Conclusions:

Proteomics data can effectively refine LLM variant classification.
This approach significantly improves the discovery potential of human genetic studies.