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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: Jun 23, 2025

Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot

Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot

Published on: October 28, 2021

Koina: Democratizing machine learning for proteomics research.

Ludwig Lautenbacher¹, Kevin L Yang², Tobias Kockmann³

¹Computational Mass Spectrometry, Technical University of Munich (TUM), Freising, Germany.

Biorxiv : the Preprint Server for Biology

|June 19, 2024

Summary

This summary is machine-generated.

Koina is an open-source service making machine-learning (ML) and deep-learning (DL) models accessible for proteomics. This integration enhances data analysis pipelines, improving peptide identification and spectral library generation.

Keywords:

AI in life science DDA DIA FAIR PRM deep learning democratization federated machine learning mass spectrometry peptide property predictions proteomics web service

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Last Updated: Jun 23, 2025

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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

Area of Science:

Proteomics
Computational Biology
Bioinformatics

Background:

Machine-learning (ML) and deep-learning (DL) offer significant potential for advancing proteomics applications, including spectral library generation, peptide identification, and targeted acquisition.
Despite frequent publication of new ML/DL models, community adoption remains slow due to technical implementation challenges.

Purpose of the Study:

To address the slow adoption of ML/DL models in proteomics by enhancing their usability and accessibility.
To develop and demonstrate an accessible platform for integrating advanced ML/DL models into existing proteomics workflows.

Main Methods:

Development of Koina, an open-source, containerized, decentralized, and online-accessible high-performance prediction service.
Integration of Koina with the FragPipe computational platform to showcase its compatibility with existing proteomics software.

Main Results:

Koina enables seamless integration of ML/DL models into various proteomics software pipelines.
Demonstrated improvement in data analysis through the integration of Koina with existing tools like FragPipe.

Conclusions:

Koina facilitates broader community access and utilization of state-of-the-art ML/DL models in proteomics.
The developed service streamlines the application of advanced computational methods, enhancing the efficiency and scope of proteomics data analysis.