Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Evaluating the Performance of Photon- and Electron-Based Fragmentation Methods in Omnitrap-LCMS Analysis of <i>N</i>-Glycopeptides.

Analytical chemistry·2026

Same author

Spatial distribution of the proteome in the human body and in cancers.

Nature·2026

Same author

Integrated proteogenomic and metabolomic profiling of acute myeloid leukemias to identify molecular subtypes and associated therapy targets.

Nature cancer·2026

Same author

iDeepLC: Chemical Structure Information Yields Improved Retention Time Prediction of Peptides with Unseen Modifications.

Analytical chemistry·2026

Same author

Predicting Discrete Structural Transformations in Small Molecules from Tandem Mass Spectrometry.

bioRxiv : the preprint server for biology·2026

Same author

Glucuronidation metabolomic fingerprinting to map host-microbe metabolism.

Nature communications·2026

Same journal

Inherent tissue homeostasis of the juvenile metaphysis provides a foundation for osteosarcoma development.

Nature communications·2026

Same journal

Slowing the spread of treatment failure to artemisinin-based combination therapies in Uganda.

Nature communications·2026

Same journal

Harnessing interfacial click polymerization using pyridinium-yne films as photochromic, radical generation and sensing platforms.

Nature communications·2026

Same journal

Potential role of a CRISPR-Cas-activated toxin-antitoxin system in bacterial immunity.

Nature communications·2026

Same journal

Diverse radical transformations of allenic C(sp)-C(sp<sup>2</sup>) and C(sp<sup>3</sup>)-C(sp<sup>2</sup>) bonds enabled by silyl substitution.

Nature communications·2026

Same journal

Experience-dependent modulation of collective behavior in larval zebrafish.

Nature communications·2026

See all related articles

Search research articles

Home
Koina: Democratizing Machine Learning For Proteomics Research.

Home
Koina: Democratizing Machine Learning For Proteomics Research.

Related Experiment Video

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

Koina: Democratizing machine learning for proteomics research.

Ludwig Lautenbacher^1,2, Kevin L Yang³, Tobias Kockmann⁴

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

Nature Communications

|November 11, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

We introduce Koina, an open-source repository for machine learning (ML) models in proteomics. Koina enhances model accessibility and integration into data analysis pipelines, accelerating ML adoption in the field.

More Related Videos

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

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Related Experiment Videos

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

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

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Machine learning (ML) and deep learning show promise for proteomics applications like spectral library generation and peptide identification.
Slow adoption of new ML models in proteomics is hindered by poor findability, accessibility, and integration challenges.
Existing proteomics software often lacks straightforward methods for incorporating novel ML models.

Purpose of the Study:

To present Koina, an open-source, decentralized, and online-accessible repository for ML models in proteomics.
To facilitate the publication, discovery, and usability of ML models within the proteomics community.
To demonstrate the seamless integration of ML models into existing proteomics data analysis workflows.

Main Methods:

Development of Koina, an open-source, decentralized, and online-accessible platform for ML model repository.

Implementation of an easy-to-use online interface for accessing and utilizing ML models.

Integration of Koina with the FragPipe computational platform for proteomics data analysis.

Main Results:

Koina provides a centralized solution for ML model findability and accessibility in proteomics.
The platform enables straightforward integration of ML models into established data analysis pipelines.
Demonstrated successful integration with FragPipe, showcasing improved proteomics data analysis capabilities.

Conclusions:

Koina significantly lowers the barrier for adopting ML models in proteomics research.
The repository fosters reproducibility and reusability of ML models for end-users.
Koina represents a key advancement in computational proteomics, enabling broader application of ML.