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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: May 12, 2025

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Published on: April 11, 2019

Open-Source and FAIR Research Software for Proteomics.

Yasset Perez-Riverol¹, Wout Bittremieux², William S Noble³

¹European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SD, U.K.

Journal of Proteome Research

|April 23, 2025

Summary

This summary is machine-generated.

Open-source software (OSS) is crucial for advancing proteomics research. By embracing OSS and FAIR principles, the field can overcome challenges and foster a more transparent and collaborative scientific community.

Keywords:

FAIR principles best practices computational proteomics data reuse mass spectrometry open data open source proteomics

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Last Updated: May 12, 2025

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

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Area of Science:

Computational biology
Proteomics software development

Background:

Scientific discovery, particularly in proteomics, heavily relies on sophisticated software for tasks like mass spectrometry analysis.
The current landscape of proteomics software faces challenges including data complexity, reproducibility issues with proprietary tools, and integration difficulties with other omics data.
Closed-source software limits innovation, introduces inefficiencies, and incurs hidden costs for the research community.

Purpose of the Study:

To explore the pivotal role of open-source software (OSS) in computational proteomics.
To examine how OSS aligns with the FAIR Principles (Findable, Accessible, Interoperable, Reusable).
To present a vision for a future proteomics community driven by OSS and FAIR principles.

Main Methods:

Literature review and analysis of existing proteomics software.
Examination of the benefits and challenges of OSS in scientific research.
Discussion of the application of FAIR Principles to proteomics software.

Main Results:

Open-source software promotes transparency, reproducibility, and community-driven innovation in proteomics.
Adherence to FAIR Principles enhances the accessibility and interoperability of proteomics tools.
OSS offers a viable solution to the limitations imposed by proprietary software.

Conclusions:

Open-source software is essential for addressing current challenges in computational proteomics.
Implementing OSS and FAIR principles can lead to a more collaborative, transparent, and innovative proteomics ecosystem.
Adoption of OSS and FAIR principles will accelerate scientific discovery in proteomics and facilitate integration with other omics fields.