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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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TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

Published on: June 8, 2020

Is DIA proteomics data FAIR? Current data sharing practices, available bioinformatics infrastructure and

Andrew R Jones¹, Eric W Deutsch², Juan Antonio Vizcaíno³

¹Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.

|September 8, 2022

Summary

Data independent acquisition (DIA) proteomics requires better FAIR data principles. Recommendations include open data standards for spectral libraries and improved ProteomeXchange support for DIA data.

Keywords:

data independent acquisition data repositories data standards proteomics data spectral libraries

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Last Updated: Aug 29, 2025

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

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Published on: June 8, 2020

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

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Published on: April 11, 2019

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Published on: January 20, 2022

Area of Science:

Proteomics
Bioinformatics
Data Science

Background:

Data independent acquisition (DIA) proteomics has advanced significantly due to instrumentation and data analysis improvements.
Current public databases and data standards for proteomics are primarily designed for data-dependent acquisition (DDA) data, limiting FAIR data principles for DIA.
There is a need to enhance the Findability, Accessibility, Interoperability, and Reusability (FAIR) of DIA proteomics data.

Purpose of the Study:

To assess the current state of FAIR data principles in DIA proteomics.
To propose recommendations for improving FAIR data practices specifically for DIA proteomics data.

Main Methods:

Review of existing literature and data standards in proteomics.
Analysis of the applicability of current FAIR data principles to DIA techniques.
Formulation of recommendations based on identified gaps.

Main Results:

DIA proteomics data currently faces challenges in adhering to FAIR principles.
Existing data standards and public databases are not fully optimized for DIA data.
Specific areas for improvement include spectral library standards, data sharing practices, and metadata requirements.

Conclusions:

Enhancing FAIR data principles is crucial for the advancement of DIA proteomics.
Key recommendations include developing open spectral library standards, mandating spectral library availability in ProteomeXchange, and improving DIA data support in Proteomics Standards Initiative standards and ProteomeXchange.
Implementing these recommendations will improve data sharing and reusability in DIA proteomics research.