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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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Updated: Aug 6, 2025

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Published on: November 17, 2019

promor: a comprehensive R package for label-free proteomics data analysis and predictive modeling.

Chathurani Ranathunge¹, Sagar S Patel¹, Lubna Pinky¹

¹Eastern Virginia Medical School, School of Health Professions, Norfolk, VA 23501, USA.

Bioinformatics Advances

|March 16, 2023

Summary

The promor R package simplifies label-free quantification proteomics data analysis and the creation of predictive models using machine learning. It offers a user-friendly interface for identifying top protein candidates.

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Area of Science:

Proteomics
Bioinformatics
Machine Learning

Background:

Label-free quantification (LFQ) proteomics is crucial for identifying protein expression changes.
Analyzing large LFQ datasets and building predictive models can be complex and time-consuming.

Purpose of the Study:

To introduce promor, an R package designed to streamline LFQ proteomics data analysis.
To facilitate the development of machine learning-based predictive models using top protein candidates from proteomics data.

Main Methods:

Development of a comprehensive and user-friendly R package named promor.
Integration of tools for label-free quantification proteomics data analysis.
Implementation of machine learning algorithms for predictive modeling based on protein candidates.

Main Results:

promor provides a streamlined workflow for LFQ proteomics data analysis.
The package enables efficient building of predictive models with identified protein candidates.
It enhances the accessibility and usability of complex proteomics data analysis.

Conclusions:

promor offers a valuable, open-source tool for the proteomics research community.
The package simplifies complex analyses, promoting wider adoption of machine learning in proteomics.
It supports researchers in identifying key protein biomarkers and building predictive signatures.