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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 proteomics...

Peptide Identification Using Tandem Mass Spectrometry

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Related Experiment Video

Updated: Jun 2, 2026

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

A computer-based technique for automated spot detection in proteomics images.

Eleftheria A Mylona¹, Michalis A Savelonas, Dimitris Maroulis

¹Realtime Systems and Image Analysis Group, Department of Informatics and Telecommunications, University of Athens, 15784 Athens,Greece. emylona@di.uoa.gr

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society

|April 12, 2011

Summary

This study presents a new automated method for detecting protein spots in proteomics images. The technique accurately identifies spots, minimizes errors, and outperforms existing software.

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Area of Science:

Proteomics
Computational Biology
Image Analysis

Background:

Accurate protein spot detection is crucial in proteomics.
Existing methods may struggle with noise, artifacts, and overlapping spots.

Purpose of the Study:

To develop a novel, automated computer-based technique for protein spot detection in proteomics images.
To improve accuracy, reduce false negatives, and handle complex image features like overlapping spots and artifacts.

Main Methods:

Localization of regional intensity maxima corresponding to protein spots.
Implementation of constraints to manage noise and artifacts.
Formulation to ignore rectangular streaks and detect overlapping spots.

Main Results:

Achieved predictive value (PV) and detection sensitivity (DS) exceeding 90%.
Outperformed the Melanie software package in PV, specificity, and DS.
Successfully ignored artifacts, distinguished overlapping spots, and located spots within streaks.

Conclusions:

The proposed automated technique is efficient and accurate for protein spot detection.
It offers superior performance compared to existing software, particularly in challenging image conditions.
This method enhances the reliability of quantitative proteomics analysis.