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Peptide Identification Using Tandem Mass Spectrometry

Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...

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Updated: May 11, 2026

Quantitative Analysis of Chromatin Proteomes in Disease

Quantitative Analysis of Chromatin Proteomes in Disease

Published on: December 28, 2012

A combinatorial approach to the peptide feature matching problem for label-free quantification.

Hao Lin¹, Lin He, Bin Ma

¹David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1.

Bioinformatics (Oxford, England)

|May 14, 2013

Summary

This study introduces a novel combinatorial model for peptide feature matching in label-free quantification. The proposed method enhances biomarker discovery by accurately aligning and matching peptide features across datasets, outperforming existing approaches.

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Last Updated: May 11, 2026

Quantitative Analysis of Chromatin Proteomes in Disease

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Published on: December 28, 2012

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

Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Label-free quantification is crucial for identifying biomarkers by measuring peptide quantity changes.
Accurate peptide feature matching between datasets is a fundamental, yet challenging, step.
Existing software tools lack a robust combinatorial model for this matching problem.

Purpose of the Study:

To develop a combinatorial model for peptide feature matching in label-free quantification.
To address the limitations of current ad hoc software tools.
To improve the accuracy and efficiency of biomarker discovery.

Main Methods:

A combinatorial model is proposed, utilizing mass and retention time values for feature pairing.
The problem is framed as a maximum-weighted matching problem on a bipartite graph.
A time alignment function is incorporated, rendering the problem NP-hard.
Practical algorithms are developed and implemented.

Main Results:

The proposed combinatorial model effectively matches peptide features across datasets.
The developed algorithms demonstrate favorable performance compared to existing methods in experiments with real data.
The study proves the NP-hard nature of the time-aligned feature matching problem.

Conclusions:

The novel combinatorial model provides a significant advancement for peptide feature matching in label-free quantification.
The developed algorithms offer a practical and effective solution for biomarker identification.
This work lays the foundation for more sophisticated computational approaches in proteomics data analysis.