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Updated: Mar 24, 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

Protein inference: A protein quantification perspective.

Zengyou He¹, Ting Huang², Xiaoqing Liu³

¹School of Software, Dalian University of Technology, Dalian, China; Key Laboratory for Ubiquitous Network and Service Software of Liaoning, Dalian, China.

Computational Biology and Chemistry

|March 4, 2016

Summary

This summary is machine-generated.

This study proposes a novel approach to protein inference in mass spectrometry-based proteomics by reframing it as a protein quantification problem. Three quantification methods effectively solved the inference task, demonstrating a new perspective for algorithm development.

Keywords:

Linear programming Protein inference Protein quantification Shotgun proteomics Spectral counting

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Last Updated: Mar 24, 2026

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Area of Science:

Proteomics
Computational Biology
Mass Spectrometry

Background:

Protein identification and quantification are key computational challenges in mass spectrometry-based shotgun proteomics.
Current methods address protein inference and quantification separately.
A conceptual link exists between protein inference and quantification, where present proteins have non-zero abundance.

Purpose of the Study:

To investigate the feasibility of using protein quantification methods to solve the protein inference problem.
To demonstrate that protein inference can be modeled as a special case of protein quantification.
To develop and evaluate novel protein inference algorithms from a quantification perspective.

Main Methods:

Framing protein inference as a quantification problem where absent proteins have zero abundance.
Applying three simple protein quantification methods to address protein inference.
Experimental validation on six diverse datasets.

Main Results:

The three tested quantification methods proved competitive with existing protein inference algorithms.
The experimental results support the hypothesis that protein inference can be effectively modeled as a quantification task.
The study provides a new perspective for developing more effective protein inference algorithms.

Conclusions:

Protein inference can be successfully modeled as a special protein quantification problem.
This novel approach opens avenues for developing advanced protein inference algorithms.
The proposed methods offer a competitive alternative to traditional protein inference techniques.