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Related Concept Videos

Peptide Identification Using Tandem Mass Spectrometry01:33

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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Related Experiment Video

Updated: Jun 11, 2026

Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot
10:12

Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot

Published on: October 28, 2021

Comparative shotgun proteomics using spectral count data and quasi-likelihood modeling.

Ming Li1, William Gray, Haixia Zhang

  • 1Department of Biostatistics, Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee 37232-8575, USA.

Journal of Proteome Research
|July 1, 2010
PubMed
Summary
This summary is machine-generated.

Shotgun proteomics can now reliably compare complex proteomes using a new statistical analysis strategy. This method accurately identifies protein expression differences between biological samples, improving proteomic data analysis.

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Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples
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Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples

Published on: November 13, 2021

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Last Updated: Jun 11, 2026

Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot
10:12

Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot

Published on: October 28, 2021

Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples
14:51

Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples

Published on: November 13, 2021

Area of Science:

  • Proteomics
  • Bioinformatics
  • Mass Spectrometry

Background:

  • Shotgun proteomics using liquid chromatography-tandem mass spectrometry (LC-MS/MS) is powerful for global proteome analysis.
  • Current methods face challenges in accurately comparing proteomes due to incomplete sampling and variability in spectral counting.
  • This variability complicates the assessment of protein inventories across different biological states.

Purpose of the Study:

  • To develop and validate a robust statistical analysis strategy for comparing shotgun proteomic data.
  • To address the limitations of existing methods in identifying differential protein expression between complex biological samples.
  • To improve the reliability and accuracy of proteomic data interpretation.

Main Methods:

  • Developed a quasi-likelihood Generalized Linear Modeling (GLM) strategy implemented in the software package QuasiTel.
  • Compared the new GLM approach with Student t test, Wilcoxon rank test, Fisher's Exact test, and Poisson-based GLM.
  • Validated the method using a spiked protein dataset and by comparing normal tonsil epithelium with head and neck squamous cell carcinoma (HNSCC) proteomes.
  • Verified differential protein expression using liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM-MS).

Main Results:

  • The quasi-likelihood GLM and Fisher Exact test showed adequate performance, with GLM approaches having unique advantages.
  • Identified 86 proteins with differential spectral counts between normal tonsil epithelium and HNSCC.
  • LC-MRM-MS confirmed the magnitude and direction of expression differences for 6 proteins, validating the shotgun proteomics findings.
  • Demonstrated that shotgun proteomic datasets contain sufficient quantitative information for statistically significant biological insights.

Conclusions:

  • The developed quasi-likelihood GLM strategy enhances the ability to compare complex proteomes from different biological states.
  • This approach effectively identifies statistically significant protein expression differences, reflecting underlying biological variations.
  • Shotgun proteomics data, when analyzed appropriately, can yield reliable quantitative information for biological discovery.