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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...
Proteomics01:33

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...

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Quantification of Proteins Using Peptide Immunoaffinity Enrichment Coupled with Mass Spectrometry
06:09

Quantification of Proteins Using Peptide Immunoaffinity Enrichment Coupled with Mass Spectrometry

Published on: July 31, 2011

A probabilistic framework for peptide and protein quantification from data-dependent and data-independent LC-MS

Keith Richardson1, Richard Denny, Chris Hughes

  • 1Waters Corporation, Floats Road, Wythenshawe, Manchester, UK. keith_richardson@waters.com

Omics : a Journal of Integrative Biology
|August 9, 2012
PubMed
Summary

This study introduces a probability-based framework for quantifying relative peptide and protein abundance in mass spectrometry proteomics data. The method accounts for data uncertainty, providing credible intervals and regulation probabilities for robust analysis.

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Computational Biology

Background:

  • Accurate quantification of peptide and protein abundance is crucial in proteomics.
  • Existing methods may not fully account for data uncertainties and variable peptide assignments.

Purpose of the Study:

  • To develop a probability-based quantification framework for label-free and label-dependent LC-MS proteomics data.
  • To provide credible intervals and regulation probabilities alongside abundance estimations.

Main Methods:

  • Utilizes a modified Poisson statistics approach to incorporate data uncertainties.
  • Employs a novel algorithm that accepts prior probabilities for peptide assignments and automatically reweights outliers.
  • Offers two discrete normalization methods: user-defined peptide subsets or endogenous peptide background.

Main Results:

  • The framework calculates relative peptide and protein abundance with associated credible intervals and regulation probabilities.
  • Demonstrates robust quantification by automatically handling data uncertainties and variable peptide assignments.
  • Illustrates performance on example datasets for typical proteomics applications.

Conclusions:

  • The presented framework offers a reliable method for relative protein quantification in diverse LC-MS proteomics datasets.
  • The algorithm's ability to handle data uncertainties and peptide assignment variability enhances quantitative accuracy.
  • This approach supports both label-free and various label-dependent quantification strategies.