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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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Related Experiment Video

Updated: Jun 27, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Integrated multi-level quality control for proteomic profiling studies using mass spectrometry.

David A Cairns1, David N Perkins, Anthea J Stanley

  • 1Cancer Research UK Clinical Centre, Leeds Institute of Molecular Medicine, St James's University Hospital, Leeds, UK. d.a.cairns@leeds.ac.uk

BMC Bioinformatics
|December 6, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces quality control methods for mass spectrometry (MS) proteomic profiling to identify and remove low-quality spectra. Implementing these quality control (QC) processes enhances biomarker discovery and inter-experimental comparisons.

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Last Updated: Jun 27, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling
09:35

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

Published on: April 1, 2017

Area of Science:

  • Proteomics and Mass Spectrometry
  • Biomarker Discovery
  • Bioinformatics and Data Analysis

Background:

  • Proteomic profiling using mass spectrometry (MS) is crucial for biomarker discovery in complex biological samples like serum, urine, and tissue.
  • Techniques such as MALDI-TOF and SELDI-TOF MS are commonly employed for protein expression analysis.
  • A critical, often overlooked aspect is the implementation of robust quality control (QC) processes to ensure data reliability.

Purpose of the Study:

  • To describe rigorous methods for assessing the quality of spectral data in proteomic profiling experiments.
  • To develop a user-friendly, web-based program for implementing these QC procedures.
  • To examine post-QC data using variance components analysis to quantify experimental design factors.

Main Methods:

  • Development and application of rigorous algorithms for the assessment of spectral data quality in MS-based proteomic profiling.
  • Utilizing a web-based program for user-friendly implementation of quality control (QC) procedures.
  • Employing variance components analysis to quantify sources of variation in experimental data.

Main Results:

  • Algorithms effectively detected systematic variability within and between sample replicates, pooled samples, and SELDI chips/spots in a SELDI-TOF MS study of serum.
  • Manual inspection confirmed the efficacy of the algorithms in identifying poor-quality spectral data.
  • Variance components analysis revealed minimal technical variance attributable to factors like day of profile generation and experimental array.

Conclusions:

  • The described techniques reliably detect and enable the removal of poor-quality data in MS proteomic profiling.
  • Removing low-quality spectra early in the analysis significantly enhances confidence in putative biomarker identification.
  • The QC methods facilitate more reliable inter-experimental comparisons, improving the overall robustness of proteomic studies.