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Related Concept Videos

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...
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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Updated: Jun 18, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Proteomics facing the combinatorial problem.

Rune Matthiesen1, António Amorim

  • 1Instituto de Patologia e Imunologia Molecular da Universidad do Porto - IPATIMUP, Porto, Portugal.

Methods in Molecular Biology (Clifton, N.J.)
|December 4, 2009
PubMed
Summary
This summary is machine-generated.

Accurate proteomics search strategies are crucial for clinical applications. This chapter details search algorithms, addressing challenges from amino acid modifications and substitutions to prevent data misinterpretation.

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

Published on: November 15, 2017

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Last Updated: Jun 18, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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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

Area of Science:

  • Proteomics
  • Mass Spectrometry Data Analysis

Background:

  • Numerous scoring functions exist for peptide identification in MS/MS spectra.
  • Search strategies and algorithms for proteomics data analysis are less explored and incompletely described.
  • Clinical applications of proteomics require robust methods to handle combinatorial complexities.

Purpose of the Study:

  • To provide a detailed discussion of various search algorithm strategies in proteomics.
  • To highlight the importance of search strategies in accurate peptide identification.
  • To address the challenges posed by amino acid modifications and single amino acid substitutions (SAPs) in MS/MS data analysis.

Main Methods:

  • Review and detailed discussion of different MS/MS search algorithm strategies.
  • Exploration of how search algorithms handle combinatorial problems arising from modifications and SAPs.
  • Analysis of the impact of search strategy on the accuracy of peptide matching.

Main Results:

  • Identified a gap in the literature regarding comprehensive descriptions of proteomics search algorithms.
  • Highlighted that inadequate search strategies can lead to incorrect peptide matching and downstream errors.
  • Emphasized the critical need to consider amino acid modifications and SAPs within search algorithms for reliable results.

Conclusions:

  • Effective search strategies are as vital as scoring functions for accurate peptide identification in proteomics.
  • Addressing combinatorial challenges in search algorithms is essential for the success of clinical proteomics.
  • Further research and detailed descriptions of search algorithms are needed to improve the reliability of proteomics data analysis.