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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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Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot
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Analyzing shotgun proteomic data with PatternLab for proteomics.

Paulo C Carvalho1,2, John R Yates Iii2, Valmir C Barbosa1

  • 1Systems Engineering and Computer Science Program, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.

Current Protocols in Bioinformatics
|June 4, 2010
PubMed
Summary
This summary is machine-generated.

PatternLab for proteomics offers a user-friendly computational environment for analyzing shotgun proteomic data. It identifies differentially expressed and unique proteins/peptides, clusters expression profiles, and aids in Gene Ontology interpretation.

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Last Updated: Jun 12, 2026

Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot
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Published on: October 28, 2021

Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples
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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Shotgun proteomics generates complex datasets requiring specialized analysis tools.
  • Identifying differentially expressed and unique proteins is crucial for understanding biological states.
  • Interpreting large-scale proteomic data, including time-course experiments, poses a significant challenge.

Purpose of the Study:

  • To introduce PatternLab, a comprehensive computational environment for shotgun proteomic data analysis.
  • To provide tools for identifying differentially expressed and state-specific proteins/peptides.
  • To facilitate the interpretation of proteomic results using Gene Ontology.

Main Methods:

  • PatternLab integrates multiple modules for data analysis.
  • The software enables pinpointing differentially expressed proteins and peptides.
  • It offers clustering of proteins with similar expression profiles in time-course studies.

Main Results:

  • PatternLab successfully identifies proteins and peptides that are differentially expressed between conditions.
  • The environment can distinguish proteins unique to specific biological states.
  • Gene Ontology-based interpretation of results is supported.

Conclusions:

  • PatternLab provides a user-friendly, graphical interface for comprehensive shotgun proteomic data analysis.
  • The software aids in identifying key proteins and understanding their biological relevance.
  • It simplifies complex proteomic data interpretation, including time-course experiments.