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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 13, 2026

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions
09:00

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Published on: April 18, 2025

PepC: proteomics software for identifying differentially expressed proteins based on spectral counting.

N L Heinecke1, B S Pratt, T Vaisar

  • 1Insilicos LLC, Seattle, WA 98109, USA.

Bioinformatics (Oxford, England)
|April 24, 2010
PubMed
Summary
This summary is machine-generated.

PepC software automates proteomic data analysis using spectral counting. It identifies differentially expressed proteins while managing false discovery rates, enhancing reproducibility and interpretability for researchers.

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

Published on: November 15, 2017

Area of Science:

  • Proteomics
  • Bioinformatics
  • Statistical Analysis

Background:

  • Analyzing proteomic data to identify significant protein abundance changes between conditions is crucial.
  • Spectral counting is a common label-free method for this analysis.
  • Existing statistical methods require biochemical validation and can be complex to automate.

Purpose of the Study:

  • To develop an automated statistical method for analyzing proteomic data.
  • To create software that balances the identification of differentially expressed proteins with the false discovery rate.
  • To provide a rapid, reproducible, and interpretable tool for proteomic data analysis.

Main Methods:

  • Developed PepC, a Java-implemented software program.
  • Integrated PepC into the Trans Proteomic Pipeline's 'Petunia' web interface.
  • Also available as a command-line program; source code is open-source (GNU Lesser General Public License).

Main Results:

  • PepC automates a statistical approach combining t-test, G-test, and random permutation analysis.
  • The software effectively balances the trade-off between identified differentially expressed proteins and the false discovery rate.
  • Biochemical validation confirmed the approach's significance.

Conclusions:

  • PepC offers a robust and automated solution for differential protein expression analysis in proteomics.
  • The tool enhances the speed, reproducibility, and interpretability of proteomic data analysis.
  • Applicable to diverse proteomic datasets for both specialists and non-specialists.