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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...

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Sample Preparation for Endopeptidomic Analysis in Human Cerebrospinal Fluid
10:23

Sample Preparation for Endopeptidomic Analysis in Human Cerebrospinal Fluid

Published on: December 4, 2017

Search engine processor: Filtering and organizing peptide spectrum matches.

Paulo C Carvalho1, Juliana S G Fischer, Tao Xu

  • 1Laboratory for Toxinology, Oswaldo Cruz Institute, Rio de Janeiro, Brazil. paulo@pcarvalho.com

Proteomics
|February 8, 2012
PubMed
Summary
This summary is machine-generated.

The search engine processor (SEPro) is a novel tool that improves protein identification accuracy in proteomics. SEPro utilizes a unique Bayesian approach, outperforming existing commercial software for reliable data analysis.

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Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry
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Last Updated: May 25, 2026

Sample Preparation for Endopeptidomic Analysis in Human Cerebrospinal Fluid
10:23

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Published on: December 4, 2017

Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry
11:54

Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

Published on: March 23, 2020

Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Shotgun proteomics generates large datasets of peptide spectrum matches.
  • Accurate protein identification is crucial for biological discovery.
  • Existing tools face challenges in reliably filtering and organizing complex proteomic data.

Purpose of the Study:

  • To introduce the Search Engine Processor (SEPro), a novel computational tool for analyzing peptide spectrum matches.
  • To enhance the accuracy and reliability of protein identifications from shotgun proteomic data.
  • To compare SEPro's performance against a leading commercial competitor.

Main Methods:

  • Development of SEPro, a tool employing a three-tier Bayesian approach.
  • Integration of SEPro within the PatternLab for proteomics environment.
  • Benchmarking SEPro using the semi-labeled decoy approach for performance evaluation.

Main Results:

  • SEPro effectively filters, organizes, and displays peptide spectrum matches.
  • The three-tier Bayesian logic converges data to reliable protein identifications.
  • SEPro demonstrated significantly superior performance compared to a commercial competitor.

Conclusions:

  • SEPro offers a robust and accurate method for protein identification in proteomics.
  • The Bayesian approach provides a reliable framework for analyzing complex proteomic datasets.
  • SEPro represents a significant advancement in the analysis of shotgun proteomics data.