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Updated: May 15, 2026
14:51
Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples
Published on: November 13, 2021
PatternLab: from mass spectra to label-free differential shotgun proteomics
Paulo C Carvalho1, Juliana S G Fischer1, Tao Xu2
1Carlos Chagas Institute-Fiocruz, Paraná, Brazil.
Current Protocols in Bioinformatics
|December 21, 2012
Summary
PatternLab for proteomics offers enhanced computational tools for analyzing shotgun proteomic data. New modules improve protein identification, quantitation, and differential expression analysis, advancing proteomics standards.
Area of Science:
- Proteomics
- Computational Biology
- Bioinformatics
Background:
- Shotgun proteomics generates complex data requiring sophisticated analysis tools.
- Existing computational environments may lack integrated modules for advanced data processing and interpretation.
- The need for robust and efficient analysis pipelines is critical in modern proteomics research.
Purpose of the Study:
- To introduce and detail recent improvements and novel modules within the PatternLab for proteomics computational environment.
- To provide comprehensive instructions for operating and understanding these enhanced analytical tools.
- To facilitate more confident and accurate analysis of shotgun proteomic datasets.
Main Methods:
- Integration of new modules: FastaDBXtractor for sequence database preparation, ProLuCID runner for managing protein identification searches.
- Implementation of SEPro for semi-labeled decoy approach to enhance peptide spectrum match filtering.
- Inclusion of SEProQ for label-free quantitation using extracted ion chromatograms and a distributed normalized ion abundance factor (dNIAF) approach.
- Enhancement of existing modules like TFold for pinpointing differentially expressed proteins.
Main Results:
- PatternLab now offers a more comprehensive suite of tools for shotgun proteomic data analysis.
- New modules enable more confident protein identification and improved filtering of peptide spectrum matches.
- Novel quantitation module (SEProQ) facilitates accurate label-free quantification.
- Improved TFold module aids in identifying differentially expressed proteins.
Conclusions:
- The enhanced PatternLab for proteomics provides a powerful, integrated computational environment for advanced proteomic data analysis.
- These improvements push the boundaries of proteomics standards, particularly in protein identification and quantitation.
- The detailed instructions facilitate user adoption and maximize the utility of the PatternLab toolkit for researchers.
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