UNIT 13.19 PatternLab: From Mass Spectra to Label-Free Differential Shotgun Proteomics

  1. Paulo C. Carvalho1,
  2. Juliana S. G. Fischer1,
  3. Tao Xu2,
  4. John R. Yates III2,
  5. Valmir C. Barbosa3

Published Online: 1 DEC 2012

DOI: 10.1002/0471250953.bi1319s40

Current Protocols in Bioinformatics

Current Protocols in Bioinformatics

How to Cite

Carvalho, P. C., Fischer, J. S. G., Xu, T., Yates, J. R. and Barbosa, V. C. 2012. PatternLab: From Mass Spectra to Label-Free Differential Shotgun Proteomics. Current Protocols in Bioinformatics. 40:13.19:13.19.1–13.19.18.

Author Information

  1. 1

    Carlos Chagas Institute–Fiocruz, Paraná, Brazil

  2. 2

    Department of Cell Biology, The Scripps Research Institute, La Jolla, California

  3. 3

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

Publication History

  1. Published Online: 1 DEC 2012


PatternLab for proteomics is a self-contained computational environment for analyzing shotgun proteomic data. Recent improvements incorporate modules to facilitate the computational analysis, such as FastaDBXtractor for sequence database preparation and ProLuCID runner for simplifying and managing the protein identification search engine; modules for pushing the limits on proteomics standards, such as SEPro, which relies on a semi-labeled decoy approach for increasing confidence in filtering and organizing peptide spectrum matches; and modules with novel features, such as SEProQ for enabling label-free quantitation by extracted ion chromatograms according to a distributed normalized ion abundance factor approach (dNIAF). Existing modules were also improved, such as the TFold module for pinpointing differentially expressed proteins. These new modules are integrated into the previously described arsenal of tools for further data analysis. Here we provide detailed instructions for operating and understanding them. Curr. Protoc. Bioinform. 40:13.19.1-13.19.18. © 2012 by John Wiley & Sons, Inc.


  • semi-labeled decoy approach;
  • filtering PSMs;
  • dNIAF;
  • quantitative proteomics;
  • protein identification