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Integrated analysis of shotgun proteomic data with PatternLab for proteomics 4.0

Nature Protocols volume 11, pages 102–117 (2016)Cite this article

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Abstract

PatternLab for proteomics is an integrated computational environment that unifies several previously published modules for the analysis of shotgun proteomic data. The contained modules allow for formatting of sequence databases, peptide spectrum matching, statistical filtering and data organization, extracting quantitative information from label-free and chemically labeled data, and analyzing statistics for differential proteomics. PatternLab also has modules to perform similarity-driven studies with de novo sequencing data, to evaluate time-course experiments and to highlight the biological significance of data with regard to the Gene Ontology database. The PatternLab for proteomics 4.0 package brings together all of these modules in a self-contained software environment, which allows for complete proteomic data analysis and the display of results in a variety of graphical formats. All updates to PatternLab, including new features, have been previously tested on millions of mass spectra. PatternLab is easy to install, and it is freely available from http://patternlabforproteomics.org.

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Figure 1: Overview of PatternLab's workflow.
Figure 2: PatternLab's main screen.
Figure 3
Figure 4: SEPro's Result Browser.
Figure 5: PatternLab's Project Organizer.
Figure 6: PatternLab's XIC Browser.
Figure 7: The XIC Browser's completion tab allows for establishing rules for grouping files that can be used to search for m/z and chromatographic retention times of possibly undersampled peptides.
Figure 8: Result Browser for PatternLab's Isobaric Analyzer, two conditions experiment.
Figure 9: PatternLab's Isobaric Analyzer.

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Acknowledgements

We thank Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Fundação do Câncer, Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) for its BBP grant and Programa de Apoio à Pesquisa Estratégica em Saúde da Fiocruz (PAPES VII). J.R.Y. acknowledges funding from the US National Institutes of Health (P41 GM103533, R01 MH067880, and R01 MH100175) and the National Heart, Lung and Blood Institute (NHBLI) Proteomics Center at the University of California at Los Angeles (UCLA) (HHSN268201000035C). J.J.M. acknowledges NIH research resources (5P41RR011823) and funding from the National Institute of General Medical Sciences (8 P41 GM103533).

Author information

Author notes

  1. Paulo C Carvalho and Diogo B Lima: These authors contributed equally to this work.

Authors and Affiliations

  1. Computational Mass Spectrometry Group, Carlos Chagas Institute, Fiocruz Paraná, Curitiba, Brazil.,

    Paulo C Carvalho, Diogo B Lima, Felipe V Leprevost, Marlon D M Santos & Juliana S G Fischer

  2. Laboratory of Toxinology, Oswaldo Cruz Institute, Fiocruz, Rio de Janeiro, Brazil

    Paulo C Carvalho

  3. Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA

    Felipe V Leprevost

  4. Leonidas e Maria Deane Institute, Fiocruz Amazonas, Manaus, Brazil

    Priscila F Aquino

  5. Laboratory for Biological Mass Spectrometry, The Scripps Research Institute, La Jolla, California, USA

    James J Moresco & John R Yates III

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

    Valmir C Barbosa

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Contributions

P.C.C., J.R.Y. and V.C.B. have participated in the PatternLab project since its beginning in 2008. D.B.L. participated in updating features from several modules and the graphical user interface, as well as in helping migrate to the new PatternLab project file format. F.V.L. developed the PepExplorer module together with P.C.C. M.D.M.S. developed several functions in PepExplorer and had a major participation in the development of the isobaric quantification module. J.S.G.F., P.F.A. and J.J.M. have been participating in PatternLab since early versions by continuously performing beta testing, pointing out required features and providing suggestions on how to make the software more user-friendly. P.C.C. and D.B.L. created the supplementary video. P.C.C. and V.C.B. wrote the manuscript. All authors read and approved the manuscript.

Corresponding author

Correspondence to Paulo C Carvalho.

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Integrated supplementary information

Supplementary Figure 1 PatternLab’s target-decoy sequence database generation module.

This module provides options for parsing data from UniProt, NCBI, IPI, and a Generic Format. The module can automatically include the sequences of 127 common contaminants to proteomics and simplify datasets by eliminating subset sequences or sequences having an identification threshold above a given user specification. In these cases, a note is appended to the description of the remaining sequence to indicate the eliminated sequence(s).

Supplementary Figure 2 The modification library window.

New modifications can be included by typing the data in the corresponding cells and then clicking on the ‘Update my lib’ button. Modifications can be included in the search by selecting the desired rows and then clicking on the ‘Add selected row to my search.xml’ button.

Supplementary Figure 3 SEPro’s Entry Screen.

PatternLab for proteomics 4.0 makes available preset configurations for filtering results from high-resolution and low-resolution MS1 acquisitions. Regardless, all SEPro filtering parameters are made available in the ‘Advanced Parameters’ tab.

Supplementary Figure 4 XICs quantitation histogram.

A histogram of minus the logarithm of the label-free quantitation values for all the XICs obtained by simultaneously analyzing 26 3-hour LC/MS/MS shotgun proteomic experiments on an Orbitrap Elite (Thermo, San Jose).

Supplementary Figure 5 PatternLab’s Isobaric Analyzer.

The lower-right panel contains three plots. The topmost one shows the total signal, obtained only from identified spectra of a given run, for each isobaric marker before normalization. The middle one shows the signals after applying the Channel Signal normalization. The bottommost plot shows the total signal, obtained from all mass spectra of a given run, regardless of identification status, for each isobaric marker before normalization.

Supplementary Figure 6 PatternLab’s XD Scoring module.

This module relies on the delta XCorr distribution to fit a Gaussian mixture model that ultimately results in p-values for the phosphosites.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6 (PDF 994 kb)

PatternLab 4.0 in action

An overview of the main modules in action. (MP4 51995 kb)

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Carvalho, P., Lima, D., Leprevost, F. et al. Integrated analysis of shotgun proteomic data with PatternLab for proteomics 4.0. Nat Protoc 11, 102–117 (2016). https://doi.org/10.1038/nprot.2015.133

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