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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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).
The authors declare no competing financial interests.
Integrated supplementary information
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).
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.
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.
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).
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.
This module relies on the delta XCorr distribution to fit a Gaussian mixture model that ultimately results in p-values for the phosphosites.
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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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