Summary: We describe an integrative software platform, Prequips, for comparative proteomics-based systems biology analysis that: (i) integrates all information generated from mass spectrometry (MS)-based proteomics as well as from basic proteomics data analysis tools, (ii) visualizes such information for various proteomic analyses via graphical interfaces and (iii) links peptide and protein abundances to external tools often used in systems biology studies. Availability: http://prequips.sourceforge.net Contact: dhhwang@postech.ac.k

Aebersold, Ruedi

Gehlenborg, Nils

Hood, Leroy

Hwang, Daehee

Lee, Inyoul Y.

Nieselt, Kay

Yan, Wei

Yoo, Hyuntae

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BIOINFORMATICS APPLICATIONS NOTE Vol. 25 no. 5 2009, pages 682–683doi:10.1093/bioinformatics/btp005Systems biologyPrequips—an extensible software platform for integration,visualization and analysis of LC-MS/MS proteomics dataNils Gehlenborg1,2,3, Wei Yan1, Inyoul Y. Lee1, Hyuntae Yoo1, Kay Nieselt2,Daehee Hwang1,4,5,∗, Ruedi Aebersold1,6 and Leroy Hood11Institute for Systems Biology, Seattle, USA, 2Center for Bioinformatics Tübingen, University of Tübingen, Tübingen,Germany, 3European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK, 4I-Bio Program,5Department of Chemical Engineering, POSTECH, Pohang, Republic of Korea and 6Institute for Molecular SystemsBiology, ETH and Faculty of Natural Science, University of Zürich, Zürich, SwitzerlandReceived on October 19, 2008; revised on December 18, 2008; accepted on December 30, 2008Advance Access publication January 6, 2009Associate Editor: John QuackenbushABSTRACTSummary: We describe an integrative software platform, Prequips,for comparative proteomics-based systems biology analysis that: (i)integrates all information generated from mass spectrometry (MS)-based proteomics as well as from basic proteomics data analysistools, (ii) visualizes such information for various proteomic analysesvia graphical interfaces and (iii) links peptide and protein abundancesto external tools often used in systems biology studies.Availability: http://prequips.sourceforge.netContact: dhhwang@postech.ac.kr1 INTRODUCTIONRecently, liquid chromatography (LC) coupled tandem MS-basedproteomics technologies have been extensively used in systemsbiology studies. The improved reliability and widespread use ofsuch LC-MS/MS approaches have led to huge amounts of largedatasets together with a growing number of software tools fordiverse analyses of MS data (Trans-Proteomic Pipeline (TPP), Kelleret al., 2005). Despite such software tools, effective integration,visualization and analysis of MS data for systems biologyinvestigations are still complicated due to lack of (i) integration ofdiverse data types from different MS platforms and analytical outputdata types from various software tools—MSn spectra, peptides(sequences, search outputs, abundances and modifications) andproteins (coverage, abundances, modifications and significances),(ii) effective visualization of such complex integrated data forvarious analyses of MS data to examine characteristics and qualityof data and optimize experimental conditions in comparativeproteomics and (iii) automatic exchanges of such integratedinformation with software used in systems biology. The first problemis still challenging due to the complex relationships among datafrom multiple samples, despite several tools having been developedfor MS data analysis (e.g. TPP). There is still a lack of toolsfor visualizing information from multiple samples to facilitatevarious analyses in comparative proteomics. Pep3D (Li et al., 2004)resolved the second problem in part, especially for a single sample.∗To whom correspondence should be addressed.Moreover, only few tools with limited functionalities to resolvethe third problem have been introduced. Therefore, there has beena need for a computational framework providing functionalitiesfor (i) integration of complex MS data, (ii) visualization of thecomplex data using spectrum/chromatogram and tabular viewers,peptide feature maps and (iii) network modeling and analysis (e.g.Cytoscape and its plug-ins) and statistical analyses such as datanormalization, identification of differentially expressed proteins,functional enrichment analyses and classification/clustering. Here,we present Prequips, a Java-based workbench of computational toolsefficiently achieving these three tasks.2 METHODS AND FEATURESPrequips provides the following three key features. To demonstrate thefeatures in Prequips, we used four datasets obtained from LC-MS/MSanalysis of samples to examine changes in sub-cellular localization afterchemical treatment.2.1 Integration of multiple MS data and diverseproteomic informationPrequips is built on a generic data model with two main parts, core dataand meta information elements obtained from the data sources shown inFigure 1A. Core data refers to key information from MS experiments withhierarchical structure—spectra, peptide sequences and protein IDs (Fig. 1B).Meta information represents data generated by the basic data analysispipeline (e.g. SEQUEST and TPP) at all stages and in various forms, whichdescribes the core data more closely. Meta data of peptides include DBsearch outputs (sequences, SEQUEST outputs such as Xcorr, PeptideProphetprobabilities; Keller et al., 2002), abundances and modifications, whilethose of proteins include sequence coverage, abundances, modificationsand ProteinProphet probabilities. These core data and meta informationare integrated via the data model of Prequips (Fig. 1A). In ‘single sampleanalysis’, both core and meta data for a single dataset are organized in a datamodel including spectrum-, peptide- and protein-level information (Fig. 1A).In ‘multi-sample analysis’, the core and meta information for multiplesamples can be combined in the data model based on any kinds of uniqueproteins (Fig. 1B). A vector of values including all spectrum- and peptide-level data and related meta data over all samples can be created for eachprotein. To effectively load core data and meta information, Prequips employsso-called ‘data providers’, which enhances data integration capabilities(Fig. 1A). Data providers, designed in plug-in interfaces, separate Prequips682 © The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.orgPrequips—an extensible software platformFig. 1. Overview of Prequips’ design and functionality. (A) Data model structure. (B) Schematics of data structure and exchange. (C) Spectrum, peptidefeature map and table viewers in single sample analysis. (D) Peptide feature maps in multi-sample analysis. (E) Broadcasting to Cytoscape via the Gaggleinterface. (F) Network analysis using broadcasted proteins in Cytoscape.from the data sources (e.g. mzXML, pepXML and protXML; Fig. 1A) andmake the software independent of data formats.2.2 Visualization of integrated core data and metainformationPrequips supports interactive data exploration through a graphical userinterface (GUI) permitting to efficiently interact with the data throughgraphical viewers and data filters. Figure 1C shows summary windows,spectrum viewers, peptide feature maps and a tabular viewer showingintegrated core and meta information for ‘single sample analysis’: (i) theproject manager allows users to select a particular sample (e.g. a chemical-treated sample) or a group of samples (e.g. control samples); (ii) a spectrumsummary window showing core and meta information (peak numbers,retention time, etc.) for the MS2 scan being displayed in the spectrumviewer; (iii) MS/MS2 spectrum viewers; (iv) chromatogram viewers (notshown); and (v) peptide feature map. Note that the data being displayed areall connected across the viewers through the integration of core and metainformation in the data model (see the linked selection in Fig. 1C). Also, asophisticated filtering framework has been implemented in Prequips, basedon Boolean operators AND, OR and XOR (exclusive-or), by which a groupof objects can be selected using multiple data filters. For instance, userscan select peptides whose PeptideProphet probabilities are larger than 0.95.Once those peptides were selected in the tabular viewer, the correspondingpeptides and scans are highlighted in the other viewers. Prequips enablesusers to open a virtually arbitrary number of viewers to investigate the databased on the Model-View-Controller architecture. Figure 1D shows fourpeptide feature map viewers from four MS datasets, as an example of a‘multi-sample analysis’. Exploring multiple MS datasets using the viewersand data filters assist users to examine characteristics and quality of data, thusaiding optimizing experimental conditions to improve particular aspects ofresults (e.g. increase the detected proteome size).2.3 Mapping proteomic information into systemsbiology toolsBesides data integration and visualization tools for data exploration, Prequipsalso provides extensive support for systems biology analyses of MS-basedproteomic data. Prequips employs the Gaggle (Shannon et al., 2006) toexchange core and meta data of interest (e.g. a list of proteins detected frommultiple experiments) with external software tools (Fig. 1B) being used fornetwork analysis (Cytoscape, Biotapestry and String) and statistical analyses(TIGR MeV; Saeed et al., 2003). Prequips is able to broadcast both lists ofnames and matrices via the Gaggle plug-in. Figure 1E schematically showsbroadcasting protein IDs from Prequips to Cytoscape via the Gaggle. Asshown in Figure 1F, we can reconstruct a network model for the broadcastedproteins using protein–protein interactions obtained by Cytoscape plug-ins. The network shows cellular processes (see circled network modules)enriched by the proteins whose sub-cellular locations were changed afterthe chemical treatment. The Gaggle in Prequips also passes protein IDs viaFirefox to web-based tools (DAVID) for functional enrichment analysis andalso to R-package as inputs to R-scripts for sophisticated analyses.3 IMPLEMENTATIONPrequips is open-source software implemented in Java based onthe Eclipse Rich Client Platform and released under the GNUGPL. The modularity of Prequips enables third party developersto adapt it into their specific software environment or add theirown functionalities to Prequips as plug-ins (e.g. the Inclusion ListBuilder; Schmidt et al., 2008). The use of Java renders the softwareplatform-independent. To efficiently handle large datasets, the dataproviders were designed to dynamically load data from data sourcesinto memory: Prequips could load a 589 MB mzXML in 1.5 min onan iMac with 2.16 GHz and 2 GB RAM.Conflict of Interest: none declared.Funding: GAES (grant DAMD17-03-1-0321, DAMD17-03-1-0425); NIH (grant NS41997); NHLBI (grant N01-HV-28179); DOD(grant DAMD17-3-1-0321); Korean MEST (grant FPR08A1-050,SC-2120); BioGreen21 (grant 20080401-034-041-008-02-00).REFERENCESKeller,A. et al. (2002) Empirical statistical model to estimate the accuracy of peptideidentifications made by MS/MS and database search. Anal. Chem., 74, 5383–5392.Keller,A. et al. (2005) A uniform proteomics MS/MS analysis platform utilizing openXML formats. Mol. Syst. Biol., 1, 1–8.Li,X.-J. et al. (2004) A tool to visualize and evaluate data obtained by liquidchromatography-electrospray ionization-mass spectroscopy. Anal. Chem., 76,3856–3860.Saeed,A. et al. (2003) TM4: a free, open-source system for microarray data managementand analysis. Biotechniques, 34, 374–378.Shannon,P.T. et al. (2006) The gaggle: an open-source software system for integratingbioinformatics software and data sources. BMC Bioinformatics, 7, 176.Schmidt,A. et al. (2008) An integrated, directed mass spectrometric approach forin-depth characterization of complex peptide mixtures. Mol. Cell. Proteomics, 7,2138–2150.683

Prequips—an extensible software platform for integration, visualization and analysis of LC-MS/MS proteomics data

Prequips—an extensible software platform for integration, visualization and analysis of LC-MS/MS proteomics data

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