GProX, a User-Friendly Platform for Bioinformatics Analysis and Visualization of Quantitative Proteomics Data

Abstract

Recent technological advances have made it possible to identify and quantify thousands of proteins in a single proteomics experiment. As a result of these developments, the analysis of data has become the bottleneck of proteomics experiment. To provide the proteomics community with a user-friendly platform for comprehensive analysis, inspection and visualization of quantitative proteomics data we developed the Graphical Proteomics Data Explorer (GProX) 1The abbreviations used are:GProXGraphical Proteomics Data ExplorerEGFepidermal growth factorGOGene OntologyHGFhepatocyte growth factorKEGGKyoto Encyclopedia of Genes and GenomesOSoperating systemSPIASignaling Pathway Impact Analysis.. The program requires no special bioinformatics training, as all functions of GProX are accessible within its graphical user-friendly interface which will be intuitive to most users. Basic features facilitate the uncomplicated management and organization of large data sets and complex experimental setups as well as the inspection and graphical plotting of quantitative data. These are complemented by readily available high-level analysis options such as database querying, clustering based on abundance ratios, feature enrichment tests for e.g. GO terms and pathway analysis tools. A number of plotting options for visualization of quantitative proteomics data is available and most analysis functions in GProX create customizable high quality graphical displays in both vector and bitmap formats. The generic import requirements allow data originating from essentially all mass spectrometry platforms, quantitation strategies and software to be analyzed in the program. GProX represents a powerful approach to proteomics data analysis providing proteomics experimenters with a toolbox for bioinformatics analysis of quantitative proteomics data. The program is released as open-source and can be freely downloaded from the project webpage at http://gprox.sourceforge.net. Recent technological advances have made it possible to identify and quantify thousands of proteins in a single proteomics experiment. As a result of these developments, the analysis of data has become the bottleneck of proteomics experiment. To provide the proteomics community with a user-friendly platform for comprehensive analysis, inspection and visualization of quantitative proteomics data we developed the Graphical Proteomics Data Explorer (GProX) 1The abbreviations used are:GProXGraphical Proteomics Data ExplorerEGFepidermal growth factorGOGene OntologyHGFhepatocyte growth factorKEGGKyoto Encyclopedia of Genes and GenomesOSoperating systemSPIASignaling Pathway Impact Analysis.. The program requires no special bioinformatics training, as all functions of GProX are accessible within its graphical user-friendly interface which will be intuitive to most users. Basic features facilitate the uncomplicated management and organization of large data sets and complex experimental setups as well as the inspection and graphical plotting of quantitative data. These are complemented by readily available high-level analysis options such as database querying, clustering based on abundance ratios, feature enrichment tests for e.g. GO terms and pathway analysis tools. A number of plotting options for visualization of quantitative proteomics data is available and most analysis functions in GProX create customizable high quality graphical displays in both vector and bitmap formats. The generic import requirements allow data originating from essentially all mass spectrometry platforms, quantitation strategies and software to be analyzed in the program. GProX represents a powerful approach to proteomics data analysis providing proteomics experimenters with a toolbox for bioinformatics analysis of quantitative proteomics data. The program is released as open-source and can be freely downloaded from the project webpage at http://gprox.sourceforge.net. Graphical Proteomics Data Explorer epidermal growth factor Gene Ontology hepatocyte growth factor Kyoto Encyclopedia of Genes and Genomes operating system Signaling Pathway Impact Analysis. During the last decade, identification and quantitation of proteomes has been facilitated by the constant developments in mass spectrometry instrumentation, fractionation techniques, quantitation-strategies, and data analysis software. Using state-of-the-art technology it has become possible to quantify several thousands of proteins (1Rigbolt K.T. Prokhorova T.A. Akimov V. Henningsen J. Johansen P.T. Kratchmarova I. Kassem M. Mann M. Olsen J.V. Blagoev B. 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Ringwald M. Rubin G.M. Sherlock G. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.Nat. Genet. 2000; PubMed Scopus Google Scholar) such as S.P. H. B. S. D. C. D. J. A. to a in the analysis of mass spectrometry-based quantitative proteomics 2009; PubMed Scopus Google Scholar), N. a statistical tool for quantitative analysis of 2008; PubMed Scopus Google Scholar), and B. Heck A.J. a analysis toolbox for quantitative mass 2009; PubMed Scopus Google Scholar) provide a range of advanced statistical for analysis of protein abundance the P. A. J.T. D. N. B. T. a software for of interaction Res. 2003; PubMed Scopus Google Scholar) has a for the analysis in interaction data and protein these and tools are for not complex experimental setups and the requirements for data and and of several analysis To allow experimenters to several tools, such as the A.R. M. data management for the Bioinformatics 2007; 23: PubMed Scopus Google Scholar) and N. W. H. K. D. Aebersold R. L. software platform for visualization and analysis of proteomics 2009; PubMed Scopus Google Scholar), which both use the program P.T. R. The an open-source software system for bioinformatics software and data 2006; 7: PubMed Scopus Google Scholar) for data provide for data The solutions are powerful particularly the users are be for several solutions are e.g. the Pathway and (Thermo the high with these and the of software solutions a to the application of These to the Graphical Proteomics Data Explorer a software for comprehensive and bioinformatics analysis and visualization of large proteomics data The of GProX is to provide a data to spreadsheet applications and from interface available an of for analyzing proteomics data. The of GProX is to allow experimenters without in bioinformatics to data and graphical to be used in scientific GProX on available a of analysis functions within a single platform and particularly on a user-friendly interface and the of graphical The as well as the complete source is freely available for from http://gprox.sourceforge.net. The and of GProX is in The program and the interface are in the Basic programming the The and the large of graphical available in the of user-friendly graphical which the large of high-level in makes it an platform for data and with the system of the is that it a not mass software is available for most proteomics for data and of the features in GProX for data and of graphical are as for the software for statistical and A and for Scholar), has during for by the number of available from the B. M. S. B. L. Y. J. K. T. W. S. R. F. Li C. M. A.J. G. C. G. L. Yang J. open software for computational and Biol. 5: PubMed Google Scholar). In is well for the large of data produced by quantitative proteomics and a range of functions for as well as advanced graphical in a number of formats. The the based interface and is that are used as for of the are to the program and graphical are and in the program. During the is not with the which are as in the for both and from the is the program and functions in GProX are in and which are with the program. These and functions can be used as a source of for experimenters with to the currently in GProX and The GProX can be downloaded freely and without from the project as a The program requires a desktop computer with a have the program on of and and of the the program on a working of and several these be on the During the of GProX the is to and these To the with we have an information of and is in the GProX the is in in the with the program. we have a GProX Google users can and can be from the project from The required by GProX is a with in the and protein in The information for protein in the is the database and quantitative information available from data can be from data for applications within information be e.g. peptide information database such as Gene Ontology (22Ashburner M. Ball C.A. Blake J.A. Botstein D. Butler H. Cherry J.M. Davis A.P. Dolinski K. Dwight S.S. Eppig J.T. Harris M.A. Hill D.P. Issel-Tarver L. Kasarskis A. Lewis S. Matese J.C. Richardson J.E. Ringwald M. Rubin G.M. Sherlock G. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.Nat. Genet. 2000; PubMed Scopus Google Scholar) J. J. P. A. J.E. P. G. K. L. A. The protein Acids Res. 2010; PubMed Scopus Google Scholar). As a of generic the application of GProX is not to a mass spectrometry quantitation data and quantitation software. of data GProX is an in which the is to the the and quantitation and the experimental To the experimental data quantitative data can be to experiments. The of the experimental the analysis of quantitative proteomics e.g. the temporal are In a single experiment quantitation data from for can be analyzed and within of such a all information required to a is the as a from which users can to an GProX a data management in which the data is as a from which are in a data information for data data from the can be on during analysis and is to the of the that all data is on the the is in the is have to the produced by GProX as to a as users to in an such as e.g. using for in To several vector and bitmap the to open and freely in The interface of GProX is in to that of spreadsheet as e.g. within the program are accessible a and The is a interface to The all information the a of all produced and graphical as well as an of the experimental The Data the as of a a single is during the of an analysis the can of to Data analysis are on the the to and of the complete data During the of an analysis a large of graphical can be and these are in a To the graphical the an to and from analysis is in the as to the Data the can be the is used as a database without its have to the software as intuitive and user-friendly as the advanced analysis allow several To the in these and to in the use of the a and in in using the software. a an in GProX is with the program to the users with the program. algorithms and data analysis strategies are in the and in to To the features of a quantitative proteomics data and epidermal growth factor hepatocyte growth factor R. Kratchmarova I. Blagoev B. analysis of and Proteome Res. 2010; 9: PubMed Scopus Google Scholar) used experimental is in To these data the data to GProX the as protein and the quantitation to two experiments. of the analysis in the is in of GProX is to provide a for quantitative proteomics data in a Basic functions such as of and as well as as e.g. organization and of experimental data. In data can be to data providing data based on e.g. functional the experimental during data import can be in the of an analysis e.g. to experimental to for data within data in the of requires to GProX and can be readily the data to (13Cox J. Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.Nat. Biotechnol. 2008; 26: 1367-1372Crossref PubMed Scopus (8607) Google Scholar, J.E. Gygi S.P. for in large-scale protein by mass 2007; 4: PubMed Scopus Google and and to a and the protein feature in GProX is its plotting the quantitation data from proteins can be as is as it a of of proteins and of protein is and is experimental quantitative data can be in two is generated for all experimental is generated for experimental data from all proteins for To the plotting we in the of growth factor these it is that the proteins and are the a is for the it is required to all available information from a complete J. A. Y. E. R. The an database for proteomics 4: PubMed Scopus Google Scholar) and The in Acids Res. 2010; PubMed Scopus Google Scholar) database which to information can be for the proteins of To the of quantitative GProX a of These can be to allow the users to features of quantitative data. and can be produced for to protein and the of to the in the of proteins are not the of quantitation is to at the with the for the complete of quantitation in can provide a comprehensive of protein experimental can be readily for all data to in an of the data. most of the proteins in the pathway and therefore be used to possible functional of these To find proteins with clustering is a and powerful a number of protein with quantitation can be In GProX clustering based on the B. clustering of Biol. 2005; PubMed Scopus Google Scholar) has been for clustering is for clustering of quantitative proteomics data as in to the to a with a the The are used to well a the and to of to the The analysis can be by the with for e.g. the number of number of and of and graphical To the of the clustering we the data to clustering and the result that the proteins in the data can be with of to to the growth factor of growth factor such as the are in a number of as and can be in and of analysis is that it can as a for identification of features proteins with a as exemplified in the The by the scientific community provide a of information protein and and are of analyzing proteomics data. To allow of for all proteins in the data GProX information from J. A. Y. E. R. The an database for proteomics 4: PubMed Scopus Google Scholar), The in Acids Res. 2010; PubMed Scopus Google Scholar), P. D. K. S. Y. P. G. S. S. L. M. T. N. A. D. S. R. F. E. P. I. W. B. B. D. M. M. D. G. S. J. A.J. White S. S.P. A. J. E. F. I. R. J. A. G. J. Acids Res. 2011; PubMed Scopus Google Scholar) using the S. Y. A. Davis S. B. A. W. and a powerful biological and microarray data 2005; 21: PubMed Scopus Google Scholar) and by the Bioinformatics In the protein stored and in these can be and to the data information can the in an of protein can provide the for functional of proteins analysis of the of features in of In to providing the with the to the database from which to the database information can be with from the is with a peptide can be used to which to use for the database the data we the to and used these to the GO biological process (22Ashburner M. Ball C.A. Blake J.A. Botstein D. Butler H. Cherry J.M. Davis A.P. Dolinski K. Dwight S.S. Eppig J.T. Harris M.A. Hill D.P. Issel-Tarver L. Kasarskis A. Lewis S. Matese J.C. Richardson J.E. Ringwald M. Rubin G.M. Sherlock G. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.Nat. Genet. 2000; PubMed Scopus Google Scholar) from the To approach such as GO terms are for the proteins proteins in a GProX a that can be used to enrichment of features in of the data The can be used to for enrichment of feature in the data In the is essentially generic in terms of the features and the enrichment analysis can be using a range of statistical and The of the GO can be used to for the most which the of the data. The of the enrichment is a as well as a graphical of the The the and for The of graphical on the the is In the the is for features in the proteins a is produced which the of the features within the and the analysis can a feature is in a from a clustering analysis in which a is in which feature are the data the GO biological process used to an of in of the As from enrichment of a number of terms to growth factor are in and The of both annotation and enrichment analysis allow the even in an of data analysis, to a of protein and of with these These as a e.g. to identify of proteins to be targeted in experiments. In biological proteins not as as of both of which can be analyzed by quantitative proteomics C. Mann M. by mass spectrometry-based Mol. Biol. 2010; PubMed Scopus Google Scholar, M. Hubner N.C. Mann M. of using quantitative mass Biotechnol. 2008; PubMed Scopus (132) Google Scholar, B. Ong S.E. Kratchmarova I. Mann M. analysis of by quantitative Biotechnol. PubMed Scopus Google Scholar, J. Kratchmarova I. Blagoev B. by quantitative Mol. Biol. 2010; 658: PubMed Scopus (21) Google Scholar). A powerful of protein is therefore to it in the of GProX the to protein quantitation to the pathway and information by the Kyoto Encyclopedia of Genes and Genomes M. S. M. M. M. for and analysis of and Acids Res. 2010; PubMed Scopus Google Scholar) of proteins can be to to from the large pathway To identify GProX on the pathway analysis in the for S. P. S.S. P. J.S. J.P. R. A pathway 2009; PubMed Scopus Google Scholar). find well it enrichment of information on pathway and the of a are for the a the result of the analysis is and are to all of pathway can be in two the of are in a from to high the annotation of proteins in the pathway is the pathway with a a comprehensive of the the of the by enables the to e.g. a In the of is the can that of the proteins in the pathway have to the are as and are at comparison the of proteins in the pathway and the information to the have the and features of GProX using an data exemplified a is not possible to all features and applications in we that GProX is to allow the users to and to a of information from quantitative proteomics data. is that the range of within a single platform accessible for can both as a platform for data and as a toolbox for the of quantitative proteomics data. it is that it is the of software to all the features of the in the we find that GProX a powerful in the analysis of quantitative proteomics data. In the of software for bioinformatics analysis to quantitation is the in we that GProX provide a to the in the and experimenters to in bioinformatics all for and and Ong for and for the data with