A Mass Spectrometry Proteomics Data Management Platform

Mass spectrometry-based proteomics is increasingly being used in biomedical research. These experiments typically generate a large volume of highly complex data, and the volume and complexity are only increasing with time. There exist many software pipelines for analyzing these data (each typically with its own file formats), and as technology improves, these file formats change and new formats are developed. Files produced from these myriad software programs may accumulate on hard disks or tape drives over time, with older files being rendered progressively more obsolete and unusable with each successive technical advancement and data format change. Although initiatives exist to standardize the file formats used in proteomics, they do not address the core failings of a file-based data management system: (1) files are typically poorly annotated experimentally, (2) files are “organically” distributed across laboratory file systems in an ad hoc manner, (3) files formats become obsolete, and (4) searching the data and comparing and contrasting results across separate experiments is very inefficient (if possible at all). Here we present a relational database architecture and accompanying web application dubbed Mass Spectrometry Data Platform that is designed to address the failings of the file-based mass spectrometry data management approach. The database is designed such that the output of disparate software pipelines may be imported into a core set of unified tables, with these core tables being extended to support data generated by specific pipelines. Because the data are unified, they may be queried, viewed, and compared across multiple experiments using a common web interface. Mass Spectrometry Data Platform is open source and freely available at http://code.google.com/p/msdapl/. Mass spectrometry-based proteomics is increasingly being used in biomedical research. These experiments typically generate a large volume of highly complex data, and the volume and complexity are only increasing with time. There exist many software pipelines for analyzing these data (each typically with its own file formats), and as technology improves, these file formats change and new formats are developed. Files produced from these myriad software programs may accumulate on hard disks or tape drives over time, with older files being rendered progressively more obsolete and unusable with each successive technical advancement and data format change. Although initiatives exist to standardize the file formats used in proteomics, they do not address the core failings of a file-based data management system: (1) files are typically poorly annotated experimentally, (2) files are “organically” distributed across laboratory file systems in an ad hoc manner, (3) files formats become obsolete, and (4) searching the data and comparing and contrasting results across separate experiments is very inefficient (if possible at all). Here we present a relational database architecture and accompanying web application dubbed Mass Spectrometry Data Platform that is designed to address the failings of the file-based mass spectrometry data management approach. The database is designed such that the output of disparate software pipelines may be imported into a core set of unified tables, with these core tables being extended to support data generated by specific pipelines. Because the data are unified, they may be queried, viewed, and compared across multiple experiments using a common web interface. Mass Spectrometry Data Platform is open source and freely available at http://code.google.com/p/msdapl/. Mass spectrometry-based proteomics is a rapidly developing technology that is increasingly being applied to biological and biomedical research. Many software applications have been developed to analyze mass spectrometry data and each of these programs typically use (as input) and produce (as output) files of various formats. Not only are these formats often specific to certain analysis software, but it is often specific to certain versions of that software as well. And as technology advances, these file formats often change or entirely new formats are introduced. Data viewers, converters, and analysis software are developed or updated to support these new formats, rendering older files progressively more obsolete and unlikely to be supported as input to current programs. In an effort to improve data portability and address this issue of many disparate proprietary file formats, important work has gone into the development of standardized and open data formats. Chief among this work are the XML-based data formats mzML (1Deutsch E.W. Mass spectrometer output file format mzML.Methods Mol. Biol. 2010; 604: 319-331Crossref PubMed Scopus (32) Google Scholar), mzIdentML (2Eisenacher M. mzIdentML: an open community-built standard format for the results of proteomics spectrum identification algorithms.Methods Mol. Biol. 2011; 696: 161-177Crossref PubMed Scopus (29) Google Scholar), mzData (3Orchard S. Montechi-Palazzi L. Deutsch E.W. Binz P.A. Jones A.R. Paton N. Pizarro A. Creasy D.M. Wojcik J. Hermjakob H. Five years of progress in the Standardization of Proteomics Data 4th Annual Spring Workshop of the HUPO-Proteomics Standards Initiative April 23–25, 2007 Ecole Nationale Superieure (ENS), Lyon, France.Proteomics. 2007; 7: 3436-3440Crossref PubMed Scopus (46) Google Scholar) (deprecated), mzXML (4Pedrioli P.G. Eng J.K. Hubley R. Vogelzang M. Deutsch E.W. Raught B. Pratt B. Nilsson E. Angeletti R.H. Apweiler R. Cheung K. Costello C.E. Hermjakob H. Huang S. Julian R.K. Kapp E. McComb M.E. Oliver S.G. Omenn G. Paton N.W. Simpson R. Smith R. Taylor C.F. Zhu W. Aebersold R. A common open representation of mass spectrometry data and its application to proteomics research.Nat. Biotechnol. 2004; 22: 1459-1466Crossref PubMed Scopus (652) Google Scholar), and pepXML (5Keller A. Eng J. Zhang N. Li X.J. Aebersold R. A uniform proteomics MS/MS analysis platform utilizing open XML file formats.Mol. Syst. Biol. 2005; 1 (2005.0017)Crossref PubMed Scopus (598) Google Scholar); although non-XML formats have also been proposed (MGF (http://www.matrixscience.com/help/data_file_help.html), MS2 and SQT (6McDonald W.H. Tabb D.L. Sadygov R.G. MacCoss M.J. Venable J. Graumann J. Johnson J.R. Cociorva D. Yates 3rd., J.R. MS1, MS2, and SQT-three unified, compact, and easily parsed file formats for the storage of shotgun proteomic spectra and identifications.Rapid Commun. Mass Spectrom. 2004; 18: 2162-2168Crossref PubMed Scopus (294) Google Scholar), and SQLite.(7Webber J.T. Askenazi M. Marto J.A. mzResults: an interactive viewer for interrogation and distribution of proteomics results.Mol. Cell. Proteomics. 2011; 10 (M110.003970)Abstract Full Text Full Text PDF Scopus (8) Google Scholar)). Effort has also been put toward developing a universal application programming interface, dubbed mzAPI (8Askenazi M. Parikh J.R. Marto J.A. mzAPI: a new strategy for efficiently sharing mass spectrometry data.Nat. Methods. 2009; 6: 240-241Crossref PubMed Scopus (53) Google Scholar), to allow third-party applications direct access to proprietary proteomics formats. As a proof of concept, mzAPI has been used to develop both a desktop environment (9Parikh J.R. Askenazi M. Ficarro S.B. Cashorali T. Webber J.T. Blank N.C. Zhang Y. Marto J.A. Multiplierz: an extensible API based desktop environment for proteomics data analysis.BMC Bioinformatics. 2009; 10: 364Crossref PubMed Scopus (57) Google Scholar) and web-based environment (10Askenazi M. Webber J.T. Marto J.A. mzServer: web-based programmatic access for mass spectrometry data analysis.Mol. Cell. Proteomics. 2011; 1010.1074/mcp.M110.003988Abstract Full Text Full Text PDF Scopus (7) Google Scholar) for accessing and viewing proteomics data. Although these efforts are well-developed and portable, they are less than ideal as a data archival and search format; and they suffer from drawbacks intrinsic to any file-based data solution:•Files are often spread across file systems, hard drives, and tape backups in an ad hoc manner, making them hard to locate.•Files may be poorly annotated with regards to experimental conditions, software parameters, or even the software used to generate the data.•File formats become obsolete and eventually unsupported by current tools.•Visualization tools and analysis software must be installed and maintained to view or interrogate the data files. Government and journal rules regarding public data dissemination have led to the development of online proteomics data repositories such as PRIDE (11Vizcaino J.A. Côté R. Reisinger F. Foster J.M. Mueller M. Rameseder J. Hermjakob H. Martens L. A guide to the Proteomics Identifications Database proteomics data repository.Proteomics. 2009; 9: 4276-4283Crossref PubMed Scopus (202) Google Scholar), Peptide Atlas (12Deutsch E.W. Lam H. Aebersold R. PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows.EMBO Rep. 2008; 9: 429-434Crossref PubMed Scopus (444) Google Scholar), Global Proteome Machine (GPM) (13Fenyö D. Eriksson J. Beavis R. Mass spectrometric protein identification using the global proteome machine.Methods Mol. Biol. 2010; 673: 189-202Crossref PubMed Scopus (44) Google Scholar), Human Proteinpedia (14Goel R. Muthusamy B. Pandey A. Prasad T.S. Human protein reference database and human proteinpedia as discovery resources for molecular biotechnology.Mol. Biotechnol. 2011; 48: 87-95Crossref PubMed Scopus (66) Google Scholar), and the Yeast Research Center (YRC) PDR (15Riffle M. Malmström L. Davis T.N. The Yeast Resource Center Public Data Repository.Nucleic Acids Res. 2005; 33: D378-D382Crossref PubMed Scopus (22) Google Scholar) all of which provide unified web-based interfaces to underlying proteomics data stored in relational databases. Although these resources are very important in terms of data distribution, they are not typically designed to be part of a laboratory's archival and workflow process. Tranche (16Smith B.E. Hill J.A. Gjukich M.A. Andrews P.C. Tranche distributed repository and ProteomeCommons.org.Methods Mol. Biol. 2011; 696: 123-145Crossref PubMed Scopus (33) Google Scholar), a distributed storage and archival network for proteomics data, provides an effective means of archiving and distributing data, but it distributes only files and requires that the end-user have access to visualization and analysis tools capable of working with files of that specific format before they may be of use. LabKey Server (17Nelson E.K. Piehler B. Eckels J. Rauch A. Bellew M. Hussey P. Ramsay S. Nathe C. Lum K. Krouse K. Stearns D. Connolly B. Skillman T. Igra M. LabKey Server: an open source platform for scientific data integration, analysis and collaboration.BMC Bioinformatics. 2011; 12: 71Crossref PubMed Scopus (100) Google Scholar) and its accompanying proteomics module (formerly known as CPAS) is a comprehensive and robust proteomics pipeline and workflow management system. It is highly customizable and includes options for configuring and running multistep proteomics pipelines, and includes options for viewing the output of the respective analysis programs. Here we present the Mass Spectrometery Data Platform (MSDaPl) 1The abbreviations used are:MSDaPlmass spectrometery data platformMS/MStandem mass spectrometryXMLextensible markup languagePSMpeptide spectrum match. 1The abbreviations used are:MSDaPlmass spectrometery data platformMS/MStandem mass spectrometryXMLextensible markup languagePSMpeptide spectrum match., a proteomics data management system that, instead of driving proteomics workflows, focuses on long-term archiving, searching, evaluating, and performing simple analysis of the data that result from the workflows and may be used to compliment systems such as the LabKey Server (Fig. 1). mass spectrometery data platform tandem mass spectrometry extensible markup language peptide spectrum match. mass spectrometery data platform tandem mass spectrometry extensible markup language peptide spectrum match. At its core MSDaPl uses a unified relational database architecture that supports storing data generated by multiple pipelines, and may be extended to support more. MSDaPl includes import algorithms for major proteomics pipelines, and once in the database, there is no longer any dependence on specific file formats. In addition, the data are searchable and may be compared across separate experiments (even if the experimental data were generated by different pipelines). Built on the database is a web application designed for searching the data archive, evaluating the quality of tandem MS (MS/MS) runs, and viewing results. To ensure data portability for subsequent re-analysis by additional proteomics software and inclusion in public repositories, data may be exported as mzML and mzIdentML XML, regardless of the original input format. The web application is developed in Java, and as such, is cross-platform. MSDaPl is open source and may be freely downloaded at http://code.google.com/p/msdapl/. MSDaPl is supported by four primary databases (referred to here as “msData,” “NR_SEQ,” “Job Queue,” and “Projects Database”) and several ancillary databases created to provide biological context to MS/MS results (Fig. 2). The structured query language (SQL) required to generate these databases for the Database is available with the MSDaPl distribution at http://code.google.com/p/msdapl/. At the of MSDaPl is a database dubbed designed to be of any specific mass spectrometry is by mass spectrometry analysis into (1) the mass (2) peptide and (3) protein Data each of these of the analysis have common as they are the of These common are into a core set of tables in each of these and these core tables are for MS peptide or protein result set into the Because all data are in these core tables, these core may be and using a common and results compared and across experiments and disparate pipelines. The core tables may be extended in to data specific to mass spectrometry analysis programs (Fig. is by a new in the specific to an analysis each may be a of the from the core a a result in the peptide search results core may have a in the Creasy D.M. protein identification by searching databases using mass spectrometry PubMed Scopus Google Scholar) results that as a of this in the data specific to that that search support for new pipelines to the database a of the core database with tables that that, although support to the database for new programs is the data development of new that the by the import in for data generated by the programs in the J.R. Eng J.K. D. to tandem mass spectra of to in the protein PubMed Scopus Google R. Beavis with tandem mass 2004; PubMed Scopus Google and E.W. L. D. T. Lam H. N. Nilsson E. Pratt B. B. Eng J.K. Aebersold R. A of the 2010; 10: PubMed Scopus Google searching for peptide identification from tandem mass Proteome Res. 2011; 10: PubMed Scopus Google T. Venable Cociorva D. B. L. J. J. Yates J.R. a and tandem mass protein identification Cell. Proteomics. L. J. MacCoss M.J. for peptide identification from shotgun proteomics Methods. 2007; PubMed Scopus Google Scholar) MSDaPl uses a database dubbed that for by identification is designed a protein and a protein is as a protein with a Center for identification E.W. T. E. K. D.M. M. S. M. W. Y. D. T. A. J. A. L. E. M. K. D. A. G. L. Y. E. J. Database resources of the Center for Acids Res. 2011; PubMed Scopus Google protein may have multiple and from many data and each reference is to both the protein and the respective data to these data may be files and protein PubMed Scopus Google Scholar), and these may as a the used to in files and protein identification used to of any Proteomics data files typically to by the present in the file used to the The databases used to generate these files may change the with or the with over time. to the protein across experiments that used different files by from database to even multiple versions of the is an the even used a file supported by the To address this data are to MSDaPl these are to protein identification in the database by the in the protein reference for the respective These protein are stored as experimental results in the database to the original which that MSDaPl and to the protein the across all of which database of that used to a may analysis across experiments to from database to current and for may be using protein databases instead of may be have been used to generate the In to this the file must be parsed into the database to data. is in more MSDaPl uses a database that is designed to and data by data with and experiments with database includes support for and access to data with to specific The database that data may be in the context of a experimental context to the data that may be files are stored on The “Job database to data by of the web Because proteomics data files are typically large to be a web interface, to data are in this database and a separate application this database for and is in more in the “Job and Data In to provide and analysis in a biological such as M. J.A. D. H. J.M. Davis K. J.T. M.A. Hill L. A. S. M. G. for the of The PubMed Scopus Google Scholar) MSDaPl use of several ancillary databases created by protein databases. These databases may be downloaded from the MSDaPl distribution The software developed for MSDaPl a web application running on of the databases a and data designed for MS/MS results to the database, and a designed to to protein in the software, source is available at the MSDaPl at http://code.google.com/p/msdapl/. The MSDaPl web application developed using and Server using the web application It is to using an open source also in Because it is the MSDaPl web application may be to any capable of running Java, and The web has been developed using standard web and is with all current web to the of MSDaPl is the of a and data are to and only to any may view data with that access to MSDaPl requires and the the for which the is as a a progress and with the may be by any as a of a by using the present on any may also own such as and by using the present at the of all To data, may use the present on any to a the provides experimental and the of the data. Because data files with proteomics experiments are typically large to be the data from this are to the database and by a running as a on the on a different is for the data and it into the MSDaPl database software is in the “Job and Data data are imported into the database they may be from the data is into and multiple experiments may be to each each the may view the peptide search results (as a of and to view the underlying or protein results (as a of underlying and underlying spectra may be using the spectrum viewer which requires the use of no software or may also all results from a as a mzData and mzIdentML XML files for data dissemination and to public proteomics data MSDaPl tools for and MS/MS data that its relational database are MSDaPl provides tools for and viewing protein from tools for comparing and contrasting protein across multiple experiments (Fig. a may protein results by the of the protein and peptide comparing results across are with of in each a the of among all and a of in all experiments that includes in which each protein may this protein using not only the options but may also the based on of the in any of the experiments being the may to only from specific experiments if (1) present in all experiments being compared (2) present or in experiments (3) not present in experiments being compared or (4) only present in of the experiments being but not the The protein may also be to of the spectrum which provides a for comparing protein across the The may be as a or as an interactive the may on of the to to that in the protein or view a that spectrum across the experiments being that the results may be compared across and across separate disparate software pipelines and files were used in the There are no on specific pipelines or databases that must be used in to this To biological context to protein MSDaPl provides of analysis to The provides a and of the terms based on the for the in a The provides a and of the terms based on an analysis that a for each based on the distribution the of in the of in the respective annotated with the the of in the annotated with the and the of annotated for the respective As of this analysis is to S. C. D. and H. of the protein identification in the B. Tabb D.L. analysis and Proteome Res. 2007; 6: PubMed Scopus Google Scholar) for protein has been into a peptide all that are into a protein that the may or with protein results results that have been imported from or analysis such as for As of this protein only be on and generated by the algorithms is a simple of developing an for based on produced by and is very to be in experiments peptide search analysis that peptide spectrum only is may the in the peptide search result to view that provide a for the quality of the mass spectrometry (Fig. The the of with than a to all and the of MS/MS that in a quality to all MS/MS for data into MSDaPl has been developed and is in the MSDaPl The import system has been designed as a of the MSDaPl web to data and these are to the database as to be by a separate is for for new the data, the data to the MSDaPl database, and the The and may be and the web interface. The import supports files with the pepXML (5Keller A. Eng J. Zhang N. Li X.J. Aebersold R. A uniform proteomics MS/MS analysis platform utilizing open XML file formats.Mol. Syst. Biol. 2005; 1 (2005.0017)Crossref PubMed Scopus (598) Google Scholar), (5Keller A. Eng J. Zhang N. Li X.J. Aebersold R. A uniform proteomics MS/MS analysis platform utilizing open XML file formats.Mol. Syst. Biol. 2005; 1 (2005.0017)Crossref PubMed Scopus (598) Google Scholar), and MS2, and MS2 The import for each of these formats a common that may be by to support for file formats to the import system. may be to the system web in to support programs or proteomics pipelines. As in the MSDaPl use of a database to the present in specific files to protein identification in to to the protein across experiments and to provide current and and for in results. To this the file used in the analysis must be parsed into the database to the data being A for files and storing the results in the database is with It is important to that the uses the of the file to specific files. Data to the MSDaPl database also use the of the file used in the to to to protein identification that any new or of a file used to search data have a there is no longer a that an for a to a MSDaPl developed in with the and the of Proteomics Resource of these multiple proteomics each with software analysis pipelines and support In these MSDaPl has been used to or analyze data from many mass spectrometry To the of data in the of MSDaPl includes which includes MS/MS runs, and The of Proteomics Resource of MSDaPl includes which includes runs, and of these and the data has to support the respective of data. with may use these as a guide for for data. in MSDaPl are by by and proteomics is on developing support for protein and peptide which be designed to of data in developing core support for data that is of specific pipelines, this core support to data generated by specific pipelines. In addition, it is to the data in MSDaPl to third-party applications web that allow them to or to the a system to allow to of the database from the web be MSDaPl is a data data and data dissemination for mass spectrometry-based It has been designed to address the of a file-based and is and cross-platform. MSDaPl includes support for many of the proteomics pipelines, and development for any pipeline has been by The software has been designed to be and And it is that by MSDaPl to the as an MSDaPl by support for more proteomics tools and file formats.