proposal for a standard representation of two-dimensional

Comparative and Functional GenomicsComp Funct Genom 2003; 4: 492–501.Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cfg.323

Primary Research Paper

Proposal for a standard representation oftwo-dimensional gel electrophoresis data

Andrew Jones1*, Jonathan Wastling2 and Ela Hunt11Department of Computing Science, University of Glasgow, UK2 Institute of Biomedical and Life Sciences, University of Glasgow, UK

*Correspondence to:Andrew Jones, Department ofComputing Science, University ofGlasgow, 17 Lilybank Gardens,Glasgow G12 8QQ, UK.E-mail: [email protected]

AbstractThe global analysis of proteins is now feasible due to improvements in techniques suchas two-dimensional gel electrophoresis (2-DE), mass spectrometry, yeast two-hybridsystems and the development of bioinformatics applications. The experiments formthe basis of proteomics, and present significant challenges in data analysis, storage andquerying. We argue that a standard format for proteome data is required to enablethe storage, exchange and subsequent re-analysis of large datasets. We describe thecriteria that must be met for the development of a standard for proteomics. We havedeveloped a model to represent data from 2-DE experiments, including differencegel electrophoresis along with image analysis and statistical analysis across multiplegels. This part of proteomics analysis is not represented in current proposals forproteomics standards. We are working with the Proteomics Standards Initiative todevelop a model encompassing biological sample origin, experimental protocols, anumber of separation techniques and mass spectrometry. The standard format willfacilitate the development of central repositories of data, enabling results to be verifiedor re-analysed, and the correlation of results produced by different research groupsusing a variety of laboratory techniques. Copyright 2003 John Wiley & Sons, Ltd.

Keywords: proteomics; electrophoresis; standard; ontology

Introduction

Proteomics uses experimental techniques for thelarge-scale study of proteins. The experiments aimto determine all of the proteins expressed in aparticular sample, or search for protein–proteininteractions that may be crucial to the functionof the system. Proteomics is a part of func-tional genomics, which also includes microarrayanalysis, phenotypic studies and small moleculearrays. The integration of all the diverse typesof data is vital, and new bioinformatics tools arerequired (Tyers and Mann, 2003). In this work, wefocus on the development of standards for stud-ies of protein expression in which proteins areseparated by two-dimensional gel electrophoresis(2-DE) and identified by mass spectrometry (MS).There is a major requirement for the development

of a central proteome database that includes 2-DE images, analysis and MS data. For such aplan to be realized, it is vital that a standarddata model is adopted by the research commu-nity to enable experiments from different labo-ratories to be compared or queried. A centraldatabase must contain sufficient detail about exper-imental protocols for the context of the exper-iment to be fully understood. It is also impor-tant that statistical analysis is captured, to ensurethat new results derived from data are electron-ically accessible and can be verified. In otherareas of biology, the deposition of data in acentral repository is a prerequisite for publication:DNA sequences must be deposited in GenBank(http://www.ncbi.nlm.nih.gov/Genbank/) andprotein structures in the PDB (http://www.rcsb.org/pdb/). Proteomics datasets can be very large,

Copyright 2003 John Wiley & Sons, Ltd.

Standard representation of 2-DE data 493

and not highly amenable to publication only inpaper format, therefore a repository for publisheddata would enable results to be obtained andunderstood more easily. A model representingthe workflow of a proteomics experiment hasbeen presented, known as PEDRo (Taylor et al.,2003). The Proteomics Standards Initiative (PSI;http://psidev.sourceforge.net/) has been started bythe Human Proteome Organisation (HUPO) andmeetings have been held at the European Bioin-formatics Institute (Orchard et al., 2003a, 2003b).PSI is using PEDRo as an initial framework forthe development of a standard. Our contribu-tion is to develop a model for 2-DE data, dif-ference gel electrophoresis, image analysis andstatistical analysis of large datasets. We believethese data types are not adequately covered in thecurrent proposals and offer our model as addi-tional information that should be captured in thecommunity standard. To justify this view, we

focus on the proteomics techniques, the require-ments of standards and the development of a datamodel.

Proteomics techniques

A diagram describing the workflow of a proteomicsexperiment is displayed in Figure 1. Proteins areseparated according to their charge and molecularweight using 2-DE, and are visualized by staining.The gel is scanned and analysis software detectsproperties of protein spots, including coordinatesand an estimate of the volume of protein inthe gel. Software can match spots produced ondifferent gels corresponding to the same protein,and determine the relative difference in the spotsize and intensity between two or more gels. Spotmatching between gels is not perfect, thereforeother techniques, such as MS, may be required

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Mass Spectrometry MS/MSMALDI

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ID Vol X Y Protein1 454 23 242 222 28 87 abc13 12 20 12 4 662 262 101

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Figure 1. The data flow in a proteomics experiments. The area for which we have developed a model is boxed

Copyright 2003 John Wiley & Sons, Ltd. Comp Funct Genom 2003; 4: 492–501.

494 A. Jones et al.

to verify that matched spots belong to the sameprotein. In many cases, it is possible to determinethe global expression profile of proteins in one ormore samples of interest.

A new technology in protein separation is two-dimensional difference gel electrophoresis (Unluet al., 1997) or DIGE (Ettan DIGE produced byAmersham Biosciences; http://www.amersham-biosciences.com). DIGE enables two (or more)samples to be labelled with different fluorescentdyes, mixed and separated on a single gel. A pro-portion of the two samples can be mixed priorto gel loading and a third dye is applied, act-ing as an internal standard for calibration. Thegel is scanned at different wavelengths, creat-ing images that can be compared. This removesvariability due to gel-specific differences andshould greatly improve the detection of differentialexpression.

Protein spots may be excised from the gel,digested and analysed in a mass spectrometer.Information from an MS trace can be used tosearch sequence databases to determine the identityof a protein (a review of techniques is givenby Mann et al., 2001). After a protein has beenidentified there are a number of databases thatmay be accessed to characterize the protein. Theseinclude genomic or annotated sequence databases,repositories of protein motifs or families, andbibliographic references. It is important that thedetails of database searches are stored alongsideexperimental data to provide support for future re-evaluation.

Requirement for standards

Proteome experiments are labour-intensive but canproduce very large datasets which provide a sum-mary of many proteins in a given sample. Thedevelopment of robotic equipment, such as auto-mated spot pickers, for high-throughput analysis,will further increase sizes of dataset. We have pre-viously studied proteomics research activities inmicrobial pathogenesis and cancer research (Joneset al., 2003). The studies highlighted several typesof enquiry that benefit from proteomics techniques:

• Proteome cataloguing : determine the entire setof proteins expressed in a cell type, organelle ormicroorganism.

• Hypothesis generation: discover proteins whosefunction may be important in the condition ofinterest.

• Protein regulation: discover sets of proteins thatshare patterns of expression across a range ofsample conditions.

• Correlating gene and protein expression.• Post-translational modifications : identified by

MS, which cannot be determined using microar-rays.

The information from a proteomics experimentcan provide an additional level of information tosequence databases (Aebersold and Mann, 2003).For example, if experiments to determine the pro-teome of human liver cells reveal that a specificprotein is abundantly expressed, the informationis functionally significant and should be avail-able to researchers accessing sequence databases.Additionally, gel spots analysed by MS mayreveal peptides that match a region of genomicsequence that has not been annotated. There-fore, the peptide sequences can be used to dis-cover new genes, or edit incorrectly annotatedgenes.

The global protein profile generated by exper-iments depends upon the conditions under whichthe sample was produced and processed, prior toseparation by 2-DE. The data may be valuable toresearchers in diverse fields, who could obtain newresults from datasets originally intended for anotherpurpose. Therefore, it is vital that experimental pro-tocols are rigorously documented according to astandard, and stored in a structured format thatallows searches over biological conditions: species,cell, tissue type or experimental conditions, suchas: gel constituents, stain, or MS instrument param-eters.

Model development

We have previously developed a prototype databasefor 2-DE and MS data (Jones, 2001) that high-lighted challenges in data integration and captur-ing experimental protocol information in a struc-tured format. We discovered that many types ofquestions cannot be answered using current tech-nology, which would be solved by the devel-opment of a central repository and appropriatequery tools. This motivated us to develop a model



to describe data from 2-DE, difference gel elec-trophoresis, image analysis and statistical process-ing. The development of the model was driven byanalysis of real datasets and an understanding ofthe types of queries that researchers would like topose.

A repository for proteomics data must includea structured, detailed description of the originof the sample. We believe that the standardiza-tion of sample origin should occur in conjunctionwith MGED (Microarray Gene Expression Datasociety; http://www.mged.org), who are develop-ing microarray standards. There has already beensignificant work towards formal descriptions ofsample origin which is highly applicable for pro-teomics.

Our model allows researchers to store data fromany of the image analysis applications that areavailable. Statistical analyses performed on dataproduced from image analysis, such as software,algorithms and the associated parameters, can alsobe captured. The model is further specialized tomanage difference gel electrophoresis data. Ourmodel links spots visualized on a gel, to identifiedproteins, via MS. We do not include a proposal forannotation standards for MS; however, there area number of groups working towards a standardfor MS under the auspices of PSI, described in thenext section. PSI will oversee the development ofa complete model for proteomics that encompassessample origin, 2-DE and MS. The complete modelwill provide the basis for a data exchange languageand the schema for a central repository of data.The repository will provide the framework forintegrating the diverse data types that encompass aproteomics experiment, and will enable researchersto query and analyse large datasets in ways that arenot currently possible.

An overview of previous work

Microarray standards

A project was initiated for the standardizationof microarray data formats, known as MIAME(Brazma et al., 2001) (Minimum InformationAbout a Microarray Experiment). MIAME definesall the data that must be stored about an experi-ment to allow it to be reproduced or analysed inlight of other experiments. A formal specification

of the requirements was released as an object modeland a mark-up language called MicroArray andGene Expression Markup Language (MAGE-ML)(Spellman et al., 2002), in XML format (eXtensibleMarkup Language). The project is now managed bythe MGED society. The work is highly relevant tothe development of proteome standards for two rea-sons. First, there are a number similarities betweenmicroarray and proteomics experiments, thereforere-using aspects of the MAGE model will avoidrepetition of work. Second, it is important thatmicroarray and proteome data can be integrated, toenable co-analysis of gene and protein expressionlevels and determine the correlation between thetwo. The integration will be facilitated by similarrepresentations of data.

MAGE captures details of the origin of a bio-logical sample. This stage precedes the extrac-tion of mRNA (microarrays) or proteins (2-Dgels) and should be the same for both experi-ment types. There is a large variety of types ofsample that could be used, therefore defining amodel that allows all the information to be cap-tured in a machine-readable format is a majorchallenge (Stoeckert and Parkinson, 2003). Thestarting sample could be cell culture, such as amammalian cell line, therefore strain identifiers,growth media and timings must be captured. Exper-iments are also carried out on whole organisms,requiring the capture of genotypic and phenotypicinformation. The MGED group is developing anontology of terms expressing how samples havebeen generated and the related parameters, e.g.the constituents of a bacterial growth medium.The function of the ontology is to provide thedata types and values with which to populate theMAGE model. The controlled vocabulary is avail-able from the MGED Ontologies working group(http://mged.sourceforge.net/ontologies/), and isused in the ArrayExpress database (http://www.ebi.ac.uk/arrayexpress/). It is likely that theMGED ontology will develop with contributorsfrom different fields, including proteomics resear-chers adding terms relating to their own researcharea.

PEDRo

PEDRo (Proteomics Experiment Data Repository;Taylor et al., 2003) is a formal model to representa proteomics workflow. PEDRo has been accepted


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by PSI as a draft framework for the developmentof a standard. It consists of:

• Biological sample origin.• Separation techniques, including 2-DE.• Mass spectrometry laboratory protocols.• Mass spectrometry data analysis.

PEDRo is designed to allow an experimentinvolving a number of stages of protein separationto be described, including 2-DE, affinity columnsand chemical treatments. MS data is also describedin the PEDRo model, including support for storageof database searches with results. There are anumber of organizations developing standards forMS to serve different purposes (described below),therefore it is important that a consensus is reached.

The coverage of biological sample origin inPEDRo is focused on cell-based samples withlimited coverage for whole organism proteomics.We believe that the work of the MGED soci-ety on sample origins should be incorporated intoa proteomics standard. PEDRo covers many ofthe parameters needed to describe separation tech-niques; however, it has limited coverage of imageanalysis of single gels, and does not adequatelycapture multiple gel comparisons. We believe thatPEDRo does not have sufficiently detailed classesto capture difference gel electrophoresis data com-pletely.

Proteomics databases

There are a number of databases accessible viathe Internet which store proteomics data. SWISS-2DPAGE was initially developed in 1993, stor-ing 2-DE images and information about pro-teins identified on gels with links to Swiss-Prot(http://ca.expasy.org/sprot/) and other databases.SWISS-2DPAGE has an interface with gel imagemaps that can be used to access information aboutprotein spots (Hoogland et al., 2000). Scripts areavailable for developing a database and interfacebased on SWISS-2DPAGE, known as make2ddb,which has been used to create a number ofdatabases (links to the databases can be found at theSWISS-2DPAGE website: http://ca.expasy.org/ch2d/). SWISS-2DPAGE stores entries in a textformat that includes bibliographic references, spe-cies of origin and pI/mW of proteins. However,the text format does not store substantial infor-mation about experimental protocols. The format

stores peptide masses produced by MS, but with-out raw data or information about database searchesthat have been carried out, therefore the reliabilityof protein identification is difficult to determine.

A database has been developed by the JapaneseHuman Proteome Organisation (J-HUPO: http://www.jhupo.org/), which has an output formatknown as HUP-ML. HUP-ML is centred around 2-DE data and experimental protocols, allowing theconstituents of solutions and timings to be speci-fied, similar to sample preparation stages describedin MAGE-ML. The format is likely to be mappedto the final format decided by PSI. There are anumber of domain specific proteome databases,storing 2-DE or MS data (a summary of pro-teomics databases can be found at WORLD2D-PAGE: http://us.expasy.org/ch2d/2d-index.html).In general, the databases store only limited infor-mation about experimental protocols and are notfully integrated with other types of protein databa-ses. It is a major challenge to integrate distributedproteomics databases, because data is not format-ted in a uniform manner and the databases rarelyoffer wide-ranging query facilities.

Mass spectrometry

Mass spectrometry is used in proteomics to iden-tify proteins. An experiment generates raw data,in the form of a trace and processed data: a peaklist corresponding to peptide masses. A major issueis coping with proprietary data formats generatedby mass spectrometer manufacturers. Instrumentsare supplied with software for data collection andanalysis. The software only provides the func-tionality to save analysis within a data formatthat cannot be interpreted by any other software.Researchers often manually enter the peak heightsinto a text editor, for input into database search pro-grams. Proprietary formats pose a major problemfor research throughput and data archiving. It can-not be assumed that the software needed to interpretthe spectra will still be available in the future. Itis also not feasible for researchers wishing to anal-yse the spectra deposited in databases to obtain thesoftware that produced them. Therefore, there isa great need for a data exchange standard that canbe interpreted without specialist software. The stan-dard will support algorithm development for largescale database searches.



There are several proposals for MS standardsincluding GAML (Generalized Analytical MarkupLanguage: http://www.gaml.org), and SpectroML(developed by the National Institute for Standardsand Technology: http://www.nist.gov). GAMLis an industry-generated effort to develop anXML-based data format for analytical instru-ments. GAML stores values of x /y coordinatesfrom a trace, and the parameters entered in theinstrument. SpectroML has similar goals and hasbeen developed in collaboration with ASTM, aninternationally recognized standards organization(http://www.astm.org). The PEDRo model alsosupports MS data. The goal of PSI is to unify theefforts, and work with instrument manufacturers toproduce data formats conforming to the standard,once it is finalized.

A model for 2-D gel electrophoresis andanalysis

The data flow shown in Figure 1 outlines thestages in which information must be capturedin a proteomics experiment. We have developeda model in UML (Unified Modeling Language:http://www.uml.org/) (Figure 2), to represent 2-DE data, image analysis and statistical process-ing for cross-gel analysis (see Figure 3). UMLis a methodology designed to improve softwareengineering and requirements capture. UML classdiagrams represent real-world objects or con-cepts using the definition of a class with certainattributes (see Figure 2). Relationships betweenclasses can be expressed, representing the true rela-tionships between concepts or datatypes. A UML

model can subsequently be converted into a rela-tional database schema or validation document forXML.

Two-dimensional gel electrophoresis

A complex mixture of proteins can be separated bya number of techniques, including two-dimensionalgel electrophoresis (2-DE), chromatography, affin-ity column and others. In this work we focus on2-DE, which is the most widely used technique forprotein separation in proteomics. A standard for2-DE must capture the conditions under which thegel was run. The conditions include the dimensionsand voltages applied to the pH strip, gel dimen-sions, buffers and staining procedures. Many ofthese parameters are covered in PEDRo, and wehave reproduced certain attributes from PEDRo inthe 2D-PAGE class. Once a gel has been run, thereis a significant amount of information that mustbe captured, which is not adequately covered inPEDRo. Initially, a gel is scanned and a raw imageis produced. Our model incorporates this process,capturing the details of the scanner and the imageproduced, in the class ScannedImage. The modelallows multiple instances of a scanning event incases where researchers have re-scanned a gel,e.g. with scans at different resolutions. The imagederived from a scanned gel becomes the input forthe next part of the model — image analysis.

Image analysis

A number of software packages can be used toanalyse scanned 2-D gels. The software is able toperform edge detection on an image to determine

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Figure 2. The main components of a UML class diagram


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IDEvidence

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Mass spectrometry standards are under development by several organisations, see main text for a full description

The stages preceding image analysishave been presented in models: MAGEhttp://www.mged.org and PEDRohttp://pedro.man.ac.uk

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version : StringURI : String

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id1 : Stringid2 : StringnormalisedRatio : Doublequality : StringratioType : String

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scanner : StringfileURI : Stringresolution : Doublecontrast : Doublebrightness : Doublewavelength : DoubledimensionX : IntegerdimensionY: Integer

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id : StringmW : DoublepI : Doubleaccession : StringswissProtID : StringpirID : String

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software : Stringversion : Stringalgorithm : StringdataFile : StringanalysisType : String

Spot

volume : DoublenormalisedVolume : Doublearea : Double peakHeight : DoublexCoord : IntegeryCoord : Integerexperiment_pI : Doubleexperiment_mW : Doubleradius : Double

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spotID : String0..n

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ExternalReference

exportedFromServer : StringexportedFromDB : StringexportID : StringexportName : String

DescribableAll classes are subclasses of Identifiable and Describable (not shown). Therefore, all classes can

have an identifier attached and be linked to annotation classes.

ScannedImage

scanner : StringfileURI : Stringresolution : Doublecontrast : Doublebrightness : Doublewavelength : DoubledimensionX : IntegerdimensionY: Integer

Databaseversion : StringURI : String

BibliographicReference

title : Stringauthors : Stringpublication : Stringpublisher : Stringeditor : Stringyear : Datevolume : Stringissue : String pages : StringURI : String

Description

text : String

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Figure 3. UML class diagram representing the relationships between types of data in an experiment using two-dimensionalgel electrophoresis and mass spectrometry

the coordinates, volume, area and other propertiesof protein spots. The class Spot accommodatesmany of the properties produced by current soft-ware packages; however, it is not possible toinclude all measures that may be produced bycurrent or future software versions. A class contain-ing the attributes parameterName, parameterValueand parameterUnit is used to cover datatypes that

are not explicitly included in the model. Valuesfor these attributes will be obtained from a con-trolled vocabulary to ensure consistency. The classImageAnalysis records the software package and adescription of image processing that has occurred.

Image analysis applications have the ability tomatch spots on different gels, believed to corre-spond to the same protein: from replicate gels for



the same samples, or from gels over which a samplecondition is varied, such as a time-course exper-iment. The overview of an experiment describesthe number of replicates, with pointers to theprotocol for the gel separation and the data. Spotsthat have been matched are linked via a specificclass: MatchedSpots and the class MultipleAnaly-sis stores a description of the type of matching thathas been carried out.

Protein spots

There are separate classes for spots identified ona gel (Spot), and proteins (Protein) to which spotsmay be matched. The relationship between Spotand Protein allows one or more spot records to belinked to one or more protein records because thereare known instances where a single spot in fact con-tains a number of different proteins. In the oppositedirection, a protein record is likely to be matched toa number of spots on different gels, or possibly on asingle gel. The relationship is linked via an attributethat includes the evidence for the match, such asany MS data that is available. This is vital becauseany findings based upon the predicted expressionof a protein should take into account the probabil-ity that the protein has been identified correctly.The protein class contains sufficient informationsuch that a repository based on the model can linkdirectly to external databases. A single protein mayhave entries in a number of databases that may berelevant to the experiment, e.g. GenBank, Swiss-Prot, PIR (http://pir.georgetown.edu/) or domain-specific databases.

Two-dimensional difference gel electrophoresis

Data produced from a difference gel electrophore-sis experiment is described in our model. Amer-sham Biosciences produce DIGE technology andDeCyder software, for analysis of gels. DeCy-der can export data in an XML format, known asDeCyderML (personal communication from Amer-sham Biosciences), which we have mapped to ourmodel. A single gel produced using DIGE tech-nology can produce several images, correspondingto the fluorescent dyes used for different samples.DeCyderML contains a class for the single channelimage, with attributes such as dye type, which wehave mapped to DIGESingleImage, which is a sub-class of ScannedImage. A second class exists for

single image spots (DIGESingleSpot). DeCyderMLalso has a class for storing information about co-migrated spots, which we have mapped to the gen-eral Spot class in our model. DeCyderML includesinformation about spots that have been matchedacross gels, which we have mapped to the Multiple-Analysis and MatchedSpot classes that exist in ourmodel for storing non-DIGE data. DeCyder soft-ware calculates ratios between pairs of single imagespots that have co-migrated, captured in SpotRatio.

Statistical analysis

Statistical analysis techniques, such as ANOVA(analysis of variance), are used to locate spotswhose volume is significantly different betweentwo samples, indicating a change in protein expres-sion under a certain condition. It is vital thatthe exact details of the analysis are preserved toensure that the same procedure can be reproducedby other research groups. A number of statisticaltechniques can be applied to large datasets, suchas analysis over a number of replicates, or overa number of gels analysing a varying condition.An example is cluster analysis, as performed onmicroarray data sets (Eisen et al., 1998), to detectgroups of proteins sharing similar expression pat-terns over a number of gels. The StatisticalAnaly-sis class accommodates a description of the soft-ware or algorithm used to perform the analysis,and appropriate parameters and significance levelsused. Our model has a link from a description ofthe analysis to the raw data. The analysis can belinked to individual spot records, or spots matchedbetween gels. A formal description of statisticalanalysis presented by Papageorgiou et al. (2001)covers most of the attributes that are applicable toproteomics analysis, but is possibly too complexfor use in biological applications. Our model hasfew attributes, with the intention that the detailsof the analysis will be described with data typesobtained from controlled vocabularies. It is desir-able that future versions of a proteomics standardincorporate future statistical standards.

Annotation

We allow for annotation of all aspects of the experi-ment including raw data, experiment protocols andanalysis. Annotation may be in the form of freetext or links to external databases or ontologies.


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MAGE includes classes that allow annotation to beadded and linked to any other part of the model.All classes in our model are subclasses of Iden-tifiable, which allows an identifier to be added toeach class. Identifiable is a subclass of Describable,which has a relationship to the annotation classes,therefore every class inherits this relationship.

Discussion

Web access to date

It has been recognized that past funding forlarge databases of scientific data has not beensufficient and, as a result, vital information islost (Maurer et al., 2000). An activity whichattempts to remedy this situation is the effort todevelop biochemical pathway databases, such asKEGG (http://www.genome.ad.jp/kegg/). Infor-mation regarding reaction kinetics and functionalinformation has been published over several deca-des, but is not generally available in electronicform. Only papers published in the last decade maybe available on the Internet, and data is not pre-sented in any kind of format that can be minedautomatically. Instead, information retrieval tech-niques must be used with significant manual inter-vention. This process is time consuming and willmiss substantial amounts of information. In the cur-rent era, data regarding one biological system isoften too extensive for a single researcher to gainaccess to by reading published literature, and auto-mated methods are required. Microarray expertshave previously recognized these needs and effortsare under way to develop large central repositories(Brazma et al., 2000). The databases are intendedto allow re-analysis of published data as new sta-tistical techniques are developed. Microarray andproteomics experiments generate large amounts ofdata that is of potential use to researchers in manyother fields. Improvements to algorithms for mul-tiple analysis of 2-DE images will enable betteruse of repositories of gel images and will pro-vide significant information about the mechanismsinvolved in protein regulation.

Status of proteome standards

Our model represents data from one section ofa proteomics workflow and complements otherwork undertaken by various organisations. PSI is

overseeing the development of a standard, and isusing PEDRo as an initial framework from whichto develop a unified model. Our model coversimage analysis of 2-DE, multiple gel comparison,DIGE and statistical analysis of large datasets, andrepresents additional information that we believeshould be included in the next version of the com-munity standard. Our model will be supplied toPSI, complementing PEDRo as the current pro-posal. We are currently integrating PEDRo with ourmodel, to describe proteome-specific technologies,using MAGE components to capture experimentalprotocols. We believe that the MGED ontologiesshould be used to describe biological samples, andPSI should ensure close collaboration with MGED.

The development of a standard requires signifi-cant contribution from the proteomics communitybefore consensus can be reached. The completemodel should be flexible with regard to new tech-nologies and experimental protocols. A data stan-dard should not prescribe how researchers carry outexperiments, but should capture enough detail toensure that useful data archives can be developed.If a standard is to be accepted, it is vital that toolsare developed that enable researchers to capturedata conforming to the standard without substantialmanual data entry. Laboratory Information Man-agement Systems (LIMS) are available from com-mercial software vendors. They capture instrumentparameters and track solutions using bar-coding. Itis likely that future versions will be specificallytailored for proteomics applications, and softwarevendors should provide an output file conformingto the proposed standard. A dataset containing 2-DE images, MS traces, analysis and annotation isfairly bulky, therefore the development of a single,public database covering all aspects of proteomicsis unlikely. A more feasible solution is the devel-opment of distributed, domain-specific proteomedatabases, such as single organism or diseasedatabases. It is vital that databases provide wideranging query facilities to enable the developmentof applications that search for data sets of interest.Data integration applications will be developed tolink proteome databases to other repositories, suchas databases of sequences, motifs and structures.

Conclusions

We have developed a model to represent 2-DEimage analysis, difference gel electrophoresis and



statistical processing. Our model is a response toprevious proposals for a standard for proteomics.PSI will oversee the development of a completestandard for proteomics. The standard will providethe basis for a central repository of data which willserve as a platform to integrate the diverse elec-tronic resources that encompass a proteomics dataset. The repository should facilitate re-evaluationof data and allow new data to be correlated withpublished results. We are currently developing alocal repository for 2-DE and MS, with a schemadeveloped from MAGE, PEDRo and our model.Our future work includes the development of XMLstorage technology to provide an integrated querysystem to a number of protein databases.

Acknowledgements

The authors wish to thank Ashwin Kotwaliwale at the Beat-son Institute for collaboration on the development of pro-totype databases. The development of the model was aidedby discussions at the 2003 Proteomics Standards Initiativemeeting at the European Bioinformatics Institute. AndrewJones and Ela Hunt are supported by grants from the Med-ical Research Council. Jonathan Wastling’s laboratory isfunded by a grant from the BBSRC (17/513819).

Related Websites

Amersham Biosciences; http://www.amersham-biosciences.com/

ArrayExpress; http://www.ebi.ac.uk/arrayex-press/

ASTM International; http://www.astm.orgGAML; http://www.gaml.orgGenBank; http://www.ncbi.nlm.nih.gov/Gen-

bank/J-HUPO; http://www.jhupo.org/KEGG; http://www.genome.ad.jp/kegg/MGED; http://www.mged.orgMGED Network Ontology Working Group; http:

//mged.sourceforge.net/ontologies/NIST; http://www.nist.govPDB; http://www.rcsb.org/pdb/PEDRO; http://pedro.man.ac.uk/PIR; http://psidev.sourceforge.net/SWISS-2DPAGE; http://ca.expasy.org/ch2d/Swiss-Prot; http://ca.expasy.org/sprot/UML; http://www.uml.org/WORLD2D-PAGE; http://us.expasy.org/ch2d/2d-

index.html

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