RESEARCH ARTICLE Open Access

MetNetAPI A flexible method to access andmanipulate biological network data from MetNetYves Sucaet12 Eve Syrkin Wurtele12

Abstract

Background Convenient programmatic access to different biological databases allows automated integration ofscientific knowledge Many databases support a function to download files or data snapshots or a webservice thatoffers ldquoliverdquo data However the functionality that a database offers cannot be represented in a static data downloadfile and webservices may consume considerable computational resources from the host server

Results MetNetAPI is a versatile Application Programming Interface (API) to the MetNetDB database It abstractscaptures and retains operations away from a biological network repository and website A range of databasefunctions previously only available online can be immediately (and independently from the website) applied to adataset of interest Data is available in four layers molecular entities localized entities (linked to a specificorganelle) interactions and pathways Navigation between these layers is intuitive (eg one can request themolecular entities in a pathway as well as request in what pathways a specific entity participates) Data retrievalcan be customized Network objects allow the construction of new and integration of existing pathways andinteractions which can be uploaded back to our server In contrast to webservices the computational demand onthe host server is limited to processing data-related queries only

Conclusions An API provides several advantages to a systems biology software platform MetNetAPI illustrates aninterface with a central repository of data that represents the complex interrelationships of a metabolic andregulatory network As an alternative to data-dumps and webservices it allows access to a current and ldquoliverdquodatabase and exposes analytical functions to application developers Yet it only requires limited resources on theserver-side (thin serverfat client setup) The API is available for Java MicrosoftNET and R programmingenvironments and offers flexible query and broad data- retrieval methods Data retrieval can be customized toclient needs and the API offers a framework to construct and manipulate user-defined networks The designprinciples can be used as a template to build programmable interfaces for other biological databases The APIsoftware and tutorials are available at httpwwwmetnetonlineorgapi

BackgroundAnalysis of the topology of biological networks providesunderstanding of structure function and interactionamong cellular entities [1] As knowledge and under-standing of living systems expands biological networkdatabases are becoming increasingly sophisticated interms of data complexity and overall functionality Tofacilitate integration with various bioinformatics soft-ware packages many online pathway and networkdatabases offer static data download and conversion

methods [2] Several larger databases including KEGG[3] BioCyc [4] and Reactome [5] also offer ApplicationProgramming Interfaces (APIs) These resources areused by the community to incrementally enrich datasetssuch that each iteration is better and more completethan the previous oneThe MetNet systems biology platform is a suite of

software programs that model metabolic and regulatorypathways in plants [6] At its core is MetNetDB whichrepresents an integrated pathway-database for plant spe-cies and combines various data sources such as AraCyc[7] TAIR [8] AGRIS [9] and atPID [10] The databaseallows users to integrate pathways and interactions andkeep track of entities of interest in a customizable way

Correspondence mashiastateedu1Department of Genetics Development and Cell Biology Iowa StateUniversity Ames IA 50011 USAFull list of author information is available at the end of the article

Sucaet and Wurtele BMC Research Notes 2010 3312httpwwwbiomedcentralcom1756-05003312

copy 2010 Sucaet and Wurtele licensee BioMed Central Ltd This is an Open Access article distributed under the terms of the CreativeCommons Attribution License (httpcreativecommonsorglicensesby20) which permits unrestricted use distribution andreproduction in any medium provided the original work is properly cited

Through a public website httpwwwmetnetonlineorg users control various network resources including1) the ability to bookmark pathways 2) tracking user-defined components of interest and 3) a localizationdatalayer Users can create new pathways by combiningand modifying existing pathways and then save the newpathways Using the website pathways can be exportedto SBML (for visualization with CellDesigner [11]) orXGMML (for visualization with Cytoscape [12]) All theabove functions are now available through our APIMetNetAPI is an alternative interface to the MetNetplaform for tasks that are not easily performed withalready existing software tools time-consuming orrepetitiveAn API allows data to be approached and viewed in

several modi Unlike statically-exported files such asdata dumps and standardized schema which offer only asingle view of the data whereas an API enables muchmore user customization such that a researcher canview or computationally manipulate the data in multipleways Consider the static SBML BioModels datasetwherein each file represents a single pathway [13]Assume someone downloads this dataset and wants togain a more complete understanding of it by creating alist of all the molecular entities that participate in all thepathways This list can then in turn be used to connectpathways with overlapping components (eg pathways inwhich starch participates can be combined to studystarch metabolism and its regulation [14]) Howevercomposing a complete list of entities that make up allthe pathways in which starch participates entails writinga piece of custom parsing software In contrast an APIcan implement a method that automatically extracts alist of participating entities for a collection of the path-ways in which they occur

ImplementationThe choice of an APISeveral options exist to share information contained in abiological database One option for transfer of databasecontent is a data dump This exposes all the informationcontained in the database However it may require sig-nificant effort to understand (and possibly reconstruct)the original database schemaA second option is to support a standard data format

Chado and BioSQL are two examples of standardizeddata schema specific to sequence databases [15] BioPaxSBML and PSI are the most widespread file formats forrepresentation of biological networks [16] Each stan-dard has its own set of limitations as to what types andresolution of data it can represent Supporting multipleformats is time-consumingA third option is providing an Application Program-

ming Interface (API) A major advantage is that content

and functionality are combined [17] Tying an APIdirectly to a biological database has been done by othergroups MetaCyc [18] is based on the Lisp programminglanguage and interfaces with local MetaCyc-deriveddatabases while MetNetAPI offers broader program-ming language support and always connects to theremote ldquoliverdquo MetNetDB database BioMart [19] is ageneric biological repository and configuring it to sup-port complex network data takes a long time Its gen-eral-purpose nature also makes it slow to run complexqueries due to the meta-data that needs to be inter-preted first KEGG [20] and Reactome [21] offer a web-service interface based on XML and SOAPREST Awebservice can be considered as a special type of APIand provides its own particular problems A wrappermust be provided around the webservice to facilitatecommunication and data exchange This effectivelymeans a secondary API has to be provided to communi-cate with the initial API Even as this process can oftenbe automated (through frameworks such as JAX-WShttpjax-wsdevjavanet Axis httpwsapacheorgaxis or XFire httpxfirecodehausorg) it is far fromefficient REST-based webservices are somewhat lesscumbersome in this regard (they are lightweight pro-duce human readable results and require no toolkitslike SOAP does) but they have their own peculiaritiesEvery resource needs to be accessible through a uniqueURI This means that information is represented in ahierarchy which can become complicated very quicklyand cumbersome to browse It is possible however tocircumvent this problem by allowing querying of thedataset at a different location on the website The URLto a REST-resource is then a query-string in its ownright While the messaging protocol involves less over-head than its SOAP-counterpart the lack of requiredmessage meta-data makes these environments at thesame time less intuitive and harder to query for complexdata Reactome is one such pathway database [21] thatsupports REST through BioMartrsquos MartService [19]Doodle is another resource that supports REST [22]while GenMAPP [23] WikiPathways [24] and CPDB[25] choose to provide SOAP-based servicesIf functionality is added to the webservice (either

REST or SOAP) supplemental resources - CPU mem-ory hard disk - for the server hosting the service mustbe considered Webservices therefore seem to be des-tined to either offer limited functionality (and thus beless useful) or offer extensive functionality but artifi-cially limit access to them because no institution cangather unlimited bandwidth and resources to serve theworld MetNetAPI offers close proximity (strong data-typing) to the MetNetDB database and underlyingmodel while still able to provide flexibility and abstrac-tion in regard to biological information content

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Processing of information mostly occurs on the clientrunning the API which results in a more distributedload This presents opportunities to better plan (and dis-tribute) resources across various projects

API implementationMetNetAPI is designed as an object model that abstractsand encapsulates the data in the underlying MetNetDBrepository We chose Java R and MicrosoftNET as tar-get programming languages because they are platformindependent and are widely in use today Users do notneed to understand the internal intricacies of the back-end database model The goal is to hide complex datamodelling techniques and allow the bioinformatics soft-ware developer (and by extension the biologist) to getstarted using novel integrated datasets quickly Overheadis kept to a minimum as there is no WSDL-file to beparsed as with SOAP The structure of the informationin MetNetAPI is exposed intuitively through Java reflec-tion mechanisms that are provided in most developmentenvironmentsMetNetAPI is a Java jar-file (or NET Assembly) which

contains several logically-ordered namespaces (abstractcontainers that express semantic categories of code)The main namespace is eduiastatemetnet (EduIastateMetnet in NET) Underlying namespaces and classesallow reasoning by type and allow a programmer tobring biological semantics into the program code Thisis in contrast with many other APIs which result ingeneric Dictionary-objects which still require furtherinterpretation and parsing after retrieval The sameargument applies to webservices (especially REST)where the returned output is text-based that requiresfurther processingQuerying of MetNetDB through MetNetAPI is opti-

mized for efficient memory use Similar to the LazyLoad concept in the Java persistence library Hibernatehttpwwwhibernateorg we adapted Just In Time (JIT)compilation for data retrieval When retrieving a path-way only the main data is obtained from the databaseInformation represented in linked tables (one-to-manyor many-to-many) is retrieved when the respectivemethods are invoked This occurs transparently so a cli-ent application should function optimally and use aminimal footprint whether retrieving a list of ldquoall path-waysrdquo or constructing an integrated network from ldquoallamino-acid biosynthesis-related reactionsrdquo The JIT dataretrieval mechanism not only encapsulates a complexdata model it also makes retrieval and reconstruction ofnetwork data efficient This behaviour is impossible toimplement through webservices as the server cannotldquoguessrdquo what clients want to do with a returned piece ofinformation in the future One option would be toprovide a verbose-like parameter when calling the

webservice which introduces additional overhead forthe programmer consuming the service Another optionwould be that the server assumes a worst-case scenarioand streams all available (hierarchical) information backto the client leading to increased (and possibly unneces-sary) server-load and network traffic

ResultsMetNetAPIMetNetAPI is a flexible API that interacts with andretrieves data from MetNet an established informationresource and suite of software applications for modelorganisms currently including Arabidopsis soybean andgrapevine httpwwwmetnetonlineorg [6] By accessingMetNet infrastructure the researcher can obtain inte-grated metabolic and regulatory biological network datain addition to other new layers of information that werenot previously available in any central locationThe API allows a software developer to navigate the

database from multiple points of view without having tounderstand the underlying database schema The data-base can be navigated either as a list of pathways a list ofentities or a collection of organism-centric networks Incontrast static data files allow only one such point ofview and require customized parsing to determine theanswers to specialized questions Examples of userqueries would include ldquowhich elements in a list of entitiesparticipate in at least two pathwaysrdquo or ldquofor a given col-lection of pathways single out and reconstruct a regula-tory networkrdquo MetNetAPI can answer such querieswithout extensive programming for any respective list ofentities (eg genes RNAs polypeptides protein com-plexes metabolites or combinations thereof) The APIapproach allows a database platform to abstract andexpose its repository data along with its functionalities

Core classesThe MetNetAPI is designed to capture MetNet architec-ture which centers around four central classesAn Entity represents any type of molecular entity that

can be found in a biological environment Entities havea general categorical descriptor that describes the typeof an entity such as ldquogenerdquo ldquoRNArdquo or ldquoProtein Com-plexrdquo They can be organism-specific (in the case of agene) or not (universal metabolites such as ATP orglucose)A LocalEntity represents a particular entity found

within a sub cellular location An example is the mole-cule (Entity) ATP which is found in several compart-ments (locations) in the cell including mitochondrionnucleus plastid and cytosol Therefore the Entity ATPhas four associated LocalEntitiesAn Interaction represents the impacts or transformations

among entities Due to the diversity and generalization of

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the Entity class Interactions are kept equally generic Likeentities they are classified Interactions include enzymaticreactions transport transcription translation and variousclasses of regulatory inhibition and activation such as allos-teric effector or indirect positive regulationA Pathway represents a group of multiple interactions

and the associated biomolecules organized into a conve-nient functional unit The pathway concept in MetNe-tAPI is defined as an unordered collection of Interactionobjects In order to allow developers to determine thestart- and end-points of a pathway getSources() andgetSinks() methods are providedPeripheral classes are provided to further define pathways

and represent MetNet-specific data The Organism classrepresents information about organisms currently in Met-NetDB EntityType and InteractionType represent thedifferent types of respective entities and interactionsPathwayClass provides a Pathway Ontology to navigatethrough the collection of all pathways which is based onAraCyc pathway classes CellLocation provides a similar hier-archy that can be used as an alternate pathway ordering treePathways are arbitrary groupings of interactions Even

for well-defined pathways such as glycolysis and TCAcycle different views can be created which may or maynot include the genes and the transcriptional and regula-tory framework of the various enzymes involved Asmore knowledge is acquired through scientific experi-mentation pathways may become so complex that it isbeneficial to break them into smaller units for some

applications Conversely smaller pathways may be joinedinto a larger unit or a super-pathway for meta-analysisTo model these evolving datasets a Network class is

provided It serves the purpose of providing customgranularity A Network object consists of a custom col-lection of interactions A Network incorporates the con-cept of a pathway yet it is not confined to theboundaries of a predefined pathway Networks can beconstructed either by combining existing pathways or byadding individual interactionsSeveral APIs offer top-down approaches to network

data An example is libSBML in which a pathway con-sists of reactions which consist of molecular species[26] It is currently not possible through libSBML towork backward (eg to see which interaction a molecu-lar species participates in) MetNetAPI offers easy navi-gation and conversion between all its core classes(see Figure 1) This makes it particularly easy to writep-neighbourhood applications where one is interestedin examining the connectedness between networkcomponents

Searching and filteringMost all network database websites have a search-func-tion Upon downloading files for offline use the onlinefunctionality is no longer available This means that adata dump does not always offer the correct amount ofinformation one is interested in Much effort needs tobe invested in study of the original data format and

Figure 1 Interconnectivity between the APIrsquos classes All core classes in MetNetAPI are interconnected This allows for upward anddownward navigation (eg one can as easily ask ldquowhat entities make up a particular pathwayrdquo as ldquowhat pathways does a particular entityparticipate inrdquo)

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writing parser code to extract the information ofinterestThrough MetNetAPI online search-capabilities are

extended and can be integrated in desktop and otherapplications (these do still need to have network-connectivity to allow communication between the APIand our back-end database) This makes it convenient toexecute a large number of queries against MetNet Theinvestigator can automatically determine which pathwaysa given list of metabolites participates in restrict a path-way to its regulatory interactions or request a list ofaffected pathways for a set of up-regulated genes MostJava-classes in the MetNetAPI library have a static search() method which allows developers to launch queriesagainst MetNetDB in real time without having to go to awebsite fill out a form and submit itFiltering using MetNetAPI is similar to searching but

zeros in on results within results For example a usercould extract all gene regulatory interactions from a pre-viously defined set of pathways (combined as a Networkobject) Alternatively a user could look at a complexpathway with 100+ interactions and decide to removetemporary clutter caused by transcriptional and transla-tional events The resulting ldquocorerdquo pathway makes iteasier to understand the metabolic functions performedby the pathway

ApplicationsThe availability of a dynamic code-driven class hierarchyinstead of a collection of static rigid files allows develo-pers to rapidly provide MetNet data and bring itsfunctionality to their own applications MetNetAPI isobject-oriented which allows for code to be mixed withdata (methods and properties) When a collection ofpathways is represented by a PathwayVector objectfunctions to manipulate the member objects are pro-vided This is preferable to the use of rigid files or thepassing back and forth of Dictionary-like structuresSource code is provided [Additional file 1] that creates

a distance matrix among all 403 pathways in the data-base The algorithm results in a GraphViz-compatiblehttpwwwgraphvizorg dot-file details of which areshown in Figure 2 The complete rendered png-file isavailable as [Additional file 2] Additional examples areavailable on the MetNetAPI tutorial websiteMetNetAPI exports data to standard data formats suchas SBML or XGMML (used in Cytoscape) This func-tionality is available for developers that wish to exploitthe richness of MetNetDB It also allows integration ofMetNet-originated data into a more expansive researchpipeline The Network class contains a set of methodsthat allow export to a variety of standards To ensurecompatibility with a wide spectrum of software the

Figure 2 Details of a map that illustrates shared genes between pathway With MetNetAPI it is straightforward to compute a distancematrix between a set of pathways The matrix can then be visualized with a tool like GraphViz (thicker lines indicate a closer distance) Details ofthe visualized matrix are shown here the full rendering is available in Supplemental file distancepng

Sucaet and Wurtele BMC Research Notes 2010 3312httpwwwbiomedcentralcom1756-05003312

Page 5 of 9

writing parser code to extract the information ofinterestThrough MetNetAPI online search-capabilities are

extended and can be integrated in desktop and otherapplications (these do still need to have network-connectivity to allow communication between the APIand our back-end database) This makes it convenient toexecute a large number of queries against MetNet Theinvestigator can automatically determine which pathwaysa given list of metabolites participates in restrict a path-way to its regulatory interactions or request a list ofaffected pathways for a set of up-regulated genes MostJava-classes in the MetNetAPI library have a static search() method which allows developers to launch queriesagainst MetNetDB in real time without having to go to awebsite fill out a form and submit itFiltering using MetNetAPI is similar to searching but

zeros in on results within results For example a usercould extract all gene regulatory interactions from a pre-viously defined set of pathways (combined as a Networkobject) Alternatively a user could look at a complexpathway with 100+ interactions and decide to removetemporary clutter caused by transcriptional and transla-tional events The resulting ldquocorerdquo pathway makes iteasier to understand the metabolic functions performedby the pathway

ApplicationsThe availability of a dynamic code-driven class hierarchyinstead of a collection of static rigid files allows develo-pers to rapidly provide MetNet data and bring itsfunctionality to their own applications MetNetAPI isobject-oriented which allows for code to be mixed withdata (methods and properties) When a collection ofpathways is represented by a PathwayVector objectfunctions to manipulate the member objects are pro-vided This is preferable to the use of rigid files or thepassing back and forth of Dictionary-like structuresSource code is provided [Additional file 1] that creates

a distance matrix among all 403 pathways in the data-base The algorithm results in a GraphViz-compatiblehttpwwwgraphvizorg dot-file details of which areshown in Figure 2 The complete rendered png-file isavailable as [Additional file 2] Additional examples areavailable on the MetNetAPI tutorial websiteMetNetAPI exports data to standard data formats suchas SBML or XGMML (used in Cytoscape) This func-tionality is available for developers that wish to exploitthe richness of MetNetDB It also allows integration ofMetNet-originated data into a more expansive researchpipeline The Network class contains a set of methodsthat allow export to a variety of standards To ensurecompatibility with a wide spectrum of software the

Figure 2 Details of a map that illustrates shared genes between pathway With MetNetAPI it is straightforward to compute a distancematrix between a set of pathways The matrix can then be visualized with a tool like GraphViz (thicker lines indicate a closer distance) Details ofthe visualized matrix are shown here the full rendering is available in Supplemental file distancepng

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Page 5 of 9

depth of information has been restricted to a minimumSo while the Network class is recommended to preparedata for external software such as Jarnac (SBML) orCytoscape (XGMML) specialized needs would require adeveloper to generate customized export-routinesMetNetAPI facilitates the creation of static files based

on dynamic actions An example would be to gather the5 pathways in the database that describe the metabolismand signalling associated with the plant hormones bras-sinosteroids and auxins into a single Network objectand to export this network to a single XGMML fileThis file can be directly imported into Cytoscapeto enable visualization and further analysis of a user-specified unit of biology (eliminating the need to importmultiple files that represent individual pathways)

Initial adaptationsSeveral proof-of-concept applications using MetNetAPIhave already been developed We have developed theMetNetScape plugin to allow a user to select an organism

and pathway to be imported into Cytoscape An exampleof an imported pathway is shown in Figure 3 The pluginis available through our website httpwwwmetnetonlineorgapicytoscape and source code is available uponrequest so its functionality may be extendedA more complex plugin has been developed for Cell-

Designer [11] to allow exchange and integration of Bio-Cyc and MetNet pathways The plugin uses the eduiastatemetnetedit namespace to publish new pathwaysin MetNetDB This makes MetNet useful as a commu-nity annotation platform The plugin allows for seamlessone-click publication of newly constructed pathwaysinto MetNet [27] It is being used to bring manually-constructed grapevine pathways [28] into MetNetDBMetaOmGraph (MOG) is an application to display

large expression datasets [629] Subsets of entities(genes or metabolites) can be selected in MetNet basedon user-specified criteria These lists can be sent toMOG for further analysis via a userrsquos MetNet profile (afree personal account created through our website [30])

Figure 3 Cytoscape plugin developed with MetNetAPI As a proof of concept a Cytoscape plugin was developed that brings pathway dataalong with localization information into the Cytoscape environment

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Integration works both ways genes can be selected inMOG and published to a personal MetNet account [31]Large biological networks often benefit from visualiza-

tion in 3D [32] Walrus httpwwwcaidaorgtoolsvisualizationwalrus is a desktop-application to visualize 3D-data A proof-of-concept application has been developedthat enables a user visualize MetNet pathways in 3D on astandard computer [33] The application retrieves datathrough MetNetAPI to compute the optimal spanningtree to be used by Walrus to create the environmentMetNetGE [34] is an environment that uses Google

Earth infrastructure to produce layered representationsof pathways in MetNet Pathways are visualized asstacked planes whereby each plane represents a certaintype of entity (genes RNA polypeptides or metabo-lites) MetNetGE uses MetNetAPI to retrieve pathwayontology data and gene information

DiscussionWe have adopted the API as a method to standardize devel-opment of applications that exploit the MetNetDB datasetIn addition to facilitating prototyping and rapid applicationdevelopment this approach ensures consistency across end-user interfaces command line interfaces and graphical userinterfaces MetNetAPI is flexible and can be modifiedbased on needs of internal and external software developersWe are exploring the possibilities of using the API in

environments other than Java This has already lead tointegration of MetNetAPI into Microsoft NET and Rhttpwwwr-projectorg through the rJava bridging soft-ware httpwwwrforgenetrJavaAdvanced programming knowledge (such as SQL or

JDBC) is not required for using MetNetAPI The complex-ity of the underlying data model is encapsulated within theAPI The interface is only slightly less universal thanthe socket-based protocol provided by BioCyc [18] and thechoice of Java allows the API to be used by a broad audi-ence of software developers and bioinformatics researchersImportantly unlike a socket-based approach installationand troubleshooting of MetNetAPI is easy since it relies onbasic Java coding practices MetNetDB represents a largecomplex metabolic and regulatory network and containsmultiple interaction types kinetic information and manu-ally curated subcellular localization assignments

ConclusionsOnline databases often provide data export by means ofstatic downloadable files or dynamic webservices MetNe-tAPI provides an additional approach to data export TheAPI provides a method to standardize development ofapplications that exploit MetNetDB but may also serve asa framework and template for other pathway databasesA standardization of terminology among different databaseswould certainly benefit developers that work on integrative

applications Many databases expose similar types of dataand the definition of a minimal set of interfaces that path-way database APIs may be expected to implement wouldbe helpful MetNetAPI can be a first step in this directionApart from facilitating prototyping and rapid application

development our approach ensures consistency and dataintegrity across command line interfaces and graphicaluser interfaces alike The choice of Java and MicrosoftNET allows the API to be used by a broad audience ofsoftware developers and bioinformaticists The complexityof the underlying data model is encapsulated within theAPI Because it is a Java-API rather than a webservicemore functionality can be provided without requiringextensive computational resources on the server-sideFor a densely populated and information-rich database

(such as MetNetDB) our API model offers many advan-tages It has the ability to incorporate online search cap-abilities into custom-built applications It also offers theoption to customize the granularity of pathways of inter-est MetNetAPI captures user-defined network structuresinto self-contained semantic objects Through Networkobjects combinations of existing or putative novel path-ways can easily be constructed manipulated and refinedMetNet is an information resource as well as an activetoolkit to develop new hypotheses Many complicatedoperations which would be difficult to implement via xmlor text-based files can be accomplished through MetNe-tAPI These feature flexible capabilities to agglomeratedata over multiple pathways to examine connectivityamong different datatypes and prepare custom datasetsfor use in other downstream applications MetNetAPI isfully documented free of charge and can be downloadedfrom httpwwwmetnetonlineorgapicytoscape

Availability and requirementsProject name MetNetAPIProject home page httpwwwmetnetonlineorgapiOperating system(s) Platform independentProgramming language Java MicrosoftNET ROther requirements

Java-specific Java 131 or higher MySQL JDBCdriverMicrosoftNET-specific MicrosoftNET 11 orhigher MySQL NET driver

License NoneAny restrictions to use by non-academics No

Additional material

Additional file 1 Sample program code to generate an inter-pathway map Sample program that creates a distance matrix betweena set of pathways The distance is based on the number of genes that

Sucaet and Wurtele BMC Research Notes 2010 3312httpwwwbiomedcentralcom1756-05003312

Page 7 of 9

are shared between two pathways Output is sent to a csv-file as well asa GraphViz-compatible dot-file A rendering of the dot-file is provided asa separate Supplemental file

Additional file 2 Inter-pathway map based on shared genesbetween pathways A complete rendering of the distance matrix thatwas computed with the sample program provided in distancejava

AbbreviationsAPI Application Programming Interface CSV file comma-separated valuesfile KEGG Kyoto Encyclopaedia of Genes and Genomes MOGMetaOmGraph REST REpresentational State Transfer SBML Systems BiologyMarkup Language SQL Structured Query Language SOAP Simple ObjectAccess Protocol URI Uniform Resource Identifier WSDL WebServiceDescription Language XGMML eXtensible Graph Markup and ModelingLanguage

AcknowledgementsWe thank all members of the MetNet group for their valuable input andsuggestions in particular Jie Li Mohammed S Alabsi and Nick RansomSpecial thanks go to John L Van Hemert and Xiaoyong Sun as well asMartijn Van Iersel for off-site interface testing We greatly appreciate the kindadvice of Gary Bader about the organization of this manuscriptFunding This project is in part funded by National Science FoundationArabidopsis 2010 grant 052026 This material is based upon worksupported by the National Science Foundation under Awards EEC-0813570and MCB-0951170

Author details1Department of Genetics Development and Cell Biology Iowa StateUniversity Ames IA 50011 USA 2Interdepartmental Program inBioinformatics amp Computational Biology Iowa State University Ames IA50011 USA

Authorsrsquo contributionsYS carried out API design and development ESW conceived of the studyand participated in its design and coordination and helped to draft themanuscript All authors read and approved the final manuscript

Competing interestsThe authors declare that they have no competing interests

Received 15 June 2010 Accepted 18 November 2010Published 18 November 2010

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26 Bornstein BJ Keating SM Jouraku A Hucka M LibSBML an API library forSBML Bioinformatics (Oxford England) 2008 24880-881

27 Pathway data integration between datasources via CellDesigner [httpwwwpubliciastateedu~jlvcelldesignerpluginsshtml]

28 Grimplet J Cramer GR Dickerson JA Mathiason K Van Hemert JFennell AY VitisNet ldquoOmicsrdquo Integration through Grapevine MolecularNetworks PloS one 2009 4e8365

29 Mentzen WI Wurtele ES Regulon organization of Arabidopsis BMC plantbiology 2008 899

30 MetNet Online [httpwwwmetnetonlineorg]31 MetaOmGraph [httpwwwmetnetdborgMetNet_MetaOmGraphhtm]32 Yang Y Engin L Wurtele ES Cruz-Neira C Dickerson JA Integration of

metabolic networks and gene expression in virtual reality Bioinformatics(Oxford England) 2005 213645-3650

Sucaet and Wurtele BMC Research Notes 2010 3312httpwwwbiomedcentralcom1756-05003312

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33 Tools and ideas for visualizing Systems Biology data in 3D [httpvraciastateedu~jlv3D]

34 Ming J Swaminathan S Wurtele ES Dickerson JA MetNetGE Visualizingbiological networks in hierarchical views and 3D tiered layouts IEEEInternational Conference on Bioinformatics and Biomedicine WorkshopWashington DC 2009 287-294

doi1011861756-0500-3-312Cite this article as Sucaet and Wurtele MetNetAPI A flexible method toaccess and manipulate biological network data from MetNet BMCResearch Notes 2010 3312

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