Distribution of immunodeficiency fact files with XML – from Web to WAP
© Väliaho et al; licensee BioMed Central Ltd. 2005
Received: 11 March 2004
Accepted: 26 June 2005
Published: 26 June 2005
Although biomedical information is growing rapidly, it is difficult to find and retrieve validated data especially for rare hereditary diseases. There is an increased need for services capable of integrating and validating information as well as proving it in a logically organized structure. A XML-based language enables creation of open source databases for storage, maintenance and delivery for different platforms.
Here we present a new data model called fact file and an XML-based specification Inherited Disease Markup Language (IDML), that were developed to facilitate disease information integration, storage and exchange. The data model was applied to primary immunodeficiencies, but it can be used for any hereditary disease. Fact files integrate biomedical, genetic and clinical information related to hereditary diseases.
IDML and fact files were used to build a comprehensive Web and WAP accessible knowledge base ImmunoDeficiency Resource (IDR) available at http://bioinf.uta.fi/idr/. A fact file is a user oriented user interface, which serves as a starting point to explore information on hereditary diseases.
The IDML enables the seamless integration and presentation of genetic and disease information resources in the Internet. IDML can be used to build information services for all kinds of inherited diseases. The open source specification and related programs are available at http://bioinf.uta.fi/idml/.
Biomedical information is often very complex. Deciphering the roles of genes in human health and disease is a grand challenge for many reasons, including impediments to defining phenotypes, difficulties in identifying and quantifying environmental effects, technical problems in generating genotypic information, and the difficulties of studying humans . The completion of the draft sequence of the human genome [2, 3] and advances in molecular biology provide new opportunities to increase our understanding of the role of genetic factors in human health and disease . The number of identified genetic diseases has increased exponentially . The new knowledge can be applied to the prevention, diagnosis and treatment of diseases. This far, the knowledge of genetics has had a large role in the health care of only a few patients and a small role in the health care of many . The biomedical informatics holds great promise for developing informatics methods that will be crucial in the development of genomic medicine .
Most hereditary diseases are rare and the diagnosed patients for a condition are often randomly spread out in the world. One doctor usually has only a few patients with a disease. It is often difficult to find comprehensive and validated biomedical information related to rare diseases. In addition, it is more and more difficult to publish results in scientific journals only from a few cases even when they are interesting . Still, all these pieces of information can contain clues to understanding the fundamental defects at molecular level and can help to develop targeted treatments. The scattering of the disease-related information to literature and Internet is a big obstacle especially for those interested in rare diseases. First of all, there may not be that much data for these diseases and secondly it may be very difficult to find and collect. Further, the user has often difficulties in assessing the quality of data.
There is an increasing need for tools and services capable of integrating information from a variety of sources. Clinicians and researchers could benefit from a more consolidated and unified view of the available biomedical data. Systems biology researchers need to integrate disparate information from multiple public sources to merge with their own experimental data to generate models of processes. Biomedical data mining attempts to extract information from biomedical databases by using e.g. automated natural language processing (NLP) techniques . Processing of biomedical texts presents many challenges such as in the areas of terminology or ontology building, information extraction from texts, knowledge discovery from collections of documents, as well as sharing and integrating knowledge from factual and textual data bases, semantic annotation, etc. Without standardized nomenclature the information extraction (IE) about a particular subject from various resources is difficult. Due to ambiguity of terms, a search for a particular term often retrieves results for unrelated entities. Since there are also some technical problems arising from the diversity of computer hardware and software, there is a need for such a data form, that can be handled by any computer and which can be easily presented on any platform.
The Extensible Markup Language (XML) is a standard created by the World Wide Web Consortium (W3C) for characterizing the content and structure of documents . It is designed to improve the functionality of the Web by enabling more flexible and adaptable information identification and presentation. XML allows to define tags and document structures for own context-specific use. It was derived from SGML (Standard Generalized Markup Language), the international standard for defining descriptions of the structure and content of different types of electronic documents . XML is simpler than SGML, but it allows the use of richly structured documents over the Internet. Information encoded in XML is easy to read and understand, and easy to process by computers. In XML files, structured data are bounded by tags and attributes. XML tags, attributes and element structure provide context information that facilitates the interpretation of the meaning of content, thereby making it feasible to develop efficient search engines and agents and perform intelligent data mining, etc. The XML allows the separation of content, logic and presentation.
Beyond XML there are a number of additional specifications such as Document Object Model (DOM) , XML Schemas , XSL Transformations , and Resource Description Framework (RDF) . XML will have a big role in integration and interoperation of biological databases. Some biomedical information models have been implemented using XML specifications [15, 16], many of them being clinical models for electronic healthcare documents [17–19].
A unified data format of resources is required for comparison between similar diseases and reutilization of information. Here we present a new data model called fact file, which integrates biomedical information related to hereditary diseases into a Web and WAP accessible knowledge base. Our scope is wider than e.g. in gene oriented knowledge bases such as GeneCards , UniGene , or LocusLink . The disease information sources are even more diverse than those for genetic information. The fact files concentrate on sharing and integrating biomedical knowledge from different sources. The presented data model can be applied to any hereditary disease.
The fact files were applied to build a comprehensive, validated knowledge base for primary immunodeficiencies (PIDs) called ImmunoDeficiency Resource (IDR) [23, 24]. It is designed for different user groups such as researchers, physicians and nurses as well as patients and their families and the general public. The IDR is the major information source to immunodeficiencies in the Web. Fact files serve as the core of the IDR knowledge base.
The description of high-level concepts in the fact file document model
The root element for IDML-based fact file document
(GeneralInformation, ClinicalInformation, MolecularBiology, Other)
Describes the disease in general terms
(DiseaseName, Abbreviation*, AlternativeNames?, Description, Classification?, Omim*, CrossReferences?, Incidence?)
The short overview of characteristic clinical features
(ClinicalDescription?, Diagnosis?, TherapeuticOptions?, ResearchPrograms?)
Molecular genetic elements
(GeneInformation?, AnimalModels?, ProteinInformation?, ExpressionPattern?)
Other related information
(Publications?, Societies?, OtherSites?)
The components of the fact file model are defined as IDML elements. According to XML, elements have distinct names and they are delimited with start and end tag, e.g. <DiseaseName>X-linked agammaglobulinemia</DiseaseName>. Elements may contain other elements or attributes, they may store text, or they may be empty. Elements may appear as often as required. Many IDML elements contain href attribute for hyperlinking to more detailed information by using globally unique idenfier URL (Unified Resource Locator). The element naming convention follows the approach used by Electronic Business XML (ebXML) core components . The IDML specified element names are in upper camel case (UpperCamelCase) and attribute names are in lower camel case (lowerCamelCase) notations. The usage of acronyms has been avoided, but when they are used the capitalization remains (example: ReferenceDNA).
The description of IDML: GeneralInformation element
Abbreviation for disease name
List of alternatively used disease names
General description of disease
(Glink | Italic)*
Classifies document explicitely in the fact files hierarchy
A collection of the related references to the OMIM database
Refers to the related fact files
(PhenotypeRelatedDiseases?, OtherRelatedDiseases?, GeneRelatedDiseases?)
Description of incidence
The description of IDML: ClinicalInformation element
Describes characteristic clinical features
(Glink | Italic)*
A collection of diagnostic guidelines and laboratories
(DiagnosticRecommendations?, AdditionalInformation?, DiagnosticLaboratories?)
A collection of available therapeutic options
A collection of related studies
The description of IDML: MolecularBiology element
Contains information on the gene name, aliases, reference sequences, chromosomal location, maps, markers, variations and other gene related resources
(Name?, Aliases?, ReferenceSequences?, OtherSequences?, ChromosomalLocation?, Maps?, Markers?, Variations?, OtherResources?)
A collection of related transgenic animal studies
Contains information on protein characteristic features, structures, domains, motifs and other protein resources
(ProteinDescription?, Structures?, Domains?, Motifs?, ProteinResources?)
Gene expression levels in a variety of cells and tissues
The <ProteinInformation> element stores characteristic structural and functional properties of the protein. The <ProteinDescription> contains several subelements e.g. <Function>, <SubcellularLocation>, <CatalyticActivity>, which are inherited from the Swiss-Prot entry model . The <Structures> element refers to solved protein structures available in Protein DataBank (PDB) . The domain and motif elements describe conserved protein regions. Each <Domain>, <Motif> and further <ProteinResources> element includes links to related resources for example in Pfam , InterPro , ProDom , SMART  or PROSITE . The <ExpressionPattern> stores information on gene or protein expression. This information is mainly from SOURCE , which is a web-based resource bringing together genetic information from different sources.
The description of IDML: Other element
A collection of related publications
List of related general and disease specific societies
A collection of other related Web sites
The IDML schema version 1.0 (idml.xsd file), examples of IDML-document and documentation on the syntax are available at our web site http://bioinf.uta.fi/idml/. The IDML document type definition file (idml.dtd) is also available, althougth we prefer to use the IDML schema for validation. Many IDML elements are optional. The syntax allows one to put comments, both within and outside of the XML markup. The parser must pass internal comments to the application programs, which can then properly treat the information. IDML documents specify which version of the schema is to be used to validate their content, eliminating possible confusion when several versions exist. IDML is open access, however, a licence is needed for building other services. Contact the authors for details.
The ImmunoDeficiency Resource is a comprehensive knowledge base on immunodeficiencies. IDR is developed and maintained by IMT Bioinformatics group in collaboration with experts on individual immunodeficiencies. All the information in the IDR will be validated by expert curators. However, all changes, additions and corrections to the fact files are made by our group. IDR is designed for different user groups such as researchers, physicians and nurses as well as patients and their families and the general public. IDR contains fact files for practically all known PIDs. The numerous individual data items in IDR have been collected partly manually, usually with simple Perl scripts written for datamining from numerous local and Internet databases and services.
We selected Apache AxKit XML Application Server version 1.61 for implementation of the IDML-encoded web service. AxKit is an application and document server that uses XML processing pipelines to generate and process content and to deliver it to clients in a wide variety of formats, such as HTML, WML, PDF and plain text using either standard techniques of World Wide Web Consortium (XSLT) , or flexible custom codes (XPathScript XPS, eXtensible Server Pages XSP).
Similar XML application server called Cocoon, has been written in Java. We settled on AxKit, because it is built in Perl, which makes it easy to integrate with bioinformatic applications many of which are written in Perl. It is important to note that AxKit is not limited to XML source documents. Non-XML documents and data sources can be converted to XML when necessary. AxKit separates the content, logic and presentation. The content reuse was implemented with XInclude  and XPointer  techniques. The root element of IDML schema is <FactFiles> and according to W3C Recommendation "Namespaces in XML"  we declared a default namespace attribute in the root element xmlns:idml="http://bioinf.uta.fi/idml" to avoid the problems of ambiquity and name collisions.
Each fact file is stored in an IDML file, that has a unique name and url address. When a fact file requests the pipeline it might look like this in diagramatic terms : Request > [XSP] > (XML) > [XSLT] > (HTML) > Browser, where processors are in square brackets and products in round brackets. The output of XSP pages is structured XML content, which can pipe through XSLT to produce HTML. The XSP feature is not currently in use in the IDR.
The information on fact files can be easily transformed and presented in any platform. It is easy to write platform or even browser and screen specific pages. We have implemented a transformation from IDML to WML for portable devices (such as mobile phones) with WAP compliance (Wireless Application Protocol). The fact files are available via bioinformatics related WAP service, BioWAP [47, 48]http:///bioinf.uta.fi/wml/welcome.wml practically anywhere, anytime.
New web techniques are developed continuously. During this project a number of new specifications and software appeared, requiring upgrading of the system many times. The separation of content and presentation enables to share the project for people who are responsible for information content and people who develop the knowledge management techniques. Once the data model was created, we have not had to touch it hardly at all in spite of technical improvements, content additions and deletions.
As far as we know there are no other efforts to develop a markup language to describe connections between disease and genetic information. The IDML was designed with following purposes in mind. First, we wanted a markup that is able to present disease, clinical, diagnostic and genetic information and relations between them. Secondly, the data model structure had to be intuitive, hierarchical, flexible, but still machine and human readable. Sometimes the relatively large XML files can appear verbose for human readers, but hierarchically and logically organized structure in addition to semantic markup facilitate the interpretation of documents. Thirdly, an application and platform independent data format was needed. Its portability, extensibility and robustness are primary advantages for interoperating heterogeneous systems. The availability of open source and free tools for processing files in all major programming languages is important. The openness of source code as well as data formats and data itself allows better integration and interoperation between data resources. The IDML enables the seamless integration of genetic and disease information resources in the Internet. The data model is appropriate for the implementation of automated decision support systems such as diagnostic consultations. Fourthly, the data have to be unambiguous and validated.
A fact file is a user oriented user interface, which serves as a good starting point to explore information on hereditary diseases. For some time now, there has been many advanced search facilities in the Internet such as Google, that are able to find very fast web pages that contain given keywords. However, the web searches typically turn up innumerable completetely irrelevant "hits", requiring much manual filtering by the user. Navarro et al. lists some issues related to database searching and accessibility that can cause difficulties  including inaccurate and redundant search results, nomenclature issues, lack of internal access, non-availability of the source code, lack of customization and differing data formats. New methods are needed for improving search results.
There is an increasing number of biomedical data sources in the Internet. The Human Genome Initiative  and other genome research projects have generated enormous quantities of data. The genetic data is well organized in web accessible databases for example EMBL , GenBank , Swiss-Prot , etc. Several organizations offer public interfaces for obtaining biomedical information across a range of domains. They provide numerous tools and applications for genetic data retrieval and analysis for example with Sequence Retrieval System SRS  and BioPerl . In addition to the sequence information, databases contain a lot of valuable information in annotations. There are also some genetic knowledge bases such as GeneCards and GeneLynx that comprise the essential information on genes. Swiss-Prot contains also some disease related annotations. The most comprehensive database on hereditary diseases is OMIM , which contains descriptions for known hereditary diseases.
Almost all pages in the Internet have been written in HyperText Markup Language (HTML) where it is used for style description. It provides some possibilities for simple description about a document. It is able to use special metatags that contain simple keywords or more advanced descriptions like Dublin Core Languages, but they are very little utilised and only the most sophisticated search engines can exploit them.
There are some efforts to integrate heterogeneous biomedical databases [15, 54, 55]. Some level of standardization is required for more automatic integration. Development of integration techniques is moving databases towards the Internet and XML-based systems . In the future, Web services will use standard Internet protocols including SOAP, WSDL, and UDDI for interoperability with other resources. Thereby the flexible and expandable integration of diverse scientific tools will be achieved.
The XML-based language IDML and fact file data model were developed for integrating, storing and exchanging information on inherited diseases. The IDML language and fact file model are implemented in the IDR knowledge base. The fact files can be easily transformed from IDML to any format such as HTML or WML using either standard W3C techniques or flexible custom code. The content management as well as the exchange of presentation are facilitated by separating document content and presentation. The IDML-based information system was proved to be a viable and applicable specification for inherited diseases. Numerous downloads (altogether more than 250,000) from the IDR knowledge base during the last two years have proved the applicability and adaptability of the fact file model.
List of abbreviations
An XML Delivery Toolkit for Apache
Bioinformatics service for portable devices
Document Object Model
Document Type Definition
Electronic Business XML
European Directory of DNA Diagnostic Laboratories
Genetic sequence database by European Molecular Biology Laboratory
Genetic sequence database by National Center for Biotechnology Information
Hypertext Markup Language
Inherited Disease Markup Language
Natural language processing
Online Mendelian Inheritance in Man
Portable Document Format
Protein Families Database
Protein Domain Database
Database of Protein Families and Domains
Resource Description Framework
Standard Generalized Markup Language
Simple Modular Architecture Research Tool
Single nucleotide polymorphism
Simple Object Access Protocol
Genomic resource in the Internet
Universal Description, Discovery, and Integration
Unified Resource Locator
World Wide Web Consortium
Wireless Application Protocol
Wireless Markup Language
Web Services Definition/Description Language
Extensible Markup Language
Extensible Style Language
eXtensible Server Pages
Financial support from the European Union, the National Technology Agency of Finland and the Medical Research Fund of Tampere University Hospital is gratefully acknowledged.
- Collins FS, Green ED, Guttmacher AE, Guyer MS: A vision for the future of genomics research. Nature. 2003, 422: 835-847. 10.1038/nature01626.View ArticlePubMedGoogle Scholar
- Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W, Funke R, Gage D, Harris K, Heaford A, Howland J, Kann L, Lehoczky J, LeVine R, McEwan P, McKernan K, Meldrim J, Mesirov JP, Miranda C, Morris W, Naylor J, Raymond C, Rosetti M, Santos R, Sheridan A, Sougnez C, Stange-Thomann N, Stojanovic N, Subramanian A, Wyman D, Rogers J, Sulston J, Ainscough R, Beck S, Bentley D, Burton J, Clee C, Carter N, Coulson A, Deadman R, Deloukas P, Dunham A, Dunham I, Durbin R, French L, Grafham D, Gregory S, Hubbard T, Humphray S, Hunt A, Jones M, Lloyd C, McMurray A, Matthews L, Mercer S, Milne S, Mullikin JC, Mungall A, Plumb R, Ross M, Shownkeen R, Sims S, Waterston RH, Wilson RK, Hillier LW, McPherson JD, Marra MA, Mardis ER, Fulton LA, Chinwalla AT, Pepin KH, Gish WR, Chissoe SL, Wendl MC, Delehaunty KD, Miner TL, Delehaunty A, Kramer JB, Cook LL, Fulton RS, Johnson DL, Minx PJ, Clifton SW, Hawkins T, Branscomb E, Predki P, Richardson P, Wenning S, Slezak T, Doggett N, Cheng JF, Olsen A, Lucas S, Elkin C, Uberbacher E, Frazier M, Gibbs RA, Muzny DM, Scherer SE, Bouck JB, Sodergren EJ, Worley KC, Rives CM, Gorrell JH, Metzker ML, Naylor SL, Kucherlapati RS, Nelson DL, Weinstock GM, Sakaki Y, Fujiyama A, Hattori M, Yada T, Toyoda A, Itoh T, Kawagoe C, Watanabe H, Totoki Y, Taylor T, Weissenbach J, Heilig R, Saurin W, Artiguenave F, Brottier P, Bruls T, Pelletier E, Robert C, Wincker P, Smith DR, Doucette-Stamm L, Rubenfield M, Weinstock K, Lee HM, Dubois J, Rosenthal A, Platzer M, Nyakatura G, Taudien S, Rump A, Yang H, Yu J, Wang J, Huang G, Gu J, Hood L, Rowen L, Madan A, Qin S, Davis RW, Federspiel NA, Abola AP, Proctor MJ, Myers RM, Schmutz J, Dickson M, Grimwood J, Cox DR, Olson MV, Kaul R, Shimizu N, Kawasaki K, Minoshima S, Evans GA, Athanasiou M, Schultz R, Roe BA, Chen F, Pan H, Ramser J, Lehrach H, Reinhardt R, McCombie WR, de la Bastide M, Dedhia N, Blocker H, Hornischer K, Nordsiek G, Agarwala R, Aravind L, Bailey JA, Bateman A, Batzoglou S, Birney E, Bork P, Brown DG, Burge CB, Cerutti L, Chen HC, Church D, Clamp M, Copley RR, Doerks T, Eddy SR, Eichler EE, Furey TS, Galagan J, Gilbert JG, Harmon C, Hayashizaki Y, Haussler D, Hermjakob H, Hokamp K, Jang W, Johnson LS, Jones TA, Kasif S, Kaspryzk A, Kennedy S, Kent WJ, Kitts P, Koonin EV, Korf I, Kulp D, Lancet D, Lowe TM, McLysaght A, Mikkelsen T, Moran JV, Mulder N, Pollara VJ, Ponting CP, Schuler G, Schultz J, Slater G, Smit AF, Stupka E, Szustakowski J, Thierry-Mieg D, Thierry-Mieg J, Wagner L, Wallis J, Wheeler R, Williams A, Wolf YI, Wolfe KH, Yang SP, Yeh RF, Collins F, Guyer MS, Peterson J, Felsenfeld A, Wetterstrand KA, Patrinos A, Morgan MJ, Szustakowki J, de Jong P, Catanese JJ, Osoegawa K, Shizuya H, Choi S, Chen YJ: Initial sequencing and analysis of the human genome. Nature. 2001, 409: 860-921. 10.1038/35057062.View ArticlePubMedGoogle Scholar
- Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, Smith HO, Yandell M, Evans CA, Holt RA, Gocayne JD, Amanatides P, Ballew RM, Huson DH, Wortman JR, Zhang Q, Kodira CD, Zheng XH, Chen L, Skupski M, Subramanian G, Thomas PD, Zhang J, Gabor Miklos GL, Nelson C, Broder S, Clark AG, Nadeau J, McKusick VA, Zinder N, Levine AJ, Roberts RJ, Simon M, Slayman C, Hunkapiller M, Bolanos R, Delcher A, Dew I, Fasulo D, Flanigan M, Florea L, Halpern A, Hannenhalli S, Kravitz S, Levy S, Mobarry C, Reinert K, Remington K, Abu-Threideh J, Beasley E, Biddick K, Bonazzi V, Brandon R, Cargill M, Chandramouliswaran I, Charlab R, Chaturvedi K, Deng Z, Di Francesco V, Dunn P, Eilbeck K, Evangelista C, Gabrielian AE, Gan W, Ge W, Gong F, Gu Z, Guan P, Heiman TJ, Higgins ME, Ji RR, Ke Z, Ketchum KA, Lai Z, Lei Y, Li Z, Li J, Liang Y, Lin X, Lu F, Merkulov GV, Milshina N, Moore HM, Naik AK, Narayan VA, Neelam B, Nusskern D, Rusch DB, Salzberg S, Shao W, Shue B, Sun J, Wang Z, Wang A, Wang X, Wang J, Wei M, Wides R, Xiao C, Yan C, Yao A, Ye J, Zhan M, Zhang W, Zhang H, Zhao Q, Zheng L, Zhong F, Zhong W, Zhu S, Zhao S, Gilbert D, Baumhueter S, Spier G, Carter C, Cravchik A, Woodage T, Ali F, An H, Awe A, Baldwin D, Baden H, Barnstead M, Barrow I, Beeson K, Busam D, Carver A, Center A, Cheng ML, Curry L, Danaher S, Davenport L, Desilets R, Dietz S, Dodson K, Doup L, Ferriera S, Garg N, Gluecksmann A, Hart B, Haynes J, Haynes C, Heiner C, Hladun S, Hostin D, Houck J, Howland T, Ibegwam C, Johnson J, Kalush F, Kline L, Koduru S, Love A, Mann F, May D, McCawley S, McIntosh T, McMullen I, Moy M, Moy L, Murphy B, Nelson K, Pfannkoch C, Pratts E, Puri V, Qureshi H, Reardon M, Rodriguez R, Rogers YH, Romblad D, Ruhfel B, Scott R, Sitter C, Smallwood M, Stewart E, Strong R, Suh E, Thomas R, Tint NN, Tse S, Vech C, Wang G, Wetter J, Williams S, Williams M, Windsor S, Winn-Deen E, Wolfe K, Zaveri J, Zaveri K, Abril JF, Guigo R, Campbell MJ, Sjolander KV, Karlak B, Kejariwal A, Mi H, Lazareva B, Hatton T, Narechania A, Diemer K, Muruganujan A, Guo N, Sato S, Bafna V, Istrail S, Lippert R, Schwartz R, Walenz B, Yooseph S, Allen D, Basu A, Baxendale J, Blick L, Caminha M, Carnes-Stine J, Caulk P, Chiang YH, Coyne M, Dahlke C, Mays A, Dombroski M, Donnelly M, Ely D, Esparham S, Fosler C, Gire H, Glanowski S, Glasser K, Glodek A, Gorokhov M, Graham K, Gropman B, Harris M, Heil J, Henderson S, Hoover J, Jennings D, Jordan C, Jordan J, Kasha J, Kagan L, Kraft C, Levitsky A, Lewis M, Liu X, Lopez J, Ma D, Majoros W, McDaniel J, Murphy S, Newman M, Nguyen T, Nguyen N, Nodell M, Pan S, Peck J, Peterson M, Rowe W, Sanders R, Scott J, Simpson M, Smith T, Sprague A, Stockwell T, Turner R, Venter E, Wang M, Wen M, Wu D, Wu M, Xia A, Zandieh A, Zhu X: The sequence of the human genome. Science. 2001, 291: 1304-1351. 10.1126/science.1058040.View ArticlePubMedGoogle Scholar
- Hamosh A, Scott AF, Amberger J, Bocchini C, Valle D, McKusick VA: Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2002, 30: 52-55. 10.1093/nar/30.1.52.View ArticlePubMedPubMed CentralGoogle Scholar
- Guttmacher AE, Collins FS: Genomic medicine - a primer. N Engl J Med. 2002, 347: 1512-1520. 10.1056/NEJMra012240.View ArticlePubMedGoogle Scholar
- Maojo V, Kulikowski CA: Bioinformatics and medical informatics: collaborations on the road to genomic medicine?. J Am Med Inform Assoc. 2003, 10: 515-522. 10.1197/jamia.M1305.View ArticlePubMedPubMed CentralGoogle Scholar
- Grivell L: Mining the bibliome: searching for a needle in a haystack? New computing tools are needed to effectively scan the growing amount of scientific literature for useful information. EMBO Rep. 2002, 3: 200-203. 10.1093/embo-reports/kvf059.View ArticlePubMedPubMed CentralGoogle Scholar
- Yandell MD, Majoros WH: Genomics and natural language processing. Nat Rev Genet. 2002, 3: 601-610.View ArticlePubMedGoogle Scholar
- World Wide Web Consortium: Extensible Markup Language (XML) 1.0 (Second Edition). [http://www.w3.org/TR/REC-xml]
- International Organization for Standardization: Standard Generalized Markup Language (SGML). [http://www.iso.org/iso/en/CatalogueDetailPage.CatalogueDetail?CSNUMBER=16387&ICS1=35&ICS2=240&ICS3=30]
- World Wide Web Consortium: Document Object Model (DOM) Level 1 Specification. [http://www.w3.org/TR/REC-DOM-Level-1/]
- World Wide Web Consortium: XML Schema Part 0: Primer. [http://www.w3.org/TR/xmlschema-0/]
- World Wide Web Consortium: XSL Transformations (XSLT) Version 1.0. [http://www.w3.org/TR/xslt]
- World Wide Web Consortium: RDF/XML Syntax Specification (Revised). [http://www.w3.org/TR/rdf-syntax-grammar/]
- Mork P, Halevy A, Tarczy-Hornoch P: A model for data integration systems of biomedical data applied to online genetic databases. Proc AMIA Symp. 2001, 473-477.Google Scholar
- Achard F, Vaysseix G, Barillot E: XML, bioinformatics and data integration. Bioinformatics. 2001, 17: 115-125. 10.1093/bioinformatics/17.2.115.View ArticlePubMedGoogle Scholar
- Coyle JF, Mori AR, Huff SM: Standards for detailed clinical models as the basis for medical data exchange and decision support. Int J Med Inf. 2003, 69: 157-174. 10.1016/S1386-5056(02)00103-X.View ArticleGoogle Scholar
- McDonald CJ, Huff SM, Suico JG, Hill G, Leavelle D, Aller R, Forrey A, Mercer K, DeMoor G, Hook J, Williams W, Case J, Maloney P: LOINC, a universal standard for identifying laboratory observations: a 5-year update. Clin Chem. 2003, 49: 624-633. 10.1373/49.4.624.View ArticlePubMedGoogle Scholar
- Lee KP, Hu J: XML Schema Representation of DICOM Structured Reporting. J Am Med Inform Assoc. 2003, 10: 213-223. 10.1197/jamia.M1042.View ArticlePubMedPubMed CentralGoogle Scholar
- Rebhan M, Chalifa-Caspi V, Prilusky J, Lancet D: GeneCards: encyclopedia for genes, proteins and diseases. [http://bioinformatics.weizmann.ac.il/cards]
- Schuler GD: Pieces of the puzzle: expressed sequence tags and the catalog of human genes. J Mol Med. 1997, 75: 694-698. 10.1007/s001090050155.View ArticlePubMedGoogle Scholar
- Pruitt KD, Maglott DR: RefSeq and LocusLink: NCBI gene-centered resources. Nucleic Acids Res. 2001, 29: 137-140. 10.1093/nar/29.1.137.View ArticlePubMedPubMed CentralGoogle Scholar
- Väliaho J, Pusa M, Ylinen T, Vihinen M: IDR: the ImmunoDeficiency Resource. Nucleic Acids Res. 2002, 30: 232-234. 10.1093/nar/30.1.232.View ArticlePubMedPubMed CentralGoogle Scholar
- Väliaho J, Riikonen P, Vihinen M: Novel immunodeficiency data servers. Immunol Rev. 2000, 178: 177-185. 10.1034/j.1600-065X.2000.17807.x.View ArticlePubMedGoogle Scholar
- Wain HM, Lush MJ, Ducluzeau F, Khodiyar VK, Povey S: Genew: the Human Gene Nomenclature Database, 2004 updates. Nucleic Acids Res. 2004, Database issue: D255-7. 10.1093/nar/gkh072.View ArticleGoogle Scholar
- Boeckmann B, Bairoch A, Apweiler R, Blatter MC, Estreicher A, Gasteiger E, Martin MJ, Michoud K, O'Donovan C, Phan I, Pilbout S, Schneider M: The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res. 2003, 31: 365-370. 10.1093/nar/gkg095.View ArticlePubMedPubMed CentralGoogle Scholar
- Diehn M, Sherlock G, Binkley G, Jin H, Matese JC, Hernandez-Boussard T, Rees CA, Cherry JM, Botstein D, Brown PO, Alizadeh AA: SOURCE: a unified genomic resource of functional annotations, ontologies, and gene expression data. Nucleic Acids Res. 2003, 31: 219-223. 10.1093/nar/gkg014.View ArticlePubMedPubMed CentralGoogle Scholar
- Eisenberg B, Nicull D: ebXML Technical Architecture Specification v1.0.4. [http://www.ebxml.org/specs/ebTA.pdf]
- Samarghitean C, Valiaho J, Vihinen M: Online Registry of Genetic and Clinical Immunodeficiency Diagnostic Laboratories, IDdiagnostics. J Clin Immunol. 2004, 24: 53-61. 10.1023/B:JOCI.0000018063.55963.0c.View ArticlePubMedGoogle Scholar
- Tarczy-Hornoch P, Shannon P, Baskin P, Espeseth M, Pagon RA: GeneClinics: a hybrid text/data electronic publishing model using XML applied to clinical genetic testing. J Am Med Inform Assoc. 2000, 7: 267-276.View ArticlePubMedPubMed CentralGoogle Scholar
- Conley ME, Notarangelo LD, Etzioni A: Diagnostic criteria for primary immunodeficiencies. Clin Immunol. 1999, 93: 190-197. 10.1006/clim.1999.4799.View ArticlePubMedGoogle Scholar
- Vihinen M, Arredondo-Vega FX, Casanova JL, Etzioni A, Giliani S, Hammarström L, Hershfield MS, Heyworth PG, Hsu AP, Lähdesmäki A, Lappalainen I, Notarangelo LD, Puck JM, Reith W, Roos D, Schumacher RF, Schwarz K, Vezzoni P, Villa A, Väliaho J, Smith CI: Primary immunodeficiency mutation databases. Adv Genet. 2001, 43: 103-188.View ArticlePubMedGoogle Scholar
- Birney E, Andrews D, Bevan P, Caccamo M, Cameron G, Chen Y, Clarke L, Coates G, Cox T, Cuff J, Curwen V, Cutts T, Down T, Durbin R, Eyras E, Fernandez-Suarez XM, Gane P, Gibbins B, Gilbert J, Hammond M, Hotz H, Iyer V, Kahari A, Jekosch K, Kasprzyk A, Keefe D, Keenan S, Lehväslaiho H, McVicker G, Melsopp C, Meidl P, Mongin E, Pettett R, Potter S, Proctor G, Rae M, Searle S, Slater G, Smedley D, Smith J, Spooner W, Stabenau A, Stalker J, Storey R, Ureta-Vidal A, Woodwark C, Clamp M, Hubbard T: Ensembl 2004. Nucleic Acids Res. 2004, Database issue: D468-70. 10.1093/nar/gkh038.View ArticleGoogle Scholar
- Frezal J: Genatlas database, genes and development defects. C R Acad Sci III. 1998, 321: 805-817.View ArticlePubMedGoogle Scholar
- Gilbert DG: euGenes: a eukaryote genome information system. Nucleic Acids Res. 2002, 30: 145-148. 10.1093/nar/30.1.145.View ArticlePubMedPubMed CentralGoogle Scholar
- GDB Human Genome Database. [http://www.gdb.org/]
- Lenhard B, Hayes WS, Wasserman WW: GeneLynx: a gene-centric portal to the human genome. Genome Res. 2001, 11: 2151-2157. 10.1101/gr.199801.View ArticlePubMedPubMed CentralGoogle Scholar
- Bernstein FC, Koetzle TF, Williams GJ, Meyer EFJ, Brice MD, Rodgers JR, Kennard O, Shimanouchi T, Tasumi M: The Protein Data Bank: a computer-based archival file for macromolecular structures. J Mol Biol. 1977, 112: 535-542.View ArticlePubMedGoogle Scholar
- Bateman A, Birney E, Cerruti L, Durbin R, Etwiller L, Eddy SR, Griffiths-Jones S, Howe KL, Marshall M, Sonnhammer EL: The Pfam protein families database. Nucleic Acids Res. 2002, 30: 276-280. 10.1093/nar/30.1.276.View ArticlePubMedPubMed CentralGoogle Scholar
- Mulder NJ, Apweiler R, Attwood TK, Bairoch A, Barrell D, Bateman A, Binns D, Biswas M, Bradley P, Bork P, Bucher P, Copley RR, Courcelle E, Das U, Durbin R, Falquet L, Fleischmann W, Griffiths-Jones S, Haft D, Harte N, Hulo N, Kahn D, Kanapin A, Krestyaninova M, Lopez R, Letunic I, Lonsdale D, Silventoinen V, Orchard SE, Pagni M, Peyruc D, Ponting CP, Selengut JD, Servant F, Sigrist CJ, Vaughan R, Zdobnov EM: The InterPro Database, 2003 brings increased coverage and new features. Nucleic Acids Res. 2003, 31: 315-318. 10.1093/nar/gkg046.View ArticlePubMedPubMed CentralGoogle Scholar
- Servant F, Bru C, Carrere S, Courcelle E, Gouzy J, Peyruc D, Kahn D: ProDom: automated clustering of homologous domains. Brief Bioinform. 2002, 3: 246-251.View ArticlePubMedGoogle Scholar
- Letunic I, Copley RR, Schmidt S, Ciccarelli FD, Doerks T, Schultz J, Ponting CP, Bork P: SMART 4.0: towards genomic data integration. Nucleic Acids Res. 2004, Database issue: D142-4. 10.1093/nar/gkh088.View ArticleGoogle Scholar
- Sigrist CJ, Cerutti L, Hulo N, Gattiker A, Falquet L, Pagni M, Bairoch A, Bucher P: PROSITE: a documented database using patterns and profiles as motif descriptors. Brief Bioinform. 2002, 3: 265-274.View ArticlePubMedGoogle Scholar
- World Wide Web Consortium: XML Inclusions (XInclude) Version 1.0. [http://www.w3.org/TR/xinclude/]
- World Wide Web Consortium: XML Pointer Language (XPointer). [http://www.w3.org/TR/xptr/]
- World Wide Web Consortium: Namespaces in XML. [http://www.w3.org/TR/REC-xml-names/]
- Riikonen P, Boberg J, Salakoski T, Vihinen M: BioWAP, mobile Internet service for bioinformatics. Bioinformatics. 2001, 17: 855-856. 10.1093/bioinformatics/17.9.855.View ArticlePubMedGoogle Scholar
- Riikonen P, Boberg J, Salakoski T, Vihinen M: Mobile access to biological databases on the Internet. IEEE Trans Biomed Eng. 2002, 49: 1477-1479. 10.1109/TBME.2002.805459.View ArticlePubMedGoogle Scholar
- Navarro JD, Niranjan V, Peri S, K. JC: From biological databases to platforms for biomedical discovery. Trends in Biotechnol. 2003, 21: 263-268. 10.1016/S0167-7799(03)00108-2.View ArticleGoogle Scholar
- Kulikova T, Aldebert P, Althorpe N, Baker W, Bates K, Browne P, van den Broek A, Cochrane G, Duggan K, Eberhardt R, Faruque N, Garcia-Pastor M, Harte N, Kanz C, Leinonen R, Lin Q, Lombard V, Lopez R, Mancuso R, McHale M, Nardone F, Silventoinen V, Stoehr P, Stoesser G, Tuli MA, Tzouvara K, Vaughan R, Wu D, Zhu W, Apweiler R: The EMBL Nucleotide Sequence Database. Nucleic Acids Res. 2004, Database issue: D27-30. 10.1093/nar/gkh120.View ArticleGoogle Scholar
- Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL: GenBank: update. Nucleic Acids Res. 2004, Database issue: D23-6. 10.1093/nar/gkh045.View ArticleGoogle Scholar
- Zdobnov EM, Lopez R, Apweiler R, Etzold T: The EBI SRS server-new features. Bioinformatics. 2002, 18: 1149-1150. 10.1093/bioinformatics/18.8.1149.View ArticlePubMedGoogle Scholar
- Stajich JE, Block D, Boulez K, Brenner SE, Chervitz SA, Dagdigian C, Fuellen G, Gilbert JG, Korf I, Lapp H, Lehväslaiho H, Matsalla C, Mungall CJ, Osborne BI, Pocock MR, Schattner P, Senger M, Stein LD, Stupka E, Wilkinson MD, Birney E: The Bioperl toolkit: Perl modules for the life sciences. Genome Res. 2002, 12: 1611-1618. 10.1101/gr.361602.View ArticlePubMedPubMed CentralGoogle Scholar
- Lawrence R, Barker K: 2001, ACM Press, 225-230., Integrating relational database schemas using a standardized dictionary: ; Las Vegas, Nevada, United States., Proceedings of the 2001 ACM symposium on Applied computing
- Stevens RD, Robinson AJ, Goble CA: myGrid: personalised bioinformatics on the information grid. Bioinformatics. 2003, Suppl 1: I302-I304. 10.1093/bioinformatics/btg1041.View ArticleGoogle Scholar
- Das M, Lawhead PB: Information storage and management in large web-based applications using XML. J Comput Small Coll. 2003, 18: 72-79.Google Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6947/5/21/prepub