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.
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