Genome Data and Tool Interoperation
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Genome Data and Tool Interoperation over the “Semantic” Web By Kei-Hoi Cheung, Ph.D. Assistant Professor Yale Center for Medical Informatics MB&B 452b/752b, April 20, 2005, Yale University Outline • Introduction • Semantic Web – Resource Description Framework (RDF) – Life Sciences Identifiers (LSID) – YeastHub: yeast genome data interoperation – Web Services for tool interoperation • Collaborative projects – Biosphere – Taverna • Semantic Web Services • Conclusion • Future directions Eras of Computing • Mainframe computing (many people share one computer) • Personal computing (one person uses one computer) • Ubiquitous computing (one person is served by many computers over the network) – Client/server computing, grid computing, peer-to-peer computing, distributed/parallel computing, component-based computing, etc – World Wide Web (WWW) is one of the main driving forces – It provides a globally distributed communication framework that is essential for almost all scientific collaboration, including bioinformatics The World Wide Web • On the order of 108 users – Used in every country on Earth • On the order of 1010 indexed web resources (text) in Google etc – Essentially Infinite if one includes “dynamic” web pages • Massively distributed and open It is difficult to keep track of these resources Data Heterogeneity • Data are exposed in different ways – Programmatic interfaces – Web forms or pages – FTP directory structures • Data are presented in different ways – Structured text • Tab delimited format, XML format, etc – Free text – Binary • Images • Naming conflicts (e.g., synonyms and homonyms) Tool heterogeneity • Server applications – Web server applications – Application programming interfaces (API) • Client applications (downloadable software) • Different programming languages • Different operating systems From Web to Semantic Web • Human processing Machine processing • Free text description ontological description • HTML XML RDF or its extensions • Metadata! HTML Example Readme 1 1 1 1 1 1 1 2 3 4 5 6 0 0 1 1 1 1 0 0 2 2 2 2 1 2 2 1 1 1 1 0 0 0 1 0 Col# 1 2 3 4 5 6 Description pedigree id Person id Father id Mother id Sex Status <html> <body> … <a href=“http://ycmi.med.yale.edu/ped_readme.html”> Readme</a> <table> <tr> <td>1</td> <td>1</td> <td>0</t> <td>0</td> … </tr> … </table> … </body> </html> XML Example Other Advantages of Using XML • It is simple, hierarchical, self-describing, and computer-readable • It can be validated using DTD or XSchema • It is a W3C standard • It has a large base of software support (both commercial and public domain software tools) – Editing tools, DOM, SAX, XSL, etc Proliferation of Bio-XML Formats Sequence BSML AGAVE Microarray Gene Expression GEML MAML BIND MAGE-ML RDF (e.g., BioPax) Semantically rich ontologies Reasoning (machine intelligence) Pathway SBML PSI-MI Definition of an Ontology • Conceptualization of a domain of interest – Concepts, relations, attributes, constraints, objects, values • An ontology is a specification of a conceptualization – Formal notation – Documentation • A variety of forms, but includes: – A vocabulary of terms – Some specification of the meaning of the terms • Ontologies are defined for reuse Roles of Ontologies in Bioinformatics • Success of many biological DBs depends on – High fidelity ontologies – Clearly communicating their ontologies • Prevent errors on data entry and interpretation • Common framework for multidatabase queries • Controlled vocabularies for genome annotation – GO – EC numbers • Information-extraction applications • Reuse is a core aspect of ontologies – Reuse of existing ontologies faster than designing new ones – Reuse decreases semantic heterogeneity of DBs • Schema-driven Software – Knowledge-acquisition tools – Query tools Example Bio-ontologies • Gene Ontologies – http://www.geneontology.org/ • MGED Ontologies – http://mged.sourceforge.net/ • Open Biomedical Ontologies (OBO) – http://obo.sourceforge.net/ Are current bio-ontologies adequate? Ontology desiderata • Precision – Formal, unambiguous – High fidelity • Explicitness – Clarity – Commitment – Reuse • Systematic – Quality – Clarity • Flexibility – Expressivity – Evolution machine computable Semantic Web • It provides a common framework that allows semantic interoperability among multiple resources through the use of ontologies • It is a collaborative effort led by W3C with participation from a large number of researchers and industrial partners • It is based on the Resource Description Framework (RDF) Resource Description Framework (RDF) • It is a standard data model (directed acyclic graph) for representing information (metadata) about resources in the World Wide Web • In general, it can be used to represent information about “things” that can be identified (using URI’s) on the Web • It is intended to provide a simple way to make statements (descriptions) about Web resources RDF Statement A RDF statement consists of: • Subject: resource identified by a URI • Predicate: property (as defined in a name space identified by a URI) • Object: property value or a resource For example, the “dbSNP Website” is a subject, “creator” is a Predicate, “NCBI” is an object. A resource can be described by multiple statements. Graphical Representation RDF/XML Representation <?xml version="1.0"?> <rdf:RDF xmlns:rdf=“http://www.w3.org/1999/02/22-rdf-syntax-ns#” xmlns:dc=“http://purl.org/dc/elements/1.1” xmlns:ex=“http://www.example.org/terms”> <dc: creator rdf:resource=“http://www.example.org/staffid/85740”></dc:creator> <dc:language>en</dc:language> <ex:creation-date>August 16, 1999</dc:creation-date> <rdf:RDF> Data Integration Using RDF atagccgta cctgcgagt ctagaagct derives from human hemoglobin GenBank derives from atagccgta cctgcgagt ctagaagct + human hemoglobin is a oxygen transport protein human hemoglobin is a Gene Ontology + has 3D structure human hemoglobin has 3D structure Unified view Protein Data Bank oxygen transport protein Reification • Making statements about statements • For example, GenBank provides the following statement: “human hemoglobin derives from atagccgtacctgcgagtctagaagct” Example <rdf:RDF xmlns:rdf=“http://www.w3.org/1999/02/22-rdf-syntax-ns#” xmlns:s=“http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?val=29436”> <rdf:Description about=“http://www.ncbi.nlm.nih.gov/Genbank”> <s:derive_from rdf:ID=“statement1”> atag… </s:derive_from> </rdf:Description> <rdf:Description about=“#statement1”> <s:providedBy>GenBank</s:providedBy> </rdf:Description> </rdf:RDF> Other RDF-Based Ontology Languages • RDFS • DAML+OIL • OWL Life Science Identifiers (“LSID”) Addresses Data Access Problems • LSID is a naming standard for distributed data, specifically: –Scientifically significant data –Geographically distributed –Files, database records, and data objects managed by N-tier applications –Public and/or private networks –And owned, managed, by different organizations LSID Syntax • 5 Part Format: URN:LSID:Authority:Namespace:Object:[Revision-ID] – URN:LSID: is a mandatory prefix – Authority is the Internet domain of the organization that assigns an LSID to a resource – Namespace constrains the scope of the object – Object is an alphanumeric describing the object – Revision-ID is an optional version of the object • Examples – URN:LSID:ncbi.nlm.nih.gov:genbank:AF271072:1 – URN:LSID:ncbi.nlm.nih.gov:pubmed:12571434 LSID: a single naming schema • One standard naming scheme – Named data is unique – Data integrity is maintained • Breaking down of “data silos” – Names no longer only useful in a specific proprietary context – Integrate any data source using standard naming scheme – Single LSID protocol replaces proprietary source specific programs • Access to more data – Integrate data across discovery and development cycles • Metadata features – Standard access to specific data allows them to easily be related semantically. These semantic links can lead to new insights LSID-Enabled Applications • LaunchPad • BioHaystack LaunchPad • • • it takes an LSID; resolves it; attempts to match the local applications one uses to process/view this data. YeastHub (a semantic web approach to yeast data integration) (Collaboration between YCMI and Gerstein Lab: Kevin Yip, Andrew Smith, Andy Masiar, Remko deKnikker) (Accepted for publication and presentation in ISMB 2005) Yeast Genome Data • The budding yeast Saccharomyces cerevisiae was the first fully sequenced eukaryotic genome. • Ease of genetic manipulation and many of its genes are strikingly similar to human genes • It has been studied extensively through a wide range of biological experiments (e.g., microarray experiments). • A large variety of yeast genome data (e.g., gene expression data) have been made available through many resources (e.g., SGD, MIPS, YPD, TRIPLES, Yeast World, etc) • Integration of such a variety of yeast data can facilitate whole genome analysis Data Conversion and Integration Resource1 Resource2 Resourcen <xml> … </xml> DOM/SAX RDF1 DB-specific tool XSLT RDF2 RDFn RDF/DB RDQL Users/Agents (Sesame) Two Levels of RDF Description • Resource description • Data description Resource Description (Use of Dublin Core Metadata) Metadata Example RDF Modeling of Tabular Data Data Conversion RDF Example Query Form RQL Syntax and Query Results Semantic Web Technologies Employed in YeastHub • RDF Site Summary (RSS) • D2RQ (mapping from relational databases to RDF) • Semantic Web Database (Sesame) • RDF Query Languages (e.g., RQL and SeRQL) Tool Interoperation An Example Scenario • Comparative genomics Manual Interoperation A Better Way of Interoperation A Better Way of Interoperation (cont’d) Web Services “Creating a Bioinformatics Nation” (Lincoln Stein) Web Services UDDI WSDL SOAP SOAP • It stands for Simple Object Access Protocol • It is an XML syntax for exchanging messages between applications • It is based on HTTP • It codifies existing practice of using XML and HTTP together • It is language and platform independent RPC implementation WSDL • It describes the syntax of Web Service interfaces and their locations • Programmers can create WSDL files to describe their Web Services and make them available over the Internet WSDL Contents WSDL Example (XEMBL) Bioinformatics Web Services Projects • DAS (http://biodas.org/) • DDBJ’s Biological Web Services (http://www.xml.nig.ac.jp/) • BioMoby (http://biomoby.org) – Moby-S – Semantic Moby • myGrid (http://www.ebi.ac.uk/mygrid/) – SoapLab (http://industry.ebi.ac.uk/soaplab/) – Talisman (http://www.ebi.ac.uk/talisman/index.html) – Taverna (http://taverna.sourceforge.net/) Bioinformatics Web Service Collaboration • Biosphere (YCMI and University of Hong Kong) • Web Service Workflow (University of New Castle Upon Tyne, University of Hong Kong, and YCMI) Biosphere Taverna Semantic Web Services Semantic Web Service • Description using OWL-S – Profile – Process – Grounding (e.g., WSDL) What to describe? Resource provides Service supports presents What it does describedby Service profile description Service grounding Service model How to access it How it works functionalities functional attributes Semantic Web Services • • • • Discovery Invocation Composition Monitoring Conclusion • The World Wide Web affords unprecedented access to “globally distributed information” • Metadata, or structured data about data, helps automate discovery of and access to such information • RDF – is the W3C Recommendation defining an infrastructure that enables the encoding, exchange, and reuse of structured metadata – allows that ontologies defined by different communities can be shared – facilitates data and tool interoperability Future Directions: The Semantic Wave “Once the web has been sufficiently "populated" with rich metadata, what can we expect? First, searching on the web will become easier as search engines have more information available, and thus searching can be more focused. Doors will also be opened for automated software agents to roam the web, looking for information for us or transacting business on our behalf. The web of today, the vast unstructured mass of information, may in the future be transformed into something more manageable and thus something far more useful.” (Ora Lassila) Doing this humanely! “No amount of automation will replace human beings, but clumsy and belligerent automation will alienate them and suppress their creativity.” (Tony Kazic) Thanks!
