genome data and tool interoperation over the “semantic” web
Post on 11-Jan-2016
23 Views
Preview:
DESCRIPTION
TRANSCRIPT
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
• 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
Eras of Computing
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
Col#Description1 pedigree id2 Person id3 Father id4 Mother id5 Sex6 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>
1 1 0 0 1 11 2 0 0 2 01 3 1 2 2 01 4 1 2 1 01 5 1 2 1 11 6 1 2 1 0
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
Pathway
BIND SBML PSI-MI
MAGE-ML
RDF (e.g., BioPax)
Semantically rich ontologies
Reasoning (machine intelligence)
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 aPredicate, “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
humanhemoglobin
oxygentransportprotein
atagccgtacctgcgagtctagaagct
derives from
is a
humanhemoglobin
humanhemoglobin
has 3D structure
GenBank
Gene Ontology
Protein Data Bank
humanhemoglobin
atagccgtacctgcgagtctagaagctderives from
oxygentransportprotein
is a
has 3D structure
Unified view
+
+
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
RDF1 RDF2 RDFn
DOM/SAX
RDF/DB
Resource1 Resource2 Resourcen
<xml> …</xml>
XSLTDB-specific tool
Users/AgentsRDQL
(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
SOAP
WSDL
UDDI
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 Service
Service profile
Service model
Service grounding
provides
presents
describedby
supports
What it does
How it works
How to access itdescription
functionalitiesfunctional 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!
top related