Intersection of Semantic Web and Life Sciences

1
Intersection of Semantic Web and Life Sciences

• Kei Cheung
• Yale Center for Medical Informatics

Genomics and Bioinformatics (MBB 452a), November
2, 2005
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Outline

• Introduction
• Overview of RDF and LSID
• Semantic web applications
• Connotea
• Piggy Bank
• YeastHub

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Two scientific/technological endeavors that have
impacted the world greatly in the past 15 years

• Human Genome Project (HGP)
• International collaboration that began in 1990
  and completed in 2003
• Understand the blueprint of life (moon-landing of
  the nineties)
• Sequence the entire human genome
• World Wide Web (WWW)
• It was born in 1989/1990 at CERNS (developed by
  Tim Berners-Lee)
• Revolutionize information access and sharing over
  the Internet (Gutenbergs printing press)
• Web browsers (e.g., IE, Netscape, FireFox)

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Relationship between HGP and WWW

• HGP transformed life sciences into an information
  science, as large amounts of data have been
  generated, which need to be stored and analyzed
• GenBank, EMBL, and DDBJ have recently reached a
  milestone of 100 billion bases from gt 165,000
  organisms
• Pubmed has gt 300,000 articles from gt 150 life
  sciences journals
• WWW has become the most popular medium for life
  scientists to distribute, access, share, and
  integrate different types of biological data over
  the Internet
• As of 2005, there are 719 publicly available
  databases listed in NAR molecular biology
  database compilation

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Spider-Man Spidey science gets a genetic makover
http//www.genomenewsnetwork.org/articles/05_02/sp
iderman.php
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Spider-Man (Tim Berners-Lee) Weaving the Web
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Semantic Web

• "The Semantic Web is an extension of the current
  web in which information is given well-defined
  meaning, better enabling computers and people to
  work in cooperation." -- Tim Berners-Lee, James
  Hendler, Ora Lassila, The Semantic Web,
  Scientific American, May 2001
• It provides a common framework that allows data
  to be shared and reused across application,
  enterprise, and community boundaries
• It is based on the Resource Description Framework
  (RDF), which integrates a variety of applications
  using XML for syntax and URIs for naming.

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Semantic Web for Life Sciences(TBL, Bio-IT World
Conference, May 2005)

• also the people involved in the Semantic Web
  pushing it along are also excited about getting
  involved in the life sciences its one of those
  areas that affect humankind, finding drugs,
  curing AIDS and cancer, etc. There seems to be a
  huge energy, and lots of practical technical
  reasons why this area is crying out to be one of
  the flagship areas that the Semantic Web really
  takes off

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Data ? Information ? Knowledge
Navarro JD, Niranjan V, Peri S, Jonnalagadda CK,
Pandey A. (2003) From biological databases to
platforms for biomedical discovery. Trends
Biotechnol. (6)263-8.
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Problem with the Current WWW
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Problem with the Current Web
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Keyword Search regulatory variation mammals
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Data Heterogeneity

• Lack of standard detailed description of
  resources
• Data are exposed in different ways
• Programmatic interfaces
• Web forms or pages
• FTP directory structures
• Data are presented in different ways
• Structured text (e.g., tab delimited format and
  XML format)
• Free text
• Binary (e.g., images)

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Data Heterogeneity (contd)

• Nomenclature problem
• Gene/protein names (based on phenotype, sequence,
  function, organisms, etc)
• Armadillo (fruitflies) vs. i-catenin (mice)
• PSM1 (human) PSM2 (yeast) PSM1 (yeast) PSM2
  (human)
• Sonic Hedgehog
• ID proliferation
• Different ID schemes 1OF1  (PDB ID) and P06478
  (SwissProt ID) correspond to Herpes Thymidine
  Kinase
• Lexcial variation GO1234, GO1234, GO-1234
• Synonyms vs. homonyms
• Dopamine receptor D2 DRD2, DRD-2, D2
• PSA prostate specific antigen,
  puromycin-sensitive aminopeptidase, psoriatric
  arthritis, pig serum albumin
• Biologists would rather share their toothbrush
  than a gene name Gene nomenclature is beyond
  redemption, said Michael Ashburner

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From Web to Semantic Web(contd)

• Human processing ? Machine processing
• Use of Metadata
• Free text description ? ontological description
• HTML ? XML ? RDF or its extensions
• Vision ? implementation

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HTML Example
Readme
1 1 0 0 1 1 1 2 0 0 2 0 1 3
1 2 2 0 1 4 1 2 1 0 1 5 1
2 1 1 1 6 1 2 1 0
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XML Example
What is XML?

• eXtensible Markup Language
• It is self describing
• It is hierarchical
• It is human- and computer-readable
• It is a World Wide Web Consortium (W3C) standard
• It can be validated using DTD or XSchema
• There is a large software base support

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Proliferation of Bio-XML Formats
Reasoning (machine intelligence)
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XML Representation of Proteomics Data
AGML
HUP-ML
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RDF Representation
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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 URIs) on the Web
• It is intended to provide a simple way to make
  statements (descriptions) about Web resources

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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 (literal) 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.
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Graphical XML Representation
http//www.ncbi.nlm.nih.gov/SNP
http//purl.org/dc/elements/1.1/creator
http//purl.org/dc/elements/1.1/language
http//www.ncbi.nlm.nih.gov
en
lt?xml version"1.0"?gt ltrdfRDF
xmlnsrdfhttp//www.w3.org/1999/02/22-rdf-syntax
-ns xmlnsdchttp//purl.org/dc/elements/1.1
xmlnsexhttp//www.example.org/termsgt ltrdfDe
scription abouthttp//www.ncbi.nlm.nih.gov/SNPgt
ltdccreator rdfresourcehttp//www.ncbi.nlm.nih
.govgtlt/dccreatorgt ltdclanguagegtenlt/dclanguagegt
dategt lt/rdfDescriptiongt lt/rdfRDFgt
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Life Sciences Identifiers (LSIDs)

• URL vs. URI vs. URN
• URL http//www.gleaners.org/faq.html
• URI http//www.gleaners.org/faq.htmlQ04
• URN www.gleaners.org/faq.htmlQ04
• LSID is a form of URN

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Problems of URIs

• The web server referenced by the URL may be
  broken or become unavailable
• The syntax of the URL may change over time as the
  underlying data retrieval program evolves
• The data returned by a URL may change over time
  as the underlying database contents change.

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LISD Format and Examples

• URNLSIDnamespacedatabaseobject_idrevision_id
  
• Examples
• URNLSIDncbi.nlm.nih.govgenbankAF271072'
• URNLSIDchemacx.cambridgesoft.comACXCAS9675821

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LSID (contd)

• Globalness A LSID is a name with global scope
  that does not imply a location. It has the same
  meaning everywhere.
• Uniqueness The same LSID will never be assigned
  to two different objects.
• Persistence It is intended that the lifetime of
  an LSID be permanent.
• Scalability LSIDs can be assigned to any data
  element that might conceivably be available on
  the network, for hundreds of years.
• Legacy Support The LSID naming scheme must
  permit the support of existing legacy naming
  systems
• Extensibility Any scheme for LSIDs must permit
  future extensions to the scheme.
• Independence It is solely the responsibility of
  a name issuing authority to determine conditions
  under which it will issue a name.
• Resolution A URN will not impede resolution
  i.e., translation to a URL..."

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Semantic Web Applications

• Connotea (on-line management of web resources)
• Piggy bank (semantic web browser)
• YeastHub yeast genome data integration

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Connotea Online Reference Management Service
(Nature Publishing Group)www.connotea.org

• To keep links to the articles/websites of your
  interest
• To discover new articles and websites through
  sharing your links with other users
• It is web-accessible

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TBLs original vision of the Web

• Active vs. passive
• Collaborative vs. authoritative
• Decentralized vs. centralized
• Semantic vs. syntactic

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Connotea Online Reference Management Service
(Nature Publishing Group)
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ALFRED Population Sample
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Connotea (ALFRED Example)
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ALFRED Example
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Google Earth Example
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Data Integration Using RDF
atagccgtacctgcgagtctagaagct
humanhemoglobin
derives from
atagccgtacctgcgagtctagaagct
GenBank
derives from

humanhemoglobin
oxygentransportprotein
humanhemoglobin
oxygentransportprotein
is a
is a
Gene Ontology

has 3D structure
humanhemoglobin
has 3D structure
Unified view
Protein Data Bank
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Piggy Bank

• http//simile.mit.edu/piggy-bank
• It is an extension to the Firebox Web browser
• It turns the Firebox Web browser into a Semantic
  Web browser
• It supports tagging and links to Google Map

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RDF is the Common Currency
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Peggy Bank (Data Integration Example)
TRIPLES (Expr. Data)
HubMed
Keyword search
D2RQ
RDF Expr. Dataset
RDF Bib.. Info.
import
import
Pluggin
Browse/ query
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TRIPLES Expression Data in RDF
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Peggy Bank (PIM1 Gene)
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Semantic Bank
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Yeast Hub
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Yeast Hub Team
Kei Cheung
Mark Gerstein
Andrew Smith
Kevin Yip
Andy Masiar
Remko deKnikker
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RDF Technologies

• Description of data source using Rich Site
  Summary (RSS)
• Data Conversion into RDF
• Relational Database to RDF (D2RQ)
• Tabular-RDF-Conversion
• RDF Database (Sesame)
• RDF-based query languages

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Rich Site Summary (RSS)
User (Application)
Yeast Hub Resource
No RSS
No RSS
RSS
RSS
Resources
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Resource Description(Use of Dublin Core Metadata)
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RDF Metadata Example (RSS1.0)
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Data Conversion and Integration
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RDF Modeling of Tabular Data
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Tabular-RDF Data Conversion
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Example of Data Converted into RDF
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Motivating Example

• Genomic analysis of essentiality within protein
  networks.
• H Yu, D Greenbaum, H Xin Lu, X Zhu, M Gerstein
  (2004) Trends Genet 20 227-31.
• Jeong, H., Mason, S., Barabási, A.-L., and
  Oltvai, Z. 2001. Lethality and centrality in
  protein networks. Nature 411 4142
• Fraser, H., Hirsh, A., Steinmetz, L., Scharfe,
  C., and Feldman, M. 2002. Evolutionary rate in
  the protein interaction network. Science 296
  750752
• Important but hard

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Example Integrated Query
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Query Form
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RQL Syntax and Query Results
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Next step Data Mining

• Whole yeast genome analysis (Y6K)
• Subcellular localization of the yeast proteome.
• A Kumar, S Agarwal, JA Heyman, S Matson, M
  Heidtman, S Piccirillo, L Umansky, A Drawid, R
  Jansen, Y Liu, KH Cheung, P Miller, M Gerstein,
  GS Roeder, M Snyder (2002) Genes Dev 16 707-19.
• A Bayesian system integrating expression data
  with sequence patterns for localizing proteins
  comprehensive application to the yeast genome.
• A Drawid, M Gerstein (2000) J Mol Biol 301
  1059-75.
• Doing systematic dataming to predict the remining
  3K localizations
• Important but hard .

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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)
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Automate humanely!

• No amount of automation will replace human
  beings, but clumsy and belligerent automation
  will alienate them and suppress their
  creativity.
• (Tony Kazic)

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Thanks!Questions?
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Semantic Graph
Find the most current image of Kei Cheung who is
affiliated with YCMI
affiliated with
Kei Cheung
YCMI
images
Files
member of
member of
File n
File 1
date
date
Feb 1, 2005
Oct 1, 1990
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Research/Technologies Related to Semantic Web

• Text mining
• Agent computing
• Web services
• Ontological research

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Knowledge representation
Jill

• A person (Joe) is an uncle iff
• Joe is male
• He has a parent (Jill) who has a second child
  (Sue) who is parent

has_child
has_parent
Sue
Joe
has_child
?
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Other things to mention?

• Taxonomy vs. ontology
• OWL overview and example(s)