Transcript Semantic Mashup of Neuroscience Data

Semantic Web Technologies
in Biosciences
Kei Cheung, Ph.D.
Yale Center for Medical Informatics
Outline
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Introduction
Past and current Web (Syntactic Web)
Future Web (Semantic Web)
Semantic Web technologies with
examples in the biosciences
Data Growth
• The Human Genome Project created a
paradigm shift in biology (experimental ->
computational) due to the flood of DNA
sequence data produced.
• Since HGP, other types of high throughput biotechnologies have emerged and produced vast
quantities of data of diverse types (transcript
profiling, protein profiling, genotyping, next
generation sequencing, etc).
• An increasing number of bio-data providers have
made their data available through the Web.
Problems and Issues
• Each database represents a data silo accessed
by local applications written in specific
languages
• The web pages display data but they do not
expose the structure of data in a machine
readable format
• Different user/query interfaces
• No uniform/global data schema
• Lack of standard ID’s, terminology, vocabulary,
data formats, etc
Available Tools/Approaches
• Web search engines (e.g., google, yahoo)
• One-stop shopping (e.g., NCBI)
• Gateway or directory listing (e.g.,
Neuroscience Database Gateway)
• Use screen scraping methods to extract
data from web content (e.g., Perl scripts)
I’m NOT a company!
Kei (Hui) Cheung
Not me!
Kei (Hoi) Cheung
(15 years ago)
Find the most recent image
of the person “Kei Hoi Cheung”
Kei (Hoi) Cheung
(more recent)
Semantic Web = Brilliant Web!
Knowledge-driven bioscience data
integration on the Semantic Web
Knowledge-based applications
targets
Receptor
Knowledge layer
Image
is-a
has-part
Protein
Pathway
underlies
underlies
has-image
Cell
Sequence
has-sequence
PDB
Data layer
underlies
encodes
Drug treats Disease
Gene
DrugBank
CCDB
is_involved_in
Gene
Expression
Onmibus
KEGG
Neuron
DB
GenBank
Gene
Cards
Semantic Web Stack
Problems with XML
• DTD has limited expressiveness of the XML language
• XML is designed as a language for message encoding
• XML is only self-descriptive about the following structural
relationships:
– containment, adjacency, co-occurrence, attribute and opaque
reference.
– All these relationships are useful for serialization, but are not
optimal for modeling objects of a problem domain
• For example, the relationship between the <spot> and
<coord_*> of AGML tags is no different from that
between <spot> and <dia_*>.
– A computer algorithm must treat them differently to develop
meaningful applications. To calculate the distance between two
<spot>s, an algorithm shall use the value of <coord_*>, but to
calculate the area of each <spot>, it shall retrieve the value of
<dia_*> instead
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
From XML to RDF
Semantic Web
• The Semantic Web provides a common
machine-readable framework that allows data to
be shared and reused across application,
enterprise, and community boundaries
– The Semantic Web is a web of data
• The Semantic Web is about two things
– It is about common formats for identification,
integration and combination of data drawn from
diverse sources
– It is also about languages for recording how the data
relates to real world objects
RDF
• The foundational semantic web technology is
the resource-description framework (RDF)
• RDF is a system to describe resources
• RDF has a very simple and yet elegant data
model (directed acyclic graph)
– everything is a resource that connects with other
resources via properties
• A resource is anything that is identifiable by a
uniform resource identifier (URI)
Characteristics of RDF
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The DAG structure offered by RDF makes it extensible and evolvable.
Adding nodes and edges to a DAG doesn’t change the structure of any
existing subgraph.
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RDF has an open-world assumption in that allows anyone to make
statements about any resource
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RDF is monotonic in that new statements neither change nor negate the
validity of previous assertions, making it particularly suitable in an academic
environment, in which consensus and disagreement about the same
resources have a useful coexistence that needs to be formally recorded.
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All RDF terms share a global naming scheme in URI, making distributed
data and ontologies possible
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The combined effect of global naming, universal data structure and openworld assumption is that resources exist independently but can be readily
linked with little precoordination.
Linked Data
• Linked Data is about using the Web to connect related
data that wasn't previously linked
• Wikipedia defines Linked Data as "a term used to
describe a recommended best practice for exposing,
sharing, and connecting pieces of data, information, and
knowledge on the Semantic Web using URIs and RDF."
• In addition to providers and consumers of linked data,
there are link creators who create semantic links
between different RDF datasets (e.g., links can be
created between protein kinases and drugs)
Linked Data Cloud (linkeddata.org)
RDFS and OWL
• RDF Schema (RDFS) – it supports
classes and class hierarchy
• Web Ontology Language (OWL): OWL
Lite, OWL DL, OWL Full
• While RDFS and OWL are layered on top
of RDF, they offer support for inference
and axiom, making Semantic Web capable
of supporting knowledge-based querying
and inferencing
Uniform Resource Identifiers
(URIs)
• A URI is a string of characters used to identify or name a
resource on the Internet.
• URLs (Uniform Resource Locators) are a particular type of
URI, used for resources that can be accessed on the WWW
(e.g., web pages)
• In RDF, URIs typically look like “normal” URLs, often with
fragment identifiers to point at specific parts of a document:
– http://www.semantic-systems-biology.org/SSB#CCO_B0000000
(id for “core cell cycle protein” in Cell Cycle Ontology)
RDF Triple/Graph
• The basic information unit in RDF is an RDF statement in the form of
– (subject, property, object)
• Each RDF statement can be modeled as a graph comprising two nodes
connected by a directed arc
• A triple example
• A set of such triples can jointly form a directed labeled graph (DLG) that
can in theory model a significant part of domain knowledge.
• An RDF graph can be represented in different formats (XML, Turtle, N3…)
Cell Cycle Ontology (CCO)
(Antezana et al, 2009, Genome Biology)
http://genomebiology.com/2009/10/5/R58
Named Graph
• RDF graphs are nameable by URIs
• This enables RDF statements to be
created to describe graphs
• This helps establish provenance and trust
• Representation formats: TriX and TriG
:G2 { :G1 swp:assertedBy _:w1 .
_:w1 swp:authority :Erick .
_:w1 dc:date "2009-05-29"^^xsd:date .
_:w1 dc:license "Creative Commons Attribution License“^^xsd:string .
:Erick rdf:type ex:Person .
:Erick ex:email <mailto:[email protected]> }
SPARQL
• It is a standard query language for RDF
• It can be used to express queries across diverse
data sources, whether the data is stored natively
as RDF or viewed as RDF via middleware.
• It contains capabilities for querying required and
optional graph patterns along with their
conjunctions and disjunctions.
• The results of SPARQL queries can be results
sets or RDF graphs.
RDF Graph Match (SPARQL)
core cell cycle protein
BASE <http://www.semantic-systems-biology.org/ webcite>
PREFIX rdfs:<http://www.w3.org/2000/01/rdf-schema# webcite>
PREFIX ssb:<http://www.semantic-systems-biology.org/SSB# webcite>
SELECT ?protein_label
WHERE {
GRAPH <cco_S_pombe> {
?protein ssb:is_a ssb:CCO_B0000000.
?protein rdfs:label ?protein_label
}
}
SPARQL (Cont’d)
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The following SPARQL query on the A. thaliana graph allows users to infer a
putative location for proteins with no documented cellular locations. The
assumption behind such a query is that two proteins that participate in the same
interaction are likely to share the same cellular location, the 'nucleus'
(CCO_C0000252):
BASE <http://www.semantic-systems-biology.org/ webcite>
PREFIX rdfs:<http://www.w3.org/2000/01/rdf-schema# webcite>
PREFIX ssb:<http://www.semantic-systems-biology.org/SSB# webcite>
SELECT
?prot_in_the_nucleus
?prot_to_study
?interaction_label
WHERE {
GRAPH <cco_A_thaliana> {
?interaction a ssb:interaction.
?interaction rdfs:label ?interaction_label.
?prot_A ssb:participates_in ?interaction.
?prot_B ssb:participates_in ?interaction.
?prot_A rdfs:label ?prot_in_the_nucleus.
?prot_B rdfs:label ?prot_to_study.
?prot_A ssb:located_in ssb:CCO_C0000252.
OPTIONAL {
?prot_B ssb:located_in ?location_B.
}
FILTER (!bound(?location_B))
}
}
OWL DL Representation
:Nucleus
a owl:Class ;
rdfs:subClassOf
[ a owl:Restriction ;
owl:onProperty :part_of ;
owl:someValuesFrom :Cell
]
Necessary but not sufficient condition: part of a nucleus is also part of a cell,
but part of a cell is not necessarily part of a nucleus
OWL Reasoning
• Which proteins participate in “mitosis”
:Protein
a owl:Class ;
rdfs:subClassOf
[ a owl:Restriction ;
owl:onProperty :participates_in ;
owl:someValuesFrom :Mitosis
]
Visualization Application
Semantic Web Rules
• Semantic Web Rule Language (SWRL) –
it combines the sublanguages of the OWL
Web Ontology Language with those of the
Rule Markup Language
• It can help increase the expressivity of
OWL ontologies by augmenting such
ontologies with rules
• Rules are easier to understand than
description logic.
SWRL Examples
• Protein (?p1) Λ cellularLocation (?p1, Nucleus) 
NuclearProtein (?p1)
• participatesInteraction(?protein1, ?interaction1) Λ
participatesInteraction(?protein2, ?interaction1) Λ
participatesInteraction(?protein2, ?interaction2) Λ
participatesInteraction(?protein3, ?interaction2) 
proteinInteraction (?protein1, ?protein3)
Enabling Technologies/Tools
• Triplestores (e.g., Virtuoso, Oracle,
AllegroGraph, …) – SPARQL Endpoint
• Ontology editors (e.g., Protégé, SWOOP,
OBO-Edit, …)
• OWL reasoners (e.g., Pellet, RacerPro,
FaCT++, …)
Semantic Web Related Communities
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National Center for Biomedical Ontology
OBO Foundry
BioPAX
Semantic Web Activity of the World Wide
Web Consortium
– Semantic Web for Health Care and Life
Sciences Interest Group
• BioRDF, COI, LODD, Sci. Discourse, Terminology,
Translational Medicine Ontology
Roads to Semantic Web
• Provide data in RDF format (data providers)
– UniProt, Gene Ontology, NCI Metathesauras
• Convert non-RDF data to RDF data (third party
efforts)
– YeastHub
– D2RQ, TRIPLIFY
• Mix RDF data with non-RDF data
– RDFa (e.g., Fuzz Firefox extension)
– GRDDL
Merge between Web 2.0 and
Semantic Web
• People (FOAF)
• Yahoo!Pipes (Semantic Web Pipes
developed at DERI)
• Dapper (Semantify Dapper)
• MediaWiki (Semantic MediaWiki)
• Google Map (Semantic Google Map)
The End