Bio2RDF and Beyond!

Bio2RDF and Beyond! Large Scale, Distributed Biological Knowledge Discovery
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EBI : 14-01-10
Michel Dumontier, Ph.D.
Associate Professor of Bioinformatics
Carleton University
Department of Biology
School of Computer Science
Institute of Biochemistry
Ottawa Institute of Systems Biology
Ottawa-Carleton Institute of Biomedical Engineering

Web-based Knowledge Discovery
a very painful process
Carole Goble (ISWC 2005)
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Syntactic Web…
It takes a lot of digging to get answers
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Portals provide structured information
and give better results
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We need to expose the deep web
Surface web:167 terabytes
Deep web:91,000 terabytes
545-to-one
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Data silos – not made for sharing
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How do we integrate these resources?
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We want to simultaneously query the 1000+ biological databases
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The Semantic Web is a web of knowledge.
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It is about standards for publishing, sharing and querying
knowledge drawn from diverse sources
It enables the answering of
sophisticated questions

A growing web of linked data
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Life Science Data Contributors
HCLS (LODD)
Neurocommons
Bio2RDF
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Resource Description Framework (RDF)
Allows one to talk about anything
Uniform Resource Identifier (URI) can be used as entity names
Bio2RDF specifies the naming convention
http://bio2rdf.org/uniprot:P05067
is a name for Amyloid precursor protein
http://bio2rdf.org/omim:104300
is a name for Alzheimer disease
uniprot:P05067
omim:104300
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Resource Description Framework (RDF)
Allows one to express statements
A RDF statement consists of:
Subject: resource identified by a URI
Predicate: resource identified by a URI
Object: resource or literal
uniprot:P05067
is a
uniprot:Protein
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Multi-Source Data Integration
depends on consistent naming
uniprot:P05067
uniprot:Protein
uniprot:Protein
is a
UniProt
has name
+
uniprot:P05067
go:Membrane
uniprot:P05067
go:Membrane
located in
located in
Gene Ontology
+
uniprot:P05067
interacts with
uniprot:P05067
uniprot:P05067
interacts with
Unified view
iRefIndex
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Building statements creates knowledge
Amyloid precursor protein
Alzheimer
Disease
label
label
is involved in
uniprot:P05067
omim:104300
is a
is a
Protein
Disease
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EBI : 14-01-10

RDF has multiple representations
RDF/XML
<?xml version="1.0"?>
<rdf:RDF
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:u="http://bio2rdf.org/uniprot:"
<rdf:Descriptionrdf:about=“&u;Q16665">
<rdf:typerdf:resource=“&u;Protein"/>
</rdf:Description>
</rdf:RDF>
RDF/N3
PREFIX u: <http://bio2rdf.org/uniprot:>
<u:Q16665> a <u:Protein> .
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Bio2RDF is a framework to create and provision linked data networks
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Francois Belleau, Laval University
Marc-Alexandre Nolin, Laval University
Peter Ansell, Queensland University of Technology
Michel Dumontier, Carleton University

Bio2RDF’s RDFized data fits together
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Bio2RDF now serving over 5 / 15 billion triples of linked biological data
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Bio2RDF linked data
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Bioinformatics Discovery Registry
SharedName initiative to provide stable URI patterns for data records.
We added the relationship between entities and records
Directory Service
~1700 datasets & dozens of resolvers.
Discovery Service
Registry links entities to data records, their formats (RDF/XML, HTML, etc) and provider (Bio2RDF, Uniprot)
Redirection Service
Automatic redirection to data provider document

something you can lookup or search for with rich descriptions
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Bio2RDF: Raw Data!
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SPARQL is the newcool kid on the query block
SQLSPARQL
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Bio2RDF’s describe service uses SPARQL
CONSTRUCT {
?s ?p ?o .
}
WHERE {
?s ?p ?o .
FILTER(?s = <http://bio2rdf.org/ns:id>).
}
Sent to http://ns.bio2rdf.org/sparql?query=...
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http://bio2rdf.org/ns:id

Bio2RDF’s search service uses SPARQLhttp://bio2rdf.org/search/hexokinase
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bio2rdf.org
kegg
gene
uniprot

Bio2RDF
Scalable, Decentralized Data Provision
Globally Mirrored and Point Provision
Customizable Query Resolution
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Customizable Configuration (in N3)Single Query, Single Provider
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Query Resolution
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700,000 queries in November 2009

Yai for data!
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But how do we discover more than what was in the data?

Ontology as Strategy
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Reasoning and Inference through Semantics
fact
uniprot:P05067
is a
is a
Uniprot:Protein
is a
chebi:Polyatomic
Entity
ontology
Knowledge base
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The Web Ontology Language (OWL) Has Explicit Semantics
Can therefore be used to capture knowledge in a machine understandable way
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Over 170 bio-ontologies
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From linked data to linked knowledge through syntactic and semantic normalization.

Multiple Ways To Represent Knowledge
Three ways to model the relationship between a protein and the volume it occupies.

Web-based Knowledge Discovery
Some of our queries need services
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The Holy Grail:
Align the promoters of all serine threoninekinases involved exclusively in the regulation of cell sorting during wound healing in blood vessels.
Retrieve and align 2000nt 5' from every serine/threoninekinase in Musmusculus expressed exclusively in the tunica [I | M |A] whose expression increases 5X or more within 5 hours of wounding but is not activated during the normal development of blood vessels, and is <40% similar in the active site to kinases known to be involved in cell-cycle regulation in any other species.
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Semantic Automated Discovery and Integration
http://sadiframework.org
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Mark Wilkinson, UBC
Michel Dumontier, Carleton University
Christopher Baker, UNB

SADI – described oriented service matching based on registered predicates

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What pathways does UniProt protein P47989 belong to?
PREFIX pred: <http://sadiframework.org/ontologies/predicates.owl#>
PREFIX ont: <http://ontology.dumontierlab.com/>
PREFIX uniprot: <http://lsrn.org/UniProt:>
SELECT ?gene ?pathway
WHERE {
uniprot:P47989 pred:isEncodedBy ?gene .
?gene ont:isParticipantIn ?pathway .
}
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SADI
Describe the input and output using OWL-DL classes
Subject of input and output mustbe the same
Web services correspond to predicates
Biocatalogue to register SADI-compliant services
Simplified migration path for existing web services (java, perl)
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Build a
knowledge base
from a series of questions
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You want to join the knowledge web
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Share your data
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Build semantic web services
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Get to where you want to be … faster!

Next Steps
Service and Data Buildout
Formal Partnerships
Applications
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dumontierlab.com
michel_dumontier@carleton.ca
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We’re interested in Personalized Medicine
The ability to offer
The Right Drug
To The Right Patient
For The Right Disease
At The Right Time
With The Right Dosage
Genetic and metabolic data will allow drugs to be tailored to patient subgroups
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PHARMGKB
is an emerging resource for pharmacogenomics
+ Role of genes, gene variants , drugs
+ pharmacokinetics
+ pharmacodynamics
+ clinical outcomes.
+ Links to publications
- Natural language descriptions
- Variant details in publications
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Pharmacogenomics of Depression KNOWLEDGE BASE
contains statements from 11/40 relevant publications involving 45 genes / gene variants, 57 drugs annotated with 19 classes of antidepressants, 45 drug treatments, 47 drug-gene interactions, 29 clinical outcomes, 10 drug-induced side-effects, and 8 gene-disease interactions.
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Protégé 4, FaCT++, DL Query Tab
Querying the PDKB
Nortriptyline induced side effects for ABCB1 gene variants
‘side effect’ that
‘is realized by’ some
(‘drug treatment’ that
‘involves’ some ‘nortriptyline’ and
‘involves’ some (‘variant of’ some ‘ABCB1’))
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postural hypotension is a side effect of nortriptyline treatment of depression for individuals presenting the 3435C>T genotype

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Bio2RDF and Beyond!

by Michel Dumontier on Jan 14, 2010

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