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EA 3888 – Conceptual Modeling of Biomedical KnowledgeFaculty of Medicine - University of Rennes 1
http://www.ea3888.univ-rennes1.fr
Integrating and querying disease and pathway ontologies:
building an OWL model and using RDFS queries
Julie Chabalier, Olivier Dameron, Anita Burgun
EA 3888 – University of Rennes 1
Introduction
Disease description in current medical ontologies • Clinical features
• Etiology
• Location
• Morphology
Example: SNOMED Clinical Terms® (SNOMED CT®)
DiseaseDefinitional manifestation causative agent
finding siteassociated morphology
http://www.snomed.org/
EA 3888 – University of Rennes 1
Introduction
Characterization of diseases : biological knowledge required
• Genes A gene mutation may result in a disease
• Metabolic pathways - A pathway may be shared by different phenotypes
• Biological processes- Different processes may explain different grades of a disease
Biological knowledge Absent from medical ontologies
EA 3888 – University of Rennes 1
Objectives
Integration of disease and pathway ontologies
• Ontology integration Identify candidate ontologies
Get candidate ontologies in an adequate formalism
Integrate formalized ontologies
• Querying the resulting ontology Consistency checking
Exploiting biomedical knowledge
EA 3888 – University of Rennes 1
Candidate ontologies
KEGG Orthology (KO) hierarchy
• Organization of metabolic pathway and disease maps in the KEGG knowledge base
• DAG of four levels
EA 3888 – University of Rennes 1
Candidate ontologies
~ 20000 terms organized according to 3 hierarchies :
- Molecular Function
- Cellular Component
- Biological Process
Used to enrich the KO pathway definitions
Gene Ontology (GO) the Gene Ontology
EA 3888 – University of Rennes 1
Candidate ontologies
SNOMED-CT: clinical description of diseases
Alzheimer's disease
findingSite
Intracranial glioma
Brain structure
Disorder of brain
Dementia
Cerebral structure
Used to enrich the KO disease definitions
findingSite
Organic mental disorder
Neoplasm of brain
EA 3888 – University of Rennes 1
Formalism
OWL as a common formalism• Unambiguous combination of several ontologies (URI, namespaces)
• Defined semantics
• Expressiveness (e.g disjointness)
Getting candidate ontologies in OWL-DL• KO: conversion of the 3 upper levels (available in text)
• GO: extraction of Biological Process hierarchy (available in OWL)
• SNOMED: extraction and conversion of the relevant concepts and relations (from UMLS)
EA 3888 – University of Rennes 1
Ontology integration
Setting up relationships between ontologies
• Aligning: defining relationships between terms (is-a, part-of, etc.)
• Mapping: defining equivalence relationships between terms
EA 3888 – University of Rennes 1
Integration framework
BioMed Ontology
GOBiologicalProcesses
DiseaseandPathwaydescriptions
KOPathwaysDiseases
SNOMEDDiseases
Pathwaydescriptions
Diseasedescriptions
EA 3888 – University of Rennes 1
Mapping GO processes – KO pathways
GObiologicalprocesses
KOPathwaysDiseases
Metamap program*: lexical mapping (labels and synonyms)
KO: Metabolism
KO: Carbohydrate metabolism
GO: Metabolism
GO: Macromolecule metabolism
GO: Carbohydrate metabolism
KO: Fructose and mannose metabolism
SNOMEDDiseases
*Aronson, A.R. (2001) Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program, Proceedings of the AMIA Symp., 17-21
EA 3888 – University of Rennes 1
Aligning GO processes – KO pathways
GO: Carbohydrate metabolism
GO: Cellular carbohydrate metabolism
GO: Monosaccharide metabolism
GO: Hexose metabolism
GO: Fructose metabolism
GO: Mannose metabolism
KO: Carbohydrate metabolism
KO: Fructose and mannose metabolism
GO: atomic concepts KO: composite concepts
Patterns to segment and recompose KO terms before the mapping
KO: Fructose and mannose metabolism
Fructose mannosemetabolism metabolism
EA 3888 – University of Rennes 1
Mapping & aligning GO processes – KO pathways
GO: Carbohydrate metabolism
GO: Cellular carbohydrate metabolism
GO: Monosaccharide metabolism
GO: Hexose metabolism
GO: Fructose metabolism
GO: Mannose metabolism
KO: Carbohydrate metabolism
KO: Fructose and mannose metabolism
EA 3888 – University of Rennes 1
Mapping of KO diseases and SNOMED diseases
GObiologicalprocesses
KOPathwaysDiseases
SNOMEDDiseases
Metamap program
SN: Alzheimer's disease
SN: Organic mental disorder
SN: Dementia
KO: Human diseases
KO: Neurodegenerative disorders
KO: Alzheimer's disease
SN: Disorder of brain
EA 3888 – University of Rennes 1
Alignment of pathways and diseases
GOBiologicalProcesses
KOPathwaysDiseases
• Condition of alignment : if, at least, one gene is involved in both a disease D and a pathway P :
12
SNOMEDDiseases
• Alignment: inferring relationships between :
1 - GO processes and KO diseases
2 - KO pathways and KO diseases
D PhasPathway
EA 3888 – University of Rennes 1
1
Alignment of GO processes and KO diseases
GOBiologicalProcesses
KOPathwaysDiseases 2
SNOMEDDiseases
KEGG mapping (KEGG geneId - Uniprot id) GOA
Genes
Uniprot id
GO id
1hasPathway
EA 3888 – University of Rennes 1
GOBiologicalProcesses
KOPathwaysDiseases
12
SNOMEDDiseases
Alignment of KO pathways and KO diseases
KO: MetabolismKO: Carbohydrate metabolism
KO: Glycolysis/GluconeogenesisKO: gene1KO: gene3
KO: MetabolismKO: Carbohydrate metabolism
KO: Glycolysis/GluconeogenesisKO: gene1KO: gene3
KO: Human diseases
KO: Neurodegenerative disorders KO: Alzheimer's disease
KO: gene1KO: gene2
KO: Human diseases
KO: Neurodegenerative disorders KO: Alzheimer's disease
KO: gene1KO: gene2 hasPathway
EA 3888 – University of Rennes 1
Integration result
BioMed Ontology
13982 classes:
• 13555 classes from GO
• 281 classes from KO- 252 pathways classes- 19 disease classes
• 146 classes from SNOMED
EA 3888 – University of Rennes 1
Integration results
• 144 KO pathways associated with GO processes (57%)
• 15 KO diseases associated with SNOMED Diseases (94%)
• 15 KO diseases associated with 836 distinct pathways (GO & KO)
3144 hasPathway relationships
BioMed Ontology
EA 3888 – University of Rennes 1
Querying the BioMed Ontology
Exploiting knowledge and checking consistency
• Taking into account the explicit relationships
• RDFS is sufficient
RDF query language : SeRQL
• Implementation of SeRQL in Sesame is able to exploit RDFS semantics
• Exploitation of explicit relationships
EA 3888 – University of Rennes 1
SeRQL queries
Example of an exploiting queryWhich pathways are shared by 2 neurological disorders : glioma &
Alzheimer’s disease?
SELECT DISTINCT Pathway, label(PathwayName) FROM
{kpath:ko05010} rdfs:subClassOf {SuperClass}, {SuperClass} rdf:type {owl:Restriction}, {SuperClass} owl:onProperty {ea3888hp:hasPathway}, {SuperClass} owl:someValuesFrom {Pathway}, {Pathway} rdfs:label {PathwayName}
INTERSECT SELECT DISTINCT Pathway, label(PathwayName) FROM
{kpath:ko05214} rdfs:subClassOf {SuperClass}, {SuperClass} rdf:type {owl:Restriction}, {SuperClass} owl:onProperty {ea3888hp:hasPathway}, {SuperClass} owl:someValuesFrom {Pathway}, {Pathway} rdfs:label {PathwayName}
SELECT DISTINCT Pathway, label(PathwayName) FROM
{kpath:ko05010} rdfs:subClassOf {SuperClass}, {SuperClass} rdf:type {owl:Restriction}, {SuperClass} owl:onProperty {ea3888hp:hasPathway}, {SuperClass} owl:someValuesFrom {Pathway}, {Pathway} rdfs:label {PathwayName}
INTERSECT SELECT DISTINCT Pathway, label(PathwayName) FROM
{kpath:ko05214} rdfs:subClassOf {SuperClass}, {SuperClass} rdf:type {owl:Restriction}, {SuperClass} owl:onProperty {ea3888hp:hasPathway}, {SuperClass} owl:someValuesFrom {Pathway}, {Pathway} rdfs:label {PathwayName}
EA 3888 – University of Rennes 1
Query resultsWhich pathways are shared by 2 neurological disorders : glioma & Alzheimer’s disease? 37 pathways:
MAPK signaling pathwayFocal adhesionInsulin signaling pathwayMelanogenesisB cell receptor signaling pathwayheart developmentcentral nervous system developmentaxon guidancepeptidyl-serine phosphorylationprotein amino acid phosphorylationcell cyclecell-cell signalingcell cycle arrestlipid catabolic processlipid metabolic processubiquitin cycletransport
ErbB signaling pathwayWnt signaling pathwayprotein tetramerizationintracellular signaling cascadeprotein modification processglycogen metabolic processanageninduction of apoptosisnegative regulation of apoptosisapoptosisanti-apoptosisNatural killer cell mediated cytotoxicitycell proliferationDNA replicationchromosome organization and biogenesis calcium ion homeostasissignal transductionresponse to UVnegative regulation of cell growthcytoskeleton organization and biogenesis
EA 3888 – University of Rennes 1
hasPathway
Query results
By leveraging the pathway hierarchy: 66 pathways (37 + 29)
Alzheimer’s disease
Intracellular protein transport
Protein transport into nucleus, translocation
GliomahasPathway
EA 3888 – University of Rennes 1
Query results
Example of a consistency query:
• Detect if a specific pathway and a more general one are associated with a same disease
Disease1 Pathway1
Pathway2
hasPathway
hasPathway
Removal of redundant relationships
EA 3888 – University of Rennes 1
Conclusion
Biomed Ontology project Integration
• Automatic method of integration of biomedical ontologies Deals with the huge quantity of biomedical data
Takes into account the frequent updates of biomedical sources
• BioMed ontology Integrates 3 biomedical ontologies (KO, GO, SNOMED)
Takes into account the formal evolution of the biomedical ontologies (OWL)
Querying• RDFS queries are enough:
to detect some basic inconsistencies of the BioMed ontology to exploit the BioMed ontology
EA 3888 – University of Rennes 1
Perspectives
• Biological evaluation: study of glioma
• Increase the number of integrated biomedical sources (e.g. OMIM, BioPax)
• Improve the mapping/alignment techniques by taking into account the semantics in the patterns
• Associate a degree of confidence to the Disease/Pathway relationships (based for example on the GO evidence code)
EA 3888 – University of Rennes 1
BioMed ontology project :
http://www.ea3888.univ-rennes1.fr/biomed_ontology/