Ontologies for representing, integrating and analyzing phenotypes

Ontologies for representing, integrating and analyzingphenotypes

Robert Hoehndorf

Department of GeneticsUniversity of Cambridge

21 June 2011

Robert Hoehndorf (University of Cambridge) Phenotype ontologies 21 June 2011 1 / 40

Introduction Motivation

Motivation

Introduction Motivation

Motivation

Introduction Ontology

Open Biomedical Ontologies (OBO)

Molecule

Tissue

Population

Transcript

Organelle

Individual

Physical object Quality Function Process

Gene OntologyCelltype

Sequence Ontology

ChEBI Ontology

AnatomyOntology

PhenotypeOntology

OntologyPhenotype and anatomy ontologies

anatomy ontologies: > 100,000 classes

FMA, MA, WA, ZFA, FA, GO-CC, ...

phenotype ontologies: > 20,000 classes

HPO, MP, WBPhenotype, FBcv, APO, ...

quality ontology: > 2,000 classes

process and function ontologies: > 25,000 classes

Gene Ontology, ...

alignments between anatomy ontologies

UBERON, various mappings

OntologyChallenges for interoperability

“merely using ontologies [...] does not reduce heterogeneity: itjust raises heterogeneity problems to a higher level” [Euzenat,2007]

implicit knowledge

implicit semantics

weakly formalized

very large

OntologyExample query

Find all regions in the human and mouse genome sequences that areassociated with Tetralogy of Fallot.

Phenotype ontology Tetralogy of Fallot

Tetralogy of Fallot

Tetralogy of FallotHuman phenotypes

Overriding aorta (HP:0002623)

Ventricular septal defect (HP:0001629)

Pulmonic stenosis (HP:0001642)

Right ventricular hypertrophy (HP:0001667)

Tetralogy of FallotPhenotype description syntax

Overriding aorta (HP:0002623):

Q: overlap with (PATO:0001590)

E1: Aorta (FMA:3734)

E2: Membranous part of interventricular septum (FMA:7135)

HP:0002623 EquivalentTo:

phene-of some (has-part some (FMA:3734 and

has-quality some (PATO:0001590 and towards some

FMA:7135)))

HP:0002623 EquivalentTo:

phene-of some (has-part some (FMA:3734 and

overlaps-with some FMA:7135))

Tetralogy of FallotUBERON human-mouse anatomy equivalences

FMA:3734 EquivalentTo: MA:0000062

FMA:7135 EquivalentTo: MA:0002939

Tetralogy of FallotPhenotype equivalence

Overriding aorta (MP:0000273):

E1: Aorta (MA:0000062)

E2: Membranous interventricular septum (MA:0002939)

MP:0000273 EquivalentTo:

phene-of some (has-part some (MA:0000062 and

MA:0002939)))

Consequence: MP:00000273 EquivalentTo: HP:0002623

Tetralogy of FallotPhenotype equivalence

Overriding aorta (MP:0000273):

E1: Aorta (MA:0000062)

E2: Membranous interventricular septum (MA:0002939)

MP:0000273 EquivalentTo:

phene-of some (has-part some (MA:0000062 and

MA:0002939)))

Consequence: MP:00000273 EquivalentTo: HP:0002623

Phenotype ontology Absence

AbsenceAbsent appendix

Absent appendix:

Q: lacks all parts of type (PATO:0002000)

E1: Human body (FMA:20394)

E2: Appendix (FMA:14542)

AbsentAppendix ≡LacksParts u ∃towards.Appendix u ∃inheresIn.HumanBody (Horrocks,2007)

AbsentAppendix ≡LacksParts u ∃towards.{Appendix} u ∃inheresIn.HumanBody(Mungall, 2007)

AbsentAppendix v ∃pheneOf .(HumanBody u ¬∃hasPart.Appendix)(H et al., 2007, 2011)

Absent appendix:

AbsentAppendix v ∃pheneOf .(HumanBody u ¬∃hasPart.Appendix)

FMA: HumanBody v ∃hasPart.Appendix

HumanBody(John),AbsentAppendix(x), hasPhene(John, x)

Removal of conflicting axioms (has-part/part-of in anatomy)

Contextualize anatomy:

Normal u HumanBody v ∃hasPart.(Normal u Appendix)

Use of non-monotonic reasoning:

Normally: HumanBody v ∃hasPart.AppendixCircumscription of ¬NormalImplementation in dlvhexIC-has-part(X,Y) :- ind(X),class(Y),inst(X,Z),

CC-normally-has-part(Z,Y), not IC-lacks-has-part(X,Y),

class(Z).

Ontology of phenotypes

Different formal expressions for phenotypes based on

qualities,

anatomical parts,

functions,

processes

enable cross-species integration of phenotypes.

Phenotype ontology Discovering mouse models

Tetralogy of Fallot

Phenotype alignmentsMouse model: Phc1

Phenotype alignmentsTetralogy of Fallot: Phc1

Knowledge representation Modularization

Complexity of automated reasoning

ontologies based on OWL

OWL 2 is based on description logic (SROIQ)

satisfiability in SROIQ is 2NEXPTIME-complete

Modularization

tractable subsets of OWL 2: EL, QL, RL

problem: identify a large (EL, QL, RL)-module of an OWL ontology

AbnormalityOfAppendix ≡∃pheneOf .(¬∃hasPart.(Normal u Appendix)) (��ZZEL)

AbsentAppendix ≡ ∃pheneOf .(¬∃hasPart.Appendix) (��ZZEL)

Inference: AbsentAppendix v AbnormalityOfAppendix (EL)

Modularization

ModularizationEL Vira

http://el-vira.googlecode.com

ontology modularization

retain signature of ontology

identify EL, QL, RL axioms in deductive closure

completeness is open problem

ModularizationEL Module

AbnormalityOfAppendix ≡∃pheneOf .(¬∃hasPart.(Normal u Appendix))

AbsentAppendix ≡ ∃pheneOf .(¬∃hasPart.Appendix)

AbsentAppendix v AbnormalityOfAppendix

H et al., 2011. A common layer of interoperability for biomedical ontologies based on OWL EL. Bioinformatics, 27(7), 1001–1008.

Knowledge representation Applications and evaluation

Phenotype alignmentsPhenomeBLAST

apply to yeast, fly, worm, fish, mouse and human phenotypes

phenotype alignment through OWL reasoning

more than 300,000 classes and 1,000,000 axioms

combination of HermiT (for modularization), CB and CEL reasoner

classification time: 7 minutes

http://phenomeblast.googlecode.org

ApplicationComparison of phenotypes

direct comparison of phenotypes:

disease phenotypes, e.g., tetralogy of Fallotphenotypes associated with genetic mutations (genotypes in mouse,fish, etc.)

ApplicationComparison of phenotypes

phenotype of mutations subclass of disease phenotype allows inference ofgene-disease association if

disease phenotypes sufficient for having the disease

mutation phenotypes necessary for having a specific genotype

ApplicationSimilarity-based comparison

pairwise comparison of phenotypes

semantic similarity: weighted Jaccard index

result: similarity matrix between phenotypes

(quantitative) evaluation based on predicting orthology, pathway,disease

identify novel gene-disease associations

ApplicationSimilarity-based comparison: ROC

0 0.2 0.4 0.6 0.8 1

False positive rate

DiseaseOrthologyPathway

ApplicationSimilarity-based comparison: gene-disease associations

Adam19 and Fgf15 genes in mice may be involved in Tetralogy ofFallot

Aberrant pathways

Cytokine-cytokine receptor interaction pathway (ko04060) issignificantly correlated with Tetralogy of Fallot (p = 5 · 10−7, Wilcoxonsigned-rank test)

Gene disease associations for orphan diseases

Slc34a1 (MGI:1345284) and Fanconi renotubular syndrome 1(OMIM:134600)

ApplicationPhenomeBrowser

Conclusions

SummaryAspects of ontology-based information systems in biology

knowledge representation language

expressivenessnon-monotonicitycomplexity of inferences

ontological decisions

anatomy (parthood, connectedness)physiology (function)pathology, disease (normality, abnormality)

statistical/similarity-based framework

semantic similarityaccount for incomplete informationaccount for noisy data

Conclusions

Challenges and future researchKnowledge representation

establish reasoning infrastructure (OWLlink, ...)

improve reasoning performance (OWL profiles, modularity,approximate reasoning)

OWL reasoning with prototypes, non-monotonic reasoning, abduction

explore alternatives to OWL

Conclusions

Challenges and future researchOntology

Molecule

Tissue

Population

Transcript

Organelle

Individual

Physical object Quality Function Process

Gene OntologyCelltype

Sequence Ontology

ChEBI Ontology

AnatomyOntology

PhenotypeOntology

Conclusions

Challenges and future researchBiology

add phenotype information

20,000 knockout micedog, rat, slime mold, ...

define disease phenotypes

extension to other domains

functional genomicspharmacology, drug discoverysystems biologyclinical research, decision support

quantifiable evaluation

Conclusions

Acknowledgements

George Gkoutos

Heinrich Herre

Janet Kelso

Michel Dumontier

DietrichRebholz-Schuhmann

Nico Adams

Dan Cook

Bernard de Bono

John Gennari

Pierre Grenon

Pascal Hitzler

Frank Loebe

Anika Oellrich

Kay Pruefer

Paul Schofield

Stefan Schulz

Robert Stevens

Sarala Wimalaratne

Conclusions

Thank you!

Ontologies for representing, integrating and analyzing phenotypes

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