1. PATO & Phenotypes: From model organisms to clinical medicine Suzanna Lewis September 4th, 2008 Signs, Symptoms and Findings Workshop First Steps Toward an Ontology of Clinical Phenotypes

2. Describing phenotype using ontologies will aid in the identification of models of disease & candidate causative genes  GWAS: Genome Wide Association Studies  Any study of genetic variation across the entire human genome that is designed to identify genetic associations with observable traits (such as blood pressure or weight), or the presence or absence of a disease or condition.  Given an identified gene, then what?

3. Animal disease models Animal models Mutant Gene Mutant or missing Protein Mutant Phenotype (disease model)

4. Mutant Gene Mutant or missing Protein Mutant Phenotype (disease) HumansAnimal models Mutant Gene Mutant or missing Protein Mutant Phenotype (disease model) Animal disease models

5. Mutant Gene Mutant or missing Protein Mutant Phenotype (disease model) Mutant Gene Mutant or missing Protein Mutant Phenotype (disease) Animal disease models HumansAnimal models

6. Mutant Gene Mutant or missing Protein Mutant Phenotype (disease) HumansAnimal models Mutant Gene Mutant or missing Protein Mutant Phenotype (disease model) Animal disease models

7. Phenotype data mining = text searching?  Text-based phenotype resources:  OMIM (NCBI)  DECIPHER (Sanger)  HGMD (Cardiff)  Disease-specific databases  MODs  PubMed

8. Query# of records “ large bone”713 "enlarged bone"136 "big bones"16 "huge bones"4 "massive bones"28 "hyperplastic bones"8 "hyperplastic bone"34 "bone hyperplasia"122 "increased bone growth"543 Thanks to: M Ashburner Information retrieval from text-based resources (OMIM) is not straightforward:

9. Even if we can find what we are looking for in one organism, how can we associate that with phenotypes observed in different organisms? Methods to link phenotypic descriptions of human diseases to animal models currently don’t exist.

10. Goal: Turn text-based phenotypes into ontology-based computable annotations  Define a model for representing phenotypes

11. SHH -/+ SHH -/- shh -/+ shh -/-

12. Phenotype (clinical sign) = entity + attribute

13. Phenotype (clinical sign) = entity + attribute P 1 = eye + hypoteloric

14. Phenotype (clinical sign) = entity + attribute P 1 = eye + hypoteloric P 2 = midface + hypoplastic

15. Phenotype (clinical sign) = entity + attribute P 1 = eye + hypoteloric P 2 = midface + hypoplastic P 3 = kidney + hypertrophied

16. Phenotype (clinical sign) = entity + attribute P 1 = eye + hypoteloric P 2 = midface + hypoplastic P 3 = kidney + hypertrophied PATO: hypoteloric hypoplastic hypertrophied ZFIN: eye midface kidney +

17. Phenotype (clinical sign) = entity + attribute Anatomical ontology Cell & tissue ontology Developmental ontology Gene ontology biological process molecular function cellular component + PATO (phenotype and trait ontology)

18. Phenotype (clinical sign) = entity + attribute P 1 = eye + hypoteloric P 2 = midface + hypoplastic P 3 = kidney + hypertrophied Syndrome = P 1 + P 2 + P 3 (disease) (package) = holoprosencephaly

19. EntityQuality EvidenceQualifier relationship Units EnvironmentGenetic Phenotype annotation model Source Attribution Who makes the assertion Properties When, what organization Assertion

20. OBD and annotations Shh Absence of aorta publish/ create Experiment/ investigation query/ meta-analysis Direct annotation Shh - Absence Of aorta X observation Computational representation Agent (human/computer) Community/expert Information entity investigator read bio-entity Shh + Heart development Dev Biol 2005 Jul 15;283(2):357-72 “Sonic hedgehog is required for cardiac outflow tract and neural crest cell development” communicate local db Multiple schemas influences Participates in represents subjobj relation annotation submit/ consume

21. Goal: Turn text-based phenotypes into ontology-based computable annotations  Define a model for representing phenotypes  Develop and extend requisite ontologies  For the entities being described: anatomies, processes, …

22. It is critical that ontologies are developed cooperatively so that their classification strategies augment one another. Building a suite of orthogonal interoperable reference (evidence based) ontologies in the biomedical domain. Truth springs from arguments amongst friends. (David Hume)

23. RELATION TO TIME GRANULARITY CONTINUANTOCCURRENT INDEPENDENTDEPENDENT ORGAN AND ORGANISM Organism (NCBI Taxonomy?) Anatomical Entity (FMA, CARO) Organ Function (FMP, CPRO) Phenotypic Quality (PaTO) Biological Process (GO) CELL AND CELLULAR COMPONENT Cell (CL) Cellular Component (FMA, GO) Cellular Function (GO) MOLECULE Molecule (ChEBI, SO, RnaO, PrO) Molecular Function (GO) Molecular Process (GO)

24. Requisite ontologies  An ontology of qualities (PATO)  Organism specific anatomies  A controlled vocabulary of homologous and analogous anatomical structures (Uberon)  Gene Ontology  Cell Types

25. Goal: Turn text-based phenotypes into ontology-based computable annotations  Define a model for representing phenotypes  Develop and extend requisite ontologies  For the entities being described: anatomies, processes, …  Develop an intuitive annotation environment for rigorously capturing phenotypes (“semantic authoring”)

26. Phenote: Simple software for annotating using ontologies  Provide tool for ontology-based annotation  Standardized model to record annotations for increased compatibility of data between disparate communities.  Simple & intuitive user interface  (especially for users that don’t know/care about what an ontology is)  Easy-to-configure for different user-communities  Pluggable architecture for external applications to interface/embed in application  Provide interfaces with external SOAP and REST services for streamlined workflow (OBD, NCBI, EBI, etc).  www.phenote.org www.phenote.org

27. CVS BioPortal External site Local file Ontologies can be utilized from various resources in OWL and OBO format

28. Phenote tour

29. Editor

30. Refining terms on- the-spot  Post-composition:  Join together 2 (or more) terms for specificity:  Apoptosis of neuron in skin (GO,CL,FMA)  S-phase of colon cancer cell (GO,CL)  Aster of human spermatocyte (GO,FMA)  Combine terms from different ontologies  Increase “information content” of an annotation  Pre-composed:  Have decomposed definitions of ~2/3 rd s of MP terms available to incorporate mouse data

31. Term Info Browser

32. Annotation Table

33. Retrieve data from NCBI: OMIM, PUBMED, … (SOAP plug-in)

34. Graphical Viewer

35. Goal: Turn text-based phenotypes into ontology-based computable annotations  Define a model for representing phenotypes  Develop and extend requisite ontologies  For the entities being described: anatomies, processes, …  Develop an intuitive annotation environment for rigorously capturing phenotypes (“semantic authoring”)  Develop a set of guidelines for biocurators  Annotate mutant phenotypes (OMIM and models)

36. General Annotation Standards  Remarkable normality  Absence  Relative qualities (what does “small” mean?)  Rates/frequencies  does it inhere in the heart or a process?  Homeotic transformation  Phenotypes specific to a stage or temporal duration

37. Testing the methodology  Annotated 11 gene-linked human diseases described in OMIM, and their homologs in zebrafish and fruitfly.  ATP2A1, BRODY MYOPATHY  EPB41, ELLIPTOCYTOSIS  EXT2, MULTIPLE EXOSTOSES  EYA1, EYES ABSENT  FECH, PROTOPORPHYRIA  PAX2, RENAL-COLOBOMA SYNDROME  SHH, HOLOPROSENCEPHALY  SOX9, CAMPOMELIC DYSPLASIA  SOX10, PERIPHERAL DEMYELINATING NEUROPATHY  TNNT2, FAMILIAL HYPERTROPHIC CARDIOMYOPATHY  TTN, MUSCULAR DYSTROPHY

38. An OMIM Record

43. Goal: Turn text-based phenotypes into ontology-based computable annotations  Define a model for representing phenotypes  Develop and extend requisite ontologies  For the entities being described: anatomies, processes, …  Develop an intuitive annotation environment for rigorously capturing phenotypes (“semantic authoring”)  Develop a set of guidelines for biocurators  Annotate mutant phenotypes (OMIM and models)  Collect & store annotations in a common resource (OBD) and make these broadly available

44. 4355 genes and genotypes in OBD 17782 entity-quality annotations in OBD

45. OBD model: Requirements  Generic  We can’t define a rigid schema for all of biomedicine  Let the domain ontologies do the modeling of the domain  Expressive  Use cases vary from simple ‘tagging’ to complex descriptions of biological phenomena  Formal semantics  Amenable to logical reasoning  First Order Logic and/or OWL1.1  Standards-compatible  Integratable with semantic web

46. OBD Model: overview  Graph-based: nodes and links  Nodes: Classes, instances, relations  Links: Relation instances  Connect subject and object via relation plus additional properties  Annotations: Posited links with attribution / evidence  Equivalent expressivity as RDF and OWL  Links aka axioms and facts in OWL  Attributed links:  Named graphs  Reification  N-ary relation pattern  Supports construction of complex descriptions through graph model

47. OBD Dataflow

48. key Post-composition of phenotype classes (PATO EQ formalism) Post-composition of complex anatomical entity descriptions Example of Annotation in OBD

49. OBD Architecture  Two stacks 1.Semantic web stack  Built using Sesame triplestore + OWLIM  Future iterations: Science-commons Virtuoso 2.OBD-SQL stack  Current focus  Traditional enterprise architecture  Plugs into Semantic Web stack via D2RQ

50. OBD-SQL Stack  Alpha version of API implemented  Test clients access via SOAP  Phenote current accesses via org.obo model & JDBC  Wraps org.obo model and OBD schema  Share relational abstraction layer  Org.obo wraps OWLAPI  Phenote currently connects via JDBC connectivity in org.obo

51. Goal: Turn text-based phenotypes into ontology-based computable annotations  Define a model for representing phenotypes  Develop and extend requisite ontologies  For the entities being described: anatomies, processes, …  Develop an intuitive annotation environment for rigorously capturing phenotypes (“semantic authoring”)  Develop a set of guidelines for biocurators  Annotate mutant phenotypes (OMIM and models)  Collect & store annotations in a common resource (OBD) and make these broadly available  Develop tools & resources for mining data for novel discovery  Developed a similarity search algorithm to identify genotypes with similar phenotype.

52. sox9 mutations curated in PATO syntax Human, SOX9 (Campomelic dysplasia) Zebrafish, sox9a (jellyfish) Male sex determination: disrupted Scapula: hypoplasticScapulocorocoid: aplastic Lower jaw: decreased size Cranial cartilage: hypoplastic Heart: malformed or edematousHeart: edematous Phalanges: decreased lengthPectoral fin: decreased length Long bones: bowedCartilage development: disrupted

53. EYA1SOX10SOX9PAX2 0.780.710.610.72 # Annotations congruence total annotations similar annotations Average annotation consistency

54. Reasoning over phenotype descriptions recorded with ontologies provides linkages in annotations.

55. Ontologies and reasoning can reveal similarities in phenotype annotations.

56. GeneSimilarityCitationRole in hedgehog pathway smo0.445Ochi, et al. 2006Membrane protein binds shh receptor ptc1 disp10.444Nakano, et al. 2004 Regulates secretion of lipid modified shh from midline prdm1 a 0.43Roy, et al., 2001Zinc-finger domain transcription factor, downstream target of shh signaling hdac10.427Cunliffe and Casaccia-Bonnefil, 2006 Transcriptional regulator required for shh mediated expression of olig2 in ventral hindbrain scube 2 0.398Hollway et al., 2006 May act during shh signal transduction at the plasma membrane wnt110.380Mullor et al., 2001Extracellular cysteine rich glycoprotein required for gli2/3 induced mesoderm development gli2a0.348Kalstrom, et al., 1999 Zinc finger transcription factor target of shh signaling bmp2b0.303Ke et al., 2008Downstream target of gli2 gene repression gli10.303Karlstrom, et al., 2003 Zinc finger transcription factor target of shh signaling ndr20.289Muller, et al., 2000 TGFbeta family member upstream of hedgehog signaling in the ventral neural tube hhip0.265Ochi et al., 2006Binds shh in membrane and modulates interaction with smo A zebrafish shh similar-phenotype query returns known hedgehog pathway members

57. OBD similarity query  A computational search that enables comparison of phenotypes within and across species.  Given a set of phenotype annotations recorded for a mutant allele we can identify other alleles in the same gene.  We can identify other known pathway members in the same species and known gene orthologs in other species simply by comparing phenotypes alone.  This annotation and search method provides a novel means for laboratory researchers to identify potential gene candidates participating in regulatory and/or disease pathways.

58. Summary of (some of) the challenges  Curating the information  Efficiency (pre-composed vs. post-composed)  Consistency between curators  Missing contextual information (genetic background and environment)  Observation vs. the inference made from this  Representing homology (bones named by relative position)  Those attempting this anyway: zebrafish, Drosophila, C. elegans, Cyprinoid fish (evolution), Dictyostelium, mouse (many), Xenopus, paramecium…

59. Credit to Berkeley  Christopher Mungall  Mark Gibson  Nicole Washington  Rob Bruggner U of Oregon  Monte Westerfield  Melissa Haendel National Institutes of Health U of Cambridge  Michael Ashburner  George Gkoutos (PATO)  David Osumi-Sutherland OBO Foundry  Michael Ashburner  Christopher Mungall  Alan Ruttenberg  Richard Scheuermann  Barry Smith