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OBD : technical overview

OBD : technical overview. Chris Mungall. Outline. The annotation lifecycle OBD Model and modeling requirements Current OBD architecture Roadmap. The need for OBD. The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data

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OBD : technical overview

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  1. OBD : technical overview Chris Mungall

  2. Outline • The annotation lifecycle • OBD Model and modeling requirements • Current OBD architecture • Roadmap

  3. The need for OBD • The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data • Current annotations using ontologies are fragmented across multiple databases, multiple schemas • OBD provides a common means of accessing and querying across these annotations

  4. OBD - What is it? • General purpose biomedical knowledgebase • Repository of biomedical annotations • Ontology-based queries and analysis • Annotations from multiple sources can be compared through use of ontologies and ontology mappings • Current primary use • Genotype-phenotype associations for DBPs • Future uses • Annotation of information entities • Documents, datasets, records, images • Annotation of any biomedical entity using bio-ontologies

  5. Dev Biol 2005 Jul 15;283(2):357-72 “Sonic hedgehog is required for cardiac outflow tract and neural crest cell development” The annotation lifecycle Lab db Absence of aorta investigator read observation publish/ create Information entity bio-entity Experiment/ investigation communicate X Direct annotation query/ meta-analysis Agent+tools (human/computer) Community/expert Shh- Absence Of aorta annotation Shh bio-entity Shh+ Heart development Computational representation

  6. What is an annotation? • OBD has a very inclusive definition of annotation • An attributed statement positing some relation(s) between entities • Typically accompanied by associations to evidence-oriented entities and metadata • Examples: • Shh participates_in heart development • p53 implicated_in cancer • p53 has_function DNA repair • PMID:1234 mentions melanoma • http://… depicts (lesion that located_in CA4) • Abc[-] influences blood pressure • Trial3456 has_inclusion_criteria (age that < 65) Shh+ Heart development Participates in

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

  8. Flexibility of OBD • Most ontology-based bio-curation focuses on stating associations between bio-entities and types as represented in ontologies • Where bio-entities can be types or instances • Genes, proteins, genotypes, cells, organisms, strains • OBD can also accommodate ‘tagging’ annotations • E.g. Ontrez, term extraction from literature • Associations between information entities and ontology terms • E.g. documents, document parts, datasets, images

  9. representation Dev Biol 2005 Jul 15;283(2):357-72 “Sonic hedgehog is required for cardiac outflow tract and neural crest cell development” Ontrez in OBD subj relation obj annotation Absence of aorta investigator PMID:1234 read observation publish/ create Information entity bio-entity Experiment/ investigation communicate X Direct annotation query/ meta-analysis Agent (human/computer) Community/expert annotation Cardiac outflow tract PMID:1234 abstract local db describes local db local db Shh bio-entity Shh describes PMID:1234 abstract Multiple schemas Computational representation

  10. OBD model: Requirements • Generic • We can’t define a rigid schema for all of biomedicine • Let the ontology do the modeling • Expressive • Use cases vary from simple ‘tagging’ to complex descriptions of biological phenomena • Formal semantics • Amenable to logical reasoning • Standards-compatible • Integration with semantic web • OWL-1.1

  11. OBD Model: overview • Graph-based: nodes and links • Nodes: Classes, instances, relations • Links: Relation instances • 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

  12. Constructing descriptions • The ability to compose descriptions is a key requirement for biomedical annotation • Logical expressions built using multiple classes • Post-composed at annotation time • Example (in owl manchester syntax*): • GODendrite_spine thatpart_of CLGolgi_cell • Genus-differentia description • Can be nested: • PATODecreased_length thatinheres_in (GODendrite_spine thatpart_ofCLGolgi_cell) • Representing and reasoning over these is a key OBD requirement * Existential quantifier omitted

  13. Reasoning over descriptions • Query requirement • Queries for annotations to “CNS neuron cell projection” • Should return: • Annotations to: GODendrite_spine thatpart_ofCLGolgi_cell • Computational Requirements • Entailments • EL++ or greater • OWL constructs • intersectionOf • equivalentClass • Representing Phenotypes in OWL (OWLED 2007)

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

  15. OBD Architecture • Two stacks • Semantic web stack • First iteration • Built using Sesame triplestore + OWLIM • Limited developer resources • Future iterations: Science-commons Virtuoso • OBD-SQL stack • Current focus • Traditional enterprise architecture • Plugs into Semantic Web stack via D2RQ

  16. OBD Architecture: Two stacks

  17. 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 OBD-SQL Stack

  18. OBDAPI illustrative examples • node = getNodeById(“OMIM:601653”) • nodes = getNodesBySearch(“p53*”) • nodes = getNodesBySource(“OMIM”) • nodes = getNodesByQuery(queryExpr) • graph = getAnnotationGraphAroundNode(“PATO:0001050”, true) • statements = getAnnotationStatementsForAnnotatedEntity(“Entrez:2138”)

  19. Phenote as an OBD client Currently Implemented

  20. Genome browser mashup Sensory neuron Vulva Uterine muscle locomotion oviposition Under Development (Holmes lab)

  21. OBD Mediator Architecture • OBDAPI can act as client to other OBDAPIs • Mediator node distributes queries to source nodes

  22. OBD-SQL Database • Generic minimal table model • Makes heavy use of views for core capabilities • E.g. analyzing information content of classes based on annotation • Views can be materialized for speed • Deductive closure of classes (named and class expressions) pre-computed • Not a blind transitive closure • OWL semantics (EL++) http://www.bioontology.org/wiki/index.php/OBD:OBD-SQL-Schema

  23. OBD Dataflow

  24. Analysis requirements • The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data • OBD must have capabilities for using to ontologies to query and analyze data effectively

  25. Inter-ontology reasoning

  26. Annotation comparison Within species Across species Translational research Visualisation and display of annotations OBD web-based interface prototype

  27. Architecture Roadmap

  28. OBD API in BioPortal: two choices • Choice A: Two separate APIs • Ontology API • Annotation API • Choice B: Unified API • Use same API for search, implementing same behaviour • Same query model

  29. Requirements for unified API • Expressive model • Logical entailment for both named classes and class expressions • Expressive queries • Compatible with OWL • Easy to express common queries

  30. end

  31. Open Questions • Classes vs instances

  32. API Extensions • Data mining support • Complex queries

  33. Current OBD Nodes http://www.bioontology.org/wiki/index.php/OBD:Querying

  34. Distribution • Distribution is optional • Not required for supporting current DBPs • OBD Nodes should be easy to set up • Lightweight DBMS • Query mediator • Integrates queries across multiple resources • Caches nodes in links in local node • Registry

  35. Similar Systems • BIRN System • RDF DB (IODT) • Semantic Mediator • FreeBase/MetaWeb etc • RDF Based? • Wiki model • Currently centralized • Referent Tracking Model • Formal basis

  36. Timeline • Current focus • OBD API • Formal representation of genotypes • Clinical Trials • Post May meeting • Distributed querying • Post BIRN meeting

  37. Representing the world in OBD • Requires formal mappings from other models into OBD constructs • What kind of entities are being represented? • How are they related? • Example: • Qualities and their bearers • See: Representing Phenotypes in OWL (OWLED 2007)

  38. OBD Requirements • Model: • Generic, cross-domain • Formal semantics • Supports complex annotation • Queries: • Deductive capabilities • Data mining capabilities • Efficient • Distributable • Standards-compatible

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