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Presentation on theme: " The SOFG Anatomy Entry List - SAEL Helen Parkinson, EBI On behalf of the Standards and Ontologies for Functional Genomics SAEL Working Group."— Presentation transcript:

1 The SOFG Anatomy Entry List - SAEL Helen Parkinson, EBI On behalf of the Standards and Ontologies for Functional Genomics SAEL Working Group

2 Some History SOFG 1 Hinxton 2002, Anatomists had a breakout group to discuss integration of Anatomy ontologies. Outcome website set up listing known anatomy resources and view, and intent to integrate expressed

3 Gratuitous Advertising – SOFG2

4 SOFG/SAEL Workshop In April 2004 an international workshop was held in Edinburgh to consider issues raised by the SOFG discussion Participants representing users of Anatomy Ontologies – ArrayExpress, RAD, GXD, EMAGE Participants representing Anatomy Ontologies: – Foundational Model of Anatomy (FMA) – GALEN, Mouse Adult Anatomy – Mouse Developmental Anatomy (EMAP) – Edinburgh Human Developmental Anatomy – CBIL Controlled Vocabulary for Anatomy David Shotton – what if the ontologies are not orthogonal?

5 Integration issues Tissues Things made of the tissues Cells making up the tissues (scale) Correspondences Homologies/Orthologies – mouse tail/C.elegans tail Stages Developmental derivation Relationship types, part-of, is-a, etc and how these are used differently Considered a specific use case: – The use of anatomy ontologies in the functional genomics domain Slide: Alan Rector, Jeremy Rogers

6 Functional Genomics Experiments Sample-based functional genomics experiments are usually limited to what is obtainable by conventional dissection High level terms are useful Detailed ontologies are also useful where experimenters need these, for example when laser capturing samples There are excellent resources already Truth: Biologists are resistant to data sharing, annotation, standardisation, ….

7 Standard terms are needed for querying

8 MGED Ontology Supports MIAME, provides terms for annotation of experiments (where they do not exist externally) Creates a framework to reference external ontologies – therefore we need external resources Requires that external terms be identifiable Is implemented in data capture applications An anatomy list for this domain needs to be simple and be flexible Annotation needs are diverse Multiple resources can be confusing

9 A multiplicity of resources Many: resources, formats, philosophies, purposes, variable content, OntologyAccessibilityContent CBIL Controlled Vocab. For AnatomyCBIL browser~600 Adult Mouse AnatomyDAG-Edit/Jax Viewer~2400 EMAP- Mouse developmentDAG-Edit, Anatomy Browser ~8000 GALEN (Anatomy Only)Protégé, Demo-GCE or OpenKnoME ~10,000 FMAFM explorer70,000 concepts >110,000 terms Compare the FMA vs. the adult mouse

10 Anatomy Terminologies and Ontologies Slide: Cornelius Rosse NCI MeSH GALENSNOMED Jax is-a Hollow viscus (body structure Heart structure (body structure) Entire thoracic viscus (body structure) TubularCardiovascularComponent IntrathoracicCardiovascularComponent IntraMediastinalStructure Thoracic cavity structure FMA Organ with cavitated organ parts Body Organ Cardiac Structure Cardiopulmonary System Cardiovascular System Cardiovascular System -part-

11 Foundational Model of Anatomy (FMA) FMA uses a frame-based formalism concerned with the representation of concepts and relationships in a form that is understandable to humans and machine readable Human/vertebrate Definition: structural attributes Content: organism to biological macromolecule Serves as a reference ontology Slide: Cornelius Rosse

12 FM Explorer

13 Adult Jax Vocab browser Anatomical structures are organized spatially and functionally, using 'is a' and 'part of' relationships For TS28 Purpose, encoding and integration of mouse gene expression data

14 SAEL.. is a simple list of ~120 terms is for low-resolution descriptions of sample origin terms have ids: SAEL:1 contains vertebrate terms at present is NOT a new anatomy ontology does NOT have defined relationship types terms do not have definitions is a first step to considering the relationships between the SAEL source ontologies is NOT intended to replace deeper integration efforts

15 The SAEL current version 1.0 Download from In plain text/OBO format Will be maintained by MGED Ontology working gp + Terry Hayamizu (Jax) Suggestions through MGED Ontology sourceforge trackerMGED Ontology sourceforge Report on the workshop is available Review publication from this workshop in CFG

16 Testing the content SAEL maps to ~ 80% of current terms tested – So far been tested vs. current annotation in ArrayExpress/MIAMExpress – OrganismPart –82 terms HGMP – microarray mouse and human only –97 terms SMD - free text microarray sample annotations – 22 terms GXD - * 80% for blot and cDNA data only RAD – microarray, uses CBIL

17 Implementation MIAMExpress – ArrayExpress data capture tool uses SAEL Data in ArrayExpress will be mapped to SAEL Future submissions will use SAEL We will encourage users of the MGED ontology to use SAEL where appropriate

18 COBrA XSPAN demo takes place on Wednesday, August 4 th at 11.30am in room Alsh 2 – Albert Burger

19 Mapping source ontologies to SAEL Adult mouse anatomy, FMA mapped to date Mouse Developmental, GALEN, CBIL + others to do Using COBrA from the XSPAN project - – Allows manual mapping between ontologies – Creates an OWL format mapping file – Reads: DAGEdit flat file format, GO XML/RDF, GO RDFS and OWL – Mappings available from

20 Web services A WSDL has been defined for SAEL: – dev_stage, is_tissue, is_cell_type, is_organ, is_system, superclass, subclass, part, part_of, uri, definition, authority, history, name, synonym Source ontologies will provide a web service supporting queries and returning the attribute list WSDL has been tested vs. Adult mouse anatomy, FMA and developmental mouse anatomy, will be tested further

21 Proposed web services architecture SAEL and will be made available via user interface and programmatically Querying multiple ontologies will be supported by the central SAEL web service The SAEL Portal provides a graphical user interface for researchers to look up the mappings between the SAEL list of anatomical entities and the target ontologies. Will be implemented in 2 phases, SAEL portal first, then local ws

22 Future We welcome collaboration and mapping of other relevant ontologies proceeds e.g. EVOC Building web services architecture, XSPAN Refining SAEL, completing mapping Inclusion in MGED Extended ontology (v1.2) Deeper integration of anatomy ontologies Decisions on handling of null mappings Modification of relationship types in current version Protégé version from Alan Rector

23 Acknowledgements SAEL workshop participants: Stuart Aitken, Albert Burger, Richard Baldock, Jonathan Bard, Duncan Davidson, Terry Hayamizu, Helen Parkinson, Alan Rector, Jeremy Rogers, Martin Ringwald, Cornelius Rosse, Chris Stoeckert John Gennari Niran Abeygunawardena, EBI Website, MIAMExpress implementation Funders: MRC-HGU, MGED, EU – TEMBLOR Jeremy Gollub -SMD, Naran Hirani/Tom Freeman – HGMP

24 Gratuitous Advertising – SOFG2

25 Bio-Ontologies Panel Discussion Michael Ashburner, Dept of Genetics Univeristy of Cambridge Crispin Miller, Bioinformatics and Onco-informatics Group Jeremy Rogers, Medical Informatics Group, University of Manchester Barry Smith, Institute for Formal Ontology and Medical Information Science, University of Leipzig and Buffalo, State University of NY

26 SWOT analysis 131,000 hits on Google

27 SWOT 2 Strengths Weaknesses – Orientated towards the internal aspects of bio-ontologies or an individual ontology Opportunities Threats – Those factors external to bio-ontologies or an individual ontology

28 Panel Perspective Michael Ashburner – Pragmatic ontologies for real utility in biology Jeremy Rogers –Pragmatic economic or user-led development risks the lowest common denominator or the mediocre: the Trabant or the Ford Mondeo. Theory-led development may ignore practicality and expense: the Formula One race car. How can such extremes be avoided in ontology engineering ? Crispin Miller – There is generally a difference between what we would like to say, and what our computers are capable of interpreting. How do we build ontology-based systems that can successfully resolve the tensions arising from this Barry Smith – Physics has pure mathematics as its formal backbone. What is the counterpart of pure mathematics in biology? Answer: formal ontology.

29 Criteria for future chairs combination of School Mistress sternness, eloquence, and opinionation Phil Lord ability to pick people out of the audience Robert Stevens

30 Michael GO – Strengths:wide uptake, community project, designed for a single problem – gene product attributes, dev of ontol pragmatic (weakness?), open, to sw – Weakness – Pragmatic design and build, qc mechanism, and implementation issues in DB, lack of formalism, no idea would be universal – Opp. - world domination, achieving greater integration across species – Threats – long term stability – academia, funding models, diversion into philoso

31 Crispin S - structure info W - O – abstraction level problematic T - Knowing where to stop, ontology is linked to tools, cultural issues,

32 Jeremy Strengths: community of eager users – scope, where to start Open source T – semantic web hype, succession of curators

33 Helen S – collaboration W – legacy data management, costs, maintenance, user uptake O – improved data retrieval, query, formalisation, T – reinvention of the wheel

34 Barry Smith MA – completion/failure S – exists O – data, make it interoperable W – pragmatic decisions become entrenched, T – fools paradise – OWL is not expressive enough expressive power – cheating is-a overloading, need a top level of ontology – philosophical qu. way that instance is used Ontology – reality – knowledge – describing knowledge, - assay ontology ? Ontology of scientific expts Perfection is the enemy of the good ! Solution: more precison

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