Jie Zheng , Elisabetta Manduchi and Christian J. Stoeckert Jr - PowerPoint PPT Presentation

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Jie Zheng , Elisabetta Manduchi and Christian J. Stoeckert Jr

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  1. Development of an Application Ontology for Beta Cell Genomics Based On the Ontology for Biomedical Investigations JieZheng, ElisabettaManduchi and Christian J. Stoeckert Jr Department of Genetics, Perelman School of Medicine, University of Pennsylvania ICBO July 2013, Montreal

  2. Beta Cell Genomics Database • http://genomics.betacell.org/gbco/ • A functional genomics resource focused on pancreatic beta cell research supporting a consortium of 62 investigators and their groups • 128 studies (version 4.1) addressing the biology of beta cells, aspects of diabetes, and the production of functional beta cells from • embryonic stem cells • mature cells of other types such as exocrine cells

  3. Desired Features of A Beta Cell Genomics Ontology • Support semantic annotation of beta cell studies with enough granularity covering both biological and experimental aspects • Specimen characteristics, species, strain, anatomical entity, cell type, etc. • Assay, protocol, data analysis methods, etc. • Enable queries of increasing complexity (competency questions) • Find gene expression data of endocrine cells • Find studies using cells which develop from either mesoderm or endoderm • Find high throughput sequencing gene expression data in samples obtained during the embryo stage from mouse strains with genetic background C57BL/6J • Enable knowledge discovery based on computable definitions • Automated cell type classification based on cell phenotype/functions and/or genetic signatures using reasoners • Leverages existing efforts covering the domains of investigations, cells, anatomy, proteins, and genes • OBO Foundry ontologies

  4. OBO Foundry Reference Ontologies • Shared common upper level ontology, Basic Formal Ontology (BFO) and common relations • Orthogonal interoperable ontologies – reuse existing terms defined in OBO Foundry ontologies • Each reference ontology covers a specific domain: • Cell type ontology (CL) : cell type • Gene ontology (GO): biological process, molecular function, cell components • Protein ontology (PR): protein (cross species) • Uber anatomy ontology (UBERON): cross-species anatomy • Ontology for biomedical investigations (OBI): all aspects of an experiments Facilitate ontology integration

  5. Motivation for Developing An Application Ontology for Beta Cell Genomics Research • No single OBO Foundry ontology can meet our needs • No ontology available covers enough granularity needed by beta cell genomics research • Typical use of disconnected multiple ontologies loses semantic power

  6. Principles of Beta Cell Genomics Ontology (BCGO) Development • Reuse terms existing in the OBO Foundry ontologies if possible • Reuse existing ontology design patterns • Use OBI as the ontology framework and integrate subsets of other OBO Foundry ontologies into it • Enrich the ontology with additional axioms when needed

  7. Ontology for Biomedical Investigations (OBI) subClass of is a specimen • Cover all aspects of an investigation • Contains classes that connect OBI with other OBO Foundry reference ontologies, such as CL, UBERON, and GO, and serve as the parent of referenced external terms OBI Cell CL cultured cell material entity gross anatomical entity CLO molecular entity UBERON cellular_component biological_process ChEBI process assay data transformation GO data item information content entity . . . protocol measurement unit label UO

  8. Development of BCGO • Identification of terms defined in OBO Foundry Ontologies • Extraction of terms from OBO Foundry ontologies • Integration of terms from different OBO Foundry ontologies • Enrichment of BCGO by adding additional terms and axioms

  9. Step 1: Identification of Terms Defined in OBO Foundry Ontologies • Draw terms from the MO to OBI mapping list • Beta Cell Genomics Database was annotated using multiple controlled vocabularies and ontologies including the MGED Ontology (MO) • Bioportal Annotation Tool • High accuracy (>95%) • May not include the latest version of ontologies • Bioportal Search Tool • Includes partial and exact matches of input text • Requires more manual review as compared to the Bioportal Annotation Tool

  10. Most Terms Needed Could Be Matched to Small Subsets of Many Ontologies • 852 terms used in the Beta Cell Genomics database • 644 terms were matched to 543 ontology terms • Mapped terms defined in 24 OBO Foundry ontologies including BFOand IAO *: application ontology BTO: BRENDA tissue / enzyme source CARO: Common Anatomy Reference Ontology EnVO: Environment Ontology ERO: eagle-i resource ontology FMA: Foundational Model of Anatomy GAZ: Gazetteer MP: Mammalian Phenotype OGMS: Ontology for General Medical Science RS: Rat Strain ontology SO: Sequence types and features SWO: Software Ontology EFO: Experimental Factor Ontology ChEBI: Chemical entities of biological interest CLO: cell line ontology NCBITaxon: NCBI organismal classification PR: protein ontology UO: Units of measurement PATO: Phenotypic quality

  11. Step 2: Extraction of Terms from OBO Foundry Ontologies • Ontodog tool: OBI subset extraction • Generates a community view including all related terms and axioms Reference: Zheng et al. International Conference on Biomedical Ontology (ICBO), Graz, Austria, July 2012 • OntoFox tool for extracting terms from all other OBO Foundry ontologies • Option 1: MIREOT • Option 2: include minimal intermediate ontology terms • Option 3: all related terms and axioms Reference: Xiang et al. (2010) BMC Research Notes, 3:175

  12. Extraction Option 1 • Applied when five or less terms in an ontology were used by BCGO • MIREOT: minimum information to reference an external ontology term Reference:Courtot et al. (2011) Applied Ontology, 6:23 • IRI of the term • IRI of the source ontology • IRI of the term parent in the target ontology • Can be done manually

  13. Extraction Option 2 • Keep hierarchical structure with minimal intermediates • Example: reference human, mouse, rat in NCBITaxon Include computed intermediate classes Include all intermediate classes MIREOT … 14 intermediate classes Option 2

  14. Extraction Option 3 • Reuse logical axioms of terms defined in source ontologies • Example – ontology design pattern of cell in CL Meehan et al. BMC Bioinformatics 2011, 12:6

  15. Summary of Extraction Methods And Results

  16. ontology Step 3: Integration of Terms Extracted From Different OBO Ontologies (1) OntoFox output file subClass of is a terms of interest In other OBO Foundry ontologies Subset of OBI specimen Beta Cell Genomics view of OBI Import retrieved terms into OBI subset (BCGO community view) under corresponding parent classes Cell subset of CL cultured cell material entity gross anatomical entity subset of CLO molecular entity subset of UBERON cellular_component biological_process subset of ChEBI process assay data transformation subset of GO data item information content entity . . . protocol measurement unit label subset of UO - Using OWL:imports - Keep retrieved terms belong to same source ontology in one OWL file - Contains 2389 classes

  17. Step 3: Integration of Terms Extracted From Different OBO Ontologies (2) To avoid inconsistencies caused by integrating terms from different paths we remove textual and logical definitions of terms referenced to external ontologies PATO PATO terms retrieved from OBI deprecated Removal of definitions of PATO terms in retrieved OBI subset Retrieval of definitions from PATO

  18. Summary of Extraction Methods And Results

  19. Step 4: Enrichment of BCGO • 208 terms that could not be matched to OBO Foundry ontologies • 42 new terms have been added into BCGO • Example – ‘insulin-expressing mature beta cell’ insulin secretion insulin secretion detection of glucose type B pancreatic cell mature islet of Langerhans islet of Langerhans insulin-expressing mature beta cell insulin Meehan et al. BMC Bioinformatics 2011, 12:6

  20. Ontology Validation • Annotation: 83% terms covered by BCGO • Competency questions can be answered: • Find gene expression data of endocrine cells • Find studies using cells which develop from either mesoderm or endoderm • Find high throughput sequencing gene expression data in samples obtained during the embryo stage from mouse strains with genetic background C57BL/6J • Automated cell type classification: ongoing

  21. Challenges • OBO Foundry ontologies use different versions of upper level ontology – BFO • Inconsistent representation of the same entities in different OBO Foundry ontologies • Example, ‘cell line cell’, alignment work has been done by CL, CLO and OBI developers • Resolution: Alignment work presented in the ICBO poster session with title ‘Alignment of Cultured Cell Modeling Across OBO Foundry Ontologies: Key Outcomes and Insights’ by Dr. Matthew Brush

  22. Summary • BCGO is available on: http://purl.obolibary.org/obo/bcgo.owl • All related documents are available on: http://code.google.com/p/bcgo-ontology/ • Development of a cross-domain application ontology • based on the OBI framework • reuse existent reference ontologies and ontology design patterns • The approach should be generally applicable when using interoperable source ontologies • Orthogonal interoperable OBO Foundry ontologies facilitate ontology integration

  23. Acknowledgements • Emily Greenfest-Allen • Matthew Brush • And OBI, CLO, CL developers • Oliver He and Allen Xiang • NIH grant 1R01GM093132-01 and by 5 U01 DK 072473

  24. Questions?

  25. Advantages Of Using OntoFox • Provide many different options for ontology terms extractions • Backend RDF store contains all OBO Foundry ontologies and reload daily if updated • Input settings can be saved as a text format file and can be reused