Computational Biology and Informatics Laboratory Development of an Application Ontology for Beta Cell Genomics Based On the Ontology for Biomedical Investigations Jie Zheng, Elisabetta Manduchi and Christian J. Stoeckert Jr Department of Genetics, Perelman School of Medicine, University of Pennsylvania ICBO July 2013, Montreal

Computational Biology and Informatics Laboratory Beta Cell Genomics Database 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

Computational Biology and Informatics Laboratory 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

Computational Biology and Informatics Laboratory 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

Computational Biology and Informatics Laboratory 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

Computational Biology and Informatics Laboratory 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

Computational Biology and Informatics Laboratory Ontology for Biomedical Investigations (OBI) 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 gross anatomical entity cellular_component molecular entity material entity specimen Cell cultured cell data transformation biological_process processassay data item measurement unit label information content entity protocol OBI UBERON GO CL CLO UO ChEBI... subClass of is a

Computational Biology and Informatics Laboratory Development of BCGO 1.Identification of terms defined in OBO Foundry Ontologies 2.Extraction of terms from OBO Foundry ontologies 3.Integration of terms from different OBO Foundry ontologies 4.Enrichment of BCGO by adding additional terms and axioms

Computational Biology and Informatics Laboratory Step 1: Identification of Terms Defined in OBO Foundry Ontologies 1.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) 2.Bioportal Annotation Tool – High accuracy (>95%) – May not include the latest version of ontologies 3.Bioportal Search Tool – Includes partial and exact matches of input text – Requires more manual review as compared to the Bioportal Annotation Tool

Computational Biology and Informatics Laboratory 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 BFO and 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

Computational Biology and Informatics Laboratory 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

Computational Biology and Informatics Laboratory 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

Computational Biology and Informatics Laboratory Extraction Option 2 Keep hierarchical structure with minimal intermediates Example: reference human, mouse, rat in NCBITaxon … 14 intermediate classes MIREOT Include all intermediate classes Include computed intermediate classes Option 2

Computational Biology and Informatics Laboratory 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

Computational Biology and Informatics Laboratory Summary of Extraction Methods And Results

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

Computational Biology and Informatics Laboratory 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 terms retrieved from OBI PATO deprecated Removal of definitions of PATO terms in retrieved OBI subset Retrieval of definitions from PATO

Computational Biology and Informatics Laboratory Summary of Extraction Methods And Results

Computational Biology and Informatics Laboratory 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’ Meehan et al. BMC Bioinformatics 2011, 12:6 insulin-expressing mature beta cell mature insulin islet of Langerhans insulin secretion detection of glucose type B pancreatic cell insulin secretion islet of Langerhans

Computational Biology and Informatics Laboratory 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

Computational Biology and Informatics Laboratory 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

Computational Biology and Informatics Laboratory Summary BCGO is available on: All related documents are available on: 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

Computational Biology and Informatics Laboratory Acknowledgements Emily Greenfest-Allen Matthew Brush And OBI, CLO, CL developers Oliver He and Allen Xiang NIH grant 1R01GM and by 5 U01 DK

Computational Biology and Informatics Laboratory Questions?

Computational Biology and Informatics Laboratory 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