1. Primary Immunodeficiency Disease (PID) PhenomeR (An integrated web-based ontology resource towards establishment of PID E-clinical decision support system) Phenotype ontology database PID Phenotype KnowledgeBase Search and Query interface - "PhenomeR" OWL, RDF files generation No Locality principle PID quality check by semi- automated method Yes Consistency principle Conservativity principle Conservativity principle PID quality check by Logic based assessment method Mapped terms using Standard sources Human Disease (DOID) Human Phenotype Ontology (HPO) Online Mendelian Inheritance in Man - Metathesaurus source processing (OMIM-MTHU) Symptom Ontology (SYMP) Systematized Nomenclature of Medicine Clinical Terms (SNOMEDCT) The Unified Medical Language System - Concept Unique Identifiers (UMLS_CUI) Mapped terms using Standard sources Human Disease (DOID) Human Phenotype Ontology (HPO) Online Mendelian Inheritance in Man - Metathesaurus source processing (OMIM-MTHU) Symptom Ontology (SYMP) Systematized Nomenclature of Medicine Clinical Terms (SNOMEDCT) The Unified Medical Language System - Concept Unique Identifiers (UMLS_CUI) Collected PID Phenotypes terms Phenotype annotation tool RAPID, IDR and Literature Is Mapped ? CONCLUSION Overall, this kind of analysis should bridge a gap between genotype and phenotype correlation thereby improving phenotype-based genetic analysis of PID genes. Moreover, it should facilitate clinicians in confirming early PID diagnosis and also helpful in implementing proper therapeutic interventions. We sincerely believe that the presented structured data format in RPO should help in augmenting biomedical researchers to do further analysis computationally and also assisting clinicians in identification of diagnosed PID ABSTRACT The main challenge for in silico genotype-phenotype correlation for any genetic diseases is to standardize phenotype ontology terms and the genotype data. Earlier, we have developed and established a molecular disease database named RAPID— Resource of Asian Primary Immunodeficiency Diseases (PID) (http://rapid.rcai.riken.jp), a web-based informatics platform which enables PID experts to easily mine collected genomic, transcriptomic, and proteomic data of PID causing genes. At present, RAPID comprises a total of 265 PIDs and 243 genes, out of which 233 genes are reported with over 5000 unique disease-causing mutations annotated from about 1800 PubMed citations as of February 2013. We, hereby, introduce a newly developed PID ontology browser, “PhenomeR” (http://rapid.rcai.riken.jp/ontology/v1.0/phenomer.php), for systematic integration and analysis of PID phenotype with the genotype data that are taken from RAPID. It currently holds 1438 PID- phenotype terms that are mapped and standardized using logic based assessment approach and represented in the form of Web Ontology Language (OWL) and Resource Description Framework (RDF) formats using semantic web technology for easy data exchange and validation, and interpretation of PID phenotype-genotype correlation using various computational approaches. The motivation for the development of PhenomeR is mainly to assist researchers and clinicians to identify reported and novel PID-causing genes as well as to determine genes involved in PID through the identification of reported disease-causing mutations and their respective observed symptoms. In essence, PID PhenomeR serves as an active integrated platform for PID phenotype data, wherein the generated semantic framework is implemented in the integrated knowledge-base query interface i.e. SPARQL Protocol and RDF Query Language (SPARQL) endpoint for establishing a well-informed PID e-clinical decision support system.http://rapid.rcai.riken.jphttp://rapid.rcai.riken.jp/ontology/v1.0/phenomer.php Successful outcome and challenges PhenomeR aims to build hierarchical ontology class structures and entities of all observed PID phenotypic terms that can be further used as integrated knowledgebase query interface - SPARQL Protocol and RDF Query Language (SPARQL) for screening and implementing algorithms to compile data from multiple sources to measure statistically significant dataset with greater sensitivity, specificity and degree of confidence towards well-informed clinical decision support system. The mapping of unmapped terms from the PhenomeR is a challenging task, since some of them are not available in any of the databases. This ongoing pursuit will soon implement a systematic integrated approach for mapping all these unmapped new terms towards an open community- driven semantic web (SW) technology. PhenomeR enables easy access, search, query and analyze PID phenotype terms associated with genes, diseases and mutations Masuya, H., Y. Makita, et al. (2011). "The RIKEN integrated database of mammals." Nucleic Acids Res. 39:D861-70. Acknowledgements The authors acknowledge RIKEN for providing necessary computing resources, the research team at the Institute of Bioinformatics (IOB), Bangalore India for their collaboration in developing RAPID, and alumni of our lab as well as all PID physicians involved in the PID Japan project for their valuable input and suggestions. Collaboration and funding The PID project has been initiated by the IOB and the Immunogenomics research group at Research Centre for Allergy and Immunology (RCAI), RIKEN Yokohama Institute, Japan and it was funded by The Asia S&T Strategic Cooperation Promotion Program, Special Coordination Funds for Promoting Science and Technology, MEXT, Japan. Overview of PID-phenomeR Contact: sujatha@rcai.riken.jp Search result of PID phenotype term with category ‘Cardiovascular’ Subazini Thankaswamy Kosalai and Sujatha Mohan 1 1 Research Unit for Immunoinformatics, RIKEN Research Center for Allergy and Immunology, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. Statistics RPO summary page in NCBO BioPortal Registration form for submitting new PID terms (A) DATA COLLECTION (B) DATA STANDARDIZATION (C) DATA STORAGE & RETRIEVAL Database StatisticsOWL Statistics Phenotype terms 1466 Classes 1549 Semantic types 24 Individuals - Category 29 Classes with single subclass 144 Subcategory 45 Classes with more than 25 subclasses 1346 Terms in Multiple Category 17 Average number of Siblings 276 Terms in Multiple subcategory 10 Object Property 161 Newly mapped terms 51 Data Property 9 Home page PID PhenomeR Database Schema RESPONSERESPONSE QUERYQUERY Reported list of genes Reported list of mutation data Primary information page of STK4 gene in RAPID Mutation analysis of STK4 gene Multiple terms search output Hyperlinked PubMed reference citation Term C3 deficiency viewed using Protégé 4.1 OntoGraf RDF file generated using OWL Syntax Converter Master list of PID phenotype terms, associated features and relationships in Excel format PID PhenomeR – Download Option (http://bioportal.bioontology.org/ontologi es/3114)http://bioportal.bioontology.org/ontologi es/3114 Search result of phenotype term beginning with ‘Recurrent’ Term hierarchy visualization using NCBO widget from NCI thesaurus PID PhenomeR Advanced search options Reported list of mutation data All distinct subjects from RPO ontology queried using SPARQL http://bioportal.bioontology.org/projects/171 PID PhenomeR project in NCBO BioPortal PID PhenomeR – Download Option – OWL format RAPID - Home page Search result of PID phenotype term with semantic type - ‘Acquired Abnormality’ PID-phenomeR features  Presents a web-based user friendly interface for accessing, querying browsing and analyzing PID phenotype terms  Integrates semantically standardized phenotype vocabularies from RAPID along with PIDs, genes and disease-causing mutations into a relational ontology for inference of genotype-phenotype correlation  Provides PID-phenotype data in various standardized downloadable options - OWL, RDF and Excel formats for easy sharing and data exchange among other interested research groups  Displays the phenotype terms in tree structure using NCBO widget  Facilitates integrated knowledgeBase query interface - SPARQL Protocol and RDF Query Language (SPARQL)  Promotes a network of active open community-driven semantic web technology Subazini Thankaswamy Kosalai and Sujatha Mohan. PID PhenomeR- An integrated platform for developing phenotype ontology structures for primary immunodeficiency diseases (Database, Oxford University Press - In communication) Publications – PID project No Yes No RDF and OWL formats viewed in Link Data and Protégé