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Standards The P 3 G knowledge database

International Biobank and Cohort Studies: Developing a Harmonious Approch February 7-8, 2005, Atlanta; GA. Jan-Eric Litton Karolinska Institutet, Stockholm Sweden. Standards The P 3 G knowledge database. Sharing data. ID MURA_BACSU STANDARD; PRT; 429 AA. DE PROBABLE UDP. -.

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Standards The P 3 G knowledge database

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  1. International Biobank and Cohort Studies: Developing a Harmonious ApprochFebruary 7-8, 2005, Atlanta; GA Jan-Eric Litton Karolinska Institutet, Stockholm Sweden • Standards • The P3G knowledge database

  2. Sharing data ID MURA_BACSU STANDARD; PRT; 429 AA. DE PROBABLE UDP - N - ACETYLGLUCOSAMINE 1 - CARBOXYVINYLTRANSFERASE DE (EC 2.5.1.7) (ENOYLPYRUVATE TRANSFERASE) (UDP - N - ACETYLGLUCOSAMINE DE ENOLPYRUVYL TRANSFERASE) (EPT). GN MURA OR MURZ. OS BACILLUS SUBTILIS. OC BACTERIA; FIRMICUTES; BACILLUS/CLOSTRIDIUM GROUP; BACILLACE AE; OC BACILLUS. KW PEPTIDOGLYCAN SYNTHESIS; CELL WALL; TRANSFERASE. FT ACT_SITE 116 116 BINDS PEP (BY SIMILARITY). FT CONFLICT 374 374 S - > A (IN REF. 3). SQ SEQUENCE 429 AA; 46016 MW; 02018C5C CRC32; MEKLNIAGGD SLNGTVHISG AKNSAVALIP ATILANSEVT IEGLPEISDI ETLR DLLKEI GGNVHFENGE MVVDPTSMIS MPLPNGKVKK LRASYYLMGA MLGRFKQAVI GLPG GCHLGP RPIDQHIKGF EALGAEVTNE QGAIYLRAER LRGARIYLDV VSVGATINIM LAAV LAEGKT IIENAAKEPE IIDVATLLTS MGAKIKGAGT NVIRIDGVKE LHGCKHTIIP DRIE AGTFMI

  3. A historical essay: The Machine Screw • Principle discovered around 400 BC • Limited use until machine tools made mass production possible (18th cent.) • Every machine shop and foundry made unique sizes and thread dimensions • 1841: Joseph Whitworth presented “The Uniform System of Screw-Threads” to Britain’s Institute of Civil Engineers • 1864: William Sellers proposes “On a Uniform System of Screw Threads” to the Franklin Institute, Philadelphia • Enabled interchangeable parts and tooling for mechanization and mass production • 1945: British and American standards merged

  4. Point-to-point integration of data Merge results • Application includes subprogram • to each different data source • Operations on data must be • processed by an application • Lots of coding efforts • Fully dependent of • data resources

  5. Data Warehouse • Data are loaded in the database • Data need filtering, cleaning, • transformation • Data must be refreshed • Scripts must be written • Timeconsuming to refresh data • Up-to-date data can not be • guaranteed ODBC - JDBC

  6. ODBC – JDBC and more Federated data • Data stay untouched • Integrates • heterogeneous local or • remote data sources • through wrappers • Just need to know what • data should be available • to whom and how to access them • It makes all data look • like it is one virtual database • hiding the data layer complexity

  7. Ontologies • Controlled vocabulary means • only one controlled term is used for a given concept • Data Model: • Data structuring mechanism in which an ontology is expressed

  8. Data model

  9. World Wide Biobanking 124 .ca .us 840 .se ISO-code 3166 Sweden=752 The National Board of Health and Welfare id=1 id=1

  10. World Wide Biobanking • Communication with other biobanks • XML

  11. Sample identification 752-08-123456789-4 2D Matrix code for DNA storage at normalized concentration SE KI Biobank # Sample ID

  12. P3G Knowledge Database Knowledge Curation and Information Technology International Working Group on Knowledge Curation And Information Technology P3Gdb Knowledgebase on Phenotypes, Genetic Analysis Methods, and Policies related to Biobanks and Population Genetics Research Data Entry core IT core

  13. P3G Knowledge Database Knowledge Curation and Information Technology • The advantages of integrating databases in different aspects of Biobanks as public resources. • The first requirement that has to be fulfilled to enable biobank communication is a unique identity for each biobank • Second, a common nomenclature is needed in order to communicate between biobanks.

  14. P3G Knowledge Database The potential impact of integrating will be: • Promote communication within and between major biobanking initiatives thereby helping to overcome existing fragmentation of population genomic research. • Enhance the effective sharing and synthesis of information, thereby addressing the need for very large sample sizes and helping to promote collaborative international genetic epidemiological and clinical research. • Avoid the expensive mistakes and inefficiencies that can arise when individual initiatives repeatedly “re-invent the wheel”, thereby saving funders and researchers a lot of time and money

  15. P3G Knowledge Database Knowledge Curation and Information Technology The Road Map: • WG 1: Nomenclature • WG 2: Sample handling • WG 3: Biobank information • WG 4: Phenotype data • WG 5: Genotype data • WG 6: Data modeling • WG 7: Database Integration • WG 8: Security • WG 9: Output and analysis • WG 10: Documentation

  16. P3G Knowledge Database The road map: Phenotype • Describe data format naming conventions • P3G data format standard (Start with GenomEUtwin documents) • Describe relations between the entities • Describe entities and their attributes • Sync genotype data • Questionnaires (validation) • Clinical measures • Laboratory phenotypes

  17. P3G Knowledge Database The road map: Data modeling • Conceptual data modeling using UML (Unified Modeling Language) • Build conceptual harmonized data model for genotype and phenotype data • Sequence variation standardization • Provide standardized data transfer format • Tracking of samples • XML and OWL for future use

  18. P3G Knowledge Database The road map: Sampling handling • Sample collection • Sample identification • Data collection • Structure and standardization of data • Quality control procedures • Ethical and legal aspects

  19. P3G Knowledge Database The road map:

  20. P3G Knowledge Database Physical entities

  21. P3G Knowledge Database Physical entities

  22. P3G Knowledge Database Physical entities

  23. P3G Knowledge Database Physical entities

  24. P3G Knowledge Database Physical entities

  25. P3G Knowledge Database Donor entities

  26. P3G Knowledge Database Sampling entities

  27. P3G Knowledge Database The road map: • Using models which remain stable as the technological landscape changes around them - Model Driven Architecture

  28. P3G Knowledge Database Knowledge Curation and Information Technology The Road Map: Starting point • 1: Nomenclature • 2: Sample handling Biobank information • 3: Phenotype data Genotype data Data modeling • 4: Database Integration Security • 5: Ethics, governance, policy, socio- demographic

  29. P3G Knowledge Database Knowledge Curation and Information Technology The Road Map: Starting point • Name IWG-leaders • Name Cores • Now, open a KDB members area under www.p3gconsortium.org, to start the knowledge database • IWG-KDB meeting late spring 2005 • Coordinate with other activities

  30. jan-eric.litton@meb.ki.se Isabel.fortier@Mail.mcgill.ca Mdeschenes@p3gconsortium.org

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