Standards and Ontologies to Enable Discovery Data and Information Integration

1. Standards and Ontologies to Enable Discovery Data and Information Integration Robin McEntire GlaxoSmithKline 19 Nov, 2002

2. Q: What non-existing technology do you most wish you had? A: A technology that would allow you to put in a DNA sequence and then spit out the specific protein function, disease association, known pharmacophores that could be developed into small molecules, and market value of small molecule or protein therapeutic (antibody) drugs generated from that gene. Martin Leach CuraGen Director of Bioinformatics Bioinform4(26), 10 (6 Nov 2000)

3. Drug Discovery Process, circa 2002 target target identify optimize identification/ chemical validation ‘hit’ structure ‘hit’ diversity validation data mining in vivo testing SAR HT chemistry HT Screening microarrays genotyping transgenics cheminformatics bioinformatics

4. Discovery Select target Develop Assay Prepare reagents Candidate targets Sequencing Compound design Synthesis Planning Analytical Analyze Results Inventory Synthesis Screening Discovery Process IT

5. Solution Genomics Combi-chem HTS & uHTS Pharmaco-genomics New Bottleneck Data analysis, interpretation, & integration Drug Discovery Today • Bottleneck • Few novel targets • Lead explosion in a series • Too long to screen • Relating genes to disease 

6. Integration of discovery information 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; BACILLACEAE; 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 ETLRDLLKEI GGNVHFENGE MVVDPTSMIS MPLPNGKVKK LRASYYLMGA MLGRFKQAVI GLPGGCHLGP RPIDQHIKGF EALGAEVTNE QGAIYLRAER LRGARIYLDV VSVGATINIM LAAVLAEGKT IIENAAKEPE IIDVATLLTS MGAKIKGAGT NVIRIDGVKE LHGCKHTIIP DRIEAGTFMI

7. What technologies can help? • Integration - to assist the transformation of data to information and to knowledge • Text Mining - to expose the information/knowledge locked in text documents (internal and external) • Grid computing • Open source and public domain initiatives • . . .

8. Two fundamental problems for information integration • Heterogeneous software systems • hardware platforms • operating systems • network protocols • programming languages & application formats • Heterogeneous data semantics • naming conflicts • measurement conflicts • representation conflicts • computational conflicts • granularity conflicts

9. Convert all software to single language, OS, hardware platform Require all information providers to use a single consistent vocabulary Solutions This works until the next scientific advance This works until the next merger

10. Alternatively ... • Focus on interoperability • Collaboratively develop standards to support software interoperability • Collaboratively develop tools and shareable ontologies Use the Tom Sawyer approach!

11. How to cope? • Don’t rely on particular hardware platforms • Your system will outlive hardware • Don’t rely on one operating system • There will always be many — perhaps from one vendor • Don’t rely on a single programming language • They come and go faster than hardware • Do follow the first principle of good design • Define small, well-documented interfaces between modules • Define common terminologies and common business objects

12. Coping -- Software Architecture • The real issue isn’t how many tiers you have, it’s understanding how to organize a distributed application • what are the components? • where do they live? • how do they talk? • Most applications tend to follow a common structural pattern: presentation, “business model” (analysis), and data storage

13. Two-tier systems Back end physical storage, legacy applications, etc. Data representation is medium of exchange (brittle, low-level) Flat file, ASN.1, XML, ... “Business model” is embedded in presentation (“fat client”) or data storage (stored procedures, triggers)

14. Three-tier systems Back end physical storage, legacy applications, etc. Middle layer provides abstract model of business process and information, encapsulates back end Local objects on desktop manage presentation, act as clients to middle tier Distributed object technologyis the established technologyof choice for the middle tier

15. Focus on modeling business behavior • Business logic/process is a first-class citizen • business logic focuses on behavior, not data • insulates client from data representation • encapsulates (hides) implementation, legacy systems • “Middle” layer should embody an abstract model of business process • its development is a long-term, core investment • this is where component technology is headed

16. Component Interfaces are needed - but are not the whole story Integration of life sciences information across scientific disciplines and business areas is essential, however ... • Terminology is inconsistent – information searches are usually incomplete and inaccurate • Definitions and descriptions of objects across a business area differ among data sources – integrating multiple sources is labor-intensive, expensive, and time-consuming • Make common, shareable ontologies a part of the component marketplace

17. Text Mining

18. Text Mining - Challenges and Possibilities • Information overload. There’s too much. • Free text is a large category: most bio-information is only in text • Medline indexes about 600K entries/year. • Pharmas make heavy use of full-text ejournals • The USPTO has over 2 million full-text patents online • Business needs to • find documents/information • screen and sort inputs • discover relationships and mine information

19. Text Mining • We would like • Better retrieval • Help with handling the documents we have • Help finding specific pieces of information without having to read each document • What might help? • Statistical techniques • Natural language processing techniques • Knowledge domain based techniques • Controlled vocabularies and ontologies are key

20. Grid Computing

21. Grid Computing • Still being defined to some extent. A good working definition for a large part of The Grid is “A heterogeneous, location-transparent pool of network accessible computation, data and application resources within a secure, managed common namespace.” • Unifies compute, data and application resources • Allows use of resources regardless of location • Allows aggregation of discrete resources • Analogous to the electric power grid. Resource available to the user can come from anywhere

22. The Grid • More than technology for high performance computing -- it’s a different way of looking at computing and network-accessible resources • There is an explosion in the complexity, diversity and distribution of hardware, software and information • Mergers, acquisitions, joint ventures, and partnerships in all industries are creating the need for distributed and virtual organizations • Consortial efforts to build consensus and standards (Global Grid Forum, GGF) • Controlled vocabularies and ontologies are key

23. Build Shareable Ontologies • Express formalized ontologies in a common language (or a small number of languages), facilitating representation and exchange of ontological knowledge • Establish consortia and community-based initiatives to build common ontologies to establish shared understandings within the industry • Do the experiment -- insert ontologies into the component, text mining and grid computing space!

24. Role of External Alliances and Collaborations in the Enterprise Architecture

25. External Alliances and Collaborations • Two essentials; • The job is too big for any one organisation • Standard components, infrastructure and ontologies promote best-of-breed • External alliances can play a vital role in defining & developing suitable services & standards

26. Engagement with alliances • Shopper / Victim No alliance engagement: shop for (or simply accept) vendor-supported standards • Watcher Semi-passive acceptance: evaluate & select from alliance (& other) products • Navigator Active participant: influences software & component development to suit enterprise strategic needs

27. Standards selection criteria • Robustness • Architectural fit • Availability of implementations • Stability • Continuing development • Level of adoption / acceptance • Size & vigor of user community • Cost of adoption / migration

28. Infrastructure standards (examples) • Data Interchange Services (e.g., PDF, HTML, ISO/IEC 10918 [JPEG], XML) • Data Management Services (ISO 9075:1992 [SQL], SQL CLI) • Graphics & Imaging Services (GIF, TIFF, GKS, CGM) • International Operation Services (ISO/IEC 10646-1 Universal Multiple-Octet Coded Character Set) • Location & Directory Services (IETF RFC1738 [URL], RFC2251 [LDAP]) • Network Services (IETF RFC 821 SMTP, X.400, IETF RFC 793 TCP) • Object-Oriented Provision of Services (CORBA, X/Open G302) • Operating System Services (IEEE Std 1003 [POSIX]) • Security Services (ISO/IEC 7498-2, SSL, IETF RFC 2222 SASL) • Software Engineering Services (ISO/IEC DIS 14882 [C++], Java JDK, VM) • System & Network Management Services (SNMP) • User Interface Services (X Window system) Source: Standards Information Base (The Open Group) www.opengroup.org/sib2/

29. Information standards examples

30. Component/Service/Ontology Selection Criteria • Fitness to purpose • Architectural fit • Platform requirements • Availability • Open source • Vendor supported • Flexibility, configurability • Staff training • Longevity, stability • Total cost of use (licensing terms)

31. Standardized components & services

32. Sources of standards • Vendors • Information Providers • Academic Research Projects • Standards Organizations • Industry Consortia • Home-grown

33. Component & standards development alliances & consortia • ISO, ANSI, IEEE, IETF, OASIS, W3C • Health Level Seven (HL7) • Life Sciences Research DTF (OMG LSR) • Open Bioinformatics Foundation: Biopython, BioJava, BioCORBA, Bioperl, BioDAS, BioMOBY, BioSOAP • Microarray Gene Expression Database Group (MGED) • Clinical Data Interchange Standards Consortium (CDISC) • Interoperable Informatics Infrastructure Consortium (I3C) • Global Grid Forum (GGF)

34. Alliance selection criteria • Technical scope of alliance mission (roadmap) • Alliance architectural commitments • Membership (breadth of industry participation) • Standards adoption process • Ability to influence • Ease of participation (cost, mechanism, openness) • Track record (i.e., stability, longevity, productivity) • IP Issues • Alliance staff support • Total cost of membership • Other benefits of membership?

35. Acknowledgements • David Benton • Jim Butler • Filip Fuma • Scott Harker • Paula Matuszek • Richard Moore