standards and ontologies to enable discovery data and information integration robin mcentire...
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Standards and Ontologies to Enable Discovery Data and
Information Integration
Robin McEntireGlaxoSmithKline
19 Nov, 2002
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
Bioinform 4(26), 10 (6 Nov 2000)
Drug Discovery Process, circa 2002
data miningmicroarraystransgenics
cheminformaticsbioinformatics
HT chemistry
chemicaldiversity
HT Screening SAR
identify‘hit’
optimize‘hit’ structure
targetvalidation
targetidentification/
validation
in vivo testing genotyping
Discovery Process IT
Sequencing
Synthesis Screening
SynthesisPlanning Inventory
Compound design
AnalyzeResults
Preparereagents
DevelopAssay
Candidate targets
Select target
Discovery
Analytical
Drug Discovery Today
Solution• Genomics
• Combi-chem
• HTS & uHTS
• Pharmaco-genomics
New Bottleneck
Data analysis, interpretation, & integration
Bottleneck• Few novel
targets
• Lead explosion in a series
• Too long to screen
• Relating genes to disease
ID MURA_BACSU STANDARD; PRT; 429 AA.DE PROBABLE UDP-N-ACETYLGLUCOSAMINE 1-CARBOXYVINYLTRANSFERASEDE (EC 2.5.1.7) (ENOYLPYRUVATE TRANSFERASE) (UDP-N-ACETYLGLUCOSAMINEDE 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
Integration of discovery information
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• . . .
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
Solutions
This works until the next scientific advance
This works until the next merger
Require all information providers to use a single consistent vocabulary
Convert all software to single language, OS, hardware platform
Alternatively ...
• Focus on interoperability
• Collaboratively develop standards to support software interoperability
• Collaboratively develop tools and shareable ontologies
Use the Tom Sawyer approach!
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
Coping -- Software ArchitectureCoping -- 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
Two-tier systemsTwo-tier systems
“Business model” is embedded in presentation (“fat client”) or data storage (stored procedures,triggers)
Back end physical storage,legacy applications, etc.
Data representation is medium of exchange (brittle, low-level)
Flat file, ASN.1, XML, ...
Three-tier systemsThree-tier systems
Local objects on desktopmanage presentation, actas clients to middle tier
Middle layer providesabstract model of business process and information,encapsulates back end
Back end physical storage,legacy applications, etc.
Distributed object technologyis the established technologyof choice for the middle tier
Focus on modeling business behavior
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
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
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
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
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
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
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!
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
Engagement with alliances• Shopper / Victim
No alliance engagement: shop for (or simply accept) vendor-supported standards
• WatcherSemi-passive acceptance: evaluate & select from alliance (& other) products
• NavigatorActive participant: influences software & component development to suit enterprise strategic needs
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
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/
Information standards examplesStandard Source Status(CML) Chemical MarkupLanguage
P. Murray-Rust Parsers and other toolsavailable
SMILES Daylight Commercial software packagesMOL2 Tripos Commercial software packagesMIF (Molecular Information File) Allen, et al. PublishedMolfile, Rgfile, Rxnfile, Sdfiles,RDfiles
MDL Info. Sys. Published. Commercial softwarepackages
CXF-10 CAS PublishedmmCIF IUCr Parsers, file manipulation
routines, viewersASN.1 Bioseq NCBI Published. Parsers and other
tools availableAGAVE DoubleTwist PublishedBSML LabBook Published. Viewers availableMicroarray and GeneExperiment (MAGE)-OM, -ML
OMG LSR Adoption vote in progress,implementations available
Gene Ontology GO Consortium Browsers and parsers available
• Fitness to purpose• Architectural fit• Platform requirements• Availability
– Open source– Vendor supported
• Flexibility, configurability• Staff training• Longevity, stability• Total cost of use (licensing terms)
Component/Service/Ontology Selection Criteria
Standardized components & servicesService / Component Source ScopeBiomolecular SequenceAnalysis
OMG LSR (EBI,NetGenics)
Generalized analysis engine forsequences, alignments
Open Genomic Maps OMG LSR(GSK/EBI)
Repository server for genomicmaps
Macromolecular Structure OMG LSR(SDSC)
Repository server for proteinand nucleic acid structures
Bibliographic Query Service OMG LSR(EBI)
Repository server forbibliographic information
BioJava, v. 1.10 BioJava Project Open-source Java classes formanuipulating and analyzingsequences
BioPerl, v. 0.7.2 BioPerl Project Open-source Perl classes formanuipulating and analyzingsequences
DAS (Distributed AnnotationSystem), v. 1.01
BioDAS Project Web-based system forexchanging genomicannotations
Sources of standards
• Vendors
• Information Providers
• Academic Research Projects
• Standards Organizations
• Industry Consortia
• Home-grown
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)
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?
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