Director’s Challenge IT Overview                                     NCICB NCICB-SAIC

Agenda Goal Development Overview Future Enhancements (Use Cases) Build a Microarray Data and Analysis Portal Development Overview Object and Data Models Software Development Process Application Architecture Currently Developed and Deployed Functionality Future Enhancements (Use Cases) Data Analysis Biological Analysis (Integration with caBIO) LIMS EVS

Overall Goal of Microarray Data Portal Transform Numerical Data into Biological Data                                                                                                                                                                                                                                                                      Provide Convenient Means of Submitting Experiments Variety of Methods to Query the Database Integrate and Develop Cluster and Pattern Analysis Tools Integrate Ontology and Annotation Tools Develop Architecture to Facilitate Items 1-4

Reporting a Microarray Experiment Experimental Data Image Files Data Files                                                                                                                                                                                                                                                                      Experimental Description Purpose of Study Experimental Details Sample information Clinical Data Standard Needed to Describe Microarray Experiment

Microarray Standards MIAME MAML MAGEML Minimum Information About a Microarray Experiment Experimental Design, Array Design, Hybridization, Samples, Measurements and Normalization Controls                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           MAML Microarray Markup Language XML Implementation of the MIAME Standard Industry moving towards MAGEML MAGEML XML Implementation of the MIAME Standard Formed Via Merge of MAML and GEML Standards De Facto Widespread Industry Support

Director’s Challenge Data Model Based Upon MAGEML Object Model Facilitates Data Exchange and Standard Upload Support for Annotation, Ontology, and Analysis Tools Tables Required for Integration with caBIO Additional tables to hold clinical data in upcoming months                                                                                                                                                                                                                                                                      One of the First Public MAGEML Databases Instantiated and Populated Schema Available for Download ErWIN Diagram Available for Download

Director’s Challenge Artifacts dc.nci.nih.gov/informatics Object Model Use Cases Sequence Diagrams Data Models SQL script ErWIN diagram Java API Links to Industry Standards Evolution of Existing Microarray Tools                                    

Director’s Challenge Object Model Based Upon the MAGEML Standard Classes Model MAGEML Elements and Relationships Objects Encapsulate Data and Methods to Access Data Java Applications can Easily Exchange Objects Objects Written to MAGEML (XML) for Non-Java Applications                                     Integration with NCICB caBIO Objects

NCICB Development Standards Java Programming Language Objects Encapsulate Data and Methods to Manage Data Many API’s to Facilitate Rapid Application Development Open Standards                                     Java Community Process Industry Standards (i.e., MAGEML) Open Source Architecture Web and Application Servers (Apache,Tomcat,JBoss) No Database-Specific Code (Triggers,Stored Procedures) Open Access XML, HTTP, SOAP, RDF– Variety of Languages

Software Development Process Rational Unified Process (RUP) Use Cases to Capture Business Requirements Sequence Diagrams Mapping Process Across Application Layers Class Diagrams to Map Business Concepts to Class Objects                                     eXtreme Programming Assignments Partitioned Between Small Teams Application Segmented into Smaller Deliverables Tailored Specifically for Dynamic Requirements Environment NCICB-SAIC Approach Combines Both, Resulting in an Iterative, Flexible, and Highly Responsive Software Development Process

Director’s Challenge API Design Patterns– Judicious Use of GoF and J2EE Patterns DynamicJavaBean– Versatile Implementation of JavaBean Object Factories– Control of Object Instantiation UserProfileBean– Customizes User Experience Metadata-Driven Configuration– Ease of Development Result is an Extensible and Configurable API

Available Configuration Parameters Database or DTD Metadata Error Codes (Type,Message) Dependent Fields TextParseBean Field—Element/Column Field Name—Field Title Form Name– Elements/Tables Placeholder Name—Column/Element Name Required Fields Retrieved Element Name—Id Non-Persisted Elements Auto Assigned Elements—Form Query Statement Metadata Configuration Parameters Loaded into Memory on Startup

Metadata-Driven Configuration – Mapping Pkg Metadata XML File Generated by DatabaseMetadataUtil Class Encapsulates Referential Constraints for XML or Database Element—Data Type Mapping (for Conversion or Type Check) Element—Primary Key or Id Mapping Exported Keys Map– Associative Table or IDREFS Constraints Metadata Parameters Generated and Loaded on Startup

Benefits of Dynamic Configuration Changes to Database—Auto Update of O/R Mapping Layer Specify Application Behavior via XML Configuration Files Facilitates DynamicJavaBean Implementation Object Reuse via Object Factories Redeployment or Reconfiguration via XML Files– No Recompile Configuration Parameters Loaded into Memory on Startup

Dynamic Java Beans Properties are Not Hardcoded into DynamicJavaBean Implementing Classes Extend or Composed of Hashtable Facilitates Object Reuse via Factory Design Pattern Metadata-Driven,Dynamic Object Definition Changes to Class Definition– XML File Update

Director’s Challenge Architecture ManagerServlet RequestHandler Input Persistence RDBMS

Challenge of Experiment Submission Capturing Rich Set of MIAME Information Vs. Ease of Use Prepopulate Fields with UserProfile and FormInputBean Data Dynamically Tailor Form Fields Based Upon Previous Entries Personalize Drop Down Lists via UserProfile Preferences Capture Common Field Data and Autogenerate Missing Items Transform Numerical Data into Biological Data

Standard “Catch All” Form

Customizing Page View

Targeted Submission Functionality XML or Form-Based Submission of MIAME-MAGE Information Upload of Data Text Files Upload or Manual Submission of Image Files Leverage Architecture Design to Facilitate Ease of Use

Prepopulating a Page

Queries Currently Implemented Coming Soon Basic search/detail Hardware Search Software Search Coming Soon Advanced Search/detail Protocol Search Chip Search

Future Directions Implement Domain Object Model Fully Implement All Search Use Cases Develop Annotation and Onotology Tools (Integrate with caBIO) Integrate xClust Cluster Analysis Tool Data Retrieval and Processing to Support Analysis Tools Develop Pattern Analysis Tools Batch Upload/Download Generate MAGEML XML file upon experiment submission                                    

Integration with NCICB caBIO and CGAP                                    

Integration with NCICB caBIO Value-added Functionalities Java API for Annotations and Ontologies Easy Retrieval of Information in the Form of Objects                                    

Annotation Using caBIO to Access Gene Information Reporter on Chip: IMAGE clone Affy probe set Genbank ID UniGene ID Gene Info: Annotation Ontology caBIO Sequence Gene AnnotationBean

Gene Ontology Gene Expression by Functional Aspects OntologyBean (GoOntology) getGenes () getAllGenes() (ontology/children) Genes Sequences Categorize genes of interest Explore data by gene categories Reporters on Chip

Gene Ontology Implementation Goal: Enable user to obtain microarray data for a list of genes based on gene ontology term Steps: G1. Get GO term by browsing GO Browser and by searching cGAP’s GO database G2. Get a gene list based on user specified GO term G3. Get expression data for a gene list by searching microarray database

Enable user to obtain an accurate GO term Gene Ontology Term Goal: Enable user to obtain an accurate GO term Approaches: G1. Get GO term by browsing GO Browser G 2. Get GO term by searching cGAP’s GO database Vocabulary Control Help users determine a GO term for their biological question

Summary Capture MIAME data in a MAGEML compliant database Data Portal – valued added functionality Bioinformatics Integration Analytic tools                                    

Acknowledgements Development Team John Yost Jennifer Long Cheng-Cheng Huang Nick Xiao Johnita Beasley Additional Thanks caBIO CGAP madB