1. Director’s Challenge IT Overview
                                   
NCICB
NCICB-SAIC

2. 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

3. 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

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

5. 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

6. 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

7. 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
                                   

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. Director’s Challenge Architecture
ManagerServlet
RequestHandler
Input
Persistence
RDBMS

17. 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

18. Standard “Catch All” Form

19. Customizing Page View

20. 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

21. Prepopulating a Page

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

23. 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
                                   

24. Integration with NCICB caBIO and CGAP
                                   

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

26. 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

27. 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

28. 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

29. 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

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

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