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Director’s Challenge IT Overview. NCICB. NCICB-SAIC. Agenda. Goal. Build a Microarray Data and Analysis Portal. Development Overview. Object and Data Models Software Development Process Application Architecture Currently Developed and Deployed Functionality.

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Director s challenge it overview

Director’s Challenge IT Overview





  • Goal

  • 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

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

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

Microarray Standards


  • 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


  • XML Implementation of the MIAME Standard

  • Formed Via Merge of MAML and GEML Standards

  • De Facto Widespread Industry Support

Director s challenge data model

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

Director’s Challenge Artifacts


  • 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

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

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

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

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

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-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

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

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

Director’s Challenge Architecture






Challenge of experiment submission

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

Targeted submission functionality

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



  • Currently Implemented

  • Basic search/detail

  • Hardware Search

  • Software Search

  • Coming Soon

  • Advanced Search/detail

  • Protocol Search

  • Chip Search

Future directions

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

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

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


Gene ontology gene expression by functional aspects
Gene OntologyGene 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
Gene Ontology Implementation


Enable user to obtain microarray data for a list of genes based on gene ontology term


  • 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

Gene ontology term
Gene Ontology Term


Enable user to obtain an accurate GO term


  • G1. Get GO term by browsing GO Browser

    • G

      2. Get GO term by searching cGAP’s GO database

  • G

    • Vocabulary Control

    • Help users determine a GO term for their biological question



    • Capture MIAME data in a MAGEML compliant database

    • Data Portal – valued added functionality

    • Bioinformatics Integration

    • Analytic tools



    • Development Team

    • John Yost

    • Jennifer Long

    • Cheng-Cheng Huang

    • Nick Xiao

    • Johnita Beasley

    • Additional Thanks

    • caBIO

    • CGAP

    • madB