# Presentation: 746702

## caBIG CDE/V Kickoff Meeting PresentationFred Hutchinson Cancer Research Center Daniel E. Geraghty, Heather Kincaid, Derek Walker, Rahul Joshi, Robert Robbins, Mark Thornquist. 

## Geraghty – from individual site to community
Thornquist – bringing community to individual site

** ****Data Sharing Continuum**

**Unstructured Local Data**

**Formally Structured Data**

**Published Digital Data**

**Integrated Data**

**Integrated Ideas and Concepts**

**Wide Area Collaborative Workspace**

- Mark Thornquist – EDRN

- Dan Geraghty – GeMS

- Geraghty – from individual site to community
- Thornquist – bringing community to individual site

- Data Flow

## Development Principles

- _Roadmap Driven_: all pieces align with a reference architecture / roadmap
- _Flexibility in inputs and outputs_: allows variety of data types and meta data classifications to  co-exist within the same system
- _Scalable Design_: retain system performance under increasing system load
- _Wide Ranging_: retain consistency with other information technology initiatives
- _Technology Agnostic_: allow for variety of technologies to exchange data
- _Open source_: allow interested parties to adopt, modify and improve the current state

##  Different Approaches for Different Circumstances

**Thornquist – EDRN **
- Integration through middleware
- Map existing databases to common data elements
**Geraghty – GeMS**
- Integration through usage
- Provide useful, needed tools – resulting in *de facto* common data elements

## 5-Year collaboration supported by NCI
Goal: Identify, evaluate, and validate promising biomarkers to support the early detection of cancer
Comprised of:
18 Biomarker Laboratories
9 Clinical and Epidemiology Centers
3 Biomarker Validation Laboratories
Data Management and Coordinating Center
Informatics Approach:
Cross-disciplinary team of biomedical and computer science researchers
Common Data Elements to standardize data definitions for databases and forms
Informatics infrastructure that allows for capture and exchange of information across EDRN centers
Leverage JPL/NASA’s experience and software in developing IT infrastructures to support planetary science
Use existing EDRN databases without requiring changes
Develop a common IRB protocol template
Common portals to access data (secure, validation, public, etc) as a single entry point

- Goal: Identify, evaluate, and validate promising biomarkers to support the early detection of cancer
- Comprised of:
*18 Biomarker Laboratories*
*9 Clinical and Epidemiology Centers*
*3 Biomarker Validation Laboratories*
*Data Management and Coordinating Center*
- Informatics Approach:
*Cross-disciplinary team of biomedical and computer science researchers*
*Common Data Elements to standardize data definitions for databases and forms*
*Informatics infrastructure that allows for capture and exchange of information across EDRN centers*
*Leverage JPL/NASA’s experience and software in developing IT infrastructures to support planetary science*
*Use existing EDRN databases without requiring changes*
*Develop a common IRB protocol template*
*Common portals to access data (secure, validation, public, etc) as a single entry point*

**Thornquist Lab: Early Detection Research Network (EDRN)**

## Virtual Specimen Repository
Informatics infrastructure created for EDRN 
Existing sites specimen databases maintained locally
Uses EDRN Common Data Elements (CDEs)
Maps institutions local data definitions to EDRN CDEs
Secure and Confidential
Secure Dynamic Portal

- Informatics infrastructure created for EDRN
- Existing sites specimen databases maintained locally
- Uses EDRN Common Data Elements (CDEs)
- Maps institutions local data definitions to EDRN CDEs
- Secure and Confidential
- Secure Dynamic Portal

**EDRN Resource Network Exchange (ERNE)**

## EDRN Informatics Tools

**EDRN Informatics Tools**

- EDRN Secure Website - CDE Tools
*CDE Repository*
*Form Tools*
*Mapping Tools*
- EDRN Resource Network Exchange (ERNE)
*An *infrastructure* for sharing data resources across EDRN*
*Supports *real time* (on demand) *distribution* of data to users*
*First release - *Specimen sharing tool* *
*EDRN CDE Mapping Tool*
- Validation Infrastructure (VSIMS)
*Provide common infrastructure across validation studies*
*Online Forms - Data Driven from CDE Repository*

## Semantic Architecture
Many institutions have existing specimen repositories with locally defined data models
EDRN Common Data Elements (CDEs)
ISO/IEC 11179
Data Model Mapping
Communicating EDRN CDEs
EDRN CDE Mapping Tool
EDRN CDE Repository

*Many institutions have existing specimen repositories with locally defined data models*
*EDRN Common Data Elements (CDEs)*
*ISO/IEC 11179*
- Data Model Mapping
*Communicating EDRN CDEs*
*EDRN CDE Mapping Tool*
*EDRN CDE Repository*

**Legacy Data and Mapping**

## Shared needs of the local (small) genetics data generating labs.

**Laboratory organization and data flow.**
*Solid informatics infrastructure essential for data retrieval **(i.e., a lab notebook). *
*Efficient data tracking improves data quality and lowers costs.*
**Collaboration Potential. Ability to easily share data in a secure manner.**
*Labs at different localities collaborating on a project.*
*Acquiring genomic data developed in another lab (e.g. for genotyping).*
*Pooling data among labs to increase sample size.*
*Pooling genetic data from common samples (e.g. building haplotypes).*
*Sharing data for standardization (e.g. STRs).*
***To address these issues we are building a ***_**Ge**_***netics***** **_**M**_***anagement ***_**S**_***oftware suite.***

## Wide area data integration is seen as stack of activities

Focus on bringing full power of high throughput DNA sequencing instruments into hands of small (R01-funded) laboratory

- Focus on bringing full power of high throughput DNA sequencing instruments into hands of small (R01-funded) laboratory

**Geraghty Lab: GeMS Approach**

**Instruments**

**Lab reports**

**Clinical Care**

**Unstructured Local Data**

**Formally Structured Data**

**Published Digital Data**

**Integrated Data**

**Integrated Ideas and Concepts**

**Wide Area Collaborative Workspace**

- Data Flow

## GeMS System Overview

- Data generated from sequencer
- Converted to standardized text formats
- Populated into published schema which relates variables to one another

- Export Server

## GeMS Architecture

- The data store is accessed through a file storage service API that acts as a DAO (Data Access Object) Layer.
- Core services is made available above J2EE application server. These services are used by the plugins to carry out their functions.
*File Storage Service– manages file system*
*Authentication – identify validation*
*Authorization – users level of access*
*Messaging – local workflow processes andcollaboration with remot sites*
*Plugin Manager – manages the resigration of plugin components*
*Workflow – manages the workflow agents, their states, and the associated triggers*
- Plugins represent the functional components that use the core services.

## GeMS Data Schema

- Schema currently relates all key variables in automated high throughput DNA sequencing to the output files for data analysis, sharing and comparison including
*DNA Source information*
*SNP Identification*
*Primers*
*Haplotypes*
*Sequencers*
*Technicians*
*PCR Thermocycles*

## From Data Generation to Data Publication

**Instruments**

**Unstructured Local Data**

**Formally Structured Data**

**Published Digital Data**

- Data Flow

- Nightly Data pick up by system
- Unstructured and unrelated data sent to GeMS server for processing
- Data related to associated parameters
- Subset of data made available to the Geraghty website

## Proposed Collaboration/Contribution to caBIG 

## Summary

- Support establishment and maintenance of Common Data Elements by
*Fostering the generation of CDE/Vs from the data gathering instruments (GeMS)*
*Developing tools to interpret and integrate legacy data (EDRN)*
*Understanding the need to build and share mapping tools (GeMS - EDRN)*
- Experience and Lessons Learned
*Managing and integrating data sets from a variety of sources*
*How to share data effectively across data grids*
*Data publishing in real time as it becomes available*
- Flexibility is Essential
*Consider the variability in data sets that must be assembled in a grid environment*
*Depends on the perspective of the study itself, or point of view of the researcher*
*Support for evolving data elements and classifications in discovery-oriented research*
*Supporting the scientist by delivering tools that add value as a mechanism for delivering established CDEs.*