Transcript eagleiintro.ppsx

eagle-i
making the
invisible visible
Lee M. Nadler, M.D. on
behalf of the eagle-I
Consortium
www.eagle-i.org
NCRR 56 Day ARRA Challenge
Convene a “diverse” group of at least 6
institutions to deliver:
 An approach to identify research resources
 A method to catalogue, enter, and store the information
locally
 A federated network capable of querying member
institutions and prove that it works
 A product that can be validated, exported across America
and sustained
eagle-i consortium --From Sea to Shining Sea
NINE institutions diverse in geography, culture and resources
Institution
NCRR Programs
Harvard University
CTSA, BIRN, NPRC
Oregon Health &
Science
University
CTSA, NPRC
Dartmouth College
COBRE, INBRE
Jackson State
University
RCMI, RTRN
Montana State
University
COBRE, INBRE
Morehouse School
of Medicine
RCMI, RCRII, RTRN,
CCRE
University of Alaska
Fairbanks
COBRE, INBRE
University of Hawaii
Manoa
RCMI, RCRII, RTRN,
CCRE,
COBRE,
INBRE,
University of Puerto
Rico
RCMI, INBRE,
RTRN, CCHD,
NPRC
Deliver a national research resource discovery network
eagle-i must create:
 Onsite teams each capable of discovering and
inventorying research resources
 A data inquiry and inventory management system
at each site
 Cycles of resource discovery, curation,
dissemination, and assessment
 A semantic search application that can find
available research resources that are often invisible
Deliverables
•
•
•
Federated system with 9 sites
Effectiveness – “make the invisible visible”
Scalability
Resource types
Quantity of resources
Number of sites
•
Functionality (obesity use case)
eagle-i Architecture
Build Team
Search Application
Data
Curators
Federated Network
(SPIN)
eagle-i
ontology
Institutional
Repositories
(RDF)
Data Entry & Curation Tools
Data
Resource Navigators
Key Architecture Elements
 Distributed Network – for local control and incremental
expansion
 Ontology Driven – for rich search semantics, linking to
outside data and flexibility for change/expansion of resource
types over time
 Open Interfaces – for connectivity with outside data and
systems
 Data Privacy Controls – to encourage contribution of
“sensitive” resources
Building The Product
• Inventory Management System
• Research Resources Inventory
• User Interface Query
Product
Resource
Navigation
Product
All Sites
Build Team -- Harvard
• Application Team
• Data Tools Team
Data
Administration
• Inventory Management
System Team
Data Curation Teams
(OHSU and Harvard)
• Data Models
Product
Data Curators
• Ontologies
Data Entry Tools
Data Tools
Search
Data Entry Tools
Field names and drop
down lists in the data
entry tool are
populated by the
ontology
Finding What You Need
External (Gene/OMIM)
Users may want
to query
disease
gene
Users may want
to query
resource
eagle-i
A junior researcher studying obesity wants to
investigate the genetic basis of insulin resistance
in model systems and humans.
Types insulin resistance into the search box
Results are returned for all resources from all institutions related to
insulin resistance.
Interested in reagents thus refines search to reagents only.
The result set was too broad. “Entrez Gene” provides access to genes
related to human disease to help narrow search results.
The investigator wants to find and animal model, so the resource is
refined from insulin resistance to insulin resistance in the mouse.
IRS-1 looks promising, so the researcher clicks on the link to go to
Entrez Gene for more information.
The researcher clicks through to Entrez Gene to confirm that IRS-1 is a
gene of interest, and searches eagle-i for resources related to IRS-.1
Plasmids for IRS-1 found and the investigator contacts the
researcher to determine their availability.
Much Work Left To Complete During Year 2
 Populating resources from all sites, curation, use
cases, sprint test cycles
 Improve and expand the system based on user
feedback (integration with PubMed, MGI, other
repositories)
 Implement connections to outside systems via
standard interfaces
 Begin planning expansion to other institutions
Challenges to Adoption and Sustainability
Develop sustainable models for data collection
 Provide value back to the data stewards
 Provide value back to the lab
Develop sustainable models for institutional
investment
 Ensure that local IT systems are low cost and easy to administer
 Provide value back to the institution
Address data privacy concerns
 Sensitive resources
eagle-i consortium
Dartmouth College
(NH)
Jason H. Moore,
PhD
Harvard University
(MA)
Lee Nadler, MD;
Douglas
MacFadden
MCS
Jackson State
University (MS)
Morehouse School of
Medicine (GA)
Montana State
University (MT)
Oregon Health and
Science University
(OR)
David W.
Robinson, PhD
University of Alaska
Fairbanks (AK)
Bert Boyer, PhD
University of Hawaii
Manoa (HI)
Richard
Yanagihara, MD
University of Puerto
Rico (PR)
Emma
FernandezRepollet
James L. Perkins,
PhD
Gary H.
Gibbons, MD
Sara L.Young,
MEd