1 BioGrids in the US: Current status and future opportunities Craig A. Stewart 15 April 2004 [email protected] Director, Research and Academic Computing Director,

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BioGrids in the US: Current status and future opportunities

Craig A. Stewart

15 April 2004

[email protected]

Director, Research and Academic Computing

Director, Information Technology Core, Indiana Genomics Initiative

License Terms• Please cite this presentation as: Stewart, C.A. BioGrids in the US:

Current status and future opportunities. 2004. Presentation. Presented at: International School on Physics and Industry workshop on Particle Accelerators and Detectors: from Physics to Medicine (Ettore Majorana Foundation and Center for Scientific Culture, Erice, Italy, 15 Apr 2005). Available from: http://hdl.handle.net/2022/14780

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What is a grid?• A grid is a system including

• computational resources • data storage resources • visualization resources• specialized instruments • tied together by high-performance networks

• Why grids?– Transcend limits of location– Use resources that would otherwise not be

accessible– To do things that would otherwise not be

possible

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Types of grids

By area of focus:• Collaboration grid• Computational grid

– Supercomputer grids

– Cycle scavenging• Data grids• Hybrid grids

Not included as part of this classification system: openness of software or organizational structure

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Computational Grid: TeraGrid

• US key national grid effort• Based on Globus infrastructure• Attempts to solve grid technology challenges in a

very general fashion• Currently 9 sites• Little application thus far specifically in the area

of biology• Construction project: first we build it, then…

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Special purpose Computational Grid: IU/HLRS 2003 HPC Challenge

• Global analysis of Arthropod evolution• One application: fastDNAml

• 8 types of systems; 641 processors; 6 continents

• 200 trees analyzed

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Cycle scavenging Computational Grids

• Folding at home (www.stanford.edu/ group/pandegroup/ folding/)

• Fight AIDS at home (fightaidsathome. scripps.edu/)

• Evolution@home (www.evolutionary-research.net/)

http://www.stanford.edu/group/ pandegroup/folding/results.html

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Data grids in biology

• Research data grids– Centralized Life Sciences Data Service– Teragrid (www.teragrid.org)

• Research and clinical data grids– SPIN (Shared Pathology Informatics Network)– Central Indiana Hospitals

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Centralized Life Sciences Data Service at Indiana University

• Goal: transparent and integrated access to multiple data sources

• Federated database approach focuses on establishing glue between existing databases

• “Private” databases stay where they are – under local control

• “Public” databases may be replicated locally for performance

• Queries are entered as standard SQL

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NR

EST

Swiss prot

BLAST Data sources

BLAST engine

CLSD Engine (IBM II)

LIGAND

BIND

ENZYME

dbSNP

Public data sources

MS SQL Server

IUSM workgroup databases

Custom Web Application Portal

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CLSD: Finding Genes

• Queries multiple databases, linking expression data (local and remote) and location data

• Built by research lab in IUSM

• Portal, built with CLSD as a grid back end

Hereditary Diseases and Family Studies Division, Dept. of Medical and Molecular Genetics, IU School of Medicine. Supported in part by NIH R01 NS37167.

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Understanding Microarray Data

The Microarray Data Portal was created by the Center for Medical Genomics at IU School of Medicine.Supported in part by the 21st Century Research & Technology Fund and the Indiana Genomics Initiative.The Indiana Genomics Initiative is supported in part by a grant from the Lilly Foundation, Inc.

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Clinical data grids in Indiana

• SPIN (Shared Pathology Informatics Network)– Distributed database of anonymized data

about pathology specimens provides– Data in compliance with US privacy regulations– SPIN software runs at participating institutions

• Regenstrief Institute– From data vaults to data grids– Hundreds of millions of patient records– Clinical service grid serving central Indiana

hospitals

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Semantic requirements for BioData Grids

• Interoperability of nomenclature and metadata a critical challenge!

• “A biologist would rather use another biologist’s toothbrush than another biologist’s terminology” – Thomas Kaufman

• Consistent semantics are required!• Example projects:

– GO: Gene Ontology– SBML: Systems Biology Markup Language– MAGE-ML: MicroArray Gene Expression Markup

Language – SNOMED – CT: SNOMED Clinical Terms

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Hybrid Grids• SCrAPS

– Advanced Photon Source at Argonne National Laboratories

– “Better than being there” functionality– Real time integration of remote instruments,

collaboration, computation, and visualization– Near real time data movement

• BIRN– Key NIH funded biogrid– Includes data, computation, visualization

• Encyclopedia of Life• eDiamond

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Where are we today?

By area of focus:• Collaboration grid• Computational grid

– Supercomputer grids

– Cycle scavenging• Data grids• Hybrid grids

By status:• Very general

construction projects• Handcrafted grid

solutions• Special projects

(heroic efforts involved)

• Ongoing production services

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Looking Ahead• Access to computing power via grids is still

largely experimental• Access to data via grids has transformed

biomedical research and is transforming clinical practice

• Access to instruments is still experimental• Great opportunities to advance biomedical

research through use of grids Biology is different– Data is always collected somewhere– Affinities between grid structure and future

software structure• Sometimes grids are not the answer

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Acknowledgments• This research was supported in part by the Indiana Genomics

Initiative. The Indiana Genomics Initiative of Indiana University is supported in part by Lilly Endowment Inc.

• This work was supported in part by Shared University Research grants from IBM, Inc. to Indiana University, and in particular by IU’s relationship with IBM as an IBM Life Sciences Institute of Innovation.

• This material is based upon work supported by the National Science Foundation under Grant No. 0116050 and Grant No. CDA-9601632. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).

• UITS staff: Mary Papakhian, Stephen Simms, Richard Repasky, Matt Link, John Samuel, Eric Wernert, Anurag Shankar, Andrew Arenson, John Herrin, Malinda Lingwall, W. Les Teach

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Thank you!

Further information available at:http://about.uits.iu.edu/divisions/rac/cv_stewart.html