Transcript Transforming life science research with advanced Information Technology at Indiana University Craig A. Stewart [email protected] University Information Technology Services, Indiana University © Copyright Trustees of Indiana University.

Transforming life science
research with advanced
Information Technology at
Indiana University
Craig A. Stewart
[email protected]
University Information Technology Services, Indiana University
© Copyright Trustees of Indiana University 2004
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License Terms
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Please cite this presentation asStewart, C.A. Transforming life science research
with advanced Information Technology at Indiana University. 2004. Presentation.
Presented at: IBM Life Sciences Symposium (Pallisades, NY, 31 May 2004).
Available from: http://hdl.handle.net/2022/14785
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Except where otherwise noted, the contents of this presentation are copyright
2004 by the Trustees of Indiana University. This content is released under the
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Outline
• IU overview
• Data, data grids, and life sciences (Centralized Life
Science Data Service)
• Computational grids and life science research (HPC
Challenge Award at SC2003)
• Looking forward – Institute of Innovation projects
• Strategy and execution: how did we get here?
• Delivering benefits
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I-Light & Abilene
• I-light
– connects IUB, IUPUI, and
Purdue University, to be
extended within Indiana
– first higher ed owned
statewide network in nation
– The networking infrastructure
for collaboration of many sorts
• Abilene
– Nation’s (current) highestspeed national network
– NOC in Indianapolis
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IU in a nutshell
• $2B Annual Budget
• One university with
• 8 campuses
• 90,000 students
• 3,900 faculty
• 878 degree programs
• Nation’s 2nd largest school of medicine
• CIO: Vice President Michael A. McRobbie
• ~$100M annual IT budget
• Indiana Genomics Initiative - $105M Lilly
Endowment, Inc. grant
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IBM Research SP
(Aries/Orion Complex)
• 1.005 TeraFLOPS. 1st
University-owned
supercomputer in US to
exceed 1 TFLOPS peak
theoretical processing
capacity.
• Geographically
distributed at IUB and
IUPUI
• Initially 50th, now 170th
in Top 500
supercomputer list
• An enabler of
collaborative research
using very large scale
computations
Photo: Tyagan Miller. May be reused by IU for noncommercial
purposes. To license for commercial use, contact the photographer
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AVIDD
• Analysis and
Visualization of
Instrument-Driven Data
• Distributed Linux cluster.
Three locations: IUN,
IUPUI, IUB
• 2.164 TFLOPS (peak
theoretical), 0.5 TB RAM,
10 TB Disk
• First distributed Linux
cluster to achieve more
than 1 TFLOPS on
Linpack benchmark
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Massive Data Storage System
• Reliable and robust
• HPSS (High Performance
Software System)
• Automatic replication of
data between Indianapolis
and Bloomington, via Ilight.
• 180 TB capacity with
existing tapes; total
capacity of 2.4 PB.
• >100 TB currently in use;
>5 TB for biomedical data
Photo: Tyagan Miller. May be reused by IU for noncommercial
purposes. To license for commercial use, contact the photographer
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John-E-Box
Design licensed to central Indiana manufacturer
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Data, data grids, and life sciences
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Federated Databases
• 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 SQL,
and the Federated Database
System knows enough about
the structure of the
databases to select data from
the right sources
• Integrate the right data in the
right way
Lab
Result
s
You!
Clinica
l Data
Toxicit
y Data
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IBM’s Federated Database approach
• Based on Discovery Link
• Wrappers
– program that sits between a database and
DiscoveryLink, allowing on the fly queries by DL from
the database
– Database registration. Each particular database must
be registered once
– Accessing a calculation as one might a database
(BLAST)
• Parsers
– Programs to import data from one format into
another that permits higher-performance queries
• Accessing a calculation from within a database query
(BLAST, HMMR)
• Accessing a database from within a calculation (SAS)
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Microarray Data Portal
• Web application and database designed for
annotation and analysis of microarray experiments.
• Annotation: Designed for users to set up
experimental design first minimizing amount of
time for sample entry but still getting in the
essential info
• Analysis
– Allows user to partition data into groups based
on their annotation.
– Extensive filtering, search, and display options
– T-test, Clustering, SVD, etc.
– Allows different views of data based on
informatics associated with the genes (e.g.
KEGG, GO, Chromosome Location)
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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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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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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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Under development: Linking Cancer
data within IUSM
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Thousands of cancer and normal tissue samples
De-identified, select phenotype data
Database system that manages IRB approvals
DiscoveryLink is planned ‘glue’ to tie tissue data to
data generated by other IUSM cores
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Protein identification
• Problem: categorize thousands of protein identifications
from proteomic experiments
• Planned solution: Use CLSD interface with LocusLink to
obtain information about proteins
• Data Generation:
– Peptide Extracts from experiment
– Separate peptides using Liquid 2D Chromatography
– Identify Mass/Charge using Mass Spectrometer
– Creates raw data (LOTS of it!)
• Data Analysis:
– SAS, using queries into CLSD
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HPC Challenge @ SC2003
Are Hexapods a single evolutionary group? Are ecdysozoans a
single evolutionary group?
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Computational grids and life
science research (HPC Challenge
Award at SC2003)
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A partial bestiary
All organism illustrations copyright
Jennifer Fairman, 2003.
www.fairmanstudios.com
Used by agreement
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Software and data analysis
• Non-grid preparatory work
– Download sequences from NCBI (67 Taxa, 12,162 bp,
mitochondrial genes for 12 proteins)
– Align sequences with Multi-Clustal
– Determine rate parameters with TreePuzzle
• Grid preparatory work
– Analyze performance of fastDNAml with Vampir
– Meetings via Access Grid & CoVise
• The grid software
– PACXMPI – Grid/MPI middleware (HLRS – High
Performance Computing Center Stuttgart)
– Covise – Collaboration and visualization (HLRS)
– fastDNAml – Maximum Likelihood phylogenetics (IU)
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• ML analysis of
phylogenetic
trees based on
DNA sequences
• Foreman/worker
MPI program
• Heuristic search
for best trees
• For 67 taxa:
2.12 ~10109 trees
• Goal: 300
bootstraps, 10
jumbles per –
3000 executions
(more than 3x
typical!)
fastDNAml
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It worked!
• Grid of 6 continents, 5 functional units, 6+ vendors, 8
types of systems, 641 processors… all analyzing
evolutionary relationships of arthropods
• HPC Challenge Award winner at SC03 conference –
demonstrates new capabilities in grid computing while
advancing research in evolutionary biology
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Looking forward – Institute of
Innovation projects
• IBM Life Sciences Institute of Innovation in 3-D
Cell Modeling
• Center for Cell and Virus Theory
• Biocomplexity Institute (talk tomorrow by Debasis
Dan)
• Model repository
• Markup Languages and Cell Models
• To the TeraGrid (and beyond!)
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Strategy and execution: how
did we get here
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IU’s IT Strategic Plan
• Real plans and real execution of those plans
• Strong focus on centralization and enablement of
capability computing
• Economy of scale
• Advantages of centralization while minimizing
disadvantages
• Engagement with researchers and vendors in projects
and grants
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Support strategy
• CS research is wonderful,
but what biomedical
researchers care about is
tools!
• Considerable effort is put
into seeking out
collaborators and people we
can assist
• If a particular application is
useful it doesn’t matter if it
seems sophisticated to a
computer scientist
• When a problem is
sophisticated we need the
computer scientists!
• Gradual enhancement of
community
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Collaboration and Outreach
• AVIDD – 20 faculty,
dozens of staff,
$1.8M in NSF funding
• Research in Indiana –
3 universities, dozens
of faculty
• IP-Grid – 2
universities, dozens
of faculty, $3M in NSF
funding
• INGEN – 100+ faculty,
hundreds of staff,
$105M funding from
Lilly Endowment, Inc.
• In-state, national, and
international outreach
are all essential
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Delivering Benefits
• 9 inventions disclosed
since 1997; 6 of these are
open source software
(BSD-like). Participation in
the community behind
community codes
essential! [IBM has
supported this strongly]
• John-E-Box design
licensed to a central
Indiana firm for
commercial production!
• A software product has
just been commercialized
• Results explainable to a
voter are essential for
continued public support!
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For further information
• fastDNAml:
http://www.indiana.edu/~rac/hpc/fastDNAml/
• about.uits.iu.edu/divisions/rac/index.html
• about.uits.iu.edu/divisions/rac/pubsstaff.html
• ingen.iu.edu
• it.iu.edu
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Acknowledgments
• This work was supported in part by Shared University
Research grants from IBM, Inc. to Indiana University. IU’s
life science research has benefited from collaboration
with IBM researchers since 1997.
• 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 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 authors) and do not necessarily
reflect the views of the National Science Foundation
(NSF).
• Assistance with this presentation: John Herrin, Malinda
Lingwall, W. Les Teach
• For HPC Challenge: thanks to the SciNet team, SC2003
organizers, HLRS, and especially Prof. Dr. Michael Resch
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& Dr. Matthias Müller.
Thank you
Any questions?
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