meeting the bioinformatics challenges of functional genomics

Meeting the Bioinformatics Meeting the Bioinformatics Challenges of Functional Challenges of Functional

GenomicsGenomics

VanBUGVanBUG

11 September 200311 September 2003

The TIGR Gene Index TeamThe TIGR Gene Index TeamFoo CheungFoo Cheung

Svetlana KaramychevaSvetlana KaramychevaYudan LeeYudan Lee

Babak ParviziBabak ParviziGeo PerteaGeo Pertea

Razvan SultanaRazvan SultanaJennifer TsaiJennifer Tsai

John QuackenbushJohn QuackenbushJoseph WhiteJoseph White

Funding provided by the Department of EnergyFunding provided by the Department of Energyand the National Science Foundationand the National Science Foundation

TIGR Human/Mouse/Arabidopsis TIGR Human/Mouse/Arabidopsis Expression TeamExpression Team

Emily ChenEmily ChenBryan FrankBryan Frank

Renee GaspardRenee GaspardJeremy HassemanJeremy Hasseman

Lara LinfordLara LinfordFenglong LiuFenglong LiuSimon KwongSimon Kwong

John QuackenbushJohn QuackenbushShuibang WangShuibang WangYonghong WangYonghong Wang

Ivana YangIvana YangYan YuYan Yu

Array Software Hit TeamArray Software Hit TeamNirmal BhagabatiNirmal Bhagabati

John BraistedJohn BraistedTracey CurrierTracey Currier

Jerry LiJerry LiWei LiangWei Liang

John QuackenbushJohn QuackenbushAlexander I. SaeedAlexander I. Saeed

Vasily SharovVasily SharovMathangi ThiagarajanMathangi Thiagarajan

Joseph WhiteJoseph WhiteAssistantAssistantSue MineoSue MineoFunding provided by the National Cancer Institute,Funding provided by the National Cancer Institute,

the National Heart, Lung, Blood Institute,the National Heart, Lung, Blood Institute,and the National Science Foundationand the National Science Foundation

H. Lee Moffitt Center/USFH. Lee Moffitt Center/USFTimothy J. YeatmanTimothy J. Yeatman

Greg BloomGreg Bloom

TIGR PGA CollaboratorsTIGR PGA CollaboratorsNorman LeeNorman LeeRenae MalekRenae Malek

Hong-Ying WangHong-Ying WangTruong LuuTruong Luu

Bobby BehbahaniBobby Behbahani

TIGR Faculty, IT Group, and StaffTIGR Faculty, IT Group, and Staff

<[email protected]><[email protected]>AcknowledgmentsAcknowledgments

PGA CollaboratorsPGA CollaboratorsGary Churchill (TJL)Gary Churchill (TJL)Greg Evans (NHLBI)Greg Evans (NHLBI)Harry Gavras (BU)Harry Gavras (BU)

Howard Jacob (MCW)Howard Jacob (MCW)Anne Kwitek (MCW)Anne Kwitek (MCW)Allan Pack (Penn)Allan Pack (Penn)

Beverly Paigen (TJL)Beverly Paigen (TJL)Luanne Peters (TJL)Luanne Peters (TJL)

David Schwartz (Duke)David Schwartz (Duke)

EmeritusEmeritusJennifer Cho (TGI)Jennifer Cho (TGI)

Ingeborg Holt (TGI)Ingeborg Holt (TGI)Feng Liang (TGI)Feng Liang (TGI)

Kristie Abernathy (Kristie Abernathy (A)A)Sonia Dharap (Sonia Dharap (A)A)

Julie Earle-Hughes (Julie Earle-Hughes (A)A)Cheryl Gay (Cheryl Gay (A)A)Priti Hegde (Priti Hegde (A)A)

Rong Qi (Rong Qi (A)A)Erik Snesrud (Erik Snesrud (A)A)Heenam Kim (Heenam Kim (A)A)

<[email protected]><[email protected]>

AcknowledgmentsAcknowledgments

Thanks to Thanks to SyntekSyntek, Inc. , Inc. <http://www.syntek.com><http://www.syntek.com>

for GeneShaving MeV module and assistance for GeneShaving MeV module and assistance with MyMADAMwith MyMADAM

Thanks to Thanks to DataNautDataNaut, Inc. , Inc. <http://www.datanaut.com><http://www.datanaut.com>

for RelNet and Terrain Map modules and for RelNet and Terrain Map modules and assistance with Client/Server MeVassistance with Client/Server MeV

<[email protected]><[email protected]>

Science is built with facts as a house is with Science is built with facts as a house is with stones – but a collection of facts is no more a stones – but a collection of facts is no more a science than a heap of stones is a house.science than a heap of stones is a house. – – Jules Henri PoincareJules Henri Poincare

There are 10There are 101111 stars in the galaxy. That stars in the galaxy. That used to be a huge number. But it's only a used to be a huge number. But it's only a hundred billion. It's less than the national hundred billion. It's less than the national deficit! We used to call them astronomical deficit! We used to call them astronomical

numbers. Now we should call them numbers. Now we should call them economical numbers.economical numbers.

- Richard Feynman, physicist, Nobel laureate - Richard Feynman, physicist, Nobel laureate (1918-1988)(1918-1988)

Microarray Analysis at TIGRMicroarray Analysis at TIGR

Step 1: Experimental DesignStep 1: Experimental Design

Step 2: Data CollectionStep 2: Data Collection

Step 3: Data AnalysisStep 3: Data Analysis

Step 4: Consulting with the ArraySW gang in the trailerStep 4: Consulting with the ArraySW gang in the trailer

Step 5: Sharing data with our collaboratorsStep 5: Sharing data with our collaborators

Select array elements and annotate themSelect array elements and annotate them

Build a database to manage stuffBuild a database to manage stuff

Print arrays and manage the labPrint arrays and manage the lab

Hybridize and analyze images; manage dataHybridize and analyze images; manage data

Analyze hybridization data and get resultsAnalyze hybridization data and get results

Steps in the ProcessSteps in the Process

TIGR Gene Indices TIGR Gene Indices home page home page

www.tigr.org/tdb/tgiwww.tigr.org/tdb/tgi

~60 species~60 species

>16,000,000 sequences>16,000,000 sequences

~60 species~60 species

>16,000,000 sequences>16,000,000 sequences

TGICL Tools are available – with more comingTGICL Tools are available – with more coming

Available with sourceAvailable with source

Geo PerteaGeo PerteaRazvan SultanaRazvan Sultana

Valentin AntonescuValentin Antonescu

High stringency pair-High stringency pair-wise comparisons to wise comparisons to

buildbuild ClustersClusters

Gene Index Assembly processGene Index Assembly process

reduce reduce redundancyredundancy

Expressed Transcripts (ET)Expressed Transcripts (ET)from GenBank CDSfrom GenBank CDS

remove vector, poly-A, remove vector, poly-A, adapter,mitochondrial adapter,mitochondrial

and ribosomal sequenceand ribosomal sequence

ESTs from ESTs from GenBank GenBank (dbEST)(dbEST)

TIGR ESTsTIGR ESTs

Each cluster is Each cluster is assembled to obtainassembled to obtain

Tentative Tentative ConsensusConsensus

sequences (TCs)sequences (TCs)

Annotate TCs Annotate TCs and releaseand release

The Mouse Gene IndexThe Mouse Gene Index <http://www.tigr.org/tdb/mgi><http://www.tigr.org/tdb/mgi>

A TC ExampleA TC Example

Babak ParviziBabak Parvizi

GO Terms GO Terms and EC Numbersand EC Numbers

The TIGR Gene IndicesThe TIGR Gene Indices <http://www.tigr.org.tdb/tdb/tgi><http://www.tigr.org.tdb/tdb/tgi>

Dan Lee, Ingeborg HoltDan Lee, Ingeborg Holt

Tentative OrthologuesTentative Orthologues

And ParaloguesAnd Paralogues

Building TOGs: Reflexive, Transitive ClosureBuilding TOGs: Reflexive, Transitive Closure

Thanks to Woytek Makałowski and Mark Boguski Thanks to Woytek Makałowski and Mark Boguski

TOGA: An Sample Alignment: TOGA: An Sample Alignment: bithoraxoid-like proteinbithoraxoid-like protein

Gene Finding in HumansGene Finding in Humans is easy!is easy!

Razvan SultanaRazvan Sultana

Gene Finding in HumansGene Finding in Humans is easy?is easy?


Gene Finding in HumansGene Finding in Humans is difficult?is difficult?


Gene Finding in HumansGene Finding in Humans is difficult?is difficult?


A genome and its annotation is A genome and its annotation is onlyonly a a hypothesis that must be tested.hypothesis that must be tested.

http://pga.tigr.org/tools.shtmlhttp://pga.tigr.org/tools.shtml

RESOURCERER RESOURCERER Jennifer TsaiJennifer Tsai

RESOURCERER: An ExampleRESOURCERER: An Example

RESOURCERER: Using Genetic MarkersRESOURCERER: Using Genetic Markers

Just added: Integrated QTLsJust added: Integrated QTLs

SOPs are availableSOPs are available

<http://pga.tigr.org/tools.shtml><http://pga.tigr.org/tools.shtml>

cDNA/template prepcDNA/template prep PCR purificationPCR purification

PrintingPrinting RNA labelingRNA labeling

HybridizationHybridization

Coming: Data QC SOPComing: Data QC SOP

What data should we collect?What data should we collect?Nature GeneticsNature Genetics 29, December 2001 29, December 2001

<http://www.mged.org><http://www.mged.org>MAGE-ML – XML-based data exchange formatMAGE-ML – XML-based data exchange format

EVERYTHINGEVERYTHING

MIAME Relational SchemaMIAME Relational Schema

What’s Wrong with MIAME?MIAME was designed as a model for capturing information MIAME was designed as a model for capturing information necessary to create public databases.necessary to create public databases.

MIAME-based databases lack LIMS capabilities, which are MIAME-based databases lack LIMS capabilities, which are necessary for large-scale studies.necessary for large-scale studies.

We do not want to store images in our database for We do not want to store images in our database for practical reasons – limited space.practical reasons – limited space.

We needed to develop a variety of tools adapted to our We needed to develop a variety of tools adapted to our existing infrastructure and existing infrastructure and legacy data and databaseslegacy data and databases..

Probes are labeled and applied to the arraysProbes are labeled and applied to the arrays An “experiment” is a hybridizationAn “experiment” is a hybridization A “study” is a collection of hybridization experimentsA “study” is a collection of hybridization experiments

MAD Microarray Database SchemaMAD Microarray Database Schema

Conceptual Schema: MADConceptual Schema: MAD

ProtocolProtocolPrimer_pairPrimer_pair

PrimerPrimer

PCRPCR

New_plateNew_plate SlideSlide Slide_typeSlide_type SpotSpot

ScanScan AnalysisAnalysis NormalizeNormalize

ExperimentExperiment ExpressionExpression

Expt_probeExpt_probe

HybHyb

StudyStudy

ProbeProbe Probe_sourceProbe_source

GeneGene

CloneClone

MADAM: Microarray Data ManagerMADAM: Microarray Data Manager

Available with source and MySQLAvailable with source and MySQL

Marie-Michelle Cordonnier-Pratt, UGAMarie-Michelle Cordonnier-Pratt, UGAconverted MySQL to Oracle and madeconverted MySQL to Oracle and madeMADAM work!MADAM work!

ExpDesignerExpDesigner

Microbial

ORFs

Design PCR Primers

PCR Products

Eukaryotic

Genes

Select cDNA clones

PCR Products

Microarray Overview IMicroarray Overview I

For each plate set,For each plate set,many identical replicasmany identical replicas

Microarray SlideMicroarray Slide(with 60,000 or more(with 60,000 or more

spotted genes)spotted genes)

+

Microtiter PlateMicrotiter Plate

Many different plates Many different plates containing different genescontaining different genes

Microarray OverviewMicroarray Overview

PCR AmplificationPCR Amplification

Selected GenesSelected Genes

Primer DesignPrimer Design

Gel-based ScoringGel-based Scoring

Primer SynthesisPrimer Synthesis

MAD

PCR ScorerPCR Scorer

Reads/loads primer data file Reads/loads primer data file to MAD and allows PCR data entry,to MAD and allows PCR data entry,

and translation of 96 and translation of 96 384. 384.(Alex Saeed, developer and maintainer(Alex Saeed, developer and maintainer

enhancements: Wedge Smith)enhancements: Wedge Smith)

Clone SelectionClone Selection

The Beast: Microarray Robot from Intelligent AutomationThe Beast: Microarray Robot from Intelligent Automation <http://www.ias.com><http://www.ias.com>

Additional Software for Arrays: SchedulerAdditional Software for Arrays: Scheduler

Microarray SchedulerMicroarray SchedulerAllows scheduling Allows scheduling of all instrumentsof all instruments

Designed and maintained Designed and maintained by Jerry Li by Jerry Li



MAD

Amplified/Purified GenesAmplified/Purified Genes

Loaded in ArrayerLoaded in Arrayer

Slides PrintedSlides Printed

Run Parameters SetRun Parameters Set

SliTrack/ControllerSliTrack/ControllerTakes Slide Order Takes Slide Order

and Run parameters,and Run parameters,generates spot order,generates spot order,

IAS control file,IAS control file,launches IAS run software,launches IAS run software,

loads database.loads database.(J. Li, developer and maintainer)(J. Li, developer and maintainer)

Microarray Overview IIMicroarray Overview II

Prepare FluorescentlyPrepare FluorescentlyLabeled ProbesLabeled Probes

ControlControl

TestTest

Hybridize,Hybridize,WashWash

MeasureMeasureFluorescenceFluorescencein 2 channelsin 2 channels

redred//greengreen

Analyze the dataAnalyze the datato identifyto identifypatterns ofpatterns of

gene expressiongene expression



ControlControl

TestTest


MeasureMeasureFluorescenceFluorescencein 2 channelsin 2 channels

redred//greengreen

Analyze the dataAnalyze the datato identifyto identifypatterns ofpatterns of

gene expressiongene expression

WeedWeed

BushBush



ControlControl

TestTest


MeasureMeasureFluoresenceFluoresencein 2 channelsin 2 channels

red/greenred/green

Analyze the dataAnalyze the datato identify to identify

differentiallydifferentiallyexpressed genesexpressed genes

Obtain RNA SamplesObtain RNA Samples


MAD

MADAMMADAMAllows data entryAllows data entry

(J. Li & J. White, web prototype;(J. Li & J. White, web prototype;A. Saeed, J. White, J.Li, A. Saeed, J. White, J.Li, & V. Sharov, developers)& V. Sharov, developers)


ControlControl

TestTest

Hybridize,Hybridize,WashWashObtain RNA SamplesObtain RNA Samples


MAD

MABCOSMABCOSUses Bar Codes to track samplesUses Bar Codes to track samples

(J. Li developer)(J. Li developer)

Obtain RNA SamplesObtain RNA Samples Prepare FluorescentlyPrepare FluorescentlyLabeled ProbesLabeled Probes

ControlControl

TestTest



Microarray OverviewMicroarray OverviewPaired TIFFPaired TIFFImage FilesImage Files

MADAM + MADAM + MAPMAPAllows data entry, Allows data entry,

moves files/renames to moves files/renames to long-term storagelong-term storage

(A. Saeed, J. White, J.Li, (A. Saeed, J. White, J.Li, & V. Sharov, developers)& V. Sharov, developers)

MAD

NetAPP


NetAPP

Spotfinder Spotfinder Provides Image Analysis, Provides Image Analysis,

writes data towrites data toflat files or directly to dbflat files or directly to db

(V. Sharov, developer and maintainer)(V. Sharov, developer and maintainer)

MAD

Available as Executable for Windows;Available as Executable for Windows;device-independent C/C++ comingdevice-independent C/C++ coming

The TIGR Array Software System

SLITRACK

MADAM

PCRSCORE

ExpDesigner

SpotFinder

MABCOS

McCoder

MeVMIDASMAD

Data Normalization and FilteringData Normalization and Filtering

Lowess NormalizationLowess Normalization

Why LOWESS?Why LOWESS?

-3

-2

-1

0

1

2

3

7 8 9 10 11 12 13 14

log(Cy3*Cy5)

A SD = 0.346

ObservationsObservations1.1. Intensity-dependent structureIntensity-dependent structure2.2. Data not mean centered at logData not mean centered at log22(ratio) = 0(ratio) = 0

LOWESS (Cont’d)LOWESS (Cont’d)

Local linear regression model Local linear regression model

Tri-cube weight function Tri-cube weight function

Least SquaresLeast Squares

Estimated Estimated values of values of loglog22(Cy5/Cy3) as (Cy5/Cy3) as

function of function of loglog1010(Cy3*Cy5)(Cy3*Cy5)

-3

-2

-1

0

1

2

3

7 8 9 10 11 12 13 14

log(Cy3*Cy5)

AA SD = SD = 0.3460.346

WYXWXX

xyxw

xyxw

xy

iii

iii

ii

')'(

0)()(

)()(

1

2

2

LOWESS Results

“Slice Analysis” (Intensity-dependent Z-score)

MIDAS: Data AnalysisMIDAS: Data Analysis Wei LiangWei Liang

Available with OSI sourceAvailable with OSI source

Adding Error Models,Adding Error Models,MAANOVA,MAANOVA,

Automated ReportingAutomated Reporting


MAD

MAD

MIDAS MIDAS Performs data normalizationPerforms data normalizationand filtering, including, soon, ANOVAand filtering, including, soon, ANOVA

MIDASMIDAS

MeV: Data Mining ToolsMeV: Data Mining Tools Alexander SaeedAlexander SaeedAlexander SturnAlexander SturnNirmal BhagabatiNirmal Bhagabati

John BraistedJohn BraistedSyntek Inc.Syntek Inc.

Datanaut, Inc.Datanaut, Inc.

Available with OSI sourceAvailable with OSI source

MeV: Metabolic pathway analysis is comingMeV: Metabolic pathway analysis is coming

Maria Klapa and Chris KoenigMaria Klapa and Chris Koenig

Analyses available in MeV...Hierarchical clustering (HCL)Hierarchical clustering (HCL)Bootstrapped/Jackknifed HCLBootstrapped/Jackknifed HCLk-k-means clustering (KMC)means clustering (KMC)k-k-means support (iterative KMC)means support (iterative KMC)Self-Organizing Maps (SOMs)Self-Organizing Maps (SOMs)Cluster Affinity Search Technique (CAST)Cluster Affinity Search Technique (CAST)Figure of Merit for CAST and KMC (soon SOM)Figure of Merit for CAST and KMC (soon SOM)QTQT-clust (Heyer Jackknife)-clust (Heyer Jackknife)Principal component analysis (PCA)Principal component analysis (PCA)Gene ShavingGene ShavingRelevance NetworksRelevance NetworksSupport Vector Machines (SVM)Support Vector Machines (SVM)Self-Organizing TreesSelf-Organizing TreesClassification approaches, including Template MatchingClassification approaches, including Template Matchingt-testst-testsSignificance Analysis of Microarrays (SAM)Significance Analysis of Microarrays (SAM)

ANOVA toolsANOVA toolsGO, Metabolic Pathway, and Genome Localization annotation/clusteringGO, Metabolic Pathway, and Genome Localization annotation/clustering

Client-server mode with well-defined APIClient-server mode with well-defined API

Missing from MeV...

MAGE-ML output for direct submission to MAGE-ML output for direct submission to databases ... Coming in the next MADAM release.databases ... Coming in the next MADAM release.

Links to BioConductor … are coming.Links to BioConductor … are coming.

Array CGH module from Barb Weber and Adam Array CGH module from Barb Weber and Adam Margolin ... is coming.Margolin ... is coming.

EASE module from Doug Hosack ... is comingEASE module from Doug Hosack ... is coming

Lots of stuff we are not smart enough to think Lots of stuff we are not smart enough to think about.about.

00 33 66 99 1212

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zz zzzz

zz zzzz

zz zzzz

zz zzzz

Sleep Deprivation Studies in MouseSleep Deprivation Studies in Mouse

zz zzzz

zz zzzz

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zz zzzz

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zz zzzz

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Experimental ParadigmExperimental ParadigmCompare gene expression between sleeping and Compare gene expression between sleeping and sleep-deprived mice in cortex and hypothalamussleep-deprived mice in cortex and hypothalamus

Perform 3 biological replicatesPerform 3 biological replicates

Normalize and filter data and use data mining techniques to Normalize and filter data and use data mining techniques to select distinct patterns of gene expressionselect distinct patterns of gene expression

Use Gene Ontology (GO) assignments to classify genes by Use Gene Ontology (GO) assignments to classify genes by cellular localization, molecular function, biological processcellular localization, molecular function, biological process

Use GO analysis to develop an understanding of responseUse GO analysis to develop an understanding of response

Differential Expression in CortexDifferential Expression in Cortex

Energy MetabolismTranscription;Mitochondrial and Ribosomal Proteins

Stress Response

IntermediateMetabolism andSignal Transduction

Differential Expression in HypothalamusDifferential Expression in Hypothalamus

Sleep signaling

Cortex – Up-regulated Genes GO Class GO Category p-value

GO Cellular Component endoplasmic reticulum 6.0610-03 GO Molecular Function heat shock protein activity 8.7810-04

pyruvate dehydrogenase (lipoamide) phosphatase activity 3.1710-03 chaperone activity 7.3810-03

Cortex – Down-regulated Genes GO Class Gene Category p-value

GO Biological Process protein biosynthesis 2.8510-25 protein metabolism 1.0010-11 electron transport 6.0410-03

GO Cellular Component ribosome 5.9510-37 ribonucleoprotein complex 1.1710-32 eukaryotic 48S initiation complex 9.7410-18 eukaryotic 43S pre-initiation complex 2.6810-15 mitochondrial inner membrane 3.7010-03

GO Molecular Function structural constituent of ribosome 6.4610-39 RNA binding activity 4.8310-21 cytochrome c oxidase activity 9.7910-04 hydrogen ion transporter activity 1.8810-03

EASE Analysis of GO termsEASE Analysis of GO terms

Hosack, Hosack, et al. 2003et al. 2003

ThemesThemes: : General biological trends based on representation of General biological trends based on representation of functional roles on the arrayfunctional roles on the array

ProblemProblem: : Requirement of functional class assignment limits utility Requirement of functional class assignment limits utility for discovery of new functional networksfor discovery of new functional networks

Thanks to Doug Hosack, NIAIDThanks to Doug Hosack, NIAID

Now available... The TGI databases, including RESOURCERERThe TGI databases, including RESOURCERER

The TGICL Gene Index Clustering and Assembly ToolsThe TGICL Gene Index Clustering and Assembly Tools

A freely-available MySQL version of our MIAME-A freely-available MySQL version of our MIAME-supportive databasesupportive database

A freely-available, open source, java-based set of tools:A freely-available, open source, java-based set of tools: MADAM: MADAM: MMicroicroaarray rray DaData ta MManager anager MIDAS: MIDAS: MiMicroarray croarray DData ata AAnalysis nalysis SSystemystem MeV: MeV: MMultiultieexperiment xperiment VVieweriewer

A freely-available, image processing software system A freely-available, image processing software system linked to the database: TIGR Spotfinderlinked to the database: TIGR Spotfinder

Nobody in the game of football Nobody in the game of football should be called a genius. should be called a genius.

A genius is somebody like Norman Einstein. A genius is somebody like Norman Einstein.

-Joe Theisman, Former quarterback-Joe Theisman, Former quarterback

A theory has only the possibility of being A theory has only the possibility of being right or wrong. A model has a third right or wrong. A model has a third

possibility; it may be right but irrelevant.possibility; it may be right but irrelevant.

–– Manfred EigenManfred Eigen

Unless a reviewer has the courage Unless a reviewer has the courage to give you unqualified praise, I say to give you unqualified praise, I say

ignore the bastard.ignore the bastard.

- John Steinbeck- John Steinbeck

The TIGR Gene Index TeamThe TIGR Gene Index TeamFoo CheungFoo Cheung

Svetlana KaramychevaSvetlana KaramychevaYudan LeeYudan Lee

Babak ParviziBabak ParviziGeo PerteaGeo Pertea

Razvan SultanaRazvan SultanaJennifer TsaiJennifer Tsai

John QuackenbushJohn QuackenbushJoseph WhiteJoseph White

Funding provided by the Department of EnergyFunding provided by the Department of Energyand the National Science Foundationand the National Science Foundation

TIGR Human/Mouse/Arabidopsis TIGR Human/Mouse/Arabidopsis Expression TeamExpression Team

Emily ChenEmily ChenBryan FrankBryan Frank

Renee GaspardRenee GaspardJeremy HassemanJeremy Hasseman

Heenam KimHeenam KimLara LinfordLara Linford

Simon KwongSimon KwongJohn QuackenbushJohn Quackenbush

Shuibang WangShuibang WangYonghong WangYonghong Wang

Ivana YangIvana YangYan YuYan Yu

Array Software Hit TeamArray Software Hit TeamNirmal BhagabatiNirmal Bhagabati

John BraistedJohn BraistedTracey CurrierTracey Currier

Jerry LiJerry LiWei LiangWei Liang

John QuackenbushJohn QuackenbushAlexander I. SaeedAlexander I. Saeed

Vasily SharovVasily SharovMathangi ThaiagarjianMathangi Thaiagarjian

Joseph WhiteJoseph WhiteAssistantAssistantSue MineoSue MineoFunding provided by the National Cancer Institute,Funding provided by the National Cancer Institute,

the National Heart, Lung, Blood Institute,the National Heart, Lung, Blood Institute,and the National Science Foundationand the National Science Foundation

H. Lee Moffitt Center/USFH. Lee Moffitt Center/USFTimothy J. YeatmanTimothy J. Yeatman

Greg BloomGreg Bloom

TIGR PGA CollaboratorsTIGR PGA CollaboratorsNorman LeeNorman LeeRenae MalekRenae Malek

Hong-Ying WangHong-Ying WangTruong LuuTruong Luu

Bobby BehbahaniBobby Behbahani

TIGR Faculty, IT Group, and StaffTIGR Faculty, IT Group, and Staff

<[email protected]><[email protected]>AcknowledgmentsAcknowledgments

PGA CollaboratorsPGA CollaboratorsGary Churchill (TJL)Gary Churchill (TJL)Greg Evans (NHLBI)Greg Evans (NHLBI)Harry Gavaras (BU)Harry Gavaras (BU)

Howard Jacob (MCW)Howard Jacob (MCW)Anne Kwitek (MCW)Anne Kwitek (MCW)Allan Pack (Penn)Allan Pack (Penn)

Beverly Paigen (TJL)Beverly Paigen (TJL)Luanne Peters (TJL)Luanne Peters (TJL)

David Schwartz (Duke)David Schwartz (Duke)

EmeritusEmeritusJennifer Cho (TGI)Jennifer Cho (TGI)

Ingeborg Holt (TGI)Ingeborg Holt (TGI)Feng Liang (TGI)Feng Liang (TGI)

Kristie Abernathy (mA)Kristie Abernathy (mA)Sonia Dharap(mA)Sonia Dharap(mA)

Julie Earle-Hughes (mA)Julie Earle-Hughes (mA)Cheryl Gay (mA)Cheryl Gay (mA)Priti Hegde (mA)Priti Hegde (mA)

Rong Qi (mA)Rong Qi (mA)Erik Snesrud (mA)Erik Snesrud (mA)

meeting the bioinformatics challenges of functional genomics

Documents

razvan sultanagene

protein gene

humansis difficult

humansis easy

national heart

national science foundationh

national deficit

dataanalyze hybridization