european bioinformatics institute mged society establishing the infrastructure for sharing...

European Bioinformatics InstituteMGED Society

Establishing the infrastructure

for sharing microarray data

Alvis Brazma

European Bioinformatics Institute EMBL-EBI

Microarray Gene Expression Data Society


Outline

Establishing the infrastructure for sharing microarray data – MGED, MIAME, MAGE-ML, databases

Microarray Informatics at the EBI

Microarrays

- a tool for the golden age of genome discoveries


Some questions for the golden age of genomics How gene expression differs in different cell

types? How gene expression changes when the

organism develops and cells are differentiating?

How gene expression differs in a normal and diseased (e.g., cancerous) cell?

How gene expression changes when a cell is treated by a drug?

How gene expression is regulated – which genes regulate which and how?


Potential amounts of microarray data

Experiments:~ 30 000 genes in a human genome~ 320 cell types in a human organism– 2000 compounds for screening – 2 concentrations – 3 time points– 5 replicates

Data~ 1012 data-points 1 Tera Byte


Making microarray data available to the public Authors web-sites Local, lab based public databases

(Stanford University, Whitehead,…) Journal web-sites There is a wide community

consensus that there is a need for public repositories for microarray data, analogous to DDBJ/EMBL/Genbank for sequence data

Raw data

Array scans

Spo

ts

Quantitations

Quantitationmatrices

Gen

es

Samples

Gene expressiondata matrix

Gene expression levels

Which data to share?

SamplesG

enes

Gene expression levels – problem 2

Sample annotations problem 1

Gene annotations

Gene expression matrix

Annotations

hybridisationlabelled

nucleic acidarray

RNA extract

source

Sample treatment

elements(spots)

Design

protocols

image

quantitationmatrix

Sample annotation

Gene annotation


nucleic acidarray

RNA extract

Sample

elements(spots)

Design


nucleic acidarray

RNA extract

Sample

elements(spots)

Design


nucleic acidarray

RNA extract

Sample

elements(spots)

Design


nucleic acidarray

RNA extract

Sample

elements(spots)

Design


nucleic acidarray

RNA extract

Sample

elements(spots)

Design

Experiment

Gene expression data matrix

transformation

integration

Gene expression measurements


Problem 4

The nature and structure of the above described gene expression data and annotations are complex

For the public repositories to make the maximum use out of these data, standards for representing and communicating it should be established


Standards for microarray data Understanding and agreement what

data and annotations should be provided

Standard controlled vocabularies (ontologies) that can be used in such annotations

Standard format for exchange of annotated data

Understanding how to compare different datasets


Microarray Gene Expression Database meeting was organised in Cambridge, UK, November 1999 to discuss these problems


MGED 1 – some participants Affymetrix DDBJ DKFZ EMBL Gene Logic Incyte Max Plank Institute NCGR

NHGRI Sanger Centre Stanford

University Uni Pennsylvania Uni Washington,

Seattle Whitehead

Institute


MGED working groups

Experiment annotation Data exchange format and

modelling Ontologies Data normalisation and

transformations Queries


MGED meetings

MGED 2, Heidelberg, May 2000MGED 3, Stanford University, April 2001MGED 4, Boston, February 2002MGED 5, Tokyo, September 2002


MGED Society was founded in June 2002

Microarray Gene Expression Data (MGED) society is an international organisation for facilitating sharing of functional genomics and proteomics array data

Board of 17 directors

www.mged.org

http://www.mged.org/




MGED standards

Annotation content – MIAME Data representation and exchange

format MAGE-OM (MAGE-ML) – jointly with OMG


MIAME – Minimum Information About a Microarray experiment An attempt to outline the

minimum information required to interpret unambiguously and potentially reproduce and verify an array based gene expression experiment

www.mged.org/miame


MGED standards


nucleic acidarray

RNA extract

Sample

elements(spots)

Design


nucleic acidarray

RNA extract

Sample

elements(spots)

Design


nucleic acidarray

RNA extract

Sample

elements(spots)

Design


nucleic acidarray

RNA extract

Sample

elements(spots)

Design


nucleic acidarray

RNA extract

Sample

elements(spots)

Design

Experiment

Gene expression data matrix

normalization

integration

MIAME – the content (annotation) of all boxes and lines should be given


MIAME ‘checklist’ to authors and reviewers Experimental design Samples used, RNE extraction and

labelling Hybridisation Measurement data and specifications Array Design

– (Row images)– Image quantitation (data and specification)– Gene expression data matrix (data and

transformations)


MIAME ‘checklist’

An open letter was sent to the journals last week - all the information in MIAME ‘checklist’ should be made available as a requirement for accepting publications

The Lancet has indicated that it will adopt MIAME checklist as a requirement

Nature will adjust its policy in the line with MIAME recommendations


A need for a supporting infrastructure MIAME itself will not solve the

problem A standard format is needed for

representing and exchanging this information


MGED standards 2 Data exchange format – MicroArray Gene

Expression Mark-up language – MAGE-ML – an XML based file format able to capture all MIAME required information

Based on object model MAGE-OM (Paul Spellman, Michael Miller, Jason Stewart, Ugis Sarkans, …)

Adopted by OMG as a standard for microarrays

www.mged.org/mage

Treatment

Transformation

BioEvent

Experiment

ArrayDesign

BioMaterial

BioAssayData BioAssay

DesignElement

UML Packages of MAGE

HigherLevelAnalysis

BioSequence

ArrayQuantitationType

Description

Protocol

Measurement

AuditAndSecurity

BQS

MAGE – an example diagram


Use case of MAGE:ArrayExpress architecture

ArrayExpress(Oracle)

Browser

MIAMEexpress

MAGE-ML(DTD)

MAGE-OM

MAGE-ML (doc)MAGE-ML (doc)MAGE-ML (doc)

dataloader

Velocitytemplateengine

Castor

object/relationalmapping

Web pagetemplateWeb pagetemplate

Java servlets Tomcat


MGED standards 3

MGED ontologies – organism part, cell type, diseased state, genotype, chemical compounds (Chris Stoeckert, Helen Parkinson, Susanna Sansone,…)

Symposium “Standards and Ontologies for Functional Genomics” – November 17-20, Cambridge, UK

www.mged.org/ontology


MGED standards 4

Data transformation and normalisation (Cathy Ball, John Quackenbush, Gavin Sherlock, …) www.mged.org/normalization

http://www.mged.org/normalization


Infrastructure for sharing microarray data

Standard for experiment annotation

Standard for data exchange Public repositories Local databases and LIMS Ways of comparing the data


ArrayExpress – a MIAME/MAGE supportive public repository for microarray data at EBI

ArrayExpress

MIAMExpressExpressionProfiler

MAGE-ML

Internet

www

MAGE-ML

Submissions Queries, Analysis


Microarray data sharing infrastructure

Public repositories

MAGE-MLMAGE-ML

www

www

www

Data queries, retrieval, and analysis

Data submissions

Array descriptions(from manufacturers)

Data analysissoftware

MIAMExpresslocal instalations

LIMS

MAGE-ML

LIMS

Data analysissoftware

html htmlOther databases

MAGE-ML

www

www


MIAME/MAGE supportive software Sanger Institute LIMS (MIDAS) TIGR LIMS Gene Traffic (Iobion) Affymetrix MAXDB (Manchester) Rosetta Resolver (Rosetta Biosoftware) Base (Lund) J-Express (Molmine) MIAMExpress (EBI) ArrayExpress (EBI)


Acknowledgements MGED board

– Cathy Ball (Stanford)– Helen Causton (Imperial Col)– Terry Gaasterland (Rockefel)– Jason Gonzales (Iobion)– Pascal Hingamp (Marseille)– Barbara Jasny (Science)– Helen Parkinson (EBI)– John Quackenbush (TIGR)– Martin Ringwald (Jackson)– Gavin Sherlock (Stanford)– Paul Spellman (Berkely)– Jason Stewart (Open Inf)– Chris Stoeckert (Uni Penns)– Yoshio Tateno (DDBJ)– Ron Taylor (Colorado)– Charles Troup (Agilent)

– MGED supporters– Rob Andrews (Sanger)– Wilhelm Ansorge (EMBL)– Mike Cherry (Stanford)– Peter Dansky (Affymetrix)– David Hancock (Manchester)– Frank Holstege (Utrecht)– Michael Miller (Rosetta)– Kate Rice (Sanger)– Christian Schwager (EMBL)– Joe White (TIGR)– Rick Young (MIT)

– EBI Microarry Team– Niran Abeygunawardena– Helen Parkinson– Philippe Rocca-Sera– Susanna Sansone– Ugis Sarkans– Mohammadreza Shojatalob– Jaak Vilo

Microarray informatics at the EBI ArrayExpress (Helen Parkinson) Expression profiler data analysis

tool and promoter analysis (Jaak Vilo)

Reconstructing and analysing gene networks


AEP2

AKR1

CMK2

ANP1

RAD16

AFR1

CEM1

CUP5

SST2

DIG1

UBP10

STE2

ERG2

PHO89ERG6

GAS1 PTP2

GYP1

HIR2HPT1

ISW1

FIG1 ISW2

KIN3

MAC1MRPL33

MSU1

NPR2

PET111

RAD57

RIP1

RRP6

ASG7

STE6RTS1

SCS7

SGS1

MFA1

SHE4AGA1

SWI4

FUS1SWI5

VAC8

VMA8

YAL004W

YAR014C

YEL044W

YER050C

FUS3

GPA1

BAR1

MFA2

YER083C

RTT104

YMR014W

YMR029C AGA2YMR031W-A

YMR293C

YOR078W

ADE2

AFG3

BNI1

CLA4

ERG3

FKS1

KAR4

YAR064W

CHS3

VAP1

ICS2

YCLX09W

YDL009C

STP4

PMT1

VCX1HO

THI13

ADR1

YDR249C PAM1

YDR275W

HXT7

HXT6 YDR366CYDR534C

URA3

YEL071W

MNN1

ICL1

RNR1

YER130C

YER135C

SPI1 DMC1

HSP12

NIL1

GSC2

KSS1

MUP1

YGR138C

SKN1

YGR250C

YHR097C YHR116W

YHR122W

YHR145C

YIL060W

YIL096C

YIL117C

RHO3

YIL122W FKH1

NCA3

YJL145W

RPL17B

YJL217W

CYC1

DAN1

PGU1

GFA1

HAP4

RRN3

STE3

PRY2

KTR2

SRL3

YLR040C

YLR042C

SSP120

HSP60

YLR297W

RPS22B YLR413W

HOF1

DDR48

RNA1

YMR266W

YNL078W

SPC98

YNL133C

YNL217W

WSC2YPT11

RFA2

YNR009W

YNR067C

MDH2

YOL154W

NDJ1

WSC3

CDC21

PFY1

RGA1

MSB1

SRL1

YOR248W

YOR296W

YOR338W

GDS1PDE2

FRE5

YPL080C

RPS9A

BBP1

YPL256C

SUA7

MEP3

YPR156C

HMG1

HOG1

MED2

QCR2

RAD6

RAS2

RPD3

RPS24A

CRS4CYC8

YAR031W

YBR012C

HIS7

YCLX07W

YCRX18C PCL2

YDR124W

ECM18APA2

YER024W

HOM3

THI5

YGL053W

NRC465

YGR161C YHR055C

YIL037C

YIL080W

YIL082W

HIS5

YJL037W

SAG1

CPA2

AAD10

HYM1

MET1

MID2

YML047C

KAR5

CIK1

FUS2 SCW10

BOP3

YNL279WTHI12

YOL119C

YOR203W

TEA1

ISU1

YPL156C

YPL192CYPL250C

KAR3YIL082W

-A

YML048W-A

YMR085W

STE11

STE12 STE18

URA1

URA4

STE24

STE4

STE5

STE7SWI6

MAK1

TUP1

YER044C YJL107C

Gene Networks – graphs: nodes are genes, arcs are relationships


Different ways to build a gene network

G1 G2- The product of gene G1 is a transcription factor, which binds to the promoter of gene G2 – physical interaction network

G1 G2- The disruption of gene G1 changes the expression level of gene G2 – data interpretation network

G1 G2- Gene G2 is mentioned in a paper about gene G1 – literature networks

Data for over 200 gene disruptions in Yeast

Hughes et al, Cell, 102 (2000)


Discretization of the data:The normalized expression log(ratios) are discretized using different thresholds = 2, 2.1 , … , 4 :

X < d(X) = 1 X d(X) = 0X > d(X) = 1


Gene disruption network

A C

B D

A B Cgene B

gene C

gene D

gene A

Data for over 200 gene disruptions in Yeast

Hughes et al, Cell, 102 (2000)


Mutation network for S. Cerevisiae


AEP2

AKR1

CMK2

ANP1

RAD16

AFR1

CEM1

CUP5

SST2

DIG1

UBP10

STE2

ERG2

PHO89ERG6

GAS1 PTP2

GYP1

HIR2HPT1

ISW1

FIG1 ISW2

KIN3

MAC1MRPL33

MSU1

NPR2

PET111

RAD57

RIP1

RRP6

ASG7

STE6RTS1

SCS7

SGS1

MFA1

SHE4AGA1

SWI4

FUS1SWI5

VAC8

VMA8

YAL004W

YAR014C

YEL044W

YER050C

FUS3

GPA1

BAR1

MFA2

YER083C

RTT104

YMR014W

YMR029C AGA2YMR031W-A

YMR293C

YOR078W

ADE2

AFG3

BNI1

CLA4

ERG3

FKS1

KAR4

YAR064W

CHS3

VAP1

ICS2

YCLX09W

YDL009C

STP4

PMT1

VCX1HO

THI13

ADR1

YDR249C PAM1

YDR275W

HXT7

HXT6 YDR366CYDR534C

URA3

YEL071W

MNN1

ICL1

RNR1

YER130C

YER135C

SPI1 DMC1

HSP12

NIL1

GSC2

KSS1

MUP1

YGR138C

SKN1

YGR250C

YHR097C YHR116W

YHR122W

YHR145C

YIL060W

YIL096C

YIL117C

RHO3

YIL122W FKH1

NCA3

YJL145W

RPL17B

YJL217W

CYC1

DAN1

PGU1

GFA1

HAP4

RRN3

STE3

PRY2

KTR2

SRL3

YLR040C

YLR042C

SSP120

HSP60

YLR297W

RPS22B YLR413W

HOF1

DDR48

RNA1

YMR266W

YNL078W

SPC98

YNL133C

YNL217W

WSC2YPT11

RFA2

YNR009W

YNR067C

MDH2

YOL154W

NDJ1

WSC3

CDC21

PFY1

RGA1

MSB1

SRL1

YOR248W

YOR296W

YOR338W

GDS1PDE2

FRE5

YPL080C

RPS9A

BBP1

YPL256C

SUA7

MEP3

YPR156C

HMG1

HOG1

MED2

QCR2

RAD6

RAS2

RPD3

RPS24A

CRS4CYC8

YAR031W

YBR012C

HIS7

YCLX07W

YCRX18C PCL2

YDR124W

ECM18APA2

YER024W

HOM3

THI5

YGL053W

NRC465

YGR161C YHR055C

YIL037C

YIL080W

YIL082W

HIS5

YJL037W

SAG1

CPA2

AAD10

HYM1

MET1

MID2

YML047C

KAR5

CIK1

FUS2 SCW10

BOP3

YNL279WTHI12

YOL119C

YOR203W

TEA1

ISU1

YPL156C

YPL192CYPL250C

KAR3YIL082W

-A

YML048W-A

YMR085W

STE11

STE12 STE18

URA1

URA4

STE24

STE4

STE5

STE7SWI6

MAK1

TUP1

YER044C YJL107C

Mutation network =2, filtered for the genes marked in red (mating)

Thomas Schlitt, Johan Rung


Comparison to literature network derived from YPD

ResultOverlap between calculated networks and YPD-graph is always larger than overlap between randomised networks and the YPD-graph


Network modularity

Is there one “big” dominant connected component and possibly a number of small components, or several components of comparable sizes?

Can the network be broken down in several components of comparable size by removing nodes of high degree (i.e., nodes with many incoming or outgoing edges)?

Number of connected components in the networks

component

full network

1% removed

5% removed

10% removed

2.0 largestsecond

total

5383

1

4707

1

368222

261452

3.0 largestsecond

total

355622

246122

138549

7646

17

4.0 largestsecond

total

235434

120537

5426

22

452851


Other opinions

Wagner, 2002 (Genome Res) – there exists many independent modules

Feathersone, 2002 (Bioessays) - there is only one giant module

All depends on the definition of the ‘module’


Disruption network properties In and out degree of genes

distributed according to power-low There are no obvious modules in

this particular network ‘Local’ networks make sense(J.Rung, T.Schlitt et al, to appear in

ECCB special issue of Bioinformatics)


Gaurab Mukherjee, Alvis Brazma, Gonzalo Garcia Lara, Ugis Sarkans, Koichi Tazaki, Ahmet Ociamen, Helen Parkinson, Mohammadreza Shojatalab, Thomas Schlitt, Katja Kivinen, Misha Kapushesky, Ele Holloway, Nastja Samsonova, Philppe Rocca-Serra, Johan Rung, Niran Abeygunawardena, Susanna Sansone, Jaak Vilo

Microarray Informatics at the EBI

european bioinformatics institute mged society establishing the infrastructure for sharing...

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